Internal arteriovenous fistula remote monitoring information acquisition system

Abstract

The invention discloses an internal arteriovenous fistula remote monitoring information acquisition system, which comprises a label processing module used for carrying out tagging processing on acquired internal arteriovenous fistula monitoring data based on a unique identifier of a patient, a wireless communication module used for transmitting the tagged data, a receiving and extracting module, and a communication module used for receiving and extracting the tagged data. The monitoring module is used for receiving and analyzing received data and extracting unique identification information of a patient, the storage module is used for storing the data in a database corresponding to the patient according to the extracted unique identification information, and the reading generation module is used for regularly reading monitoring data of the patient from the database, generating a health report and sending the health report to a doctor terminal; the internal arteriovenous fistula remote monitoring information acquisition system solves the problems that in the prior art, when multiple patients use equipment or systems at the same time, data cross and confusion are prone to occurring, recognition errors are brought, diagnosis and treatment accuracy is affected, and illness state judgment and treatment are delayed.

Description

Technical Field

[0001] The present invention relates to the technical field of medical monitoring and remote health management, and in particular to an arteriovenous fistula remote monitoring information collection system. Background Art

[0002] In multi-patient use scenarios of medical devices, accurate data transmission and identification are important links to ensure accurate diagnosis and treatment information. Existing medical data management systems are usually designed based on a single patient data record at the device terminal. When multiple patients are connected to the device or system at the same time, the complexity of information identification and management increases significantly. In this case, the device or system may be misplaced due to the superposition of patient data or incomplete isolation of the device, resulting in data confusion.

[0003] The existing multi-patient management solutions have the following major problems: Data allocation and storage defects: When multiple patients are connected at the same time, the system lacks effective patient data isolation means, which makes it easy for the physiological data of different patients to cross or overlap when stored, resulting in data identification errors. Identification conflicts in information transmission: The existing system cannot guarantee the absolute accuracy of patient information during transmission, and it is easy to have data misplacement or identification errors caused by signal or data packet mismatch, especially in multi-signal transmission or wireless transmission environments, this conflict is more obvious. Synchronization problems in the monitoring system: Because the system does not have a complete data isolation strategy, the monitoring system is difficult to synchronize and distinguish when facing multi-patient data, which is easy to cause misinterpretation of real-time monitoring and reduce the accuracy of diagnosis and treatment. The above problems lead to data cross-confusion when multiple patients use equipment or systems at the same time, resulting in identification errors, which not only affects the accuracy of diagnosis and treatment, but also delays disease diagnosis and treatment. Summary of the invention

[0004] The purpose of the present invention is to provide an arteriovenous fistula remote monitoring information acquisition system to solve the defects of data allocation and storage in the prior art, the identification conflicts in information transmission and the synchronization problems in the monitoring system, which lead to data cross-confusion and identification errors when multiple patients use the equipment or system at the same time, which not only affects the accuracy of diagnosis and treatment, but also delays the diagnosis and treatment of the disease.

[0005] To achieve the above object, the present invention provides the following technical solution: an arteriovenous fistula remote monitoring information collection system, the system comprising:

[0006] A label processing module, used for labeling the collected arteriovenous fistula monitoring data based on the patient's unique identification;

[0007] A wireless communication module, used for transmitting the labeled data;

[0008] The receiving and extracting module is used to receive and parse the received data, extract the patient's unique identification information, set the prior probability of the patient's identification based on the previous historical data, calculate the posterior probability of the label matching based on the received data, and extract and confirm the patient's unique identification based on the matching result with the highest posterior probability. The formula used to calculate the posterior probability of the label matching is:

[0009]

[0010] Among them, P(A|B) represents the posterior probability that the received label belongs to a certain patient, P(B|A) represents the conditional probability that the label exists in the patient, P(A) represents the prior probability of the patient, and P(B) represents the total probability of receiving label B;

[0011] The storage module is used to store the data in the database of the corresponding patient according to the extracted unique identification information. Specifically, the dynamic monitoring data of the patient is stored in chronological order, and a timestamp index is established. The data is stored in the time series database to improve the query speed of the time series data. The time series data is stored in a compressed format and indexed with the timestamp as the primary key. The specific disclosure is as follows:

[0012] TS={(t1, υ1), (t2, υ2),…, (t n , n )};

[0013] Among them, TS represents the entire time series data set, t i represents the timestamp of the i-th time point, υ i Indicates that at time point t i The monitoring data value, n represents the total length of the time series data;

[0014] The reading and generating module is used to regularly read the patient's monitoring data from the database, generate a health report and send it to the doctor's terminal.

[0015] Preferably, the label processing module performs labeling processing on the collected arteriovenous fistula monitoring data based on the patient's unique identifier, including:

[0016] Generate a symmetric key and save it in a safe place, encrypt the patient's unique identifier, generate ciphertext, store or transmit the encrypted ciphertext, decrypt it using the same key, and encrypt the patient's unique identifier and publish it as:

[0017] C=E k (M);

[0018] Among them, C represents the encrypted ciphertext, M represents the unique identifier of the patient, and E k represents encryption operation, and k represents the secret key.

[0019] Preferably, the wireless communication module transmits the labeled data including:

[0020] Initialize the state variables and noise covariance of the data during wireless transmission, update the estimated state for each data packet, and use the updated state to make the next prediction. The state update formula is:

[0021]

[0022] in, Indicates the current estimated state, represents the predicted state of the previous moment, K m represents the Kalman gain, z m represents the observation value, and H represents the observation matrix.

[0023] Preferably, the reading and generating module periodically reads the patient's monitoring data from the database, generates a health report and sends it to the doctor's terminal, including:

[0024] Read health parameter data from the database, apply harmony analysis to each set of data, calculate the correlation between different health data, determine whether it is within the normal range, and generate a health report. If the data correlation is abnormal, it indicates potential health risks. The specific calculation formula for the correlation between health data is:

[0025]

[0026] Among them, M represents the correlation between health data, x i and i represents the numerical value of different health parameters, and g represents the total number of parameters.

[0027] Preferably, the label processing module further includes:

[0028] Generate a patient identity authentication request and send it to the verification server. Update the patient's identity status information based on the received response verification result. After the patient's identity authentication passes, start monitoring data collection. If the patient's identity authentication fails, stop data collection and send an alarm message to the administrator. The specific conditions for patient identity authentication are: if the verification result R=1, data collection is allowed, otherwise data collection is stopped. The parameter R represents the identity authentication result, R=1 means the verification is passed, and R=0 means the verification fails.

[0029] Preferably, updating the patient's identity status information based on the received response verification result includes:

[0030] Read the verification result returned by the verification server, update the patient's local status information based on the verification result, send the updated status information to the cloud server, and update the patient's identity status information in the cloud server. The specific conditions for status update are: if the status update is successful S=1, continue with the subsequent steps, otherwise resend the identity authentication request, and the parameter S represents the status update result, S=1 indicates a successful update, and S=0 indicates a failed update.

[0031] Preferably, updating the patient's local status information based on the verification result includes:

[0032] Obtain the status update confirmation information fed back by the cloud server, update the local cache status based on the confirmation information, record the updated status in the log file, and check the log file to confirm the validity of the status update. The specific conditions for confirming the status update are: if the confirmation information C=1, record the update log, otherwise mark it as an update failure. The parameter C represents the confirmation information, C=1 indicates that the update confirmation is successful, and C=0 indicates that the update confirmation fails.

[0033] Preferably, updating the local cache status based on the confirmation information includes:

[0034] Read the update status record in the log file, update the status information of the local cache based on the status record, re-read the local cache verification status after the cache is updated, and compare the verified status information with the log record. If the local cache status B is equal to the log record status L, the update is considered valid, otherwise it is updated again. B represents the local cache status and L represents the log record status.

[0035] Preferably, updating the locally cached state information based on the state record includes:

[0036] Get the latest status record, update the locally cached status information based on the latest status record, send the updated status information to the monitoring system, monitor and record the status in the monitoring system, where if the updated status information I is equal to the log record status L, the update is considered valid, otherwise an abnormal log is recorded, where I represents the updated status information and L represents the log record status.

[0037] Preferably, the label processing module also includes storing the encrypted ciphertext in blocks, and independently verifying each data block during transmission.

[0038] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0039] The arteriovenous fistula remote monitoring information collection system labels the collected arteriovenous fistula monitoring data based on the patient's unique identification through a label processing module, the wireless communication module transmits the labeled data, the receiving and extracting module receives and parses the received data, extracts the patient's unique identification information, the storage module stores the data in a database corresponding to the patient according to the extracted unique identification information, and the reading and generating module regularly reads the patient's monitoring data from the database, generates a health report and sends it to the doctor's terminal. This can effectively avoid the intersection or overlap of multiple patient data during storage and processing, ensure that the physiological data of each patient can be independently and accurately allocated and stored, reduce the occurrence of data recognition errors, improve the accuracy of data management, and enhance the quality of multiple patients. It improves the reliability of data transmission in multi-patient scenarios, ensures the accuracy and completeness of real-time monitoring data, provides doctors with real and reliable diagnosis and treatment data support, improves the real-time and accuracy of diagnosis and treatment, improves the data management efficiency of the system, and reduces diagnosis and treatment delays caused by data confusion, ensuring that the patient's condition can be diagnosed and treated in a timely and correct manner, which can improve the operating efficiency of the system and the accuracy of diagnosis and treatment decisions, thereby ensuring medical safety in multi-patient use scenarios, and solves the defects of data allocation and storage in existing technologies, identification conflicts in information transmission, and synchronization problems in monitoring systems, which lead to data cross-confusion when multiple patients use equipment or systems at the same time, resulting in identification errors, which not only affects the accuracy of diagnosis and treatment, but also delays the diagnosis and treatment of the condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention. DETAILED DESCRIPTION

[0041] 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.

[0042] like Figure 1 As shown, the present invention provides a technical solution: an arteriovenous fistula remote monitoring information collection system, the system comprising:

[0043] A label processing module, used for labeling the collected arteriovenous fistula monitoring data based on the patient's unique identification;

[0044] A wireless communication module, used for transmitting the labeled data;

[0045] The receiving and extracting module is used to receive and parse the received data, extract the patient's unique identification information, set the prior probability of the patient's identification based on the previous historical data, calculate the posterior probability of the label matching based on the received data, and extract and confirm the patient's unique identification based on the matching result with the highest posterior probability. The formula used to calculate the posterior probability of the label matching is:

[0046]

[0047] Among them, P(A|B) represents the posterior probability that the received label belongs to a certain patient, P(B|A) represents the conditional probability that the label exists in the patient, P(A) represents the prior probability of the patient, and P(B) represents the total probability of receiving label B;

[0048] The storage module is used to store the data in the database of the corresponding patient according to the extracted unique identification information. Specifically, the dynamic monitoring data of the patient is stored in chronological order, and a timestamp index is established. The data is stored in the time series database to improve the query speed of the time series data. The time series data is stored in a compressed format and indexed with the timestamp as the primary key. The specific disclosure is as follows:

[0049] TS={(t1,υ1),(t2,υ2),…,(t n , n )};

[0050] Among them, TS represents the entire time series data set, t i represents the timestamp of the i-th time point, υ i Indicates that at time point t i The monitoring data value, n represents the total length of the time series data;

[0051] The reading and generating module is used to regularly read the patient's monitoring data from the database, generate a health report and send it to the doctor's terminal.

[0052] Label processing module: This module is used to label the unique identifier extracted from the patient's monitoring data. Based on the label, the system can distinguish the patient's data from other data to achieve personalized tracking and monitoring. By labeling the unique identification information in the monitoring data, the consistency and accuracy of the data processing process are ensured.

[0053] Wireless communication module: This module is responsible for wireless transmission of tagged data. The introduction of wireless communication technology enables fast and reliable data transmission between different devices and locations, providing support for remote monitoring.

[0054] Receiving and Extracting Module: The function of the receiving and extracting module is to receive and parse the data transmitted to the system and extract the patient's unique identification information from it. Combining historical data and prior probability, the posterior probability that the received label belongs to the patient is calculated. The system confirms the patient's unique identification based on the matching result with the highest calculated posterior probability, thereby enhancing the accuracy of data processing. The calculation formula of the posterior probability is:

[0055]

[0056] Among them, P(A|B) represents the posterior probability that the received label belongs to a certain patient, P(A|B) represents the conditional probability that the label exists in the patient, P(A) represents the prior probability of the patient, and P(B) represents the total probability of receiving label B;

[0057] Storage module: The storage module is used to store the patient's monitoring data in the corresponding patient's data warehouse in chronological order. The system creates a timestamp index so that the data is indexed with time as the primary key when it is stored, and the storage space is compressed. The storage of time series data adopts a compressed format to improve the data query efficiency of the system. The specific time series representation is:

[0058] TS={(t1,υ1),(t2,υ2),…,(t n ,υ n )};

[0059] Among them, TS represents the entire time series data set, t i represents the timestamp of the i-th time point, υ i Indicates that at time point t i The monitoring data value, n represents the total length of the time series data;

[0060] Reading and generating module: This module regularly reads the patient's monitoring data from the stored data, generates a health report, and sends the report to the doctor's terminal. Through this module, doctors can obtain the patient's health status in a timely manner, which is convenient for remote monitoring and subsequent diagnosis and treatment decisions.

[0061] The signal acquisition system of the present invention can accurately and efficiently remotely monitor intravenous tumors. The designed label processing and receiving extraction mechanism ensures a high degree of match between the monitoring data and the patient's unique identifier, which helps to reduce the risk of misidentification of data. The compressed storage and indexing mechanism of time series data significantly improves the storage and query efficiency of data, and facilitates the long-term preservation and rapid reading of data. At the same time, by regularly generating health reports, doctors can obtain patients' health information in real time, support remote diagnosis and treatment, and improve the practicality and reliability of the system.

[0062] The label processing module labels the collected arteriovenous fistula monitoring data based on the patient's unique identifier, including:

[0063] Generate a symmetric key and save it in a safe place, encrypt the patient's unique identifier, generate ciphertext, store or transmit the encrypted ciphertext, decrypt it using the same key, and encrypt the patient's unique identifier and publish it as:

[0064] C=E k (M);

[0065] Among them, C represents the encrypted ciphertext, M represents the unique identifier of the patient, and E k represents encryption operation, and k represents the secret key.

[0066] In this implementation, the system first generates a unique symmetric key k and saves the key in a secure location (such as a hardware security module or an encrypted database). When the system needs to label the patient's unique identifier M, the label processing module will call the key k to perform symmetric encryption on the identifier to obtain the encrypted ciphertext C. The encrypted ciphertext is transmitted to the receiving end or secure storage through the data transmission module, avoiding the privacy leakage risk caused by directly transmitting the patient's identification information.

[0067] The wireless communication module transmits the tagged data including:

[0068] Initialize the state variables and noise covariance of the data during wireless transmission, update the estimated state for each data packet, and use the updated state to make the next prediction. The state update formula is:

[0069]

[0070] in, Indicates the current estimated state, represents the predicted state of the previous moment, K m represents the Kalman gain, z m represents the observation value, and H represents the observation matrix.

[0071] In this embodiment, the system first initializes the state variables and noise covariance during the wireless transmission process to ensure the accuracy of the transmitted data. m and predicted status Calculate the Kalman gain K m , and use the gain to update the state to get a new estimated state The new estimated state not only improves the accuracy of data transmission, but also predicts the state at the next moment, thereby effectively reducing the impact of noise interference during continuous transmission. The Kalman filter algorithm is used in this system to predict and update the state of monitoring data, making the data transmission process smoother and more stable. This algorithm can dynamically adjust the system's response to noise and improve the accuracy of data transmission, and is particularly suitable for high-frequency data transmission scenarios in remote monitoring of intravenous tumors.

[0072] By introducing the Kalman filter algorithm, the wireless transmission module of the present invention significantly reduces the impact of noise interference during remote monitoring. The state update and prediction mechanism ensures the accurate transmission of monitoring data by the system, reducing data distortion caused by signal attenuation and noise. Compared with traditional wireless transmission methods, the system of the present invention has obvious advantages in the real-time and accuracy of data transmission, and can provide higher quality data support for telemedicine. In addition, due to the adaptability of the Kalman filter algorithm, the system can adapt to interference changes in different transmission environments, improving the robustness and reliability of the remote monitoring system.

[0073] The reading and generating module periodically reads the patient's monitoring data from the database, generates a health report and sends it to the doctor's terminal, including:

[0074] Read health parameter data from the database, apply harmony analysis to each set of data, calculate the correlation between different health data, determine whether it is within the normal range, and generate a health report. If the data correlation is abnormal, it indicates potential health risks. The specific calculation formula for the correlation between health data is:

[0075]

[0076] Among them, M represents the correlation between health data, x i and i represents the numerical value of different health parameters, and g represents the total number of parameters.

[0077] In this embodiment, the reading generation module reads the patient's health monitoring parameters from the stored data at a set time interval. The system analyzes the relationship between these parameter data to calculate the differences between the various health parameters and judge their correlation. Specifically, the system calculates the average value M of the absolute difference for each set of health data pairs (such as blood pressure, blood oxygen, heart rate, etc.), and uses it as a measure of correlation. If the value exceeds the preset normal range, the system will mark it as abnormal and prompt potential health risks. The health report includes the monitoring values ​​and correlation index values ​​of each parameter. Doctors can understand the patient's current status and potential health risks by viewing the health report, so as to provide targeted health management suggestions. Since correlation analysis can identify abnormal correlations between multiple health parameters, this system can effectively enhance the sensitivity of remote monitoring to early signs of disease.

[0078] The tag processing module also includes:

[0079] Generate a patient identity authentication request and send it to the verification server. Update the patient's identity status information based on the received response verification result. After the patient's identity authentication passes, start monitoring data collection. If the patient's identity authentication fails, stop data collection and send an alarm message to the administrator. The specific conditions for patient identity authentication are: if the verification result R=1, data collection is allowed, otherwise data collection is stopped. The parameter R represents the identity authentication result, R=1 means the verification is passed, and R=0 means the verification fails.

[0080] In this embodiment, the system generates an identity authentication request before data collection begins, and sends the request to the verification server. The verification server returns the result parameter R of the identity authentication, where R=1 indicates that the verification is successful and data collection is allowed; if R=0, it means that the verification failed, the system will stop data collection, and immediately send an alarm message to the administrator. Through this verification mechanism, the system can effectively prevent unauthorized monitoring data collection. The present invention ensures the security and accuracy of data collection through the patient identity authentication mechanism, and reduces the risk of unauthorized data collection. The alarm function when verification fails enables the administrator to deal with potential security issues in a timely manner, thereby improving the security protection capabilities of the system. In addition, the design of automatically stopping data collection not only improves security, but also simplifies the operating process and reduces the need for human intervention.

[0081] Updating the patient's identity status information based on the received response verification result includes:

[0082] Read the verification result returned by the verification server, update the patient's local status information based on the verification result, send the updated status information to the cloud server, and update the patient's identity status information in the cloud server. The specific conditions for status update are: if the status update is successful S=1, continue with the subsequent steps, otherwise resend the identity authentication request, and the parameter S represents the status update result, S=1 indicates a successful update, and S=0 indicates a failed update.

[0083] In this embodiment, the system first reads the verification result from the verification server, and updates the patient's identity status information locally based on the result. Then, the system sends the updated status information to the cloud server to synchronize the patient's status information in the cloud. If the update confirmation parameter S=1 returned by the cloud server, the status update is successful, and the system continues the subsequent monitoring data collection process; if S=0 indicates that the update failed, the system resends the identity authentication request to ensure the synchronization accuracy of the identity status information. Through the synchronization update mechanism between the local and cloud, the present invention realizes efficient management and consistency maintenance of the patient's identity status. When the status update fails, the design of automatically resending the request increases the robustness of the system and ensures the security of data collection. In addition, the cloud synchronization function improves the reliability and real-time performance of patient identity information in a remote monitoring environment, which is conducive to the application of telemedicine.

[0084] Based on the verification results, the patient's local status information is updated, including:

[0085] Obtain the status update confirmation information fed back by the cloud server, update the local cache status based on the confirmation information, record the updated status in the log file, and check the log file to confirm the validity of the status update. The specific conditions for confirming the status update are: if the confirmation information C=1, record the update log, otherwise mark it as an update failure. The parameter C represents the confirmation information, C=1 indicates that the update confirmation is successful, and C=0 indicates that the update confirmation fails.

[0086] In this embodiment, after obtaining the status update confirmation information fed back by the cloud server, the system uses the confirmation result to update the status of the local cache. The system records the updated status in a log file to form a traceability record. Check the log file regularly to ensure the validity of all status update operations. If the confirmation information C=1, it means that the update is successful, and the system marks the log file as a successful record; if the confirmation information C=0, the update fails, and the system marks the status as abnormal. Through the log record and regular inspection mechanism, the present invention ensures the traceability and reliability of the patient identity status update process, and effectively improves the transparency and operational safety of status synchronization. In the case of update failure, the system implements the error location function by marking the exception, which is convenient for subsequent maintenance and troubleshooting.

[0087] Updating the local cache status based on the confirmation information includes:

[0088] Read the update status record in the log file, update the status information of the local cache based on the status record, re-read the local cache verification status after the cache is updated, and compare the verified status information with the log record. If the local cache status B is equal to the log record status L, the update is considered valid, otherwise it is updated again. B represents the local cache status and L represents the log record status.

[0089] In this embodiment, the system first reads the update status record in the log file, and updates the status information of the local cache according to the record. The system re-verifies after the cache is updated to ensure that the cache status is consistent with the log record. Through this double verification mechanism, the system can promptly discover and repair data problems caused by cache inconsistencies, thereby ensuring the consistency and accuracy of the data. The double verification mechanism of the present invention improves the reliability of data consistency management and prevents the situation where the cache is inconsistent with the log record. By re-updating the cache status, the system can automatically correct errors when data inconsistencies occur, thereby enhancing the stability of the system. In addition, the cache status inspection process optimizes the real-time update capability of remote monitoring data, making the system more suitable for scenarios with high-frequency data updates.

[0090] The status information of the local cache is updated based on the status record, including:

[0091] Get the latest status record, update the locally cached status information based on the latest status record, send the updated status information to the monitoring system, monitor and record the status in the monitoring system, where if the updated status information I is equal to the log record status L, the update is considered valid, otherwise an abnormal log is recorded, where I represents the updated status information and L represents the log record status.

[0092] In this embodiment, the system updates the locally cached status information by obtaining the latest status record, and sends the updated status information to the monitoring system to achieve real-time monitoring of the data. The system compares the updated status information with the log record status L to confirm the validity of the update. If the two are inconsistent, the system records an exception log for subsequent troubleshooting and maintenance. This embodiment enhances the accuracy and reliability of the data update process through real-time monitoring of status information by the monitoring system. When the status is inconsistent, the design of automatically recording the exception log provides an effective fault tracking mechanism for system maintenance. This mechanism ensures the stability of the system in complex environments and helps to achieve comprehensive tracking and management of remote monitoring data.

[0093] The label processing module also includes a block storage method for the encrypted ciphertext, and an independent verification of each data block during transmission. In this embodiment, the encrypted data is divided into several data blocks, and each data block is independently stored in a distributed database. When the data block is transmitted, the system generates a check code for each data block, and the transmission receiving end verifies the integrity of the data block through the check code. The block verification mechanism effectively reduces the bit error rate in data transmission and ensures the accuracy of the data. Through the design of block storage and independent verification, the present invention improves the reliability and anti-interference ability during data transmission. The independent verification of each data block can quickly locate and repair the data when an error occurs during transmission, reduce the overhead of retransmission, and improve the efficiency and stability of the system. It is particularly suitable for remote monitoring scenarios that require high data transmission integrity.

[0094] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An arteriovenous fistula remote monitoring information collection system, characterized in that: The system comprises: A label processing module, used for labeling the collected arteriovenous fistula monitoring data based on the patient's unique identification; A wireless communication module, used for transmitting the labeled data; The receiving and extracting module is used to receive and parse the received data, extract the patient's unique identification information, set the prior probability of the patient's identification based on the previous historical data, calculate the posterior probability of the label matching based on the received data, and extract and confirm the patient's unique identification based on the matching result with the highest posterior probability. The formula used to calculate the posterior probability of the label matching is: Among them, P(A|B) represents the posterior probability that the received label belongs to a certain patient, P(B|A) represents the conditional probability that the label exists in the patient, P(A) represents the prior probability of the patient, and P(B) represents the total probability of receiving label B; The storage module is used to store the data in the database of the corresponding patient according to the extracted unique identification information. Specifically, the dynamic monitoring data of the patient is stored in chronological order, and a timestamp index is established. The data is stored in the time series database to improve the query speed of the time series data. The time series data is stored in a compressed format and indexed with the timestamp as the primary key. The specific disclosure is as follows: TS={(t1,y1),(t2,y2),...,(t n ,u n )}; Among them, TS represents the entire time series data set, t i represents the timestamp of the i-th time point, υ i Indicates that at time point t i The monitoring data value, n represents the total length of the time series data; The reading and generating module is used to regularly read the patient's monitoring data from the database, generate a health report and send it to the doctor's terminal.

2. The arteriovenous fistula remote monitoring information collection system according to claim 1, characterized in that: The label processing module performs label processing on the collected arteriovenous fistula monitoring data based on the patient's unique identifier, including: Generate a symmetric key and save it in a safe place, encrypt the patient's unique identifier, generate ciphertext, store or transmit the encrypted ciphertext, decrypt it using the same key, and encrypt the patient's unique identifier and publish it as: C=E k (M); Among them, C represents the encrypted ciphertext, M represents the unique identifier of the patient, and E k represents encryption operation, and k represents the secret key.

3. The arteriovenous fistula remote monitoring information collection system according to claim 1, characterized in that: The wireless communication module transmits the labeled data including: Initialize the state variables and noise covariance of the data during wireless transmission, update the estimated state for each data packet, and use the updated state to make the next prediction. The state update formula is: in, Indicates the current estimated state, represents the predicted state of the previous moment, K m represents the Kalman gain, z m represents the observation value, and H represents the observation matrix.

4. The arteriovenous fistula remote monitoring information collection system according to claim 1, characterized in that: The reading and generating module periodically reads the patient's monitoring data from the database, generates a health report and sends it to the doctor's terminal, including: Read health parameter data from the database, apply harmony analysis to each set of data, calculate the correlation between different health data, determine whether it is within the normal range, and generate a health report. If the data correlation is abnormal, it indicates potential health risks. The specific calculation formula for the correlation between health data is: Among them, M represents the correlation between health data, x i and i represents the numerical value of different health parameters, and g represents the total number of parameters.

5. The arteriovenous fistula remote monitoring information collection system according to claim 1, characterized in that: The label processing module also includes: Generate a patient identity authentication request and send it to the verification server. Update the patient's identity status information based on the received response verification result. After the patient's identity authentication passes, start monitoring data collection. If the patient's identity authentication fails, stop data collection and send an alarm message to the administrator. The specific conditions for patient identity authentication are: if the verification result R=1, data collection is allowed, otherwise data collection is stopped. The parameter R represents the identity authentication result, R=1 means the verification is passed, and R=0 means the verification fails.

6. The arteriovenous fistula remote monitoring information collection system according to claim 5, characterized in that: The updating of the patient's identity status information based on the received response verification result includes: Read the verification result returned by the verification server, update the patient's local status information based on the verification result, send the updated status information to the cloud server, and update the patient's identity status information in the cloud server. The specific conditions for status update are: if the status update is successful S=1, continue with the subsequent steps, otherwise resend the identity authentication request, and the parameter S represents the status update result, S=1 indicates a successful update, and S=0 indicates a failed update.

7. The arteriovenous fistula remote monitoring information collection system according to claim 6, characterized in that: The updating of the patient's local status information based on the verification result includes: Obtain the status update confirmation information fed back by the cloud server, update the local cache status based on the confirmation information, record the updated status in the log file, and check the log file to confirm the validity of the status update. The specific conditions for confirming the status update are: if the confirmation information C=1, record the update log, otherwise mark it as an update failure. The parameter C represents the confirmation information, C=1 indicates that the update confirmation is successful, and C=0 indicates that the update confirmation fails.

8. The arteriovenous fistula remote monitoring information collection system according to claim 7, characterized in that: The updating of the local cache status based on the confirmation information includes: Read the update status record in the log file, update the status information of the local cache based on the status record, re-read the local cache verification status after the cache is updated, and compare the verified status information with the log record. If the local cache status B is equal to the log record status L, the update is considered valid, otherwise it is updated again. B represents the local cache status and L represents the log record status.

9. The arteriovenous fistula remote monitoring information collection system according to claim 8, characterized in that: The updating of the state information of the local cache based on the state record includes: Get the latest status record, update the locally cached status information based on the latest status record, send the updated status information to the monitoring system, monitor and record the status in the monitoring system, where if the updated status information I is equal to the log record status L, the update is considered valid, otherwise an abnormal log is recorded, where I represents the updated status information and L represents the log record status.

10. The arteriovenous fistula remote monitoring information collection system according to claim 2, characterized in that: The label processing module also includes a block storage method for the encrypted ciphertext, and an independent verification of each data block during transmission.

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