AI health monitoring management system based on intelligent diagnosis

Abstract

The invention discloses an AI health monitoring management system based on intelligent diagnosis, and relates to the technical field of data analysis, and the method comprises the steps: obtaining a timestamp which is uploaded to an intelligent diagnosis system in a preset time window for each type of user health data, and obtaining a data updating fluctuation abnormal coefficient and a data updating delay coefficient; calculating a data uploading incomplete coefficient according to the uploading quantity of each type of user health data; calculating a data false coefficient, and dividing the real state of the user health data into a false state and a temporary real state in combination with a preset threshold value; for the user health data in the temporary real state, a data false coefficient sequence is obtained to judge the final real state of the user health data, and the authenticity of the health data can be effectively verified; the potential problem that errors or deviations possibly exist in transmitted data can be recognized and corrected, the accuracy of an intelligent diagnosis result is ensured, and misleading caused by making improper health decisions or taking invalid health management measures for users is reduced.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to an AI health monitoring and management system based on intelligent diagnosis. Background Art

[0002] The AI ​​health monitoring and management system based on intelligent diagnosis is a system that uses artificial intelligence (AI) technology to achieve real-time monitoring, diagnosis and management of the health status of individuals or groups. This type of system usually combines sensors, wearable devices, health data analysis, machine learning and big data technologies to conduct in-depth analysis of users' health data, and provide health predictions, risk assessments, diagnostic recommendations and personalized health management plans through intelligent algorithms; this system is widely used in chronic disease management, elderly health monitoring, sports health management, mental health monitoring and other fields. Its advantage is that it can achieve early detection and intervention of health conditions, improve disease prevention capabilities, and improve the efficiency of medical resource utilization.

[0003] AI health monitoring management systems based on intelligent diagnosis rely on the user's health data transmitted by wearable devices and health monitoring sensors, and often assume that these data are accurate and perform intelligent diagnosis; however, these systems often lack an effective verification mechanism for the authenticity of health data; since sensors may be affected by a variety of factors, the transmitted data may contain errors or deviations; if the system fails to identify and correct these potential data problems, it may lead to errors in the intelligent diagnosis results, thereby misleading users to make inappropriate health decisions or take ineffective health management measures. Summary of the invention

[0004] The purpose of the present invention is to solve the above-mentioned problem that may cause errors in intelligent diagnosis results, thereby misleading users to make inappropriate health decisions or take ineffective health management measures, and to provide an AI health monitoring and management system based on intelligent diagnosis.

[0005] The present invention proposes an AI health monitoring management system based on intelligent diagnosis, the system comprising:

[0006] Update module: for each type of user health data, obtain the timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain the data update fluctuation anomaly coefficient and data update delay coefficient according to the timestamp;

[0007] Incomplete module: obtains the number of uploaded health data of each type of user within a preset time window, and calculates the data upload incomplete coefficient according to the number of uploaded health data of each type of user;

[0008] False coefficient module: obtains the data false coefficient according to the data update fluctuation abnormal coefficient, the data update delay coefficient and the data upload incomplete coefficient; and divides the real state of the user's health data into a false state and a temporary real state in combination with the preset data false coefficient threshold;

[0009] Final judgment module: For the user health data in a temporary true state, continuously obtain the data false coefficient, and sequence it according to time to obtain the data false coefficient sequence, and judge the final true state of the user health data based on the data false coefficient sequence.

[0010] Optionally, obtaining the data update fluctuation anomaly coefficient according to the timestamp includes:

[0011] For each type of user health data, obtain the actual timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain a timestamp sequence based on time order;

[0012] Calculate the time interval between every two adjacent timestamps in the timestamp sequence, and sequence them to obtain the upload interval sequence;

[0013] The upload interval in the upload interval sequence is marked as E 实 u , u represents the sequence number of the upload interval in the upload interval sequence, u=1, 2, 3, 4, ..., p, p is the total number of upload intervals in the upload interval sequence and p is a positive integer;

[0014] Calculate the mean of the upload interval sequence using the following formula:

[0015] Calculate the data update fluctuation anomaly coefficient, the calculation formula is:

[0016]

[0017] Among them, YK is the data update fluctuation anomaly coefficient, f is the type number of the user health data, m is the total number of types of user health data, and m is a positive integer.

[0018] Optionally, obtaining a data update delay coefficient according to a timestamp includes:

[0019] For each type of user health data, obtain the actual timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain a timestamp sequence based on time order;

[0020] Calculate the time interval between every two adjacent timestamps in the timestamp sequence, and sequence them to obtain the upload interval sequence; mark the upload interval in the upload interval sequence as E 实 u, u represents the sequence number of the upload interval in the upload interval sequence, u=1, 2, 3, 4, ..., p, p is the total number of upload intervals in the upload interval sequence and p is a positive integer;

[0021] Set the upload interval E in the upload interval sequence 实 u and within the preset upload interval range (T 快 , T 慢 ) are re-marked as Q w , w represents E 实 u Within the preset data upload interval range (T 快 , T 慢 ), w = 0, 1, 2, 3, 4, ..., d, d is a positive integer; calculate the data update delay coefficient AG, the calculation formula is: f is the type number of the user health data, m is the total number of types of the user health data, and m is a positive integer.

[0022] Optionally, calculating the data upload incomplete coefficient according to the number of uploaded user health data of each type includes:

[0023] Get the actual number of health data uploaded by each type of user and the corresponding preset health data upload number within the preset time window, and mark the actual data upload number and the corresponding preset data upload number as G respectively. 实 f and G 预 f , f is the type number of the user health data, m is the total number of types of user health data, and m is a positive integer;

[0024] According to G 实 f and G 预 f The data upload incomplete coefficient DF is obtained, and the calculation formula is:

[0025] Optionally, obtaining the data false coefficient according to the data update fluctuation abnormality coefficient, the data update delay coefficient and the data upload incompleteness coefficient includes:

[0026]

[0027] Where Dax is the data false coefficient, YK, AG and DF are the data update fluctuation abnormality coefficient, data update delay coefficient and data upload incomplete coefficient respectively, α, β, γ are the preset proportional coefficients of YK, AG and DF respectively, and α, β, γ are all greater than 0.

[0028] Optionally, dividing the real state of the user health data into a false state and a temporary real state in combination with a preset data false coefficient threshold includes:

[0029] Compare the data false coefficient with the preset data false coefficient threshold. If the data false coefficient is not less than the preset data false coefficient threshold, it indicates that the real state of the user's health data is a false state, and a warning signal is issued;

[0030] The data false coefficient is compared with the preset data false coefficient threshold. If the data false coefficient is less than the preset data false coefficient threshold, it means that the user health data is in a temporary true state and needs further analysis.

[0031] Optionally, judging the final true state of the user health data according to the data false coefficient sequence includes:

[0032] Calculate the standard deviation and mean of the data false coefficient sequence, and compare the standard deviation and mean of the data false coefficient sequence with the preset standard deviation and preset mean respectively;

[0033] If the standard deviation of the data false coefficient sequence is less than the preset standard deviation and the mean is less than the preset mean, it means that the final true state of the user's health data is true; otherwise, it means that the final true state of the user's health data is a false state.

[0034] Beneficial effects of the present invention:

[0035] The present invention proposes an AI health monitoring and management system based on intelligent diagnosis. For each type of user health data, the timestamp of the data uploaded to the intelligent diagnosis system within a preset time window is obtained to obtain the data update fluctuation abnormality coefficient and the data update delay coefficient; and the data upload incomplete coefficient is calculated according to the number of uploaded user health data of each type; the data false coefficient is obtained according to the data update fluctuation abnormality coefficient, the data update delay coefficient and the data upload incomplete coefficient; and the real state of the user health data is divided into a false state and a temporary real state in combination with a preset data false coefficient threshold; for the user health data in the temporary real state, the data false coefficient sequence is obtained to judge the final real state of the user health data, which can effectively verify the authenticity of the health data; the system can identify and correct potential problems that may exist in errors or deviations in the transmitted data, ensure the accuracy of the intelligent diagnosis results, and reduce the misleading of users to make inappropriate health decisions or take ineffective health management measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] Figure 1The framework diagram of an AI health monitoring management system based on intelligent diagnosis. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0040] The embodiment of the present invention provides an AI health monitoring and management system based on intelligent diagnosis. Figure 1 , Figure 1 A framework diagram of an AI health monitoring management system based on intelligent diagnosis provided by an embodiment of the present invention. The system includes:

[0041] Update module: for each type of user health data, obtain the timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain the data update fluctuation anomaly coefficient and data update delay coefficient according to the timestamp;

[0042] Incomplete module: obtains the number of uploaded health data of each type of user within a preset time window, and calculates the data upload incomplete coefficient according to the number of uploaded health data of each type of user;

[0043] False coefficient module: obtains the data false coefficient according to the data update fluctuation abnormal coefficient, the data update delay coefficient and the data upload incomplete coefficient; and divides the real state of the user's health data into a false state and a temporary real state in combination with the preset data false coefficient threshold;

[0044] Final judgment module: For the user health data in a temporary true state, continuously obtain the data false coefficient, and sequence it according to time to obtain the data false coefficient sequence, and judge the final true state of the user health data based on the data false coefficient sequence.

[0045] An AI health monitoring and management system based on intelligent diagnosis provided in an embodiment of the present invention can effectively verify the authenticity of health data; the system can identify and correct potential problems of errors or deviations in the transmitted data, ensure the accuracy of the intelligent diagnosis results, and reduce the misleading of users into making inappropriate health decisions or taking ineffective health management measures.

[0046] In one embodiment, obtaining the data update fluctuation anomaly coefficient according to the timestamp includes:

[0047] For each type of user health data, obtain the actual timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain a timestamp sequence based on time order;

[0048] Calculate the time interval between every two adjacent timestamps in the timestamp sequence, and sequence them to obtain the upload interval sequence;

[0049] The upload interval in the upload interval sequence is marked as E 实 u , u represents the sequence number of the upload interval in the upload interval sequence, u=1, 2, 3, 4, ..., p, p is the total number of upload intervals in the upload interval sequence and p is a positive integer;

[0050] Calculate the mean of the upload interval sequence using the following formula:

[0051] Calculate the data update fluctuation anomaly coefficient, the calculation formula is:

[0052]

[0053] Among them, YK is the data update fluctuation anomaly coefficient, f is the type number of the user health data, m is the total number of types of user health data, and m is a positive integer.

[0054] It should be noted that the preset time window is set by professionals based on actual circumstances and is not specifically limited or elaborated.

[0055] It should be noted that each type of user health data refers to various physiological health indicators collected by wearable devices or health monitoring sensors, such as heart rate, blood pressure, body temperature, blood oxygen saturation, number of steps, sleep quality, weight, etc. These health data usually change over time and are uploaded to the intelligent diagnosis system by the device in real time or periodically. The way to obtain these data is usually through real-time connection and synchronization with wearable devices (such as smart watches, fitness bracelets) or other health sensors (such as blood pressure monitors, blood glucose meters). The device will regularly measure the user's physiological parameters and transmit the data to the system through the network. These uploaded health data will be marked with a timestamp to record the specific time when the data is uploaded. The system analyzes these timestamp sequences, calculates the upload interval and its fluctuations, and thus evaluates the stability and authenticity of the data. Therefore, the acquisition of health data not only depends on the measurement accuracy of the device, but also involves the reliability and stability of data transmission to ensure that the acquired health data is representative and conforms to the expected regularity.

[0056] It should be noted that the data update fluctuation anomaly coefficient is an indicator used to measure the fluctuation of the health data upload interval. It evaluates the stability of the uploaded data by calculating the ratio of the standard deviation of the upload interval to the mean in the timestamp sequence. When there is a large fluctuation in the upload interval of health data, this may mean that there are unstable factors in the data collection or transmission process, such as device failure, network delay, user failure to wear the device in time, or inaccurate measurement. The larger the fluctuation anomaly coefficient, the more drastic the change in the time interval of the uploaded data, and the higher the instability of the data. This fluctuation usually makes it difficult for the system to accurately obtain continuous and reliable health information. Therefore, the increase in the data update fluctuation anomaly coefficient means that the authenticity of the data is questioned, and the system may not be able to make accurate diagnosis or prediction based on these unstable data, thereby increasing the possibility of errors in the intelligent diagnosis results. A higher fluctuation anomaly coefficient may imply poor quality of sensor data, which in turn affects the accuracy of health monitoring and even misleads users to make wrong health decisions.

[0057] In one implementation, the benefit of analyzing the data update fluctuation anomaly coefficient for judging the authenticity of user health data is: it provides a means to quantify the regularity of data upload. By monitoring the fluctuations in the time intervals for uploading health data, the system can promptly identify abnormal upload patterns, such as upload intervals that are too long, too short, or frequently fluctuate, which may be potential problem signals in data collection or transmission. For example, if the time interval for uploading health data shows abnormal large fluctuations, it may mean device failure, network instability, or inconsistent user wearing behavior, all of which will cause data distortion and affect the accuracy of intelligent diagnosis. By calculating the fluctuation anomaly coefficient, the system can identify these abnormal fluctuations, thereby warning in advance that there may be problems with the authenticity of the data and avoiding relying on unreliable data to make incorrect health judgments. In addition, this coefficient can also help the system distinguish which data belongs to the normal fluctuation range and which may be abnormal due to external factors or equipment problems, and then take corresponding remedial measures, such as re-collecting data or notifying users to check their equipment, to ensure that the final diagnosis results are more accurate and reliable.

[0058] In one embodiment, obtaining a data update delay coefficient according to a timestamp includes:

[0059] For each type of user health data, obtain the actual timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain a timestamp sequence based on time order;

[0060] Calculate the time interval between every two adjacent timestamps in the timestamp sequence, and sequence them to obtain the upload interval sequence; mark the upload interval in the upload interval sequence as E 实 u, u represents the sequence number of the upload interval in the upload interval sequence, u=1, 2, 3, 4, ..., p, p is the total number of upload intervals in the upload interval sequence and p is a positive integer;

[0061] Set the upload interval E in the upload interval sequence 实 u and within the preset upload interval range (T 快 , T 慢 ) are re-marked as Q w , w represents E 实 u Within the preset data upload interval range (T 快 , T 慢 ), w = 0, 1, 2, 3, 4, ..., d, d is a positive integer; calculate the data update delay coefficient AG, the calculation formula is: f is the type number of the user health data, m is the total number of types of the user health data, and m is a positive integer.

[0062] It should be noted that the preset upload interval range is set by professionals based on experience and data analysis according to the actual application scenarios and the user's health monitoring needs. This range usually takes into account the collection frequency of different types of health data and the user's daily activity patterns. Setting a reasonable upload interval range helps ensure the timeliness and integrity of the data, while avoiding data loss or inconsistency due to too frequent or too long uploads. Through this customized upload interval setting, the system can more accurately evaluate the normality of data updates and promptly identify abnormal upload behaviors, thereby ensuring the accuracy and reliability of intelligent diagnostic results.

[0063] It should be noted that the data update delay coefficient is an indicator used to measure the degree of delay in uploading user health data. It evaluates the timeliness of data updates by analyzing the time intervals for uploading health data, especially those that exceed the preset upload interval range. When the upload interval is too long or irregular, the data update delay coefficient will increase, which means that there may be delays or abnormalities in the data collection and upload process. This delay usually reflects the system's untimely acquisition of real-time health information, which may be caused by equipment failure, network problems, or users not wearing the device on time. The larger the data update delay coefficient, the worse the frequency and timeliness of data updates, resulting in the system being unable to obtain the user's latest health status in real time. This delay may cause the intelligent diagnosis system to rely on lagging or incomplete data when performing health analysis, thereby affecting the accuracy of the diagnosis results. Especially in some health monitoring scenarios with high timeliness requirements (such as heart disease monitoring, blood sugar monitoring, etc.), data delays may cause the system to be unable to respond to sudden health problems in a timely manner, increasing the risk of diagnostic errors. Therefore, a higher delay coefficient usually means that the data authenticity is lower, the probability of error in the diagnosis results is higher, and it may mislead users to make inappropriate health decisions.

[0064] In one implementation, the benefit of analyzing the data update delay coefficient for judging the authenticity of user health data is: it provides a means to quantify the regularity of data upload. By monitoring the fluctuations in the time intervals for uploading health data, the system can promptly identify abnormal upload patterns, such as upload intervals that are too long, too short, or frequently fluctuate, which may be potential problem signals in data collection or transmission. For example, if the time interval for uploading health data shows abnormal large fluctuations, it may mean device failure, network instability, or inconsistent user wearing behavior, all of which will cause data distortion and affect the accuracy of intelligent diagnosis. By calculating the fluctuation anomaly coefficient, the system can identify these abnormal fluctuations, thereby warning in advance that there may be problems with the authenticity of the data and avoiding relying on unreliable data to make incorrect health judgments. In addition, this coefficient can also help the system distinguish which data belongs to the normal fluctuation range and which may be abnormal due to external factors or equipment problems, and then take corresponding remedial measures, such as re-collecting data or notifying users to check their equipment, to ensure that the final diagnosis results are more accurate and reliable.

[0065] In one embodiment, obtaining the number of each type of user health data uploaded within a preset time window, and calculating the data upload incomplete coefficient according to the number of each type of user health data uploaded includes:

[0066] Get the actual number of health data uploaded by each type of user and the corresponding preset health data upload number within the preset time window, and mark the actual data upload number and the corresponding preset data upload number as G respectively. 实 f and G 预 f , f is the type number of the user health data, m is the total number of types of user health data, and m is a positive integer;

[0067] According to G 实 f and G 预 f The data upload incomplete coefficient DF is obtained, and the calculation formula is:

[0068] It should be noted that the preset data upload quantity is set by professionals based on actual circumstances and is not limited or elaborated on in detail.

[0069] It should be noted that the data upload incomplete coefficient is an indicator used to measure the gap between the actual number of user health data uploaded and the preset upload number within the preset time window. Specifically, it reflects whether the data upload is carried out at the expected frequency. When the actual upload number is less than the preset upload number, the data upload incomplete coefficient will increase, indicating that the health data upload within the time window is missing or untimely. The larger the data upload incomplete coefficient, the less complete or discontinuous the health data received by the system may be, which may affect the timeliness and accuracy of the data. This will directly affect the judgment ability of the intelligent diagnosis system because the system relies on these health data for health analysis and prediction. If the uploaded data is incomplete, the intelligent diagnosis system may not be able to obtain sufficient and real health information, and thus make wrong judgments. For example, if a certain type of health data (such as blood pressure, heart rate, etc.) is missing or the interval is too long, the system may not be able to detect health changes in time, miss the opportunity for early warning, and even lead to misdiagnosis. Therefore, the larger the data upload incomplete coefficient, the lower the accuracy and reliability of intelligent diagnosis, the greater the risk of incorrect diagnosis results and health advice, and may mislead users to make inappropriate health decisions.

[0070] In one implementation, the benefit of analyzing the incomplete data upload coefficient for judging the authenticity of the user's health data is that it can help the system to promptly identify the missing or irregular data upload, thereby ensuring that the health data based on the intelligent diagnosis system is complete and representative. By calculating this coefficient, the system can effectively detect which health data has insufficient upload frequency or interruption, and then determine whether these data can truly reflect the user's health status. The larger the data upload incomplete coefficient, the more serious the degree of data missing, which may lead to inaccurate or untimely diagnosis results. For example, for some important health indicators (such as heart rate, blood sugar, etc.), incomplete upload may miss important health changes, affecting the system's early warning ability and decision support ability. By analyzing and monitoring this coefficient, the system can identify potential health data problems in advance and take remedial measures in time (such as reminding users to upload data or resynchronize devices) to ensure that the intelligent diagnosis system relies on accurate and comprehensive data when making health assessments, thereby improving the accuracy and reliability of diagnosis and reducing the risk of misleading users.

[0071] In one embodiment, obtaining the data false coefficient according to the data update fluctuation abnormality coefficient, the data update delay coefficient and the data upload incomplete coefficient includes:

[0072]

[0073] Where Dax is the data false coefficient, YK, AG and DF are the data update fluctuation abnormality coefficient, data update delay coefficient and data upload incomplete coefficient respectively, α, β, γ are the preset proportional coefficients of YK, AG and DF respectively, and α, β, γ are all greater than 0.

[0074] In one embodiment, dividing the real state of the user's health data into a false state and a temporary real state in combination with a preset data false coefficient threshold includes:

[0075] Compare the data false coefficient with the preset data false coefficient threshold. If the data false coefficient is not less than the preset data false coefficient threshold, it indicates that the real state of the user's health data is a false state, and a warning signal is issued;

[0076] The data false coefficient is compared with the preset data false coefficient threshold. If the data false coefficient is less than the preset data false coefficient threshold, it means that the user health data is in a temporary true state and needs further analysis.

[0077] It should be noted that the preset data false coefficient threshold is set by professionals based on actual circumstances and is not limited or elaborated on in detail.

[0078] It should be noted that if the data false coefficient is not less than the preset threshold: this indicates that there are large deviations or problems in the health data, which may be inaccurate or incomplete due to equipment failure, data loss, upload delays, etc. The system will issue an early warning signal to remind users or medical management personnel that the data may be false and need to be verified or corrected immediately. In this case, the system may stop relying on these data for diagnosis or decision-making to avoid misleading users to conduct incorrect health management; if the data false coefficient is less than the preset threshold: this means that the user's health data is temporarily true, that is, the integrity and timeliness of the data upload are within a reasonable range, but there is still a certain degree of uncertainty, which requires further analysis. The system will continue to monitor the update of the data, collect more data, and further verify the authenticity of the data through time series analysis and other methods. If the false coefficient of the data continues to be at a low level, it can be considered that the data is gradually becoming true, and the system can rely on these data for more accurate health assessment and diagnosis.

[0079] In one implementation, this division method based on data false coefficients and thresholds can provide a real-time health data quality control mechanism to ensure that the judgments made by the intelligent diagnosis system are based on reliable and accurate data. When the system detects that the data false coefficient is too high, it can effectively avoid misdiagnosis and protect users from health misleading caused by inaccurate data. At the same time, when the data is judged to be temporarily true, the system can continue to analyze the data in detail to improve the accuracy of health monitoring. Through this dynamic evaluation and timely early warning mechanism, users can not only obtain more reliable health diagnoses, but also take timely measures when potential problems arise, such as checking equipment, re-uploading data, etc., thereby improving the overall effect of health management and the reliability of the intelligent diagnosis system.

[0080] In one embodiment, judging the final true state of the user health data according to the data false coefficient sequence includes:

[0081] Calculate the standard deviation and mean of the data false coefficient sequence, and compare the standard deviation and mean of the data false coefficient sequence with the preset standard deviation and preset mean respectively;

[0082] If the standard deviation of the data false coefficient sequence is less than the preset standard deviation and the mean is less than the preset mean, it means that the final true state of the user's health data is true; otherwise, it means that the final true state of the user's health data is a false state.

[0083] It should be noted that the preset standard deviation and preset mean are set by professionals based on actual conditions and are not limited or elaborated on in detail.

[0084] It should be noted that the mean of the data false coefficient sequence represents the overall reliability of the health data within the time window. If the mean is small, it means that the health data is relatively true overall; if the mean is large, it may indicate that the data is generally inaccurate; the standard deviation reflects the degree of fluctuation of the data false coefficient. If the standard deviation is small, it means that the data changes relatively smoothly and the fluctuation is small, indicating that the data quality is relatively stable; if the standard deviation is large, it may indicate that there are large inconsistencies in the update and upload of health data, and the fluctuation is large, which may affect the authenticity of the data. Compare the calculated standard deviation and mean of the data false coefficient sequence with the preset standard deviation and mean: if the standard deviation of the data false coefficient sequence is less than the preset standard deviation, it means that the data fluctuation is small and the stability is strong. The system can assume that the quality of the health data is high and the credibility is strong; if the mean of the data false coefficient sequence is less than the preset mean, it means that the data is less false. The system can infer that the data is closer to the truth and the data accuracy is higher. Through the above comparison, if the standard deviation and mean of the data false coefficient sequence meet the preset standards (that is, the standard deviation is less than the preset standard deviation and the mean is less than the preset mean), the system considers that the final true state of the user health data of the segment is true, indicating that the data is credible, and intelligent diagnosis can rely on these data for health analysis and decision-making. On the contrary, if the standard deviation and mean of the data false coefficient do not meet the preset standards (that is, the standard deviation is greater than the preset standard deviation or the mean is greater than the preset mean), the system determines that the health data is false, indicating that the accuracy and credibility of the data are low, and the intelligent diagnosis results may have large deviations, and further verification or correction measures are needed.

[0085] In one implementation, this method of judging the final true state of health data by the standard deviation and mean of the data false coefficient sequence has great advantages. It not only helps the system monitor the stability and credibility of the data in real time, but also evaluates its authenticity based on the long-term trend of the data, rather than relying on a single data verification. By dynamically adjusting the preset thresholds of the standard deviation and mean, the system can flexibly perform data quality control based on the characteristics of health data for different users and in different environments. If the system detects false data, it can quickly issue a warning and provide solutions or optimization measures to ensure the accuracy of health monitoring and diagnosis. Ultimately, this method improves the reliability of health data and the accuracy of intelligent diagnosis, reduces health misdiagnosis and decision-making errors caused by data problems, and enhances user trust and security of the system.

[0086] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be used to artificially limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An AI health monitoring and management system based on intelligent diagnosis, characterized in that: The system comprises: Update module: for each type of user health data, obtain the timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain the data update fluctuation anomaly coefficient and data update delay coefficient according to the timestamp; Incomplete module: obtains the number of uploaded health data of each type of user within a preset time window, and calculates the data upload incomplete coefficient according to the number of uploaded health data of each type of user; False coefficient module: obtains the data false coefficient according to the data update fluctuation abnormal coefficient, the data update delay coefficient and the data upload incomplete coefficient; and divides the real state of the user's health data into a false state and a temporary real state in combination with the preset data false coefficient threshold; Final judgment module: For the user health data in a temporary true state, continuously obtain the data false coefficient, and sequence it according to time to obtain the data false coefficient sequence, and judge the final true state of the user health data based on the data false coefficient sequence.

2. According to claim 1, an AI health monitoring and management system based on intelligent diagnosis is characterized in that: The data update fluctuation anomaly coefficients obtained according to the timestamp include: For each type of user health data, obtain the actual timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain a timestamp sequence based on time order; Calculate the time interval between every two adjacent timestamps in the timestamp sequence, and sequence them to obtain the upload interval sequence; The upload interval in the upload interval sequence is marked as E 实 u , u represents the sequence number of the upload interval in the upload interval sequence, u=1, 2, 3, 4, ..., p, p is the total number of upload intervals in the upload interval sequence and p is a positive integer; Calculate the mean of the upload interval sequence using the following formula: Calculate the data update fluctuation anomaly coefficient, the calculation formula is: Among them, YK is the data update fluctuation anomaly coefficient, f is the type number of the user health data, m is the total number of types of user health data, and m is a positive integer.

3. The AI ​​health monitoring and management system based on intelligent diagnosis according to claim 1 is characterized in that: The data update delay coefficients obtained based on the timestamp include: For each type of user health data, obtain the actual timestamp of uploading it to the intelligent diagnosis system within the preset time window, and obtain a timestamp sequence based on time order; Calculate the time interval between every two adjacent timestamps in the timestamp sequence, and sequence them to obtain the upload interval sequence; mark the upload interval in the upload interval sequence as E 实 u , u represents the sequence number of the upload interval in the upload interval sequence, u=1, 2, 3, 4, ..., p, p is the total number of upload intervals in the upload interval sequence and p is a positive integer; Set the upload interval E in the upload interval sequence 实 u and within the preset upload interval range (T 快 , T 慢 ) are re-marked as Q w ,w represents E 实 u Within the preset data upload interval range (T 快 , T 慢 ), w = 0, 1, 2, 3, 4, ..., d, d is a positive integer; calculate the data update delay coefficient AG, the calculation formula is: f is the type number of the user health data, m is the total number of types of the user health data, and m is a positive integer.

4. The AI ​​health monitoring and management system based on intelligent diagnosis according to claim 1 is characterized in that: The data upload incompleteness coefficient is calculated based on the number of uploaded health data of each type of user, including: Get the actual number of health data uploaded by each type of user and the corresponding preset health data upload number within the preset time window, and mark the actual data upload number and the corresponding preset data upload number as G respectively. 实 f and G 预 f , f is the type number of the user health data, m is the total number of types of user health data, and m is a positive integer; According to G 实 f and G 预 f The data upload incomplete coefficient DF is obtained, and the calculation formula is:

5. The AI ​​health monitoring and management system based on intelligent diagnosis according to claim 1 is characterized in that: The data false coefficients obtained based on the data update fluctuation anomaly coefficient, data update delay coefficient and data upload incomplete coefficient include: Where Dax is the data false coefficient, YK, AG and DF are the data update fluctuation abnormality coefficient, data update delay coefficient and data upload incomplete coefficient respectively, α, β, γ are the preset proportional coefficients of YK, AG and DF respectively, and α, β, γ are all greater than 0.

6. The AI ​​health monitoring and management system based on intelligent diagnosis according to claim 1 is characterized in that: Combining the preset data false coefficient threshold, the real state of the user's health data is divided into a false state and a temporary real state, including: Compare the data false coefficient with the preset data false coefficient threshold. If the data false coefficient is not less than the preset data false coefficient threshold, it indicates that the real state of the user's health data is a false state, and a warning signal is issued; The data false coefficient is compared with the preset data false coefficient threshold. If the data false coefficient is less than the preset data false coefficient threshold, it means that the user health data is in a temporary true state and needs further analysis.

7. The AI ​​health monitoring and management system based on intelligent diagnosis according to claim 1 is characterized in that: Judging the final true state of user health data based on the data false coefficient sequence includes: Calculate the standard deviation and mean of the data false coefficient sequence, and compare the standard deviation and mean of the data false coefficient sequence with the preset standard deviation and preset mean respectively; If the standard deviation of the data false coefficient sequence is less than the preset standard deviation and the mean is less than the preset mean, it means that the final true state of the user's health data is true; otherwise, it means that the final true state of the user's health data is a false state.

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