Pediatric patient nursing guidance method and system based on data analysis

Abstract

The invention relates to the technical field of intelligent medical treatment, in particular to a pediatric patient nursing guidance method and system based on data analysis. According to the method and the device, the short-time fluctuation and the long-term trend of the physiological characteristics of the patient can be dynamically captured by acquiring the physiological sign data in the specified time period, calculating the fluctuation condition and forming the health state fluctuation information instead of only depending on single-point measurement data, so that the change characteristics of the health state are reflected more accurately. According to the method, the physiological signs conforming to the mean value or standard deviation range are screened, the individual health baseline is established, and the deviation between the individual health baseline and the physiological signs is compared, so that health assessment can be adaptively adjusted according to the reference states of different individuals instead of unified standard assessment, and misjudgment caused by individual differences is reduced. The nursing intervention level is set based on the change trend of the physiological signs, and the nursing intensity can be flexibly adjusted through factors such as duration and frequency of the fluctuation trend, so that the nursing suggestions have higher pertinence.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical technology, and particularly to a pediatric patient care guidance method and system based on data analysis. Background Art

[0002] The field of intelligent medical technology includes methods based on computer technology and data analysis for optimizing medical services, improving diagnosis and treatment efficiency, and enhancing patient care. The core of this technology field involves the collection, storage, analysis, and application of medical data, covering aspects such as electronic medical records, clinical decision support, remote monitoring, and health management. Intelligent medical care uses medical knowledge bases and artificial intelligence algorithms to process patient information to assist doctors in making diagnoses, recommending treatment plans, and predicting disease conditions.

[0003] Among them, the pediatric patient care guidance method refers to a specific method for providing care guidance for pediatric patients based on data analysis technology, including the collection and analysis of children's physiological and disease condition data, and the assessment of children's disease states in combination with a medical knowledge base. Specific methods include using wearable devices or medical sensors to collect physiological parameters such as the body temperature, heart rate, and respiratory rate of children, and screening and classifying abnormal indicators through data processing methods, and evaluating the health status in combination with children's medical records and past diagnosis and treatment records.

[0004] Existing care guidance methods mainly measure patients' physiological parameters based on wearable devices or medical sensors, and evaluate the health status in combination with children's medical records and past diagnosis and treatment records. However, they lack in-depth analysis of the physiological sign fluctuation patterns, and cannot distinguish short-term abnormal fluctuations from long-term trend changes, resulting in some short-term abnormalities being misjudged as continuous abnormalities, thus triggering unnecessary care interventions. Existing technologies usually use fixed health standards for evaluation without considering the underlying differences in individual physiological characteristics, which may lead to some children with relatively high or low resting heart rates and blood oxygen levels being misclassified as abnormal states, affecting the accuracy of care recommendations. The adjustment of care intensity only depends on static disease classification without optimizing in combination with the dynamic feedback during the implementation of care. The care plan cannot be adaptively adjusted during the implementation process, which may lead to a mismatch between the care frequency and the actual needs. The assessment of the correlation between care measures and seasonal pathogens is missing, and care recommendations cannot be adapted according to the activity levels of the main epidemic pathogens in different seasons, which may lead to a lag in the care plan and the inability to give early warnings and interventions for the high-incidence periods of seasonal diseases, reducing the timeliness and effectiveness of care. Summary of the Invention

[0005] The objective of the present invention is to address the deficiencies in the prior art, and to propose a pediatric patient care guidance method and system based on data analysis.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A pediatric patient care guidance method based on data analysis, comprising the following steps,

[0007] S1: Obtain the daily physiological signs of pediatric patients within a specified time, count the fluctuations of the physiological signs, and obtain the health status fluctuation information;

[0008] S2: Screen the physiological signs within the corresponding mean or standard deviation range within the specified time from the health status fluctuation information, establish an individual health baseline, and compare the deviation between the health baseline and the physiological signs to obtain individual health change data;

[0009] S3: Obtain the change trend of each physiological sign from the individual health change data, set the nursing intervention level according to the fluctuation situation, and obtain the nursing intensity classification result;

[0010] S4: According to the nursing intensity classification result, count the adjustment frequency of the corresponding nursing measures that have been implemented, screen the physiological signs that still show continuous fluctuations after nursing intervention, and judge whether the screened physiological signs need to prompt the adjustment of the nursing method to obtain the nursing dynamic adjustment information;

[0011] S5: Obtain the clinical infection data of pediatric patients within the current season, compare the nursing intensity classification result with the symptom characteristics of the disease, judge whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, evaluate the adjustable nursing measures, and generate nursing guidance information.

[0012] The present invention is improved in that the health status fluctuation information includes the change amplitude of the physiological sign peak value, the occurrence frequency of the physiological sign peak value, the instantaneous fluctuation data points of the physiological sign, and the continuous fluctuation data points of the physiological sign. The individual health change amplitude is specifically the deviation of the physiological sign fluctuation intensity, the change amplitude of the physiological sign, and the comparison result of the physiological sign health baseline. The nursing intensity classification result includes the abnormal fluctuation frequency, the abnormal fluctuation duration, and the nursing intervention level. The nursing dynamic adjustment information includes the adjustment frequency of the nursing measures, the influence degree of the nursing measures, the adaptability of the nursing measures, and the nursing method adjustment prompt. The nursing guidance information includes the nursing monitoring frequency, the nursing intervention method, the nursing intervention duration, and the correlation assessment of the nursing needs and the seasonal pathogen activity.

[0013] The present invention is improved in that the specific steps of obtaining the daily physiological signs of pediatric patients within a specified time and counting the fluctuations of the physiological signs to obtain the health status fluctuation information are as follows:

[0014] S101: obtaining daily physiological signs of the pediatric patient including body temperature, resting heart rate, and blood oxygen level within a specified time period, recording time series data of the physiological signs, calculating the upper and lower fluctuation amplitudes of each physiological sign, screening the change values ​​between adjacent time points, and determining whether they exceed a set reference range, accumulating the numerical distribution of the fluctuation amplitudes, and generating the fluctuation amplitude of the physiological signs;

[0015] S102: calling the fluctuation amplitude of the physiological sign, counting the fluctuation duration of each physiological sign, screening the fluctuation data points whose change rate in the specified time exceeds the preset threshold, classifying them as instantaneous fluctuation data points, and screening the fluctuation data points whose change rate in the specified time exceeds the preset threshold and multiple time points, classifying them as continuous fluctuation data points, and obtaining the physiological sign fluctuation type classification result;

[0016] S103: Based on the physiological sign fluctuation type classification result, analyze the fluctuation peak values ​​of instantaneous fluctuation data points and continuous fluctuation data points, count the frequency of occurrence of the peak value of each physiological sign within a specified time period, accumulate the fluctuation peak value data, and generate health status fluctuation information.

[0017] The present invention is improved in that the specific steps of selecting physiological signs within the corresponding mean or standard deviation range within a specified time from the health status fluctuation information, establishing an individual health baseline, and comparing the deviation between the health baseline and the physiological signs to obtain individual health change data are as follows:

[0018] S201: extracting data sequences of body temperature, resting heart rate, and blood oxygen level from the health status fluctuation information, calculating the mean and standard deviation of each physiological sign, screening physiological sign data whose fluctuation amplitude falls within the mean or standard deviation interval within a specified time range, removing abnormal data points beyond the specified range, and generating physiological sign screening results;

[0019] S202: Calculate the continuous and stable data points within a specified time range according to the physiological sign screening results, obtain the trend mean of body temperature, resting heart rate, and blood oxygen level, analyze the baseline fluctuation range of each physiological sign, and establish an individual health baseline;

[0020] S203: Compare the individual health baseline with the physiological sign fluctuation intensity in the health status fluctuation information, calculate the deviation degree of each physiological sign, count the change deviation of body temperature, resting heart rate, and blood oxygen level relative to the individual health baseline, and generate individual health change data.

[0021] The present invention is improved in that the specific steps of obtaining the change trend of each physiological sign from the individual health change data, setting the nursing intervention level according to the fluctuation situation, and obtaining the nursing intensity grading result are as follows:

[0022] S301: Based on the individual's health change data, analyze the change trends of body temperature, resting heart rate, and blood oxygen level within a specified time range, analyze the increase and decrease directions at consecutive time points, determine whether a stable, rising, or falling trend is presented, count the proportion of each trend within the time period, and generate the analysis result of the physiological sign fluctuation trend;

[0023] S302: Based on the analysis result of the physiological sign fluctuation trend, count the number of time points when the fluctuation deviation exceeds the individual's health baseline range, calculate the occurrence frequency of abnormal fluctuations within the specified time, record the start time and end time of each fluctuation, and obtain the information on the abnormal fluctuation frequency and abnormal duration;

[0024] S303: According to the information on the abnormal fluctuation frequency and abnormal duration, determine whether the physiological sign fluctuation returns to stability within the specified time. According to the two situations of returning to stability and continuous fluctuation, set the corresponding nursing intervention levels to obtain the nursing intensity grading result.

[0025] The improvement of the present invention is as follows. The specific steps for obtaining the nursing dynamic adjustment information by counting the adjustment frequency of the corresponding nursing measures implemented according to the nursing intensity grading result, screening the physiological signs that still show continuous fluctuations after nursing intervention, and determining whether the screened physiological signs need to prompt the adjustment of the nursing method are as follows:

[0026] S401: According to the nursing intensity grading result, obtain the nursing measures of the implemented nursing monitoring frequency, nursing intervention method, and nursing intervention duration, count the adjustment times of the same type of nursing measures within the specified time, analyze the implementation changes of the nursing measures, and generate the nursing measure adjustment frequency information;

[0027] S402: Based on the nursing measure adjustment frequency information, analyze the change trends of body temperature, resting heart rate, and blood oxygen level, calculate the physiological sign fluctuation situation after nursing intervention, compare the fluctuation amplitudes before and after the intervention, screen the physiological signs that still show continuous fluctuations after nursing intervention, and calculate the adaptability of the nursing measures to each screened physiological sign to generate the nursing measure adaptability information;

[0028] S403: Compare the nursing measure adaptability information with the preset nursing adaptability threshold. If the adaptability is lower than the nursing adaptability threshold, mark that the current nursing measure needs to be adjusted to generate the nursing dynamic adjustment information.

[0029] The improvement of the present invention is as follows. The specific steps for obtaining the nursing guidance information by obtaining the clinical infection data of pediatric patients in the current season, comparing the nursing intensity grading result with the symptom characteristics of the disease, determining whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, and evaluating the adjustable nursing measures are as follows:

[0030] S501: Obtain the clinical infection data of pediatric patients within the current season, extract the infection cases of multiple pathogens such as influenza virus, rotavirus, and respiratory syncytial virus, count the infection frequencies of all pathogens, calculate the distribution ratios of each pathogen within the specified time period, and generate pathogen infection frequency data;

[0031] S502: Based on the pathogen infection frequency data, compare the abnormal physiological signs in the nursing intensity grading results, analyze the typical symptom characteristics of the infection cases, match the nursing needs corresponding to the nursing dynamic adjustment information, calculate the correlation between the nursing needs and the pathogen activity, and generate nursing need correlation data;

[0032] S503: Based on the nursing need correlation data, evaluate the direction of nursing measure adjustment, screen the nursing monitoring frequencies, nursing intervention methods, and nursing intervention durations that are suitable for the seasonal pathogen infection situation, and generate nursing guidance information.

[0033] A pediatric patient nursing guidance system based on data analysis, the system includes:

[0034] The physiological sign fluctuation analysis module obtains the daily physiological signs of pediatric patients within the specified time, counts the fluctuations of the physiological signs, and obtains the health status fluctuation information;

[0035] The individual health baseline establishment module screens the physiological signs within the corresponding mean or standard deviation range within the specified time from the health status fluctuation information, establishes an individual health baseline, and compares the deviation between the health baseline and the physiological signs to obtain individual health change data;

[0036] The nursing intensity grading module obtains the change trend of each physiological sign from the individual health change data, sets the nursing intervention level according to the fluctuation situation, and obtains the nursing intensity grading result;

[0037] The nursing dynamic adjustment module counts the adjustment frequencies of the corresponding nursing measures that have been implemented according to the nursing intensity grading result, screens the physiological signs that still show continuous fluctuations after the nursing intervention, and determines whether the screened physiological signs need to prompt the adjustment of the nursing method to obtain the nursing dynamic adjustment information;

[0038] The nursing guidance plan generation module obtains the clinical infection data of pediatric patients within the current season, compares the nursing intensity grading result with the symptom characteristics of the disease, determines whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, evaluates the adjustable nursing measures, and generates nursing guidance information.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are:

[0040] In the present invention, by acquiring physiological sign data within a specified time period, calculating the fluctuations, and forming health state fluctuation information, it is possible to dynamically capture the short-term fluctuations and long-term trends of the patient's physiological characteristics, rather than relying solely on single-point measurement data, thereby more accurately reflecting the changing characteristics of the health state. Screening physiological signs that meet the mean or standard deviation range, establishing an individual health baseline, and comparing the deviation between the individual health baseline and the physiological signs enable the health assessment to be adaptively adjusted according to the baseline state of different individuals, rather than using a unified standard for assessment, reducing misjudgments caused by individual differences. Setting the nursing intervention level based on the changing trend of physiological signs allows for flexible adjustment of the nursing intensity through factors such as the duration and frequency of the fluctuation trend, making the nursing recommendations more targeted and avoiding excessive or insufficient nursing measures. Combining the implementation of the nursing intensity, counting the frequency of nursing measure adjustments, calculating the nursing measure fitness, and screening the physiological signs that still show abnormal fluctuations after nursing can dynamically optimize the nursing method, improving the pertinence and effectiveness of the nursing measures. Through the data analysis of the activity of seasonal pathogens, matching the nursing needs with pathogen infections, and adjusting the nursing strategy according to the correlation assessment, the nursing measures can be optimized for different pathogen epidemic periods, enhancing the accuracy of the nursing plan and reducing the waste of nursing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a flowchart of the present invention;

[0042] Figure 2 is a schematic diagram of the detailed process of step S1 of the present invention;

[0043] Figure 3 is a schematic diagram of the detailed process of step S2 of the present invention;

[0044] Figure 4 is a schematic diagram of the detailed process of step S3 of the present invention;

[0045] Figure 5 is a schematic diagram of the detailed process of step S4 of the present invention;

[0046] Figure 6 is a schematic diagram of the detailed process of step S5 of the present invention;

[0047] Figure 7 is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In order to make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0050] Please refer to Figure 1 , the present invention provides a technical solution: a pediatric patient care guidance method based on data analysis, including the following steps:

[0051] S1: Obtain the daily physiological signs of pediatric patients such as body temperature, resting heart rate, and blood oxygen level within a specified time, calculate the upper and lower fluctuation ranges of each physiological sign and count the duration, screen the data points of instantaneous fluctuations and continuous fluctuations, accumulate the peak change amplitude of the physiological sign and the frequency of its occurrence, and obtain the health status fluctuation information;

[0052] S2: Screen the physiological signs within the corresponding mean or standard deviation range within the specified time from the health status fluctuation information, establish an individual health baseline, compare the deviation of the fluctuation intensity of each physiological sign from the individual health baseline, calculate the change deviation of the corresponding health index, and obtain the individual health change data;

[0053] S3: Obtain the change trend of each physiological sign from the individual health change data, calculate the frequency of abnormal fluctuations and collect the corresponding duration, determine whether there is abnormal recovery or continuous abnormal fluctuation within the specified time, and set the nursing intervention level according to the judgment result to obtain the nursing intensity classification result;

[0054] S4: Count the adjustment frequency of the corresponding nursing measures already implemented according to the nursing intensity classification result, analyze the influence degree of the current nursing measures on the physiological signs, screen the physiological signs with continuous fluctuations still existing after nursing intervention according to the influence degree, calculate the nursing measure fitness, and if the fitness is lower than the preset fitness threshold, it is prompted that the nursing method needs to be adjusted to obtain the nursing dynamic adjustment information;

[0055] S5: Obtain the clinical infection data of pediatric patients within the current season and count the infection frequency of pathogens, compare the nursing intensity classification result with the typical symptom characteristics of the disease, judge whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, and evaluate the adjustable nursing measures according to the correlation, including the nursing monitoring frequency, nursing intervention method, and nursing intervention duration, to generate the nursing guidance information.

[0056] The health status fluctuation information includes the variation range of physiological sign peaks, the frequency of physiological sign peaks, instantaneous physiological sign fluctuation data points, and continuous physiological sign fluctuation data points. The specific individual health variation range is the deviation of physiological sign fluctuation intensity, the variation range of physiological signs, and the comparison result of the physiological sign health baseline. The nursing intensity classification result includes the abnormal fluctuation frequency, the duration of abnormal fluctuation, and the nursing intervention level. The nursing dynamic adjustment information includes the frequency of nursing measure adjustment, the impact degree of nursing measures, the adaptability of nursing measures, and the nursing method adjustment prompt. The nursing guidance information includes the nursing monitoring frequency, the nursing intervention method, the duration of nursing intervention, and the assessment of the correlation between nursing needs and the activity level of seasonal pathogens.

[0057] Please refer to Figure 2 , obtain the daily physiological signs of pediatric patients within a specified time, and count the fluctuations of physiological signs. The specific steps to obtain the health status fluctuation information are as follows:

[0058] S101: Obtain the daily physiological signs of body temperature, resting heart rate, and blood oxygen level of pediatric patients within a specified time, record the time series data of physiological signs, calculate the upper and lower fluctuation ranges of each physiological sign, screen the change values between adjacent time points, and determine whether they exceed the set benchmark range. Cumulate the numerical distribution of the fluctuation range to generate the physiological sign fluctuation range;

[0059] To obtain the data of body temperature, resting heart rate, and blood oxygen level of pediatric patients within a specified time, it is necessary to record the time series data of the patients' physiological signs through hospital ward, ICU monitoring equipment, wearable devices, etc. Set the recording interval, for example, record once every 5 minutes, store and mark the data timestamp for subsequent calculation and analysis. For the obtained data, calculate the upper and lower fluctuation ranges of each physiological sign, and use differential operation, that is, for the time series data {X t}, calculate the change value ΔX t = X t - X t-1 , where ΔX t represents the change value at the t-th moment, X t represents the physiological sign value at the t-th moment, and X t-1 represents the physiological sign value at the (t - 1)-th moment. If |ΔX tIf it exceeds the reference range, record its fluctuations. The reference range can be set according to the normal physiological fluctuation range. For example, the reference fluctuation range of body temperature is ±0.3°C, the resting heart rate is ±10 bpm, and the blood oxygen level is ±2%. Further screen the change values between adjacent time points to determine whether they exceed the set reference range. If the number of time points at which a certain physiological sign continuously exceeds the reference range reaches the set threshold (such as continuous exceeding the standard for more than 10 minutes), then mark the data during this period as abnormal fluctuations. Finally, accumulate the numerical distribution of the fluctuation amplitudes, and statistically analyze the distribution range of the fluctuation values of different physiological signs. For example, calculate the maximum fluctuation amplitude of body temperature within a specified time period (highest body temperature - lowest body temperature), record the extreme change range of the blood oxygen level, and generate the physiological sign fluctuation amplitude.

[0060] S102: Invoke the physiological sign fluctuation amplitude, count the fluctuation duration of each physiological sign, screen the fluctuation data points whose change rate at a specified time exceeds the preset threshold, and classify them as instantaneous fluctuation data points. At the same time, screen the fluctuation data points whose change rate at a specified time exceeds the preset threshold and have fluctuations at multiple time points, and classify them as continuous fluctuation data points to obtain the classification result of the physiological sign fluctuation type.

[0061] Invoke the physiological sign fluctuation amplitude, count the fluctuation duration of each physiological sign, obtain the continuous fluctuation information of adjacent time points, define the fluctuation duration as the time interval from the first time exceeding the reference range to returning to the normal range, screen the fluctuation data points whose change rate exceeds the preset threshold. The preset threshold is based on the normal change rate of each physiological sign. For example, the body temperature change rate is set to ±0.5°C per hour, the resting heart rate change rate is ±15 bpm per minute, and the blood oxygen level change rate is set to ±3% per minute. If the change rate at a certain time point exceeds this threshold, it is marked as a high-variation point and classified as an instantaneous fluctuation data point. In addition, count the situations where the fluctuation exceeds the threshold at multiple consecutive time points. If the fluctuation duration reaches the set threshold (such as the body temperature abnormality lasting for more than 30 minutes), it is classified as a continuous fluctuation data point. Further calculate the proportion of instantaneous fluctuation data points and continuous fluctuation data points to judge the main types of instantaneous fluctuations and continuous fluctuations. For example, if there are multiple short-time fluctuation points in the blood oxygen level but no continuous fluctuation occurs, then mark the blood oxygen fluctuation of this patient as mainly instantaneous fluctuations, and finally obtain the classification result of the physiological sign fluctuation type.

[0062] S103: Based on the classification result of the physiological sign fluctuation type, analyze the fluctuation peaks of the instantaneous fluctuation data points and continuous fluctuation data points, count the frequency of the peak appearance of each physiological sign within a specified time period, accumulate the fluctuation peak data, and generate the health status fluctuation information.

[0063] The peak values ​​of instantaneous fluctuation data points and continuous fluctuation data points are extracted, and the peak value changes of physiological signs within the specified time period are calculated. The peak value is defined as the time point when a physiological sign reaches a local maximum or minimum. For example, if a patient's highest body temperature in a day is 39.2°C and the lowest body temperature is 36.4°C, then the patient's peak temperature change is 2.8°C. The frequency of peak occurrence is further counted, and time windows (such as 1 hour, 4 hours, 24 hours) are set to calculate the peak fluctuations in each time window. If a physiological sign has multiple peak values ​​in a short period of time (such as a heart rate peak of more than 120bpm three times within 1 hour), it is judged that the physiological sign has frequent fluctuations. Finally, the fluctuation peak data is accumulated, and the total amount of physiological sign fluctuations of all patients in the specified time period is calculated. For example, the frequency of all abnormal blood oxygen fluctuations in a day is counted, and the number of occurrences of blood oxygen drop below 92% is calculated, and finally the health status fluctuation information is generated.

[0064] See also Figure 3 , the specific steps of selecting physiological signs within the corresponding mean or standard deviation range within a specified time from the health status fluctuation information, establishing an individual health baseline, and comparing the deviation between the health baseline and the physiological signs to obtain the individual health change data are as follows:

[0065] S201: extracting data sequences of body temperature, resting heart rate, and blood oxygen level from health status fluctuation information, calculating the mean and standard deviation of each physiological sign, screening physiological sign data whose fluctuation range falls within the mean or standard deviation interval within a specified time range, removing abnormal data points beyond the specified range, and generating physiological sign screening results;

[0066] First, determine the time range of data recording, such as the past 24 hours, 7 days or 30 days, obtain all data points of each physiological sign within the time range, and sort them by timestamp to ensure the continuity and time correlation of the data. Then calculate the mean and standard deviation of each physiological sign. The mean is used to represent the average level of physiological signs, and the standard deviation is used to measure the degree of fluctuation of physiological signs. Physiological sign data with fluctuation amplitude falling within the mean or standard deviation interval within the specified time range are screened, and the standard deviation screening range is set to μ±2σ (i.e., the interval within which 95% of the data falls). Taking blood oxygen level as an example, if the patient's blood oxygen mean μ=96.5% and standard deviation σ=1.2% in the past 24 hours, the screening range is 94.1% to 98.9%, and data points outside this range will be regarded as abnormal fluctuations. Similarly, the same calculation is performed for body temperature and resting heart rate, and abnormal data points outside the specified range are eliminated. For example, if a patient's blood oxygen level drops to 89% within 30 minutes, the data point will not be included in the subsequent analysis, and the physiological sign screening results are finally obtained.

[0067] S202: Based on the physiological sign screening results, calculate the continuously stable data points within a specified time range, obtain the trend means of body temperature, resting heart rate, and blood oxygen level, analyze the baseline fluctuation range of each physiological sign, and establish an individual health baseline;

[0068] Based on the physiological sign screening results, calculate the continuously stable data points within a specified time range. Define the continuously stable data points as the points whose fluctuation range is within the mean ± standard deviation interval within a continuous time period. Statistically calculate the stable time length of each time period. If a physiological sign remains within the screening range for at least 90% of the recording time, then it is determined that the physiological sign state is stable during this time period. Obtain the trend means of body temperature, resting heart rate, and blood oxygen level. Use the sliding window method to calculate the trend means of physiological signs. For example, for the body temperature data within 24 hours, calculate the sliding mean every 1 hour to determine whether there is a long-term trend change in body temperature. Analyze the baseline fluctuation range of each physiological sign. The set baseline fluctuation range can be adjusted based on the patient's individual historical data. For example, if the mean resting heart rate of a patient in the past 7 days is 72 bpm and the standard deviation is 5 bpm, then the baseline fluctuation range can be set as 67 - 77 bpm. If the patient's resting heart rate continuously exceeds this range during subsequent monitoring, it can be determined as an abnormal trend. Finally, establish an individual health baseline.

[0069] S203: Compare the individual health baseline with the physiological sign fluctuation intensity in the health status fluctuation information, calculate the deviation degree of each physiological sign, statistically calculate the variation deviation of body temperature, resting heart rate, and blood oxygen level relative to the individual health baseline, and generate individual health variation data.

[0070] Compare the individual health baseline with the physiological sign fluctuation intensity in the health status fluctuation information, calculate the deviation degree of each physiological sign. Define the deviation degree as the percentage deviation of the actual measured value relative to the individual health baseline. For example, if the mean body temperature of a patient's individual health baseline is 36.8°C and a certain measured value is 38.2°C, then the deviation degree is calculated as (38.2 - 36.8) / 36.8×100% = 3.8%. Statistically calculate the variation deviation of body temperature, resting heart rate, and blood oxygen level relative to the individual health baseline. Calculate the deviation mean of each physiological sign within a specified time range. If the deviation mean of a physiological sign exceeds the preset deviation threshold (such as ±5%), then it is determined that the state of this physiological sign deviates from the individual health baseline. Further evaluate the duration of the fluctuation. If 70% of the time of a patient's resting heart rate exceeds the threshold within the past 24 hours, then it is determined as a continuous deviation. Finally, generate individual health variation data.

[0071] Please refer to Figure 4 , obtain the variation trend of each physiological sign from the individual health variation data, set the nursing intervention level according to the fluctuation situation, and the specific steps to obtain the nursing intensity grading result are as follows:

[0072] S301: Analyze the change trends of body temperature, resting heart rate, and blood oxygen level within a specified time range based on individual health change data, analyze the increase or decrease direction at consecutive time points, determine whether a stable, rising, or falling trend is presented, count the proportion of each trend within the time period, and generate the analysis result of physiological sign fluctuation trends.

[0073] First, obtain the time series data of each physiological sign, arrange them in chronological order, calculate the numerical changes at adjacent time points. If the value at the later time point is greater than that at the previous time point, it is determined that the physiological sign is in an upward trend; if the value at the later time point is less than that at the previous time point, it is determined to be in a downward trend; if the two are equal, it is determined to be in a stable state. Then, count the proportion of time that each physiological sign is in the upward, downward, and stable states within the entire time range. For example, if the proportion of time when a patient's body temperature rises within 24 hours is 40%, the proportion of time when it falls is 30%, and the proportion of time when it is stable is 30%, it can be judged that the patient's body temperature has a relatively frequent fluctuation trend. Further analyze the change patterns between different physiological signs, such as whether the resting heart rate increases synchronously when the body temperature rises, and whether the blood oxygen level decreases when the resting heart rate rises. Finally, generate the analysis result of physiological sign fluctuation trends.

[0074] S302: Based on the analysis result of physiological sign fluctuation trends, count the number of time points when the fluctuation deviation exceeds the individual health baseline range, calculate the occurrence frequency of abnormal fluctuations within the specified time, record the start time and end time of each fluctuation, count the duration of abnormal fluctuations, and obtain the information on abnormal fluctuation frequency and abnormal duration.

[0075] Count the number of time points when the fluctuation deviation exceeds the individual health baseline range. Set the baseline range as μ ± 2σ (the interval in which 95% of the data falls). For each physiological sign, check whether its actual measured value exceeds this range. If it does, mark this time point as an abnormal fluctuation point and accumulate the number of abnormal fluctuation points. Calculate the occurrence frequency of abnormal fluctuations within the specified time. Define the abnormal fluctuation frequency as the proportion of abnormal fluctuation points in the total measurement time. For example, a patient's heart rate was measured 1440 times within 24 hours, and 60 of them exceeded the baseline range. Then the abnormal fluctuation frequency is 60 / 1440 × 100%. At the same time, record the start time and end time of each fluctuation. If an abnormal fluctuation starts at 12:05 and returns to normal at 12:20, the duration of this abnormal fluctuation is 15 minutes. Count the duration distribution of all abnormal fluctuations. Finally, obtain the information on abnormal fluctuation frequency and abnormal duration.

[0076] S303: According to the information on abnormal fluctuation frequency and abnormal duration, judge whether the physiological sign fluctuation returns to stability within the specified time. Set the corresponding nursing intervention levels according to the two situations of returning to stability and continuous fluctuation, and obtain the nursing intensity grading result.

[0077] Set the judgment criteria for recovery to stability. For example, if the abnormal fluctuation returns to the individual's healthy baseline range within 1 hour, it is judged as short-term recovery. If the abnormal fluctuation still has not recovered after multiple consecutive monitoring periods (such as more than 12 hours), it is judged as continuous fluctuation. According to the two situations of recovery to stability and continuous fluctuation, set the corresponding nursing intervention levels. If the physiological signs recover after short-term fluctuations, the nursing intervention level is set to low, such as increasing the frequency of routine monitoring. If the physiological signs fluctuate continuously for a long time, the nursing intervention level is set to high, such as increasing the monitoring frequency and adjusting the nursing measures. For example, for patients with persistent heart rate abnormalities, 24-hour ambulatory electrocardiogram monitoring may be required, and finally the nursing intensity grading results are obtained.

[0078] Please refer to Figure 5 , according to the nursing intensity grading results, count the adjustment frequencies of the corresponding nursing measures that have been implemented, screen the physiological signs that still show continuous fluctuations after nursing intervention, and judge whether the screened physiological signs need to prompt the adjustment of the nursing method. The specific steps to obtain the nursing dynamic adjustment information are as follows:

[0079] S401: According to the nursing intensity grading results, obtain the nursing measures of the implemented nursing monitoring frequency, nursing intervention method, and nursing intervention duration, count the adjustment times of the same type of nursing measures within the specified time, analyze the implementation changes of the nursing measures, and generate nursing measure adjustment frequency information;

[0080] Obtain the nursing measures such as the implemented nursing monitoring frequency, nursing intervention method, and nursing intervention duration. First, extract the patient's nursing records, including nursing time, nursing type, and corresponding physiological signs, and count the adjustment times of the same nursing measure within the specified time. For example, if a patient adjusts the nursing monitoring frequency 5 times within 24 hours, the adjustment frequency of this nursing measure is 5. Then analyze the implementation changes of the nursing measures, calculate the adjustment ratio of the nursing measures. For example, if it is adjusted 3 times at night (22:00 - 06:00) and 2 times during the day (06:00 - 22:00), the night adjustment ratio of the nursing monitoring frequency is 3 / 5×100% = 60%. Further calculate the stability of the nursing measures. If a nursing measure is adjusted frequently in different time periods, it indicates that the nursing plan is unstable. If the nursing measures are concentratedly adjusted in a specific period, there may be a peak in nursing needs. Finally, generate nursing measure adjustment frequency information.

[0081] S402: Based on the nursing measure adjustment frequency information, analyze the change trends of body temperature, resting heart rate, and blood oxygen level, calculate the physiological sign fluctuations after nursing intervention, compare the fluctuation amplitudes before and after the intervention, screen the physiological signs that still show continuous fluctuations after nursing intervention, calculate the fitness of the nursing measures for each screened physiological sign, and generate nursing measure fitness information;

[0082] Obtain the measurement data before and after the nursing intervention, calculate the fluctuations of physiological signs after the nursing intervention, and use normalization to calculate the changes in the fluctuations of physiological signs: Among them, ΔX is the degree of fluctuation of physiological signs, and X after is the measured value of physiological signs after the nursing intervention, and X before is the measured value before the nursing intervention, and σ before is the standard deviation of physiological signs before the nursing intervention. If ΔX > 1, it indicates that the physiological signs are still in an abnormal fluctuation state after nursing. Further screen the physiological signs that still show continuous fluctuations after the nursing intervention, count the time required for the recovery of physiological sign fluctuations, calculate the proportion of abnormal fluctuations that continue after the nursing intervention. If the proportion of the time when the physiological signs return to the individual's healthy baseline range within 60 minutes after nursing is less than 30%, then this nursing measure may have a weak effect on this physiological sign. Finally, calculate the fitness of the nursing measure using the following formula: Among them, A is the fitness of the nursing measure, ΔX is the amplitude of physiological sign fluctuations after nursing, max(ΔX) is the maximum fluctuation amplitude among all physiological signs, T unstable is the total time of abnormal fluctuations of physiological signs, T total is the total observation time, N ineffective is the number of times without improvement after nursing, N total is the total number of nursing times. The judgment range of nursing fitness is as follows: Fitness A ≥ 0.8: The nursing measure is highly adaptable, the fluctuations of physiological signs recover stably, the intervention is effective, and no adjustment is required; Fitness 0.6 ≤ A < 0.8: The nursing measure is moderately adaptable, the recovery of physiological signs is relatively good, and continuous observation can be carried out; Fitness 0.4 ≤ A < 0.6: The nursing measure is lowly adaptable, the recovery of physiological signs is not ideal, and the nursing strategy can be considered for optimization; Fitness A < 0.4: The nursing measure is not adaptable, the physiological signs show continuous abnormal fluctuations, and the nursing plan needs to be adjusted. Suppose that before and after nursing for a certain patient, the blood oxygen level drops from 95% to 91%, and its standard deviation is 1.2%. Then calculate ΔX as: Suppose the abnormal duration of the patient's blood oxygen level after nursing is 45 minutes, and the observation time is 180 minutes. Then: If the total number of nursing times is 10 times, and the number of ineffective nursing times is 3 times. Then: Suppose the maximum fluctuation amplitude of the patient's physiological signs is 4.5. Then: Substitute into the fitness formula: A = 1 - (0.74 + 0.25 + 0.3) = 1 - 1.29 = -0.29. Since the fitness cannot be negative, the lowest fitness is set to 0, that is, A = 0, indicating that this nursing measure is completely not adaptable. The fitness is lower than 0.4, and the nursing method should be adjusted immediately, and finally generate the nursing measure adaptation information.

[0083] S403: Compare the nursing measure adaptation information with the preset nursing adaptation threshold. If the adaptation degree is lower than the nursing adaptation threshold, mark that the current nursing measure needs to be adjusted and generate nursing dynamic adjustment information.

[0084] Compare the nursing measure adaptation information with the preset nursing adaptation threshold, and set the nursing adaptation degree threshold. For example, when the nursing adaptation degree is lower than 0.6, it is determined that the adaptability of this nursing measure is poor. If the calculated nursing measure adaptation degree is lower than this threshold, mark that the current nursing measure needs to be adjusted, screen the nursing measures with lower nursing adaptation degrees, and analyze possible adjustment directions. For example: when the nursing adaptation degree of blood oxygen level is lower than 0.6, it is possible to consider increasing the oxygen concentration, adjusting the oxygen inhalation mode (such as changing from low-flow nasal catheter to high-flow oxygen therapy), or increasing the frequency of blood oxygen monitoring; when the nursing adaptation degree of body temperature is lower than 0.6, it is possible to adjust the cooling method (such as changing from physical cooling to drug cooling), or increasing the frequency of body temperature measurement to more closely monitor the body temperature change; when the nursing adaptation degree of resting heart rate is lower than 0.6, it may be necessary to optimize the nursing process, such as adjusting the patient's rest time, reducing stimulating factors, or increasing the use of electrocardiogram monitoring equipment. Finally, generate nursing dynamic adjustment information according to the nursing measure adaptation information to ensure the continuous optimization of the nursing plan.

[0085] Please refer to Figure 6 , obtain the clinical infection data of pediatric patients within the current season, compare the nursing intensity grading results with the symptom characteristics of the disease, determine whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, evaluate the adjustable nursing measures, and the specific steps for generating nursing guidance information are as follows:

[0086] S501: Obtain the clinical infection data of pediatric patients within the current season, extract the infection cases of multiple pathogens such as influenza virus, rotavirus, and respiratory syncytial virus, count the infection frequencies of all pathogens, calculate the distribution ratio of each pathogen within the specified time period, and generate pathogen infection frequency data;

[0087] Obtain the clinical infection data of pediatric patients within the current season, extract the infection cases of pathogens such as influenza virus, rotavirus, and respiratory syncytial virus. First, screen the cases diagnosed or suspected of infection within the specified time period (such as the past 30 days), count the infection numbers of various pathogens, calculate the distribution ratio of each pathogen within this time period, and use the following formula: Among them, P i is the infection ratio of pathogen i within the specified time period, N i is the number of infection cases of this pathogen, N totalis the total number of all infection cases during this time period. For example, in the past 30 days, a total of 1000 infection cases were confirmed in a certain area, including 500 cases infected with influenza virus, 300 cases infected with rotavirus, and 200 cases infected with respiratory syncytial virus. Then the infection proportions of the three are as follows: Further analyze the change trends of each pathogen in different time periods. For example, count whether there is an upward or downward trend in the infection proportions in the past 7 days, 14 days, and 30 days. If the infection proportion of a certain pathogen increases by more than 10% in the past 7 days, it can be judged that the activity of this pathogen is relatively high in the current season, and finally generate pathogen infection frequency data.

[0088] S502: Based on the pathogen infection frequency data, compare the abnormal physiological signs in the nursing intensity grading results, analyze the typical symptom characteristics of the infection cases, match the nursing needs corresponding to the nursing dynamic adjustment information, calculate the correlation between the nursing needs and the pathogen activity, and generate nursing need correlation data;

[0089] First, screen the cases with higher nursing grades and count the abnormal physiological signs related to the infection of specific pathogens. For example, cases infected with influenza virus may show high fever (≥38.5°C), increased heart rate (≥110 bpm), and decreased blood oxygen level (≤94%). Cases infected with rotavirus may show persistent diarrhea (≥5 times a day), body temperature fluctuation (37.5°C - 38.2°C), and mild dehydration (change in blood sodium level of 5 - 10 mmol / L). Use symptom matching to calculate the correlation between the nursing needs and the pathogen activity. The definition of the correlation calculation is as follows: Among them, R is the correlation between the nursing needs and the pathogen activity, and M match is the number of cases where the nursing need symptoms match the pathogen infection symptoms, and M total is the total number of nursing need cases, and T total is the total number of infection cases of this pathogen, and T overlap is the number of cases with high nursing needs among the cases infected with this pathogen, that is, the intersection of the nursing need cases and the pathogen infection cases. For example, in the past 30 days, there were a total of 500 cases with high-intensity nursing needs, among which 400 cases matched the symptoms of influenza virus infection, and 350 cases were both infected with influenza virus and had high nursing needs. Then the correlation calculation is as follows: If the correlation of a certain pathogen is higher than 70%, it indicates that the infection of this pathogen is highly correlated with the nursing needs and may be the main influencing factor for the increase in nursing intensity. If the correlation is between 50% - 70%, it means that the infection of this pathogen has a certain impact on the nursing needs, but there may be other factors interfering. If the correlation is lower than 50%, it indicates that the infection of this pathogen has a relatively small impact on the current nursing needs, and finally generate nursing need correlation data.

[0090] S503: Based on the data related to nursing needs, evaluate the direction of nursing measure adjustment, screen the nursing monitoring frequency, nursing intervention methods, and nursing intervention duration suitable for seasonal pathogen infections, and generate nursing guidance information.

[0091] To screen the nursing monitoring frequency, nursing intervention methods, and nursing intervention duration suitable for seasonal pathogen infections, first screen the pathogens with high nursing need relevance (relevance ≥ 70%). Adjust the nursing monitoring frequency according to the main symptoms of the pathogen. For example, if the relevance of influenza virus infection is high, increase the body temperature measurement frequency (once every 2 hours) and blood oxygen monitoring frequency (once every 4 hours). If the relevance of rotavirus infection is high, increase the assessment of fluid replacement (monitor urine output daily) and record the frequency of diarrhea (record each time). For nursing intervention methods, if the relevance of influenza virus is high, increase physical cooling measures (such as warm water sponge bath). If the relevance of respiratory syncytial virus is high, increase airway humidification treatment (such as nebulization inhalation). For the nursing intervention duration, if the nursing needs of a pathogen-infected case last for more than 5 days, the corresponding nursing intervention cycle is extended. If the nursing needs of a pathogen-infected case significantly decrease within 3 days, the nursing intervention can be appropriately reduced. Finally, generate nursing guidance information.

[0092] Please refer to Figure 7 , a pediatric patient nursing guidance system based on data analysis. The system includes:

[0093] The physiological sign fluctuation analysis module obtains the daily physiological signs of pediatric patients within a specified time, statistically analyzes the fluctuations of the physiological signs, and obtains the health status fluctuation information;

[0094] The individual health baseline establishment module screens the physiological signs within the corresponding mean or standard deviation range within a specified time from the health status fluctuation information, establishes an individual health baseline, compares the deviation between the health baseline and the physiological signs, and obtains the individual health change data;

[0095] The nursing intensity grading module obtains the change trend of each physiological sign from the individual health change data, sets the nursing intervention level according to the fluctuation situation, and obtains the nursing intensity grading result;

[0096] The nursing dynamic adjustment module counts the adjustment frequency of the corresponding nursing measures that have been implemented according to the nursing intensity grading result, screens the physiological signs that still show continuous fluctuations after nursing intervention, and judges whether the screened physiological signs need to prompt the adjustment of nursing methods, and obtains the nursing dynamic adjustment information;

[0097] The nursing guidance plan generation module obtains the clinical infection data of pediatric patients in the current season, compares the nursing intensity grading results with the symptom characteristics of the disease, determines whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, evaluates the adjustable nursing measures, and generates nursing guidance information.

[0098] The above are only the preferred embodiments of the present invention, and are not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as the technical solution content of the present invention is not departed from, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A pediatric patient care guidance method based on data analysis, characterized in that, It includes the following steps: S1: Obtain the daily physiological signs of pediatric patients within a specified time, count the fluctuations of the physiological signs, and obtain the health status fluctuation information; S2: Screen the physiological signs within the corresponding mean or standard deviation range within the specified time from the health status fluctuation information, establish an individual health baseline, compare the deviation between the health baseline and the physiological signs, and obtain the individual health change data; S3: Obtain the change trend of each physiological sign from the individual health change data, set the nursing intervention level according to the fluctuation situation, and obtain the nursing intensity grading result; S4: According to the nursing intensity grading result, count the adjustment frequency of the corresponding nursing measures that have been implemented, screen the physiological signs that still show continuous fluctuations after the nursing intervention, and judge whether the screened physiological signs need to prompt the adjustment of the nursing method to obtain the nursing dynamic adjustment information; S5: Obtain the clinical infection data of pediatric patients within the current season, compare the nursing intensity grading result with the symptom characteristics of the disease, judge whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, evaluate the adjustable nursing measures, and generate the nursing guidance information.

2. The method for guiding the care of pediatric patients based on data analysis according to claim 1, wherein: The health status fluctuation information includes the amplitude of the physiological sign peak change, the frequency of the physiological sign peak appearance, the instantaneous fluctuation data points of the physiological sign, and the continuous fluctuation data points of the physiological sign. The individual health change amplitude is specifically the deviation of the physiological sign fluctuation intensity, the change amplitude of the physiological sign, and the comparison result of the physiological sign health baseline. The nursing intensity grading result includes the abnormal fluctuation frequency, the abnormal fluctuation duration, and the nursing intervention level. The nursing dynamic adjustment information includes the nursing measure adjustment frequency, the nursing measure influence degree, the nursing measure adaptability, and the nursing method adjustment prompt. The nursing guidance information includes the nursing monitoring frequency, the nursing intervention method, the nursing intervention duration, and the correlation assessment of the nursing needs and the seasonal pathogen activity.

3. The pediatric patient care guidance method based on data analysis according to claim 1, characterized in that: The specific steps to obtain the daily physiological signs of pediatric patients within a specified time, count the fluctuations of the physiological signs, and obtain the health status fluctuation information are as follows: S101: Obtain the daily physiological signs of body temperature, resting heart rate, and blood oxygen level of pediatric patients within a specified time, record the time series data of the physiological signs, calculate the upper and lower fluctuation amplitudes of each physiological sign, screen the change values between adjacent time points, and judge whether they exceed the set benchmark range, accumulate the numerical distribution of the fluctuation amplitudes, and generate the physiological sign fluctuation amplitude; S102: Call the physiological sign fluctuation amplitude, count the fluctuation duration of each physiological sign, screen the fluctuation data points with a change rate exceeding the preset threshold within the specified time, classify them as instantaneous fluctuation data points, and at the same time screen the fluctuation data points with a change rate exceeding the preset threshold and multiple time points, classify them as continuous fluctuation data points, and obtain the physiological sign fluctuation type classification result; S103: Based on the physiological sign fluctuation type classification result, analyze the fluctuation peaks of the instantaneous fluctuation data points and the continuous fluctuation data points, count the frequency of the peak appearance of each physiological sign within the specified time period, accumulate the fluctuation peak data, and generate the health status fluctuation information.

4. The method for guiding the care of pediatric patients based on data analysis according to claim 1, characterized in that: The specific steps of selecting physiological signs within the corresponding mean or standard deviation range within a specified time from the health status fluctuation information, establishing an individual health baseline, and comparing the deviation between the health baseline and the physiological signs to obtain individual health change data are as follows: S201: extracting data sequences of body temperature, resting heart rate, and blood oxygen level from the health status fluctuation information, calculating the mean and standard deviation of each physiological sign, screening physiological sign data whose fluctuation amplitude falls within the mean or standard deviation interval within a specified time range, removing abnormal data points beyond the specified range, and generating physiological sign screening results; S202: Calculate the continuous and stable data points within a specified time range according to the physiological sign screening results, obtain the trend mean of body temperature, resting heart rate, and blood oxygen level, analyze the baseline fluctuation range of each physiological sign, and establish an individual health baseline; S203: Compare the individual health baseline with the physiological sign fluctuation intensity in the health status fluctuation information, calculate the deviation degree of each physiological sign, count the change deviation of body temperature, resting heart rate, and blood oxygen level relative to the individual health baseline, and generate individual health change data.

5. The pediatric patient care guidance method based on data analysis according to claim 1, characterized in that: The specific steps of obtaining the change trend of each physiological sign from the individual health change data, setting the nursing intervention level according to the fluctuation situation, and obtaining the nursing intensity grading result are as follows: S301: Based on the individual health change data, analyzing the change trend of body temperature, resting heart rate, and blood oxygen level within a specified time range, analyzing the direction of increase and decrease at consecutive time points, judging whether it presents a stable, rising or falling trend, and counting the proportion of each trend within the time period to generate a physiological sign fluctuation trend analysis result; S302: Based on the physiological sign fluctuation trend analysis result, count the number of time points when the fluctuation deviation exceeds the individual health baseline range, calculate the frequency of abnormal fluctuations within a specified time, record the start time and end time of each fluctuation, and obtain the abnormal frequency and duration information of the fluctuation; S303: Based on the abnormal fluctuation frequency and abnormal duration information, determine whether the fluctuation of physiological signs has stabilized within a specified time, and set the corresponding nursing intervention level according to the two situations of restoration of stability and continuous fluctuation to obtain the nursing intensity grading result.

6. The method for guiding the care of pediatric patients based on data analysis according to claim 1, wherein: According to the nursing intensity classification results, the adjustment frequency of the corresponding nursing measures that have been implemented is counted, the physiological signs that continue to fluctuate after nursing intervention are screened, and it is determined whether the physiological signs after screening need to prompt adjustment of the nursing method. The specific steps for obtaining dynamic adjustment information of nursing are as follows: S401: According to the nursing intensity classification result, the implemented nursing monitoring frequency, nursing intervention method, and nursing intervention duration of nursing measures are obtained, the number of adjustments of similar nursing measures within a specified time is counted, the execution changes of nursing measures are analyzed, and the adjustment frequency information of nursing measures is generated; S402: Based on the frequency information of the nursing measures adjustment, analyze the changing trends of body temperature, resting heart rate, and blood oxygen level, calculate the fluctuations of physiological signs after nursing intervention, compare the fluctuation ranges before and after the intervention, screen out the physiological signs that still show continuous fluctuations after nursing intervention, calculate the fitness of each nursing measure for the screened physiological signs, and generate nursing measure fitness information; S403: Compare the nursing measure fitness information with the preset nursing fitness threshold. If the fitness is lower than the nursing fitness threshold, mark that the current nursing measure needs to be adjusted and generate nursing dynamic adjustment information.

7. The method for guiding the care of pediatric patients based on data analysis according to claim 6, wherein: For calculating the fitness of each nursing measure for the screened physiological signs, the formula is as follows: Among them, A is the adaptability of nursing measures, ΔX is the fluctuation range of physiological signs after nursing, max(ΔX) is the maximum fluctuation range among all physiological signs, T unstable is the total time of abnormal fluctuation of physiological signs, T total is the total observation time, N ineffective is the number of times without improvement after nursing, N total is the total number of nursing times.

8. The pediatric patient care guidance method based on data analysis according to claim 1, characterized in that: The specific steps for obtaining the clinical infection data of pediatric patients in the current season, comparing the nursing intensity grading result with the symptom characteristics of the disease, judging whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, and evaluating the adjustable nursing measures to generate nursing guidance information are as follows: S501: Obtain the clinical infection data of pediatric patients in the current season, extract the infection cases of multiple pathogens such as influenza virus, rotavirus, and respiratory syncytial virus, count the infection frequencies of all pathogens, calculate the distribution ratios of each pathogen within a specified time period, and generate pathogen infection frequency data; S502: Based on the pathogen infection frequency data, compare the abnormal physiological signs in the nursing intensity grading result, analyze the typical symptom characteristics of the infection cases, match the nursing needs corresponding to the nursing dynamic adjustment information, calculate the correlation between the nursing needs and the pathogen activity, and generate nursing need correlation data; S503: Based on the nursing need correlation data, evaluate the direction of nursing measure adjustment, screen out the nursing monitoring frequency, nursing intervention methods, and nursing intervention durations that are suitable for the seasonal pathogen infection situation, and generate nursing guidance information.

9. The method for guiding the care of pediatric patients based on data analysis according to claim 8, characterized in that: For calculating the correlation between the nursing needs and the pathogen activity, the formula is as follows: Among them, R is the correlation between the nursing needs and the pathogen activity, M match is the number of cases where the symptoms of nursing needs match the symptoms of pathogen infection, M total is the total number of cases of nursing needs, T total is the total number of infected cases of the pathogen, T overlap is the number of cases where the same type of nursing needs exist simultaneously among the pathogen-infected cases.

10. A pediatric patient care guidance system based on data analysis, characterized in that, Execute according to the pediatric patient nursing guidance method based on data analysis described in any one of claims 1 - 9. The system includes: The physiological sign fluctuation analysis module obtains the daily physiological signs of pediatric patients within a specified time, counts the fluctuations of the physiological signs, and obtains the health status fluctuation information; The individual health baseline establishment module screens out the physiological signs within the corresponding mean or standard deviation range within the specified time from the health status fluctuation information, establishes an individual health baseline, and compares the deviation between the health baseline and the physiological signs to obtain individual health change data; The nursing intensity grading module obtains the changing trend of each physiological sign from the individual health change data, sets the nursing intervention level according to the fluctuation situation, and obtains the nursing intensity grading result; The nursing dynamic adjustment module counts the adjustment frequencies of the corresponding nursing measures that have been implemented according to the nursing intensity grading result, screens out the physiological signs that still show continuous fluctuations after nursing intervention, and judges whether the screened physiological signs need to prompt the adjustment of nursing methods to obtain nursing dynamic adjustment information; The nursing guidance plan generation module obtains the clinical infection data of pediatric patients in the current season, compares the nursing intensity grading results with the symptom characteristics of the disease, determines whether the nursing needs corresponding to the nursing dynamic adjustment information are correlated with the seasonal pathogen activity, evaluates the adjustable nursing measures, and generates nursing guidance information.

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