Intelligent monitoring management method and system for disinfection supply center

Abstract

The present application provides an intelligent monitoring management method and system for a disinfection supply center, which relates to the field of intelligent monitoring management technology. The method includes: obtaining a first disinfection difficulty parameter through disinfection difficulty analysis; identifying surface wear parameters, predicting the formation of biofilms, and correcting the first disinfection difficulty parameter; making a decreasing prediction of equipment aging parameters; analyzing and obtaining disinfection attenuation parameters based on decreasing equipment aging parameters, and correcting the second disinfection difficulty parameter; predicting the wear coefficient after disinfection, correcting the surface wear parameters, obtaining corrected wear parameters, and combining the third disinfection difficulty parameter to obtain sampling monitoring parameters for sampling monitoring and management. The present application can solve the technical problem in the prior art that the impact of disinfection equipment characteristics and contamination characteristics on equipment is not considered, resulting in the inability to prevent disinfection risks, achieve the technical goal of monitoring the impact of disinfection equipment on equipment, and achieve the technical effect of predicting and preventing disinfection risks.

Description

Technical Field

[0001] The present application relates to the field of intelligent monitoring and management technology, and in particular to an intelligent monitoring and management method and system for a disinfection supply center. Background Art

[0002] The monitoring and management of the sterilization supply center ensures that medical devices remain sterile during the processing and sterilization process, ensuring the safety of medical devices.

[0003] At present, in the existing monitoring and management process of the disinfection supply center, if the aging degree, performance status and other characteristics of the disinfection equipment are not monitored, it is impossible to accurately evaluate the impact of the equipment on the disinfection effect, resulting in unsatisfactory disinfection effect when using equipment with poor performance. If the probability of incomplete disinfection is not analyzed, it is impossible to predict and prevent the potential risk of disinfection failure. The degree of contamination will change over time, such as increased contamination due to seasonal factors or specific events. If random changes are not taken into account, the disinfection procedure cannot be adjusted in time to cope with changes in the degree of contamination. Biofilm is a common microbial community on the surface of medical devices, which has a significant impact on the disinfection process. Ignoring the analysis of biofilms leads to disinfection strategies that are not sufficient to eliminate biofilms, thereby affecting the disinfection effect.

[0004] In summary, there are technical problems in the prior art that most of them fail to fully consider the impact of factors such as disinfection equipment characteristics and pollution characteristics on disinfection equipment, resulting in the inability to accurately assess the impact of the equipment on the disinfection effect, making it impossible to predict and prevent potential risks of disinfection failure, further affecting disinfection safety. Summary of the invention

[0005] The purpose of this application is to provide an intelligent monitoring and management method and system for a disinfection supply center, so as to solve the technical problem that most of the existing technologies fail to fully consider the impact of factors such as disinfection equipment characteristics and contamination characteristics on disinfection equipment, resulting in the inability to accurately assess the impact of equipment on the disinfection effect, resulting in the inability to predict and prevent potential disinfection failure risks, further affecting disinfection safety.

[0006] In view of the above problems, the present application provides an intelligent monitoring and management method and system for a disinfection supply center.

[0007] In a first aspect, the present application provides an intelligent monitoring and management method for a disinfection supply center, which is implemented by an intelligent monitoring and management system for a disinfection supply center, wherein the method includes: collecting device feature information and traceability feature information of medical devices to be disinfected in the current disinfection supply center, obtaining a set of device feature information and traceability feature information, performing a disinfection difficulty analysis on the medical devices, and obtaining a first disinfection difficulty parameter; identifying surface wear parameters of the medical devices, combining the device feature information and traceability feature information set, predicting the formation of biofilms on the medical devices, obtaining biofilm prediction results, correcting the first disinfection difficulty parameter, and obtaining a second disinfection difficulty parameter; obtaining a set of the device feature information and traceability feature information for the medical devices. The equipment aging parameters of the disinfection equipment used to disinfect the medical device are predicted decreasingly according to the equipment characteristic information and the traceability characteristic information set to obtain the decreasing equipment aging parameters; according to the decreasing equipment aging parameters, a disinfection attenuation parameter is obtained by analysis, and the second disinfection difficulty parameter is corrected to obtain a third disinfection difficulty parameter; based on the decreasing equipment aging parameters, the wear coefficient of the medical device after disinfection is predicted, the surface wear parameter is corrected to obtain a corrected wear parameter, and in combination with the third disinfection difficulty parameter, a sampling monitoring parameter for sampling monitoring the quality of the disinfection device after the disinfection of the medical device is completed is analyzed and obtained, and sampling monitoring and management of the disinfected medical device are performed.

[0008] In the second aspect, the present application also provides an intelligent monitoring and management system for a disinfection supply center, which is used to execute the intelligent monitoring and management method for a disinfection supply center as described in the first aspect, wherein the system includes: a first disinfection difficulty parameter acquisition module, the first disinfection difficulty parameter acquisition module is used to collect the device characteristic information and traceability characteristic information of the medical devices to be disinfected in the current disinfection supply center, obtain the device characteristic information and the traceability characteristic information set, perform disinfection difficulty analysis on the medical devices, and obtain a first disinfection difficulty parameter; a second disinfection difficulty parameter acquisition module, the second disinfection difficulty parameter acquisition module is used to identify the surface wear parameters of the medical devices, combine the device characteristic information and the traceability characteristic information set, predict the formation of the biofilm of the medical devices, obtain the biofilm prediction result, correct the first disinfection difficulty parameter, and obtain the second disinfection difficulty parameter; a decreasing equipment aging parameter acquisition module The decreasing equipment aging parameter acquisition module is used to obtain the equipment aging parameters of the disinfection equipment for disinfecting the medical device, and perform decreasing prediction on the equipment aging parameters according to the equipment feature information and the traceability feature information set to obtain the decreasing equipment aging parameters; the third disinfection difficulty parameter acquisition module is used to analyze and obtain the disinfection attenuation parameter according to the decreasing equipment aging parameters, and correct the second disinfection difficulty parameter to obtain the third disinfection difficulty parameter; the sampling monitoring management module is used to predict the wear coefficient of the medical device after disinfection based on the decreasing equipment aging parameters, correct the surface wear parameters to obtain the corrected wear parameters, and analyze and obtain the sampling monitoring parameters for sampling monitoring the quality of the disinfection equipment after the disinfection of the medical device is completed in combination with the third disinfection difficulty parameter, and perform sampling monitoring and management on the disinfected medical devices.

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

[0010] By collecting the device characteristic information and traceability characteristic information of the medical devices to be disinfected in the current disinfection supply center, obtaining the device characteristic information and traceability characteristic information set, performing disinfection difficulty analysis on the medical devices, and obtaining a first disinfection difficulty parameter; identifying the surface wear parameters of the medical devices, combining the device characteristic information and traceability characteristic information set, predicting the formation of biofilms on the medical devices, obtaining biofilm prediction results, correcting the first disinfection difficulty parameter, and obtaining a second disinfection difficulty parameter; obtaining the equipment aging parameters of the disinfection equipment used to disinfect the medical devices, and performing an aging analysis on the equipment according to the device characteristic information and traceability characteristic information set. Perform a decreasing prediction to obtain a decreasing equipment aging parameter; according to the decreasing equipment aging parameter, analyze and obtain a disinfection attenuation parameter, correct the second disinfection difficulty parameter, and obtain a third disinfection difficulty parameter; based on the decreasing equipment aging parameter, predict the wear coefficient of the medical device after disinfection, correct the surface wear parameter, obtain a corrected wear parameter, and analyze and obtain a sampling monitoring parameter for sampling monitoring the quality of the disinfection device after the disinfection of the medical device is completed in combination with the third disinfection difficulty parameter, perform sampling monitoring and management on the disinfected medical devices, achieve the technical goal of comprehensively monitoring the impact of disinfection equipment on the device, and achieve the technical effect of predicting and preventing disinfection risks.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0013] Figure 1 This is a flow chart of the intelligent monitoring and management method for a disinfection supply center used in this application;

[0014] Figure 2 This is a schematic diagram of the structure of the intelligent monitoring and management system used in the disinfection supply center in this application.

[0015] Description of reference numerals:

[0016] A first disinfection difficulty parameter obtaining module 11, a second disinfection difficulty parameter obtaining module 12, a decreasing equipment aging parameter obtaining module 13, a third disinfection difficulty parameter obtaining module 14, and a sampling monitoring management module 15. DETAILED DESCRIPTION

[0017] This application provides an intelligent monitoring management method and system for a disinfection supply center, which solves the technical problem that most existing technologies fail to fully consider the impact of factors such as disinfection equipment characteristics and contamination characteristics on disinfection equipment, resulting in the inability to accurately assess the impact of equipment on disinfection effects, resulting in the inability to predict and prevent potential disinfection failure risks, and further affecting disinfection safety. The technical goal of comprehensively monitoring the impact of disinfection equipment on equipment is achieved, and the technical effect of predicting and preventing disinfection risks is achieved.

[0018] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them.

[0019] For example, please refer to the attached Figure 1 The present application provides an intelligent monitoring and management method for a disinfection supply center, wherein the method is applied to an intelligent monitoring and management system for a disinfection supply center, and the method specifically includes the following steps:

[0020] Step 1: Collect the device feature information and traceability feature information of the medical devices to be disinfected in the current disinfection supply center, obtain a set of the device feature information and traceability feature information, perform disinfection difficulty analysis on the medical devices, and obtain a first disinfection difficulty parameter.

[0021] Specifically, medical device characteristics refer to the structural characteristics of medical devices, including the type, specification, material, frequency of use, and complexity of medical devices. Traceability characteristics include hospital, department, and pollution characteristics (such as type of surgery or environmental pollution during use). The device characteristic information and traceability characteristic information of medical devices to be disinfected in the current disinfection supply center are collected, the disinfection difficulty of medical devices is analyzed, and the first disinfection difficulty parameter is obtained.

[0022] Step 2: Identify the surface wear parameters of the medical device, combine the device feature information and the traceability feature information set, predict the formation of the biofilm of the medical device, obtain the biofilm prediction result, correct the first disinfection difficulty parameter, and obtain the second disinfection difficulty parameter.

[0023] Specifically, for example, surface wear includes the number and depth of scratches on the surface of the device, which leads to the accommodation of bacteria and viruses, thereby increasing the formation of biofilms, affecting disinfection, and then correcting the disinfection difficulty parameters. Use image recognition to analyze the surface image of the medical device, identify the degree of wear on the surface of the device, and thus obtain the surface wear parameters. Combine the identified surface wear parameters with the characteristic information of the device (such as type, material, frequency of use, etc.) and traceability characteristic information (such as the department of use, degree of contamination, history of use, etc.) to form a more comprehensive information set. Use machine learning models to predict the possibility of biofilm formation on medical devices. Biofilm is a microbial community that forms a protective film on the surface of medical devices, which has a significant impact on the disinfection process. According to the biofilm prediction results, the first disinfection difficulty parameter is corrected to obtain the second disinfection difficulty parameter.

[0024] Step three: Obtain equipment aging parameters of the disinfection equipment used to disinfect the medical device, perform a decreasing prediction on the equipment aging parameters based on the equipment characteristic information and the traceability characteristic information set, and obtain the decreasing equipment aging parameters.

[0025] Specifically, each time the device is disinfected, the aging degree of the device will increase. The current device aging parameters are collected, and based on the device feature information and traceability feature information set of the current device, the decreasing device aging parameters after the actual change during the disinfection of the current device are predicted. The decreasing device aging parameters can be used to adjust the maintenance plan of the disinfection equipment, optimize the disinfection process, ensure the disinfection effect, and extend the service life of the equipment. For example, if the prediction shows that the performance of the equipment may deteriorate, maintenance or replacement of parts can be arranged in advance. The equipment used for disinfection is tested to assess the aging status, including the age of the equipment, the number of operating hours, the most recent maintenance time, the degree of wear of key components, etc.

[0026] Step 4: Analyze and obtain the disinfection attenuation parameter based on the decreasing equipment aging parameter, and correct the second disinfection difficulty parameter to obtain a third disinfection difficulty parameter.

[0027] Specifically, based on the equipment aging parameters, the attenuation of the equipment disinfection quality is analyzed and the disinfection difficulty parameters are corrected to more accurately reflect the impact of equipment aging on the disinfection difficulty, thereby obtaining the third disinfection difficulty parameter.

[0028] Step 5: Based on the decreasing equipment aging parameters, predict the wear coefficient of the medical device after disinfection, correct the surface wear parameters to obtain corrected wear parameters, and combine with the third disinfection difficulty parameter to analyze and obtain sampling monitoring parameters for sampling monitoring of the quality of the disinfection equipment after the medical device is disinfected, and perform sampling monitoring and management on the disinfected medical devices.

[0029] Specifically, the decreasing equipment aging parameter refers to the movement and disinfection of the instrument during disinfection, which will cause wear to the instrument. The wear coefficient is analyzed to correct the surface wear parameters, and the corrected wear parameters are obtained. Combined with the third disinfection difficulty parameter, random inspections of the quality after disinfection are carried out, and management is carried out according to the inspection results, such as secondary disinfection, repair or scrapping, to improve the disinfection quality of the instrument.

[0030] The intelligent monitoring and management method for a disinfection supply center is applied to an intelligent monitoring and management system for a disinfection supply center, which can achieve the technical goal of comprehensively monitoring the impact of disinfection equipment on equipment and achieve the technical effect of predicting and preventing disinfection risks.

[0031] Furthermore, the present application also includes:

[0032] Collect device characteristic information of medical devices to be disinfected in the current disinfection supply center, wherein the medical device is a medical device; collect hospital information, department information and contamination characteristic information of the medical device as traceability characteristic information to obtain a traceability characteristic information set; perform disinfection difficulty analysis on the medical device based on the device characteristic information and the traceability characteristic information set to obtain a first disinfection difficulty parameter.

[0033] Specifically, the current sterilization supply center includes multiple batches of medical devices. Collecting device characteristic information of the medical devices to be sterilized in the current sterilization supply center, such as the type, specification, material, etc. of the devices, is helpful in determining the sterilization method and technical parameters. Among them, medical devices are a type of medical device.

[0034] Then, the traceability characteristic information of the medical device is collected, including hospital information, department information and pollution characteristic information. For example, the pollution characteristic information includes the degree of pollution, type of pollutants, etc., which is used to track the use history of the medical device and formulate disinfection strategies.

[0035] Next, using the collected device feature information and traceability feature information set, the preset machine learning algorithm is used to analyze the disinfection difficulty of each medical device, including factors such as the complexity of the medical device, the degree of contamination, and the applicability of the disinfectant. Based on the results of the disinfection difficulty analysis, a first disinfection difficulty parameter is generated for each medical device to help disinfection personnel select appropriate disinfection procedures and parameters to ensure the disinfection effect.

[0036] By improving the efficiency and quality of disinfection supply centers and reducing human errors, the automated data collection and analysis can provide decision support and process improvement basis for hospital management.

[0037] Furthermore, the present application also includes:

[0038] Based on the historical disinfection data of the disinfection supply center, multiple sample instrument feature information of multiple medical devices is obtained, and multiple sample traceability feature information sets of multiple devices are collected; based on the disinfection result data after disinfection, multiple sample first disinfection difficulty parameter sets are collected; the multiple sample traceability feature information sets and the multiple sample first disinfection difficulty parameter sets are respectively used to train multiple disinfection difficulty parameter prediction branches to obtain a disinfection difficulty predictor; based on the instrument feature information, the traceability feature information sets are respectively input into the disinfection difficulty predictor to predict multiple basic disinfection difficulty parameters, and the first disinfection difficulty parameter is calculated.

[0039] Specifically, historical disinfection data of the disinfection supply center is collected, including multiple sample device feature information (such as device type, material, etc.) of various medical devices and corresponding multiple sample traceability feature information (such as sample hospital information, sample department information, sample contamination feature information, etc.). Based on the historical disinfection data, the disinfection result data after disinfection is collected, and the first disinfection difficulty parameter set of multiple randomly obtained samples is extracted.

[0040] Then, using the collected multiple sample traceability feature information sets and multiple sample first disinfection difficulty parameter sets as input features, multiple disinfection difficulty parameter prediction branches are trained through a machine learning algorithm, and the disinfection difficulty is predicted based on the input feature information. If the output features of multiple disinfection difficulty parameter prediction branches tend to be stable, the output accuracy of multiple disinfection difficulty parameter prediction branches is extracted to determine whether the output accuracy threshold is satisfied. If the output accuracy threshold is satisfied, the training of multiple disinfection difficulty parameter prediction branches is completed, and multiple disinfection difficulty parameter prediction branches are integrated to obtain a disinfection difficulty predictor.

[0041] Next, based on the device characteristic information, the corresponding medical device is obtained, and the traceability characteristic information sets collected according to the corresponding medical device are respectively input into the disinfection difficulty predictor to predict and obtain multiple basic disinfection difficulty parameters, and the first disinfection difficulty parameter of the corresponding medical device is calculated.

[0042] Historical disinfection data is used to predict and optimize future disinfection processes, thereby improving disinfection efficiency and accuracy. Through machine learning, the predictive capabilities are continuously improved, allowing the disinfection supply center to operate more efficiently.

[0043] Furthermore, the present application also includes:

[0044] Collect surface images of the medical device to obtain a set of device surface images; based on the surface recognition history of the medical device, collect a set of sample device surface images and a set of sample device surface wear parameters, and use an image recognition network to train a device surface wear identifier; perform wear recognition on the device surface image set according to the device surface wear identifier to obtain a set of device surface wear parameters, and calculate the surface wear parameters; predict the formation of a biofilm of the medical device according to the surface wear parameters, device feature information, and traceability feature information set to obtain a biofilm prediction result, wherein the biofilm prediction result includes a biofilm disinfection influence coefficient; based on the biofilm disinfection influence coefficient, correct the first disinfection difficulty parameter to obtain a second disinfection difficulty parameter.

[0045] Specifically, an image acquisition device is used to obtain a surface image of the medical device to be sterilized, thereby obtaining a set of device surface images.

[0046] Then, based on the surface recognition history of the medical device, that is, the surface image acquisition results of the historical time, a set of sample device surface images and a set of sample device surface wear parameters are randomly acquired, and a device surface wear identifier is trained through an image recognition network (such as a convolutional neural network). The device surface wear identifier can predict the degree of wear of the device by analyzing the surface image of the medical device.

[0047] Next, the trained instrument surface wear identifier is used to analyze the instrument surface image set, identify the wear condition, and generate surface wear parameters.

[0048] Next, the surface wear parameters, device feature information, and traceability feature information are combined to predict biofilm formation. The possibility of biofilm formation is predicted based on factors such as the degree of surface wear, usage history, and contamination characteristics of the medical device. The biofilm prediction results include the biofilm disinfection influence coefficient, which indicates the degree of influence of the biofilm on the difficulty of disinfection.

[0049] Then, according to the biofilm disinfection influence coefficient, the first disinfection difficulty parameter is corrected to obtain the second disinfection difficulty parameter. Among them, the higher the biofilm disinfection influence coefficient, the more surface wear generated by biofilm disinfection, resulting in higher disinfection difficulty, and vice versa. The second disinfection difficulty parameter takes into account the actual situation of the instrument more comprehensively, including surface wear and biofilm formation, so as to guide the disinfection process more accurately.

[0050] By combining image recognition technology and machine learning algorithms, the difficulty of disinfection of medical devices can be assessed more accurately, and the disinfection strategy can be adjusted according to the specific conditions of the devices to improve the disinfection effect.

[0051] Furthermore, the present application also includes:

[0052] Perform aging detection on the disinfection equipment used to disinfect the medical device to obtain equipment aging parameters; based on the historical disinfection data set of the disinfection equipment, collect a sample equipment aging parameter set, a sample equipment feature information set, multiple sample traceability feature information sets and a sample decreasing equipment aging parameter set to train an aging decrease predictor; based on the aging decrease predictor, perform equipment aging decrease prediction on the equipment aging parameters, equipment feature information and traceability feature information set to obtain decreasing equipment aging parameters.

[0053] Specifically, disinfection equipment is regularly inspected to assess the degree of aging, including the measurement of various performance indicators of the equipment (such as temperature, pressure, chemical composition, etc.), as well as the monitoring of the equipment's operating status. Based on the test results, parameters describing the aging status of the disinfection equipment are obtained, including the equipment's service life, maintenance history, and wear of key components.

[0054] Then, a sample equipment aging parameter set, a sample instrument feature information set, a plurality of sample traceability feature information sets and a sample decreasing equipment aging parameter set are collected from the historical disinfection data set of the disinfection equipment for training the aging decrease predictor. The collected historical disinfection data set is used to train the aging decrease predictor through a machine learning algorithm, and the trend of equipment aging and the decrease of equipment aging are predicted according to the current equipment aging parameters, instrument feature information and traceability feature information, and the decreasing equipment aging parameters are obtained.

[0055] Subsequently, the decremental equipment aging parameters can be used to adjust the maintenance plan of the disinfection equipment, optimize the disinfection process, ensure the disinfection effect, and extend the service life of the equipment.

[0056] The disinfection supply center can better manage disinfection equipment, predict and respond to problems that may arise from equipment aging, thereby improving disinfection efficiency and safety, reducing maintenance costs, and ensuring the disinfection quality of medical devices.

[0057] Furthermore, the present application also includes:

[0058] Based on the disinfection quality data records of the disinfection equipment at different aging stages, a sample equipment aging parameter set and a sample disinfection attenuation parameter set are collected to construct an index table; the disinfection attenuation parameter is obtained according to the decreasing equipment aging parameter index, and the second disinfection difficulty parameter is corrected to obtain a third disinfection difficulty parameter.

[0059] Specifically, based on the disinfection quality data records of the disinfection equipment at different aging stages, a set of sample equipment aging parameters is collected, including the service life of the equipment, the number of maintenance times, and the replacement history of key components. A set of sample disinfection attenuation parameters is collected to reflect the decline in the disinfection effect of the disinfection equipment during the aging process. Using the collected set of sample equipment aging parameters and the set of sample disinfection attenuation parameters, an index table is constructed to associate the set of sample equipment aging parameters with the set of sample disinfection attenuation parameters, which is used to predict the attenuation of the disinfection effect according to the aging status of the equipment.

[0060] Then, the aging status of the current disinfection equipment is evaluated to obtain the decreasing equipment aging parameters.

[0061] Next, according to the obtained decreasing equipment aging parameter, the corresponding disinfection attenuation parameter is found through the index table, which describes the degree of reduction in disinfection effect due to equipment aging. Using the obtained disinfection attenuation parameter, the calculated second disinfection difficulty parameter is corrected to obtain the third disinfection difficulty parameter, which more comprehensively considers the impact of equipment aging and disinfection effect attenuation on disinfection difficulty.

[0062] The disinfection supply center can more accurately assess the difficulty of disinfection and take appropriate measures to ensure the quality of disinfection, which helps to improve disinfection efficiency, reduce errors, and ensure patient safety.

[0063] Furthermore, the present application also includes:

[0064] Based on the wear record data of medical devices disinfected at different aging stages of the disinfection equipment, a set of sample equipment aging parameters and a set of sample wear coefficients are collected, and an equipment disinfection wear analyzer is trained to predict the decreasing equipment aging parameters to obtain the wear coefficient; based on the wear coefficient, the surface wear parameters are corrected to obtain the corrected wear parameters; based on the corrected wear parameters and the third disinfection difficulty parameter, a decision analysis is performed on the sampling monitoring after the disinfection of the medical device is completed to obtain the sampling monitoring parameters.

[0065] Specifically, based on the wear record data of the disinfection equipment when disinfecting medical devices at different aging stages, a set of sample equipment aging parameters and a set of sample wear coefficients are collected to train the instrument disinfection wear analyzer, which can predict the wear of the instrument during the disinfection process based on the aging parameters of the equipment. The instrument disinfection wear analyzer is used to predict the current decreasing equipment aging parameters to obtain the wear coefficient, which reflects the degree of instrument wear caused by equipment aging.

[0066] Then, the identified surface wear parameters are corrected according to the predicted wear coefficient to obtain the corrected wear parameters, which more accurately describe the actual wear of the instrument during the sterilization process.

[0067] Next, combined with the modified wear parameter and the third disinfection difficulty parameter, a decision analysis is performed to determine whether sampling monitoring of medical devices is required after disinfection is completed, and sampling monitoring parameters are obtained. Sampling monitoring parameters include monitoring frequency, sample size, monitoring method, etc., to ensure that the disinfection quality meets the standards and optimize resource allocation.

[0068] The sterilization supply center can more accurately assess the wear of medical devices during the sterilization process and adjust the monitoring strategy accordingly to ensure the sterilization effect and improve patient safety, which helps to improve the efficiency and quality of the sterilization process while reducing unnecessary monitoring costs.

[0069] Furthermore, the present application also includes:

[0070] According to the historical data of random inspections of the disinfection quality of medical devices, a sample corrected wear parameter set and a sample third disinfection difficulty parameter set are collected, and according to the proportion of medical devices that do not meet the disinfection quality requirements under different sample corrected wear parameters and sample third disinfection difficulty parameters, a sample sampling monitoring parameter set is obtained, wherein the sample sampling monitoring parameters include sampling proportions and monitoring requirement parameters; the sample corrected wear parameter set and the sample third disinfection difficulty parameter set are used as decision inputs, and the sample sampling monitoring parameter set is used as decision results, and a sampling monitoring decision maker is constructed based on a decision tree; based on the sampling monitoring decision maker, the corrected wear parameter and the third disinfection difficulty parameter information are analyzed for decision-making, to obtain the sampling monitoring parameters.

[0071] Specifically, based on the historical data of random inspections of the disinfection quality of medical devices, a set of sample correction wear parameters and a set of sample third disinfection difficulty parameters are collected. The situations where the disinfection quality of medical devices does not meet the requirements under different sample correction wear parameters and sample third disinfection difficulty parameters are recorded to obtain a set of sample sampling monitoring parameters, including sampling ratios and monitoring requirement parameters.

[0072] Then, a sampling monitoring decision maker is constructed using a decision tree algorithm, with the sample correction wear parameter set and the sample third disinfection difficulty parameter set as decision inputs and the sample sampling monitoring parameter set as decision results. The decision tree will output the corresponding sampling monitoring parameters, i.e., the sampling ratio and monitoring requirement parameters, based on the input correction wear parameters and the third disinfection difficulty parameters.

[0073] Next, based on the constructed sampling monitoring decision maker, decision analysis is performed on the current corrected wear parameters and the third disinfection difficulty parameter information to obtain sampling monitoring parameters, which guide the sampling monitoring activities after disinfection and ensure that the disinfection quality meets the standards.

[0074] The disinfection supply center can intelligently determine the sampling monitoring strategy based on the actual condition of the medical device and the difficulty of disinfection, thereby improving the targetedness and efficiency of monitoring, ensuring the quality of disinfection, and avoiding the waste of resources caused by excessive monitoring.

[0075] In summary, the intelligent monitoring and management method for a disinfection supply center provided by this application has the following technical effects:

[0076] By collecting the device characteristic information and traceability characteristic information of the medical devices to be disinfected in the current disinfection supply center, obtaining the device characteristic information and traceability characteristic information set, performing disinfection difficulty analysis on the medical devices, and obtaining a first disinfection difficulty parameter; identifying the surface wear parameters of the medical devices, combining the device characteristic information and traceability characteristic information set, predicting the formation of biofilms on the medical devices, obtaining biofilm prediction results, correcting the first disinfection difficulty parameter, and obtaining a second disinfection difficulty parameter; obtaining the equipment aging parameters of the disinfection equipment used to disinfect the medical devices, and performing an aging analysis on the equipment according to the device characteristic information and traceability characteristic information set. Perform a decreasing prediction to obtain a decreasing equipment aging parameter; according to the decreasing equipment aging parameter, analyze and obtain a disinfection attenuation parameter, correct the second disinfection difficulty parameter, and obtain a third disinfection difficulty parameter; based on the decreasing equipment aging parameter, predict the wear coefficient of the medical device after disinfection, correct the surface wear parameter, obtain a corrected wear parameter, and analyze and obtain a sampling monitoring parameter for sampling monitoring the quality of the disinfection device after the disinfection of the medical device is completed in combination with the third disinfection difficulty parameter, perform sampling monitoring and management on the disinfected medical devices, achieve the technical goal of comprehensively monitoring the impact of disinfection equipment on the device, and achieve the technical effect of predicting and preventing disinfection risks.

[0077] Embodiment 2: Based on the intelligent monitoring and management method for the disinfection supply center in the previous embodiment, the present application also provides an intelligent monitoring and management system for the disinfection supply center, please refer to the attached Figure 2 , the system comprising:

[0078] The first disinfection difficulty parameter acquisition module 11 is used to collect the device characteristic information and traceability characteristic information of the medical devices to be disinfected in the current disinfection supply center, obtain the device characteristic information and traceability characteristic information set, perform disinfection difficulty analysis on the medical devices, and obtain the first disinfection difficulty parameter.

[0079] The second disinfection difficulty parameter obtaining module 12 is used to identify the surface wear parameters of the medical device, predict the formation of the biofilm of the medical device in combination with the device feature information and the traceability feature information set, obtain the biofilm prediction result, correct the first disinfection difficulty parameter, and obtain the second disinfection difficulty parameter.

[0080] The decreasing equipment aging parameter acquisition module 13 is used to obtain the equipment aging parameters of the disinfection equipment for disinfecting the medical device, and to perform decreasing prediction on the equipment aging parameters according to the equipment characteristic information and the traceability characteristic information set to obtain the decreasing equipment aging parameters.

[0081] The third disinfection difficulty parameter obtaining module 14 is used to analyze and obtain the disinfection attenuation parameter according to the decreasing equipment aging parameter, and to correct the second disinfection difficulty parameter to obtain the third disinfection difficulty parameter.

[0082] The sampling monitoring management module 15 is used to predict the wear coefficient of the medical device after disinfection based on the decreasing equipment aging parameters, correct the surface wear parameters to obtain corrected wear parameters, and analyze and obtain sampling monitoring parameters for sampling monitoring of the quality of the disinfection equipment after the disinfection of the medical device is completed in combination with the third disinfection difficulty parameter, and perform sampling monitoring and management on the disinfected medical devices.

[0083] Furthermore, the first disinfection difficulty parameter obtaining module 11 in the system is also used for:

[0084] Collecting device characteristic information of medical devices to be sterilized in the current sterilization supply center, wherein the medical device is a medical device;

[0085] Collecting hospital information, department information, and contamination characteristic information of the medical device as traceability characteristic information to obtain a traceability characteristic information set;

[0086] A disinfection difficulty analysis of the medical device is performed based on the device characteristic information and the traceability characteristic information set to obtain a first disinfection difficulty parameter.

[0087] Furthermore, the first disinfection difficulty parameter obtaining module 11 in the system is also used for:

[0088] Based on the historical disinfection data of the disinfection supply center, multiple sample device feature information of multiple medical devices is obtained, and multiple sample traceability feature information sets of multiple devices are collected, and based on the disinfection result data after disinfection, multiple sample first disinfection difficulty parameter sets are collected;

[0089] The plurality of sample traceability feature information sets and the plurality of sample first disinfection difficulty parameter sets are respectively used to train a plurality of disinfection difficulty parameter prediction branches to obtain a disinfection difficulty predictor;

[0090] Based on the instrument feature information, the traceability feature information set is respectively input into the disinfection difficulty predictor to predict and obtain a plurality of basic disinfection difficulty parameters, and then calculate and obtain a first disinfection difficulty parameter.

[0091] Further, the second disinfection difficulty parameter obtaining module 12 in the system is also used for:

[0092] Collecting surface images of the medical device to obtain a set of device surface images;

[0093] Based on the surface recognition history of medical devices, a set of sample device surface images and a set of sample device surface wear parameters are collected, and an image recognition network is used to train a device surface wear identifier;

[0094] According to the instrument surface wear identifier, wear identification is performed on the instrument surface image set to obtain an instrument surface wear parameter set, and surface wear parameters are calculated;

[0095] Predicting the formation of a biofilm of the medical device according to the surface wear parameter, the device characteristic information, and the traceability characteristic information set to obtain a biofilm prediction result, wherein the biofilm prediction result includes a biofilm disinfection influence coefficient;

[0096] According to the biofilm disinfection influence coefficient, the first disinfection difficulty parameter is corrected to obtain a second disinfection difficulty parameter.

[0097] Furthermore, the decreasing device aging parameter obtaining module 13 in the system is also used for:

[0098] Performing aging detection on the disinfection equipment used to disinfect the medical device to obtain equipment aging parameters;

[0099] Based on the historical disinfection data set of the disinfection equipment, a sample equipment aging parameter set, a sample instrument feature information set, multiple sample traceability feature information sets and a sample reduction equipment aging parameter set are collected to train an aging reduction predictor;

[0100] Based on the aging decline predictor, the equipment aging decline prediction is performed on the equipment aging parameters, the instrument feature information and the traceability feature information set to obtain the declining equipment aging parameters.

[0101] Further, the third disinfection difficulty parameter obtaining module 14 in the system is also used for:

[0102] Based on the disinfection quality data records of the disinfection equipment at different aging stages, the sample equipment aging parameter set and the sample disinfection attenuation parameter set are collected to construct an index table;

[0103] The disinfection attenuation parameter is obtained according to the decreasing equipment aging parameter index, and the second disinfection difficulty parameter is corrected to obtain a third disinfection difficulty parameter.

[0104] Furthermore, the sampling monitoring management module 15 in the system is also used for:

[0105] Based on the wear record data of disinfecting medical devices at different aging stages of disinfection equipment, a sample equipment aging parameter set and a sample wear coefficient set are collected, and an equipment disinfection wear analyzer is trained to predict the decremental equipment aging parameters to obtain the wear coefficient;

[0106] According to the wear coefficient, the surface wear parameter is corrected to obtain a corrected wear parameter;

[0107] According to the corrected wear parameter and the third disinfection difficulty parameter, a decision analysis is performed on the sampling monitoring after the disinfection of the medical device is completed to obtain the sampling monitoring parameter.

[0108] Furthermore, the sampling monitoring management module 15 in the system is also used for:

[0109] According to the historical data of random inspections of the disinfection quality of medical devices, a set of sample-corrected wear parameters and a set of sample third disinfection difficulty parameters are collected, and according to the proportion of medical devices that do not meet the disinfection quality requirements under different sample-corrected wear parameters and sample third disinfection difficulty parameters, a set of sample sampling monitoring parameters is obtained, wherein the sample sampling monitoring parameters include sampling proportions and monitoring requirement parameters;

[0110] Using the sample correction wear parameter set and the sample third disinfection difficulty parameter set as decision inputs, using the sample sampling monitoring parameter set as a decision result, and constructing a sampling monitoring decision maker based on a decision tree;

[0111] Based on the sampling monitoring decision maker, the corrected wear parameter and the third disinfection difficulty parameter information are analyzed and decided to obtain the sampling monitoring parameter.

[0112] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The intelligent monitoring and management method for a disinfection supply center and the specific examples in the aforementioned embodiment 1 are also applicable to the intelligent monitoring and management system for a disinfection supply center in this embodiment. Through the aforementioned detailed description of the intelligent monitoring and management method for a disinfection supply center, those skilled in the art can clearly know the intelligent monitoring and management system for a disinfection supply center in this embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0113] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0114] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent monitoring and management method for a disinfection supply center, characterized in that: The method comprises: Collecting device characteristic information and traceability characteristic information of medical devices to be disinfected in the current disinfection supply center, obtaining a set of device characteristic information and traceability characteristic information, performing disinfection difficulty analysis on the medical devices, and obtaining a first disinfection difficulty parameter; Identify the surface wear parameters of the medical device, combine the device feature information and the traceability feature information set, predict the formation of the biofilm of the medical device, obtain the biofilm prediction result, correct the first disinfection difficulty parameter, and obtain the second disinfection difficulty parameter; Obtaining equipment aging parameters of a disinfection device for disinfecting the medical device, performing a decreasing prediction on the equipment aging parameters according to the device characteristic information and the traceability characteristic information set, and obtaining the decreasing equipment aging parameters; According to the decreasing equipment aging parameter, a disinfection attenuation parameter is obtained by analysis, and the second disinfection difficulty parameter is corrected to obtain a third disinfection difficulty parameter; Based on the decreasing equipment aging parameter, the wear coefficient of the medical device after disinfection is predicted, the surface wear parameter is corrected to obtain the corrected wear parameter, and combined with the third disinfection difficulty parameter, the sampling monitoring parameters for sampling monitoring of the quality of the disinfection device after the disinfection of the medical device is completed are analyzed and obtained, and sampling monitoring and management of the disinfected medical devices are performed.

2. The method according to claim 1, characterized in that Collecting device feature information and traceability feature information of medical devices to be disinfected in the current disinfection supply center, obtaining a set of device feature information and traceability feature information, performing disinfection difficulty analysis on the medical devices, and obtaining a first disinfection difficulty parameter, including: Collecting device characteristic information of medical devices to be sterilized in the current sterilization supply center, wherein the medical device is a medical device; Collecting hospital information, department information, and contamination characteristic information of the medical device as traceability characteristic information to obtain a traceability characteristic information set; A disinfection difficulty analysis of the medical device is performed based on the device characteristic information and the traceability characteristic information set to obtain a first disinfection difficulty parameter.

3. The method according to claim 2, characterized in that According to the device characteristic information and the traceability characteristic information set, performing a disinfection difficulty analysis on the medical device to obtain a first disinfection difficulty parameter includes: Based on the historical disinfection data of the disinfection supply center, multiple sample device feature information of multiple medical devices is obtained, and multiple sample traceability feature information sets of multiple devices are collected, and based on the disinfection result data after disinfection, multiple sample first disinfection difficulty parameter sets are collected; The plurality of sample traceability feature information sets and the plurality of sample first disinfection difficulty parameter sets are respectively used to train a plurality of disinfection difficulty parameter prediction branches to obtain a disinfection difficulty predictor; Based on the instrument feature information, the traceability feature information set is respectively input into the disinfection difficulty predictor to predict and obtain a plurality of basic disinfection difficulty parameters, and then calculate and obtain a first disinfection difficulty parameter.

4. The method according to claim 1, characterized in that: Identifying the surface wear parameters of the medical device, combining the device feature information and the traceability feature information set, predicting the formation of a biofilm of the medical device, obtaining a biofilm prediction result, and correcting the first disinfection difficulty parameter, including: Collecting surface images of the medical device to obtain a set of device surface images; Based on the surface recognition history of medical devices, a set of sample device surface images and a set of sample device surface wear parameters are collected, and an image recognition network is used to train a device surface wear identifier; According to the instrument surface wear identifier, wear identification is performed on the instrument surface image set to obtain an instrument surface wear parameter set, and surface wear parameters are calculated; Predicting the formation of a biofilm of the medical device according to the surface wear parameter, the device characteristic information, and the traceability characteristic information set to obtain a biofilm prediction result, wherein the biofilm prediction result includes a biofilm disinfection influence coefficient; According to the biofilm disinfection influence coefficient, the first disinfection difficulty parameter is corrected to obtain a second disinfection difficulty parameter.

5. The method according to claim 1, characterized in that Obtaining equipment aging parameters of a disinfection device for disinfecting the medical device, performing a decreasing prediction on the equipment aging parameters according to the device feature information and the traceability feature information set, and obtaining the decreasing equipment aging parameters, including: Performing aging detection on the disinfection equipment used to disinfect the medical device to obtain equipment aging parameters; Based on the historical disinfection data set of the disinfection equipment, a sample equipment aging parameter set, a sample instrument feature information set, multiple sample traceability feature information sets and a sample reduction equipment aging parameter set are collected to train an aging reduction predictor; Based on the aging decline predictor, the equipment aging decline prediction is performed on the equipment aging parameters, the instrument feature information and the traceability feature information set to obtain the declining equipment aging parameters.

6. The method according to claim 1, characterized in that According to the decreasing equipment aging parameter, a disinfection attenuation parameter is obtained by analysis, and the second disinfection difficulty parameter is corrected to obtain a third disinfection difficulty parameter, including: Based on the disinfection quality data records of the disinfection equipment at different aging stages, the sample equipment aging parameter set and the sample disinfection attenuation parameter set are collected to construct an index table; The disinfection attenuation parameter is obtained according to the decreasing equipment aging parameter index, and the second disinfection difficulty parameter is corrected to obtain a third disinfection difficulty parameter.

7. The method according to claim 1, characterized in that Based on the decremental equipment aging parameter, the wear coefficient of the medical device after disinfection is predicted, the surface wear parameter is corrected to obtain the corrected wear parameter, and combined with the third disinfection difficulty parameter, the sampling monitoring parameters for sampling and monitoring the quality of the disinfection device after the disinfection of the medical device is completed are analyzed and obtained, including: Based on the wear record data of disinfecting medical devices at different aging stages of disinfection equipment, a sample equipment aging parameter set and a sample wear coefficient set are collected, and an equipment disinfection wear analyzer is trained to predict the decremental equipment aging parameters to obtain the wear coefficient; According to the wear coefficient, the surface wear parameter is corrected to obtain a corrected wear parameter; According to the corrected wear parameter and the third disinfection difficulty parameter, a decision analysis is performed on the sampling monitoring after the disinfection of the medical device is completed to obtain the sampling monitoring parameter.

8. The method according to claim 7, characterized in that According to the modified wear parameter and the third disinfection difficulty parameter, a decision analysis for sampling monitoring after the disinfection of the medical device is completed is performed, including: According to the historical data of random inspections of the disinfection quality of medical devices, a set of sample-corrected wear parameters and a set of sample third disinfection difficulty parameters are collected, and according to the proportion of medical devices that do not meet the disinfection quality requirements under different sample-corrected wear parameters and sample third disinfection difficulty parameters, a set of sample sampling monitoring parameters is obtained, wherein the sample sampling monitoring parameters include sampling proportions and monitoring requirement parameters; Using the sample correction wear parameter set and the sample third disinfection difficulty parameter set as decision inputs, using the sample sampling monitoring parameter set as a decision result, and constructing a sampling monitoring decision maker based on a decision tree; Based on the sampling monitoring decision maker, the corrected wear parameter and the third disinfection difficulty parameter information are analyzed and decided to obtain the sampling monitoring parameter.

9. Intelligent monitoring and management system for disinfection supply center, characterized in that: For implementing the method according to any one of claims 1 to 8, the system comprises: A first disinfection difficulty parameter acquisition module, which is used to collect device feature information and traceability feature information of medical devices to be disinfected in the current disinfection supply center, obtain a set of device feature information and traceability feature information, perform disinfection difficulty analysis on the medical devices, and obtain a first disinfection difficulty parameter; A second disinfection difficulty parameter acquisition module, the second disinfection difficulty parameter acquisition module is used to identify the surface wear parameter of the medical device, combine the device feature information and the traceability feature information set, predict the formation of the biofilm of the medical device, obtain the biofilm prediction result, correct the first disinfection difficulty parameter, and obtain the second disinfection difficulty parameter; A decreasing device aging parameter acquisition module, the decreasing device aging parameter acquisition module is used to obtain the device aging parameters of the disinfection device for disinfecting the medical device, and perform decreasing prediction on the device aging parameters according to the device feature information and the traceability feature information set to obtain the decreasing device aging parameters; A third disinfection difficulty parameter acquisition module, the third disinfection difficulty parameter acquisition module is used to analyze and obtain a disinfection attenuation parameter according to the decreasing equipment aging parameter, and to correct the second disinfection difficulty parameter to obtain a third disinfection difficulty parameter; A sampling monitoring management module is used to predict the wear coefficient of the medical device after disinfection based on the decreasing equipment aging parameter, correct the surface wear parameter to obtain the corrected wear parameter, and analyze and obtain the sampling monitoring parameters for sampling monitoring the quality of the disinfection device after the disinfection of the medical device is completed in combination with the third disinfection difficulty parameter, and perform sampling monitoring and management on the disinfected medical device.

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