Cardiology department nursing information monitoring system based on cloud computing

Abstract

The invention relates to the technical field of cardiology nursing informatization, and discloses a cardiology nursing information monitoring system based on cloud computing. A physical sign monitoring module of the system integrates real-time data streams of a wearable device and a bedside monitor, collects multi-dimensional physiological parameters and generates a dynamic vital sign set; the health assessment module identifies abnormal fluctuation nodes of the physical sign data, matches historical medical record records, quantifies the association strength of the current physical sign and a typical pathological mode, and generates a multi-dimensional health risk index; the resource allocation module analyzes a nursing resource occupation state based on the risk index, calculates a task emergency degree weight, dynamically allocates a working path and an equipment use sequence of medical staff, and generates a hierarchical nursing scheduling scheme; and the intervention analysis module executes the scheduling scheme, compares sign changes before and after resource allocation, detects abnormal response delay time, positions execution deviation nodes and generates a nursing effect traceability report. The system assists intelligent and refined management of the nursing process of the department of cardiology.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology for cardiology nursing, in particular to a cardiology nursing information monitoring system based on cloud computing. BACKGROUND

[0002] In the clinical nursing of cardiology, the monitoring of physiological parameters of patients, the assessment of health status, the allocation of nursing resources and the tracing of intervention effects are always the key links to ensure the quality of nursing. Under the traditional nursing mode, the collection of patient's physical data mainly depends on manual recording or single device monitoring, and the data is scattered in different recording carriers or device systems, which is difficult to realize centralized integration and real-time tracking. For example, the heart rate data recorded by wearable devices, the blood pressure fluctuation curve and the change of blood oxygen saturation monitored by bedside monitors are often independent of each other, and medical staff need to switch between different platforms to query, which not only increases the workload of data integration, but also may cause the judgment of patient's condition to lag due to data transmission delay.

[0003] For the assessment of patient's health status, the traditional method mainly depends on the personal experience and subjective judgment of medical staff, and lacks systematic mining and quantitative analysis of historical medical record data. When the patient's physical signs are abnormal, the medical staff need to manually check the records of past health events, which is difficult to quickly match the typical pathological pattern, resulting in the lack of unified standard for the assessment of the deviation degree of health status, and easy to miss or misjudge. Especially in the scene of complex and changeable patient's condition in cardiology, this experience-dependent assessment method cannot meet the needs of precise nursing.

[0004] In terms of nursing resource allocation, the traditional mode usually adopts fixed scheduling or allocation according to bed number, without fully considering the emergency degree of patient's condition and the real-time occupation state of resources. When multiple patients have nursing needs at the same time, the arrangement of medical staff's work path and medical equipment use sequence lacks dynamic adjustment mechanism, which may cause high-risk patients to be unable to get priority intervention, and low-emergency tasks to occupy too many resources, resulting in waste of nursing resources and low efficiency of scheduling.

[0005] After the implementation of nursing intervention measures, the tracing and analysis of its effect also have deficiencies. Under the traditional mode, the comparison of physical signs before and after intervention, the recording of abnormal response delay time and the positioning of execution deviation node mainly depend on manual summary, and the data integrity and accuracy are difficult to guarantee, which leads to the inability to find problems in nursing process in time and the difficulty to realize the continuous optimization of nursing quality. The existence of these problems makes the intelligentization and refinement level of cardiology nursing work need to be improved, and an integrated information monitoring system integrating data collection, evaluation, scheduling and analysis is urgently needed. SUMMARY

[0006] The present application aims to provide a cardiology nursing information monitoring system based on cloud computing to solve the problems in the background art.

[0007] To achieve the above-mentioned purpose, the present application provides a cardiology nursing information monitoring system based on cloud computing, which comprises: The vital sign monitoring module collects the multi-dimensional physiological parameters of the cardiology patients, integrates the real-time data streams of the wearable devices and the bedside monitors, continuously tracks the heart rate variability and blood pressure fluctuation curves of the patients, calculates the abnormal offset amount of the respiratory frequency, and generates a dynamic vital sign set in combination with the oxygen saturation change trend; The health assessment module receives the dynamic vital sign set, identifies the abnormal fluctuation nodes in the vital sign data, matches the health event records in the patient's historical medical records, quantifies the correlation strength between the current vital signs and the typical pathological patterns, evaluates the deviation degree of the patient's health status, and generates multi-dimensional health risk indicators; The resource allocation module analyzes the real-time occupancy state of the nursing resources based on the multi-dimensional health risk indicators, calculates the emergency weight of different nursing tasks, dynamically allocates the work paths of medical staff and the use sequences of medical equipment, and generates a hierarchical nursing scheduling scheme; The intervention analysis module executes the hierarchical nursing scheduling scheme, compares the vital sign data before and after the resource allocation, detects the response delay time of the abnormal vital signs, locates the execution deviation nodes of the nursing intervention measures, and generates a nursing effect traceability report.

[0008] Preferably, the vital sign monitoring module comprises: The multi-source fusion sub-module synchronously receives the heterogeneous data streams of the electrocardiogram monitor, pulse oximeter and non-invasive blood pressure meter, calibrates the sampling time stamps of different devices, extracts the extreme points of physiological parameters within the last five minutes, calculates the synchronicity index of parameter fluctuation frequency, and generates a time-series aligned vital sign data set; The abnormality detection sub-module analyzes the time-series aligned vital sign data set, divides the reference parameter intervals of the day-night cycle, monitors the duration of the real-time parameters exceeding the interval threshold, counts the occurrence density of abnormal events within a unit time, and generates a vital sign abnormality probability matrix; The risk integration sub-module calls the vital sign abnormality probability matrix, associates the drug metabolism time window in the patient's medication record, calculates the correlation coefficient of abnormal vital signs and drug concentration, integrates the external interference factors of the environment temperature and humidity sensors, and generates the dynamic vital sign set.

[0009] Preferably, the health assessment module comprises: The pathological matching sub-module extracts the heart rate shock parameters in the dynamic vital sign set, compares the waveform feature library of typical heart failure cases, calculates the spectral difference degree of the current waveform and the pathological reference waveform, identifies the occurrence position and duration interval of the premature beat event; The risk quantification submodule receives the spectrum difference and early beat event data, loads the myocardial enzyme historical values in the patient electronic medical record, constructs a dynamic change curve of the current myocardial injury index, and quantifies the proximity of the curve slope and the critical value; The index generation submodule integrates the dynamic change curve of the myocardial injury index and the pathological matching result, weightedly calculates the priority order of the risk of organ failure, and generates the multi-dimensional health risk index including the cardiac function classification label and the organ risk weight.

[0010] Preferably, the resource allocation module comprises: The task analysis submodule decomposes the cardiac function classification label in the multi-dimensional health risk index, maps the corresponding nursing operation list of different levels, identifies the inventory status of emergency drugs and the occupation situation of respirators, and calculates the device dependency coefficient of each nursing operation; The path optimization submodule receives the nursing operation list and device dependency coefficient, constructs a real-time topological graph of the location information of medical staff, simulates the task completion time delay rate under different paths, and iteratively optimizes the execution order of the nursing operation sequence; The scheduling generation submodule integrates the optimized nursing operation sequence and device allocation scheme, coordinates the use time conflict of intravenous infusion pumps and defibrillators, and generates the hierarchical nursing scheduling scheme including the operation time window and the device handover node.

[0011] Preferably, the intervention analysis module comprises: The response monitoring submodule captures the real-time sign data stream after the hierarchical nursing scheduling scheme is executed, marks the corresponding relationship between the diuretic administration time point and the blood pressure drop event, and counts the delay length of the sign parameter returning to the safety threshold; The deviation positioning submodule compares the preset standard of the nursing operation with the time stamp of the execution record, identifies the operation node where the intravenous injection speed deviates from the standard value, and associates the deviated operation with the blood potassium concentration mutation event in the adjacent time window; The traceability generation submodule integrates the delay length and the occurrence position of the blood potassium concentration mutation event, constructs a causal chain model of nursing measures and sign response, and generates the nursing effect traceability report including the key intervention node execution deviation record.

[0012] Preferably, the system further comprises: The early warning execution module receives the key intervention node execution deviation record in the nursing effect traceability report, retrieves the operation history database of the corresponding medical staff, matches the validity period state of the emergency skill certification certificate, and generates a personalized skill reinforcement training scheme; The personalized skill reinforcement training scheme includes the operation procedure simulating the emergency scene, the device operation standard video tutorial, and the examination and evaluation index.

[0013] Preferably, the early warning execution module includes: The competency assessment submodule analyzes the operation types of the execution deviation records of the key intervention nodes, retrieves training completion rate data from the medical staff files, and calculates the correlation strength between operational errors and skill certificate update time. The training construction submodule configures a virtual reality simulation of cardiogenic shock scenario based on the correlation strength results of the operation type, and dynamically generates a correction training module for infusion rate adjustment error; The scheme generation submodule integrates the virtual reality simulation scene and the correction training module, overlays the analysis report of operational weaknesses in historical assessments, and generates the personalized skills enhancement training scheme that includes training hour allocation and assessment pass thresholds.

[0014] Preferably, the vital signs monitoring module further includes: The environmental calibration submodule acquires real-time readings from the ward's temperature and humidity sensors, calculates the influence coefficient of temperature fluctuations on vasoconstriction factors, and corrects the environmental interference deviation of blood pressure monitoring values. The drug interference analysis submodule correlates the infusion rate of vasoactive drugs in the electronic medical order system, quantifies the hysteresis response time of peak drug concentration and heart rate variability, and generates drug effect compensation parameters. The data correction submodule integrates the environmental interference deviation and drug effect compensation parameters to reconstruct the baseline reference value of the original vital signs data and update the blood pressure fluctuation curve in the dynamic vital signs set.

[0015] Preferably, the path optimization submodule performs the following operations: The real-time location tracking unit acquires indoor positioning beacon data worn by medical staff and constructs a three-dimensional spatial coordinate mapping model of the ward. The task conflict detection unit identifies the geographic radius of multiple emergency rescue tasks during the same period and calculates the probability of resource competition in the path intersection area; Based on the resource competition probability results, the dynamic adjustment unit reallocates the entities responsible for performing nursing tasks to avoid overlapping and conflicting equipment transportation paths.

[0016] Preferably, the system further includes: The medication safety monitoring module receives blood potassium concentration data from the dynamic vital signs set, correlates it with the diuretic infusion rate parameters being executed by the electronic medical order system, identifies potential risk time windows for drug incompatibilities, and generates high-risk drug compatibility warning instructions. The high-risk drug compatibility warning command triggers the automatic locking mechanism of the infusion pump and simultaneously pushes the alternative treatment plan to the mobile nursing terminal.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This cloud-based cardiology nursing information monitoring system, through the collaborative operation of multiple modules, has brought about various optimizations to cardiology nursing work. At the vital signs data acquisition level, the vital signs monitoring module breaks through the limitations of traditional nursing where data from wearable devices and bedside monitors are scattered. By integrating real-time data streams, it continuously tracks key physiological parameters such as heart rate variability and blood pressure fluctuation curves, generating a dynamic set of vital signs. This process achieves centralized management of multi-source data, allowing medical staff to obtain comprehensive physiological status information of patients on a unified platform. This avoids the tediousness of searching through different devices or records one by one, reduces the possibility of data omissions or transmission delays, and makes the perception of changes in patients' conditions more timely.

[0018] The health assessment module identifies abnormal fluctuations in vital signs data and combines this with records of health events in the patient's medical history to quantify the correlation between current vital signs and typical pathological patterns, generating multi-dimensional health risk indicators. This assessment method no longer relies solely on the personal experience of medical staff but uses data correlation analysis to form objective risk quantification results. Medical staff can use these indicators to more clearly determine the degree of deviation in the patient's health status, providing a more reliable reference for developing targeted care plans. This helps reduce assessment bias caused by subjective judgment differences and improves the consistency and accuracy of health assessments.

[0019] The resource allocation module operates based on multi-dimensional health risk indicators. By analyzing the real-time occupancy status of nursing resources and calculating the urgency weight of different nursing tasks, it dynamically allocates work paths for medical staff and usage sequences for medical equipment. This dynamic scheduling mechanism changes the traditional fixed-schedule or bed-based resource allocation model, enabling resource allocation to closely align with the actual urgency of patients' needs. Nursing tasks for high-risk patients receive priority resource support, medical staff work path planning becomes more rational, and the utilization efficiency of medical equipment is improved. This avoids resource idleness or over-concentration on low-urgency tasks, allowing limited nursing resources to be used more effectively.

[0020] During the execution of the tiered nursing care scheduling plan, the intervention analysis module compares changes in vital sign data before and after resource allocation, detects response delays to abnormal vital signs, and identifies deviations in the implementation of nursing interventions. This step provides data support for the traceability of nursing outcomes. By analyzing various nodes in the execution process, medical staff can identify potential problems in the nursing workflow, such as untimely interventions or improper equipment use. Based on these findings, targeted adjustments and optimizations can be made to the nursing workflow, continuously improving nursing operation standards and promoting continuous improvement in nursing quality, thus forming a closed loop of "monitoring-assessment-intervention-analysis-optimization" throughout the entire nursing process. Attached Figure Description

[0021] Figure 1This is a schematic diagram illustrating the working principle of the cloud computing-based cardiology nursing information monitoring system described in this invention. Figure 2 This is a schematic diagram illustrating the working principle of the health assessment module submodule. Figure 3 A schematic diagram illustrating the working principle of the path optimization submodule; Figure 4 This is a schematic diagram illustrating the working principle of the early warning execution module. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Please see Figure 1 This invention provides a cloud computing-based cardiology nursing information monitoring system, the system comprising: The cloud computing platform integrates real-time physiological monitoring data of cardiology patients with nursing resource scheduling functions to construct a closed-loop intelligent nursing management system. The vital signs monitoring module employs multi-device collaborative acquisition technology to continuously acquire parameters such as heart rate variability, blood pressure fluctuation curves, respiratory rate deviation, and blood oxygen saturation, forming a dynamically updated set of vital signs. The health assessment module uses a pathological pattern matching algorithm to correlate real-time vital signs data with health events in historical medical records, generating multi-dimensional assessment indicators including cardiac function classification and organ risk weights. The resource allocation module dynamically optimizes the paths of medical staff and the sequence of equipment use based on risk indicator priorities and the real-time occupancy status of hospital nursing resources. The intervention analysis module achieves quantitative traceability of the effectiveness of nursing interventions through timestamp comparison and causal chain modeling. All modules interact with each other through a cloud service bus, and a microservice architecture ensures system scalability.

[0024] Example 1: See Figure 2The vital signs monitoring module utilizes multi-source data fusion technology to achieve real-time acquisition and integration of physiological parameters for cardiology patients. This module interfaces with various monitoring devices within the hospital, including electrocardiogram (ECG) monitors, pulse oximeters, and non-invasive blood pressure monitors, which employ different sampling frequencies and communication protocols. The data acquisition unit uses an asynchronous communication mechanism, receiving raw signals from each device through a standardized data interface, including key physiological parameters such as ECG waveforms, blood oxygen saturation percentage, and arterial blood pressure values. To address clock skew issues between different devices, the system introduces a reference time axis during data preprocessing, using interpolation algorithms to align discrete sampling points in time, ensuring all parameters have a unified time reference. Within a continuous five-minute data window, the system automatically identifies extreme points for each parameter, including maximum heart rate, minimum blood oxygen saturation, and peak blood pressure fluctuations, and calculates synchronicity indicators for these extreme values ​​to assess the coordination of physiological responses across multiple systems.

[0025] The anomaly detection function is implemented by establishing a dynamic threshold model. Based on individual patient differences and historical data, the system sets baseline parameter ranges for both day and night, including a safe range for heart rate, a reasonable fluctuation range for blood pressure, and a minimum threshold for blood oxygen saturation. During real-time monitoring, the system continuously tracks the duration for which each parameter deviates from the baseline range. When an indicator exceeds a threshold and remains above the preset duration, an anomaly event counter is triggered. This counter not only records the frequency of anomalies but also calculates the density of anomalies per unit time, forming a probability matrix reflecting the stability of the patient's condition. To distinguish the clinical significance of anomalies, the system combines the patient's medication records to analyze the correlation between drug metabolism cycles and fluctuations in physiological parameters, thereby differentiating between pathological changes and normal responses caused by drug intervention.

[0026] The risk integration function further incorporates environmental factors. The system connects to a network of temperature and humidity sensors within the ward to acquire real-time data on the patient's microenvironment. Addressing the susceptibility of blood pressure measurements to temperature fluctuations, the system establishes an environmental compensation algorithm to dynamically correct the raw blood pressure data based on the difference between the current temperature and the standard value. Simultaneously, for patients receiving vasoactive drug therapy, the system reads the drug infusion rate from the electronic prescription, combines it with the drug's pharmacokinetic characteristics, calculates the expected impact of drug concentration on heart rate variability, and generates corresponding compensation parameters. Finally, the system integrates the environmentally corrected and drug-compensated physiological parameters into a structured set of vital signs. This set not only includes the original monitoring values ​​but also adds multi-dimensional labels such as data source, environmental conditions, and drug effects, providing a comprehensive and reliable data foundation for subsequent health assessments.

[0027] The health assessment module achieves accurate judgment of patient status through multi-level pathological feature analysis. The pathological matching function first performs multi-scale decomposition of the electrocardiogram (ECG) signal, extracting the morphological features and temporal characteristics of the waveform. The system maintains a cloud-based pathological feature library, containing typical waveform patterns for various cardiac diseases. Using a dynamic time warping algorithm, the system calculates the similarity between the patient's real-time ECG signal and the pathological patterns in the feature library, identifying specific abnormal waveform segments and their durations. For identified abnormal waveforms, the system further analyzes their location density and temporal distribution patterns to determine whether they constitute clinically significant premature ventricular contractions or rhythm disturbances.

[0028] The risk quantification function focuses on the dynamic assessment of myocardial injury. The system periodically extracts historical myocardial enzyme data from the patient's electronic medical record, including key indicators such as troponin and creatine kinase isoenzymes. Using sliding window analysis technology, it tracks the trends of these indicators over time and calculates the rate of change within specific time intervals. When an indicator value shows a continuous upward or downward trend, and the rate of change exceeds a preset warning threshold, the system activates the corresponding risk warning mechanism. This trend-based warning method can detect potential risk signals before the absolute value reaches critical standards.

[0029] The indicator generation function employs a multi-parameter fusion strategy. The system receives analysis results from two sub-modules: pathological matching and risk quantification, comprehensively considering multiple factors such as heart rate variability characteristics, premature beat density, and myocardial injury trends. Through preset weighting rules, the system calculates risk scores for various aspects of cardiac function and provides suggested adjustments to the cardiac function classification based on the scores. Simultaneously, the system assesses the potential secondary effects of cardiac dysfunction on other organs, particularly the potential risks to renal perfusion and function, generating a comprehensive risk assessment report reflecting the interactions between multiple organ systems. This report uses a structured data format, includes machine-readable risk level labels and detailed assessment evidence, providing objective support for clinical decision-making.

[0030] Throughout the implementation process, the system employs a distributed computing architecture to process massive amounts of physiological data and complex analytical algorithms. The cloud computing platform provides elastic computing resources, dynamically adjusting processing capacity based on real-time data volume. All analysis results are validated by prior knowledge from clinical experts to ensure that algorithm outputs conform to medical logic. The system also establishes a comprehensive data quality control mechanism, including signal quality detection, outlier filtering, and missing data compensation techniques, guaranteeing the reliability of input data and the accuracy of analysis results. Through this comprehensive and meticulous implementation, the system achieves real-time monitoring and precise assessment of the health status of cardiology patients.

[0031] Example 2: See Figure 3The resource allocation module achieves efficient scheduling and management of cardiology nursing resources through intelligent task parsing and path optimization algorithms. This module receives structured risk indicators from the health assessment module and transforms them into a sequence of executable nursing tasks. The system's built-in cardiac function classification mapping table establishes a correspondence between different levels of risk assessment results and standard nursing procedures. For example, for patients with cardiac function level IV, it automatically triggers 12 basic nursing interventions, including non-invasive ventilation support, intravenous diuretic injection, and continuous ECG monitoring. During task parsing, the system queries RFID tag data from the hospital's material management system in real time to obtain key resource information such as emergency drug inventory status and ventilator usage, calculating the equipment dependence and material consumption for each nursing procedure.

[0032] After task decomposition, the path optimization function begins operation. The system tracks the location information of medical staff in real time through a UWB (Ultra-Wideband) positioning network deployed throughout the ward. Each nurse's positioning beacon updates coordinate data at a frequency of 1Hz, and the system maps this spatial information onto a 3D ward model, constructing a dynamically updated location topology map. When multiple emergency tasks occur simultaneously, the system calculates the geographical radius between the execution locations of each task and analyzes the overlap of time windows. If the geographical center distance between two emergency tasks is less than a preset threshold and the time windows overlap for more than a certain duration, the system determines there is a risk of path conflict. At this point, the dynamic adjustment algorithm begins to re-evaluate the task allocation scheme, comprehensively considering factors such as the nurse's professional skill level, the distance between the current location and the task point, and the straight-line accessibility of the equipment transportation path, to generate the optimal task reallocation scheme.

[0033] The scheduling generation function transforms optimized task sequences into executable nursing plans. The system establishes an equipment usage time conflict matrix, analyzing the demand for medical equipment from different nursing procedures. For parallel tasks requiring the same equipment resources, the system automatically inserts buffer time to ensure the orderly handover of critical equipment such as infusion pumps and ECG monitors. When generating the final scheduling plan, the system considers not only time factors but also assesses the clinical priority between tasks, ensuring that the nursing needs of high-risk patients are met first. The scheduling plan is presented visually on the mobile nursing terminal, including detailed execution time windows, equipment handover nodes, and operational precautions, facilitating accurate understanding and execution by nursing staff.

[0034] The application of indoor positioning technology provides precise spatial data support for path optimization. The deployed UWB positioning base stations cover the entire cardiology ward, calculating the distance between tags and base stations by measuring the time-of-flight of wireless signals. A three-dimensional spatial mapping model transforms the physical ward area into a digital coordinate system, accurately reconstructing the spatial relationships of key areas such as wards, nurse stations, and equipment rooms. Positioning data undergoes Kalman filtering to eliminate interference factors such as multipath effects, ensuring the stability and reliability of location information. The system periodically calibrates positioning accuracy, adjusting positioning parameters by comparing coordinates of reference points to maintain centimeter-level positioning accuracy.

[0035] Task conflict detection employs a dual spatial-temporal criterion. The system defines the impact area for each emergency rescue task, delineating a service radius centered on the execution location. When the service areas of different tasks overlap spatially and their planned execution times overlap, the system initiates a conflict resolution process. Conflict probability calculation considers not only geographical distance but also variables such as task duration and resource demand intensity, forming a multi-dimensional conflict assessment model. The system maintains a dynamically updated task queue, monitors the execution progress of each task in real time, and adjusts the sensitivity threshold for conflict judgment based on actual conditions.

[0036] The dynamic adjustment function utilizes a combinatorial optimization algorithm to reallocate resources. The system employs an improved Hungarian algorithm to handle the task-personnel matching problem, incorporating constraints such as professional skill matching and current workload while considering path distance. For equipment transportation route planning, the system calculates the length, number of turns, and difficulty of each alternative route, selecting the optimal comprehensive transportation plan. When overlapping transportation routes for large equipment such as ventilators are detected, the system automatically adjusts transportation time or selects a detour to avoid corridor congestion. The adjusted plan is pushed to the mobile terminals of relevant nursing staff in real time, displaying the updated task list and navigation path.

[0037] Equipment scheduling management ensures rational resource utilization through timeline conflict detection. The system establishes a usage timeline for each critical medical device, marking the occupied time periods and the expected release time. When a new nursing task requests the use of a device, the system checks for overlap between the requested time period and existing occupancy. In cases of complete conflict, the system automatically coordinates the usage order based on task priority; in cases of partial conflict, the system attempts to fine-tune the operation time or suggests using alternative equipment. Management of commonly used equipment such as infusion pumps adopts a reservation system, allowing nursing staff to check equipment availability and reserve usage time periods in advance via mobile terminals. The system automatically records the equipment handover process, including handover time, equipment status confirmation, and the transmission of usage precautions, forming a complete equipment circulation log.

[0038] The mobile terminal's interactive design fully considers the needs of clinical work scenarios. Nursing staff are equipped with tablets that run a dedicated application that displays their personal task list in a timeline format. Task cards display basic patient information, nursing interventions, required equipment, and estimated time. The navigation function provides optimal path guidance from the current location to the task point, updating in real-time the presence of obstacles or congestion along the path. During task execution, the system supports voice interaction and gesture control, facilitating operation by nursing staff in a sterile environment. After task completion, nursing staff confirm the execution status by scanning the patient's wristband and medication barcode; the system automatically updates the task status and releases relevant resources.

[0039] The entire resource allocation process employs a closed-loop control mechanism. The system continuously monitors the execution progress of nursing tasks, comparing the actual completion time with the planned time. When a significant delay is detected, the system analyzes the cause of the delay and triggers corresponding compensation mechanisms, such as dispatching additional personnel or adjusting the time windows for subsequent tasks. Execution details of completed tasks, including operation time, personnel involved, and equipment used, are fully recorded for subsequent quality analysis and algorithm optimization. The system periodically evaluates the actual effectiveness of the scheduling plan, continuously adjusting and optimizing parameters by analyzing historical data to improve the accuracy and practicality of resource allocation.

[0040] Through this refined implementation method, the resource allocation module achieves intelligent management of the entire process of cardiology nursing work, from task decomposition and path planning to execution monitoring. The system transforms abstract nursing needs into concrete, actionable solutions, coordinating the rational distribution of human and equipment resources in time and space, effectively improving the efficiency and quality of nursing work. The combination of 3D spatial modeling and real-time positioning technology provides precise spatial data support for resource scheduling in complex medical environments. Dynamic adjustment algorithms can quickly respond to emergencies, ensuring the rational allocation of emergency resources. The visual interactive interface lowers the technical barrier to system use, allowing nurses to focus on clinical operations rather than system operations. The entire module operates without relying on any preset fixed rules, but rather through continuous learning and adaptation, forming dynamic scheduling strategies that match specific work scenarios.

[0041] Example 3: See Figure 4The intervention analysis module establishes a correlation model between nursing interventions and patient physiological responses, enabling quantitative evaluation and traceability of clinical intervention effects. This module is activated after the implementation of the tiered nursing care scheduling plan, continuously monitoring dynamic changes in patients' vital signs and capturing the temporal relationship between key intervention nodes and fluctuations in physiological parameters. The system employs a high-frequency data acquisition mode, shortening the physiological parameter sampling interval from the conventional 5 minutes to 30 seconds after key operations such as intravenous drug administration and position adjustment, enhancing its ability to capture rapid changes. Diuretic administration triggers a dedicated blood pressure monitoring protocol; the system records the administration timestamp and establishes a two-hour data observation window before and after, analyzing the trajectories of changes in systolic blood pressure, diastolic blood pressure, and mean arterial pressure.

[0042] Physiological response analysis employed a sliding window comparison method, with the system calculating statistical differences in blood pressure parameters before and after intervention. When a decrease in systolic blood pressure exceeding a specific percentage of baseline values ​​within one hour was detected, the system marked the event as a positive drug response case. A time-series alignment algorithm precisely matched nursing operation logs with the physiological data stream, identifying deviations between execution parameters such as intravenous injection rate and drug concentration and standard operating procedures. The formula for calculating the injection rate difference is as follows:

[0043] in, This indicates the actual recorded completion time of the betting activity. This represents the injection time required by the standard operating procedure. When the calculated result exceeds a preset threshold, the system marks the deviation event on the timeline and extracts the blood potassium test results within specific time windows before and after the deviation for correlation analysis.

[0044] The deviation tracing function is achieved by constructing a multi-factor influence network. The system integrates multi-source data such as nursing operation deviations, environmental parameter fluctuations, and drug interactions to establish a Bayesian probabilistic graphical model. This model uses factors such as intravenous injection rate deviation, room temperature changes, and concomitant medication as parent nodes, and sudden changes in serum potassium concentration as child nodes, calculating the conditional probability of each factor on the abnormal physiological response. The network parameters are trained using historical case data to continuously optimize the weight relationships between nodes. When a new abnormal event occurs, the system runs a probabilistic inference algorithm to generate a causal analysis report containing the contribution of each factor. The report displays key influencing factors in the form of a heatmap and sorts them according to clinical relevance, helping medical staff quickly locate the potential root cause of the problem.

[0045] The early warning execution module initiates targeted improvement measures based on the source analysis results. The system retrieves the electronic files of nursing staff involved in operational deviations, analyzing their training records and skills certification status. When it is found that an operator has not completed the annual simulated assessment or specific skills training, the system automatically generates an intensive training plan that includes the missing course content. The training plan adopts adaptive logic, dynamically adjusting the training content and assessment standards according to the type and severity of the deviation. For intravenous injection operation deviations, the plan focuses on practical modules such as infusion pump calibration and drug calculation; for delayed emergency response, the frequency and difficulty of scenario simulation exercises are increased.

[0046] The virtual training environment is constructed using a modular design principle. The system utilizes the Unity3D engine to develop configurable virtual scenes that simulate common acute conditions in cardiology. The vasoactive drug compatibility training module reproduces blood pressure response curves under different dosing regimens, requiring trainees to adjust infusion parameters based on real-time changes in vital signs. The fluid resuscitation training scenario incorporates dynamic disease progression logic, where each trainee's decision affects the virtual patient's outcome. The system records operational details during training, including decision-making time, operational accuracy, and procedural compliance, generating personalized assessment reports with improvement suggestions.

[0047] Skills assessment employs a multi-dimensional quantitative indicator system. The system not only evaluates the final result of the operation but also analyzes performance at key points during the process. The assessment of intravenous drug administration includes sub-indicators such as the accuracy of drug calculation, the rationality of equipment parameter settings, and the precision of execution time control. Each indicator has a dynamic threshold, and the pass / fail standard is adjusted according to the trainee's job level and experience. Assessment data is integrated with the hospital's continuing education credit system, allowing nurses who complete the training to earn corresponding professional development credits.

[0048] The entire intervention analysis process forms a complete quality improvement closed loop. The system continuously tracks the subsequent operational performance of trained nurses, comparing changes in deviation rates before and after training. Analysis results are fed back to the training program optimization algorithm, dynamically adjusting the weights and content of different training modules. The nursing management platform provides a multi-dimensional quality monitoring dashboard, displaying summary data such as overall departmental operational compliance rates, training completion status, and skills improvement trends. This data supports nursing managers in developing targeted team capability enhancement plans, forming a continuous improvement professional development mechanism.

[0049] The reliability of data acquisition and processing is ensured through multiple verification mechanisms. The system performs integrity checks on all input nursing operation records, verifying the continuity and logical rationality of timestamps. Physiological monitoring data undergoes signal quality assessment, automatically identifying and eliminating outliers caused by motion artifacts or equipment interference. Matching of key time nodes employs a two-way verification mechanism, both searching for corresponding physiological data changes in nursing records and tracing back to possible interventions from significant physiological fluctuations. This two-way tracing method improves the accuracy of event correlation and reduces false alarms and missed alarms.

[0050] The system interface design supports multi-faceted data analysis needs. The clinical view focuses on the intervention response chain of a single patient, displaying the correlation between nursing procedures, physiological changes, environmental factors, and other events in a timeline format. The management view provides departmental-level quality indicator summaries and supports multi-dimensional filtering and comparative analysis by ward, shift, and personnel category. The training view integrates individual and team skill development trajectories, visually displaying weaknesses and improvement progress. All views support drill-down queries, allowing users to delve deeper from summary data to specific operational details and original records.

[0051] The intervention analysis module integrates with the hospital's existing information system using a standardized interface. The system retrieves basic patient information and historical data from the electronic medical record system via the HL7 protocol and retrieves relevant examination results from the medical imaging system via the DICOM standard. Nursing operation data undergoes format conversion and terminology mapping through specialized middleware to ensure accurate correlation between information from different sources. Analysis results are returned to the clinical system in structured document format, embedded in the patient's electronic nursing records, forming a complete chain of diagnostic and treatment evidence.

[0052] Through this refined implementation method, the intervention analysis module achieves complete closed-loop management from nursing procedures to physiological responses and quality improvement. The system not only identifies surface-level operational deviations but also delves into the root causes and influencing factors. Evidence-based training program design makes capacity-building measures more targeted, and the application of virtual simulation technology creates a safe environment for repeated practice. A multi-dimensional assessment system objectively reflects the skill level of nursing staff, and a data-driven continuous improvement mechanism promotes overall improvement in nursing quality. The entire module operates without relying on subjective judgment but provides objective and reliable decision support for clinical quality management through quantitative analysis and probabilistic reasoning.

[0053] Example 4: The early warning execution module constructs a personalized clinical skills enhancement system through in-depth analysis of nursing operation deviation data. This module receives key node execution deviation records from the intervention analysis module. Taking a deviation in the infusion rate adjustment of vasoactive drugs during a cardiogenic shock resuscitation as an example, the system first parses the operation type code "VA-203" for this event, corresponding to a vasoactive drug dosage adjustment operation. A search of the operator's electronic file shows that the nurse completed basic life support training within the past 12 months, but their Advanced Cardiovascular Life Support (ACLS) certification expired 8 months ago. The system further retrieves their operation record database from the past three years and finds that similar drug dosage adjustment deviations occurred three times in the past six months, all involving norepinephrine infusion scenarios.

[0054] The system establishes a skills deficiency analysis matrix, mapping the "VA-203" type bias to seven major skill assessment dimensions. Table 1 shows some of the nursing staff's skills assessment data.

[0055] Table 1: Partial Skills Assessment Data of the Nursing Staff

[0056] Based on this analysis, the training subsystem dynamically configures virtual reality training scenarios. For the drug calculation dimension, the system generates an interactive question bank containing concentration conversions for various vasoactive drugs, simulating common drug specification changes during emergency treatment. When a virtual patient's blood pressure suddenly drops, trainees must complete the concentration conversion from 50mg / 50ml to 2mg / ml within a limited time and correctly set the infusion pump parameters. The equipment operation dimension focuses on training the coordinated use of multi-channel infusion pumps, simulating complex scenarios of simultaneously adjusting the infusion rates of norepinephrine and dopamine. The system monitors in real-time whether the dose ratio of the two channels meets the treatment guidelines.

[0057] The virtual scenario design incorporates clinical decision tree logic. Taking a cardiogenic shock case as an example, the initial scenario presents a virtual patient with blood pressure of 82 / 45 mmHg and heart rate of 118 beats / min. Trainees must select the type and initial dose of vasoactive drugs based on real-time changes in vital signs. The system's built-in physiological model dynamically responds to each decision made by the trainee: correct drug selection and dosage adjustments gradually improve the patient's condition, while incorrect decisions lead to deterioration. Multiple decision branches are set within the scenario; for example, when the trainee chooses to use dopamine instead of norepinephrine first, the system triggers a pathophysiological change of increased myocardial oxygen consumption, guiding the trainee to understand the theoretical basis for drug selection.

[0058] The program generation subsystem integrates multi-dimensional assessment data to generate a customized training program for the nurses in the example. This program includes 8 hours of virtual reality practical training, focusing on the two weaker dimensions of emergency response and process execution. The ACLS protocol training module recreates 10 typical arrhythmia scenarios, requiring trainees to correctly identify the heart rhythm and select synchronous / asynchronous mode on a virtual defibrillator. The system automatically adjusts the assessment difficulty based on historical pass rates; for this nurse's 75% pass rate in the process execution dimension, a perfect execution of the process must be completed three consecutive times to pass. The theoretical training section focuses on the pharmacological characteristics of vasoactive drugs, including 20 drug selection scenario judgment questions based on real cases.

[0059] The training effectiveness is evaluated using a multi-time-point assessment mechanism. After completing virtual reality training, the system arranges for the nurse to undergo a physical operation assessment in a mannequin laboratory. The assessment scenario simulates an ICU environment, setting up a critical situation of sudden blood pressure drop, requiring the operator to independently complete the entire process from medication preparation to infusion rate adjustment. Evaluation indicators include quantitative parameters such as operation time, dosage calculation error rate, and equipment setting accuracy, while also recording the clinical thought process at key decision points. The system compares the assessment results with baseline data before training and generates an evaluation report including the progress in each dimension.

[0060] A continuous tracking mechanism ensures the conversion of training effectiveness. For three months after training, the system continuously monitors the nurse's actual clinical operation data. Each time vasoactive drug adjustments are performed, the mobile terminal automatically displays key operational prompts and records the deviation of actual execution parameters from standard values. The system establishes an individual skill development curve and regularly analyzes trends in operational accuracy. When signs of decline in a specific skill dimension are detected, a micro-training module is automatically triggered, pushing targeted review content and short situational tests.

[0061] The management platform supports monitoring of team capability development. Nursing managers can view a department-wide skills matrix heatmap to intuitively identify common weaknesses within the team. The system automatically generates quarterly training analysis reports, including management indicators such as average pass rates for each skill dimension, distribution of training needs, and return on investment. For the ACLS protocol implementation issues identified in the example, the system recommends organizing a department-wide scenario simulation exercise and marking a list of key personnel whose certifications are about to expire.

[0062] Data interaction design optimizes clinical workflows. The training system synchronizes in real time with the hospital's human resources management system, automatically updating nurses' qualification certification status. When ACLS certification is about to expire, a renewal reminder is sent three months in advance, and training slots are reserved. The mobile application supports offline learning, allowing nurses to complete theoretical modules during their spare time. Training progress data is integrated with the scheduling system to avoid scheduling shifts during critical operation periods for personnel undergoing key skills training.

[0063] The entire early warning execution process forms a multi-layered improvement mechanism from the individual to the system. Individual operational deviations trigger individual capacity-building programs, the identification of common problems guides team-level interventions, and continuous data tracking verifies the effectiveness of improvements. Virtual reality technology creates a safe trial-and-error environment, allowing nursing staff to repeatedly practice complex operations in risk-free scenarios. A dynamic difficulty adjustment mechanism ensures that training is both challenging and achievable. A quantitative assessment system eliminates subjective judgment bias, providing objective references for career development. The system's deep integration with the existing hospital information platform makes training management a natural extension of daily clinical work rather than an additional burden.

[0064] Example 5: The environmental calibration function of the vital signs monitoring module uses multi-sensor data fusion technology to compensate for environmental interference in physiological parameter measurements. A network of temperature and humidity sensors deployed in the ward collects environmental data at a frequency of minutes, and the system establishes a dynamic correction model between environmental parameters and physiological indicators. When the temperature sensor detects a deviation from the standard value of 22 degrees Celsius in the ward, the system dynamically adjusts the raw blood pressure value acquired by the non-invasive blood pressure monitor according to a preset temperature-vasoconstriction effect relationship. This correction considers the gradual effect of temperature changes on vascular tension and uses a nonlinear compensation algorithm to handle measurement deviations under sudden temperature changes. Simultaneously, the system monitors changes in the relative humidity of the ward; when the humidity exceeds 60%, a special compensation mode is activated to consider the impact of changes in skin conductivity on ECG signal quality under high humidity conditions.

[0065] The drug interference analysis function is deeply integrated with the data flow of the hospital pharmacy management system. The system acquires real-time infusion information of vasoactive drugs from the electronic prescription system, including the current infusion rate and cumulative dose of drugs such as norepinephrine and dopamine. For each type of drug, a pharmacokinetic model is established to calculate the expected impact of drug concentration on autonomic nervous system function. For example, for alpha-receptor agonists, the system predicts the possible heart rate variability inhibition effect within 30-90 seconds after administration and marks the potentially drug-affected time periods in the raw heart rate variability data. The system maintains a drug effect time window database, recording the typical time characteristics of drug onset, peak effect, and decline under different administration methods, providing pharmacological background references for the interpretation of physiological parameters.

[0066] The data correction engine employs a multi-level verification mechanism to process raw physiological signals. The system first verifies the reliability of environmental sensor data, eliminating abnormal readings caused by sensor malfunctions or temporary obstructions. It then performs time alignment to ensure a precise temporal correspondence between changes in environmental parameters and fluctuations in physiological indicators. During the drug effect compensation phase, the system differentiates between intravenous bolus injection and continuous infusion, employing different compensation strategies. For rapidly administered drugs, the system sets dynamic compensation windows before and after the administration time point, with the window width automatically adjusted according to drug characteristics. For continuously infused drugs, a continuous compensation mode is triggered, with the system updating compensation parameters in real time based on changes in the infusion rate. The final output corrected data retains a dual record of both the original measurement and the corrected value for clinical personnel to compare and reference.

[0067] The medication safety monitoring module constructs a dynamic response model of drug-physiological indicators. The system continuously analyzes the trend of serum potassium concentration changes in patients using diuretics and establishes a prediction curve based on the dosage and time function. When the furosemide infusion rate and the rate of decrease in serum potassium are detected to exceed the normal ratio range, the system initiates a risk warning process. The warning mechanism adopts a tiered trigger strategy, judging the risk level based on a combination of absolute serum potassium value and rate of decrease. The primary warning is triggered when serum potassium is between 3.0-3.5 mmol / L and decreases rapidly, and the system displays a warning message on the nursing terminal; the intermediate warning is activated when serum potassium is below 3.0 mmol / L, and the system automatically locks the infusion pump and prompts for immediate potassium supplementation; the advanced warning is for critical situations where serum potassium is below 2.5 mmol / L, and the system simultaneously notifies the medical team and generates an emergency consultation request.

[0068] The alternative treatment recommendation engine integrates a clinical guideline knowledge base. When the system detects high-risk drug interactions, it automatically retrieves alternative drug options from the hospital's medication knowledge base. For hypokalemia caused by furosemide, the system recommends different potassium supplementation strategies, such as 10% potassium chloride or potassium magnesium aspartate, based on the patient's renal function and current electrolyte levels. The recommended regimen includes detailed preparation methods, infusion rate ranges, and monitoring requirements, supporting rapid implementation by nursing staff. The system also provides related monitoring suggestions, such as increasing the frequency of ST-segment monitoring on ECG or arranging emergency electrolyte retesting.

[0069] The environment-medication-physiology closed-loop control system enables real-time intervention and adjustment. When a sustained increase in ambient temperature leads to an upward trend in blood pressure correction values, the system automatically adjusts the ward's air conditioning parameters to maintain a stable measurement environment. For cases where medication effects cause significant changes in physiological parameters, the system dynamically adjusts the monitoring protocol, such as adding ECG leads or shortening the non-invasive blood pressure measurement interval. This adaptive monitoring strategy is personalized based on individual patient responses, avoiding a one-size-fits-all fixed monitoring mode. The system logs all automatic adjustment operations, including adjustment time, adjustment parameters, and adjustment basis, forming a complete closed-loop control record.

[0070] The clinical decision support interface presents a multi-dimensional data correlation view. The system features a dedicated environment-drug effect dashboard, synchronously displaying temperature change curves, drug infusion rate curves, and physiological parameter curves before and after correction in a timeline format. Clinicians can use interactive controls to select different display combinations, intuitively observing the combined effects of environmental factors and drug interventions on physiological indicators. Critical safety warnings employ a color-coding system, using different background colors from light yellow to dark red to highlight information according to urgency. The interface retains manual overriding permissions for professional clinicians, allowing them to manually adjust system parameters or temporarily disable specific compensation functions in special circumstances.

[0071] A system calibration mechanism ensures long-term operational accuracy. Regular, automated sensor cross-validation procedures compare readings from temperature and humidity sensors at different locations to identify potential equipment drift. The drug effect model is updated monthly, incorporating the latest pharmacological research data and clinical medication feedback. The system maintenance team regularly reviews automated amendments, randomly checks the clinical rationale of correction algorithms, and manually adjusts compensation coefficients when necessary. This quality control mechanism, combining automated calibration and manual review, ensures the system's stability and reliability under various clinical conditions.

[0072] Deep integration with hospital infrastructure expands the system's functional boundaries. The system interfaces with building management systems to acquire air conditioning operation status and airflow organization data, predicting response delays in temperature regulation. Direct connection to laboratory information systems allows for real-time acquisition of key test results such as electrolytes and blood gas analysis, verifying the biochemical basis of physiological parameter changes. Integration with the nursing call system activates bedside call lights simultaneously when advanced alerts are triggered, drawing the attention of nursing staff. This cross-system collaboration model breaks down information silos, constructing a comprehensive patient safety protection network.

[0073] Through this refined implementation, the system intelligently compensates for environmental disturbances and drug effects, enhancing the clinical reliability of raw physiological data. Multi-parameter fusion analysis reveals risk patterns that are difficult to detect with traditional single-parameter monitoring, and the early warning mechanism buys valuable time for clinical intervention. Closed-loop control enables the monitoring system to adapt to complex and ever-changing clinical environments, maintaining stable monitoring quality. An intuitive decision support interface helps clinicians quickly understand the physiological and pathological significance behind the data, making more accurate clinical judgments. The entire system operates without interfering with routine diagnostic and treatment procedures, silently enhancing every aspect of the patient safety protection system.

[0074] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0075] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A cloud-based cardiology nursing information monitoring system, characterized in that, The system includes: The vital signs monitoring module collects multidimensional physiological parameters of cardiology patients, integrates real-time data streams from wearable devices and bedside monitors, continuously tracks the patient's heart rate variability and blood pressure fluctuation curves, calculates abnormal deviations in respiratory rate, and generates a dynamic set of vital signs by combining changes in blood oxygen saturation. The health assessment module receives the dynamic set of vital signs, identifies abnormal fluctuation nodes in the vital sign data, matches health event records in the patient's historical medical records, quantifies the correlation strength between the current vital signs and typical pathological patterns, assesses the degree of deviation of the patient's health status, and generates multi-dimensional health risk indicators. Based on the multi-dimensional health risk indicators, the resource allocation module analyzes the real-time occupancy status of nursing resources, calculates the urgency weight of different nursing tasks, dynamically allocates the work paths of medical staff and the usage sequence of medical equipment, and generates a hierarchical nursing scheduling scheme. The intervention analysis module executes the hierarchical nursing scheduling plan, compares the changes in vital sign data before and after resource allocation, detects the response delay time of abnormal vital signs, locates the deviation nodes in the execution of nursing intervention measures, and generates a nursing effect traceability report.

2. The cloud-based cardiology nursing information monitoring system according to claim 1, characterized in that, The vital signs monitoring module includes: The multi-source fusion submodule synchronously receives heterogeneous data streams from an electrocardiogram monitor, a pulse oximeter, and a non-invasive blood pressure monitor, calibrates the sampling timestamps of different devices, extracts the extreme points of physiological parameters within five consecutive minutes, calculates the synchronization index of parameter fluctuation frequency, and generates a time-aligned set of vital signs data. The anomaly detection submodule analyzes the time-aligned vital sign data set, divides the baseline parameter range of the day-night cycle, monitors the duration of real-time parameters exceeding the range threshold, counts the occurrence density of abnormal events per unit time, and generates a vital sign anomaly probability matrix. The risk integration submodule calls the abnormal vital signs probability matrix, associates the drug metabolism time window in the patient's medication record, calculates the correlation coefficient between abnormal vital signs and drug concentration, integrates external interference factors from environmental temperature and humidity sensors, and generates the dynamic vital signs set.

3. The cloud-based cardiology nursing information monitoring system according to claim 1, characterized in that, The health assessment module includes: The pathological matching submodule extracts the heart rate oscillation parameters from the dynamic vital signs set, compares them with the waveform feature library of typical heart failure cases, calculates the spectral difference between the current waveform and the pathological baseline waveform, and identifies the location and duration of premature beats. The risk quantification submodule receives the spectrum difference and premature beat event data, loads the historical values ​​of myocardial enzyme spectrum from the patient's electronic medical record, constructs the dynamic change curve of the current myocardial injury index, and quantifies the degree of closeness of the curve slope to the critical value. The indicator generation submodule integrates the dynamic change curves of the myocardial injury indicators with the pathological matching results, calculates the priority ranking of different organ failure risks using weighted calculations, and generates the multi-dimensional health risk indicators that include cardiac function classification labels and organ risk weights.

4. The cardiology nursing information monitoring system based on cloud computing according to claim 3, characterized in that, The resource allocation module includes: The task parsing submodule decomposes the cardiac function classification labels in the multi-dimensional health risk indicators, maps the nursing operation list corresponding to different levels, identifies the inventory status of emergency medicines and the occupancy of ventilators, and calculates the equipment dependence coefficient of each nursing operation. The path optimization submodule receives the nursing operation list and equipment dependency coefficient, constructs a real-time topology map of the location information of medical staff, simulates the task completion time delay rate under different paths, and iteratively optimizes the execution order of the nursing operation sequence. The scheduling generation submodule integrates and optimizes the nursing operation sequence and equipment allocation scheme, coordinates the usage time conflicts between intravenous infusion pumps and defibrillators, and generates the hierarchical nursing scheduling scheme that includes operation time windows and equipment handover nodes.

5. The cardiology nursing information monitoring system based on cloud computing according to claim 1, characterized in that, The intervention analysis module includes: The response monitoring submodule captures real-time vital sign data streams after the execution of the graded nursing scheduling plan, marks the correspondence between diuretic administration time points and blood pressure drop events, and calculates the delay time for vital sign parameters to recover to the safe threshold. The deviation localization submodule compares the preset standard of nursing operation with the timestamp of the execution record, identifies the operation node where the intravenous injection rate deviates from the standard value, and associates the deviation operation with the blood potassium concentration mutation event in the adjacent time window; The source tracing generation submodule integrates the delay duration and the location of the sudden change in blood potassium concentration, constructs a causal chain model of nursing interventions and vital sign responses, and generates a nursing effect source tracing report containing records of deviations in the execution of key intervention nodes.

6. The cloud-based cardiology nursing information monitoring system according to claim 5, characterized in that, The system also includes: The early warning execution module receives the key intervention node execution deviation records in the nursing effect traceability report, retrieves the operation history database of the corresponding medical staff, matches the validity status of the emergency skills certification certificate, and generates a personalized skills enhancement training plan. The personalized skills enhancement training program includes operating procedures for simulated emergency rescue scenarios, standard video tutorials for equipment operation, and assessment indicators.

7. The cloud-based cardiology nursing information monitoring system according to claim 6, characterized in that, The early warning execution module includes: The competency assessment submodule analyzes the operation type of the execution deviation record of the key intervention node, retrieves the training completion rate data in the medical staff files, and calculates the correlation strength between operational errors and skill certificate update time. The training construction submodule configures a virtual reality simulation of cardiogenic shock scenario based on the correlation strength results of the operation type, and dynamically generates a correction training module for infusion rate adjustment error; The scheme generation submodule integrates the virtual reality simulation scene and the correction training module, overlays the analysis report of operational weaknesses in historical assessments, and generates the personalized skills enhancement training scheme that includes training hour allocation and assessment pass thresholds.

8. The cardiology nursing information monitoring system based on cloud computing according to claim 1, characterized in that, The vital signs monitoring module also includes: The environmental calibration submodule acquires real-time readings from the ward's temperature and humidity sensors, calculates the influence coefficient of temperature fluctuations on vasoconstriction factors, and corrects the environmental interference deviation of blood pressure monitoring values. The drug interference analysis submodule correlates the infusion rate of vasoactive drugs in the electronic medical order system, quantifies the hysteresis response time of peak drug concentration and heart rate variability, and generates drug effect compensation parameters. The data correction submodule integrates the environmental interference deviation and drug effect compensation parameters to reconstruct the baseline reference value of the original vital signs data and update the blood pressure fluctuation curve in the dynamic vital signs set.

9. The cloud-based cardiology nursing information monitoring system according to claim 4, characterized in that, The path optimization submodule performs the following operations: The real-time location tracking unit acquires indoor positioning beacon data worn by medical staff and constructs a three-dimensional spatial coordinate mapping model of the ward. The task conflict detection unit identifies the geographic radius of multiple emergency rescue tasks during the same period and calculates the probability of resource competition in the path intersection area; Based on the resource competition probability results, the dynamic adjustment unit reallocates the entities responsible for performing nursing tasks to avoid overlapping and conflicting equipment transportation paths.

10. The cloud-based cardiology nursing information monitoring system according to claim 1, characterized in that, The system also includes: The medication safety monitoring module receives blood potassium concentration data from the dynamic vital signs set, correlates it with the diuretic infusion rate parameters being executed by the electronic medical order system, identifies potential risk time windows of drug incompatibilities, and generates high-risk drug compatibility warning instructions. The high-risk drug compatibility warning command triggers the automatic locking mechanism of the infusion pump and simultaneously pushes the alternative treatment plan to the mobile nursing terminal.

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