Visual access monitoring method and device for an inner ear drug delivery system

Abstract

The application discloses a visual access monitoring method and device for an inner ear drug delivery system, and relates to the technical field of intelligent monitoring. The method comprises the following steps: obtaining a drug delivery monitoring index; collecting real-time parameters through a visual monitoring page to obtain a drug delivery index monitoring eigenvalue matrix; comparing a drug delivery index setting eigenvalue matrix with the drug delivery index monitoring eigenvalue matrix to obtain a drug delivery index deviation vector matrix; performing drug delivery anomaly analysis according to the drug delivery index deviation vector matrix to obtain a drug delivery anomaly coefficient; and adjusting according to the drug delivery index setting eigenvalue matrix when the drug delivery anomaly coefficient is greater than or equal to a drug delivery anomaly coefficient threshold. The technical problem that it is difficult to accurately identify drug delivery anomalies due to the inability to analyze the overall state in the prior art is solved, accurate anomaly state identification is achieved through comprehensive analysis of multiple monitoring indexes, and drug delivery parameters are automatically adjusted, thereby achieving the technical effects of improving the accuracy and safety of the drug delivery process.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to a visual pathway monitoring method and device for an inner ear drug delivery system. Background Art

[0002] As an advanced medical technology, inner ear drug delivery systems have demonstrated tremendous potential in recent years in areas such as hearing loss treatment and drug delivery for inner ear diseases. However, traditional drug delivery control systems often suffer from limited monitoring capabilities. Most systems can only monitor and adjust a single drug delivery parameter, failing to comprehensively monitor and analyze multiple key parameters. Traditional single-indicator analysis methods are unable to accurately identify multi-dimensional drug delivery anomalies, making it difficult to promptly detect potential risks during the drug delivery process, thereby impacting treatment effectiveness. Summary of the Invention

[0003] The present application provides a method and device for visual pathway monitoring of an inner ear drug delivery system, which solves the technical problem in the prior art that it is difficult to analyze based on the overall status, resulting in the inability to accurately identify drug delivery anomalies.

[0004] In view of the above problems, the present application provides a method and device for visual pathway monitoring of an inner ear drug delivery system.

[0005] In a first aspect of the present application, a method for visualizing a pathway monitoring for an inner ear drug delivery system is provided, the method comprising:

[0006] Obtain drug administration monitoring indicators, wherein the drug administration monitoring indicators include drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval; collect real-time parameters of the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval through a visual monitoring page to obtain a drug administration indicator monitoring eigenvalue matrix; obtain a drug administration indicator setting eigenvalue matrix; compare the drug administration indicator setting eigenvalue matrix with the drug administration indicator monitoring eigenvalue matrix to obtain a drug administration indicator deviation vector matrix; perform drug administration anomaly analysis based on the drug administration indicator deviation vector matrix to obtain a drug administration anomaly coefficient; when the drug administration anomaly coefficient is greater than or equal to a drug administration anomaly coefficient threshold, adjust the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval based on the drug administration indicator setting eigenvalue matrix.

[0007] In a second aspect of the present application, a visual pathway monitoring device for an inner ear drug delivery system is provided, the device comprising:

[0008] Monitoring index acquisition module: obtains drug administration monitoring indicators, wherein the drug administration monitoring indicators include drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval; real-time parameter acquisition module: collects the real-time parameters of the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval through a visual monitoring page to obtain a drug administration index monitoring eigenvalue matrix; preset data acquisition module: obtains a drug administration index setting eigenvalue matrix; deviation calculation module: compares the drug administration index setting eigenvalue matrix and the drug administration index monitoring eigenvalue matrix to obtain a drug administration index deviation vector matrix; abnormality analysis module: performs drug administration abnormality analysis based on the drug administration index deviation vector matrix to obtain a drug administration abnormality coefficient; adjustment control module: when the drug administration abnormality coefficient is greater than or equal to the drug administration abnormality coefficient threshold, adjusts the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval according to the drug administration index setting eigenvalue matrix.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] First, obtain the drug administration monitoring indicators, wherein the drug administration monitoring indicators include the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval. Then, through the visual monitoring page, collect the real-time parameters of the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval to obtain the drug administration indicator monitoring eigenvalue matrix. At the same time, obtain the drug administration indicator setting eigenvalue matrix. Then, compare the drug administration indicator setting eigenvalue matrix and the drug administration indicator monitoring eigenvalue matrix to obtain the drug administration indicator deviation vector matrix. Next, perform drug administration anomaly analysis based on the drug administration indicator deviation vector matrix to obtain the drug administration anomaly coefficient. Finally, when the drug administration anomaly coefficient is greater than or equal to the drug administration anomaly coefficient threshold, adjust the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval based on the drug administration indicator setting eigenvalue matrix. It solves the technical problem in the existing technology that it is difficult to analyze based on the overall status, resulting in the inability to accurately identify drug administration abnormalities. By comprehensively analyzing multiple monitoring indicators, accurate abnormal status identification is achieved, and drug administration parameters are automatically adjusted, thereby achieving the technical effect of improving the accuracy and safety of the drug administration process. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic flow chart of a method for visualizing pathway monitoring for an inner ear drug delivery system provided in an embodiment of the present application;

[0013] Figure 2 Schematic diagram of the structure of a visual pathway monitoring device for an inner ear drug delivery system provided in an embodiment of the present application.

[0014] Explanation of the accompanying symbols: monitoring index acquisition module 11, real-time parameter acquisition module 12, preset data acquisition module 13, deviation calculation module 14, abnormality analysis module 15, adjustment control module 16. DETAILED DESCRIPTION

[0015] The present application solves the technical problem in the prior art that it is difficult to accurately identify drug delivery abnormalities due to the difficulty in analyzing the overall status by providing a visual pathway monitoring method and device for an inner ear drug delivery system.

[0016] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0017] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0018] Example 1, as Figure 1 As shown, the present application provides a method for visualizing pathway monitoring of an inner ear drug delivery system, wherein the method comprises:

[0019] Obtain drug administration monitoring indicators, wherein the drug administration monitoring indicators include drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval.

[0020] Based on the inner ear drug delivery system, drug delivery monitoring indicators are obtained, including drug chamber capacity, drug chamber internal pressure, drug delivery time, drug delivery speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval.

[0021] Drug storage capacity: the capacity of the drug storage tank, monitors whether the drug is sufficient and meets the drug administration needs; Drug storage internal pressure: the pressure inside the drug storage tank, used to ensure that the drug is delivered under stable pressure to prevent leakage or over-compression; Drug administration time: the duration of drug delivery, monitors the duration of the drug administration process to ensure that the drug is released on time; Drug administration speed: the rate of drug delivery, monitors the release rate of the drug to prevent drug administration from being too fast or too slow; Micro-flush pressure: the pressure used to flush the drug storage tank or pipeline to ensure smooth drug flow and prevent blockage; Circulation channel pressure: the pressure related to the drug delivery channel, monitors the pressure changes in the channel to ensure normal drug delivery; Micro-flush working interval: the time interval of micro-flush operations to ensure the effectiveness of each flush and the reasonable coordination of flushing and drug delivery; Circulation working interval: the working time interval of cyclic drug delivery to ensure that the drug can be continuously and stably supplied in multiple cycles.

[0022] Through the visual monitoring page, the real-time parameters of the drug chamber capacity, the internal pressure of the drug chamber, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are collected to obtain the drug administration index monitoring eigenvalue matrix.

[0023] The visualization monitoring page collects multiple drug administration monitoring indicators during the inner ear drug administration process in real time. These indicators are then integrated into a matrix format along the time dimension, forming a drug administration indicator monitoring eigenvalue matrix. This matrix displays the dynamic changes of each indicator during the drug administration process. Each row of the drug administration indicator monitoring eigenvalue matrix represents a sampling moment or time point, recording the values ​​of all monitoring indicators at that moment. Each column corresponds to a monitoring indicator, and each column of the matrix records the value of that indicator at different time points.

[0024] Furthermore, through the visual monitoring page, the real-time parameters of the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are collected to obtain the drug administration index monitoring eigenvalue matrix, which previously included:

[0025] Construct a three-dimensional model of an active drug delivery system and a three-dimensional model of a drug delivery target; set the parameter display positions of the drug chamber capacity, the internal pressure of the drug chamber, the drug delivery time, the drug delivery speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval; construct the visual monitoring page based on the parameter display positions, the three-dimensional model of the active drug delivery system, and the three-dimensional model of the drug delivery target.

[0026] Specifically, a three-dimensional model of the active drug delivery system is constructed based on its actual physical structure and operating characteristics, ensuring that the three-dimensional model of the active drug delivery system can accurately reflect the key components of the delivery system (such as the drug reservoir, drug delivery pipeline, etc.) and their interactions. At the same time, a three-dimensional model of the drug delivery target is constructed based on the anatomical structure and treatment goals of the inner ear, so that the three-dimensional model of the drug delivery target can accurately describe the target area of ​​drug delivery (such as the inner ear cavity) and its characteristics. In the three-dimensional model of the active drug delivery system and the three-dimensional model of the drug delivery target, the display position of each drug delivery monitoring indicator (drug reservoir capacity, drug reservoir internal pressure, drug delivery time, drug delivery rate, micro-irrigation pressure, circulation channel pressure, micro-irrigation working interval, and circulation working interval) is determined. For example, the values ​​of drug reservoir capacity and internal pressure are displayed next to the drug reservoir model, the data of drug delivery rate and time are marked next to the drug delivery pipeline model, and the data of micro-irrigation pressure and working interval are associated with the circulation channel. The three-dimensional model of the active drug delivery system and the three-dimensional model of the drug delivery target are integrated into the design of the visual monitoring page. According to the set parameter display position, corresponding data visualization elements (such as dashboards, charts, etc.) are added to the page to display real-time parameters.

[0027] Obtain the drug administration index setting eigenvalue matrix.

[0028] In drug administration monitoring, the drug administration indicator setting eigenvalue matrix is ​​generated based on set ideal parameters or expected treatment standards. The drug administration indicator setting eigenvalue matrix contains the ideal values ​​or safety ranges for each drug administration monitoring indicator and is used for comparative analysis with the drug administration indicator monitoring eigenvalue matrix collected in real time. Specifically, the set standard values ​​are organized by time or sampling point to form the drug administration indicator setting eigenvalue matrix. Each column of the matrix corresponds to the set value of a monitoring indicator, and each row of the matrix represents the drug administration set value at a certain time point or under certain specific conditions.

[0029] The medication indicator setting eigenvalue matrix and the medication indicator monitoring eigenvalue matrix are compared to obtain a medication indicator deviation vector matrix.

[0030] The difference between the elements of each data in the drug administration index setting eigenvalue matrix and the drug administration index monitoring eigenvalue matrix is ​​calculated to form a drug administration index deviation vector matrix, which represents the deviation of each drug administration parameter.

[0031] A drug administration abnormality analysis is performed based on the drug administration index deviation vector matrix to obtain a drug administration abnormality coefficient.

[0032] After comparing the medication indicator setting eigenvalue matrix and the medication indicator monitoring eigenvalue matrix and obtaining the medication indicator deviation vector matrix, a medication abnormality coefficient reflecting the degree of medication abnormality is calculated by analyzing these deviation values. The medication abnormality coefficient is a quantitative measure of the overall deviation of the medication process and is used to determine whether there is an abnormality in the medication process.

[0033] Furthermore, the medication abnormality analysis is performed according to the medication index deviation vector matrix to obtain the medication abnormality coefficient, including:

[0034] A medication indicator deviation threshold matrix of the medication monitoring indicator is set; when any medication indicator deviation vector of the medication indicator deviation vector matrix does not satisfy the medication indicator deviation threshold matrix, the medication abnormality coefficient is set to be greater than the medication abnormality coefficient threshold; when each medication indicator deviation vector of the medication indicator deviation vector matrix satisfies the medication indicator deviation threshold matrix, the medication abnormality analysis is performed on the medication indicator deviation vector matrix through the medication abnormality analysis network to obtain the medication abnormality coefficient.

[0035] The medication index deviation threshold matrix is ​​set according to treatment requirements, equipment limitations, and the safety range of drug delivery, and includes the tolerance deviation range (i.e., the allowable error range) of each monitoring indicator. The deviation value of each indicator in the medication index deviation vector matrix is ​​compared with the corresponding threshold range in the medication index deviation threshold matrix; if any deviation value in the medication index deviation vector matrix does not meet the corresponding deviation threshold range, it is considered that the indicator is abnormal, and the medication abnormality coefficient is set to be greater than the medication abnormality coefficient threshold; when each deviation vector in the medication index deviation vector matrix meets the given deviation threshold, it is further analyzed through the medication abnormality analysis network. The medication abnormality analysis network is a model based on machine learning or deep learning that can extract features from the medication index deviation vector matrix and calculate a medication abnormality coefficient. The medication abnormality coefficient reflects the severity of the abnormality in the medication process.

[0036] Furthermore, setting the medication indicator deviation threshold matrix of the medication monitoring indicator includes:

[0037] According to the medication monitoring indicator, a first medication monitoring indicator is extracted; with the first medication monitoring indicator as the only variable, a medication abnormality record data set is extracted; a central trend analysis of the over-limit standard deviation of the first medication monitoring indicator is performed on the medication abnormality record data set to obtain an over-limit standard deviation concentration interval, and the lower limit value of the over-limit standard deviation concentration interval is extracted and set as the over-limit standard deviation threshold; a central trend analysis of the deficiency standard deviation of the first medication monitoring indicator is performed on the medication abnormality record data set to obtain a deficiency standard deviation concentration interval, and the lower limit value of the deficiency standard deviation concentration interval is extracted and set as the deficiency standard deviation threshold; the over-limit standard deviation threshold and the deficiency standard deviation threshold are added to the first medication monitoring indicator deviation threshold; and the first medication monitoring indicator deviation threshold is added to the medication indicator deviation threshold matrix.

[0038] Preferably, from all medication monitoring indicators, the first indicator that needs to set a deviation threshold is selected and recorded as the first medication monitoring indicator; with the first medication monitoring indicator as the only variable, records containing abnormalities of the indicator are extracted from historical data to form a medication abnormality record data set; the first medication monitoring indicator in the medication abnormality record data set is subjected to an over-limit standard deviation analysis, the over-limit standard deviation refers to the degree to which the indicator value exceeds the normal range (i.e., the upper limit or lower limit), and the over-limit standard deviation concentration interval is obtained by calculating the central trend of these over-limit values ​​(such as the mean, median, etc.), and the over-limit standard deviation concentration interval reflects the typical deviation degree when the indicator value exceeds the normal range; the lower limit value is extracted from the over-limit standard deviation concentration interval and set as the over-limit standard deviation threshold, and the over-limit standard deviation threshold is used to judge whether the indicator value exceeds the upper limit of the acceptable range; similarly, the over-limit standard deviation value in the medication abnormality record data set is analyzed. The first medication monitoring indicator is subjected to defect standard deviation analysis. The defect standard deviation refers to the degree to which the indicator value is lower than the lower limit of the normal range. By calculating the central trend of these defect values, the defect standard deviation concentration interval is obtained, and the lower limit value is extracted from the defect standard deviation concentration interval and set as the defect standard deviation threshold. The defect standard deviation threshold is used to determine whether the indicator value is lower than the lower limit of the acceptable range; the over-limit standard deviation threshold and the defect standard deviation threshold are combined to form the deviation threshold of the first medication monitoring indicator. The deviation threshold of the first medication monitoring indicator includes an upper limit threshold (over-limit standard deviation threshold) and a lower limit threshold (defect standard deviation threshold); the deviation threshold of the first medication monitoring indicator is added to the medication indicator deviation threshold matrix; the above process is repeated for the remaining medication monitoring indicators until the deviation thresholds of all indicators are set and added to the medication indicator deviation threshold matrix.

[0039] Furthermore, setting the medication indicator deviation threshold matrix of the medication monitoring indicator also includes:

[0040] When the data volume of the medication abnormality record data set is less than or equal to the statistical analysis data volume threshold, the medication indicator deviation threshold matrix of the medication monitoring indicator is set by the user terminal.

[0041] Before starting to set the deviation threshold, it is necessary to evaluate the data volume of the medication abnormality record data set; if the data volume is greater than the statistical analysis data volume threshold (this threshold can be set based on actual conditions, such as based on the accuracy and reliability requirements of the statistical analysis), then you can continue to use the data for statistical analysis to set the deviation threshold; if the data volume is less than or equal to the statistical analysis data volume threshold, it needs to be manually set through the user end. The user can set a reasonable deviation threshold for each medication monitoring indicator based on his or her professional knowledge, experience, and understanding of the medication process.

[0042] Furthermore, when each medication indicator deviation vector of the medication indicator deviation vector matrix satisfies the medication indicator deviation threshold matrix, medication abnormality analysis is performed on the medication indicator deviation vector matrix through a medication abnormality analysis network to obtain the medication abnormality coefficient, including:

[0043] A set of historical drug administration logs of an inner ear drug administration system is obtained, wherein each drug administration indicator deviation vector of any historical drug administration log satisfies the drug administration indicator deviation threshold matrix; when the first historical drug administration log in the set of historical drug administration logs is a drug administration abnormality, the drug administration abnormality coefficient is identified as 1, and drug administration abnormality coefficient identification information is obtained, wherein 1 is greater than the drug administration abnormality coefficient threshold; when the first historical drug administration log in the set of historical drug administration logs is a normal drug administration, the drug administration abnormality coefficient is identified as 0, and drug administration abnormality coefficient identification information is obtained, wherein 0 is less than the drug administration abnormality coefficient threshold; the drug administration abnormality coefficient identification information is used as supervision and the historical drug administration indicator deviation vector matrix is ​​used as input data to train the drug administration abnormality analysis network.

[0044] A historical medication log set is obtained from the inner ear medication system, where the historical medication log set contains sufficient medication records, each record contains a medication index deviation vector, and these deviation vectors all satisfy the medication index deviation threshold matrix set previously; the historical medication log set is marked to distinguish which are records of medication abnormalities; if the first historical medication log is a medication abnormality, the medication abnormality coefficient of the record is marked as 1, which indicates that the record is abnormal, and 1 is greater than the preset medication abnormality coefficient threshold; if the first historical medication log is a medication normal, the medication abnormality coefficient of the record is marked as 0, which indicates that the record is normal, and 0 is less than the preset medication abnormality coefficient threshold.

[0045] Based on the identification results of the historical logs (1 for abnormal, 0 for normal), the drug administration anomaly coefficient identification information can be obtained. This identification information provides label data for subsequent training, helping the network learn how to distinguish between normal and abnormal drug administration processes. After obtaining the drug administration anomaly coefficient identification information of the historical logs, this identification information is then used as a supervisory signal to train the drug administration anomaly analysis network; the historical drug administration index deviation vector matrix is ​​used as input data to train the drug administration anomaly analysis network through machine learning algorithms (such as deep neural networks, support vector machines, etc.). After training is completed, the drug administration anomaly analysis network can output the drug administration anomaly coefficient based on the drug index deviation vector matrix.

[0046] When the medication abnormality coefficient is greater than or equal to the medication abnormality coefficient threshold, the eigenvalue matrix is ​​set according to the medication index to adjust the drug chamber capacity, the internal pressure of the drug chamber, the medication time, the medication speed, the micro-flush pressure, the circulation channel pressure, the micro-flush working interval, and the circulation working interval.

[0047] When it is detected that the drug administration abnormality coefficient exceeds the preset drug administration abnormality coefficient threshold, it means that an abnormality or deviation has occurred in the drug administration process. At this time, the eigenvalue matrix is ​​set according to the drug administration index to automatically adjust the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flush pressure, circulation channel pressure, micro-flush working interval and circulation working interval to ensure the accuracy and stability of the drug administration process.

[0048] Furthermore, when the medication abnormality coefficient is greater than or equal to a medication abnormality coefficient threshold, the medication chamber capacity, the medication chamber internal pressure, the medication time, the medication speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are adjusted according to the medication index setting eigenvalue matrix, further comprising:

[0049] The drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are adjusted to obtain an updated drug administration index deviation vector matrix; when the updated drug administration abnormality coefficient of the updated drug administration index deviation vector matrix is ​​less than the drug administration abnormality coefficient threshold, drug administration control is performed according to the updated drug administration index eigenvalue matrix.

[0050] When the drug administration abnormality coefficient is greater than or equal to the drug administration abnormality coefficient threshold, the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flush pressure, circulation channel pressure, micro-flush working interval, and circulation working interval will be adjusted according to the drug administration index setting eigenvalue matrix to restore the normal drug administration process; after adjusting the drug administration parameters, the actual values ​​of these parameters are monitored and recorded again to generate an updated drug administration index deviation vector matrix, which reflects the deviation between the adjusted drug administration index and the normal value; the updated drug administration index deviation vector matrix is ​​input into the drug administration abnormality analysis network for analysis to obtain an updated drug administration abnormality coefficient; if the updated drug administration abnormality coefficient is less than the drug administration abnormality coefficient threshold, it means that the drug administration process has returned to normal, and normal drug administration control will continue according to the updated drug administration index eigenvalue matrix.

[0051] In summary, the embodiments of the present application have at least the following technical effects:

[0052] First, obtain the drug administration monitoring indicators, wherein the drug administration monitoring indicators include the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval. Then, through the visual monitoring page, collect the real-time parameters of the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval to obtain the drug administration indicator monitoring eigenvalue matrix. At the same time, obtain the drug administration indicator setting eigenvalue matrix. Then, compare the drug administration indicator setting eigenvalue matrix and the drug administration indicator monitoring eigenvalue matrix to obtain the drug administration indicator deviation vector matrix. Next, perform drug administration anomaly analysis based on the drug administration indicator deviation vector matrix to obtain the drug administration anomaly coefficient. Finally, when the drug administration anomaly coefficient is greater than or equal to the drug administration anomaly coefficient threshold, adjust the drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval based on the drug administration indicator setting eigenvalue matrix. It solves the technical problem in the existing technology that it is difficult to analyze based on the overall status, resulting in the inability to accurately identify drug administration abnormalities. By comprehensively analyzing multiple monitoring indicators, accurate abnormal status identification is achieved, and drug administration parameters are automatically adjusted, thereby achieving the technical effect of improving the accuracy and safety of the drug administration process.

[0053] Example 2, based on the same inventive concept as the method for visualizing the inner ear drug delivery system in the previous embodiment, Figure 2 As shown, the present application provides a visual pathway monitoring device for an inner ear drug delivery system, wherein the device comprises:

[0054] Monitoring index acquisition module 11: obtains drug administration monitoring index, wherein the drug administration monitoring index includes drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval; real-time parameter acquisition module 12: collects the real-time parameters of the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval through a visual monitoring page to obtain the drug administration index monitoring eigenvalue matrix; preset data acquisition module 13: obtains the drug administration index setting eigenvalue matrix Matrix; Deviation calculation module 14: Compare the drug administration index setting eigenvalue matrix and the drug administration index monitoring eigenvalue matrix to obtain the drug administration index deviation vector matrix; Abnormality analysis module 15: Perform drug administration abnormality analysis according to the drug administration index deviation vector matrix to obtain the drug administration abnormality coefficient; Adjustment control module 16: When the drug administration abnormality coefficient is greater than or equal to the drug administration abnormality coefficient threshold, adjust the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval according to the drug administration index setting eigenvalue matrix.

[0055] Furthermore, the abnormality analysis module 15 is used to perform the following method:

[0056] A medication indicator deviation threshold matrix of the medication monitoring indicator is set; when any medication indicator deviation vector of the medication indicator deviation vector matrix does not satisfy the medication indicator deviation threshold matrix, the medication abnormality coefficient is set to be greater than the medication abnormality coefficient threshold; when each medication indicator deviation vector of the medication indicator deviation vector matrix satisfies the medication indicator deviation threshold matrix, the medication abnormality analysis is performed on the medication indicator deviation vector matrix through the medication abnormality analysis network to obtain the medication abnormality coefficient.

[0057] Furthermore, the abnormality analysis module 15 is used to perform the following method:

[0058] According to the medication monitoring indicator, a first medication monitoring indicator is extracted; with the first medication monitoring indicator as the only variable, a medication abnormality record data set is extracted; a central trend analysis of the over-limit standard deviation of the first medication monitoring indicator is performed on the medication abnormality record data set to obtain an over-limit standard deviation concentration interval, and the lower limit value of the over-limit standard deviation concentration interval is extracted and set as the over-limit standard deviation threshold; a central trend analysis of the deficiency standard deviation of the first medication monitoring indicator is performed on the medication abnormality record data set to obtain a deficiency standard deviation concentration interval, and the lower limit value of the deficiency standard deviation concentration interval is extracted and set as the deficiency standard deviation threshold; the over-limit standard deviation threshold and the deficiency standard deviation threshold are added to the first medication monitoring indicator deviation threshold; and the first medication monitoring indicator deviation threshold is added to the medication indicator deviation threshold matrix.

[0059] Furthermore, the abnormality analysis module 15 is used to perform the following method:

[0060] A set of historical drug administration logs of an inner ear drug administration system is obtained, wherein each drug administration indicator deviation vector of any historical drug administration log satisfies the drug administration indicator deviation threshold matrix; when the first historical drug administration log in the set of historical drug administration logs is a drug administration abnormality, the drug administration abnormality coefficient is identified as 1, and drug administration abnormality coefficient identification information is obtained, wherein 1 is greater than the drug administration abnormality coefficient threshold; when the first historical drug administration log in the set of historical drug administration logs is a normal drug administration, the drug administration abnormality coefficient is identified as 0, and drug administration abnormality coefficient identification information is obtained, wherein 0 is less than the drug administration abnormality coefficient threshold; the drug administration abnormality coefficient identification information is used as supervision and the historical drug administration indicator deviation vector matrix is ​​used as input data to train the drug administration abnormality analysis network.

[0061] Furthermore, the real-time parameter acquisition module 12 is used to perform the following method:

[0062] Construct a three-dimensional model of an active drug delivery system and a three-dimensional model of a drug delivery target; set the parameter display positions of the drug chamber capacity, the internal pressure of the drug chamber, the drug delivery time, the drug delivery speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval; construct the visual monitoring page based on the parameter display positions, the three-dimensional model of the active drug delivery system, and the three-dimensional model of the drug delivery target.

[0063] Furthermore, the adjustment control module 16 is used to perform the following method:

[0064] The drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are adjusted to obtain an updated drug administration index deviation vector matrix; when the updated drug administration abnormality coefficient of the updated drug administration index deviation vector matrix is ​​less than the drug administration abnormality coefficient threshold, drug administration control is performed according to the updated drug administration index eigenvalue matrix.

[0065] Furthermore, the abnormality analysis module 15 is used to perform the following method:

[0066] When the data volume of the medication abnormality record data set is less than or equal to the statistical analysis data volume threshold, the medication indicator deviation threshold matrix of the medication monitoring indicator is set by the user terminal.

[0067] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0069] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A method for visualizing access monitoring of an inner ear drug delivery system, characterized in that: The method comprises: Obtaining drug administration monitoring indicators, wherein the drug administration monitoring indicators include drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval; Through the visual monitoring page, real-time parameters of the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are collected to obtain a drug administration index monitoring eigenvalue matrix; Obtaining the drug administration index setting eigenvalue matrix; Comparing the medication index setting eigenvalue matrix and the medication index monitoring eigenvalue matrix to obtain a medication index deviation vector matrix; Performing medication abnormality analysis based on the medication index deviation vector matrix to obtain a medication abnormality coefficient; When the medication abnormality coefficient is greater than or equal to the medication abnormality coefficient threshold, the drug chamber capacity, the drug chamber internal pressure, the medication time, the medication speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are adjusted according to the medication index setting eigenvalue matrix; Performing medication abnormality analysis based on the medication index deviation vector matrix to obtain medication abnormality coefficients includes: Setting a medication indicator deviation threshold matrix for the medication monitoring indicator; When any one of the drug administration indicator deviation vectors in the drug administration indicator deviation vector matrix does not satisfy the drug administration indicator deviation threshold matrix, setting the drug administration abnormality coefficient to be greater than the drug administration abnormality coefficient threshold; When each medication indicator deviation vector of the medication indicator deviation vector matrix satisfies the medication indicator deviation threshold matrix, performing medication abnormality analysis on the medication indicator deviation vector matrix through a medication abnormality analysis network to obtain the medication abnormality coefficient; Setting the medication indicator deviation threshold matrix of the medication monitoring indicator includes: extracting a first drug administration monitoring indicator according to the drug administration monitoring indicator; Taking the first medication monitoring indicator as the only variable, extracting a medication abnormality record data set; performing a central trend analysis of the over-limit standard deviation of the first medication monitoring indicator on the medication abnormality record data set to obtain an over-limit standard deviation concentration interval, extracting a lower limit value of the over-limit standard deviation concentration interval, and setting the lower limit value of the over-limit standard deviation concentration interval as the over-limit standard deviation threshold; performing a defect standard deviation central trend analysis of the first medication monitoring indicator on the medication abnormality record data set to obtain a defect standard deviation concentration interval, extracting a lower limit value of the defect standard deviation concentration interval, and setting the lower limit value of the defect standard deviation concentration interval as a defect standard deviation threshold; Adding the excess standard deviation threshold and the deficiency standard deviation threshold to the first medication monitoring indicator deviation threshold; Adding the first medication monitoring indicator deviation threshold to the medication indicator deviation threshold matrix; When each medication indicator deviation vector of the medication indicator deviation vector matrix satisfies the medication indicator deviation threshold matrix, medication abnormality analysis is performed on the medication indicator deviation vector matrix through a medication abnormality analysis network to obtain the medication abnormality coefficient, including: Obtaining a set of historical drug administration logs of the inner ear drug administration system, wherein each drug administration index deviation vector of any historical drug administration log satisfies the drug administration index deviation threshold matrix; When the first historical medication log in the historical medication log set is a medication abnormality, marking the medication abnormality coefficient as 1, and obtaining medication abnormality coefficient identification information, wherein 1 is greater than the medication abnormality coefficient threshold; When the first historical medication log in the historical medication log set indicates normal medication, marking the medication abnormality coefficient as 0, and obtaining medication abnormality coefficient identification information, wherein 0 is less than the medication abnormality coefficient threshold; The medication anomaly analysis network is trained using the medication anomaly coefficient identification information as supervision and the historical medication index deviation vector matrix as input data; Through the visual monitoring page, the real-time parameters of the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are collected to obtain the drug administration index monitoring eigenvalue matrix, which previously included: Construct a three-dimensional model of the active drug delivery system and a three-dimensional model of the drug delivery target; Setting the parameter display positions of the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval; constructing the visual monitoring page based on the parameter display position, the active drug delivery system three-dimensional model, and the drug delivery target three-dimensional model; When the medication abnormality coefficient is greater than or equal to the medication abnormality coefficient threshold, the medication chamber capacity, the medication chamber internal pressure, the medication time, the medication speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval are adjusted according to the medication index setting eigenvalue matrix, further comprising: Adjusting the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval to obtain an updated drug administration index deviation vector matrix; When the updated medication abnormality coefficient of the updated medication index deviation vector matrix is ​​less than the medication abnormality coefficient threshold, medication control is performed according to the updated medication index eigenvalue matrix.

2. The visual access monitoring method for the inner ear drug delivery system according to claim 1, wherein: Setting the medication indicator deviation threshold matrix of the medication monitoring indicator also includes: When the data volume of the medication abnormality record data set is less than or equal to the statistical analysis data volume threshold, the medication indicator deviation threshold matrix of the medication monitoring indicator is set by the user terminal.

3. A visual access monitoring device for an inner ear drug delivery system, characterized in that: The device is used to implement the visual access monitoring method for the inner ear drug delivery system according to any one of claims 1 to 2, comprising: Monitoring index acquisition module: obtains drug administration monitoring indexes, wherein the drug administration monitoring indexes include drug chamber capacity, drug chamber internal pressure, drug administration time, drug administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval; Real-time parameter acquisition module: through the visual monitoring page, collects the real-time parameters of the drug chamber capacity, the drug chamber internal pressure, the drug administration time, the drug administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval to obtain the drug administration index monitoring eigenvalue matrix; Preset data acquisition module: obtain the eigenvalue matrix of drug administration index setting; Deviation calculation module: compares the medication index setting eigenvalue matrix and the medication index monitoring eigenvalue matrix to obtain the medication index deviation vector matrix; Abnormality analysis module: performing medication abnormality analysis based on the medication index deviation vector matrix to obtain medication abnormality coefficient; Adjustment control module: When the drug administration abnormality coefficient is greater than or equal to the drug administration abnormality coefficient threshold, the eigenvalue matrix is ​​set according to the drug administration index to adjust the drug chamber capacity, the internal pressure of the drug chamber, the drug administration time, the drug administration speed, the micro-flush pressure, the circulation channel pressure, the micro-flush working interval, and the circulation working interval.

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