A clean light control system with adaptive dimming

Abstract

The present invention discloses a clean light control system with adaptive dimming, and relates to the field of clean light control. The system comprises: receiving the culture dish contamination status information and culture dish deployment location information of the clean room uploaded by the user end; performing bacteria distribution analysis to obtain the bacteria distribution status of the clean room; performing ultraviolet light adjustment optimization of the clean light, generating an ultraviolet light irradiation area list, an ultraviolet light wavelength optimization value list and an irradiation time optimization value list, assigning tasks to the clean light array, generating a clean light array scheduling plan; and performing self-adjustment control on the clean light array. The technical problem that the clean light in the prior art cannot be integratedly controlled according to the contamination status of the clean room is solved. By analyzing the bacteria distribution, obtaining the contamination status of the clean room, and controlling the clean light according to the contamination status of the clean room, the technical effect of automatically adjusting the light, avoiding excessive lighting and energy waste, and performing targeted treatment on the clean room is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of clean light control, and in particular to a clean light control system with adaptive dimming. Background Art

[0002] An adaptive dimming cleanroom light control system is mainly due to the special lighting requirements of clean rooms and the shortcomings of existing lighting control technology. As a place with extremely high environmental requirements, clean rooms not only need to ensure the cleanliness of indoor air, but also need to provide workers with a comfortable and efficient lighting environment. However, traditional cleanroom light control systems can often only achieve simple switching and brightness adjustment, and cannot be intelligently adjusted according to the real-time pollution status of the cleanroom. Existing cleanroom light control systems are usually unable to obtain real-time pollution status information of the cleanroom, so they cannot automatically adjust the light according to the degree of pollution.

[0003] In summary, the prior art has the technical problem that the clean room lamp cannot be integratedly controlled according to the pollution status of the clean room. Summary of the invention

[0004] Based on this, it is necessary to provide an adaptive dimming clean light control system that can analyze the distribution of bacteria, obtain the contamination status of the clean room, control the clean light according to the contamination status of the clean room, and avoid excessive lighting and energy waste by automatically adjusting the light to address the clean room.

[0005] The present application provides a clean light control system with adaptive dimming, comprising: an information receiving module, the information receiving module is used to receive the culture dish contamination status information and the culture dish deployment location information of the clean room uploaded by the user end; a bacteria distribution analysis module, the bacteria distribution analysis module is used to perform bacteria distribution analysis according to the culture dish contamination status information and the culture dish deployment location information, and obtain the clean room bacteria distribution status; a clean light ultraviolet light adjustment optimization module, the clean light ultraviolet light adjustment optimization module is used to perform clean light ultraviolet light adjustment optimization according to the clean room bacteria distribution status, and generate an ultraviolet light irradiation area list, an ultraviolet light wavelength optimization value list and an irradiation time optimization value list; a task allocation module, the task allocation module is used to perform task allocation on the clean light array according to the ultraviolet light irradiation area list, the ultraviolet light wavelength optimization value list and the irradiation time optimization value list, and generate a clean light array scheduling plan; a self-adjustment control module, the self-adjustment control module is used to perform self-adjustment control on the clean light array according to the clean light array scheduling plan.

[0006] The above-mentioned adaptive dimming clean light control system solves the technical problem in the prior art that the clean light cannot be integratedly controlled according to the pollution status of the clean room. By analyzing the distribution of bacteria, the pollution status of the clean room is obtained, and the clean light is controlled according to the pollution status of the clean room. The technical effect of automatically adjusting the light, avoiding excessive lighting and energy waste, and carrying out targeted treatment of the clean room is achieved.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a structural block diagram of a clean light control system with adaptive dimming in one embodiment;

[0009] Figure 2 The figure is a flow chart of a clean light control system with adaptive dimming in one embodiment.

[0010] Explanation of the reference numerals: information receiving module 11 , bacteria distribution analysis module 12 , clean lamp ultraviolet light adjustment optimization module 13 , task allocation module 14 , self-adjustment control module 15 . DETAILED DESCRIPTION

[0011] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0012] like Figure 1 As shown, the present application provides an adaptive dimming clean light control system, which performs the following steps: Figure 2 The process includes:

[0013] An information receiving module 11, the information receiving module 11 is used to receive the culture dish contamination status information and culture dish deployment location information of the clean room uploaded by the user end;

[0014] Adaptive dimming is a technology that uses external equipment or devices to automatically adjust the parameters according to a predetermined rule without direct human involvement, thereby adjusting the brightness of the light source. A clean light is a special-purpose lamp designed for clean places. It has multiple functions, including sterilization, smoke and dust removal, strong protective sealing, and good waterproof effect. The present application provides an adaptive dimming clean light control system, which combines the characteristics of adaptive dimming technology and clean lights, aiming to achieve more intelligent, efficient and environmentally friendly lighting control.

[0015] The contamination status information of the culture dish can reflect the contamination of microorganisms and particulate matter in the clean room. By analyzing this information, it can be determined whether the clean room has reached the predetermined cleaning standards and whether there are potential sources of contamination. At the same time, the deployment location information of the culture dish can provide the specific location where the contamination occurs, helping staff to quickly locate and deal with the source of contamination. Users can upload the contamination status information and deployment location information of the culture dish to the server through a dedicated APP or web page. After receiving the information, the system should verify the data to ensure the accuracy and completeness of the data. For example, it can be checked whether the uploaded picture is clear and whether the data is within the specified range. The verified data can be stored in the database and displayed to users or managers in a visual way. Through the above module, the contamination status information and deployment location information of the culture dish of the clean room uploaded by the user end are received. By effectively using the above information, the management efficiency of the clean room can be improved and the cleanliness and safety of the clean room can be ensured.

[0016] A bacteria distribution analysis module 12, the bacteria distribution analysis module 12 is used to perform bacteria distribution analysis according to the culture dish contamination status information and the culture dish deployment position information to obtain the clean room bacteria distribution status;

[0017] The received culture dish contamination status information and culture dish deployment location information are integrated, including matching the contamination degree data with the corresponding culture dish location information, cleaning and preprocessing the integrated data, removing outliers and duplicate data, etc., to ensure the accuracy and reliability of the data; using visualization tools, the bacterial distribution status is displayed in an intuitive way. Based on the visualization results, the bacterial distribution is analyzed. This includes counting the number, type and distribution density of bacteria in different areas, analyzing the source and transmission path of bacteria, and evaluating the cleanliness and sanitation of the clean room. The results of the bacterial distribution analysis are output in the form of a report or chart to obtain the output results, which include the clean room bacterial distribution status, including counting the number, type and distribution density of bacteria in different areas. Through the above modules, the bacterial distribution in the clean room can be accurately understood, providing strong data support for the management and maintenance of the clean room.

[0018] A clean room lamp ultraviolet light adjustment optimization module 13, the clean room lamp ultraviolet light adjustment optimization module 13 is used to perform clean room lamp ultraviolet light adjustment optimization according to the clean room bacteria distribution state, and generate an ultraviolet light irradiation area list, an ultraviolet light wavelength optimization value list and an irradiation time optimization value list;

[0019] According to the clean room bacteria distribution state, the clean lamp ultraviolet light adjustment optimization is performed. The clean room bacteria distribution state data includes information such as the number of colonies, colony distribution density, and space volume in different areas, which are obtained through professional bacteria detection equipment and software and serve as the basis for subsequent adjustment optimization. According to the bacteria distribution state data, the area that needs to be sterilized by ultraviolet light is determined, including areas with high bacteria density or key areas that need special attention. For each determined area, its edge space coordinates are extracted to define the irradiation range of ultraviolet light in the future; the particle swarm optimization algorithm is used to optimize in the adjustment space of ultraviolet light wavelength and irradiation time, and the fitness value of each particle, that is, each group of ultraviolet light wavelength and irradiation time combination, is calculated according to the bacteria distribution state data, ultraviolet light sterilization log and the preset fitness function. The fitness function comprehensively considers multiple factors such as sterilization rate, energy efficiency, equipment life, etc. to ensure that the optimal combination of ultraviolet light parameters is found. Through the iterative optimization process, the optimal value of ultraviolet light wavelength and irradiation time that meet the fitness constraint conditions are found. The optimization value will be generated for each determined area to ensure that the bacteria in the area are effectively killed. Add the edge space coordinates of each area to the UV irradiation area list so that the UV device can be controlled to irradiate according to these coordinates. Add the UV wavelength optimization value and irradiation time optimization value of each area to the corresponding list as the parameter basis for controlling the UV device to ensure that the device can work according to the optimal parameters.

[0020] A task allocation module 14, which is used to allocate tasks to the clean light array according to the ultraviolet light irradiation area list, the ultraviolet light wavelength optimization value list and the irradiation time optimization value list, and generate a clean light array scheduling plan;

[0021] According to the edge space coordinates in the UV irradiation area list, determine the specific location of each area to be sterilized in the clean room, and match each area with the appropriate lamp in the clean light array. The coverage, location layout and correspondence with the area boundary of the lamp should be considered during the matching process. According to the UV wavelength optimization value list, assign the corresponding UV wavelength to each matched clean lamp. Ensure that the UV wavelength emitted by the lamp meets the optimization requirements to improve the sterilization effect; similarly, assign the corresponding irradiation time to each lamp according to the irradiation time optimization value list. This ensures that each area can get enough UV irradiation time to achieve the expected sterilization effect; formulate the scheduling order of the clean light array according to the priority of the area, the availability of the lamp and the possible sterilization conflict. The priority can be determined based on factors such as the distribution status of bacteria and the importance of the area. The information such as lamp allocation, wavelength allocation, irradiation time and scheduling order determined in the above steps are integrated to form a detailed clean light array scheduling plan. The scheduling plan can include a timetable to specify when each lamp starts and ends irradiation, as well as the wavelength changes during irradiation. Through the above modules, accurate task allocation and efficient scheduling of the clean lamp array can be achieved, ensuring that the UV sterilization equipment can perform sterilization tasks according to optimal parameters in different areas, thereby improving the sterilization effect and efficiency of the entire clean room.

[0022] The self-adjusting control module 15 is used to perform self-adjusting control on the clean lamp array according to the clean lamp array scheduling plan.

[0023] Read the determined clean light array scheduling plan from the storage system, including all necessary information, such as lamp position, irradiation area, irradiation time, wavelength setting, etc., activate the control system of the clean light array, ensure that all lamps and sensors are in working condition, check the current status of each lamp, including position, angle, working status, etc., ensure that the lamp has no faults or abnormalities, and is ready for self-adjustment. According to the lamp position distribution information in the scheduling plan, control the lamp to move to the specified position, accurately adjust the irradiation angle of each lamp according to the horizontal and vertical angle information, and set the emission wavelength of each lamp according to the ultraviolet wavelength optimization value in the plan. According to the irradiation time optimization value in the scheduling plan, set the corresponding irradiation time for each area, and perform self-adjustment control on the clean light array according to the clean light array scheduling plan. Through the above modules, self-adjustment control is performed on the clean light array according to the clean light array scheduling plan.

[0024] Furthermore, the system also includes:

[0025] A region division module, the region division module is used to extract the number of colonies in the culture dishes in the first region, the number of colonies in the culture dishes in the second region, and the number of colonies in the culture dishes in the Nth region according to the culture dish contamination state information and the culture dish deployment position information, wherein the first region, the second region, and the Nth region are sub-regions for dividing the clean room space;

[0026] A clean list generation module, the clean list generation module is used to perform neighborhood growth aggregation on the number of colonies in the first area culture dish, the number of colonies in the second area culture dish, and the number of colonies in the Nth area culture dish to generate a clean room bacteria area list and a clean room colony count list, wherein the clean room bacteria area list and the clean room colony count list correspond to each other one by one;

[0027] A bacteria distribution status module is used to add the clean room bacteria area list and the clean room colony count list into the clean room bacteria distribution status.

[0028] The clean room space is divided into several sub-areas, such as the first area, the second area, and the Nth area, which can be based on the layout of the clean room, functional requirements, or airflow characteristics. According to the deployment location information of the culture dish, each culture dish is matched with its area to ensure that the contamination status information of each culture dish can accurately correspond to the corresponding area. For each area, the number of colonies of all culture dishes in it is extracted, which is completed by counting the number of colonies on the culture dishes or using a special colony counting instrument. The number of colonies in the culture dishes in the first area, the number of colonies in the culture dishes in the second area, and the number of colonies in the culture dishes in the Nth area are obtained; the neighborhood relationship between different areas is determined based on the physical location of the area, and the neighborhood relationship helps to identify the continuity and change trend of bacterial distribution. According to the neighborhood relationship, the number of colonies in adjacent areas is aggregated. This can be achieved by calculating the average, maximum, or sum of the number of colonies in adjacent areas. The purpose of aggregation is to identify hot spots or continuous areas of bacterial distribution. Through neighborhood growth aggregation, a list of clean room bacteria areas and a list of clean room colony counts are generated. The bacterial area list records the continuous areas or hot spots of bacterial distribution, while the colony count list corresponds to the number of colonies in these areas. These two lists are one-to-one corresponding and can clearly reflect the distribution of bacteria in the clean room. The generated clean room bacterial area list and clean room colony count list are added to the clean room bacterial distribution status. The clean room bacterial distribution status not only contains the colony count information in each area, but also contains the continuity and hot spot area information of bacterial distribution. Through the above modules, the colony counts in different areas can be extracted and neighborhood growth aggregation can be performed based on the contamination status information and deployment location information of the culture dish, thereby generating a detailed clean room bacterial distribution status.

[0029] Furthermore, the system also includes:

[0030] A culture dish colony number acquisition module, wherein the culture dish colony number acquisition module is used to obtain the number of culture dish colonies in the kth region and the number of culture dish colonies in the k+1th region, wherein the kth region and the k+1th region are adjacent regions;

[0031] A culture dish colony number judgment module, the culture dish colony number judgment module is used to merge the kth region and the k+1th region to generate an updated region when the colony number deviation between the colony number of the culture dish in the kth region and the colony number of the culture dish in the k+1th region is less than or equal to the colony number deviation threshold, wherein the updated region colony number of the updated region is equal to the weighted mean of the colony number of the culture dish in the kth region and the colony number of the culture dish in the k+1th region;

[0032] A first weight module, wherein the first weight module is used in which the first weight of the number of colonies in the culture dish of the kth region is equal to the ratio of the spatial volume of the kth region to the sum of the spatial volume of the kth region and the spatial volume of the k+1th region;

[0033] A second weight module, wherein the second weight of the number of colonies in the culture dish of the k+1th region is equal to the ratio of the spatial volume of the k+1th region to the sum of the spatial volume of the kth region and the spatial volume of the k+1th region;

[0034] A neighborhood growth aggregation module is used to perform neighborhood growth aggregation cyclically. When the colony count deviation between any two adjacent areas is greater than a colony count deviation threshold, the clean room bacteria area list and the clean room colony count list are output.

[0035] The number of colonies in the culture dish of the kth region and the number of colonies in the culture dish of the k+1th region are obtained. The two regions are adjacent. A subsequent study is conducted on the number of colonies in the culture dish of the kth region and the number of colonies in the culture dish of the k+1th region, and the deviation of the number of colonies in the two adjacent regions is calculated. The deviation of the number of colonies in the culture dish of the kth region is obtained by subtracting the number of colonies in the culture dish of the k+1th region. A colony number deviation threshold is preset. The colony number deviation threshold is used to determine whether the deviation between the number of colonies in the culture dish of the kth region and the number of colonies in the culture dish of the k+1th region meets the fusion condition. If the calculated colony number deviation is less than or equal to the preset colony number deviation threshold, it means that the colony distribution of the two regions is relatively similar, and they can be considered to be merged into an updated region. When the above conditions are met, the kth region and the k+1th region are merged into an updated region. The number of colonies in the updated region is weighted according to their spatial volume. The number of colonies in the updated area is obtained by calculating the weighted mean of the number of colonies in the culture dish of the kth area and the number of colonies in the culture dish of the k+1th area; wherein the weight of the number of colonies in the culture dish of the kth area is equal to the ratio of the spatial volume of the kth area to the sum of the spatial volumes of the kth area and the k+1th area; similarly, the weight of the number of colonies in the culture dish of the k+1th area is equal to the ratio of the spatial volume of the k+1th area to the sum of the spatial volumes of the two areas. It is ensured that the area with a larger spatial volume contributes more to the number of colonies in the updated area. The loop of neighborhood growth aggregation represents that the above steps are a cyclic process, and the same operation is performed on the updated area and its adjacent areas until the deviation of the number of colonies in any two adjacent areas is greater than the colony number deviation threshold. This means that there is a significant difference in the colony distribution between the two adjacent areas, and it is not suitable for further fusion. When the loop ends, the final clean room bacterial area list and clean room colony number list are obtained. The clean room colony number list reflects the distribution of bacteria in different areas of the clean room and the number of colonies in each area. Through the above modules, the distribution of bacteria in the clean room can be more accurately understood, providing strong data support for subsequent cleaning and disinfection work.

[0036] Furthermore, the system also includes:

[0037] A colony and space coordinate extraction module, wherein the colony and space coordinate extraction module is used to extract the number of colonies in the first area and the space coordinates of the edge of the first area according to the distribution state of bacteria in the clean room;

[0038] An ultraviolet sterilization log indexing module, the ultraviolet sterilization log indexing module is used to index the ultraviolet sterilization log according to the number of colonies in the first area and the spatial volume of the first area, wherein the ultraviolet sterilization log includes an ultraviolet wavelength label, an irradiation time label and a sterilization rate label, and the sterilization rate label is equal to the ratio of the number of colonies killed to the original number of colonies;

[0039] An ultraviolet light adjustment optimization module, the ultraviolet light adjustment optimization module is used to perform ultraviolet light adjustment optimization according to the ultraviolet light sterilization log, and generate an optimized value of the ultraviolet light wavelength in the first area and an optimized value of the irradiation time in the first area;

[0040] An edge space coordinate adding module, the edge space coordinate adding module is used to add the edge space coordinates of the first area into the ultraviolet light irradiation area list, add the ultraviolet light wavelength optimization value of the first area into the ultraviolet light wavelength optimization value list, and add the irradiation time optimization value of the first area into the irradiation time optimization value list.

[0041] The first area is identified from the distribution of bacteria in the clean room, and the number of colonies in the area is extracted. The number of colonies reflects the degree of bacterial contamination in the area and is an important basis for the subsequent adjustment of the UV sterilization parameters. The edge space coordinates of the first area are extracted to define the specific area boundary that the UV light needs to irradiate, ensuring that the sterilization operation can accurately cover the target area. According to the number of colonies and the spatial volume of the first area, the UV sterilization log is indexed. The UV sterilization log records the relationship between different UV wavelengths, irradiation time and sterilization rate. The sterilization rate label is the ratio of the number of colonies eliminated to the original number of colonies, which directly reflects the effect of UV sterilization. By comparing the sterilization rates under different conditions, the UV parameters that best suit the current sterilization needs of the first area can be selected. Based on the data in the UV sterilization log, the UV adjustment optimization is performed to find a combination of UV wavelength and irradiation time that can maximize the sterilization rate while also taking into account energy efficiency and equipment life. After the optimization process, the optimized value of the ultraviolet wavelength and the optimized value of the irradiation time of the first area are obtained, which are calculated according to the specific conditions of the first area, such as the number of colonies, the volume of the space, etc., and can more effectively kill the bacteria in the area. The edge space coordinates of the first area are added to the ultraviolet irradiation area list, so that the ultraviolet light equipment can be subsequently controlled to irradiate according to these coordinates. At the same time, the optimized value of the ultraviolet wavelength and the optimized value of the irradiation time of the first area are added to the corresponding optimization value list, respectively, to control the actual operating parameters of the ultraviolet light equipment. Through the above steps, the ultraviolet light sterilization parameters can be dynamically adjusted according to the distribution status of bacteria in the clean room, so as to achieve efficient and accurate sterilization of different areas, improve the cleanliness of the clean room, and reduce the risk of bacterial contamination.

[0042] Furthermore, the system also includes:

[0043] An adjustment optimization space construction module, wherein the adjustment optimization space construction module is used to construct an adjustment optimization space with the ultraviolet light wavelength as the first coordinate axis and the irradiation time as the second coordinate axis;

[0044] A particle distribution information generating module, the particle distribution information generating module is used to generate first particle distribution information in the adjustment optimization space according to the ultraviolet light wavelength label and the irradiation time label of the ultraviolet light sterilization log;

[0045] A fitness calculation module, the fitness calculation module is used to perform fitness calculation on the first particle distribution information in combination with the sterilization rate label to generate a first particle fitness set;

[0046] An optimization value output module, wherein the optimization value output module is used to output the optimization value of the ultraviolet light wavelength of the first region and the optimization value of the irradiation time of the first region when the number of particles in the first particle fitness set that satisfy the fitness constraint condition is not equal to 0;

[0047] An expanded particle set generation module, wherein when the number of particles in the first particle fitness set that satisfy the fitness constraint condition is equal to 0, the expanded particle set generation module performs particle expansion based on the first particle fitness set and the first particle distribution information to generate an expanded particle set;

[0048] An execution loop module is used to configure an expanded particle sterilization rate label set for the expanded particle set based on big data, update the first particle distribution information according to the expanded particle set and the expanded particle sterilization rate label set, and execute a loop.

[0049] A two-dimensional adjustment optimization space is constructed with the ultraviolet wavelength as the first coordinate axis and the irradiation time as the second coordinate axis. The adjustment optimization space represents all possible combinations of ultraviolet wavelengths and irradiation times. According to the ultraviolet wavelength label and irradiation time label in the ultraviolet light sterilization log, a certain number of particles, i.e., potential combinations of ultraviolet wavelengths and irradiation times, are randomly generated in the adjustment optimization space. Each particle represents a solution, generating the first particle distribution information; combined with the sterilization rate label, the fitness is calculated for each particle, i.e., each group of ultraviolet wavelength and irradiation time combinations. Fitness can be defined as a function of the sterilization rate, for example, the sterilization rate can be directly used as the fitness value, or a more complex fitness function can be defined according to actual needs; the number of particles in the first particle fitness set that meet the fitness constraint condition is checked. The fitness constraint condition can be set according to actual needs. In the present application, the fitness constraint condition has a first fitness factor threshold and a second fitness factor threshold; when the first fitness factor is greater than or equal to the first fitness factor threshold, and the second fitness factor is greater than or equal to the second fitness factor threshold, it is judged to converge. If the number of particles that meet the fitness constraint is not equal to 0, it means that a suitable combination of ultraviolet wavelength and irradiation time has been found, and these optimized values ​​are output as the first area ultraviolet wavelength optimization value and the first area irradiation time optimization value; if the number of particles that meet the fitness constraint is equal to 0, it means that there is no suitable solution in the current particle distribution, and particle expansion is required, for example, select a better solution, then select a worse solution, and then calculate the distance between the better solution and the worse solution, randomly select a value from 0-1 and multiply the distance to obtain the perturbation distance; then based on the worse solution position, take the better solution position as the target, travel the perturbation distance, and obtain a new solution. Generate an expanded particle set based on the first particle fitness set and the first particle distribution information. The expanded particle set can be generated by adding new particle points near the existing particles to increase the coverage of the search space. Based on big data or other available information, a corresponding expanded particle sterilization rate label set is configured for the expanded particle set. However, direct retrieval in big data lacks directionality, and the amount of data to be retrieved is large, and the amount of calculation is also large. Therefore, we first search for a batch of preliminary optimizations, and then optimize the new solution in a directionally optimal way. For example, according to the irradiation wavelength and duration of the new solution, we collect the sterilization rate of the solution under the same control state in the big data and configure it as the sterilization rate label of the new solution, which reduces the amount of data that needs to be retrieved from the big data and improves the calculation efficiency. We repeat the above steps until we find a particle that meets the fitness constraint or reach the preset maximum number of iterations. In each cycle, the particle swarm will be updated according to the fitness value and certain rules to gradually approach the optimal solution.Through the above modules, the particle swarm optimization algorithm is used to find the optimal combination of ultraviolet light wavelength and irradiation time in the adjustment optimization space to achieve efficient sterilization of clean room bacteria. It combines the data of ultraviolet light sterilization log and the search capability of particle swarm optimization, and can automatically adapt to the bacterial distribution status in different areas to improve the sterilization effect and efficiency.

[0050] Furthermore, the system also includes:

[0051] A particle neighborhood set construction module, wherein the particle neighborhood set construction module is used to traverse the first particle distribution information with a preset distance threshold as a radius to construct a first particle neighborhood set;

[0052] A frequent coefficient set setting module, wherein the frequent coefficient set setting module is used to traverse the first particle neighborhood set, respectively calculate the ratio of the number of particles in the neighborhood to the total number of particles, and set it as the first frequent coefficient set;

[0053] An adaptation factor set generation module, the adaptation factor set generation module is used to configure a first weight for the frequent coefficient, configure a second weight for the sterilization rate label, and based on the second weight and the first weight, add the first frequent coefficient set and the sterilization rate label to generate a first adaptation factor set;

[0054] A sterilization rate label setting module, the sterilization rate label setting module is used to set the sterilization rate label to a second adaptation factor set;

[0055] An associated storage module is used to associate and store the first adaptation factor set with the second adaptation factor set to generate the first particle fitness set.

[0056] Taking the first particle as the center and the preset distance threshold as the radius, the first particle distribution information is traversed to find other particles within the radius, and the first particle neighborhood set is constructed, which reflects the relative position relationship of the particles in space. The first particle neighborhood set is traversed, and for each particle, the proportion of the number of particles in its neighborhood to the total number of particles is calculated to obtain the first frequent coefficient, which reflects the density of particles in space, that is, the frequency of particle distribution. The first frequent coefficients of all particles are combined into a first frequent coefficient set, and a first weight is configured for the frequent coefficient, and a second weight is configured for the sterilization rate label. The specific values ​​of the two weights can be adjusted according to actual conditions to balance the importance of the frequent coefficient and the sterilization rate in the fitness calculation; based on the first weight and the second weight, the first frequent coefficient set and the sterilization rate label are weighted and summed to generate the first fitness factor set. The first fitness factor set comprehensively considers the distribution of particles and the sterilization effect. The sterilization rate label is directly used as the second fitness factor set, and the second fitness factor set only considers the sterilization effect, without considering the distribution of particles. The first fitness factor set and the second fitness factor set are associated and stored to generate the first particle fitness set. This fitness set contains two types of fitness information with different focuses, which can be selected as needed. Through the above modules, the fitness of particles is evaluated by comprehensively considering the distribution of particles and the sterilization effect. The first fitness factor set emphasizes the balance between the frequency of particle distribution and the sterilization effect, while the second fitness factor set only focuses on the sterilization effect. In practical applications, the appropriate fitness set can be selected according to the characteristics and needs of the problem for subsequent optimization operations.

[0057] Furthermore, the system also includes:

[0058] A random adjustment module, the random adjustment module is used to randomly adjust the clean lamp array based on the horizontal angle constraint interval of the lamp, the vertical angle constraint interval of the lamp, and the ultraviolet light wavelength constraint interval, and when the random adjustment scheme satisfies the ultraviolet light irradiation area list, the ultraviolet light wavelength optimization value list and the irradiation time optimization value list within the sterilization constraint time, it is added to the initial clean lamp array scheduling scheme set;

[0059] An affected area evaluation value set generation module, wherein when the number of the initial clean lamp array scheduling solution set meets the preset solution number, the affected area evaluation is performed by traversing the initial clean lamp array scheduling solution set to generate an affected area evaluation value set;

[0060] A minimum solution extraction module is used to extract an initial clean lamp array scheduling solution of the minimum value of the affected area evaluation value set, and set it as the clean lamp array scheduling solution.

[0061] The horizontal angle constraint interval, vertical angle constraint interval, and ultraviolet wavelength constraint interval of the lamp are set by the staff. The horizontal angle constraint interval of the lamp is, for example, [0°, 180°]; the vertical angle constraint interval of the lamp is, for example, [0°, 90°]; the ultraviolet wavelength constraint interval is, for example, [200nm, 300nm]. In each adjustment, a horizontal angle, a vertical angle, and an ultraviolet wavelength are randomly generated for each lamp to ensure that these values ​​are within the corresponding constraint intervals. According to the generated random angle and wavelength, the irradiation range and effect of the lamp are simulated to check whether this random adjustment scheme can meet the requirements of the ultraviolet irradiation area list, the ultraviolet wavelength optimization value list, and the irradiation time optimization value list within the sterilization constraint time. If the random adjustment scheme meets all conditions, it is added to the initial clean lamp array scheduling scheme set, and the above random adjustment and evaluation process is repeated until the number of initial clean lamp array scheduling scheme sets reaches the preset number of schemes. Traverse each plan in the initial clean lamp array scheduling plan set, and for each plan, evaluate the impact on non-target areas during irradiation, by calculating the ultraviolet light intensity, area or other relevant indicators irradiated to the non-target area. Generate an affected area evaluation value, the smaller the value, the smaller the impact of the plan, that is, the more accurately the target area is irradiated, and the affected area evaluation values ​​of all plans are combined into an evaluation value set. Extract the initial clean lamp array scheduling plan corresponding to the minimum value from the evaluation value set, and set this plan as the final clean lamp array scheduling plan. Through the above module, extract the initial clean lamp array scheduling plan with the minimum value of the affected area evaluation value set, and set it as the clean lamp array scheduling plan. It provides a basis for subsequent self-regulating control.

[0062] Furthermore, the system also includes:

[0063] A regional control information acquisition module, the regional control information acquisition module is used to obtain the first regional control information of the first initial clean light array scheduling scheme of the initial clean light array scheduling scheme set, wherein the first regional control information includes clean light position distribution information, clean light horizontal rotation angle information, and clean light vertical rotation angle information;

[0064] A simulation irradiation module, the simulation irradiation module is used to perform simulation irradiation according to the clean light position distribution information, the clean light horizontal angle information, and the clean light vertical angle information to obtain the number of passes in the first irrelevant area;

[0065] The regional evaluation value adding module is used to add the number of passes of the first irrelevant area, the number of passes of the second irrelevant area, and so on until the number of passes of the Nth irrelevant area when all areas of the first initial clean lamp array scheduling plan are traversed, to generate a first affected area evaluation value, and add it to the affected area evaluation value set.

[0066] The first initial clean lamp array scheduling plan is extracted from the initial clean lamp array scheduling plan set. For the first initial clean lamp array scheduling plan, its control information for each area is obtained, including: clean lamp position distribution information, that is, the specific position of each lamp in the clean room, clean lamp horizontal angle information, the horizontal rotation angle of each lamp relative to its initial position, and clean lamp vertical angle information, the vertical rotation angle of each lamp relative to its initial position. According to the layout and size of the clean room, a corresponding three-dimensional model is established in the simulation software, and the clean lamp array is placed in the model according to the extracted position distribution information. According to the extracted clean lamp horizontal angle information and vertical angle information, the angle of each lamp in the simulation is adjusted to simulate the ultraviolet light irradiation process emitted by the lamp, record the propagation path and irradiation range of the ultraviolet light, define the irrelevant area as the area that does not need to be sterilized, that is, the area outside the target area, and count the number of times the ultraviolet light passes through the irrelevant area during the simulation irradiation process, that is, the number of times the first irrelevant area passes. It is obtained by calculating the number of intersections between the ultraviolet light path and the boundary of the irrelevant area; for the first initial clean lamp array scheduling plan, it is necessary to traverse all the areas controlled by it, and for each area, repeat the simulation irradiation and irrelevant area pass number calculation process; the irrelevant area pass number calculated for each area is accumulated to obtain the total irrelevant area pass number of the first initial clean lamp array scheduling plan, and the pass number refers to the first affected area evaluation value, which reflects the degree of impact of the plan on the irrelevant area during the irradiation process. The calculated first affected area evaluation value is added to the affected area evaluation value set for subsequent plan comparison and selection.

[0067] In summary, the beneficial effects of this system include:

[0068] The present invention solves the technical problem that the clean lamp in the prior art cannot be integratedly controlled according to the pollution status of the clean room. By analyzing the distribution of bacteria and obtaining the pollution status of the clean room, the clean lamp is controlled according to the pollution status of the clean room. The technical effect of automatically adjusting the light, avoiding excessive lighting and energy waste, and carrying out targeted treatment of the clean room is achieved.

[0069] The specific embodiment of a clean light control system with adaptive dimming can be referred to the embodiment above, which will not be described here. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0070] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0071] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. An adaptive dimming clean light control system, characterized in that: include: An information receiving module, the information receiving module is used to receive the culture dish contamination status information and culture dish deployment location information of the clean room uploaded by the user end; A bacteria distribution analysis module, the bacteria distribution analysis module is used to perform bacteria distribution analysis according to the culture dish contamination status information and the culture dish deployment position information to obtain the clean room bacteria distribution status; A clean room lamp ultraviolet light adjustment optimization module, which is used to perform clean room lamp ultraviolet light adjustment optimization according to the distribution state of bacteria in the clean room, and generate an ultraviolet light irradiation area list, an ultraviolet light wavelength optimization value list, and an irradiation time optimization value list; A task allocation module, the task allocation module is used to allocate tasks to the clean light array according to the ultraviolet light irradiation area list, the ultraviolet light wavelength optimization value list and the irradiation time optimization value list, and generate a clean light array scheduling plan; A self-regulating control module, the self-regulating control module is used to perform self-regulating control on the clean lamp array according to the clean lamp array scheduling plan; Wherein, the clean lamp ultraviolet light adjustment optimization module also includes: A colony and space coordinate extraction module, wherein the colony and space coordinate extraction module is used to extract the number of colonies in the first area and the space coordinates of the edge of the first area according to the distribution state of bacteria in the clean room; An ultraviolet sterilization log indexing module, the ultraviolet sterilization log indexing module is used to index the ultraviolet sterilization log according to the number of colonies in the first area and the spatial volume of the first area, wherein the ultraviolet sterilization log includes an ultraviolet wavelength label, an irradiation time label and a sterilization rate label, and the sterilization rate label is equal to the ratio of the number of colonies killed to the original number of colonies; An ultraviolet light adjustment optimization module, the ultraviolet light adjustment optimization module is used to perform ultraviolet light adjustment optimization according to the ultraviolet light sterilization log, and generate an optimized value of the ultraviolet light wavelength in the first area and an optimized value of the irradiation time in the first area; An edge space coordinate adding module, the edge space coordinate adding module is used to add the edge space coordinates of the first area into the ultraviolet light irradiation area list, add the ultraviolet light wavelength optimization value of the first area into the ultraviolet light wavelength optimization value list, and add the irradiation time optimization value of the first area into the irradiation time optimization value list; Wherein, the ultraviolet light adjustment optimization module also includes: An adjustment optimization space construction module, wherein the adjustment optimization space construction module is used to construct an adjustment optimization space with the ultraviolet light wavelength as the first coordinate axis and the irradiation time as the second coordinate axis; A particle distribution information generating module, the particle distribution information generating module is used to generate first particle distribution information in the adjustment optimization space according to the ultraviolet light wavelength label and the irradiation time label of the ultraviolet light sterilization log; A fitness calculation module, the fitness calculation module is used to perform fitness calculation on the first particle distribution information in combination with the sterilization rate label to generate a first particle fitness set; An optimization value output module, wherein the optimization value output module is used to output the optimization value of the ultraviolet light wavelength of the first region and the optimization value of the irradiation time of the first region when the number of particles in the first particle fitness set that satisfy the fitness constraint condition is not equal to 0; An expanded particle set generation module, wherein when the number of particles in the first particle fitness set that satisfy the fitness constraint condition is equal to 0, the expanded particle set generation module performs particle expansion based on the first particle fitness set and the first particle distribution information to generate an expanded particle set; An execution loop module, wherein the execution loop module is used to configure an expanded particle sterilization rate label set based on big data for the expanded particle set, update the first particle distribution information according to the expanded particle set and the expanded particle sterilization rate label set, and execute a loop; Wherein, the fitness calculation module also includes: A particle neighborhood set construction module, wherein the particle neighborhood set construction module is used to traverse the first particle distribution information with a preset distance threshold as a radius to construct a first particle neighborhood set; A frequent coefficient set setting module, wherein the frequent coefficient set setting module is used to traverse the first particle neighborhood set, respectively calculate the ratio of the number of particles in the neighborhood to the total number of particles, and set it as the first frequent coefficient set; An adaptation factor set generation module, the adaptation factor set generation module is used to configure a first weight for the frequent coefficient, configure a second weight for the sterilization rate label, and based on the second weight and the first weight, add the first frequent coefficient set and the sterilization rate label to generate a first adaptation factor set; A sterilization rate label setting module, the sterilization rate label setting module is used to set the sterilization rate label to a second adaptation factor set; An associated storage module is used to associate and store the first fitness factor set with the second fitness factor set to generate the first particle fitness set.

2. The system according to claim 1, characterized in that The bacterial distribution analysis module also includes: A region division module, the region division module is used to extract the number of colonies in the culture dishes in the first region, the number of colonies in the culture dishes in the second region, and the number of colonies in the culture dishes in the Nth region according to the culture dish contamination state information and the culture dish deployment position information, wherein the first region, the second region, and the Nth region are sub-regions for dividing the clean room space; A clean list generation module, the clean list generation module is used to perform neighborhood growth aggregation on the number of colonies in the first area culture dish, the number of colonies in the second area culture dish, and the number of colonies in the Nth area culture dish to generate a clean room bacteria area list and a clean room colony count list, wherein the clean room bacteria area list and the clean room colony count list correspond to each other one by one; A bacteria distribution status module is used to add the clean room bacteria area list and the clean room colony count list into the clean room bacteria distribution status.

3. The system according to claim 2, characterized in that The area division module also includes: A culture dish colony number acquisition module, wherein the culture dish colony number acquisition module is used to obtain the number of culture dish colonies in the kth region and the number of culture dish colonies in the k+1th region, wherein the kth region and the k+1th region are adjacent regions; A culture dish colony number judgment module, the culture dish colony number judgment module is used to merge the kth region and the k+1th region to generate an updated region when the colony number deviation between the colony number of the culture dish in the kth region and the colony number of the culture dish in the k+1th region is less than or equal to the colony number deviation threshold, wherein the updated region colony number of the updated region is equal to the weighted mean of the colony number of the culture dish in the kth region and the colony number of the culture dish in the k+1th region; A first weight module, wherein the first weight module is used in which the first weight of the number of colonies in the culture dish of the kth region is equal to the ratio of the spatial volume of the kth region to the sum of the spatial volume of the kth region and the spatial volume of the k+1th region; A second weight module, wherein the second weight of the number of colonies in the culture dish of the k+1th region is equal to the ratio of the spatial volume of the k+1th region to the sum of the spatial volume of the kth region and the spatial volume of the k+1th region; A neighborhood growth aggregation module is used to perform neighborhood growth aggregation cyclically. When the colony count deviation between any two adjacent areas is greater than a colony count deviation threshold, the clean room bacteria area list and the clean room colony count list are output.

4. The system according to claim 1, characterized in that The task allocation module includes: A random adjustment module, the random adjustment module is used to randomly adjust the clean lamp array based on the horizontal angle constraint interval of the lamp, the vertical angle constraint interval of the lamp, and the ultraviolet light wavelength constraint interval, and when the random adjustment scheme satisfies the ultraviolet light irradiation area list, the ultraviolet light wavelength optimization value list and the irradiation time optimization value list within the sterilization constraint time, it is added to the initial clean lamp array scheduling scheme set; An affected area evaluation value set generation module, wherein when the number of the initial clean lamp array scheduling solution set meets the preset solution number, the affected area evaluation is performed by traversing the initial clean lamp array scheduling solution set to generate an affected area evaluation value set; A minimum solution extraction module is used to extract an initial clean lamp array scheduling solution of the minimum value of the affected area evaluation value set, and set it as the clean lamp array scheduling solution.

5. The system according to claim 4, characterized in that Traversing the initial clean lamp array scheduling solution set to evaluate the affected area, generating an affected area evaluation value set, including: A regional control information acquisition module, the regional control information acquisition module is used to obtain the first regional control information of the first initial clean light array scheduling scheme of the initial clean light array scheduling scheme set, wherein the first regional control information includes clean light position distribution information, clean light horizontal rotation angle information, and clean light vertical rotation angle information; A simulation irradiation module, the simulation irradiation module is used to perform simulation irradiation according to the clean light position distribution information, the clean light horizontal angle information, and the clean light vertical angle information to obtain the number of passes in the first irrelevant area; The regional evaluation value adding module is used to add the number of passes of the first irrelevant area, the number of passes of the second irrelevant area, and so on until the number of passes of the Nth irrelevant area when all areas of the first initial clean lamp array scheduling plan are traversed, to generate a first affected area evaluation value, and add it to the affected area evaluation value set.

Images