An adaptive sterilization control method for clean lamp

Abstract

The present invention discloses an adaptive sterilization control method for a clean lamp, and relates to the field of sterilization technology. The method comprises: collecting bacterial characteristic information in a target area, and collecting user characteristic information; analyzing and obtaining multiple sensitive bacterial species according to the user characteristic information, and obtaining characteristic information of multiple sensitive bacterial species; constructing a sensitive bacteria distribution, and screening and obtaining multiple key sterilization areas; analyzing the sterilization resistance information of multiple sensitive bacterial species according to the sterilization data of the clean lamp in the target area within a preset historical time range; optimizing the position and irradiation control parameters of the clean lamp in the target area, and obtaining the optimal position and optimal irradiation control parameters; according to the optimal position and optimal irradiation control parameters, the clean lamp is arranged and controlled in the target area for sterilization. The method solves the technical problem in the prior art that it is difficult for the clean lamp to adaptively adjust the sterilization parameters according to the environment, resulting in poor sterilization effect, and achieves the technical effect of improving the sterilization effect of the clean lamp.

Description

Technical Field

[0001] The invention relates to the technical field of sterilization, and in particular to an adaptive sterilization control method for a clean lamp. Background Art

[0002] In today's society, with the improvement of people's living standards and the increasing attention to public health and environment, the demand for clean environment is becoming more and more urgent. Especially in places with extremely high requirements for cleanliness, such as medical treatment, food production, and laboratories, the control of bacteria in the air has become an issue that cannot be ignored. As a new type of sterilization equipment, clean lamps have the advantages of no pollution, no residue, and harmlessness to the human body, and have gradually become one of the preferred equipment for clean environment control. However, traditional clean lamps have limitations in fixed position and parameter settings in terms of sterilization effects, and cannot adapt to actual needs in different environments. To this end, an adaptive sterilization control method for clean lamps is proposed, which can monitor the distribution and characteristics of bacteria in the target area in real time, and automatically adjust the position and irradiation control parameters of the clean lamp according to the characteristics and sterilization resistance of different bacteria, so as to achieve accurate and efficient sterilization. Summary of the invention

[0003] The embodiment of the present application provides an adaptive sterilization control method for a clean lamp, which solves the technical problem in the prior art that it is difficult for a clean lamp to adaptively adjust sterilization parameters according to the environment, resulting in poor sterilization effect.

[0004] In view of the above problems, an embodiment of the present application provides an adaptive sterilization control method for a clean lamp.

[0005] The present application provides an adaptive sterilization control method for a clean lamp, the method comprising:

[0006] Collect characteristic information of bacteria in the air at multiple monitoring locations within a target area, and collect characteristic information of users within the target area;

[0007] Analyze and obtain multiple sensitive bacterial species according to the user characteristic information, and filter the bacterial characteristic information of the multiple sensitive bacterial species from the multiple bacterial characteristic information to obtain multiple sensitive bacterial species characteristic information;

[0008] According to the characteristic information of the multiple sensitive bacteria species, construct the distribution of sensitive bacteria in the target area, and screen and obtain multiple sterilization key areas and multiple key distribution information;

[0009] Analyzing the sterilization resistance information of the plurality of sensitive bacteria species according to the clean light sterilization data of the target area within a preset historical time range;

[0010] According to the multiple sterilization key areas, multiple key distribution information and sterilization resistance information, the position and irradiation control parameters of the clean light in the target area are optimized to obtain the optimal position and the optimal irradiation control parameters;

[0011] According to the optimal position and the optimal irradiation control parameters, the clean lamps are arranged and controlled in the target area for sterilization.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0013] First, the bacterial characteristic information in the air at multiple monitoring positions in the target area is collected, and the user characteristic information in the target area is collected. Then, according to the user characteristic information, multiple sensitive bacteria species are analyzed and obtained, and the bacterial characteristic information of multiple sensitive bacteria species is screened from the multiple bacterial characteristic information to obtain multiple sensitive bacteria species characteristic information. Then, according to the multiple sensitive bacteria species characteristic information, the sensitive bacteria distribution in the target area is constructed, and multiple sterilization key areas and multiple key distribution information are screened. According to the sterilization data of the clean lamp in the target area within a preset historical time range, the sterilization resistance information of multiple sensitive bacteria species is analyzed. According to the multiple sterilization key areas, multiple key distribution information and sterilization resistance information, the position and irradiation control parameters of the clean lamp in the target area are optimized to obtain the optimal position and optimal irradiation control parameters. Finally, according to the optimal position and optimal irradiation control parameters, the clean lamp is arranged and controlled in the target area for sterilization. The technical problem that the clean lamp is difficult to adaptively adjust the sterilization parameters according to the environment in the prior art, resulting in poor sterilization effect, is solved, and the technical effect of improving the sterilization effect of the clean lamp is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 A schematic flow chart of an adaptive sterilization control method for a clean lamp provided in an embodiment of the present application;

[0016] Figure 2 A schematic diagram of a flow chart for analyzing the sterilization resistance information of multiple sensitive bacteria species in an adaptive sterilization control method for a clean lamp provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides an adaptive sterilization control method for a clean lamp, thereby solving the technical problem in the prior art that it is difficult for a clean lamp to adaptively adjust sterilization parameters according to the environment, resulting in poor sterilization effect.

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0019] It should be noted that the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0020] Embodiment 1

[0021] like Figure 1 As shown, the embodiment of the present application provides an adaptive sterilization control method for a clean lamp, wherein the method includes:

[0022] Collect characteristic information of bacteria in the air at multiple monitoring locations within a target area, and collect characteristic information of users within the target area;

[0023] When executing the adaptive sterilization control method, it is first necessary to collect bacterial characteristic information in the air at multiple monitoring locations in the target area, such as bacterial concentrations and types at different locations. At the same time, user characteristic information in the target area is also collected, such as the user's physical condition. Optionally, the target area is in the ward, by detecting the bacterial types and concentrations in the air at multiple locations in the ward, and collecting patient characteristics, such as symptoms and recovery stages. By collecting bacterial characteristic information and user characteristic information, the bacterial conditions and user needs in the target area can be more comprehensively understood, providing strong data support for adaptive sterilization control.

[0024] Furthermore, collecting characteristic information of bacteria in the air at multiple monitoring locations in the target area and collecting characteristic information of users in the target area include:

[0025] Collecting air samples at multiple monitoring locations in the target area, testing them, and obtaining multiple bacterial species information and multiple bacterial concentration information sets as bacterial characteristic information at multiple monitoring locations;

[0026] The monitoring data of users in the target area is collected as user characteristic information.

[0027] Select multiple monitoring locations in the target area, collect air samples for testing, and obtain multiple bacterial species information and multiple bacterial concentration information sets. Multiple bacterial species information and multiple bacterial concentration information sets will be used as bacterial characteristic information of multiple monitoring locations, where the bacterial characteristic information of each monitoring location includes bacterial species information and bacterial concentration information. At the same time, collect monitoring data of users in the target area, including the user's health status, age, gender, etc., as user characteristic information. By collecting user characteristic information, we can understand the specific needs of users and the population that may be more sensitive to certain bacteria, so as to formulate a more personalized sterilization strategy.

[0028] Analyze and obtain multiple sensitive bacterial species according to the user characteristic information, and filter the bacterial characteristic information of the multiple sensitive bacterial species from the multiple bacterial characteristic information to obtain multiple sensitive bacterial species characteristic information;

[0029] Based on the user characteristic information, analyze the sensitive bacterial species that the user may be affected by. For example, if there are many children or elderly people in the target area, you may need to pay attention to bacteria that are particularly sensitive to them. For these sensitive populations, you may need to focus on monitoring and controlling bacteria related to respiratory diseases, such as influenza virus, pneumococcus, etc. Based on the determined sensitive bacterial species, the bacterial characteristic information of multiple sensitive bacterial species is screened from the bacterial characteristic information of multiple monitoring locations to obtain multiple sensitive bacterial species characteristic information, including bacterial species, concentration, activity level, etc.

[0030] Further, according to the user characteristic information, a plurality of sensitive bacterial species are analyzed and obtained, and the bacterial characteristic information of the plurality of sensitive bacterial species is screened from the plurality of bacterial characteristic information to obtain the plurality of sensitive bacterial species characteristic information, including:

[0031] Based on the monitoring data records of the target area, a sample user characteristic information set is obtained, and a sample sensitive bacteria species information set of different sample user characteristic information is obtained;

[0032] Using the sample user characteristic information set and the sample sensitive bacterial species information set, a sensitive bacterial species classifier is constructed to identify and classify the user characteristic information to obtain multiple sensitive bacterial species;

[0033] According to the multiple sensitive bacterial species, characteristic information of sensitive bacterial species is screened from the multiple bacterial characteristic information to obtain multiple sensitive bacterial species characteristic information.

[0034] According to the monitoring data records of the target area, a sample user feature information set is obtained, and a sample sensitive strain information set of different sample user feature information is extracted therefrom. Specifically, a sample sensitive strain information set related to these user features is obtained from relevant medical and public health databases. Using machine learning technology, the sample user feature information set and the sample sensitive strain information set are used as training data to construct a sensitive strain classifier. The sensitive strain classifier can learn the association pattern between user feature information and sensitive strains, and can accurately identify and classify new user feature information. After the sensitive strain classifier is constructed, the user feature information in the target area can be input into the classifier for classification and identification. Through the processing of the classifier, multiple sensitive strains can be obtained, which are bacterial species that may pose a threat to the health of users in the area. According to the multiple sensitive strains obtained, screening is performed in multiple bacterial feature information. By comparison and matching, feature information related to sensitive strains is screened out, thereby obtaining multiple sensitive strain feature information.

[0035] According to the characteristic information of the multiple sensitive bacteria species, construct the distribution of sensitive bacteria in the target area, and screen and obtain multiple sterilization key areas and multiple key distribution information;

[0036] Based on the concentration characteristics of sensitive bacteria at multiple locations, the concentration distribution of sensitive bacteria in the target area is constructed, and then the area with the largest concentration is selected as the key sterilization area. For each selected key sterilization area, its key distribution information needs to be further determined, including the specific type of sensitive bacteria, concentration distribution, activity level, etc. By constructing the distribution of sensitive bacteria and determining the key sterilization areas and their key distribution information, the distribution of sensitive bacteria in the target area can be understood more accurately, providing strong support for subsequent sterilization work.

[0037] Furthermore, based on the characteristic information of the plurality of sensitive bacteria species, the distribution of sensitive bacteria in the target area is constructed, and a plurality of key sterilization areas and a plurality of key distribution information are obtained by screening, including:

[0038] Calculate and obtain multiple sensitive bacteria distribution information of multiple monitoring locations according to the multiple sensitive bacteria characteristic information, each sensitive bacteria distribution information includes the average concentration of multiple sensitive bacteria in the corresponding monitoring location;

[0039] Constructing the sensitive bacteria distribution in the target area based on the multiple sensitive bacteria distribution information combined with interpolation processing;

[0040] Based on the distribution of sensitive bacteria, multiple areas with the highest concentration of sensitive bacteria are screened and obtained as multiple key sterilization areas, and multiple key distribution information is obtained.

[0041] According to the characteristic information of multiple sensitive bacteria, the distribution information of multiple sensitive bacteria at multiple monitoring locations is calculated. That is to say, for each monitoring location, the average concentration of multiple sensitive bacteria in the location needs to be calculated. Optionally, the average concentration of sensitive bacteria is obtained by adding the concentration value of each sensitive bacteria at the location and then dividing it by the number of sensitive bacteria. Using these sensitive bacteria distribution information and combining interpolation processing techniques such as Kriging interpolation or inverse distance weighted interpolation, the distribution of sensitive bacteria in the target area is constructed. Through interpolation processing, the concentration of sensitive bacteria in areas where no monitoring points are set can be more accurately estimated, thereby obtaining a continuous and comprehensive distribution of sensitive bacteria. Based on the constructed sensitive bacteria distribution, multiple areas with the largest concentration of sensitive bacteria are screened out, and these areas will be regarded as key areas for sterilization. For each screened key sterilization area, its key distribution information is further obtained, including specific sensitive bacteria, concentration range, distribution, etc.

[0042] Analyzing the sterilization resistance information of the plurality of sensitive bacteria species according to the clean light sterilization data of the target area within a preset historical time range;

[0043] In order to more effectively control the sterilization of sensitive bacteria in the target area, it is necessary to analyze the resistance of these sensitive bacteria to the sterilization of clean lamps. Specifically, the preset historical time range can be set according to the actual situation, such as the past week or month. By obtaining the sterilization data of the clean lamp in the target area within the preset historical time range, including the operation record of the clean lamp, sterilization strategy, sterilization time, and corresponding bacterial detection results, etc., the sterilization resistance information of sensitive bacteria in the target area, that is, the degree of resistance to sterilization, is analyzed. If the sensitive bacteria can still maintain a certain survival rate or concentration after multiple sterilizations, it indicates that the bacteria has a strong resistance to clean lamp sterilization.

[0044] Furthermore, if Figure 2 As shown, according to the clean light sterilization data of the target area within a preset historical time range, the sterilization resistance information of the multiple sensitive bacteria species is analyzed, including:

[0045] Acquire the clean light sterilization data of the target area within a preset historical time range, wherein the clean light sterilization data includes sterilization duration and clean light control parameters;

[0046] Acquire a set of sample clean light sterilization data and a decreasing range of sterilization efficiency of the plurality of sensitive bacteria species after sterilization data of different sample clean light sterilization data as a set of sterilization resistance information of the plurality of samples;

[0047] Using the sample clean lamp sterilization data set and multiple sample sterilization resistance information sets, a sterilization resistance analyzer is constructed to analyze the clean lamp sterilization data to obtain multiple sensitive sterilization resistance information;

[0048] The sterilization resistance information is obtained by calculation based on the plurality of sensitive sterilization resistance information.

[0049] Optionally, obtain the clean light sterilization data of the target area within a preset historical time range, including the sterilization time and the clean light control parameters (such as light intensity, wavelength, irradiation mode, etc.). These data reflect the actual operation and sterilization effect of the clean light in the target area. Obtain multiple sample clean light sterilization data from the historical clean light sterilization data, covering data at different time points, different areas or under different conditions. At the same time, obtain the decreasing range of the sterilization efficiency of multiple sensitive bacteria after sterilization according to different sample sterilization data. Specifically, calculate the decreasing range of the sterilization efficiency of different sensitive bacteria according to the sterilization efficiency of different sensitive bacteria after each sterilization. The decreasing range of the sterilization efficiency reflects the change in the resistance of sensitive bacteria during the sterilization process, that is, as the number of sterilization increases, the sterilization effect of sensitive bacteria on the clean light gradually weakens, and these decreasing ranges are used as a set of sterilization resistance information for multiple samples. Based on the sample clean light sterilization data set and multiple sample sterilization resistance information sets, a sterilization resistance analyzer is constructed using machine learning technology. The sterilization resistance analyzer will learn the relationship between the clean light sterilization data and the sterilization resistance of sensitive bacteria. Specifically, the analyzer will try to identify which clean light control parameters and sterilization duration have the greatest impact on the resistance of sensitive bacteria, and predict the resistance changes of sensitive bacteria under given clean light sterilization conditions. After the sterilization resistance analyzer is constructed, the clean light sterilization data of the target area is input into the sterilization resistance analyzer for analysis. The sterilization resistance analyzer will predict the sterilization resistance information of multiple sensitive bacteria in the target area based on the learned model, including the resistance changes of sensitive bacteria and the decreasing range of sterilization efficiency under different clean light control parameters and sterilization durations. Based on the multiple sensitive sterilization resistance information output by the sterilization resistance analyzer, the final sterilization resistance information is calculated. The sterilization resistance information reflects the degree of resistance of sensitive bacteria in the target area to clean light sterilization, thus providing an important reference for formulating and adjusting the clean light sterilization strategy. For example, for sensitive bacteria with strong resistance, it is necessary to increase the sterilization time, adjust the clean light control parameters, or use other auxiliary sterilization methods to improve the sterilization effect.

[0050] According to the multiple sterilization key areas, multiple key distribution information and sterilization resistance information, the position and irradiation control parameters of the clean light in the target area are optimized to obtain the optimal position and the optimal irradiation control parameters;

[0051] After obtaining multiple key sterilization areas, multiple key distribution information and sterilization resistance information, the position and irradiation control parameters of the clean lamp in the target area will be optimized to obtain the optimal position and optimal irradiation control parameters to ensure the maximum sterilization effect.

[0052] Furthermore, according to the plurality of sterilization key areas, the plurality of key distribution information and the sterilization resistance information, the position and irradiation control parameters of the clean light in the target area are optimized, including:

[0053] Construct a clean light optimization function to optimize the position and control parameters of the clean light in the target area, as follows:

[0054] ;

[0055] Among them, CLB is bactericidal fitness, is the distance weight, is the parameter weight, is the power weight, M is the number of multiple sterilization key areas, is the weight assigned according to the size of the i-th key distribution information, is the irradiation distance between the position of the clean light and the i-th key sterilization area, To preset the irradiation distance, is the sterilization efficiency of irradiating and sterilizing the i-th sterilization key area according to the irradiation control parameters under the sterilization resistance information and the i-th key distribution information, is the power of the clean lamp under the irradiation control parameters, is the preset power;

[0056] Randomly generating a plurality of first positions and a plurality of first irradiation control parameters within the irradiation control parameters of the clean lamp as a plurality of first solutions;

[0057] Based on the clean lamp optimization function, analyzing and calculating a plurality of first sterilization fitnesses of the plurality of first solutions;

[0058] Based on the multiple first sterilization fitnesses, the multiple first solutions are divided and classified to generate multiple solution sets, and iterative optimization is performed until convergence;

[0059] Outputting a convergent solution set with the maximum sum of sterilization fitness after optimization convergence, wherein the convergent solution set includes multiple convergent solutions;

[0060] Acquire multiple convergence positions in the multiple convergence solutions, select the convergence solution output corresponding to the convergence position with the largest moving channel distance in the target area, and obtain the optimal position and optimal irradiation control parameters.

[0061] In order to optimize the position and control parameters of the clean light in the target area, it is necessary to construct a clean light optimization function and perform iterative optimization based on the function to find the optimal clean light position and irradiation control parameters. The clean light optimization function consists of three parts, which consider the distance between the clean light and the key sterilization area, the sterilization efficiency of the irradiation control parameters, and the power of the clean light. Among them, CLB is the sterilization fitness. is the distance weight, is the parameter weight, is the power weight, M is the number of multiple sterilization key areas, is the weight assigned according to the size of the i-th key distribution information, is the irradiation distance between the position of the clean light and the i-th key sterilization area, To preset the irradiation distance, is the sterilization efficiency, i.e., the sterilization ratio, of irradiating and sterilizing the i-th sterilization key area according to the irradiation control parameters under the sterilization resistance information and the i-th key distribution information, such as the ratio of the difference between the concentration before sterilization and the concentration after sterilization to the concentration before sterilization, is the power of the clean lamp under the irradiation control parameters, is the preset power. In the clean light optimization function, the smaller the irradiation distance, the better the sterilization effect, and the greater the sterilization efficiency, the better the sterilization effect.

[0062] Multiple first positions and multiple first irradiation control parameters are randomly generated within the irradiation control parameters of the clean lamp as multiple first solutions to form an initial solution set. Based on the clean lamp optimization function, the sterilization fitness of each solution in the initial solution set, i.e., the CLB value, is analyzed and calculated. Based on the first sterilization fitness, the initial solution set is divided and classified to generate multiple solution sets. Then, each solution set is iteratively optimized, and the position and irradiation control parameters of the clean lamp are continuously adjusted to reduce the corresponding sterilization fitness value until the solution set converges (i.e., the change of the solution tends to be stable). Among all the convergent solution sets, the convergent solution set with the largest sum of sterilization fitness is selected. Then, multiple convergence positions are obtained from the solution set, and the convergence solution corresponding to the convergence position with the largest distance from the moving channel in the target area is selected as the optimal solution, and the optimal position and optimal irradiation control parameters are output. In this way, it can be ensured that the position and irradiation control parameters of the clean lamp are optimal in terms of sterilization effect, power consumption, and avoiding interference with the moving channel.

[0063] Furthermore, based on the clean lamp optimization function, analyzing and calculating a plurality of first sterilization fitness of the plurality of first solutions includes:

[0064] Based on the sterilization data records of the clean lamp on the multiple sensitive bacteria, a sample irradiation control parameter set, a sample sterilization resistance information set and a sample key distribution information set are obtained, and a sample sterilization efficiency information set after sterilization is obtained;

[0065] The sample irradiation control parameter set, the sample sterilization resistance information set, the sample key distribution information set and the sample sterilization efficiency information set are used to construct a sterilization efficiency predictor, and the sterilization efficiency of the plurality of first irradiation control parameters is predicted in combination with the sterilization resistance information and the plurality of key distribution information to obtain a plurality of first sterilization efficiencies;

[0066] According to the multiple first positions, the multiple first sterilization efficiencies and the clean lamp power under the multiple first irradiation control parameters, based on the clean lamp optimization function, multiple first sterilization fitnesses are calculated and obtained.

[0067] From the sterilization data records of clean lamps on multiple sensitive bacteria, the sample irradiation control parameter set, sample sterilization resistance information set, sample key distribution information set and sample sterilization efficiency information set after sterilization are extracted. These data will be used to construct and train a sterilization efficiency predictor. Using machine learning technology, combined with the sample irradiation control parameter set, sample sterilization resistance information set, sample key distribution information set and sample sterilization efficiency information set, a sterilization efficiency predictor is constructed. The sterilization efficiency predictor can predict the corresponding sterilization efficiency based on the given irradiation control parameters, sterilization resistance information and key distribution information. For each first irradiation control parameter (i.e., the randomly generated initial irradiation control parameter), combined with the known sterilization resistance information and multiple key distribution information, the sterilization efficiency predictor is used to predict the sterilization efficiency, and multiple first sterilization efficiencies are obtained. These first sterilization efficiencies represent the sterilization effect of the clean lamp on sensitive bacteria under different positions and irradiation control parameters. According to multiple first positions (i.e., randomly generated initial positions), multiple first sterilization efficiencies, and clean lamp powers under multiple first irradiation control parameters, combined with the clean lamp optimization function, multiple first sterilization fitnesses are calculated. Each first sterilization fitness represents a comprehensive evaluation of the overall sterilization effect and power consumption under a specific position and irradiation control parameter combination.

[0068] Furthermore, based on the multiple first sterilization fitnesses, the multiple first solutions are divided and classified to generate multiple solution sets, and iterative optimization is performed, including:

[0069] According to the multiple first sterilization fitnesses, the multiple first solutions are divided, and a plurality of first solutions with the largest first sterilization fitnesses are used as multiple group heads;

[0070] Based on the multiple cluster heads, the other multiple first solutions are classified to construct multiple solution sets, wherein similar solutions are classified into one solution set;

[0071] In multiple solution sets, the cluster head is used as the optimization direction, multiple other solutions are optimized and updated, and the sterilization fitness of the updated solutions is calculated to update the cluster head;

[0072] Continue iterative optimization until convergence is reached.

[0073] According to multiple first sterilization fitness, multiple first solutions (i.e., the initial clean lamp position and irradiation control parameter combination) are sorted, and several first solutions with the largest first sterilization fitness are selected as multiple cluster heads (i.e., the representative solutions of each solution set). Based on multiple cluster heads, the other multiple first solutions are classified. Specifically, the similarity between solutions can be measured by using methods such as Euclidean distance and Manhattan distance. Similar solutions (i.e., solutions with a closer distance) are classified into the same solution set. In each solution set, the cluster head is used as the optimization direction, and local search and random perturbation are performed near the cluster head to optimize and update the other multiple solutions. The sterilization fitness of the updated solution is calculated and compared with the original solution. If the updated solution has a better sterilization fitness (i.e., a lower sterilization fitness value), it is retained; otherwise, the original solution is kept unchanged. After completing a round of optimization in the solution set, the cluster head is updated according to the updated solution set. The solution with the best sterilization fitness in the solution set can be selected as the new cluster head to guide the subsequent optimization direction. Repeat the above steps of optimization within the solution set and updating the cluster head to perform iterative optimization. In each iteration, all solution sets are updated and the sterilization fitness of the solution is recalculated. A convergence number is set as the termination condition of the iteration according to the actual situation. When the convergence number is reached, the iterative optimization is stopped and the current optimal solution (i.e. the solution with the lowest sterilization fitness) is output.

[0074] According to the optimal position and the optimal irradiation control parameters, the clean lamps are arranged and controlled in the target area for sterilization.

[0075] The clean lamps are arranged and controlled in the target area according to the optimal position and optimal irradiation control parameters to perform sterilization operations, ensuring efficient and accurate sterilization operations in the target area.

[0076] In summary, the embodiments of the present application have at least the following technical effects:

[0077] First, the bacterial characteristic information in the air at multiple monitoring positions in the target area is collected, and the user characteristic information in the target area is collected. Then, according to the user characteristic information, multiple sensitive bacteria species are analyzed and obtained, and the bacterial characteristic information of multiple sensitive bacteria species is screened from the multiple bacterial characteristic information to obtain multiple sensitive bacteria species characteristic information. Then, according to the multiple sensitive bacteria species characteristic information, the sensitive bacteria distribution in the target area is constructed, and multiple sterilization key areas and multiple key distribution information are screened. According to the sterilization data of the clean lamp in the target area within a preset historical time range, the sterilization resistance information of multiple sensitive bacteria species is analyzed. According to the multiple sterilization key areas, multiple key distribution information and sterilization resistance information, the position and irradiation control parameters of the clean lamp in the target area are optimized to obtain the optimal position and optimal irradiation control parameters. Finally, according to the optimal position and optimal irradiation control parameters, the clean lamp is arranged and controlled in the target area for sterilization. The technical problem that the clean lamp is difficult to adaptively adjust the sterilization parameters according to the environment in the prior art, resulting in poor sterilization effect, is solved, and the technical effect of improving the sterilization effect of the clean lamp is achieved.

[0078] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. Other embodiments are within the scope of the attached claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0080] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An adaptive sterilization control method for a clean lamp, characterized in that: The method comprises: Collect characteristic information of bacteria in the air at multiple monitoring locations within a target area, and collect characteristic information of users within the target area; Analyze and obtain multiple sensitive bacterial species according to the user characteristic information, and filter the bacterial characteristic information of the multiple sensitive bacterial species from the multiple bacterial characteristic information to obtain multiple sensitive bacterial species characteristic information; According to the characteristic information of the plurality of sensitive bacteria species, construct the distribution of sensitive bacteria species in the target area, and screen and obtain a plurality of key sterilization areas and a plurality of key distribution information; Analyzing the sterilization resistance information of the plurality of sensitive bacteria species according to the clean light sterilization data of the target area within a preset historical time range; According to the multiple sterilization key areas, multiple key distribution information and sterilization resistance information, the position and irradiation control parameters of the clean light in the target area are optimized to obtain the optimal position and the optimal irradiation control parameters; According to the optimal position and optimal irradiation control parameters, the clean light is arranged and controlled in the target area for sterilization; Collecting characteristic information of bacteria in the air at multiple monitoring locations in a target area, and collecting characteristic information of users in the target area, including: Collecting air samples at multiple monitoring locations in the target area, testing them, and obtaining multiple bacterial species information and multiple bacterial concentration information sets as bacterial characteristic information at multiple monitoring locations; Collecting monitoring data of users in the target area as user characteristic information; According to the user characteristic information, a plurality of sensitive bacterial species are analyzed and obtained, and the bacterial characteristic information of the plurality of sensitive bacterial species is screened from the plurality of bacterial characteristic information to obtain the plurality of sensitive bacterial species characteristic information, including: Based on the monitoring data records of the target area, a sample user characteristic information set is obtained, and a sample sensitive bacteria species information set of different sample user characteristic information is obtained; Using the sample user characteristic information set and the sample sensitive bacterial species information set, a sensitive bacterial species classifier is constructed to identify and classify the user characteristic information to obtain multiple sensitive bacterial species; According to the multiple sensitive bacterial species, screening the characteristic information of sensitive bacterial species in the multiple bacterial characteristic information to obtain multiple sensitive bacterial species characteristic information; According to the characteristic information of the plurality of sensitive bacteria species, the distribution of sensitive bacteria species in the target area is constructed, and a plurality of key sterilization areas and a plurality of key distribution information are obtained by screening, including: According to the plurality of sensitive bacterial species characteristic information, calculating and obtaining a plurality of sensitive bacterial species distribution information at a plurality of monitoring locations, wherein each sensitive bacterial species distribution information includes an average concentration of a plurality of sensitive bacterial species in a corresponding monitoring location; Based on the distribution information of the plurality of sensitive bacterial species combined with interpolation processing, construct the distribution of sensitive bacterial species in the target area; Based on the distribution of sensitive bacteria, multiple areas with the highest concentration of sensitive bacteria are screened and obtained as multiple key sterilization areas, and multiple key distribution information is obtained; According to the clean light sterilization data of the target area within a preset historical time range, the sterilization resistance information of the plurality of sensitive bacteria species is analyzed, including: Acquire the clean light sterilization data of the target area within a preset historical time range, wherein the clean light sterilization data includes sterilization duration and clean light control parameters; Acquire a set of sample clean light sterilization data and a decreasing range of sterilization efficiency of the plurality of sensitive bacteria species after sterilization data of different sample clean light sterilization data as a set of sterilization resistance information of the plurality of samples; Using the sample clean light sterilization data set and multiple sample sterilization resistance information sets, a sterilization resistance analyzer is constructed to analyze the clean light sterilization data to obtain sterilization resistance information of multiple sensitive bacteria species; Based on the sterilization resistance information of the plurality of sensitive bacteria species, the sterilization resistance information is calculated and obtained; According to the plurality of sterilization key areas, the plurality of key distribution information and the sterilization resistance information, the position and irradiation control parameters of the clean light in the target area are optimized, including: Construct a clean light optimization function to optimize the position and irradiation control parameters of the clean light in the target area, as follows: ； Among them, CLB is bactericidal fitness, is the distance weight, is the parameter weight, is the power weight, M is the number of multiple sterilization key areas, is the weight assigned according to the size of the i-th key distribution information, is the irradiation distance between the position of the clean light and the i-th key sterilization area, To preset the irradiation distance, is the sterilization efficiency of irradiating and sterilizing the i-th sterilization key area according to the irradiation control parameters under the sterilization resistance information and the i-th key distribution information, is the power of the clean lamp under the irradiation control parameters, is the preset power; Randomly generating a plurality of first positions and a plurality of first irradiation control parameters within the irradiation control parameters of the clean lamp as a plurality of first solutions; Based on the clean lamp optimization function, analyzing and calculating a plurality of first sterilization fitnesses of the plurality of first solutions; Based on the multiple first sterilization fitnesses, the multiple first solutions are divided and classified to generate multiple solution sets, and iterative optimization is performed until convergence; Outputting a convergent solution set with the maximum sum of sterilization fitness after optimization convergence, wherein the convergent solution set includes multiple convergent solutions; Acquire multiple convergence positions in the multiple convergence solutions, select a convergence solution output corresponding to a convergence position with a maximum moving channel distance in the target area, and obtain the optimal position and optimal irradiation control parameters; Based on the clean lamp optimization function, analyzing and calculating a plurality of first sterilization fitness of the plurality of first solutions, including: Based on the sterilization data records of the clean lamp on the multiple sensitive bacteria, a sample irradiation control parameter set, a sample sterilization resistance information set and a sample key distribution information set are obtained, and a sample sterilization efficiency information set after sterilization is obtained; The sample irradiation control parameter set, the sample sterilization resistance information set, the sample key distribution information set and the sample sterilization efficiency information set are used to construct a sterilization efficiency predictor, and the sterilization efficiency of the plurality of first irradiation control parameters is predicted in combination with the sterilization resistance information and the plurality of key distribution information to obtain a plurality of first sterilization efficiencies; According to the multiple first positions, the multiple first sterilization efficiencies and the clean lamp power under the multiple first irradiation control parameters, based on the clean lamp optimization function, multiple first sterilization fitnesses are calculated and obtained.

2. The method according to claim 1, characterized in that Based on the multiple first sterilization fitnesses, the multiple first solutions are divided and classified to generate multiple solution sets, and iterative optimization is performed, including: According to the multiple first sterilization fitnesses, the multiple first solutions are divided, and a number of first solutions with the largest first sterilization fitnesses are used as multiple group heads, where the group heads are representative solutions of each solution set; Based on the multiple cluster heads, the other multiple first solutions are classified to construct multiple solution sets, wherein similar solutions are classified into one solution set; In multiple solution sets, the cluster head is used as the optimization direction, multiple other solutions are optimized and updated, and the sterilization fitness of the updated solutions is calculated to update the cluster head; Continue iterative optimization until convergence is reached.

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