An intelligent control system for enzyme liquid used for cleaning equipment in medical disinfection supply rooms

Abstract

The present invention discloses an enzyme liquid intelligent control system for cleaning instruments in a medical disinfection supply room, comprising an environmental history data collection module, a data analysis module, an environmental real-time data collection module and an enzyme liquid deterioration analysis module, wherein the modules are connected via electrical and / or wireless network modes; the environmental history data collection module is used for pre-collecting environmental history test data of a disinfection supply room, and sending the collected environmental history test data to the data analysis module; the data analysis module is used for integrating environmental history test data, generating a machine learning model for evaluating the degree of enzyme liquid deterioration, and sending the trained machine learning model to the environmental real-time data collection module; the environmental real-time data collection module is used for real-time collection of environmental real-time data of a disinfection supply room, and sending attribute values ​​of the enzyme liquid deterioration corresponding to the influencing results stored in the environmental real-time data to the enzyme liquid deterioration analysis module.

Description

Technical Field

[0001] The invention relates to the field of medical device disinfection, and in particular to an enzyme liquid intelligent control system for cleaning devices in a medical disinfection supply room. Background Art

[0002] The cleaning of medical disinfection supply room equipment can be divided into manual cleaning and automatic cleaning. With the development of science and technology, the intelligence of automatic disinfection equipment for medical equipment has gradually improved in enzyme solution preparation, cleaning agent use, cleaning time, etc., which can meet the current cleaning needs of medical equipment. However, the current enzyme solution control system has limited control over environmental factors such as light, humidity and temperature during the cleaning process, and is still affected by the environment, resulting in the most critical enzyme solution in the instrument cleaning process being contaminated or the enzyme solution activity decreasing due to light, temperature or humidity.

[0003] There are some technical means for detecting enzyme liquid in the prior art to improve the control and monitoring of enzyme liquid, but most of them cannot timely analyze the cause of deterioration according to the deterioration of the enzyme liquid, and there are misdiagnosis of the cause of enzyme liquid deterioration. When the enzyme liquid deteriorates, the cause of deterioration is obtained with delay, which further affects the efficiency of subsequent cleaning of medical devices.

[0004] In order to solve the above-mentioned problems, an enzyme liquid intelligent control system for cleaning instruments in a medical disinfection supply room is proposed. Summary of the invention

[0005] The purpose of the present invention is to provide an enzyme solution intelligent control system for cleaning medical disinfection supply room equipment to solve the shortcomings of the background technology.

[0006] An enzyme liquid intelligent control system for cleaning instruments in a medical disinfection supply room comprises an environmental history data collection module, a data analysis module, an environmental real-time data collection module and an enzyme liquid deterioration analysis module, wherein the modules are connected by electrical and / or wireless network means;

[0007] Environmental history data collection module; used to collect environmental history test data of the disinfection supply room in advance, and send the collected environmental history test data to the data analysis module;

[0008] Data analysis module: used to integrate environmental historical test data, generate a machine learning model for evaluating the degree of enzyme liquid deterioration, and send the trained machine learning model to the environmental real-time data collection module;

[0009] Environmental real-time data collection module; used to collect the real-time environmental data of the disinfection supply room in real time, and send the attribute values ​​of the enzyme liquid deterioration corresponding to the impact result stored in the real-time environmental data to the enzyme liquid deterioration analysis module;

[0010] Enzyme liquid deterioration analysis module; used to receive the attribute values ​​that affect the results of enzyme liquid deterioration, input the attribute values ​​that affect the results of enzyme liquid deterioration into the corresponding machine learning model according to the corresponding environmental historical test data, and obtain the deterioration cause output by the machine learning model.

[0011] Furthermore, the environmental history test data are data related to the deterioration of the enzyme solution, and the environmental history test data are affected by the sudden environmental conditions of the disinfection supply room, and the environmental conditions are specifically the environmental impact coefficient.

[0012] Furthermore, the logic of generating the environmental impact coefficient is:

[0013] The optimal thresholds for light, humidity and temperature are set, and the light intensity, humidity and temperature in the disinfection supply room are obtained through measuring instruments. Weighted analysis is performed using the absolute value of the difference between the light intensity and the optimal light threshold, the absolute value of the difference between the humidity and the optimal humidity threshold, the absolute value of the difference between the temperature and the optimal temperature threshold, and the ratios between the optimal light threshold, the optimal humidity threshold and the optimal temperature threshold. The analyzed results are logarithmically analyzed, and the final results are analyzed in absolute value.

[0014] Furthermore, the process of the environmental history data collection module collecting environmental history test data is as follows:

[0015] Step N1, sorting the environmental history test data in the order of acquisition, and numbering the environmental history test data in the order of acquisition, marking the numbers as β, β=1, 2, 3...γ, where γ is the number of environmental history test data acquired;

[0016] Step N2, based on the environmental history test data β, collect the reasons for the deterioration of the enzyme solution between the environmental history test data and the environmental history test data β-1, and collect the results of the influence of the reasons for the deterioration of the enzyme solution on the enzyme solution in the environmental history test data β;

[0017] Step N3, collecting the attribute values ​​of the causes of enzyme solution deterioration detected by each environmental history test data β in the past and the corresponding impact results of the enzyme solution deterioration as environmental history test data.

[0018] Furthermore, the impact result of the enzyme solution is the appearance of the enzyme solution or the difference in internal component test indicators, and the specific impact results vary according to different environmental historical test data; the appearance of the enzyme solution is the change in color of the enzyme solution, precipitation of enzyme solution components, stratification of the enzyme solution, gelation of the enzyme solution or crystallization of the enzyme solution, and the difference in internal component test indicators is the change in pH value of the enzyme solution or the change in quality of the enzyme solution.

[0019] Furthermore, the appearance of the enzyme solution is represented by data in the following manner:

[0020] Further, a number of enzyme liquid appearance training photos are collected, and the expression forms of the enzyme liquid appearance training photos are annotated by digital labels. The enzyme liquid appearance training photos are trained based on a support vector machine, and a network model is generated that automatically outputs digital labels that are compatible with the enzyme liquid appearance expression form according to the enzyme liquid appearance expression form; the digital labels are manually analyzed and rated for the degree of deterioration of each enzyme liquid appearance training photo, wherein 0 represents no deterioration, 1 represents a low degree of deterioration, 2 represents a medium degree of deterioration, and 3 represents a high degree of deterioration.

[0021] Furthermore, the generation step Q1 of the machine learning module for evaluating the degree of enzyme liquid deterioration is: based on each environmental history test class data β, the attribute value of the enzyme liquid deterioration corresponding to the environmental history test class data that affects the result is used as the input of the machine learning model, the cause of enzyme liquid deterioration is used as the prediction target, and the prediction accuracy of the machine learning model is used as the training target.

[0022] Furthermore, the generation step Q2 of the machine learning module for evaluating the degree of deterioration of the enzyme solution is: training the machine learning model of each environmental history test data β, presetting the prediction accuracy threshold α, and when the prediction accuracy of the machine learning model reaches the prediction accuracy threshold α, stopping the training, and marking the machine learning model trained for the environmental history test data β as β α .

[0023] Furthermore, the step Y1 of collecting real-time environmental data by the environmental real-time data collection module is as follows: according to the actual situation, a threshold value of the degree of deterioration is preset for each environmental historical test data β, and the threshold value of the proportion of deteriorated components of the historical environmental test data β is set to K β For the environmental historical test data β, the percentage of deteriorated components detected in the enzyme solution each time is counted. The percentage of deteriorated components is the molecular content of the deteriorated components in the enzyme solution as a whole. If the percentage of deteriorated components in the real-time environmental data is less than the threshold value K of the percentage of deteriorated components, β , no processing is performed, otherwise, go to step Y2.

[0024] Furthermore, the step Y2 of collecting the real-time environmental data by the environmental real-time data collection module is: collecting the attribute values ​​of the enzyme solution deterioration corresponding to the influencing results obtained in the subsequent test and sending them to the deterioration analysis module.

[0025] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0026] In the present invention, a machine learning model is constructed for environmental history test data in a medical disinfection supply room, the attribute values ​​of the environmental history test data corresponding to the influencing results of enzyme liquid deterioration are used as the input of the machine learning model, and the cause of enzyme liquid deterioration is used as the prediction target, so as to train a machine learning module that can accurately analyze and predict environmental data in the medical disinfection supply room. In this way, when an abnormal environment occurs, the cause of enzyme liquid deterioration can be directly predicted according to the attribute values ​​of the influencing results corresponding to the enzyme liquid deterioration, thereby improving the timeliness and accuracy of environmental regulation in the medical disinfection supply room. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0028] Figure 1 The present invention is a module flow chart of an enzyme solution intelligent control system for cleaning medical disinfection supply room instruments. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 As shown, an enzyme solution intelligent control system for cleaning medical disinfection supply room equipment, the enzyme solution intelligent control system for cleaning medical disinfection supply room equipment includes an environmental history data collection module, a data analysis module, an environmental real-time data collection module and an enzyme solution deterioration analysis module, and each module is connected by an electrical and / or wireless network;

[0031] Environmental history data collection module; used to collect environmental history test data of the disinfection supply room in advance;

[0032] The environmental history test data is data related to the deterioration of the enzyme solution. The data is often caused by sudden changes in environmental conditions in the disinfection supply room. The degree of deterioration of the enzyme solution is different, resulting in different detection parameters for the deterioration of the enzyme solution. The environmental conditions are specifically the environmental impact coefficient;

[0033] The generation logic of the environmental impact coefficient is:

[0034] The optimal thresholds for light, humidity and temperature are set, and the light intensity, humidity and temperature in the disinfection supply room are obtained through measuring instruments. Weighted analysis is performed using the absolute value of the difference between the light intensity and the optimal light threshold, the absolute value of the difference between the humidity and the optimal humidity threshold, the absolute value of the difference between the temperature and the optimal temperature threshold, and the ratios between the optimal light threshold, the optimal humidity threshold and the optimal temperature threshold. The analyzed results are logarithmically analyzed, and the final results are analyzed in absolute value.

[0035] The process of the environmental history data collection module collecting environmental history test data is as follows:

[0036] Step N1, sorting the environmental history test data in the order of acquisition, and numbering the environmental history test data in the order of acquisition, marking the numbers as β, β=1, 2, 3...γ, where γ is the number of environmental history test data acquired;

[0037] Step N2, based on the environmental history test data β, collect the reasons for the deterioration of the enzyme solution between the environmental history test data and the environmental history test data β-1, and collect the results of the influence of the reasons for the deterioration of the enzyme solution on the enzyme solution in the environmental history test data β;

[0038] Step N3, collecting the attribute values ​​of the enzyme solution deterioration causes detected by each environmental history test data β in the past and the corresponding impact results of the enzyme solution deterioration as environmental history test data;

[0039] The impact result of the enzyme solution is the appearance of the enzyme solution or the difference in the test index of the internal components. The specific impact results vary according to different environmental history test data. The appearance of the enzyme solution is the color change of the enzyme solution, the precipitation of the enzyme solution components, the stratification of the enzyme solution, the gelation of the enzyme solution or the crystallization of the enzyme solution. The difference in the test index of the internal components is the change in the pH value of the enzyme solution or the change in the quality of the enzyme solution.

[0040] The way to express the appearance of the enzyme solution through data is as follows:

[0041] Based on the fact that the attributes obtained by taking the appearance expression form of the enzyme liquid cannot be expressed by data, a number of appearance training photos of the enzyme liquid are further collected, and the expression form of the appearance training photos of the enzyme liquid is annotated by digital labels. The appearance training photos of the enzyme liquid are trained based on a support vector machine to generate a network model that automatically outputs digital labels that are compatible with the appearance expression form of the enzyme liquid according to the appearance expression form of the enzyme liquid;

[0042] The digital label is a manual analysis and rating of the degree of deterioration of each enzyme solution appearance training photo; wherein 0 represents no deterioration, 1 represents a low degree of deterioration, 2 represents a medium degree of deterioration, and 3 represents a high degree of deterioration;

[0043] The environmental history data collection module sends the collected environmental history test data to the data analysis module.

[0044] Data analysis module: used to integrate environmental historical test data, generate a machine learning model for evaluating the degree of enzyme liquid deterioration, and send the trained machine learning model to the environmental real-time data collection module;

[0045] The steps for generating the machine learning module for evaluating the degree of enzyme liquid deterioration are:

[0046] Step Q1, based on each environmental history test data β, the attribute value of the enzyme solution deterioration corresponding to the environmental history test data that affects the result is used as the input of the machine learning model, the enzyme solution deterioration cause is used as the prediction target, and the prediction accuracy of the machine learning model is used as the training target;

[0047] Step Q2, train the machine learning model for each environmental history test data β, pre-set the prediction accuracy threshold α, and stop training when the prediction accuracy of the machine learning model reaches the prediction accuracy threshold α, and mark the machine learning model trained for the environmental history test data β as β α ;

[0048] The data analysis module will train the machine learning model β α Sent to the environmental real-time data collection module.

[0049] Environmental real-time data collection module; used to collect real-time environmental data of the disinfection supply room;

[0050] The steps of collecting real-time environmental data by the environmental real-time data collection module are as follows:

[0051] Step Y1: preset a deterioration threshold for each environmental historical test data β according to the actual situation, and set the deterioration component ratio threshold of the historical environmental test data β to K β For the environmental historical test data β, the percentage of deteriorated components detected in the enzyme solution each time is counted. The percentage of deteriorated components is the molecular content of the deteriorated components in the enzyme solution as a whole. If the percentage of deteriorated components in the real-time environmental data is less than the threshold value K of the percentage of deteriorated components, β , no processing is performed, otherwise, go to step Y2;

[0052] Step Y2, collecting the attribute values ​​of the enzyme solution deterioration corresponding to the influencing results obtained in the subsequent test and sending them to the deterioration analysis module;

[0053] The environmental real-time data collection module sends the attribute values ​​of the enzyme solution deterioration corresponding to the influencing results stored in the environmental real-time data to the enzyme solution deterioration analysis module.

[0054] Enzyme solution deterioration analysis module; used to receive the attribute value of the enzyme solution deterioration corresponding to the impact result, and input the attribute value of the enzyme solution deterioration corresponding to the impact result into the corresponding machine learning model β according to the corresponding environmental history test data β α and obtain the machine learning model β α The reason for the deterioration of the output.

[0055] The method involved in realizing the enzyme liquid intelligent control system for cleaning equipment in the medical disinfection supply room is:

[0056] Step 1: Collect environmental history test data of the disinfection supply room in advance;

[0057] The environmental history test data is data related to the deterioration of the enzyme solution. The data is often caused by sudden changes in environmental conditions in the disinfection supply room. The degree of deterioration of the enzyme solution is different, resulting in different detection parameters for the deterioration of the enzyme solution. The environmental conditions are specifically the environmental impact coefficient;

[0058] The generation logic of the environmental impact coefficient is:

[0059] The optimal thresholds for light, humidity and temperature are set, and the light intensity, humidity and temperature in the disinfection supply room are obtained by measuring instruments. The absolute value of the difference between the light intensity and the optimal light threshold, the absolute value of the difference between the humidity and the optimal humidity threshold, the absolute value of the difference between the temperature and the optimal temperature threshold, and the ratios of the optimal light threshold, the optimal humidity threshold and the optimal temperature threshold are used for weight analysis. The analyzed results are logarithmically analyzed, and the final results are analyzed in absolute value. The analysis formula of the environmental impact coefficient is:

[0060]

[0061] Among them, Bhj is the environmental impact coefficient, Gzp, Sdp, and Wdp are the optimal thresholds of light, humidity, and temperature, respectively; Gzc, Sdc, and Wdc are the optimal thresholds of light intensity, humidity, and temperature, respectively; L1, L2, and L3 are the weight factors of the ratio of the absolute value of the difference between light intensity and the optimal light threshold to the optimal light threshold, the ratio of the absolute value of the difference between humidity and the optimal humidity threshold to the optimal humidity threshold, and the ratio of the absolute value of the difference between temperature and the optimal temperature threshold to the optimal temperature threshold, respectively; among them, L1+L2+L3=0.8625, i is the environmental error correction constant, and i is greater than 0.

[0062] It should be noted that the smaller the value of the environmental impact coefficient Bhj, the more serious the environmental impact;

[0063] The significance of collecting data on light intensity, humidity and temperature is:

[0064] Some enzymes may be sensitive to light. Long-term exposure to strong light may lead to photolysis and degradation. Some enzymes may be more easily inactivated under light conditions. The activity of the enzyme may decrease, resulting in decreased performance of the enzyme solution. Light intensity can be measured using a light meter or photometer, usually in lux.

[0065] Changes in humidity may cause changes in the water content of the enzyme solution. High humidity may cause the enzyme solution to absorb moisture and cause hydrolysis. Excessive humidity may cause dilution of the enzyme solution and loss of activity. For some enzymes, humidity fluctuations may also promote the growth of microorganisms and cause contamination of the enzyme solution. The instrument used to measure humidity can be a hygrometer or a humidity sensor. Humidity is usually expressed as a percentage of relative humidity.

[0066] Temperature can directly affect the activity of enzymes. Increased temperature may accelerate the reaction rate of the enzyme, but may also cause inactivation. High temperature may cause denaturation and inactivation of the enzyme solution, while low temperature may slow down the activity of the enzyme. Temperature can be measured using instruments such as thermometers, infrared thermometers or digital temperature sensors, and temperature is usually expressed in degrees Celsius.

[0067] The process of the environmental history data collection module collecting environmental history test data is as follows:

[0068] Step N1, sorting the environmental history test data in the order of acquisition, and numbering the environmental history test data in the order of acquisition, marking the numbers as β, β=1, 2, 3...γ, where γ is the number of environmental history test data acquired;

[0069] It should be noted that: if there are two or more sets of abnormal data on the same day, the environmental historical test data that is obtained first will be ranked first according to the time of acquisition.

[0070] Step N2, based on the environmental history test data β, collect the reasons for the deterioration of the enzyme solution between the environmental history test data and the environmental history test data β-1, and collect the results of the influence of the reasons for the deterioration of the enzyme solution on the enzyme solution in the environmental history test data β;

[0071] The impact result of the enzyme solution is the appearance of the enzyme solution or the difference in the test indicators of internal components. The specific impact results vary according to different environmental historical test data. The appearance of the enzyme solution is the change in color of the enzyme solution, precipitation of enzyme solution components, stratification of the enzyme solution, gelation of the enzyme solution or crystallization of the enzyme solution. The difference in the test indicators of internal components is the change in pH value of the enzyme solution or the change in quality of the enzyme solution.

[0072] It should be noted that spoiled enzyme solution may show a variety of different characteristics and manifestations, depending on the type and composition of the enzyme solution and the specific cause of the spoilage;

[0073] The enzyme solution may change in color, including but not limited to becoming turbid, cloudy, discolored, and precipitated. Such phenomena may indicate that a chemical reaction has occurred or that the substances in the enzyme solution have changed. If the precipitation phenomenon in the enzyme solution is relatively serious, it may be the precipitation of the product produced by the reaction of pollutants and enzymes. The enzyme solution may be stratified to form different layers of liquid, which may be caused by the instability of the enzyme solution. The enzyme solution may also gel during storage to form a solid or semi-solid substance. Crystallization may occur in the enzyme solution, which may be caused by the crystallization or solubility change of certain components.

[0074] Differences in internal component test indicators: The pH value of the enzyme solution may change, affecting the activity and stability of the enzyme; certain components in the enzyme solution may undergo oxidation reactions, resulting in a decrease in the quality of the enzyme solution, which may be related to the sensitivity of the enzyme, especially for oxygen-sensitive enzymes.

[0075] Based on the fact that the attributes obtained by taking the appearance expression form of the enzyme liquid cannot be expressed by data, a number of appearance training photos of the enzyme liquid are further collected, and the expression form of the appearance training photos of the enzyme liquid is annotated by digital labels. The appearance training photos of the enzyme liquid are trained based on a support vector machine to generate a network model that automatically outputs digital labels that are compatible with the appearance expression form of the enzyme liquid according to the appearance expression form of the enzyme liquid;

[0076] The digital label is a manual analysis and rating of the degree of deterioration of each enzyme solution appearance training photo; wherein 0 represents no deterioration, 1 represents a low degree of deterioration, 2 represents a medium degree of deterioration, and 3 represents a high degree of deterioration;

[0077] Step N3, collecting the attribute values ​​of the enzyme solution deterioration causes detected by each environmental history test data β in the past and the corresponding impact results of the enzyme solution deterioration as environmental history test data;

[0078] Step 2: Integrate the environmental history test data to generate a machine learning model for evaluating the degree of enzyme liquid deterioration;

[0079] The steps for generating the machine learning module for evaluating the degree of enzyme liquid deterioration are:

[0080] Step Q1, based on each environmental history test data β, the attribute value of the enzyme solution deterioration corresponding to the environmental history test data that affects the result is used as the input of the machine learning model, the enzyme solution deterioration cause is used as the prediction target, and the prediction accuracy of the machine learning model is used as the training target;

[0081] Step Q2, train the machine learning model for each environmental history test data β, pre-set the prediction accuracy threshold α, and stop training when the prediction accuracy of the machine learning model reaches the prediction accuracy threshold α, and mark the machine learning model trained for the environmental history test data β as β α ;

[0082] It should be noted that the machine learning model includes but is not limited to decision trees, random forests, and support vector machines.

[0083] Step 3: Collect the real-time environmental data of the disinfection supply room in real time, and filter out the attribute values ​​that affect the results of enzyme liquid deterioration in the real-time environmental data;

[0084] The steps of collecting real-time environmental data by the environmental real-time data collection module are as follows:

[0085] Step Y1: preset a deterioration threshold for each environmental historical test data β according to the actual situation, and set the deterioration component ratio threshold of the historical environmental test data β to K β For the environmental historical test data β, the percentage of deteriorated components detected in the enzyme solution each time is counted. The percentage of deteriorated components is the molecular content of the deteriorated components in the enzyme solution as a whole. If the percentage of deteriorated components in the real-time environmental data is less than the threshold value K of the percentage of deteriorated components, β , no processing is performed, otherwise, go to step Y2;

[0086] Step Y2, collect the attribute values ​​that affect the results of subsequent enzyme liquid deterioration tests and send them to the deterioration analysis module.

[0087] Step 4: According to the corresponding environmental history test data, the attribute values ​​that affect the results of enzyme liquid deterioration are input into the corresponding machine learning model, and the deterioration cause output by the machine learning model is obtained.

[0088] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

Claims

1. An intelligent enzyme liquid control system for cleaning equipment in a medical disinfection supply room, characterized in that: The enzyme solution intelligent control system for cleaning equipment in the medical disinfection supply room includes an environmental history data collection module, a data analysis module, an environmental real-time data collection module and an enzyme solution deterioration analysis module, and each module is connected by electrical and / or wireless network means; Environmental history data collection module; used to collect environmental history test data of the disinfection supply room in advance, and send the collected environmental history test data to the data analysis module; Data analysis module; It is used to integrate environmental historical test data, generate a machine learning model for evaluating the degree of enzyme liquid deterioration, and send the trained machine learning model to the environmental real-time data collection module; Environmental real-time data collection module; Used to collect the real-time environmental data of the disinfection supply room in real time, and send the attribute values ​​of the enzyme liquid deterioration corresponding to the influencing results stored in the real-time environmental data to the enzyme liquid deterioration analysis module; Enzyme solution deterioration analysis module; Used to receive the attribute value of the impact result corresponding to the deterioration of the enzyme solution, input the attribute value of the impact result corresponding to the deterioration of the enzyme solution into the corresponding machine learning model according to the corresponding environmental history test data, and obtain the deterioration cause output by the machine learning model; The environmental history test data is data related to the deterioration of the enzyme solution, and the environmental history test data is affected by the sudden environmental conditions of the disinfection supply room, and the environmental conditions are specifically the environmental impact coefficient; The logic for generating the environmental impact coefficient is: Set the optimal thresholds for light, humidity and temperature, obtain the light intensity, humidity and temperature in the disinfection supply room through measuring instruments, perform weight analysis on the absolute value of the difference between the light intensity and the optimal light threshold, the absolute value of the difference between the humidity and the optimal humidity threshold, the absolute value of the difference between the temperature and the optimal temperature threshold, and the ratios between the optimal light threshold, the optimal humidity threshold and the optimal temperature threshold, respectively, perform logarithmic analysis on the analyzed results, and perform absolute value analysis on the final results; The process of the environmental history data collection module collecting environmental history test data is as follows: Step N1, sorting the environmental history test data in the order of acquisition, and numbering the environmental history test data in the order of acquisition, marking the numbers as β, β=1, 2, 3...γ, where γ is the number of environmental history test data acquired; Step N2, based on the environmental history test data β, collect the reasons for the deterioration of the enzyme solution between the environmental history test data and the environmental history test data β-1, and collect the results of the influence of the reasons for the deterioration of the enzyme solution on the enzyme solution in the environmental history test data β; Step N3, collecting the attribute values ​​of the enzyme solution deterioration causes detected by each environmental history test data β in the past and the corresponding impact results of the enzyme solution deterioration as environmental history test data; The impact result of the enzyme solution is the appearance of the enzyme solution or the difference in the test index of the internal components. The specific impact results vary according to different environmental history test data. The appearance of the enzyme solution is the color change of the enzyme solution, the precipitation of the enzyme solution components, the stratification of the enzyme solution, the gelation of the enzyme solution or the crystallization of the enzyme solution. The difference in the test index of the internal components is the change in the pH value of the enzyme solution or the change in the quality of the enzyme solution. The way to express the appearance of the enzyme solution through data is as follows: Further, a number of enzyme liquid appearance training photos are collected, and the expression forms of the enzyme liquid appearance training photos are annotated by digital labels. The enzyme liquid appearance training photos are trained based on a support vector machine, and a network model is generated that automatically outputs digital labels that are compatible with the enzyme liquid appearance expression form according to the enzyme liquid appearance expression form; the digital labels are manually analyzed and rated for the degree of deterioration of each enzyme liquid appearance training photo, wherein 0 represents no deterioration, 1 represents a low degree of deterioration, 2 represents a medium degree of deterioration, and 3 represents a high degree of deterioration.

2. According to claim 1, an enzyme solution intelligent control system for cleaning medical disinfection supply room equipment is characterized in that: The generation step Q1 of the machine learning module for evaluating the degree of enzyme liquid deterioration is: based on each environmental history test class data β, the attribute value of the enzyme liquid deterioration corresponding to the environmental history test class data that affects the result is used as the input of the machine learning model, the cause of enzyme liquid deterioration is used as the prediction target, and the prediction accuracy of the machine learning model is used as the training target.

3. According to claim 2, an enzyme solution intelligent control system for cleaning medical disinfection supply room equipment is characterized in that: The step Q2 of generating the machine learning module for evaluating the degree of deterioration of the enzyme solution is: training the machine learning model of each environmental history test data β, presetting the prediction accuracy threshold α, and stopping the training when the prediction accuracy of the machine learning model reaches the prediction accuracy threshold α, and marking the machine learning model trained for the environmental history test data β as β α .

4. The enzyme solution intelligent control system for cleaning medical disinfection supply room equipment according to claim 3, characterized in that: The step Y1 of collecting real-time environmental data by the environmental real-time data collection module is: according to the actual situation, a threshold value of the degree of deterioration is preset for each environmental historical test data β, and the threshold value of the proportion of deteriorated components of the historical environmental test data β is set to K β For the environmental historical test data β, the percentage of deteriorated components detected in the enzyme solution each time is counted. The percentage of deteriorated components is the molecular content of the deteriorated components in the enzyme solution as a whole. If the percentage of deteriorated components in the real-time environmental data is less than the threshold value K of the percentage of deteriorated components, β , no processing is performed, otherwise, go to step Y2.

5. The enzyme solution intelligent control system for cleaning medical disinfection supply room equipment according to claim 4, characterized in that: Step Y2 of the environmental real-time data collection module for collecting environmental real-time data is: collecting the attribute values ​​of the enzyme solution deterioration corresponding to the influencing results obtained in the subsequent test and sending them to the deterioration analysis module.

Images