Disinfection optimization treatment method for nursing environment

Abstract

The invention relates to a disinfection optimization treatment method for environment nursing, and relates to the field of process optimization, and the method comprises the following steps: according to a microorganism concentration sequence array, predicting and obtaining microorganism concentration prediction distribution after a future moment; optimizing the microorganism concentration prediction distribution according to the activity path and the activity frequency of the patient to obtain corrected microorganism concentration prediction distribution; setting an atomization concentration constraint in combination with the physical sign data, the illness state information and the historical activity range of the patient; and by taking a plurality of atomization concentration constraints as limits, carrying out disinfection scheme optimization according to the corrected microorganism concentration prediction distribution, and outputting an optimal disinfection scheme. The technical problems that matched disinfection parameters cannot be set according to the current environment state of the infectious disease department and the disinfection accuracy is insufficient in a traditional method can be solved; according to the invention, accurate, efficient and safe disinfection management can be realized, meanwhile, potential hazards of excessive disinfection to patients are reduced, and the disinfection quality and the comfort level of the patients are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of process optimization, and particularly to a method for optimizing disinfection in a nursing environment. Background Art

[0002] In the nursing process of modern hospitals, especially in the infectious disease department, environmental disinfection has always been the core link to prevent nosocomial infections and cross-infections. With the gradual improvement of hospital infection control standards, the disinfection quality has an important impact on the health and treatment effect of patients.

[0003] However, traditional disinfection methods often fail to consider the real-time changing environmental conditions, such as microbial concentration, air circulation, patient activities, etc. These methods usually adopt fixed disinfection frequencies and intensities, ignoring the actual concentration changes of microorganisms and the individual needs of patients at each time point and spatial area, and there is a problem of insufficient accuracy. Summary of the Invention

[0004] Aiming at the technical problem that the traditional disinfection method cannot set matching disinfection parameters according to the current environmental state of the infectious disease department and there is insufficient disinfection accuracy, the present invention provides a method for optimizing disinfection in a nursing environment to solve this problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides a method for optimizing disinfection in a nursing environment, including: monitoring and obtaining a plurality of microbial concentration sequences at a plurality of monitoring points within the coverage area of the infectious disease department, performing interpolation prediction for a plurality of unknown points to generate a microbial concentration sequence array; predicting and obtaining the predicted distribution of microbial concentration after a future moment according to the microbial concentration sequence array; optimizing the predicted distribution of microbial concentration according to the activity paths and activity frequencies of patients within the coverage area of the infectious disease department to obtain a corrected predicted distribution of microbial concentration; combining the physical signs data, disease conditions and historical activity ranges of a plurality of patients to set a plurality of atomization concentration constraints; taking the plurality of atomization concentration constraints as limitations, optimizing the disinfection plan according to the corrected predicted distribution of microbial concentration, outputting an optimal disinfection plan, and controlling the disinfection robot to execute the disinfection control after the future moment.

[0006] Preferably, the method for optimizing disinfection in a nursing environment further includes: regularly monitoring a plurality of monitoring points within the coverage area of the infectious disease department through a microbial monitoring system to generate a plurality of microbial concentration sequences; based on the Kriging interpolation principle, performing interpolation prediction for a plurality of unknown points according to the plurality of microbial concentration sequences to generate a plurality of microbial concentration prediction sequences; constructing a microbial concentration sequence array based on the plurality of microbial concentration sequences and a plurality of microbial concentration prediction sequences.

[0007] Preferably, the disinfection optimization processing method for a nursing environment further includes: synchronously monitoring and obtaining environmental parameters of a plurality of monitoring points to generate a plurality of environmental parameter sequences, where the environmental parameters include temperature, humidity, and air flow direction; according to the plurality of environmental parameter sequences, performing mapping compensation on the plurality of microorganism concentration prediction sequences to obtain a plurality of compensated microorganism concentration prediction sequences; constructing a microorganism concentration sequence array based on the plurality of microorganism concentration sequences and the plurality of compensated microorganism concentration prediction sequences.

[0008] Preferably, the disinfection optimization processing method for a nursing environment further includes: combining the position information of a plurality of monitoring points and a plurality of unknown points, and respectively constructing an environmental parameter sequence array and a microorganism concentration prediction sequence array according to the plurality of environmental parameter sequences and the plurality of microorganism concentration prediction sequences; collecting a sample environmental parameter sequence array set, a sample microorganism concentration prediction sequence array set, and a plurality of sample compensated microorganism concentration prediction sequence sets, and performing supervised training on a feedforward neural network until the model converges to obtain a concentration compensation analysis plug-in; inputting the environmental parameter sequence array and the microorganism concentration prediction sequence array into the concentration compensation analysis plug-in to output a plurality of compensated microorganism concentration prediction sequences.

[0009] Preferably, the disinfection optimization processing method for a nursing environment further includes: according to the microorganism monitoring records in the coverage area of the infectious disease department, collecting a sample microorganism concentration sequence array set and a sample environmental parameter sequence array set, and obtaining a sample predicted microorganism concentration array after the same historical moment for different sample microorganism concentration sequence arrays and sample environmental parameter sequence arrays to obtain a sample predicted microorganism concentration array set; using the sample microorganism concentration sequence array set, the sample environmental parameter sequence array set, and the sample predicted microorganism concentration array set to train a feedforward neural network to construct a concentration prediction plug-in; using the concentration prediction plug-in to perform prediction according to the microorganism concentration sequence array and the environmental parameter sequence array to obtain a predicted microorganism concentration array; performing concentration distribution fitting according to the predicted microorganism concentration array to generate a microorganism concentration prediction distribution.

[0010] Preferably, the disinfection optimization processing method for a nursing environment further includes: monitoring and obtaining a plurality of activity paths and a plurality of activity frequencies of a plurality of patients; determining the activity range according to the activity path, analyzing and determining the microorganism concentration enhancement coefficient according to the activity frequency, and sequentially analyzing and determining the plurality of activity ranges and the plurality of microorganism concentration enhancement coefficients of the plurality of patients; optimizing and enhancing the microorganism concentration prediction distribution according to the plurality of activity ranges and the plurality of microorganism concentration enhancement coefficients to obtain a corrected microorganism concentration prediction distribution.

[0011] Preferably, the method for optimizing disinfection treatment in a nursing environment further includes: randomly selecting the first physical sign data, the first medical condition information, and the first historical activity range of the first patient; analyzing the tolerable atomization concentration according to the disinfectant type, the first physical sign data, and the first medical condition information, and predicting and obtaining the first maximum atomization concentration; setting a first atomization concentration constraint according to the first historical activity range and the first maximum atomization concentration, and adding it to the multiple atomization concentration constraints.

[0012] Preferably, the method for optimizing disinfection treatment in a nursing environment further includes: taking the disinfectant type as a constraint, collecting a sample physical sign data set and a sample medical condition information set, and obtaining the maximum atomization concentration tolerable by the patient under different sample physical sign data and sample medical condition information, which is set as the sample atomization concentration, to obtain a sample atomization concentration set; using the sample physical sign data set, the sample medical condition information set, and the sample atomization concentration set as training data, constructing an atomization concentration analysis model based on machine learning; using the atomization concentration analysis model to analyze the tolerable atomization concentration according to the disinfectant type, the first physical sign data, and the first medical condition information, and outputting the first maximum atomization concentration.

[0013] Preferably, the method for optimizing disinfection treatment in a nursing environment further includes: configuring the disinfection atomization concentration based on the predicted distribution of the corrected microbial concentration to obtain an atomization concentration distribution; adjusting the disinfection atomization concentration distribution according to the multiple atomization concentration constraints to obtain an optimized atomization concentration distribution, wherein if the maximum atomization concentration in the atomization concentration constraint is greater than or equal to the disinfection atomization concentration within the same activity range, no adjustment is made, and if the maximum atomization concentration in the atomization concentration constraint is less than the disinfection atomization concentration within the same activity range, the maximum atomization concentration is used for replacement; generating an optimal disinfection plan according to the predetermined disinfection path and the optimized atomization concentration distribution.

[0014] The beneficial effects of the present invention are as follows: By monitoring and obtaining a number of microbial concentration sequences at several monitoring points within the coverage area of the infectious disease department, interpolation prediction is performed for multiple unknown points to generate a microbial concentration sequence array; then, based on the microbial concentration sequence array, the predicted microbial concentration distribution after a future moment is obtained; further, according to the activity paths and activity frequencies of patients within the coverage area of the infectious disease department, the predicted microbial concentration distribution is optimized to obtain a corrected microbial concentration prediction distribution; on the other hand, multiple atomization concentration constraints are set in combination with the physical signs data, condition information, and historical activity ranges of multiple patients; finally, with the multiple atomization concentration constraints as limitations, the disinfection plan is optimized according to the corrected microbial concentration prediction distribution, and the optimal disinfection plan is output to control the disinfection robot to execute the disinfection control after the future moment. That is to say, through real-time monitoring, dynamic prediction, and personalized optimization, accurate, efficient, and safe disinfection management can be achieved, while reducing the potential harm of over-disinfection to patients, effectively improving the disinfection quality of the hospital's infectious disease department and the comfort of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic flowchart of a disinfection optimization processing method for a nursing environment provided by the present invention; Figure 2 It is a schematic flowchart of generating a microbial concentration sequence array in a disinfection optimization processing method for a nursing environment provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0017] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0018] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described in the present invention as "for example" is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0019] Embodiments, such as Figure 1 As shown, an embodiment of the present invention provides a method for optimizing disinfection treatment in a nursing environment, which specifically includes the following steps: S10: Monitor and obtain a plurality of microbial concentration sequences at a plurality of monitoring points within the coverage area of the infectious disease department, perform interpolation prediction for a plurality of unknown points, and generate a microbial concentration sequence array.

[0020] Further, as Figure 2 shown, step S10 of the present invention further includes: S11: Regularly monitor a plurality of monitoring points within the coverage area of the infectious disease department through a microbial monitoring system to generate a plurality of microbial concentration sequences; S12: Based on the Kriging interpolation principle, perform interpolation prediction for a plurality of unknown points according to the plurality of microbial concentration sequences to generate a plurality of microbial concentration prediction sequences.

[0021] Specifically, the microbial concentration in the environment of the infectious disease department needs to be measured in real time or regularly through a monitoring system, which usually includes sensors and microbial sampling equipment, and can accurately measure the concentration of microorganisms such as bacteria, viruses, and fungi in the air and collect data at certain time intervals.

[0022] First, according to the specific situation of the infectious disease department (such as the number of wards, air circulation, patient activity areas, etc.), several monitoring points are selected within the coverage area. The layout of these monitoring points usually takes into account the differences in microbial concentrations in different areas and the functional requirements of the ward area, such as different monitoring needs may exist in the ward area, corridors, nurse stations, public areas, etc. Then, the monitoring system will conduct regular data collection at set time intervals (such as every 10 minutes). Each data collection will record the microbial concentration data of each monitoring point, and these data are usually stored in the form of a time series. A microbial concentration value is generated for each monitoring point after each measurement. Over time, the concentration data at multiple time points can form a microbial concentration sequence. For example, if monitoring point A is measured every 10 minutes, then after 4 hours of monitoring, 24 microbial concentration data points will be obtained, which constitute the concentration sequence of monitoring point A. These microbial concentration data will serve as the basis for subsequent analysis, and several microbial concentration sequences will be obtained.

[0023] Kriging interpolation is an interpolation method based on spatial statistics. It not only considers the data of measurement points but also combines the spatial correlation (i.e., spatial autocorrelation) between measurement points. The core idea of the Kriging method is to infer the values of unknown points through the data of known monitoring points and a spatial model. This method is very useful in environmental monitoring because environmental data (such as microbial concentrations) usually have spatial correlation, and the concentration values in adjacent areas are often similar. Through Kriging interpolation, the blank areas due to uneven distribution of monitoring points or unmonitored areas can be effectively filled. First, use the microbial concentration sequences of multiple monitoring points collected in step S11. The spatio-temporal distribution of these sequences will be used as the basic data for interpolation. The microbial concentration sequence of each monitoring point contains the concentration changes of that point at different times. Through these data, the spatial relationship between different monitoring points can be established. Then, according to the Kriging interpolation principle, for each unknown point (i.e., unmonitored area or time period), by calculating its spatial autocorrelation with known monitoring points, a spatial model (usually based on the semi-variogram function) is used to estimate the microbial concentration value of this unknown point. The semi-variogram function is a function used in Kriging interpolation to measure the similarity between spatial points and usually depends on the characteristics of the geographical space, such as distance, variance, etc. The Kriging method calculates the optimal weights of the predicted values through the semi-variogram function, thereby obtaining the predicted concentration value of each unknown point. For each unmonitored point, the Kriging algorithm will output a predicted microbial concentration value. By combining the predicted values of multiple unknown points, a complete predicted microbial concentration sequence can be generated. The predicted sequence includes not only the actual data of the monitored points but also the data of the unknown points estimated through interpolation.

[0024] By using the Kriging interpolation algorithm, the concentrations at unmonitored regions or time points are interpolated and predicted based on these microbial concentration sequences, generating multiple microbial concentration prediction sequences; this process provides a scientific prediction of the concentrations in unmonitored regions by considering spatial correlation and the spatial distribution of data, ensuring that the overall trend of microbial concentration changes can be accurately grasped during actual disinfection.

[0025] S13: Construct a microbial concentration sequence array based on the several microbial concentration sequences and multiple microbial concentration prediction sequences.

[0026] Furthermore, step S13 of the present invention further includes: S131: Synchronously monitor and obtain the environmental parameters at several monitoring points, generating several environmental parameter sequences, where the environmental parameters include temperature, humidity, and air flow direction.

[0027] Specifically, by synchronously monitoring environmental parameters, more background information can be provided for subsequent microbial concentration prediction and disinfection optimization. Environmental parameters (such as temperature, humidity, and air flow direction) have important effects on the growth, spread, and disinfection effect of microorganisms. Therefore, real-time acquisition of these environmental data is crucial for the precise control of environmental disinfection. Similar to the microbial monitoring system, the environmental parameter monitoring points should be reasonably set according to the spatial layout of the infectious disease department area. These monitoring points should cover multiple key areas within the infectious disease department, especially places that are easily affected by pathogen transmission (such as wards, corridors, nurse stations, etc.). A reasonable layout of monitoring points can ensure representative environmental data is obtained.

[0028] To ensure the real-time and accuracy of data, the monitoring of environmental parameters needs to be synchronized with the monitoring of microbial concentration. This means that each time the microbial concentration is monitored, data such as temperature, humidity, and air flow direction need to be collected simultaneously. Synchronized monitoring ensures that fluctuations in microbial concentration under different environmental conditions can be promptly reflected and an association between the two can be established. The environmental parameters (such as temperature, humidity, and air flow direction) at each monitoring point will be collected at different time points, thus forming an environmental parameter sequence. Among them, temperature directly affects the growth rate and activity of microorganisms. Generally speaking, high temperature helps inactivating many pathogens, while low temperature may promote the survival and spread of certain pathogens. Therefore, temperature monitoring helps to understand the impact of the environment on microorganisms; humidity has a significant impact on the distribution and transmission of microorganisms in the air. Excessive humidity may promote the growth of molds and bacteria, while low humidity may increase fine particulate matter in the air, affecting respiratory health. Controlling humidity within a certain range helps reduce the concentration of microorganisms in the air; air circulation is a key factor in controlling the spread of pathogens in the air. The direction and speed of air flow determine the transmission path of microorganisms indoors. A good ventilation system can effectively reduce the residence time and concentration of microorganisms in the air, avoiding cross-infection; for example, if the temperature is measured once an hour, 24 temperature data points can be obtained within a day, forming a temperature sequence. Similarly, humidity and air flow direction will also generate corresponding sequences according to the sampling frequency. These sequences not only provide environmental data at each monitoring point at different times but also provide valuable auxiliary information for changes in microbial concentration. By analyzing these environmental parameter sequences, the potential impact of environmental conditions on microbial concentration can be understood, providing data support for optimizing subsequent disinfection strategies.

[0029] By synchronously monitoring the environmental parameters (such as temperature, humidity, and air flow direction) in the infectious disease department, more background information can be provided for predicting changes in microbial concentration and optimizing disinfection plans; through the collection and analysis of environmental parameter sequences, the impact of environmental conditions on microbial concentration can be revealed, thereby providing a scientific basis for future disinfection strategies.

[0030] S132: According to the several environmental parameter sequences, perform mapping compensation on the multiple microbial concentration prediction sequences to obtain multiple compensated microbial concentration prediction sequences.

[0031] Furthermore, step S132 of the present invention further includes: S1321: Combine the location information of several monitoring points and multiple unknown points, and respectively construct an environmental parameter sequence array and a microbial concentration prediction sequence array according to the several environmental parameter sequences and multiple microbial concentration prediction sequences; S1322: Collect a sample environmental parameter sequence array set, a sample microbial concentration prediction sequence array set, and multiple sample compensated microbial concentration prediction sequence sets, and perform supervised training on the feedforward neural network until the model converges to obtain a concentration compensation analysis plug-in; S1323: Input the environmental parameter sequence array and the microbial concentration prediction sequence array into the concentration compensation analysis plug-in, and output multiple compensated microbial concentration prediction sequences.

[0032] Specifically, combine the location information of several monitoring points, and construct an environmental parameter sequence array according to the several environmental parameter sequences, that is, integrate the environmental parameter sequences of different monitoring points together to form an environmental parameter sequence array. This array contains the environmental parameter data of all monitoring points, and these data are integrated according to the time dimension. For example, assume there are N monitoring points, and each monitoring point has environmental data at T time points. Then the final environmental parameter sequence array will be an N×T matrix, where each row represents the environmental parameter data sequence of a monitoring point, and each column represents the environmental parameters of all monitoring points at the same time point. On the other hand, construct a microbial concentration prediction sequence array according to the location information of multiple unknown points and multiple microbial concentration prediction sequences.

[0033] First, collect a sample environmental parameter sequence array set. Among them, the environmental parameter data of each sample includes information such as temperature, humidity, and air flow direction. These data are arranged in a time series and can reflect environmental changes at different times and locations; then, corresponding to each environmental parameter sample, collect a microbial concentration prediction sequence array set. These concentration data reflect the microbial concentration distribution at different time points and locations and are the key inputs for training the neural network; then collect the sample compensated microbial concentration prediction sequences corresponding to the sample environmental parameter sequence array and the sample microbial concentration prediction sequence array. The compensated prediction is based on the correction of environmental parameters and the original concentration prediction, aiming to make up for the prediction errors caused by changes in environmental parameters or model biases.

[0034] Then, using the sample environmental parameter sequence array and the sample microbial concentration prediction sequence array as inputs, and the sample compensated microbial concentration prediction sequence as supervision, these collected sample datasets (environmental parameters, microbial concentration prediction sequences, compensated concentration prediction sequences) are used as the training set and input into a feedforward neural network for supervised learning. The neural network model continuously adjusts the weight and bias parameters by comparing the error between the real data and the prediction results to achieve optimal prediction performance. The goal of the neural network is to learn how environmental parameters affect the microbial concentration and obtain a concentration compensation analysis plugin through training that can perform concentration compensation. The training process continues until the error of the model converges, indicating that the model has found the optimal parameter configuration and can effectively predict and compensate for the microbial concentration from environmental data. After training, the obtained concentration compensation analysis plugin, as an independent module, can be used to perform compensation analysis on new environmental parameters and microbial concentration predictions.

[0035] Finally, the environmental parameter sequence array and the microbial concentration prediction sequence array are input into the concentration compensation analysis plugin. Based on the previously trained feedforward neural network, concentration compensation analysis is performed. The plugin applies the neural network model to perform compensation calculations according to the input environmental parameters and predicted concentrations, and outputs the predicted values of the microbial concentration after compensation. This compensation process can correct the concentration prediction deviation caused by environmental factor changes, measurement errors, or other unknown factors. After concentration compensation analysis, the plugin outputs multiple compensated microbial concentration prediction sequences. These compensated sequences have higher accuracy compared to the original prediction sequences and can more realistically reflect the actual microbial concentration distribution. The output compensated concentration sequences will be used to further adjust the disinfection strategy to ensure that the disinfection intensity and frequency match the actual microbial concentration, avoiding over-disinfection or under-disinfection.

[0036] By using this plugin to compensate the input environmental and concentration data, compensated microbial concentration prediction sequences are generated. These compensated data can provide a more accurate basis for optimizing the disinfection plan and ensure that the disinfection process is more precise, safe, and effective.

[0037] S133: Construct a microbial concentration sequence array based on the several microbial concentration sequences and multiple compensated microbial concentration prediction sequences.

[0038] Specifically, finally, the several microbial concentration sequences and multiple compensated microbial concentration prediction sequences are merged to generate a microbial concentration sequence array. By merging and optimizing the original data and the compensated data, a comprehensive concentration prediction picture is formed, which provides a more accurate basis for subsequent disinfection plans and enables precise and efficient environmental disinfection management.

[0039] S20: Predict and obtain the predicted distribution of the microbial concentration after a future time according to the microbial concentration sequence array.

[0040] Furthermore, step S20 of the present invention further includes: S21: Collect a sample microbial concentration sequence array set and a sample environmental parameter sequence array set according to the microbial monitoring records in the coverage area of the infectious disease department, and obtain the sample predicted microbial concentration arrays after the same historical time for different sample microbial concentration sequence arrays and sample environmental parameter sequence arrays, so as to obtain a sample predicted microbial concentration array set; S22: Use the sample microbial concentration sequence array set, the sample environmental parameter sequence array set and the sample predicted microbial concentration array set to train a feedforward neural network and construct a concentration prediction plug-in; S23: Use the concentration prediction plug-in to perform prediction according to the microbial concentration sequence array and the environmental parameter sequence array, and obtain a predicted microbial concentration array; S24: Perform concentration distribution fitting according to the predicted microbial concentration array to generate a predicted distribution of the microbial concentration.

[0041] Specifically, obtain the microbial monitoring records in the coverage area of the infectious disease department. The microbial monitoring records are data obtained by real-time monitoring and recording of the microbial concentration at each monitoring point in the infectious disease department. These records usually include the microbial concentration at each monitoring point in different time periods (such as the concentration of bacteria, viruses or fungi). The microbial monitoring data is the core data for optimizing environmental disinfection, which can reflect the degree of environmental pollution, the type of pollutants and the microbial distribution state in different time periods. According to the microbial monitoring records, collect a sample microbial concentration sequence array set. The sample microbial concentration sequence array set is a set composed of microbial concentration sequences of multiple samples (i.e., multiple monitoring points). Each sample concentration sequence is a time series data, which reflects the change of the microbial concentration at a monitoring point at multiple time points. By collecting these sequences, the fluctuation of the microbial concentration in different areas of the infectious disease department can be comprehensively understood; together with the microbial concentration data, environmental parameters (such as temperature, humidity, air flow direction, etc.) are also regularly monitored. These sample environmental parameter sequence arrays are sequence data obtained by monitoring different environmental factors. Environmental parameters directly affect the growth, transmission and concentration change of microorganisms. Therefore, they are essential factors in predicting the microbial concentration. The environmental parameter sequence array reflects the change of the environmental conditions at each monitoring point over time. On the other hand, obtain the microbial concentration arrays after the same historical time (such as 1 hour later) for different sample microbial concentration sequence arrays and sample environmental parameter sequence arrays, and set them as sample predicted microbial concentration arrays, so as to obtain a sample predicted microbial concentration array set.

[0042] A feedforward neural network is a basic neural network structure, commonly used for regression or classification tasks. In this scenario, the feedforward neural network is used to establish a prediction model based on historical microbial concentration data, environmental parameter data, and prediction data. Further, taking a sample microbial concentration sequence array and a sample environmental parameter sequence array as inputs, and a sample predicted microbial concentration array as supervision, using the sample microbial concentration sequence array set, sample environmental parameter sequence array set, and sample predicted microbial concentration array set as training data, the feedforward neural network is supervised and trained. The training process of the neural network includes the forward propagation of input data and the backpropagation of errors. By continuously adjusting the weights and biases in the network, the output of the network is optimized to minimize the prediction error; iterative training is performed until the loss function converges to obtain a concentration prediction plug-in. The concentration prediction plug-in is a trained neural network model that can predict the microbial concentration in real time when given new input data.

[0043] By training the feedforward neural network, inputting historical microbial concentration sequences, environmental parameter data, and predicted concentration data into the network, and training the neural network model, the model can predict the microbial concentration in future time periods. After this training process is completed, the network model is constructed into a concentration prediction plug-in, which can automatically predict and adjust the microbial concentration when new data is input, providing real-time and accurate support for disinfection decision-making.

[0044] Then, input the microbial concentration sequence array and the environmental parameter sequence array into the concentration prediction plug-in for prediction, that is, predict the future microbial concentration based on the input historical data and environmental conditions, and output a predicted microbial concentration array. Each data point in the predicted microbial concentration array corresponds to the microbial concentration value at a specific location (monitoring point) and time point. These predicted values provide a scientific basis for subsequent disinfection decision-making. Next, perform concentration distribution fitting based on the predicted microbial concentration array, that is, combine the concentration values in the predicted microbial concentration array with environmental parameters, the spatial location of the monitoring point, and time factors to generate a comprehensive concentration distribution, and perform interpolation, smoothing, or optimization processing on the concentration data, so that the concentration data presents a reasonable spatial distribution pattern throughout the region. Especially if the concentration prediction values at certain positions or time points deviate from expectations, the fitting process will adjust these values to make them more in line with the actual situation, generating a microbial concentration prediction distribution. The microbial concentration prediction distribution is the concentration data after fitting, which shows the microbial concentration distribution at different monitoring points at future times. This distribution is not just a single concentration value, but a global view that can intuitively reflect how the microbial concentration changes in space and time.

[0045] S30: Optimize the predicted microbial concentration distribution according to the activity paths and activity frequencies of patients within the coverage area of the infectious disease department to obtain a corrected predicted microbial concentration distribution.

[0046] Further, step S30 of the present invention further includes: S31: Monitor and obtain the activity paths and activity frequencies of multiple patients; S32: Determine the activity ranges according to the activity paths, analyze and determine the microbial concentration enhancement coefficients according to the activity frequencies, and sequentially analyze and determine the activity ranges and microbial concentration enhancement coefficients of multiple patients; S33: Optimize and enhance the predicted microbial concentration distribution according to the multiple activity ranges and multiple microbial concentration enhancement coefficients to obtain a corrected predicted microbial concentration distribution.

[0047] Specifically, monitor and obtain the activity paths and activity frequencies of multiple patients. In the infectious disease department, the activity path of a patient refers to the movement route or location of the patient in the hospital or within the department. The activity range of a patient may include the bed area, restroom, corridor, etc. The activity trajectories of patients are recorded through sensors (such as RFID, GPS positioning systems, or other indoor positioning technologies) or patient handheld devices. These path data can provide the specific locations and movement trends of patient activities; the activity frequency refers to the frequency of a patient's activities within a unit time. These data can be collected through sensors or regular inspections to help analyze the activity intensity of patients. By monitoring the activity paths and frequencies of multiple patients, it is possible to more accurately understand how patients affect the spread of microorganisms within the infectious disease department. The activity paths and frequencies of patients are important influencing factors when calculating the microbial concentration distribution.

[0048] Next, determine the activity range according to the activity path. The activity range refers to the area where a patient actually moves in the hospital, usually obtained comprehensively from the patient's activity path. For some patients, the activity range may be relatively limited (such as only moving near the bed), while for other patients, it may cover the entire infectious disease department area. Analyze and determine the microbial concentration enhancement coefficient according to the activity frequency. The microbial concentration enhancement coefficient is a coefficient that measures the impact of a patient's activities on the microbial concentration in the surrounding environment. Patients with frequent activities may cause an increase in the microbial concentration in the surrounding area, especially in areas with strong air flow; if a patient has frequent activities, the microbial concentration may be enhanced due to air flow, breathing, or emissions, etc. Therefore, an enhancement coefficient needs to be set to reflect this impact; by analyzing the activity frequency of patients, the corresponding enhancement coefficient can be calculated, so as to quantify the impact of patient activities on the environmental microbial concentration. By determining the activity range and calculating the microbial concentration enhancement coefficient, it can provide a basis for subsequent optimization and adjustment of the microbial concentration. The activities of patients will affect the microbial concentration, especially patients with high-frequency activities, which may lead to an increase in the microbial concentration in the air or on the ground.

[0049] After obtaining the range of motion and enhancement coefficient of each patient, the next step is to optimize the predicted distribution of microbial concentration based on this information. Since patient movement can affect the microbial concentration in the surrounding area, the distribution of microbial concentration must be adjusted according to the range of motion and frequency of each patient. Patients with a larger range of motion will affect the microbial concentration in a wider area, while patients with a high activity frequency may have a greater impact on the surrounding environment in a short period of time. By applying the range of motion and the microbial concentration enhancement coefficient, these factors can be incorporated into the prediction model to update the original microbial concentration distribution. For example, in areas with a higher activity frequency, the microbial concentration will be relatively higher, so appropriate enhancement of the concentration in this area is required. During the optimization process, the predicted distribution of microbial concentration will become more accurate, capable of reflecting the true impact of patient movement on the environmental microbial concentration, and obtaining a corrected predicted distribution of microbial concentration. Through this process, the original prediction of microbial concentration can be adjusted according to the patient's movement information, thereby improving the accuracy of the disinfection strategy.

[0050] Optimizing the predicted distribution of microbial concentration according to the range of patient movement and the enhancement coefficient to obtain a corrected predicted distribution of microbial concentration can effectively combine the patient's movement situation, making the disinfection plan more accurate and personalized, and optimizing the environmental disinfection strategy in the infectious disease department.

[0051] S40: Set multiple atomization concentration constraints by combining the physical sign data, condition information, and historical range of motion of multiple patients.

[0052] Furthermore, step S40 of the present invention further includes: S41: Randomly select the first physical sign data, the first condition information, and the first historical range of motion of the first patient.

[0053] Specifically, randomly select any one patient from multiple patients as the first patient, and obtain the first physical sign data, the first disease condition information, and the first historical activity range of the first patient. Among them, the first physical sign data includes physiological data such as the current health status, body temperature, pulse, and blood pressure of the patient. These data can help understand the patient's health status, especially whether they are in the acute stage of infection. For example, information such as fever, decreased blood pressure, and increased heart rate may indicate that the patient's immune system is challenged and requires special attention. The first disease condition information includes the patient's disease type, disease stage (such as whether it is in the acute stage or recovery stage of infection), and treatment plan. These information are very important for determining the patient's immunity, viral load, and sensitivity to disinfectants. For example, patients undergoing immunosuppressive treatment may require a higher intensity of disinfection, while patients with a strong immune system may not require excessive disinfection. The first historical activity range refers to the specific areas where the patient has previously moved within the hospital, which can reflect the patient's potential impact on the environment. These data help determine which areas may require more disinfection resources. For example, if the first patient often moves in areas with strong air flow, then these areas may require more attention and disinfection.

[0054] S42: Conduct an analysis of the tolerable atomization concentration based on the disinfectant type, the first physical sign data, and the first disease condition information, and predict and obtain the first maximum atomization concentration.

[0055] Furthermore, step S42 of the present invention further includes: S421: With the disinfectant type as a constraint, collect a sample physical sign data set and a sample disease condition information set, and obtain the maximum atomization concentration tolerable by the patient under different sample physical sign data and sample disease condition information, set as the sample atomization concentration, and obtain a sample atomization concentration set; S422: Use the sample physical sign data set, the sample disease condition information set, and the sample atomization concentration set as training data, and construct an atomization concentration analysis model based on machine learning; S423: Use the atomization concentration analysis model to conduct an analysis of the tolerable atomization concentration based on the disinfectant type, the first physical sign data, and the first disease condition information, and output the first maximum atomization concentration.

[0056] Specifically, first, obtain the type of disinfectant. The type of disinfectant determines its active ingredients, concentration, volatility, and other characteristics. Different types of disinfectants (such as chlorine-based disinfectants, hydrogen peroxide, alcohol, etc.) have different effects on the environment and the human body. Therefore, the maximum atomization concentration that the patient can tolerate must be set according to the selected disinfectant. Different disinfectants may cause different degrees of irritation or harm to the human body, especially for patients with a weakened immune system (such as patients undergoing chemotherapy). Therefore, it is crucial to select an appropriate type of disinfectant and set the maximum atomization concentration that the patient can tolerate based on this. Next, with the type of disinfectant as a constraint, collect the sample physical sign dataset and the sample disease condition information set; and collect the maximum atomization concentration that the patient can tolerate under different sample physical sign data and sample disease condition information. That is, based on the patient's physical sign and disease condition data, their tolerance to the disinfectant can be estimated. The maximum concentration of the disinfectant that the patient can tolerate under different health states (such as a weakened or normal immune system) can be determined through historical data, medical literature, expert opinions, or clinical trial data. For example, for patients with multiple health problems, the concentration of the atomized disinfectant may need to be reduced to prevent irritation to the respiratory tract or cause other discomfort. The maximum atomization concentration may also be related to factors such as the patient's age, weight, and severity of the disease. For young and healthy patients, they may be able to tolerate a higher concentration of atomized disinfectant, while older or severely ill patients require a lower concentration. Among them, each sample patient will have a maximum atomization concentration that they can tolerate. These concentration values will be used as sample atomization concentrations and collected into a sample atomization concentration set. This set contains the maximum tolerance concentrations of different patients under different physical sign and disease condition conditions. This atomization concentration set will become the basis for subsequent optimization and adjustment of the disinfection strategy, used to calculate and control the concentration of the disinfectant that each patient can tolerate, and ensure effective disinfection without causing discomfort. By reasonably setting the maximum atomization concentration that each patient can tolerate, over-disinfection can be avoided, reducing respiratory discomfort or other physical reactions caused by excessive concentration, and ensuring the comfort of the patient.

[0057] Adopt machine learning algorithms, such as random forest, to build an atomization concentration analysis model. This model will be able to predict the maximum atomization concentration that a patient can tolerate by inputting features such as the patient's physical signs data and condition information. Then, using the sample physical signs dataset, sample condition information set, and sample atomization concentration set as training data, the goal during the training process is to enable the model to recognize the relationship between the physiological characteristics and condition information of different patients and the atomization concentration they can tolerate. After training is completed, the model can predict the atomization concentration based on different input data (such as physical signs, condition, etc.) to obtain the atomization concentration analysis model. Further, input the disinfectant type, the first physical signs data, and the first condition information into the atomization concentration analysis model for analyzable tolerable atomization concentration, and output the first maximum atomization concentration. That is, the maximum atomization disinfectant concentration that the patient can tolerate under the current physical signs and condition state. This concentration value will be used as a constraint condition in the subsequent disinfection plan to ensure that the disinfection process does not exceed the patient's tolerance range while achieving sufficient disinfection effect.

[0058] By establishing a machine learning model, the atomization concentration can be adjusted personalized according to information such as the patient's physical signs, condition, and disinfectant type. This can ensure that each patient receives the most appropriate disinfectant concentration, avoiding over - or under - disinfection. By accurately predicting the maximum atomization concentration that a patient can tolerate, health problems such as respiratory tract irritation caused by excessive concentration can be effectively avoided, ensuring the comfort and safety of the patient. On the premise of ensuring safety, by adjusting the atomization concentration, environmental disinfection can be carried out more efficiently, improving the disinfection effect and reducing the risks that may be brought by over - disinfection.

[0059] S43: Set the first atomization concentration constraint according to the first historical activity range and the first maximum atomization concentration, and add it to the multiple atomization concentration constraints.

[0060] Specifically, use the first historical activity range and the first maximum atomization concentration as the first atomization concentration constraint, that is, ensure that within the patient's activity range, the concentration of atomization disinfection does not exceed their tolerance. Finally, use the same method to analyze and obtain multiple atomization concentration constraints.

[0061] By combining the patient's historical activity range and the maximum atomization concentration, disinfection concentration constraints can be customized for each patient, ensuring maximum disinfection effect while avoiding harm or discomfort to the patient. This personalized constraint based on the patient's activity range and maximum tolerance concentration can more precisely control the disinfection process, avoid over - disinfection in certain areas of the environment, and reduce the waste of disinfectant in the air and environment. By setting reasonable atomization concentration constraints, fine management of the disinfection process can be achieved without affecting the disinfection quality, ensuring that the disinfection intensity of each area matches the patient's needs.

[0062] S50: With the multiple atomization concentration constraints as limitations, optimize the disinfection plan according to the predicted distribution of the corrected microbial concentration, output the optimal disinfection plan, and control the disinfection robot to execute the disinfection control after the future moment.

[0063] Further, step S50 of the present invention further includes: S51: Configure the disinfection atomization concentration based on the predicted distribution of the corrected microbial concentration to obtain the atomization concentration distribution; S52: Adjust the disinfection atomization concentration distribution according to the multiple atomization concentration constraints to obtain the optimized atomization concentration distribution. Among them, if the maximum atomization concentration in the atomization concentration constraint is greater than or equal to the disinfection atomization concentration within the same activity range, no adjustment is made. If the maximum atomization concentration in the atomization concentration constraint is less than the disinfection atomization concentration within the same activity range, the maximum atomization concentration is used for replacement; S53: Generate the optimal disinfection plan according to the predetermined disinfection path and the optimized atomization concentration distribution.

[0064] Specifically, configuring the disinfection atomization concentration based on the predicted distribution of the corrected microbial concentration means determining the required disinfection concentration for different regions and time points according to the predicted distribution of the microbial concentration. This process uses the microbial concentration data to guide the setting of the disinfection atomization concentration to ensure that the disinfectant can reach an effective concentration for the microorganisms while avoiding over-disinfection or under-disinfection. This process involves matching the required microbial concentration with the corresponding disinfection atomization concentration and configuring the disinfection atomization concentration throughout the infectious disease department area. During the disinfection process in the infectious disease department, it is necessary to ensure that the concentration of the disinfectant matches the distribution of the microbial concentration to maximize the disinfection effect.

[0065] Then, after determining the preliminary disinfection atomization concentration distribution, further adjustment is required according to the patient's personalized constraints (such as the maximum tolerable concentration, etc.) to ensure that the disinfection process is both effective and does not cause discomfort or harm to the patient; that is, for each patient's atomization concentration constraint, check the disinfection atomization concentration within the corresponding activity range. If the disinfection concentration in this area exceeds the patient's maximum tolerable concentration (i.e., the atomization concentration constraint), adjustment is made. If the maximum atomization concentration is greater than or equal to the disinfection concentration within the activity range, no adjustment is required and the disinfection concentration remains unchanged; if the maximum atomization concentration is less than the disinfection concentration within the activity range, the disinfection concentration is adjusted to this maximum tolerable concentration, which can ensure that the concentration of the disinfectant is within the range that the patient can tolerate, reducing the discomfort or potential harm to the patient. Finally, through these adjustments, an optimized disinfection atomization concentration distribution is generated to ensure that each patient can be disinfected within the maximum safe concentration range while maintaining the disinfection effect.

[0066] Obtain a predetermined disinfection path, which refers to the sequence and manner of disinfection operations, ensuring that each area is adequately disinfected, especially areas where patients are highly active or high-risk areas; based on the optimized atomization concentration distribution, combined with the predetermined disinfection path, generate the final disinfection plan, i.e., the optimal disinfection plan. By combining microbial concentration prediction, environmental parameters, patient activity paths, and personalized atomization concentration constraints, an accurate disinfection plan can be generated, avoiding over-disinfection or missed disinfection areas, thereby improving the overall disinfection efficiency; by avoiding excessive exposure to disinfectants, it can reduce patient discomfort such as respiratory irritation and ensure that the disinfection process does not impose additional burdens or health risks on patients.

[0067] The disinfection optimization processing method for a nursing environment provided by the embodiments of the present invention has at least the following technical effects: By monitoring and obtaining a plurality of microbial concentration sequences at a plurality of monitoring points within the coverage area of the infectious disease department, interpolation prediction of multiple unknown points is performed to generate a microbial concentration sequence array; then, based on the microbial concentration sequence array, the microbial concentration prediction distribution after a future moment is predicted; further, according to the activity paths and activity frequencies of patients within the coverage area of the infectious disease department, the microbial concentration prediction distribution is optimized to obtain a corrected microbial concentration prediction distribution; on the other hand, combining the physical sign data, condition information, and historical activity ranges of multiple patients, a plurality of atomization concentration constraints are set; finally, with the plurality of atomization concentration constraints as limitations, the disinfection plan is optimized according to the corrected microbial concentration prediction distribution, and the optimal disinfection plan is output to control the disinfection robot to execute the disinfection control after the future moment. That is to say, through real-time monitoring, dynamic prediction, and personalized optimization, accurate, efficient, and safe disinfection management can be achieved, while reducing the potential harm of over-disinfection to patients, effectively improving the disinfection quality of the hospital's infectious disease department, as well as the comfort of patients.

[0068] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concept.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. A disinfection optimization method for a nursing environment, characterized in that the method Including: Monitoring and obtaining a plurality of microbial concentration sequences at a plurality of monitoring points within the coverage area of the infectious disease department, performing interpolation prediction for a plurality of unknown points, and generating a microbial concentration sequence array; Predicting and obtaining the predicted distribution of microbial concentration after a future time according to the microbial concentration sequence array; Optimizing the predicted distribution of microbial concentration according to the activity paths and activity frequencies of patients within the coverage area of the infectious disease department to obtain a corrected predicted distribution of microbial concentration; Setting a plurality of atomization concentration constraints in combination with the physical sign data, disease conditions, and historical activity ranges of multiple patients; Taking the plurality of atomization concentration constraints as limitations, optimizing the disinfection plan according to the corrected predicted distribution of microbial concentration, outputting an optimal disinfection plan, and controlling the disinfection robot to execute the disinfection control after the future time.

2. The disinfection optimization treatment method for a nursing environment according to claim 1, wherein, Monitoring and obtaining a plurality of microbial concentration sequences at a plurality of monitoring points within the coverage area of the infectious disease department, performing interpolation prediction for a plurality of unknown points, and generating a microbial concentration sequence array, including: Regularly monitoring a plurality of monitoring points within the coverage area of the infectious disease department through a microbial monitoring system to generate a plurality of microbial concentration sequences; Based on the Kriging interpolation principle, performing interpolation prediction for a plurality of unknown points according to the plurality of microbial concentration sequences to generate a plurality of microbial concentration prediction sequences; Constructing a microbial concentration sequence array based on the plurality of microbial concentration sequences and the plurality of microbial concentration prediction sequences.

3. The disinfection optimization processing method for a nursing environment according to claim 2, wherein, Constructing a microbial concentration sequence array based on the plurality of microbial concentration sequences and the plurality of microbial concentration prediction sequences, including: Synchronously monitoring and obtaining environmental parameter sequences at a plurality of monitoring points to generate a plurality of environmental parameter sequences, where the environmental parameters include temperature, humidity, and air flow direction; Performing mapping compensation on the plurality of microbial concentration prediction sequences according to the plurality of environmental parameter sequences to obtain a plurality of compensated microbial concentration prediction sequences; Constructing a microbial concentration sequence array based on the plurality of microbial concentration sequences and the plurality of compensated microbial concentration prediction sequences.

4. A disinfection optimization method for a nursing environment according to claim 3, characterized in that, Performing mapping compensation on the plurality of microbial concentration prediction sequences according to the plurality of environmental parameter sequences to obtain a plurality of compensated microbial concentration prediction sequences, including: Combining the position information of a plurality of monitoring points and a plurality of unknown points, and respectively constructing an environmental parameter sequence array and a microbial concentration prediction sequence array according to the plurality of environmental parameter sequences and the plurality of microbial concentration prediction sequences; Collecting a sample environmental parameter sequence array set, a sample microbial concentration prediction sequence array set, and a plurality of sample compensated microbial concentration prediction sequence sets, and performing supervised training on a feedforward neural network until the model converges to obtain a concentration compensation analysis plug-in; Inputting the environmental parameter sequence array and the microbial concentration prediction sequence array into the concentration compensation analysis plug-in, and outputting a plurality of compensated microbial concentration prediction sequences.

5. A disinfection optimization method for a nursing environment according to claim 4, characterized in that, Predicting and obtaining the predicted distribution of microbial concentration after a future time according to the microbial concentration sequence array, including: According to the microbial monitoring records in the coverage area of the infectious disease department, collect the sample microbial concentration sequence array set and the sample environmental parameter sequence array set, and obtain the sample predicted microbial concentration array after the same historical moment for different sample microbial concentration sequences and sample environmental parameter sequences, so as to obtain the sample predicted microbial concentration array set; Use the sample microbial concentration sequence array set, the sample environmental parameter sequence array set and the sample predicted microbial concentration array set to train a feedforward neural network and construct a concentration prediction plug-in; Use the concentration prediction plug-in to predict according to the microbial concentration sequence array and the environmental parameter sequence array, and obtain the predicted microbial concentration array; Perform concentration distribution fitting according to the predicted microbial concentration array to generate a microbial concentration prediction distribution.

6. The disinfection optimization treatment method for a nursing environment according to claim 1, wherein, Optimize the microbial concentration prediction distribution according to the activity paths and activity frequencies of patients in the coverage area of the infectious disease department to obtain a corrected microbial concentration prediction distribution, including: Monitor and obtain the multiple activity paths and multiple activity frequencies of multiple patients; Determine the activity range according to the activity path, analyze and determine the microbial concentration enhancement coefficient according to the activity frequency, and sequentially analyze and determine the multiple activity ranges and multiple microbial concentration enhancement coefficients of multiple patients; Optimize and enhance the microbial concentration prediction distribution according to the multiple activity ranges and multiple microbial concentration enhancement coefficients to obtain a corrected microbial concentration prediction distribution.

7. A disinfection optimization method for a nursing environment according to claim 1, characterized in that, Combine the physical sign data, disease conditions and historical activity ranges of multiple patients to set multiple atomization concentration constraints, including: Randomly select the first physical sign data, the first disease condition information and the first historical activity range of the first patient; Perform an analysis of the tolerable atomization concentration according to the disinfectant type, the first physical sign data and the first disease condition information, and predict and obtain the first maximum atomization concentration; Set the first atomization concentration constraint according to the first historical activity range and the first maximum atomization concentration, and add it to the multiple atomization concentration constraints.

8. A disinfection optimization method for a nursing environment according to claim 7, characterized in that, The analysis of the tolerable atomization concentration according to the disinfectant type, the first physical sign data and the first disease condition information includes: Taking the disinfectant type as a constraint, collect the sample physical sign data set and the sample disease condition information set, and obtain the maximum atomization concentration tolerable by patients under different sample physical sign data and sample disease condition information, set as the sample atomization concentration, so as to obtain the sample atomization concentration set; Taking the sample physical sign data set, the sample disease condition information set and the sample atomization concentration set as training data, construct an atomization concentration analysis model based on machine learning; Use the atomization concentration analysis model to perform an analysis of the tolerable atomization concentration according to the disinfectant type, the first physical sign data and the first disease condition information, and output the first maximum atomization concentration.

9. A disinfection optimization treatment method for a nursing environment according to claim 1, characterized in that, Taking the multiple atomization concentration constraints as restrictions, optimize the disinfection plan according to the corrected microbial concentration prediction distribution, and output the optimal disinfection plan, including: Configure the disinfection atomization concentration based on the corrected microbial concentration prediction distribution to obtain the atomization concentration distribution; Adjust the disinfection atomization concentration distribution according to the multiple atomization concentration constraints to obtain an optimized atomization concentration distribution. Among them, if the maximum atomization concentration in the atomization concentration constraint is greater than or equal to the disinfection atomization concentration within the same activity range, no adjustment is made. If the maximum atomization concentration in the atomization concentration constraint is less than the disinfection atomization concentration within the same activity range, the maximum atomization concentration is used for replacement; Generate an optimal disinfection plan according to the predetermined disinfection path and the optimized atomization concentration distribution.

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