Intelligent endoscope monitoring method and system and medium

Abstract

The invention provides an intelligent endoscope monitoring method and system and a medium, and belongs to the technical field of instrument monitoring and control. The method comprises the following steps: collecting endoscope monitoring information in a preset time period, and extracting data such as disinfectant concentration and temperature field information; a three-dimensional temperature field model is constructed according to the temperature field information, and cleaning progress data is preprocessed; constructing a cleaning effect evaluation model based on the multi-source data, and if an evaluation result does not reach the standard, generating a control instruction; cleaning and disinfection parameters are automatically adjusted according to the instruction, and alarming or equipment locking is triggered; and finally, the monitoring data is encrypted and stored according to a timestamp, a traceable cleaning and disinfection record is generated, and two-dimensional code authentication viewing is supported. According to the system, intelligent monitoring of the endoscope cleaning and disinfecting process can be achieved, and safety and traceability are improved.

Description

Technical Field

[0001] The present application relates to the field of instrument monitoring and control technology, and in particular to an endoscope intelligent monitoring method, system and medium. Background Art

[0002] Endoscopes, as a commonly used medical examination tool, must be strictly cleaned and disinfected after use to prevent cross-infection and ensure the health and safety of patients. However, the current monitoring of the endoscope cleaning and disinfection process has many shortcomings.

[0003] When it comes to monitoring disinfectant concentration, traditional methods often struggle to accurately obtain concentration information in real time and lack an effective calibration mechanism. This can result in disinfectant concentrations failing to meet disinfection requirements, impacting disinfection effectiveness. For temperature monitoring, existing technologies can mostly only obtain local or single-point temperature data, failing to comprehensively and intuitively reflect the temperature distribution within the disinfection tank. This can easily lead to uneven temperatures that affect disinfection quality. When it comes to cleaning progress monitoring, data processing is not sophisticated enough, making it difficult to accurately determine whether the cleaning process is proceeding normally. For example, it's impossible to effectively identify abnormal fluctuations in the flow and pressure of the cleaning fluid, or accurately control the timing of each cleaning stage.

[0004] Furthermore, current monitoring systems lack comprehensive evaluation models and intelligent control mechanisms, making it difficult to make timely adjustments and provide early warnings based on monitoring results. Furthermore, it is difficult to achieve traceable management of the cleaning and disinfection process, hindering the control and supervision of medical quality. Therefore, an intelligent, efficient, and comprehensive endoscopic monitoring method and system is urgently needed to address these issues. Summary of the Invention

[0005] The purpose of this application is to provide an intelligent endoscope monitoring method, system and medium. The method first collects endoscope monitoring information within a preset time period, extracts data such as disinfectant concentration and temperature field information, and monitors the disinfectant concentration by establishing a calibration curve. Then, a three-dimensional temperature field model is constructed, and the temperature distribution is visualized using interpolation algorithms and three-dimensional modeling technology, with abnormalities highlighted. The cleaning progress data is preprocessed, filtered and denoised, and timestamps are aligned. A weighted evaluation model is constructed to evaluate the cleaning effect, and the weights are determined using a particle swarm optimization algorithm. Control instructions are generated when the standards are not met. Then, the cleaning and disinfection parameters are automatically adjusted, an alarm is triggered, or the device is locked according to the instructions. Finally, the monitoring data is encrypted and stored, and a traceable record is generated, which supports code scanning and authentication viewing, thereby achieving comprehensive intelligent monitoring and traceable management of the endoscope cleaning and disinfection process.

[0006] The present application provides an endoscope intelligent monitoring method, comprising the following steps: Collect endoscope monitoring information for a preset time period, extract disinfectant concentration, temperature field information, cleaning progress data, and endoscope surface contaminant information; Constructing a three-dimensional temperature field model according to the temperature field information; Preprocessing the cleaning progress data; Building a cleaning effect evaluation model based on multi-source data and obtaining evaluation results, and generating control instructions when the evaluation results do not meet the standards; Automatically adjust cleaning and disinfection parameters according to the control instructions, and trigger sound and light alarms or equipment locking; The monitoring data is encrypted and stored with timestamps to generate traceable cleaning and disinfection records.

[0007] Among them, in the endoscope intelligent monitoring method described in this application, the concentration of the extracted disinfectant is specifically: By establishing a calibration curve between the absorbance and concentration of the disinfectant, the concentration of the disinfectant can be monitored in real time; The calibration curve establishment process includes configuring at least five standard disinfectant samples with different concentration gradients, measuring the absorbance of each sample at a specific wavelength, and obtaining a functional relationship curve between absorbance and concentration based on the measurement data.

[0008] Among them, in the endoscope intelligent monitoring method described in this application, the three-dimensional temperature field model is constructed according to the temperature field information, specifically: extracting temperature data at different spatial positions according to the temperature field information; The discrete temperature data is converted into continuous three-dimensional temperature distribution data using a preset spatial interpolation algorithm; The three-dimensional temperature distribution data is visualized based on three-dimensional modeling technology. When the temperature data deviates from the set temperature range by more than a preset deviation rate, the abnormal area is highlighted in the three-dimensional model.

[0009] Among them, in the endoscope intelligent monitoring method described in this application, the preprocessing of the cleaning progress data is specifically: Extracting cleaning fluid flow and pressure data and cleaning time data according to the cleaning progress data; Using a sliding average filtering algorithm on the cleaning fluid flow and pressure data to remove high-frequency noise; The cleaning time data is timestamped to ensure the accuracy of the time records at each stage.

[0010] Among them, in the endoscope intelligent monitoring method described in this application, the cleaning effect evaluation model is constructed based on multi-source data and the evaluation result is obtained. When the evaluation result does not meet the standard, a control instruction is generated, specifically: A weighted evaluation model was constructed, which included the factors affecting disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and the residual contaminant rate on the endoscope surface. The particle swarm optimization algorithm is used to determine the weight of each influencing factor based on historical cleaning data and cleaning effect evaluation results; The evaluation result is a cleaning effect score. When the cleaning effect score is lower than a preset effect score threshold, the cleaning effect does not meet the standard and a control instruction is generated.

[0011] Among them, in an endoscope intelligent monitoring method described in this application, the monitoring data is encrypted and stored according to the timestamp to generate a traceable cleaning and disinfection record, specifically: Encrypt data using a preset encryption algorithm; The traceable cleaning and disinfection records include real-time data of various monitoring parameters, triggering time and content of control instructions, and equipment status change information; By scanning the QR code and performing user identity authentication, you can view the cleaning and disinfection records of the corresponding endoscope.

[0012] In a second aspect, the present application provides an endoscope intelligent monitoring system, the system comprising: a memory and a processor, the memory comprising a program for an endoscope intelligent monitoring method, the program for the endoscope intelligent monitoring method, when executed by the processor, implementing the following steps: Collect endoscope monitoring information for a preset time period, extract disinfectant concentration, temperature field information, cleaning progress data, and endoscope surface contaminant information; Constructing a three-dimensional temperature field model according to the temperature field information; Preprocessing the cleaning progress data; Building a cleaning effect evaluation model based on multi-source data and obtaining evaluation results, and generating control instructions when the evaluation results do not meet the standards; Automatically adjust cleaning and disinfection parameters according to the control instructions, and trigger sound and light alarms or equipment locking; The monitoring data is encrypted and stored with timestamps to generate traceable cleaning and disinfection records.

[0013] Among them, in the endoscope intelligent monitoring system described in this application, the concentration of the extracted disinfectant is specifically: By establishing a calibration curve between the absorbance and concentration of the disinfectant, the concentration of the disinfectant can be monitored in real time; The calibration curve establishment process includes configuring at least five standard disinfectant samples with different concentration gradients, measuring the absorbance of each sample at a specific wavelength, and obtaining a functional relationship curve between absorbance and concentration based on the measurement data.

[0014] Among them, in the endoscope intelligent monitoring system described in this application, the three-dimensional temperature field model is constructed according to the temperature field information, specifically: extracting temperature data at different spatial positions according to the temperature field information; The discrete temperature data is converted into continuous three-dimensional temperature distribution data using a preset spatial interpolation algorithm; The three-dimensional temperature distribution data is visualized based on three-dimensional modeling technology. When the temperature data deviates from the set temperature range by more than a preset deviation rate, the abnormal area is highlighted in the three-dimensional model.

[0015] In a third aspect, the present application also provides a computer-readable storage medium, which includes an endoscopic intelligent monitoring method program. When the endoscopic intelligent monitoring method program is executed by a processor, it implements the steps of an endoscopic intelligent monitoring method as described in any one of the above items.

[0016] As can be seen from the above, the embodiment of the present application provides an intelligent endoscope monitoring method, system and medium. At the method level, the endoscope monitoring information of a preset time period is first collected, and the disinfectant concentration, temperature field information, cleaning progress data and endoscope surface contaminant information are accurately extracted. Among them, the concentration is monitored in real time by establishing a calibration curve of the absorbance and concentration of the disinfectant. The calibration curve is obtained by configuring at least five standard disinfectant samples with different concentration gradients and measuring the absorbance at a specific wavelength. Then, a three-dimensional temperature field model is constructed based on the temperature field information, and the discrete temperature data is converted into continuous three-dimensional temperature distribution data using a preset spatial interpolation algorithm. With the help of three-dimensional modeling technology, it is visualized and presented. Once the temperature deviates from the set range and exceeds the preset deviation rate, the abnormal area is highlighted in the model. For the cleaning progress data, the cleaning liquid flow, pressure and time data are extracted, and the sliding average filter algorithm is used to remove the high-frequency noise of the flow and pressure data. At the same time, the time data is timestamp aligned to ensure accuracy. Next, a weighted evaluation model is constructed, encompassing factors such as disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and residual contaminant rate on the endoscope surface. Using a particle swarm optimization algorithm, the weights of each factor are determined based on historical cleaning data and effect evaluations. The resulting cleaning effect score is compared with a preset threshold, and control instructions are generated if the score does not meet the standard. Subsequently, cleaning and disinfection parameters are automatically adjusted according to the instructions, triggering an audible and visual alarm or locking the device. Finally, a preset encryption algorithm is used to encrypt and store monitoring data by timestamp, generating a traceable cleaning and disinfection record that includes real-time data on each monitoring parameter, control instruction trigger information, and device status change information. This record can be viewed by scanning a QR code and completing user authentication. The system implements the aforementioned method steps by executing relevant programs on a processor. This method can also be implemented when programs stored on a computer-readable storage medium are executed, thereby achieving comprehensive, intelligent monitoring and traceable management of the endoscope cleaning and disinfection process, effectively ensuring endoscope safety and medical quality.

[0017] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A flowchart of an endoscope intelligent monitoring method provided in an embodiment of the present application; Figure 2 A flowchart of constructing a three-dimensional temperature field model for an intelligent endoscope monitoring method provided in an embodiment of the present application; Figure 3 A flowchart of preprocessing the cleaning progress data of an endoscope intelligent monitoring method provided in an embodiment of the present application; Figure 4 A flowchart of constructing a cleaning effect evaluation model and obtaining evaluation results for an endoscope intelligent monitoring method provided in an embodiment of the present application; DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0021] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions.

[0022] Please refer to Figure 1 , Figure 1 This is a flow chart of an endoscope intelligent monitoring method in some embodiments of the present application. The endoscope intelligent monitoring method is used in a terminal device, such as a computer, a mobile phone terminal, etc. The endoscope intelligent monitoring method includes the following steps: S101, collecting endoscope monitoring information for a preset time period, extracting disinfectant concentration, temperature field information, cleaning progress data, and endoscope surface contaminant information; S102, constructing a three-dimensional temperature field model according to the temperature field information; S103, pre-processing the cleaning progress data; S104: Build a cleaning effect evaluation model based on multi-source data and obtain evaluation results. If the evaluation results do not meet the standards, generate a control instruction. S105, automatically adjusting cleaning and disinfection parameters according to the control instructions, and triggering an audible and visual alarm or device locking; S106. Encrypt and store the monitoring data according to the timestamp to generate a traceable cleaning and disinfection record.

[0023] First, endoscope monitoring information is collected over a pre-set time period. This process involves the acquisition of multiple key data points, including disinfectant concentration, temperature field information, cleaning progress data, and information on endoscope surface contaminants. Disinfectant concentration information is crucial for ensuring disinfection effectiveness and is directly related to the thoroughness of endoscope disinfection. The temperature field information is then used to construct a three-dimensional temperature field model. By collecting and analyzing temperature data at different spatial locations and utilizing a specific spatial interpolation algorithm, the discrete temperature data is converted into continuous three-dimensional temperature distribution data. This is then visualized using 3D modeling technology. This provides an intuitive understanding of the temperature distribution within the disinfection tank. If the temperature deviates from the set range by exceeding a preset deviation rate, the abnormal area is highlighted in the 3D model, facilitating detection and action by staff. Cleaning progress data is pre-processed after acquisition. This includes extracting cleaning fluid flow rate, pressure, and cleaning time data. A sliding average filter algorithm is applied to the cleaning fluid flow rate and pressure data to remove high-frequency noise and ensure data accuracy. The cleaning time data is also timestamped to ensure accurate time recording at each stage, providing a reliable basis for subsequent, accurate evaluation of the cleaning process. Based on the collected multi-source data, a cleaning effectiveness evaluation model is constructed. This model comprehensively considers influencing factors such as disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and the residual contaminant rate on the endoscope surface. Using a particle swarm optimization algorithm, combined with historical cleaning data and cleaning effectiveness evaluation results, the model determines the weights of each influencing factor and generates the evaluation results. If the evaluation results indicate that the cleaning effectiveness does not meet the standards, the system automatically generates control instructions. Based on these control instructions, the system can automatically adjust cleaning and disinfection parameters, such as adjusting the disinfectant replenishment volume, changing the cleaning fluid flow rate and pressure, and triggering audible and visual alarms to alert staff, or directly lock the equipment to prevent unqualified cleaning and disinfection operations from continuing. In addition, to ensure traceability of the cleaning and disinfection process and ensure medical quality and safety, all monitoring data is encrypted and stored with timestamps, forming traceable cleaning and disinfection records. These records contain key information such as real-time data for each monitoring parameter, the triggering time and specific content of the control instructions, and information on equipment status changes. This facilitates subsequent query and analysis, providing strong support for medical management and quality control.

[0024] According to an embodiment of the present invention, the concentration of the extracted disinfectant is specifically: By establishing a calibration curve between the absorbance and concentration of the disinfectant, the concentration of the disinfectant can be monitored in real time; The calibration curve establishment process includes configuring at least five standard disinfectant samples with different concentration gradients, measuring the absorbance of each sample at a specific wavelength, and obtaining a functional relationship curve between absorbance and concentration based on the measurement data.

[0025] To accurately monitor disinfectant concentration in real time, a calibration curve was established to measure absorbance versus concentration. The construction of this calibration curve follows a rigorous process: first, at least five standard disinfectant samples with varying concentration gradients are prepared. These sample concentrations are differentiated to obtain sufficient data at a sufficient number of different concentrations. Next, the absorbance of each sample is measured at a specific wavelength. This wavelength is scientifically selected to be more sensitive to changes in disinfectant absorbance, resulting in more accurate measurements. After measuring the absorbance of all samples, mathematical analysis is used to fit the measured data to a curve representing the relationship between absorbance and concentration. This curve serves as the calibration curve. During subsequent monitoring, by measuring the real-time absorbance of the disinfectant and applying the established calibration curve, the current disinfectant concentration can be accurately determined, providing critical data support for endoscope disinfection and ensuring that the disinfection effect meets the expected standards.

[0026] Please refer to Figure 2 , Figure 2 This is a flow chart of constructing a three-dimensional temperature field model of an endoscope intelligent monitoring method in some embodiments of the present application. According to an embodiment of the present invention, constructing a three-dimensional temperature field model based on the temperature field information is specifically as follows: S201, extracting temperature data at different spatial locations according to the temperature field information; S202, using a preset spatial interpolation algorithm to convert discrete temperature data into continuous three-dimensional temperature distribution data; S203 , visualizing the three-dimensional temperature distribution data based on three-dimensional modeling technology, and highlighting the abnormal area in the three-dimensional model when the temperature data deviates from the set temperature range by more than a preset deviation rate.

[0027] Monitoring and analyzing the temperature field is crucial for ensuring the effectiveness of endoscope cleaning and disinfection. First, temperature data at different spatial locations is extracted from the collected temperature field information. These data represent discrete samples of the temperature conditions within the disinfection tank. Since comprehensive and continuous temperature distribution is required during the actual disinfection process, a preset spatial interpolation algorithm is used to process these discrete temperature data. Based on the temperature data at known discrete points, this algorithm uses specific mathematical models and calculation methods to reasonably infer the temperature values ​​at other unmeasured locations. This algorithm then converts the discrete temperature data into continuous three-dimensional temperature distribution data, constructing a more complete and accurate temperature field model. Then, using three-dimensional modeling technology, the resulting three-dimensional temperature distribution data is visualized, transforming the abstract temperature data into an intuitive three-dimensional graphical representation. This allows operators to clearly observe the temperature conditions at various locations within the disinfection tank. To ensure that the disinfection process is carried out within appropriate temperature conditions, the system pre-sets a temperature range and deviation rate. If, during monitoring, the temperature data deviates from the set temperature range by more than the preset deviation rate, the system immediately responds by automatically highlighting the abnormal area in the 3D model. This intuitive abnormal prompt method allows operators to quickly detect temperature abnormalities and take corresponding measures to make adjustments in a timely manner, thereby effectively ensuring the quality and safety of endoscope cleaning and disinfection work.

[0028] Please refer to Figure 3 , Figure 3 This is a flow chart of pre-processing the cleaning progress data of an endoscope intelligent monitoring method in some embodiments of the present application. According to an embodiment of the present invention, the pre-processing of the cleaning progress data is specifically as follows: S301, extracting cleaning liquid flow and pressure data and cleaning time data according to the cleaning progress data; S302, using a sliding average filtering algorithm to remove high-frequency noise from the cleaning fluid flow and pressure data; S303: aligning the timestamps of the cleaning time data to ensure the accuracy of the time records of each stage.

[0029] Processing cleaning progress data is crucial for accurately evaluating endoscope cleaning effectiveness. In practice, key cleaning fluid flow, pressure, and cleaning time data are first extracted from the collected cleaning progress data. Cleaning fluid flow and pressure data directly reflect the working state of the cleaning fluid during the cleaning process, while cleaning time data is crucial for ensuring that the entire cleaning process complies with standards. However, during data collection, cleaning fluid flow and pressure data are susceptible to interference from various factors, generating high-frequency noise that affects data accuracy and, in turn, interferes with the assessment of the cleaning process. Therefore, a sliding average filter algorithm is used to process cleaning fluid flow and pressure data. This algorithm effectively smooths the data by averaging data within a specific time window, removing high-frequency noise and ensuring that the cleaning fluid flow and pressure data more accurately reflect the actual situation. Furthermore, the accuracy of cleaning time data is crucial. Since the endoscope cleaning process involves multiple stages, the time records of each stage are crucial for analyzing the rationality and integrity of the cleaning process. To ensure the accuracy of the time records for each stage, the cleaning time data requires timestamp alignment. Timestamp alignment is an operation that calibrates and integrates cleaning time data collected at different times according to a unified time base, ensuring that the time data at each stage accurately corresponds to the actual cleaning process, providing a reliable data foundation for subsequent time-based analysis and evaluation.

[0030] Please refer to Figure 4 , Figure 4 This is a flowchart of a method for constructing a cleaning effect evaluation model and obtaining evaluation results for an endoscope intelligent monitoring method in some embodiments of the present application. According to an embodiment of the present invention, the cleaning effect evaluation model is constructed based on multi-source data and the evaluation results are obtained. When the evaluation results do not meet the standards, a control instruction is generated, specifically: S401. Construct a weighted evaluation model that includes factors affecting disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and endoscope surface contaminant residual rate. S402, using a particle swarm optimization algorithm to determine the weight of each influencing factor based on historical cleaning data and cleaning effect evaluation results; S403: The evaluation result is a cleaning effect score. When the cleaning effect score is lower than a preset effect score threshold, the cleaning effect does not meet the standard and a control instruction is generated.

[0031] To evaluate the effectiveness of endoscope cleaning, a comprehensive weighted evaluation model was constructed to more comprehensively and accurately measure the cleaning process. This model incorporates multiple key influencing factors, including disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and the residual contaminant rate on the endoscope surface. These factors reflect key parameters and conditions during the endoscope cleaning process from different perspectives and have direct or indirect impacts on cleaning effectiveness. To determine the relative importance of each factor in the model, or its weight, a particle swarm optimization algorithm was employed, combining historical cleaning data and cleaning effectiveness evaluation results. Particle swarm optimization is an intelligent optimization algorithm that simulates the foraging behavior of a flock of birds, searching for the optimal solution through continuous iteration. During this process, the algorithm repeatedly adjusts the weights of each factor based on the relationship between each factor and cleaning effectiveness in historical data until an optimal set of weights is found, enabling the model to most accurately reflect actual cleaning effectiveness. The results calculated using this weighted evaluation model are presented as a cleaning effectiveness score. A predefined score threshold is set to determine whether the cleaning effectiveness meets the required standards. If the calculated cleaning effectiveness score falls below this threshold, it indicates that the current cleaning effectiveness is substandard. Once this happens, the system will immediately generate control instructions to adjust the cleaning process, such as automatically adjusting the cleaning and disinfection parameters, or triggering sound and light alarms to prompt staff to take corresponding measures to ensure that the endoscope cleaning can achieve the desired effect and ensure medical safety.

[0032] According to an embodiment of the present invention, the monitoring data is encrypted and stored according to the timestamp to generate a traceable cleaning and disinfection record, specifically: Encrypt data using a preset encryption algorithm; The traceable cleaning and disinfection records include real-time data of various monitoring parameters, triggering time and content of control instructions, and equipment status change information; By scanning the QR code and performing user identity authentication, you can view the cleaning and disinfection records of the corresponding endoscope.

[0033] To ensure data security and privacy, monitoring data is encrypted using a preset encryption algorithm. This carefully selected and configured encryption algorithm converts raw data into ciphertext for storage, effectively preventing unauthorized theft or tampering during storage and transmission. The traceable cleaning and disinfection records generated by the system contain rich and critical information, including real-time data on various monitoring parameters, such as disinfectant concentration, temperature field, and cleaning fluid flow and pressure. These data accurately record the changes in various indicators during the endoscope cleaning and disinfection process. They also include the triggering time and content of control instructions, detailing the operational decisions made by the system when abnormalities are detected or adjustments are required during the cleaning and disinfection process. Furthermore, device status change information is used to record changes in the device's operating status throughout the entire process. To facilitate querying and managing these important records, the system supports scanning QR codes to access the cleaning and disinfection records for corresponding endoscopes. However, to ensure data security and privacy, users must undergo identity authentication before viewing the records. Only authenticated users can access and view the detailed cleaning and disinfection records of the corresponding endoscope. This process not only ensures convenient data query but also prevents unauthorized access, thereby effectively improving the security and standardization of medical data management.

[0034] According to an embodiment of the present invention, the further embodiment includes: Using a deep learning algorithm to process the collected information on contaminants on the endoscope surface to identify specific types of stains on the endoscope surface; Automatically select the best cleaning strategy from the preset cleaning strategy library based on the type of stain, and adjust the cleaning solution formula, cleaning time or cleaning intensity.

[0035] During actual endoscope use, various types of stains, such as blood, mucus, and tissue fragments, remain on the surface. These stains vary in composition and adhesion characteristics, and therefore require varying cleaning requirements. Traditional monitoring methods may only detect the presence of contaminants but cannot accurately distinguish the specific type of stain. To address this issue, the present invention utilizes deep learning algorithms, specifically convolutional neural networks (CNNs). CNNs are specialized for processing grid-structured data and are well-suited for processing image data. The present invention first collects a large amount of endoscope surface image data labeled with different types of stains to construct a rich training dataset. This image data should cover a wide range of common stain types, as well as varying degrees and distributions. The CNN model is then trained using this training dataset. During training, the model automatically learns the characteristics and patterns of stains in the images, enabling it to accurately identify different types of stains. For example, blood stains may have specific color and texture characteristics, mucus may appear transparent or translucent and viscous, and tissue fragments may have irregular shapes and textures. By continuously adjusting the model's parameters, the model can accurately identify the type of stain in new endoscope surface images. After accurately identifying the type of stain on the endoscope surface, the system automatically selects the optimal cleaning strategy based on a pre-set cleaning strategy library. This pre-established cleaning strategy library contains optimal cleaning solutions for different types of stains, developed based on extensive experimentation and practical experience. For example, if the stain on the endoscope surface is identified as blood, the system may select a cleaning solution with strong decontamination capabilities, appropriately extend the cleaning time, and increase the intensity of the cleaning process to ensure complete removal of the blood stain. For mucus stains, the system may select a cleaning solution with specific solubility properties and adjust the water flow rate and direction to better remove the mucus. In this way, the system can precisely clean different types of stains, avoiding the problems of incomplete or over-cleaning that can occur with traditional cleaning methods that use a uniform cleaning strategy. Incomplete cleaning can leave bacteria and viruses on the endoscope surface, increasing the risk of cross-infection; over-cleaning can damage the surface structure of the endoscope and shorten its service life. Therefore, the technical solution described above can significantly improve the effectiveness and efficiency of endoscope cleaning, ensuring the safety and quality of endoscope use.

[0036] According to an embodiment of the present invention, the further embodiment includes: Through the wireless communication module integrated in the system, the monitoring data is transmitted to the cloud server in real time for storage and management; Medical staff use mobile terminals to log in to the cloud platform through the Internet to view the real-time monitoring data of the endoscope and the operating status of the equipment.

[0037] The system integrates wireless communication modules, such as Wi-Fi, Bluetooth, or 4G / 5G. These modules enable the intelligent endoscope monitoring system to exchange data with the outside world. In actual use, the monitoring system collects various endoscope monitoring data in real time, including disinfectant concentration, temperature field information, and cleaning progress data. This data is transmitted to a cloud server via the wireless communication module. For example, the 4G / 5G communication module offers high-speed and stable data transmission, ensuring timely and accurate transmission of large amounts of monitoring data to the cloud. Even if the endoscope monitoring equipment is located in different locations, remote data transmission is possible as long as there is adequate network coverage. The cloud server is a platform with powerful storage and computing capabilities. Once the monitoring data is transmitted to the cloud server, it will be stored and managed. Cloud storage offers the advantages of large capacity and high reliability, enabling long-term storage of large amounts of monitoring data. Furthermore, the cloud server can analyze and process the data. For example, it can perform statistical analysis on historical monitoring data and generate various reports and charts to help medical staff understand the overall status and trends of endoscope cleaning and disinfection. In addition, the cloud server can analyze real-time monitoring data and, when abnormalities are detected, promptly issue alerts, prompting medical staff to take appropriate measures. Medical staff can access the cloud platform from anywhere with internet access using a mobile device, such as a phone or tablet, equipped with a dedicated application. Once logged in, medical staff can conveniently view the endoscope's real-time monitoring data and device operating status. For example, medical staff can use their mobile device to monitor the endoscope's cleaning and disinfection progress, whether the disinfectant concentration meets the required standards, and whether the temperature field is uniform, anytime and anywhere, from the ward, office, or even at home. If abnormal equipment operation or monitoring data is detected that does not meet requirements, medical staff can take timely intervention, such as adjusting cleaning and disinfection parameters or arranging equipment maintenance. This remote, real-time viewing capability significantly improves medical staff's work efficiency, enabling them to more accurately and promptly monitor endoscope usage. It also facilitates centralized management and oversight of endoscope cleaning and disinfection operations in hospitals, improving medical quality and safety. Furthermore, the long-term storage and analysis of large amounts of monitoring data in the cloud can provide data support for hospital management decisions and help continuously optimize endoscope cleaning and disinfection processes and methods.

[0038] The present invention also discloses an endoscope intelligent monitoring system, comprising a memory and a processor. The memory comprises an endoscope intelligent monitoring method program. When the endoscope intelligent monitoring method program is executed by the processor, the following steps are implemented: Collect endoscope monitoring information for a preset time period, extract disinfectant concentration, temperature field information, cleaning progress data, and endoscope surface contaminant information; Constructing a three-dimensional temperature field model according to the temperature field information; Preprocessing the cleaning progress data; Building a cleaning effect evaluation model based on multi-source data and obtaining evaluation results, and generating control instructions when the evaluation results do not meet the standards; Automatically adjust cleaning and disinfection parameters according to the control instructions, and trigger sound and light alarms or equipment locking; The monitoring data is encrypted and stored with timestamps to generate traceable cleaning and disinfection records.

[0039] First, endoscope monitoring information is collected over a pre-set time period. This process involves the acquisition of multiple key data points, including disinfectant concentration, temperature field information, cleaning progress data, and information on endoscope surface contaminants. Disinfectant concentration information is crucial for ensuring disinfection effectiveness and is directly related to the thoroughness of endoscope disinfection. The temperature field information is then used to construct a three-dimensional temperature field model. By collecting and analyzing temperature data at different spatial locations and utilizing a specific spatial interpolation algorithm, the discrete temperature data is converted into continuous three-dimensional temperature distribution data. This is then visualized using 3D modeling technology. This provides an intuitive understanding of the temperature distribution within the disinfection tank. If the temperature deviates from the set range by exceeding a preset deviation rate, the abnormal area is highlighted in the 3D model, facilitating detection and action by staff. Cleaning progress data is pre-processed after acquisition. This includes extracting cleaning fluid flow rate, pressure, and cleaning time data. A sliding average filter algorithm is applied to the cleaning fluid flow rate and pressure data to remove high-frequency noise and ensure data accuracy. The cleaning time data is also timestamped to ensure accurate time recording at each stage, providing a reliable basis for subsequent, accurate evaluation of the cleaning process. Based on the collected multi-source data, a cleaning effectiveness evaluation model is constructed. This model comprehensively considers influencing factors such as disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and the residual contaminant rate on the endoscope surface. Using a particle swarm optimization algorithm, combined with historical cleaning data and cleaning effectiveness evaluation results, the model determines the weights of each influencing factor and generates the evaluation results. If the evaluation results indicate that the cleaning effectiveness does not meet the standards, the system automatically generates control instructions. Based on these control instructions, the system can automatically adjust cleaning and disinfection parameters, such as adjusting the disinfectant replenishment volume, changing the cleaning fluid flow rate and pressure, and triggering audible and visual alarms to alert staff, or directly lock the equipment to prevent unqualified cleaning and disinfection operations from continuing. In addition, to ensure traceability of the cleaning and disinfection process and ensure medical quality and safety, all monitoring data is encrypted and stored with timestamps, forming traceable cleaning and disinfection records. These records contain key information such as real-time data for each monitoring parameter, the triggering time and specific content of the control instructions, and information on equipment status changes. This facilitates subsequent query and analysis, providing strong support for medical management and quality control.

[0040] According to an embodiment of the present invention, the concentration of the extracted disinfectant is specifically: By establishing a calibration curve between the absorbance and concentration of the disinfectant, the concentration of the disinfectant can be monitored in real time; The calibration curve establishment process includes configuring at least five standard disinfectant samples with different concentration gradients, measuring the absorbance of each sample at a specific wavelength, and obtaining a functional relationship curve between absorbance and concentration based on the measurement data.

[0041] To accurately monitor disinfectant concentration in real time, a calibration curve was established to measure absorbance versus concentration. The construction of this calibration curve follows a rigorous process: first, at least five standard disinfectant samples with varying concentration gradients are prepared. These sample concentrations are differentiated to obtain sufficient data at a sufficient number of different concentrations. Next, the absorbance of each sample is measured at a specific wavelength. This wavelength is scientifically selected to be more sensitive to changes in disinfectant absorbance, resulting in more accurate measurements. After measuring the absorbance of all samples, mathematical analysis is used to fit the measured data to a curve representing the relationship between absorbance and concentration. This curve serves as the calibration curve. During subsequent monitoring, by measuring the real-time absorbance of the disinfectant and applying the established calibration curve, the current disinfectant concentration can be accurately determined, providing critical data support for endoscope disinfection and ensuring that the disinfection effect meets the expected standards.

[0042] According to an embodiment of the present invention, constructing a three-dimensional temperature field model according to the temperature field information is specifically as follows: extracting temperature data at different spatial positions according to the temperature field information; The discrete temperature data is converted into continuous three-dimensional temperature distribution data using a preset spatial interpolation algorithm; The three-dimensional temperature distribution data is visualized based on three-dimensional modeling technology. When the temperature data deviates from the set temperature range by more than a preset deviation rate, the abnormal area is highlighted in the three-dimensional model.

[0043] Monitoring and analyzing the temperature field is crucial for ensuring the effectiveness of endoscope cleaning and disinfection. First, temperature data at different spatial locations is extracted from the collected temperature field information. These data represent discrete samples of the temperature conditions within the disinfection tank. Since comprehensive and continuous temperature distribution is required during the actual disinfection process, a preset spatial interpolation algorithm is used to process these discrete temperature data. Based on the temperature data at known discrete points, this algorithm uses specific mathematical models and calculation methods to reasonably infer the temperature values ​​at other unmeasured locations. This algorithm then converts the discrete temperature data into continuous three-dimensional temperature distribution data, constructing a more complete and accurate temperature field model. Then, using three-dimensional modeling technology, the resulting three-dimensional temperature distribution data is visualized, transforming the abstract temperature data into an intuitive three-dimensional graphical representation. This allows operators to clearly observe the temperature conditions at various locations within the disinfection tank. To ensure that the disinfection process is carried out within appropriate temperature conditions, the system pre-sets a temperature range and deviation rate. If, during monitoring, the temperature data deviates from the set temperature range by more than the preset deviation rate, the system immediately responds by automatically highlighting the abnormal area in the 3D model. This intuitive abnormal prompt method allows operators to quickly detect temperature abnormalities and take corresponding measures to make adjustments in a timely manner, thereby effectively ensuring the quality and safety of endoscope cleaning and disinfection work.

[0044] According to an embodiment of the present invention, the pre-processing of the cleaning progress data is specifically as follows: Extracting cleaning fluid flow and pressure data and cleaning time data according to the cleaning progress data; Using a sliding average filtering algorithm on the cleaning fluid flow and pressure data to remove high-frequency noise; The cleaning time data is timestamped to ensure the accuracy of the time records at each stage.

[0045] Processing cleaning progress data is crucial for accurately evaluating endoscope cleaning effectiveness. In practice, key cleaning fluid flow, pressure, and cleaning time data are first extracted from the collected cleaning progress data. Cleaning fluid flow and pressure data directly reflect the working state of the cleaning fluid during the cleaning process, while cleaning time data is crucial for ensuring that the entire cleaning process complies with standards. However, during data collection, cleaning fluid flow and pressure data are susceptible to interference from various factors, generating high-frequency noise that affects data accuracy and, in turn, interferes with the assessment of the cleaning process. Therefore, a sliding average filter algorithm is used to process cleaning fluid flow and pressure data. This algorithm effectively smooths the data by averaging data within a specific time window, removing high-frequency noise and ensuring that the cleaning fluid flow and pressure data more accurately reflect the actual situation. Furthermore, the accuracy of cleaning time data is crucial. Since the endoscope cleaning process involves multiple stages, the time records of each stage are crucial for analyzing the rationality and integrity of the cleaning process. To ensure the accuracy of the time records for each stage, the cleaning time data requires timestamp alignment. Timestamp alignment is an operation that calibrates and integrates cleaning time data collected at different times according to a unified time base, ensuring that the time data at each stage accurately corresponds to the actual cleaning process, providing a reliable data foundation for subsequent time-based analysis and evaluation.

[0046] According to an embodiment of the present invention, the cleaning effect evaluation model is constructed based on multi-source data and an evaluation result is obtained. When the evaluation result does not meet the standard, a control instruction is generated, specifically: A weighted evaluation model was constructed, which included the factors affecting disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and the residual contaminant rate on the endoscope surface. The particle swarm optimization algorithm is used to determine the weight of each influencing factor based on historical cleaning data and cleaning effect evaluation results; The evaluation result is a cleaning effect score. When the cleaning effect score is lower than a preset effect score threshold, the cleaning effect does not meet the standard and a control instruction is generated.

[0047] To evaluate the effectiveness of endoscope cleaning, a comprehensive weighted evaluation model was constructed to more comprehensively and accurately measure the cleaning process. This model incorporates multiple key influencing factors, including disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and the residual contaminant rate on the endoscope surface. These factors reflect key parameters and conditions during the endoscope cleaning process from different perspectives and have direct or indirect impacts on cleaning effectiveness. To determine the relative importance of each factor in the model, or its weight, a particle swarm optimization algorithm was employed, combining historical cleaning data and cleaning effectiveness evaluation results. Particle swarm optimization is an intelligent optimization algorithm that simulates the foraging behavior of a flock of birds, searching for the optimal solution through continuous iteration. During this process, the algorithm repeatedly adjusts the weights of each factor based on the relationship between each factor and cleaning effectiveness in historical data until an optimal set of weights is found, enabling the model to most accurately reflect actual cleaning effectiveness. The results calculated using this weighted evaluation model are presented as a cleaning effectiveness score. A predefined score threshold is set to determine whether the cleaning effectiveness meets the required standards. If the calculated cleaning effectiveness score falls below this threshold, it indicates that the current cleaning effectiveness is substandard. Once this happens, the system will immediately generate control instructions to adjust the cleaning process, such as automatically adjusting the cleaning and disinfection parameters, or triggering sound and light alarms to prompt staff to take corresponding measures to ensure that the endoscope cleaning can achieve the desired effect and ensure medical safety.

[0048] According to an embodiment of the present invention, the monitoring data is encrypted and stored according to the timestamp to generate a traceable cleaning and disinfection record, specifically: Encrypt data using a preset encryption algorithm; The traceable cleaning and disinfection records include real-time data of various monitoring parameters, triggering time and content of control instructions, and equipment status change information; By scanning the QR code and performing user identity authentication, you can view the cleaning and disinfection records of the corresponding endoscope.

[0049] To ensure data security and privacy, monitoring data is encrypted using a preset encryption algorithm. This carefully selected and configured encryption algorithm converts raw data into ciphertext for storage, effectively preventing unauthorized theft or tampering during storage and transmission. The traceable cleaning and disinfection records generated by the system contain rich and critical information, including real-time data on various monitoring parameters, such as disinfectant concentration, temperature field, and cleaning fluid flow and pressure. These data accurately record the changes in various indicators during the endoscope cleaning and disinfection process. They also include the triggering time and content of control instructions, detailing the operational decisions made by the system when abnormalities are detected or adjustments are required during the cleaning and disinfection process. Furthermore, device status change information is used to record changes in the device's operating status throughout the entire process. To facilitate querying and managing these important records, the system supports scanning QR codes to access the cleaning and disinfection records for corresponding endoscopes. However, to ensure data security and privacy, users must undergo identity authentication before viewing the records. Only authenticated users can access and view the detailed cleaning and disinfection records of the corresponding endoscope. This process not only ensures convenient data query but also prevents unauthorized access, thereby effectively improving the security and standardization of medical data management.

[0050] According to an embodiment of the present invention, the further embodiment includes: Using a deep learning algorithm to process the collected information on contaminants on the endoscope surface to identify specific types of stains on the endoscope surface; Automatically select the best cleaning strategy from the preset cleaning strategy library based on the type of stain, and adjust the cleaning solution formula, cleaning time or cleaning intensity.

[0051] During actual endoscope use, various types of stains, such as blood, mucus, and tissue fragments, remain on the surface. These stains vary in composition and adhesion characteristics, and therefore require varying cleaning requirements. Traditional monitoring methods may only detect the presence of contaminants but cannot accurately distinguish the specific type of stain. To address this issue, the present invention utilizes deep learning algorithms, specifically convolutional neural networks (CNNs). CNNs are specialized for processing grid-structured data and are well-suited for processing image data. The present invention first collects a large amount of endoscope surface image data labeled with different types of stains to construct a rich training dataset. This image data should cover a wide range of common stain types, as well as varying degrees and distributions. The CNN model is then trained using this training dataset. During training, the model automatically learns the characteristics and patterns of stains in the images, enabling it to accurately identify different types of stains. For example, blood stains may have specific color and texture characteristics, mucus may appear transparent or translucent and viscous, and tissue fragments may have irregular shapes and textures. By continuously adjusting the model's parameters, the model can accurately identify the type of stain in new endoscope surface images. After accurately identifying the type of stain on the endoscope surface, the system automatically selects the optimal cleaning strategy based on a pre-set cleaning strategy library. This pre-established cleaning strategy library contains optimal cleaning solutions for different types of stains, developed based on extensive experimentation and practical experience. For example, if the stain on the endoscope surface is identified as blood, the system may select a cleaning solution with strong decontamination capabilities, appropriately extend the cleaning time, and increase the intensity of the cleaning process to ensure complete removal of the blood stain. For mucus stains, the system may select a cleaning solution with specific solubility properties and adjust the water flow rate and direction to better remove the mucus. In this way, the system can precisely clean different types of stains, avoiding the problems of incomplete or over-cleaning that can occur with traditional cleaning methods that use a uniform cleaning strategy. Incomplete cleaning can leave bacteria and viruses on the endoscope surface, increasing the risk of cross-infection; over-cleaning can damage the surface structure of the endoscope and shorten its service life. Therefore, the technical solution described above can significantly improve the effectiveness and efficiency of endoscope cleaning, ensuring the safety and quality of endoscope use.

[0052] According to an embodiment of the present invention, the further embodiment includes: Through the wireless communication module integrated in the system, the monitoring data is transmitted to the cloud server in real time for storage and management; Medical staff use mobile terminals to log in to the cloud platform through the Internet to view the real-time monitoring data of the endoscope and the operating status of the equipment.

[0053] The system integrates wireless communication modules, such as Wi-Fi, Bluetooth, or 4G / 5G. These modules enable the intelligent endoscope monitoring system to exchange data with the outside world. In actual use, the monitoring system collects various endoscope monitoring data in real time, including disinfectant concentration, temperature field information, and cleaning progress data. This data is transmitted to a cloud server via the wireless communication module. For example, the 4G / 5G communication module offers high-speed and stable data transmission, ensuring timely and accurate transmission of large amounts of monitoring data to the cloud. Even if the endoscope monitoring equipment is located in different locations, remote data transmission is possible as long as there is adequate network coverage. The cloud server is a platform with powerful storage and computing capabilities. Once the monitoring data is transmitted to the cloud server, it will be stored and managed. Cloud storage offers the advantages of large capacity and high reliability, enabling long-term storage of large amounts of monitoring data. Furthermore, the cloud server can analyze and process the data. For example, it can perform statistical analysis on historical monitoring data and generate various reports and charts to help medical staff understand the overall status and trends of endoscope cleaning and disinfection. In addition, the cloud server can analyze real-time monitoring data and, when abnormalities are detected, promptly issue alerts, prompting medical staff to take appropriate measures. Medical staff can access the cloud platform from anywhere with internet access using a mobile device, such as a phone or tablet, equipped with a dedicated application. Once logged in, medical staff can conveniently view the endoscope's real-time monitoring data and device operating status. For example, medical staff can use their mobile device to monitor the endoscope's cleaning and disinfection progress, whether the disinfectant concentration meets the required standards, and whether the temperature field is uniform, anytime and anywhere, from the ward, office, or even at home. If abnormal equipment operation or monitoring data is detected that does not meet requirements, medical staff can take timely intervention, such as adjusting cleaning and disinfection parameters or arranging equipment maintenance. This remote, real-time viewing capability significantly improves medical staff's work efficiency, enabling them to more accurately and promptly monitor endoscope usage. It also facilitates centralized management and oversight of endoscope cleaning and disinfection operations in hospitals, improving medical quality and safety. Furthermore, the long-term storage and analysis of large amounts of monitoring data in the cloud can provide data support for hospital management decisions and help continuously optimize endoscope cleaning and disinfection processes and methods.

[0054] The third aspect of the present invention provides a computer-readable storage medium, which includes an endoscopic intelligent monitoring method program. When the endoscopic intelligent monitoring method program is executed by a processor, it implements the steps of an endoscopic intelligent monitoring method as described in any one of the above items.

[0055] The present invention discloses an intelligent endoscope monitoring method, system, and medium designed to comprehensively and accurately monitor the endoscope cleaning and disinfection process, ensuring medical safety and quality while also improving data management efficiency and remote monitoring capabilities. The method encompasses a multi-step process. First, endoscope monitoring information is collected during a preset time period, including disinfectant concentration, temperature field information, cleaning progress data, and information on endoscope surface contaminants. Disinfectant concentration is monitored in real time by establishing a calibration curve based on absorbance measurements of multiple standard samples of varying concentrations. A three-dimensional temperature field model is constructed using this temperature field information. Temperature data at different spatial locations is first extracted and then converted into continuous three-dimensional temperature distribution data using a preset spatial interpolation algorithm. This data is then visualized using three-dimensional modeling, with abnormal temperatures highlighted within the model. For the cleaning progress data, cleaning fluid flow rate, pressure, and cleaning time data are extracted. A sliding average filter algorithm is used to remove high-frequency noise from the flow and pressure data, and the cleaning time data is timestamped to ensure data accuracy. A cleaning effectiveness evaluation model is constructed based on multi-source data, comprehensively considering influencing factors such as disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and residual contaminant rate on the endoscope surface. A particle swarm optimization algorithm, combined with historical data, is used to determine the weights of each factor. The evaluation results are presented as a cleaning effectiveness score. When the score falls below a preset threshold, control instructions are generated to automatically adjust cleaning and disinfection parameters, trigger audible and visual alarms, or lock the device. Monitoring data is encrypted and stored with a timestamp, using a pre-set encryption algorithm for security. Traceable cleaning and disinfection records contain key information and can be accessed by scanning a QR code and performing user authentication. Furthermore, a convolutional neural network, part of a deep learning algorithm, processes information about endoscope surface contaminants, identifies specific stain types, and matches the optimal cleaning strategy from a pre-set cleaning strategy library, improving cleaning targeting and effectiveness. Furthermore, an integrated wireless communication module transmits monitoring data in real time to a cloud server for storage and management. Medical staff can log in to the cloud platform using a mobile terminal and remotely view real-time endoscope monitoring data and device operating status, facilitating centralized management and timely intervention, providing data support for hospital management decisions.

[0056] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0057] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0058] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0059] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0060] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. An intelligent endoscope monitoring method, characterized in that: The following steps are involved: Collect endoscope monitoring information for a preset time period, extract disinfectant concentration, temperature field information, cleaning progress data, and endoscope surface contaminant information; Constructing a three-dimensional temperature field model according to the temperature field information; Preprocessing the cleaning progress data; Building a cleaning effect evaluation model based on multi-source data and obtaining evaluation results, and generating control instructions when the evaluation results do not meet the standards; Automatically adjust cleaning and disinfection parameters according to the control instructions, and trigger sound and light alarms or equipment locking; The monitoring data is encrypted and stored with timestamps to generate traceable cleaning and disinfection records.

2. The intelligent endoscope monitoring method according to claim 1, characterized in that: The concentration of the extracted disinfectant is specifically: By establishing a calibration curve between the absorbance and concentration of the disinfectant, the concentration of the disinfectant can be monitored in real time; The calibration curve establishment process includes configuring at least five standard disinfectant samples with different concentration gradients, measuring the absorbance of each sample at a specific wavelength, and obtaining a functional relationship curve between absorbance and concentration based on the measurement data.

3. The intelligent endoscope monitoring method according to claim 1, characterized in that: The three-dimensional temperature field model is constructed according to the temperature field information, specifically: extracting temperature data at different spatial positions according to the temperature field information; The discrete temperature data is converted into continuous three-dimensional temperature distribution data using a preset spatial interpolation algorithm; The three-dimensional temperature distribution data is visualized based on three-dimensional modeling technology. When the temperature data deviates from the set temperature range by more than a preset deviation rate, the abnormal area is highlighted in the three-dimensional model.

4. The intelligent endoscope monitoring method according to claim 1, characterized in that: The pre-processing of the cleaning progress data is specifically as follows: Extracting cleaning fluid flow and pressure data and cleaning time data according to the cleaning progress data; Using a sliding average filtering algorithm on the cleaning fluid flow and pressure data to remove high-frequency noise; The cleaning time data is timestamped to ensure the accuracy of the time records at each stage.

5. The intelligent endoscope monitoring method according to claim 1, characterized in that: The cleaning effect evaluation model is constructed based on multi-source data and an evaluation result is obtained. When the evaluation result does not meet the standard, a control instruction is generated, specifically: A weighted evaluation model was constructed, which included the factors affecting disinfectant concentration, temperature uniformity, cleaning fluid flow stability, and the residual contaminant rate on the endoscope surface. The particle swarm optimization algorithm is used to determine the weight of each influencing factor based on historical cleaning data and cleaning effect evaluation results; The evaluation result is a cleaning effect score. When the cleaning effect score is lower than a preset effect score threshold, the cleaning effect does not meet the standard and a control instruction is generated.

6. The intelligent endoscope monitoring method according to claim 1, characterized in that: The monitoring data is encrypted and stored according to the timestamp to generate traceable cleaning and disinfection records, specifically: Encrypt data using a preset encryption algorithm; The traceable cleaning and disinfection records include real-time data of various monitoring parameters, triggering time and content of control instructions, and equipment status change information; By scanning the QR code and performing user identity authentication, you can view the cleaning and disinfection records of the corresponding endoscope.

7. An intelligent endoscope monitoring system, characterized in that: The system comprises a memory and a processor, wherein the memory comprises an endoscopic intelligent monitoring method program, and when the endoscopic intelligent monitoring method program is executed by the processor, the following steps are implemented, specifically: Collect endoscope monitoring information for a preset time period, extract disinfectant concentration, temperature field information, cleaning progress data, and endoscope surface contaminant information; Constructing a three-dimensional temperature field model according to the temperature field information; Preprocessing the cleaning progress data; Building a cleaning effect evaluation model based on multi-source data and obtaining evaluation results, and generating control instructions when the evaluation results do not meet the standards; Automatically adjust cleaning and disinfection parameters according to the control instructions, and trigger sound and light alarms or equipment locking; The monitoring data is encrypted and stored with timestamps to generate traceable cleaning and disinfection records.

8. The intelligent endoscope monitoring system according to claim 7, characterized in that: The concentration of the extracted disinfectant is as follows: By establishing a calibration curve between the absorbance and concentration of the disinfectant, the concentration of the disinfectant can be monitored in real time; The calibration curve establishment process includes configuring at least five standard disinfectant samples with different concentration gradients, measuring the absorbance of each sample at a specific wavelength, and obtaining a functional relationship curve between absorbance and concentration based on the measurement data.

9. The intelligent endoscope monitoring system according to claim 7, characterized in that: The three-dimensional temperature field model is constructed according to the temperature field information, specifically: extracting temperature data at different spatial positions according to the temperature field information; The discrete temperature data is converted into continuous three-dimensional temperature distribution data using a preset spatial interpolation algorithm; The three-dimensional temperature distribution data is visualized based on three-dimensional modeling technology. When the temperature data deviates from the set temperature range by more than a preset deviation rate, the abnormal area is highlighted in the three-dimensional model.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes an endoscopic intelligent monitoring method, system and medium program. When the endoscopic intelligent monitoring method, system and medium program are executed by the processor, the steps of the endoscopic intelligent monitoring method as described in any one of claims 1 to 6 are implemented.

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