A monitoring system for an education system and a method of monitoring data analysis

Abstract

The application provides a monitoring system and a monitoring data analysis method for an education system, and belongs to the technical field of data analysis, and specifically comprises the following steps: determining a monitoring analysis method of an education analysis target in a group based on analysis results of the education analysis target data in the group and monitoring data of a monitoring device of the education analysis target; determining a monitoring risk device in the monitoring device of the education analysis target by using the monitoring analysis method; determining an update result of the education analysis target in the group based on a coincidence degree of the monitoring risk devices of the education analysis target; determining abnormal analysis data of a monitoring index based on construction data of the education analysis target in the group; and determining an analysis management method of monitoring data of the group by combining the update result of the education analysis target in the group and a correlation degree of abnormal situations of the monitoring index in the history, so that the matching degree of education and physical indexes of patients is improved.

Description

Technical Field

[0001] This invention belongs to the field of data analysis technology, and in particular relates to a monitoring system and monitoring data analysis method for a publicity and education system. Background Technology

[0002] To improve patient recovery, existing technologies often require user education according to a preset cycle, enabling patients to strictly follow medical advice for self-care and medication. A similar technical solution is presented in invention patent application CN202511283833.8, "An Intelligent Education System Integrating Multimodal Data Analysis," but it suffers from the following technical problems: When updating health education in a personalized way, existing technical solutions often process education based on the user's current physical monitoring indicators or disease type. However, they neglect the risk of future abnormal changes in the user's current physical indicators, making it difficult to achieve personalized matching between education and the user. Therefore, how to determine the analysis and processing methods of monitoring data based on the user's physical indicators, and how to reliably analyze and identify the changing trends of the user's physical indicators to provide data support for updating health education has become an urgent technical problem to be solved.

[0003] Therefore, there is an urgent need for a monitoring system and a method for analyzing monitoring data in the propaganda and education system. Summary of the Invention

[0004] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a monitoring data analysis method, which includes: S1 uses the patient's physical indicator data to determine the degree of matching between the patient's physical indicators and those of other patients in the department. Based on the degree of matching, the patient is divided into different groups. Based on the degree of matching between the patient data in the group and the physical indicators of patients in the department in the past, the analysis strategy for monitoring the patient data in the group is determined. S2 uses the analysis strategy to determine the mission analysis targets, and based on the analysis results of the mission analysis target data in the group and the monitoring data of the mission analysis target monitoring equipment, determines the monitoring analysis method for the mission analysis targets in the group; S3 uses the monitoring and analysis method to identify the monitoring risk devices among the monitoring equipment of the propaganda and education analysis target, determines the update results of the propaganda and education analysis target in the group based on the degree of overlap of the monitoring risk devices of the propaganda and education analysis target, identifies the analysis data of the monitoring indicators that are abnormal based on the construction data of the propaganda and education analysis target in the group, and determines the analysis and management method of the monitoring data of the group by combining the update results of the propaganda and education analysis target in the group and the degree of correlation with the abnormal situation of the monitoring indicators in history.

[0005] The beneficial effects of this invention are as follows: Based on the degree of matching between patient data in the group and the department's historical patient physical indicators, the analysis strategy for monitoring patient data in the group is determined. For patient groups within the department, dynamic grouping is performed based on the similarity of their physical indicators, and the key comparison and analysis strategy for monitoring data of each patient group is scientifically determined. Taking into account the number of patients in the current group and the proportion of patients similar to the group in the department in the past, the group is dynamically evaluated to determine whether it has sufficient data analysis value, thereby deciding whether to initiate key monitoring data comparison and analysis processing for the group, providing a data foundation and scientific basis for subsequent optimization of personalized education strategies.

[0006] By utilizing the analytical data of abnormal monitoring indicators, the updated results of education analysis targets in the group, and the correlation with the abnormality of monitoring indicators in history, the method for analyzing and managing the monitoring data of the group is determined. For the updated target groups obtained after equipment deviation screening, the prevalence of abnormalities of each monitoring indicator in the group's historical construction is further evaluated to determine whether to continue monitoring data analysis for the group, so as to optimize the allocation of analytical resources. Combining the number of current updated targets, the number of risk indicators of concern, and the abnormal correlation of individual patients, a stratified decision is made on whether to continue investing analytical resources, ensuring that limited resources are focused on abnormal patterns that are truly representative of the group, and ensuring the timeliness and matching degree of the updated processing of education methods.

[0007] Furthermore, the patient's physical indicator data includes the patient's physical indicators and the indicator data of the physical indicators.

[0008] Furthermore, the degree of matching between the patient's physical indicators and those of other patients within the department is determined based on the number of physical indicators for which the deviation rate between the patient's and other patients' indicator data meets the requirements.

[0009] Furthermore, the patients are divided into different groups, specifically including: Patients whose number of physical indicators that meet the deviation rate requirement is greater than the preset threshold number of indicators are grouped into the same group.

[0010] Furthermore, the method for determining the analysis strategy for the monitoring data of patients in the group is as follows: S11 uses the patient data in the group to determine the number of patients in the group; S12 determines the patients who fell into the group in the past based on the degree of matching between the patient data in the group and the physical indicators of patients in the department in the past; S13 determines an analysis strategy for the monitoring data of patients in the group based on the number of patients in the group and the patients who have fallen into the group in the past.

[0011] Furthermore, the method for determining the analysis and management method of the monitoring data of the group is as follows: Based on the updated results of the mission analysis objectives, the updated mission analysis objectives in the group are determined and used as the updated objectives; Using the data from the construction of the mission analysis objectives in the group, the number of times the monitoring indicators in the group showed anomalies was determined, and the number of times the monitoring indicators showed anomalies was used as the number of correlation analysis. By utilizing the updated target data, the abnormal correlation of the updated target's monitoring indicators, and the number of correlation analyses of different monitoring indicators, the analysis and management method for the monitoring data of the group is determined.

[0012] Secondly, this application provides a monitoring system for a missionary system, employing the aforementioned monitoring data analysis method, specifically including: The analysis strategy determination module, the monitoring and analysis module, and the analysis management module are all included. The analysis strategy determination module is responsible for determining the analysis strategy for the monitoring data of patients in the group. The monitoring and analysis module is responsible for determining the monitoring and analysis methods for the missionary analysis objectives in the group; The analysis and management module is responsible for determining the analysis and management methods for the monitoring data of the group.

[0013] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0015] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0016] Figure 1 This is a flowchart of a monitoring data analysis method; Figure 2 This is a flowchart illustrating the method for determining the analysis strategy for patient monitoring data in a group; Figure 3 This is a flowchart illustrating the method for determining the monitoring and analysis of missionary analysis objectives within a group; Figure 4 This is a framework diagram of a monitoring system used in the mission and education system. Detailed Implementation

[0017] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0018] Example 1 like Figure 1 As shown, this application provides a monitoring data analysis method, specifically including: S1 uses the patient's physical indicator data to determine the degree of matching between the patient's physical indicators and those of other patients in the department. Based on the degree of matching, the patient is divided into different groups. Based on the degree of matching between the patient data in the group and the physical indicators of patients in the department in the past, the analysis strategy for monitoring the patient data in the group is determined. S2 uses the analysis strategy to determine the mission analysis targets, and based on the analysis results of the mission analysis target data in the group and the monitoring data of the mission analysis target monitoring equipment, determines the monitoring analysis method for the mission analysis targets in the group; S3 uses the monitoring and analysis method to determine the monitoring risk devices among the monitoring devices of the propaganda and education analysis target, determines the update results of the propaganda and education analysis target in the group based on the degree of overlap of the monitoring risk devices of the propaganda and education analysis target, and determines the analysis and management method of the monitoring data of the group based on the update results of the propaganda and education analysis target and in combination with the construction data of the propaganda and education analysis target in the group.

[0019] Furthermore, the patient's physical indicator data includes the patient's physical indicators and the indicator data of the physical indicators.

[0020] Furthermore, the degree of matching between the patient's physical indicators and those of other patients within the department is determined based on the number of physical indicators for which the deviation rate between the patient's and other patients' indicator data meets the requirements.

[0021] Furthermore, the patients are divided into different groups, specifically including: Patients whose number of physical indicators that meet the deviation rate requirement is greater than the preset threshold number of indicators are grouped into the same group.

[0022] Specifically, such as Figure 2 As shown, the method for determining the analysis strategy for the monitoring data of patients in the group is as follows: This invention focuses on dynamically grouping patients within a department based on the similarity of their physical indicators, and scientifically determining the key comparative analysis strategies for monitoring data of each patient group. The logic is as follows: First, patients are divided into different groups based on the degree of matching of their physical indicator data. Then, considering the number of patients in the current group and the historical proportion of patients similar to that group in the department, the system dynamically assesses whether the group has sufficient data analysis value, thereby deciding whether to initiate key monitoring data comparative analysis for that group. The purpose of this process is to accurately identify the true trends and patterns of change in physical indicators by deeply comparing the monitoring device data of patients within the group, providing a data foundation and scientific basis for subsequently optimizing personalized education strategies.

[0023] S11 uses the patient data in the group to determine the number of patients in the group; "Patient data in a group" refers to the collection information of all patients within a specific group currently defined; "Number of patients" refers to the total number of individual patients included in the group.

[0024] The number of patients is a fundamental indicator for measuring cohort size and directly relates to whether the data from that cohort is meaningful for statistical comparative analysis. Larger cohorts may reflect a common type of patient characteristics, and the patterns of change in their monitoring data are highly representative and valuable for research, making them worthy of focused comparative analysis. Smaller cohorts, on the other hand, may represent rare types, with greater randomness in their data fluctuations, requiring further evaluation of their analytical value in conjunction with historical data. Therefore, obtaining the number of patients is the starting point for subsequent decision-making.

[0025] S12 determines the patients who fell into the group in the past based on the degree of matching between the patient data in the group and the physical indicators of patients in the department in the past; "Patients in history" refers to patients previously admitted and recorded by the department; "matching degree" is measured by the number of indicators whose deviation rate among patients' physical indicators meets the requirements; when the deviation rate of a historical patient's various physical indicators meets the preset requirements with the average or typical indicators of the current group of patients, the historical patient is considered to "fall into the group".

[0026] By reviewing historical data, we can determine whether the patient characteristics represented by the current group have been consistent throughout history. If similar patients have frequently appeared in the past, it indicates that this characteristic is a long-standing and regular patient type, and the changes in its monitoring data may exhibit a traceable pattern, making it worthwhile to explore through focused comparative analysis. Conversely, if it has rarely appeared in the past, it may be a random cluster, and the changes in its monitoring data may lack a general pattern, limiting the value of focused analysis. This step provides the foundation for subsequent proportion calculations.

[0027] S13 determines an analysis strategy for the monitoring data of patients in the group based on the number of patients in the group and the patients who have fallen into the group in the past.

[0028] The above steps include the following: S131 Obtain the number of patients in the group and determine whether the number of patients in the group is greater than a preset patient number threshold. If yes, determine that the analysis strategy for the monitoring data of the patients in the group is to use the patients in the group as the target of education analysis. If no, proceed to step S132. The "preset patient number threshold" is a pre-defined value used to define whether the group size is large enough to directly determine whether its monitoring data has the basic value for focused comparative analysis. When the current number exceeds this threshold, the group is considered to have significant representativeness and sufficient data volume to support meaningful statistical analysis, and there is no need to refer to historical data; focused comparative analysis can be initiated directly.

[0029] Setting a threshold number is crucial for efficiently handling large-scale groups. If the current group already has a large number of people, it means there is a sufficient stream of monitoring data available for analysis, and the trends in indicators discovered from this data have a high degree of confidence. Direct intervention can quickly generate preliminary insights into the patterns of change in the physical indicators of this type of patient, providing timely evidence for adjusting educational strategies. This reflects the timeliness and data-driven nature of decision-making.

[0030] S132 uses patients who fell into the group in the past as historical matching patients, and determines the matching patient ratio based on the proportion of the historical matching patients in the department. It is determined whether the matching patient ratio is greater than a preset matching ratio threshold. If yes, proceed to step S133. If no, determine that the analysis strategy for the monitoring data of patients in the group is not to use patients in the group as the target of education analysis. "Historical matched patients" refers to the historical patients who fall into the current group as determined in step S12; "Matching patient ratio" refers to the percentage of historical matched patients to the total number of historical patients in the department; "Preset matching ratio threshold" is a pre-set ratio value used to determine the frequency and regularity of this feature in history.

[0031] When the current group is small, it's necessary to examine its historical frequency of occurrence. A high proportion of historically matched patients indicates that while the current characteristic may not be prominent, it is a recurring pattern in the long term, and the historical trajectory of its monitoring data may contain important regularities. Even with a small current sample size, the data fluctuations of this group, corroborated by historical data, may still be representative and valuable for analysis, thus requiring further consideration. Conversely, a low historical proportion suggests that the characteristic is indeed rare, and the fluctuations in its monitoring data may be isolated cases without general patterns, making it unworthy of focused comparative analysis. This step achieves a secondary screening of the "small group."

[0032] S133 uses the matched patient ratio to determine the threshold for the number of patients in the group, and when the number of patients in the group is greater than the threshold, the patients in the group are used as the target of the education analysis.

[0033] "Determining the quantity threshold using the proportion of matched patients" refers to dynamically adjusting the minimum quantity standard that the current group needs to reach based on the historical proportion of matched patients, i.e., recalculating a "quantity threshold" for the group. For example, the quantity threshold can be set to be inversely proportional to the proportion of matched patients; the higher the proportion, the lower the threshold, to reflect the data analysis value weight given to historical commonality. "Key comparative analysis of monitoring devices" refers to conducting in-depth horizontal and vertical comparisons of time-series data generated by medical monitoring devices (such as continuous glucose monitors, wearable ECG patches, etc.) used by patients within the group, aiming to eliminate random errors and discover the true patterns of indicator fluctuations.

[0034] A high historical proportion indicates a long-term trend for this characteristic, suggesting that its pattern of change is likely repeatable and regular. Therefore, even if the current group size does not reach a fixed threshold, it may still be worth noting due to the potential for patterns revealed by historical data. Using dynamic thresholds allows for more flexible initiation of focused analyses on groups with potential for pattern discovery, avoiding the missed opportunities to identify important clinical trends due to overly rigid fixed thresholds. This step enables fine-tuning of current data analysis decisions based on historical information.

[0035] A top-tier hospital has introduced an intelligent patient management system to optimize health education for diabetic patients through precise analysis of patient monitoring data. The department uses this invention's method to manage inpatients and outpatient follow-up patients in groups.

[0036] First, the department collects physical indicator data from all patients, including fasting blood glucose, 2-hour postprandial blood glucose, glycated hemoglobin, blood pressure, heart rate, and other specific indicators. Based on this data, the deviation rate of each indicator between every two patients is calculated (e.g., deviation rate = |Patient A's indicator - Patient B's indicator| / Patient B's indicator). If the deviation rate of all indicators is less than the preset deviation requirements (e.g., fasting blood glucose deviation ≤ 8%, postprandial blood glucose deviation ≤ 10%, glycated hemoglobin deviation ≤ 5%, and deviations in blood oxygen, blood pressure, and heart rate, etc.), then the indicators are considered matched. When the number of matched physical indicators between two patients exceeds the preset threshold (e.g., more than 4 indicators), the two patients are grouped into the same group. After cluster analysis, the 120 patients currently managed by the department were divided into 9 groups, with group H containing 15 patients characterized by "impaired fasting glucose with poor postprandial blood glucose control".

[0037] Next, we will determine the key comparative analysis strategy for monitoring data of group H.

[0038] S11: The number of patients in group H is determined to be 15.

[0039] S12: Based on the average physical indicators of patients in group H (e.g., fasting blood glucose 6.5 mmol / L, 2-hour postprandial blood glucose 10.2 mmol / L), patients were matched with 400 patients in the department's historical database from the past two years. The matching criteria were: a deviation rate of ≤8% between the historical patients' fasting blood glucose and 6.5 mmol / L, a deviation rate of ≤10% between the historical patients' 2-hour postprandial blood glucose and 10.2 mmol / L, and compliance with requirements for deviations in blood pressure, heart rate, etc. Statistical analysis showed that 42 historical patients met the criteria, meaning there were 42 historically matched patients.

[0040] S13: Proceed to the sub-step judgment.

[0041] S131: The preset patient number threshold is 20. The current number of patients in group H is 15 < 20, therefore proceed to S132.

[0042] S132: There are 42 historically matched patients, and the department has a total of 400 historical patients. The calculated matching ratio is 42 / 400 = 10.5%. The preset matching ratio threshold is 10%. 10.5% > 10%, therefore proceed to S133.

[0043] S133: A new threshold number is dynamically calculated using the matched patient ratio of 10.5%. The base threshold is set to 20, and the calculation formula is: New threshold = Base threshold × (1 - matched patient ratio) = 20 × (1 - 0.105) = 17.9, rounded down to 18. Since the current number of groups H is 15 < 18, it is determined that no focused comparison analysis processing by the monitoring device will be performed.

[0044] If we assume that the current group H has 19 people, then 19 > 18, and the system will automatically start a focused comparative analysis of the patients in this group.

[0045] Through the above process, the department prioritizes data analysis resources for groups that are large enough or have strong historical patterns, avoiding ineffective analysis work on small, random groups and ensuring the efficiency and scientific nature of monitoring data mining.

[0046] Specifically, such as Figure 3 As shown, the method for determining the monitoring and analysis objectives of the mission analysis in the group is as follows: This invention targets patient groups identified as the focus of health education analysis. By analyzing regular physiological data collected by monitoring devices, it identifies concurrent abnormalities related to key health indicators, thereby providing a basis for optimizing personalized health education strategies. Key health indicators (such as blood glucose) are core indicators reflecting the complexity of a patient's condition. Although they may not be obtainable online through monitoring devices, their levels directly determine the number and severity of health problems that patients need to address. The monitoring devices include those for real-time collection of regular indicators such as heart rate, blood pressure, and respiratory rate. Abnormalities in these indicators may be related to the condition represented by the key health indicators.

[0047] First, the stringency of the identification criteria is dynamically adjusted based on the number of patients in the group. When the sample size is small, a lenient standard is used to capture as many potential monitoring device anomalies as possible and avoid missing valuable concurrent information. When the sample size is sufficient, the complexity of the patient's condition is further judged based on the number of indicators of interest: the more indicators of interest, the more complex the patient's condition, and the higher the reliability requirement for the identification of monitoring device anomalies. Therefore, a strict standard is required (considering both the number of times the standard is exceeded and the interval between two consecutive exceedances) to ensure that the identified anomalies are truly clinically significant and to avoid misjudging complex fluctuations in the condition as equipment problems. When there are fewer indicators of interest, the identification method is determined by the group size. A lenient standard is used for smaller groups to fully explore information, while a strict standard is used for larger groups to ensure identification accuracy.

[0048] S21 Based on the mission analysis target data in the group, determine the number of mission analysis targets in the group; In the above steps, if the number of educational analysis targets in the group is less than the preset target number threshold, then the number of educational analysis targets in the group is small, making it difficult to effectively determine whether the monitoring reliability of the monitoring equipment meets the requirements, and thus making it difficult to effectively construct targeted educational programs. Therefore, the monitoring and analysis method for the educational analysis targets in the group is determined to be the preset monitoring and analysis method, that is, when the monitoring data of the monitoring equipment has a period of exceeding the standard greater than the preset threshold for the number of exceeding the standard, then the monitoring equipment is determined to be a monitoring risk device. Therefore, when the number of educational analysis targets with poor monitoring reliability does not meet the requirements, no monitoring and analysis processing is required.

[0049] It is also understood that if the number of missionary analysis targets in the group is not less than a preset target number threshold, proceed to the next step.

[0050] "Education analysis targets" refer to the individual patients identified through the preceding process who require focused education analysis. These patients are grouped into the same group based on the similarity of their focus indicators (such as blood glucose and blood pressure). "Education analysis target data" includes the patient's identification, historical test values ​​of their focus indicators, and continuous physiological data collected by their monitoring devices (such as multi-parameter monitors). "Preset target quantity threshold" is a pre-set value used to determine whether the number of education analysis targets within the group is "too few" or "too many".

[0051] The size of the group directly determines the amount of sample available for detecting concurrent anomalies. When the number is small, without a lenient approach to capture as many potential monitoring device anomalies as possible, it is impossible to effectively and accurately rule out abnormal devices. Furthermore, the small number and lack of relevant verification may lead to educational updates that do not accurately match the actual situation of users. When the number is large, the sample is sufficient, allowing for more precise identification methods. Different users can verify each other's findings, ruling out certain anomalies without relying solely on the identification of abnormal devices. This step provides the fundamental basis for the subsequent differentiation of identification standards.

[0052] S22 determines the physical indicators of concern for the mission analysis target based on the analysis results of the monitoring data from the monitoring equipment of the mission analysis target; It should be noted that the physical indicators of concern for the missionary analysis target are the physical indicators that the missionary analysis target focuses on, that is, the physical indicators that need to be monitored during the missionary process, and the specifics are determined according to the user's settings.

[0053] "Monitoring equipment" refers to medical instruments that can collect physiological data online, continuously, and regularly, and monitor indicators such as heart rate, blood pressure, and respiratory rate in real time; "Analysis results of monitoring data" refers to the changing trends and abnormal distributions of various indicators obtained by processing these continuous data; "Physical indicators of concern" refers to indicators such as blood sugar and blood pressure, which reflect the severity of the patient's condition as determined by doctors.

[0054] Clearly defining the key indicators on monitoring equipment helps determine the complexity of a patient's condition. If it is complex, there is a greater need to analyze any abnormal changes in the monitoring data during the course of the disease. Therefore, in order to comprehensively capture all abnormalities in the monitoring data, we can lay the foundation for personalized education.

[0055] The above steps include the following: S221 Obtain the body indicators of concern for the missionary analysis target, and determine whether the number of the body indicators of concern is greater than the preset threshold for the number of concern indicators. If so, the need for personalized missionary construction is high. Therefore, the monitoring and analysis method for the missionary analysis target in the group is to determine that the monitoring device belongs to the monitoring risk device when the monitoring data of the monitoring device has a period of exceeding the standard greater than the second preset threshold for the number of exceeding the standard. This increases the difficulty of identifying and processing the monitoring risk device, while also ensuring the reliability of abnormal identification. If not, proceed to step S23.

[0056] It is understandable that the second preset threshold for the number of time periods exceeding the standard is greater than the preset threshold for the number of time periods exceeding the standard.

[0057] "Preset threshold for the number of monitored indicators" is a pre-set value used to determine whether the number of monitored indicators is too large. A large number of monitored indicators directly reflects the complexity of the patient's condition. "Monitoring device" here refers to the specific individual monitor worn by the patient to monitor different indicators. "Second preset threshold for the number of times a certain indicator data collected by a certain monitor exceeds the normal range within a set time window" refers to the upper limit of the number of times a certain indicator data collected by a certain monitor exceeds the normal range. The normal range can be referred to the reference values ​​displayed by the monitor (such as heart rate 60-100 beats / min, systolic blood pressure 90-140 mmHg, respiratory rate 16-20 breaths / min). "Monitoring risk device" refers to the individual monitor that is identified as potentially having measurement deviations after being judged under multiple conditions.

[0058] The number of monitoring indicators directly reflects the complexity of a patient's condition. A higher number of monitoring indicators (such as the simultaneous need to control blood sugar and blood pressure) indicates a complex condition, potentially involving multiple systems and organs, requiring more updated patient education strategies. Employing stricter standards (examining a higher number of instances of exceeding the control limits) can effectively filter out occasional fluctuations and ensure the reliability of equipment failures.

[0059] S23 uses the number of mission analysis targets in the group and the body indicators of the mission analysis targets to determine the monitoring and analysis method for the mission analysis targets in the group.

[0060] In the above steps, based on the number of missionary analysis targets in the group, the groups are sorted from low to high according to the number of missionary analysis targets. It is then determined whether the sorting result of the group meets the requirements. If yes, the monitoring and analysis method for the missionary analysis targets in the group is determined to be the preset monitoring and analysis method. Thus, when the number of missionary analysis targets with poor monitoring reliability does not meet the requirements, there is no need to continue monitoring and analysis, and the monitoring and analysis of other groups and newly updated groups can be exited in time. If no, the monitoring and analysis method for the missionary analysis targets in the group is determined to be that when the monitoring data of the monitoring device has a period of exceeding the standard greater than the second preset threshold for the number of exceeding the standard, the monitoring device is determined to be a monitoring risk device, thereby increasing the difficulty of identifying and processing monitoring risk devices, while also ensuring the reliability of anomaly identification.

[0061] This step is applicable when the number of indicators is relatively small (i.e., the judgment in S221 is negative). In this case, further judgment is needed based on the group size to decide whether to use a lenient or strict standard. "Group ranking" refers to sorting all education analysis target groups from lowest to highest according to the number of education analysis targets they contain, thus obtaining a ranking for each group. "Whether the ranking results meet the requirements" can be determined according to preset rules. For example, for education analysis targets ranked in the top 30%, the number of high-ranking indicators is relatively small, and a lenient standard should be used to fully capture possible concurrent abnormalities; for those ranked lower, the number of indicators is relatively large, and a strict standard should be used to ensure that the identified abnormalities have clinical reliability.

[0062] When the number of indicators is small, the patient's condition is relatively simple, but the size of the cohort remains a key factor in determining the identification criteria. For smaller cohorts (ranked higher), the sample is fragile, requiring more lenient criteria to identify as many possible concurrent abnormalities as possible and avoid missing valuable clinical clues. For larger cohorts (ranked lower), the sample is sufficient, and stricter criteria can more accurately identify truly clinically significant abnormal patterns.

[0063] The endocrinology department of a top-tier hospital, through the preliminary procedures (such as...) Figure 2 As shown, several target groups for educational analysis have been identified from hospitalized patients. These groups are based on the similarity of key indicators—fasting blood glucose, 2-hour postprandial blood glucose, glycated hemoglobin, blood pressure, etc.—with the number of key indicators directly reflecting the complexity of the patient's condition. Each hospitalized patient is equipped with a multi-parameter monitor (device numbers M001-M100). The monitor can continuously and online monitor regular physiological indicators such as heart rate, blood pressure, and respiratory rate. The reference normal ranges displayed by the monitor are as follows: heart rate 60-100 beats / min, systolic blood pressure 90-140 mmHg, diastolic blood pressure 60-90 mmHg, and respiratory rate 16-20 breaths / min. Key indicators such as blood glucose are obtained through routine finger-prick blood tests and cannot be monitored online. However, the patient's hyperglycemic state may be associated with abnormalities in heart rate, blood pressure, and other indicators reflected by the monitor. The current goal is to analyze the monitor data to identify potential complications in hyperglycemic patients, providing a basis for optimizing a comprehensive educational strategy that includes blood glucose management and cardiovascular protection.

[0064] The department currently has 5 target groups for health education and analysis (based on focus indicators), and their basic information is as follows: Group A: 5 people, with 1 monitoring indicator (fasting blood glucose). Group C: 18 people, with 3 monitoring indicators (fasting blood glucose, systolic blood pressure, and respiratory rate).

[0065] Group A (5 people): S21: If the number of abnormal heart rate readings is 5 < 10, it is considered a low-number situation, and a lenient standard is used to identify abnormal patterns recorded by the monitor. Lenient standard: Judgment is based solely on the number of times the heart rate exceeds the standard. That is, if a patient's monitor shows more than 5 instances of heart rate exceeding the standard in a day, the patient is considered to have a heart rate abnormality requiring attention, and the monitor is classified as an abnormal monitoring device.

[0066] Group C (18 people): S21: If the quantity is 18 ≥ 10, proceed to S22.

[0067] S22: Based on clinical needs, the indicators that need to be monitored are heart rate, systolic blood pressure, and respiratory rate (3 items).

[0068] S221: Number of monitored indicators 3 > 2 (preset threshold for the number of monitored indicators), strict standards are adopted. Strict standards: examine the number of times the standard is exceeded. Analyze the indicators recorded by each monitor: Patient C3: Heart rate exceeded the standard 9 times (>8 times, i.e. the second preset threshold for the number of time periods exceeding the standard). Each time the heart rate exceeded the standard, the time period exceeding the standard must be at least 1 minute before the time period exceeding the standard can be recorded as 1 time. The monitoring device that recorded the heart rate is a monitoring abnormal device.

[0069] Furthermore, the method for determining the updated results of the mission analysis objectives in the group is as follows: This embodiment focuses on patient groups identified as targets for health education analysis. By analyzing regular physiological data collected from various devices such as heart rate monitoring, blood pressure monitoring, and respiratory monitoring, it identifies potential complications related to key indicators (such as blood glucose). Through group commonality analysis, it removes health education analysis targets with isolated device biases, thereby dynamically updating the groups requiring health education analysis. By analyzing the sharing of monitoring risk devices within the group, patients only related to isolated devices are removed from the health education analysis targets, retaining patients with group commonalities, thus laying the foundation for the accurate formulation of subsequent health education content.

[0070] S31 Utilizes the monitoring risk devices of the mission analysis target to determine the number of monitoring risk devices for the mission analysis target; In the above steps, the number of monitoring risk devices for the mission analysis target is obtained, and it is determined whether the number of monitoring risk devices for the mission analysis target is less than a preset risk device number threshold. If so, the updated result of the mission analysis target in the group is determined to be that the mission analysis target still belongs to the mission analysis target. If not, proceed to step S32.

[0071] "Monitoring risk devices" refers to individual monitoring devices that were identified as having abnormalities in the aforementioned steps; "Number of monitoring risk devices" refers to the number of monitoring devices marked as risk devices among the various types of monitoring devices used by a target patient in the education analysis; "Preset risk device number threshold" is a pre-set value used to initially determine whether a patient may be excluded due to device problems.

[0072] If a patient is associated with a small number of monitoring risk devices, it indicates that the data anomalies may only involve a few indicators and are still of reference value; therefore, their status as a target for educational analysis should be retained. If the number is large, further analysis of the distribution of these risk devices within the group is needed to determine whether the anomalies are common or isolated cases. This step achieves the initial screening of patients.

[0073] Patient A2 uses a heart rate monitoring device, which is marked as a risk device with a count of 1, less than the preset risk device count threshold of 2. Therefore, A2 is still within the target of the education analysis.

[0074] S32 determines, based on the degree of overlap between the monitoring risk devices of the mission analysis target and other mission analysis targets in the group, that the monitoring risk devices of the mission analysis target belong to other mission analysis targets with monitoring risk devices, and uses them as correlation analysis targets; The above steps include the following: S321 Determine whether the number of associated analysis targets of the monitoring risk equipment of the propaganda analysis target meets the requirements. If yes, it means that the propaganda analysis targets in the group generally exceed the standard in the monitoring risk equipment. Therefore, determine that the updated result of the propaganda analysis targets in the group is that the propaganda analysis target still belongs to the propaganda analysis target. If not, proceed to step S322. "Overlap rate" refers to the number of patients within a group who used the same risk monitoring device and experienced abnormalities. If a large number of patients showed abnormalities, it indicates a significant correlation between the abnormality and the patients in that group. "Association analysis target" refers to other patients who used the same batch of devices as the current patient and also experienced abnormalities.

[0075] By analyzing the sharing of risky devices within a group, it can be determined whether the device's anomalies are widespread. If multiple people experience anomalies due to the same batch of devices, it indicates a significant correlation between the patients in the group and the abnormality of that indicator; these patients' anomalies represent a common problem and should be retained. If only one person has an abnormality, it may be an isolated case, and that patient should be considered for removal. This step is crucial for transitioning from device-level anomalies to indicator-level commonalities.

[0076] Patient C7 used heart rate device H107 and blood pressure device. Upon investigation, among patients in group C who used heart rate devices, C9 and C12 also showed abnormal heart rates; among patients who used blood pressure devices in batch Y, C10 and C15 also showed abnormal blood pressure. Therefore, C9, C12, C10, and C15 were all targets for association analysis.

[0077] "The number of association analysis targets all meet the requirements" means that for all monitoring risk devices for this patient, the number of association analysis targets corresponding to each device reaches the preset minimum value (e.g., at least 1 person), indicating that the abnormality type represented by the device has a commonality within the group.

[0078] If multiple patients exhibit similar abnormalities across all of the patient's risk devices, it indicates that these abnormalities are not isolated cases but rather a representative issue affecting a group. The patient's data should be retained for the development of subsequent education strategies. This step directly reflects the core principle of "preserving common abnormalities."

[0079] Patient C7's two risk devices, H107 and B207, each have two associated analysis targets, both meeting the requirement (preset at least one person). Therefore, C7 still belongs to the education analysis target.

[0080] S322 treats monitoring risk devices whose number of associated analysis targets does not meet the requirements as isolated devices, and determines whether the proportion of isolated devices of the monitoring risk devices of the education analysis targets is greater than the preset isolated device proportion threshold. If so, it means that the education analysis targets in the group generally do not exceed the standard in the monitoring risk devices. Therefore, it is determined that the updated result of the education analysis targets in the group is that the education analysis targets no longer belong to the education analysis targets. If not, proceed to step S33. "Isolated devices" refers to monitoring risk devices for which the number of association analysis targets is insufficient; "isolated device ratio" refers to the proportion of isolated devices owned by a patient to the total number of risk devices; "preset isolated device ratio threshold" is a pre-set ratio value used to determine whether a patient's abnormality is mainly due to isolated devices.

[0081] If most of a patient's risk devices are isolated, it indicates that the abnormalities are mainly caused by devices used by a small number of people and do not represent a group-wide issue. Such patients should be removed from the education analysis to avoid interfering with the group education strategy. This step further filters out truly representative patients.

[0082] Patient C18 has two high-risk devices: H118 (batch X, associated with 7 people) and B218 (batch Y2, associated with 0 people). B218 is an isolated device, accounting for 50% of all devices. The preset threshold for the percentage of isolated devices is 50%. If the threshold is met, proceed to step S33.

[0083] S33 determines the updated results of the mission analysis targets in the group based on the number of monitoring risk devices for the mission analysis targets and the correlation analysis targets in different monitoring risk devices.

[0084] Specifically, in the above steps, the risk weight value of the associated analysis target is determined based on the number of associated analysis targets of the monitoring risk devices of the mission analysis target. It is then determined whether the sum of the risk weight values ​​of the monitoring risk devices of the mission analysis target is greater than a preset risk weight threshold. If so, the updated result of the mission analysis target in the group is determined to be that the mission analysis target no longer belongs to the mission analysis target. If not, the updated result of the mission analysis target in the group is determined to be that the mission analysis target still belongs to the mission analysis target.

[0085] This step applies when the proportion of isolated devices does not exceed the threshold. In this case, risk weights need to be introduced for comprehensive judgment. The "risk weight value of the correlation analysis target" can be determined according to the number of correlation analysis targets. The more targets there are, the higher the weight, indicating that the abnormality of the device is more common, suggesting that there is a certain correlation between the patient in the group and the target. In this case, the risk weight value is smaller. The "sum of risk weight values" refers to the cumulative result of the weight values ​​of all risky devices for the patient. The "preset risk weight threshold" is a pre-set weight and upper limit. Exceeding the upper limit indicates that the overall monitoring bias risk is relatively large, and it needs to be excluded to eliminate interference.

[0086] When the proportion of isolated devices is not high but the criteria are not fully met, weighted summation can more precisely measure the correlation between patient abnormalities and group commonalities. A higher weighted sum indicates a stronger correlation between the patient and the indicator, and the patient should be retained; a lower weighted sum indicates that the patient's abnormality is more case-specific and should be removed. This quantitative method ensures the accuracy and fairness of the decision-making process.

[0087] Patient C18's H118 has 7 associated users. When the number is greater than 2, the number is 0. When the number is less than or equal to 2, the number is 1 - 0.1 multiplied by the number of associated users. Therefore, its risk weight value is 0. B218 has a weight of 1. The weight sum of 1 is not greater than the preset risk weight threshold of 1.5. At this time, C18 belongs to the education analysis target (because the high weight sum indicates that its abnormality mainly comes from common equipment, so the risk of abnormality in its monitoring equipment is small, while the risk of abnormality in its physical indicators is high). If it is excluded, it is regarded as an education analysis target with monitoring bias risk.

[0088] Furthermore, the method for determining the analysis and management method of the monitoring data of the group is as follows: This invention targets the updated target group obtained after equipment deviation screening, and further evaluates the prevalence of anomalies in each monitoring indicator in the group's historical construction. This determines whether to continue monitoring data analysis for the group, thereby optimizing the allocation of analytical resources. The logic is as follows: by statistically analyzing the number of times the number of abnormal patients for each monitoring indicator meets the target after excluding patients at monitoring risk in multiple historical constructions of the group (i.e., the number of association analyses), the correlation strength between each indicator and the group is quantified. If a certain indicator consistently maintains a sufficient number of abnormal patients in multiple historical constructions, it indicates that the abnormality of this indicator is a stable common feature of the group and deserves close attention; if the number of times all indicators meet the target historically is scarce, it indicates that the group has no significant common anomalies, and the analysis should be terminated. Based on this, combined with the number of current updated targets, the number of risk indicators under observation, and the abnormal association of individual patients, a tiered decision is made on whether to continue investing analytical resources. This ensures that limited resources are focused on truly representative abnormal patterns of the group, guaranteeing the timeliness and appropriateness of updated educational methods.

[0089] S41 Based on the update results of the mission analysis objectives, determine the updated mission analysis objectives in the group and use them as the updated objectives; "Updated results of mission analysis objectives" refers to the results obtained after previous processes (such as...). Figure 3 After screening (S31-S33) as shown, the patient set retained after removing patients who no longer have group representativeness due to isolated equipment bias or other reasons from the original education analysis target; the "updated target" is these retained patients, who are the valid members of the group at the current time point and are the basis for subsequent analysis.

[0090] After equipment bias screening, a purer patient population was obtained, whose anomalous data are more likely to reflect real clinical problems. However, it is still necessary to assess whether this group has consistently exhibited meaningful anomalous patterns historically to determine whether to continue investing analytical resources. This step provides the current analytical subjects for subsequent decision-making.

[0091] In the most recent build of the "hyperglycemia combined with abnormal heart rate" group, after updates S31-S33, 12 patients (such as patients C2, C3, C4, etc.) were retained. These 12 patients are the current update target for this group.

[0092] S42 uses the construction data of the missionary analysis objectives in the group to determine the number of times the monitoring indicators in the group are abnormal, and uses the number of times the monitoring indicators are abnormal as the number of correlation analysis. "Construction data" refers to the patient monitoring data and clinical information used in the historical construction of this group. "Number of analyses showing abnormalities in monitoring indicators" refers to each historical construction of the group where, after excluding patients at risk of monitoring, the number of patients with abnormalities on a certain monitoring indicator still reaches a preset "effective number threshold," which is counted as one valid occurrence of that indicator. The "number of association analyses" for that indicator is obtained by summing up the number of valid occurrences in all historical constructions. This value reflects the stability and prevalence of the indicator's abnormality in the group's history—a higher number of association analyses indicates that the indicator's abnormality occurs repeatedly in the group, exhibiting strong commonalities, and warrants close attention in subsequent educational outreach.

[0093] The core of this step is to examine the stability of abnormal indicators over time. A high number of patients with abnormal indicators in a single cohort construction might be accidental, but if a sufficient number of patients with abnormal indicators are consistently observed across multiple historical constructions, it indicates that the abnormality of that indicator is an inherent characteristic of the cohort, rather than a random fluctuation. By statistically analyzing the number of times indicators were met historically, common indicators with long-term value can be identified more reliably, providing historical evidence to support subsequent decisions.

[0094] Assume the "hyperglycemia combined with abnormal heart rate group" was constructed 5 times in the past year. After each construction, monitoring risk devices were excluded, and the number of remaining patients with abnormalities on each indicator was counted. The preset "effective number threshold" is 5 people (i.e., ≥5 patients with abnormalities on each indicator are considered valid). The historical records are as follows: First test: After exclusion, 8 people had abnormal heart rate (meeting the target), 3 people had abnormal systolic blood pressure (not meeting the target), and 4 people had abnormal respiratory rate (not meeting the target). Second test: After exclusion, 6 people had abnormal heart rate (meeting the target), 5 people had abnormal systolic blood pressure (meeting the target), and 2 people had abnormal respiratory rate (not meeting the target). Third time: After exclusion, 7 people had abnormal heart rate (meeting the target), 4 people had abnormal systolic blood pressure (not meeting the target), and 5 people had abnormal respiratory rate (meeting the target). Fourth round: After exclusion, 5 people had abnormal heart rate (meeting the target), 3 people had abnormal systolic blood pressure (not meeting the target), and 3 people had abnormal respiratory rate (not meeting the target). 5th time: After exclusion, 4 people had abnormal heart rate (not up to standard), 2 people had abnormal systolic blood pressure (not up to standard), and 1 person had abnormal respiratory rate (not up to standard). The correlation analysis was performed 4 times for heart rate (the first, second, third, and fourth times met the target), 1 time for systolic blood pressure (the second time), and 1 time for respiratory rate (the third time). This indicates that the heart rate abnormality was the most stable and had the strongest correlation in this group's history.

[0095] Specifically, the number of correlation analyses refers to the number of analyses in which the number of educational analysis targets with abnormal monitoring indicators exceeds a preset target number threshold, excluding those educational analysis targets that have a risk of monitoring bias.

[0096] S43 uses the updated target data, the abnormal correlation of the monitoring indicators of the updated target, and the number of correlation analyses of different monitoring indicators to determine the analysis and management method of the monitoring data of the group.

[0097] Furthermore, the number of updated targets in the group is obtained. If the number of updated targets in the group meets the requirements, that is, it is greater than a certain number of targets, the number of updated targets is still relatively large. Therefore, the analysis and management method for the monitoring data of the group is determined to continue analysis and processing.

[0098] "Update target data" refers to the detailed information of the current target patients; "Abnormal correlation" refers to the abnormal distribution of each patient on different indicators, such as a patient having abnormal heart rate and blood pressure at the same time; "Analysis and management methods" refers to the decision on whether to continue in-depth analysis of the group (such as identifying abnormal patterns and developing education strategies).

[0099] This step serves as the entry point for comprehensive decision-making. First, a quick assessment is made based on the number of current update targets: if the number is large enough, continue analysis directly, as a large sample size is inherently valuable. If the number is insufficient, stratification is necessary, incorporating indicators such as the number of association analyses to avoid prematurely abandoning historically performing but currently small groups.

[0100] For the aforementioned group, the current update target number is 12 people. First, determine if the "quantity requirement" is met (for example, if the preset threshold for continuing analysis is set to 8 people, 12 > 8, then directly determine to continue analysis). If the current update target only has 3 people, then it is not met, and proceed to the subsequent sub-steps.

[0101] Furthermore, if the number of update targets in the group does not meet the requirements, the following is also included: S431 determines whether the number of updated targets is less than the preset target number threshold. If so, even if the analysis and processing continue, the correlation analysis of different monitoring indicators cannot be achieved, that is, the update processing of the number of correlation analysis cannot be performed. Therefore, it is determined that the analysis and management method of the monitoring data of the group does not need to be further analyzed and processed. If not, proceed to step S432. The "preset target number threshold" is a pre-set minimum number of patients. The preset "effective number threshold" is 5 people (that is, ≥5 patients with abnormal indicators are considered effective). If the number is less than 5, it is impossible to update the number of correlation analyses for different indicators, and there is no analytical value.

[0102] If there are currently 3 people, the analysis will not continue; if there are 6 people, proceed to S432.

[0103] S432 uses the number of correlation analyses of different monitoring indicators to determine the monitoring indicators whose number of correlation analyses is greater than the preset correlation number threshold, and uses them as risk indicators of concern. It then determines whether the number of risk indicators of concern is greater than the preset risk indicator number threshold. If so, it determines that the analysis and management method of the monitoring data of the group is to continue the analysis and processing. If not, it proceeds to step S433. "Preset association frequency threshold" is a numerical value used to determine whether a certain indicator has reached the effective standard frequently enough in history (e.g., at least 3 times); "Risk indicators to watch" refers to those indicators whose association analysis frequency exceeds this threshold. These indicators are high-frequency common anomalies that have repeatedly occurred in the group's history; "Preset risk indicator quantity threshold" is used to determine whether there are enough such high-risk indicators (e.g., at least 2).

[0104] Even if the current group size is small, if certain indicators have repeatedly shown anomalies historically, it suggests that these indicators may be inherent characteristics of the group, and the current small size may only be a temporary phenomenon, warranting further analysis. This step uses historical performance to compensate for the limitations of the current sample.

[0105] Assume the current group update target only has 6 people, but their historical correlation analysis counts are: heart rate 4 times, systolic blood pressure 1 time, and respiration 1 time. The preset correlation count threshold is set to 2 times, then heart rate (4>2) is a risk indicator of concern, with a quantity of 1. The preset risk indicator quantity threshold is set to 1, where 1 is not greater than 1 (it needs to be greater), therefore this does not meet the requirement, and proceed to S433.

[0106] S433 determines whether there are any abnormal updated targets among the associated risk indicators based on the abnormal correlation of the monitoring indicators of the updated target. If yes, the analysis and management method of the monitoring data of the group is to continue the analysis and processing. If no, proceed to step S434. "Abnormal associations" refers to abnormal combinations of multiple indicators for the current target patient; "Association risk indicators" are the risk indicators of concern selected in S432. This step checks whether there is at least one patient in the current target whose abnormality occurs precisely on these risk indicators of concern. If so, it indicates that historically important indicators are still supported by current patients, and further analysis is warranted.

[0107] Even if the number of risk indicators is insufficient, if patients still show abnormalities on these historically important indicators, it indicates that these indicators have not completely disappeared and may still have value for further investigation. This step further preserves indicators with historical basis and current case support.

[0108] Continuing with the previous example, if there is only one risk indicator of concern, and 3 out of the current 6 updated targets have abnormal heart rates, then there is an abnormal correlation, and the analysis continues. If no patients have abnormalities in these indicators (e.g., the risk indicator of concern is empty), then this step is invalid, and proceed to S434.

[0109] S434 determines the monitoring matching factor of the monitoring indicators of the updated target based on the number of anomalies in different monitoring indicators and the number of correlation analyses of the monitoring indicators. It then determines whether the monitoring matching factor is greater than a preset matching factor threshold. If so, it determines that the analysis and management method of the monitoring data of the group is to continue the analysis and processing. If not, it determines that the analysis and management method of the monitoring data of the group cannot continue the analysis and processing, thereby providing resources for the update and analysis and processing of new groups.

[0110] The "Matching Factor" is a comprehensive indicator used to quantify the degree of matching between the current abnormal patterns in a group and its historical common characteristics. For example, it can be defined as Matching Factor = Σ(Current number of abnormal individuals for a certain indicator × Number of historical correlation analyses for that indicator). The "Preset Matching Factor Threshold" is the lower limit for determining whether further analysis is worthwhile.

[0111] If none of the above conditions are met, the group may no longer have significant abnormal patterns. However, if the current anomaly still has a weak correlation with historical high-frequency indicators, this correlation can be quantified through weighted calculation. If the matching factor is higher than the threshold, it indicates that there is still some value; otherwise, the analysis should be terminated, and resources should be released to other groups.

[0112] Assume the current target group for updating is 6 people, with the following abnormal numbers for each indicator: heart rate 5 people, systolic blood pressure 1 person, respiration 0 people. Historical association analysis counts: heart rate 1 time, systolic blood pressure 1 time, respiration 1 time. Define the matching factor as: Heart rate (current number of people × historical count) + Systolic blood pressure (current number of people × historical count) + Respiration (current number of people × historical count) = 5 × 1 + 1 × 1 + 0 × 1 = 6. The preset matching factor threshold is set to 8; if 6 is not greater than 8, the process terminates.

[0113] This invention uses a systematic group monitoring data analysis and management method to accurately identify common abnormal indicators with historical stability: by statistically analyzing the number of times each monitoring indicator is achieved after excluding patients at risk of monitoring in multiple historical group constructions, the long-term correlation strength between the indicator and the group is quantified, effectively screening out recurring and representative common abnormalities, providing reliable targets for subsequent education strategies.

[0114] Based on the number of current update targets and the historical performance of each indicator, stratified decisions are made on whether to continue investing analysis resources, avoiding wasting computing power on groups with too few samples or mediocre historical performance, and ensuring that resources are focused on groups that have real long-term value.

[0115] By comprehensively judging from multiple perspectives such as current size, stability of historical indicators, and abnormal associations among current patients, the decision-making process takes into account both current samples and historical experience, making the decision more scientific and reasonable. Through progressive judgment conditions (such as focusing on risk indicators, existence of abnormal associations, and matching factors), even groups that are currently small in size but have a good history can be preserved, thus protecting valuable clinical clues.

[0116] Example 2 Secondly, such as Figure 4 As shown, this application provides a monitoring system for a propaganda and education system, employing the aforementioned monitoring data analysis method, specifically including: The analysis strategy determination module, the monitoring and analysis module, and the analysis management module are all included. The analysis strategy determination module is responsible for determining the analysis strategy for the monitoring data of patients in the group. The monitoring and analysis module is responsible for determining the monitoring and analysis methods for the missionary analysis objectives in the group; The analysis and management module is responsible for determining the analysis and management methods for the monitoring data of the group.

[0117] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0118] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0119] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A monitoring data analysis method, characterized in that, Specifically, it includes: Using the patient's physical indicators, determine the degree of matching between the patient's physical indicators and those of other patients in the department. Based on the degree of matching, classify the patient into different groups. Based on the degree of matching between the patient data in the group and the physical indicators of patients in the department in the past, determine the analysis strategy for monitoring the patient data in the group. The analysis strategy is used to determine the objectives of the missionary analysis. Based on the analysis results of the missionary analysis objective data in the group and the monitoring data of the missionary analysis objective monitoring equipment, the monitoring and analysis method for the missionary analysis objectives in the group is determined. The monitoring and analysis method is used to identify the monitoring risk devices among the monitoring equipment of the propaganda and education analysis targets. Based on the degree of overlap of the monitoring risk devices of the propaganda and education analysis targets, the update results of the propaganda and education analysis targets in the group are determined. Based on the construction data of the propaganda and education analysis targets in the group, the analysis data of the monitoring indicators with abnormality are identified. Combining the update results of the propaganda and education analysis targets in the group and the degree of correlation with the abnormality of the monitoring indicators in history, the analysis and management method of the monitoring data of the group is determined.

2. The monitoring data analysis method as described in claim 1, characterized in that, The patient's physical indicator data includes the patient's physical indicators and the indicator data of the physical indicators.

3. The monitoring data analysis method as described in claim 1, characterized in that, The degree of matching between the patient's physical indicators and those of other patients in the department is determined based on the number of physical indicators that meet the requirements according to the deviation rate of the patient's indicator data from those of other patients in the department.

4. The monitoring data analysis method as described in claim 3, characterized in that, The patients were divided into different groups, specifically including: Patients whose number of physical indicators that meet the deviation rate requirement is greater than the preset threshold number of indicators are grouped into the same group.

5. The monitoring data analysis method as described in claim 1, characterized in that, The method for determining the analysis strategy for the monitoring data of patients in the group is as follows: Determine the number of patients in the group based on the patient data in the group; Based on the degree of matching between the patient data in the group and the physical indicators of patients in the department in the past, the patients who fell into the group in the past are identified; Based on the number of patients in the group and the number of patients who have historically fallen into the group, an analysis strategy for monitoring the patients in the group is determined.

6. The monitoring data analysis method as described in claim 5, characterized in that, S131 Obtain the number of patients in the group. If the number of patients in the group is greater than a preset patient number threshold, then determine that the analysis strategy for the monitoring data of the patients in the group is to use the patients in the group as the target of the education analysis.

7. The monitoring data analysis method as described in claim 1, characterized in that, The method for determining the analysis and management method of the monitoring data of the group is as follows: Based on the updated results of the mission analysis objectives, the updated mission analysis objectives in the group are determined and used as the updated objectives; Using the data from the construction of the mission analysis objectives in the group, the number of times the monitoring indicators in the group showed anomalies was determined, and the number of times the monitoring indicators showed anomalies was used as the number of correlation analysis. By utilizing the updated target data, the abnormal correlation of the updated target's monitoring indicators, and the number of correlation analyses of different monitoring indicators, the analysis and management method for the monitoring data of the group is determined.

8. The monitoring data analysis method as described in claim 7, characterized in that, The number of correlation analyses refers to the number of analyses performed when the number of educational analysis targets with abnormal monitoring indicators exceeds a preset target number threshold, excluding those with monitoring bias risks.

9. The monitoring data analysis method as described in claim 8, characterized in that, The number of updated targets in the group is obtained. If the number of updated targets in the group meets the requirements, the analysis and management method for the monitoring data of the group is determined to continue analysis and processing.

10. A monitoring system for a propaganda and education system, employing a monitoring data analysis method according to any one of claims 1-9, characterized in that, Specifically, it includes: The analysis strategy determination module, the monitoring and analysis module, and the analysis management module are all included. The analysis strategy determination module is responsible for determining the analysis strategy for the monitoring data of patients in the group. The monitoring and analysis module is responsible for determining the monitoring and analysis methods for the missionary analysis objectives in the group; The analysis and management module is responsible for determining the analysis and management methods for the monitoring data of the group.

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