Visual pathway monitoring method and device for inner ear drug delivery system

Abstract

The invention 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 administration monitoring index; real-time parameters are collected through a visual monitoring page, and a drug administration index monitoring characteristic value matrix is obtained; comparing the drug administration index setting characteristic value matrix with the drug administration index monitoring characteristic value matrix to obtain a drug administration index deviation vector matrix; performing drug delivery abnormity analysis according to the drug delivery index deviation vector matrix to obtain a drug delivery abnormity coefficient; and when the drug administration abnormal coefficient is greater than or equal to the drug administration abnormal coefficient threshold, adjusting according to the drug administration index setting characteristic value matrix. The technical problem that in the prior art, drug delivery abnormity cannot be accurately recognized due to the fact that analysis is difficult to conduct according to the overall state is solved, accurate abnormal state recognition is achieved by comprehensively analyzing the multiple monitoring indexes, drug delivery parameters are automatically adjusted, and therefore the technical effect of improving the accuracy and safety of the drug delivery process is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring, and particularly relates to a visualization path monitoring method and device for an inner ear drug delivery system. Background Art

[0002] As an advanced medical technology, the inner ear drug delivery system has shown great potential in recent years in the fields of hearing loss treatment, inner ear disease drug delivery, etc. However, traditional drug delivery control systems often have the problem of single monitoring ability. Most systems can only monitor and adjust a single drug delivery parameter, and cannot achieve comprehensive monitoring and abnormal analysis of multiple key parameters. The traditional single-index analysis method cannot accurately identify multi-dimensional drug delivery abnormalities, making it difficult to timely discover potential risks during the drug delivery process, thus affecting the treatment effect. Summary of the Invention

[0003] The present application provides a visualization path monitoring method and device for 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 state, resulting in the inability to accurately identify drug delivery abnormalities.

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

[0005] In the first aspect of the present application, a visualization path monitoring method for an inner ear drug delivery system is provided. The method includes:

[0006] Obtain drug delivery monitoring indicators, where the drug delivery monitoring indicators include drug reservoir capacity, drug reservoir internal pressure, drug delivery time, drug delivery speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval; through a visualization monitoring page, collect real-time parameters of the drug reservoir capacity, the drug reservoir internal pressure, 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 to obtain a drug delivery indicator monitoring eigenvalue matrix; obtain a drug delivery indicator set eigenvalue matrix; compare the drug delivery indicator set eigenvalue matrix and the drug delivery indicator monitoring eigenvalue matrix to obtain a drug delivery indicator deviation vector matrix; perform drug delivery abnormality analysis based on the drug delivery indicator deviation vector matrix to obtain a drug delivery abnormality coefficient; when the drug delivery abnormality coefficient is greater than or equal to the drug delivery abnormality coefficient threshold, adjust the drug reservoir capacity, the drug reservoir internal pressure, 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 according to the drug delivery indicator set eigenvalue matrix.

[0007] In the second aspect of the present application, a visualization path monitoring device for an inner ear drug delivery system is provided. The device includes:

[0008] Monitoring index acquisition module: Obtain administration monitoring indexes, where the administration monitoring indexes include medicine bin capacity, medicine bin internal pressure, administration time, administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval; Real-time parameter acquisition module: Through the visualization monitoring page, collect the real-time parameters of the medicine bin capacity, the medicine bin internal pressure, the administration time, the administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval, and obtain the administration index monitoring eigenvalue matrix; Preset data acquisition module: Obtain the administration index set eigenvalue matrix; Deviation calculation module: Compare the administration index set eigenvalue matrix and the administration index monitoring eigenvalue matrix to obtain the administration index deviation vector matrix; Abnormality analysis module: Perform administration abnormality analysis based on the administration index deviation vector matrix to obtain the administration abnormality coefficient; Adjustment control module: When the administration abnormality coefficient is greater than or equal to the administration abnormality coefficient threshold, adjust the medicine bin capacity, the medicine bin internal pressure, the administration time, the administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval according to the administration index set 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 administration monitoring indexes, where the administration monitoring indexes include medicine bin capacity, medicine bin internal pressure, administration time, administration speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval. Then, through the visualization monitoring page, collect the real-time parameters of the medicine bin capacity, the medicine bin internal pressure, the administration time, the administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval, and obtain the administration index monitoring eigenvalue matrix. At the same time, obtain the administration index set eigenvalue matrix. Then, compare the administration index set eigenvalue matrix and the administration index monitoring eigenvalue matrix to obtain the administration index deviation vector matrix. Next, perform administration abnormality analysis based on the administration index deviation vector matrix to obtain the administration abnormality coefficient. Finally, when the administration abnormality coefficient is greater than or equal to the administration abnormality coefficient threshold, adjust the medicine bin capacity, the medicine bin internal pressure, the administration time, the administration speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval according to the administration index set eigenvalue matrix. This solves the technical problem in the prior art that it is difficult to analyze based on the overall state and thus unable to accurately identify administration abnormalities. By comprehensively analyzing multiple monitoring indexes, it realizes accurate identification of abnormal states and automatically adjusts administration parameters, thereby achieving the technical effect of improving the accuracy and safety of the administration process. Description of the Drawings

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 Schematic flowchart of the visualization path monitoring method for the inner ear drug delivery system provided by the embodiments of the present application;

[0013] Figure 2 Schematic structural diagram of the visualization path monitoring device for the inner ear drug delivery system provided by the embodiments of the present application.

[0014] Explanation of reference numerals: monitoring index acquisition module 11, real-time parameter acquisition module 12, preset data acquisition module 13, deviation calculation module 14, anomaly analysis module 15, adjustment and control module 16. Detailed implementation manners

[0015] By providing a visualization path monitoring method and device for the inner ear drug delivery system, the present application solves the technical problem in the prior art that it is difficult to analyze based on the overall state, resulting in the inability to accurately identify abnormal drug delivery.

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

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

[0018] Embodiment 1, as Figure 1 shown, the present application provides a visualization path monitoring method for the inner ear drug delivery system, where the method includes:

[0019] Obtain drug delivery monitoring indexes, where the drug delivery monitoring indexes include drug storage capacity, drug storage internal pressure, drug delivery time, drug delivery speed, micro-flushing pressure, circulation channel pressure, micro-flushing working interval, and circulation working interval.

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

[0021] Drug reservoir capacity: The capacity of the drug storage reservoir, which monitors whether the drug is sufficient and meets the drug delivery requirements; Pressure inside the drug reservoir: The pressure inside the drug reservoir, which is used to ensure the drug is delivered under a stable pressure, preventing leakage or over-compression; Drug delivery time: The duration of drug delivery, which monitors the length of the drug delivery process to ensure the drug is released on time; Drug delivery speed: The rate of drug delivery, which monitors the release speed of the drug, preventing too fast or too slow drug delivery; Micro-flushing pressure: The pressure used to flush the drug reservoir or pipeline, ensuring smooth drug flow and preventing blockage; Circulation channel pressure: The pressure related to the drug transmission channel, which monitors the pressure changes in the channel to ensure normal drug delivery; Micro-flushing working interval: The time interval of the micro-flushing operation, ensuring the effect of each flush and the reasonable coordination between flushing and drug delivery; Circulation working interval: The working time interval of the circulating drug delivery, ensuring the continuous and stable supply of the drug during multiple cycles.

[0022] Through the visual monitoring page, collect the real-time parameters of the drug reservoir capacity, the pressure inside the drug reservoir, 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, and obtain the monitoring eigenvalue matrix of drug delivery indicators.

[0023] Through the visual monitoring page, collect the real-time parameters of multiple drug delivery monitoring indicators during the inner ear drug delivery process, integrate the multiple drug delivery monitoring indicators into a matrix form according to the time dimension, and form the monitoring eigenvalue matrix of drug delivery indicators. The monitoring eigenvalue matrix of drug delivery indicators shows the dynamic changes of each indicator during the drug delivery process. Each row of the monitoring eigenvalue matrix of drug delivery indicators 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 will record the values of this indicator at different time points.

[0024] Furthermore, through the visual monitoring page, collect the real-time parameters of the drug reservoir capacity, the pressure inside the drug reservoir, 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, and obtain the monitoring eigenvalue matrix of drug delivery indicators. The previous steps include:

[0025] Construct a three-dimensional model of the active drug delivery system and a three-dimensional model of the drug delivery target; set the display positions of the parameters of the medicine warehouse capacity, the internal pressure of the medicine warehouse, the administration time, the administration speed, the micro-flushing pressure, the circulating channel pressure, the micro-flushing working interval, and the circulating working interval; 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, construct the visual monitoring page.

[0026] Specifically, according to the actual physical structure and working characteristics of the active drug delivery system, construct its three-dimensional model to ensure that the three-dimensional model of the active drug delivery system can accurately reflect the key components of the delivery system (such as the medicine warehouse, the drug delivery pipeline, etc.) and their interaction relationships; at the same time, according to the anatomical structure of the inner ear and the treatment target, construct a three-dimensional model of the drug delivery target 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, determine the display positions of each administration monitoring index (medicine warehouse capacity, internal pressure of the medicine warehouse, administration time, administration speed, micro-flushing pressure, circulating channel pressure, micro-flushing working interval, circulating working interval). For example, the numerical values of the medicine warehouse capacity and the internal pressure are displayed beside the medicine warehouse model, the data of the administration speed and time are marked beside the drug delivery pipeline model, and the data of the micro-flushing pressure and the working interval are associated with the circulating channel; integrate the three-dimensional model of the active drug delivery system and the three-dimensional model of the drug delivery target into the design of the visual monitoring page, and add corresponding data visualization elements (such as dashboards, charts, etc.) to the page according to the set parameter display positions for displaying real-time parameters.

[0027] Obtain the set eigenvalue matrix of the administration index.

[0028] During the monitoring of the administration process, the set eigenvalue matrix of the administration index is a matrix generated based on the set ideal parameters or expected treatment standards. The set eigenvalue matrix of the administration index contains the ideal values or safety ranges of each administration monitoring index, which is used for comparative analysis with the monitored eigenvalue matrix of the administration index collected in real time. Specifically, the set standard values are sorted by time or sampling points to form the set eigenvalue matrix of the administration index. Each column of the matrix corresponds to the set value of a monitoring index, and each row of the matrix represents the administration set value at a certain time point or under a certain specific condition.

[0029] Compare the set eigenvalue matrix of the administration index and the monitored eigenvalue matrix of the administration index to obtain the deviation vector matrix of the administration index.

[0030] Perform element-wise difference calculations on each item of data in the set eigenvalue matrix of the administration index and the monitored eigenvalue matrix of the administration index to form the deviation vector matrix of the administration index. The deviation vector matrix of the administration index represents the deviation of each administration parameter.

[0031] Perform abnormal administration analysis based on the administration index deviation vector matrix to obtain an abnormal administration coefficient.

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

[0033] Furthermore, performing abnormal administration analysis based on the administration index deviation vector matrix to obtain an abnormal administration coefficient includes:

[0034] Set the administration index deviation threshold matrix for the administration monitoring index; when any administration index deviation vector in the administration index deviation vector matrix does not meet the administration index deviation threshold matrix, set the abnormal administration coefficient to be greater than the abnormal administration coefficient threshold; when each administration index deviation vector in the administration index deviation vector matrix meets the administration index deviation threshold matrix, perform abnormal administration analysis on the administration index deviation vector matrix through an abnormal administration analysis network to obtain the abnormal administration coefficient.

[0035] The administration 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 index. Compare the deviation value of each index in the administration index deviation vector matrix with the corresponding threshold range in the administration index deviation threshold matrix; if any deviation value in the administration index deviation vector matrix does not meet the corresponding deviation threshold range, it is considered that the index is abnormal, and the abnormal administration coefficient is set to be greater than the abnormal administration coefficient threshold; when each deviation vector in the administration index deviation vector matrix meets the given deviation threshold, further analysis is performed through an abnormal administration analysis network. The abnormal administration analysis network is a model based on machine learning or deep learning that can extract features from the administration index deviation vector matrix and calculate an abnormal administration coefficient, which reflects the severity of the abnormality in the administration process.

[0036] Furthermore, setting the administration index deviation threshold matrix for the administration monitoring index includes:

[0037] According to the administration monitoring indicators, extract the first administration monitoring indicator; using the first administration monitoring indicator as the only variable, extract the administration anomaly record data set; conduct an overrun standard deviation central tendency analysis of the first administration monitoring indicator on the administration anomaly record data set to obtain an overrun standard deviation central interval, extract the lower limit value of the overrun standard deviation central interval, and set it as the overrun standard deviation threshold; conduct a defect standard deviation central tendency analysis of the first administration monitoring indicator on the administration anomaly record data set to obtain a defect standard deviation central interval, extract the lower limit value of the defect standard deviation central interval, and set it as the defect standard deviation threshold; add the overrun standard deviation threshold and the defect standard deviation threshold to the first administration monitoring indicator deviation threshold; add the first administration monitoring indicator deviation threshold to the administration indicator deviation threshold matrix.

[0038] Preferably, select the first indicator for which a deviation threshold needs to be set from all the administration monitoring indicators, and denote it as the first administration monitoring indicator; using the first administration monitoring indicator as the only variable, extract the records containing anomalies of this indicator from the historical data to form an administration anomaly record data set; conduct an overrun standard deviation analysis on the first administration monitoring indicator in the administration anomaly record data set. The overrun standard deviation refers to the degree to which the indicator value exceeds the normal range (i.e., the upper or lower limit). By calculating the central tendency (such as the mean, median, etc.) of these overrun values, obtain the overrun standard deviation central interval. The overrun standard deviation central interval reflects the typical deviation degree when the indicator value exceeds the normal range; extract the lower limit value from the overrun standard deviation central interval and set it as the overrun standard deviation threshold. The overrun standard deviation threshold is used to determine whether the indicator value exceeds the upper limit of the acceptable range; similarly, conduct a defect standard deviation analysis on the first administration monitoring indicator in the administration anomaly record data set. 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 tendency of these defect values, obtain the defect standard deviation central interval, and extract the lower limit value from the defect standard deviation central interval and set it 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; combine the overrun standard deviation threshold and the defect standard deviation threshold to form the deviation threshold of the first administration monitoring indicator. The deviation threshold of the first administration monitoring indicator includes an upper threshold (the overrun standard deviation threshold) and a lower threshold (the defect standard deviation threshold); add the deviation threshold of the first administration monitoring indicator to the administration indicator deviation threshold matrix; repeat the above process for the remaining administration monitoring indicators until the deviation thresholds of all indicators are set and added to the administration indicator deviation threshold matrix.

[0039] Furthermore, setting the administration indicator deviation threshold matrix for the administration monitoring indicators further includes:

[0040] When the data volume of the administration anomaly record data set is less than or equal to the statistical analysis data volume threshold, set the administration index deviation threshold matrix of the administration monitoring index through the client.

[0041] Before starting to set the deviation threshold, it is necessary to evaluate the data volume of the administration anomaly record data set; if the data volume is greater than the statistical analysis data volume threshold (this threshold can be set according to the actual situation, such as based on the accuracy and reliability requirements of statistical analysis), then the data can continue to be used for statistical analysis to set the deviation threshold; if the data volume is less than or equal to the statistical analysis data volume threshold, then manual setting needs to be performed through the client, and the user can set reasonable deviation thresholds for each administration monitoring index according to their professional knowledge, experience, and understanding of the administration process.

[0042] Furthermore, when each administration index deviation vector of the administration index deviation vector matrix meets the administration index deviation threshold matrix, perform administration anomaly analysis on the administration index deviation vector matrix through the administration anomaly analysis network to obtain the administration anomaly coefficient, including:

[0043] Obtain the historical administration log set of the inner ear administration system, where each administration index deviation vector of any historical administration log meets the administration index deviation threshold matrix; when the first historical administration log in the historical administration log set is an administration anomaly, mark the administration anomaly coefficient as 1 to obtain the administration anomaly coefficient identification information, where 1 is greater than the administration anomaly coefficient threshold; when the first historical administration log in the historical administration log set is a normal administration, mark the administration anomaly coefficient as 0 to obtain the administration anomaly coefficient identification information, where 0 is less than the administration anomaly coefficient threshold; use the administration anomaly coefficient identification information as supervision and the historical administration index deviation vector matrix as input data to train the administration anomaly analysis network.

[0044] Obtain the historical administration log set from the inner ear administration system. The historical administration log set contains sufficient administration records, each record contains an administration index deviation vector, and these deviation vectors all meet the previously set administration index deviation threshold matrix; mark the historical administration log set to distinguish which records are administration anomalies; if the first historical administration log is an administration anomaly, mark the administration anomaly coefficient of this record as 1, and this mark indicates that this record is abnormal, and 1 is greater than the preset administration anomaly coefficient threshold; if the first historical administration log is a normal administration, mark the administration anomaly coefficient of this record as 0, and this mark indicates that this record is normal, and 0 is less than the preset administration anomaly coefficient threshold.

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

[0046] When the drug administration anomaly coefficient is greater than or equal to the drug administration anomaly coefficient threshold, adjust the drug bin capacity, the pressure inside the drug bin, the drug administration time, the drug administration speed, the micro-flushing pressure, the pressure of the circulation channel, the micro-flushing working interval, and the circulation working interval according to the set eigenvalue matrix of the drug administration index.

[0047] When it is detected that the drug administration anomaly coefficient exceeds the preset drug administration anomaly coefficient threshold, it indicates that there is an anomaly or deviation in the drug administration process. At this time, automatically adjust the drug bin capacity, the pressure inside the drug bin, the drug administration time, the drug administration speed, the micro-flushing pressure, the pressure of the circulation channel, the micro-flushing working interval, and the circulation working interval according to the set eigenvalue matrix of the drug administration index, so as to ensure the accuracy and stability of the drug administration process.

[0048] Furthermore, when the drug administration anomaly coefficient is greater than or equal to the drug administration anomaly coefficient threshold, adjusting the drug bin capacity, the pressure inside the drug bin, the drug administration time, the drug administration speed, the micro-flushing pressure, the pressure of the circulation channel, the micro-flushing working interval, and the circulation working interval according to the set eigenvalue matrix of the drug administration index further includes:

[0049] Adjust the drug bin capacity, the pressure inside the drug bin, the drug administration time, the drug administration speed, the micro-flushing pressure, the pressure of the circulation channel, the micro-flushing working interval, and the circulation working interval to obtain an updated drug administration index deviation vector matrix; when the updated drug administration anomaly coefficient of the updated drug administration index deviation vector matrix is less than the drug administration anomaly coefficient threshold, perform drug administration control according to the updated drug administration index eigenvalue matrix.

[0050] When the administration anomaly coefficient is greater than or equal to the administration anomaly coefficient threshold, the medicine bin capacity, the pressure inside the medicine bin, the administration time, the administration speed, the micro-flushing pressure, the circulating channel pressure, the micro-flushing working interval, and the circulating working interval will be adjusted according to the set eigenvalue matrix of the administration index to restore the normal administration process; after adjusting the administration parameters, the actual values of these parameters will be monitored and recorded again, so as to generate an updated administration index deviation vector matrix, and the updated administration index deviation vector matrix reflects the deviation between the adjusted administration index and the normal value; the updated administration index deviation vector matrix will be input into the administration anomaly analysis network for analysis to obtain the updated administration anomaly coefficient; if the updated administration anomaly coefficient is less than the administration anomaly coefficient threshold, it indicates that the administration process has returned to normal, and normal administration control will continue according to the updated administration index eigenvalue matrix.

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

[0052] First, obtain the administration monitoring indexes, where the administration monitoring indexes include the medicine bin capacity, the pressure inside the medicine bin, the administration time, the administration speed, the micro-flushing pressure, the circulating channel pressure, the micro-flushing working interval, and the circulating working interval. Then, through the visual monitoring page, collect the real-time parameters of the medicine bin capacity, the pressure inside the medicine bin, the administration time, the administration speed, the micro-flushing pressure, the circulating channel pressure, the micro-flushing working interval, and the circulating working interval to obtain the administration index monitoring eigenvalue matrix. At the same time, obtain the set eigenvalue matrix of the administration index. Then, compare the set eigenvalue matrix of the administration index and the administration index monitoring eigenvalue matrix to obtain the administration index deviation vector matrix. Next, perform administration anomaly analysis according to the administration index deviation vector matrix to obtain the administration anomaly coefficient. Finally, when the administration anomaly coefficient is greater than or equal to the administration anomaly coefficient threshold, adjust the medicine bin capacity, the pressure inside the medicine bin, the administration time, the administration speed, the micro-flushing pressure, the circulating channel pressure, the micro-flushing working interval, and the circulating working interval according to the set eigenvalue matrix of the administration index. It solves the technical problem in the prior art that it is difficult to analyze according to the overall state and thus unable to accurately identify administration anomalies. By comprehensively analyzing multiple monitoring indexes, accurate anomaly state identification is realized, and the administration parameters are automatically adjusted, thereby achieving the technical effects of improving the accuracy and safety of the administration process.

[0053] Embodiment 2, based on the same inventive concept as the visual access monitoring method for the inner ear drug delivery system in the foregoing embodiment, as Figure 2 shown, the present application provides a visual access monitoring device for the inner ear drug delivery system, where the device includes:

[0054] Monitoring index acquisition module 11: Obtain administration monitoring indexes, where the administration monitoring indexes include medicine warehouse capacity, internal pressure of the medicine warehouse, administration time, administration speed, micro flushing pressure, circulation channel pressure, micro flushing working interval, and circulation working interval; Real-time parameter acquisition module 12: Through the visualization monitoring page, collect the real-time parameters of the medicine warehouse capacity, the internal pressure of the medicine warehouse, the administration time, the administration speed, the micro flushing pressure, the circulation channel pressure, the micro flushing working interval, and the circulation working interval, and obtain the administration index monitoring eigenvalue matrix; Preset data acquisition module 13: Obtain the administration index set eigenvalue matrix; Deviation calculation module 14: Compare the administration index set eigenvalue matrix and the administration index monitoring eigenvalue matrix to obtain the administration index deviation vector matrix; Abnormality analysis module 15: Perform administration abnormality analysis based on the administration index deviation vector matrix to obtain the administration abnormality coefficient; Adjustment control module 16: When the administration abnormality coefficient is greater than or equal to the administration abnormality coefficient threshold, adjust the medicine warehouse capacity, the internal pressure of the medicine warehouse, the administration time, the administration speed, the micro flushing pressure, the circulation channel pressure, the micro flushing working interval, and the circulation working interval according to the administration index set eigenvalue matrix.

[0055] Further, the abnormality analysis module 15 is used to execute the following method:

[0056] Set the administration index deviation threshold matrix of the administration monitoring indexes; When any administration index deviation vector of the administration index deviation vector matrix does not meet the administration index deviation threshold matrix, set the administration abnormality coefficient to be greater than the administration abnormality coefficient threshold; When each administration index deviation vector of the administration index deviation vector matrix meets the administration index deviation threshold matrix, perform administration abnormality analysis on the administration index deviation vector matrix through the administration abnormality analysis network to obtain the administration abnormality coefficient.

[0057] Further, the abnormality analysis module 15 is used to execute the following method:

[0058] Extract the first drug administration monitoring indicator according to the drug administration monitoring indicator; use the first drug administration monitoring indicator as the only variable to extract the drug administration abnormal record data set; perform over-limit standard deviation central tendency analysis of the first drug administration monitoring indicator on the drug administration abnormal record data set to obtain the over-limit standard deviation concentration interval, and extract the lower limit value of the over-limit standard deviation concentration interval, which is set as the over-limit standard deviation threshold; perform defect standard deviation central tendency analysis of the first drug administration monitoring indicator on the drug administration abnormal record data set to obtain the defect standard deviation concentration interval, and extract the lower limit value of the defect standard deviation concentration interval, which is set as the defect standard deviation threshold; add the over-limit standard deviation threshold and the defect standard deviation threshold to the first drug administration monitoring indicator deviation threshold; add the first drug administration monitoring indicator deviation threshold to the drug administration indicator deviation threshold matrix.

[0059] Further, the anomaly analysis module 15 is used to execute the following method:

[0060] Obtain the historical drug administration log set of the inner ear drug delivery system, where 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 historical drug administration log set is drug administration abnormal, mark the drug administration abnormal coefficient as 1 to obtain the drug administration abnormal coefficient marking information, where 1 is greater than the drug administration abnormal coefficient threshold; when the first historical drug administration log in the historical drug administration log set is drug administration normal, mark the drug administration abnormal coefficient as 0 to obtain the drug administration abnormal coefficient marking information, where 0 is less than the drug administration abnormal coefficient threshold; use the drug administration abnormal coefficient marking information as supervision and the historical drug administration indicator deviation vector matrix as input data to train the drug administration anomaly analysis network.

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

[0062] Construct a three-dimensional model of the active drug delivery system and a three-dimensional model of the drug delivery target; set the parameter display positions of the drug storage capacity, the internal pressure of the drug storage, 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; 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, construct the visualization monitoring page.

[0063] Further, the adjustment and control module 16 is used to execute the following method:

[0064] Adjust the medicine storage capacity, the internal pressure of the medicine storage, the administration time, the administration speed, the micro-flushing pressure, the pressure of the circulation channel, the micro-flushing working interval, and the circulation working interval to obtain an updated deviation vector matrix of the administration index; when the updated administration abnormality coefficient of the updated deviation vector matrix of the administration index is less than the administration abnormality coefficient threshold, perform administration control according to the updated eigenvalue matrix of the administration index.

[0065] Further, the abnormality analysis module 15 is used to execute the following method:

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

[0067] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages or disadvantages of the embodiments. And the above description of specific embodiments of this specification has been made. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0068] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0069] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. A visual pathway monitoring method for 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, 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; Obtaining a 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 according to 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 eigenvalue matrix is ​​set according to the medication index to adjust the medicine chamber capacity, the internal pressure of the medicine chamber, the medication time, the medication speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval.

2. The visual pathway monitoring method for an inner ear drug delivery system according to claim 1, characterized in that: Performing medication abnormality analysis according to the medication index deviation vector matrix to obtain medication abnormality coefficients includes: Setting a medication indicator deviation threshold matrix of the medication monitoring indicator; 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, medication abnormality analysis is performed on the medication indicator deviation vector matrix through a medication abnormality analysis network to obtain the medication abnormality coefficient.

3. The visual pathway monitoring method for an inner ear drug delivery system according to claim 1, characterized in that: Setting the medication indicator deviation threshold matrix of the medication monitoring indicator includes: Extracting a first medication monitoring indicator according to the medication 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 it as an over-limit standard deviation threshold; Performing a defect standard deviation central trend analysis of the first drug administration monitoring indicator on the drug administration abnormality record data set to obtain a defect standard deviation central interval, extracting a lower limit value of the defect standard deviation central interval, and setting it as a defect standard deviation threshold; Adding the excess standard deviation threshold and the deficiency standard deviation threshold into the first medication monitoring indicator deviation threshold; The first medication monitoring indicator deviation threshold is added to the medication indicator deviation threshold matrix.

4. The visual pathway monitoring method for an inner ear drug delivery system according to claim 2, wherein: 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 of the historical medication log set is 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 of the historical medication log set is normal medication, the medication abnormality coefficient is marked as 0, and medication abnormality coefficient identification information is obtained, 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.

5. The visual pathway monitoring method for an inner ear drug delivery system according to claim 1, characterized in that: 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 includes: Construct a 3D model of the active drug delivery system and a 3D 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; The visual monitoring page is constructed based on the parameter display position, the active drug delivery system three-dimensional model and the drug delivery target three-dimensional model.

6. The visual pathway monitoring method for an inner ear drug delivery system according to claim 1, characterized in that: 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 characteristic value matrix, and the medication index is further included: 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 medication abnormality coefficient of the updated medication indicator deviation vector matrix is ​​less than the medication abnormality coefficient threshold, medication control is performed according to the updated medication indicator eigenvalue matrix.

7. The visual pathway monitoring method for an inner ear drug delivery system according to claim 3, characterized in that: 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.

8. A visual pathway monitoring device for an inner ear drug delivery system, characterized in that: The device is used to implement the visual pathway monitoring method for an inner ear drug delivery system according to any one of claims 1 to 7, 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, collect 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 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 a medication index deviation vector matrix; Abnormal analysis module: performing abnormal analysis on medication according to the medication index deviation vector matrix to obtain a medication abnormality coefficient; Adjustment control module: 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 medicine chamber capacity, the internal pressure of the medicine chamber, the medication time, the medication speed, the micro-flushing pressure, the circulation channel pressure, the micro-flushing working interval, and the circulation working interval.

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