阅读说明：本技术 一种杀菌系统、控制方法及存储介质 (Sterilization system, control method and storage medium ) 是由 郝锐 赵伟 李文英 方家伟 赵朝荣 于 2020-05-27 设计创作，主要内容包括：本发明公开了杀菌系统、控制方法及存储介质,控制方法包括：采集并检测多个已杀菌位置的不同类型细菌的平均细菌浓度；根据平均细菌浓度及第一关联关系,反馈和优化温湿度控制系统的第一装置参数；根据平均细菌浓度及第二关联关系,反馈和优化紫外灯控制系统的第二装置参数；根据优化后的第一装置参数和第二装置参数,控制杀菌系统进行杀菌。通过检测已杀菌位置的平均细菌浓度,以及第一关联关系和第二关联关系,持续对第一装置参数和第二装置参数进行反馈和优化,实现了杀菌系统的杀菌效果监控、自动反馈和自动优化功能,逐步提高和完善杀菌系统的杀菌效果。(The invention discloses a sterilization system, a control method and a storage medium, wherein the control method comprises the following steps: collecting and detecting the average bacterial concentration of different types of bacteria at a plurality of sterilized positions; feeding back and optimizing a first device parameter of the temperature and humidity control system according to the average bacteria concentration and the first incidence relation; feeding back and optimizing a second device parameter of the ultraviolet lamp control system according to the average bacterial concentration and the second incidence relation; and controlling the sterilization system to sterilize according to the optimized first device parameter and the optimized second device parameter. The average bacteria concentration of the sterilized position and the first incidence relation and the second incidence relation are detected, the first device parameter and the second device parameter are continuously fed back and optimized, the sterilization effect monitoring, automatic feedback and automatic optimization functions of the sterilization system are realized, and the sterilization effect of the sterilization system is gradually improved and perfected.)

1. A control method of a sterilization system is characterized in that the sterilization system comprises a temperature and humidity control system and an ultraviolet lamp control system, and the control method comprises the following steps:

collecting and detecting the average bacterial concentration of different types of bacteria at a plurality of sterilized positions;

feeding back and optimizing a first device parameter of the temperature and humidity control system according to the average bacteria concentration and a first incidence relation between the first device parameter of the temperature and humidity control system and the average bacteria concentration;

feeding back and optimizing a second device parameter of the ultraviolet lamp control system according to the average bacterial concentration and a second incidence relation between the second device parameter of the ultraviolet lamp control system and the average bacterial concentration;

controlling the sterilization system to sterilize according to the optimized first device parameter and the optimized second device parameter;

wherein the first correlation is determined by training a first neural network through historical average bacterial concentrations and historical first device parameters; the second correlation is determined by training a second neural network with historical mean bacterial concentrations and historical second device parameters.

2. The method of claim 1, wherein the step of collecting and detecting the average bacterial concentration of different types of bacteria at a plurality of sterilized locations comprises the steps of:

taking an image of the sterilized site by a microscopic electron microscope;

counting the number of different types of bacteria in the image by an image recognition technology;

calculating the bacterial concentrations of different types of bacteria;

repeating the steps to obtain the bacterial concentrations of different types of bacteria at a plurality of different sterilized positions, and calculating the average bacterial concentrations of the different types of bacteria.

3. A method of controlling a germicidal system as claimed in claim 1 or 2, characterized in that the first device parameter comprises at least one of the following: the temperature value of the temperature and humidity control system and the humidity value of the temperature and humidity control system are obtained; the second device parameter includes at least one of: the light intensity of the ultraviolet lamp, the wavelength of the ultraviolet lamp and the irradiation time of the ultraviolet lamp.

4. The method of claim 1, wherein the first neural network and the second neural network are both multilayer neural networks.

5. A sterilization system, comprising:

the temperature and humidity control system comprises a temperature adjusting device and a humidity adjusting device;

an ultraviolet lamp control system comprising a plurality of ultraviolet lamps;

the detection robot is used for acquiring and detecting the average bacterial concentration of different types of bacteria at a plurality of sterilized positions;

the first optimization module is used for feeding back and optimizing a first device parameter of the temperature and humidity control system according to the average bacteria concentration and a first incidence relation between the first device parameter of the temperature and humidity control system and the average bacteria concentration;

the second optimization module is used for feeding back and optimizing the second device parameter of the ultraviolet lamp control system according to the average bacterial concentration and the second incidence relation between the second device parameter of the ultraviolet lamp control system and the average bacterial concentration;

the controller is used for controlling the sterilization system to sterilize according to the optimized first device parameter and the optimized second device parameter;

wherein the first correlation is determined by training a first neural network through historical average bacterial concentrations and historical first device parameters; the second correlation is determined by training a second neural network with historical mean bacterial concentrations and historical second device parameters.

6. A sterilization system according to claim 5, wherein the inspection robot comprises:

a microscopic electron microscope for taking an image of the sterilized position;

the image processing module is used for counting the number of different types of bacteria in the image through an image recognition technology;

and the statistical module is used for calculating the bacterial concentration.

7. A sterilization system according to claim 5 or 6 wherein said first device parameter comprises at least one of: the temperature value of the temperature and humidity control system and the humidity value of the temperature and humidity control system are obtained; the second device parameter includes at least one of: the light intensity of the ultraviolet lamp, the wavelength of the ultraviolet lamp, and the irradiation time of the ultraviolet lamp.

8. The sterilization system of claim 5 wherein the first neural network and the second neural network are both multilayer neural networks.

9. A storage medium storing executable instructions executable by a device to cause the device to perform a control method according to any one of claims 1 to 4.

Technical Field

The present invention relates to a sterilization apparatus, and more particularly, to a sterilization system, a control method, and a storage medium.

Background

At present, workers usually hold the ultraviolet sterilizing lamp for extensive sterilization, and people can only hold the ultraviolet sterilizing lamp by experience, so that the sterilization effect is not ideal; and the sterilization effect cannot be known without subsequent verification. The sterilization process and the sterilization effect cannot be correlated and fed back, which is not beneficial to the perfection of the subsequent sterilization scheme.

Disclosure of Invention

The present invention is directed to solve at least one of the problems of the prior art, and provides a sterilization system, a control method and a storage medium.

The technical scheme adopted by the invention for solving the problems is as follows:

in a first aspect of the present invention, a control method for a sterilization system, the sterilization system including a temperature and humidity control system and an ultraviolet lamp control system, the control method including the steps of:

collecting and detecting the average bacterial concentration of different types of bacteria at a plurality of sterilized positions;

feeding back and optimizing a first device parameter of the temperature and humidity control system according to the average bacteria concentration and a first incidence relation between the first device parameter of the temperature and humidity control system and the average bacteria concentration;

feeding back and optimizing a second device parameter of the ultraviolet lamp control system according to the average bacterial concentration and a second incidence relation between the second device parameter of the ultraviolet lamp control system and the average bacterial concentration;

controlling the sterilization system to sterilize according to the optimized first device parameter and the optimized second device parameter;

wherein the first correlation is determined by training a first neural network through historical average bacterial concentrations and historical first device parameters; the second correlation is determined by training a second neural network with historical mean bacterial concentrations and historical second device parameters.

According to a first aspect of the invention, said acquiring and detecting an average bacterial concentration of different types of bacteria at a plurality of sterilized sites comprises the steps of:

taking an image of the sterilized site by a microscopic electron microscope;

counting the number of different types of bacteria in the image by an image recognition technology;

calculating the bacterial concentrations of different types of bacteria;

repeating the steps to obtain the bacterial concentrations of different types of bacteria at a plurality of different sterilized positions, and calculating the average bacterial concentrations of the different types of bacteria.

According to a first aspect of the invention, the first device parameter comprises at least one of: the temperature value of the temperature and humidity control system and the humidity value of the temperature and humidity control system are obtained; the second device parameter includes at least one of: the light intensity of the ultraviolet lamp, the wavelength of the ultraviolet lamp and the irradiation time of the ultraviolet lamp.

According to a first aspect of the invention, the first neural network and the second neural network are both multilayer neural networks.

In a second aspect of the present invention, a sterilization system includes:

the temperature and humidity control system comprises a temperature adjusting device and a humidity adjusting device;

an ultraviolet lamp control system comprising a plurality of ultraviolet lamps;

the detection robot is used for acquiring and detecting the average bacterial concentration of different types of bacteria at a plurality of sterilized positions;

the first optimization module is used for feeding back and optimizing a first device parameter of the temperature and humidity control system according to the average bacteria concentration and a first incidence relation between the first device parameter of the temperature and humidity control system and the average bacteria concentration;

the second optimization module is used for feeding back and optimizing the second device parameter of the ultraviolet lamp control system according to the average bacterial concentration and the second incidence relation between the second device parameter of the ultraviolet lamp control system and the average bacterial concentration;

the controller is used for controlling the sterilization system to sterilize according to the optimized first device parameter and the optimized second device parameter;

wherein the first correlation is determined by training a first neural network through historical average bacterial concentrations and historical first device parameters; the second correlation is determined by training a second neural network with historical mean bacterial concentrations and historical second device parameters.

According to a second aspect of the present invention, the inspection robot includes:

a microscopic electron microscope for taking an image of the sterilized position;

the image processing module is used for counting the number of different types of bacteria in the image through an image recognition technology;

and the statistical module is used for calculating the bacterial concentration.

According to a second aspect of the invention, the first device parameter comprises at least one of: the temperature value of the temperature and humidity control system and the humidity value of the temperature and humidity control system are obtained; the second device parameter includes at least one of: the light intensity of the ultraviolet lamp, the wavelength of the ultraviolet lamp, and the irradiation time of the ultraviolet lamp.

According to a second aspect of the invention, the first neural network and the second neural network are both multilayer neural networks.

In a third aspect of the present invention, a storage medium stores executable instructions that are executable by a device to cause the device to perform the control method according to the first aspect of the present invention.

The scheme at least has the following beneficial effects: the average bacteria concentration of the sterilized position and the first incidence relation and the second incidence relation are detected, the first device parameter and the second device parameter are continuously fed back and optimized, the sterilization effect monitoring, automatic feedback and automatic optimization functions of the sterilization system are realized, and the sterilization effect of the sterilization system is gradually improved and perfected.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The invention is further illustrated with reference to the following figures and examples.

FIG. 1 is a flow chart illustrating a method for controlling a sterilization system according to an embodiment of the present invention;

FIG. 2 is a flowchart of step S100 in FIG. 1;

FIG. 3 is a block diagram of a sterilization system according to an embodiment of the present invention;

fig. 4 is a circuit diagram of a remote control module.

Detailed Description

Reference will now be made in detail to the present preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to like elements throughout.

In the description of the present invention, it should be understood that the orientation or positional relationship referred to in the description of the orientation, such as the upper, lower, front, rear, left, right, etc., is based on the orientation or positional relationship shown in the drawings, and is only for convenience of description and simplification of description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention.

In the description of the present invention, the meaning of a plurality of means is one or more, the meaning of a plurality of means is two or more, and larger, smaller, larger, etc. are understood as excluding the number, and larger, smaller, inner, etc. are understood as including the number. If the first and second are described for the purpose of distinguishing technical features, they are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.

In the description of the present invention, unless otherwise explicitly limited, terms such as arrangement, installation, connection and the like should be understood in a broad sense, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present invention in combination with the specific contents of the technical solutions.

Referring to fig. 1, some embodiments of the present invention provide a control method of a sterilization system, where the sterilization system includes a temperature and humidity control system 11 and an ultraviolet lamp control system 12, and the control method includes the following steps:

s100, collecting and detecting the average bacterial concentration of different types of bacteria at a plurality of sterilized positions;

step S210, feeding back and optimizing a first device parameter of the temperature and humidity control system 11 according to the average bacteria concentration and a first incidence relation between the first device parameter of the temperature and humidity control system 11 and the average bacteria concentration;

step S220, feeding back and optimizing the second device parameters of the ultraviolet lamp control system 12 according to the average bacteria concentration and the second incidence relation between the second device parameters of the ultraviolet lamp control system 12 and the average bacteria concentration;

and S300, controlling the sterilization system to sterilize according to the optimized first device parameter and the optimized second device parameter.

In this embodiment, the first correlation is determined by training a first neural network with a historical average bacterial concentration and a historical first device parameter; the second correlation is determined by training a second neural network with the historical mean bacterial concentration and the historical second device parameters. The average bacteria concentration of the sterilized position and the first incidence relation and the second incidence relation are detected, the first device parameter and the second device parameter are continuously fed back and optimized, the sterilization effect monitoring, automatic feedback and automatic optimization functions of the sterilization system are realized, and the sterilization effect of the sterilization system is gradually improved and perfected.

It should be noted that, after the sterilization system completes a round of sterilization procedure, the average bacterial concentration of the sterilized position detected after the round of sterilization procedure and the first device parameter set by the round of sterilization procedure are used for training the first neural network to optimize the first association relationship; the average bacterial concentration of the sterilized positions detected after the round of sterilization procedures and the second device parameters set by the round of sterilization procedures are used for training a second neural network to optimize a second incidence relation. I.e. the average bacterial concentration in this sterilisation procedure will be one datum of the historical average bacterial concentration in the next sterilisation procedure. Similarly, the first device parameter in the sterilization procedure will become a datum of the historical first device parameter in the next sterilization procedure; the second device parameter in the sterilization procedure will become a datum of the historical second device parameter in the next sterilization procedure.

In addition, for both step S210 and step S220, it may be performed; it is also possible to perform only one of them, i.e. perform only step S210 and not step S220 or perform only step S220 and not step S210. After a round of sterilization procedure, the parameters of the first device are maintained unchanged, and only the parameters of the second device are optimized, or the parameters of the second device are maintained unchanged, and only the parameters of the first device are optimized.

Referring to fig. 2, further to step S100, detecting the average bacterial concentration of the plurality of sterilized sites specifically comprises the following steps:

step S110, shooting an image of a sterilized position through a microscope;

step S120, counting the number of different types of bacteria in the image corresponding to the sterilization position through an image recognition technology; the image recognition technology can adopt the existing image recognition technology based on deep learning, and the recognition accuracy is high through repeated training;

step S130, calculating the bacterial concentrations of different types of bacteria;

repeatedly executing the steps S100 to S130 to obtain the bacterial concentrations of different types of bacteria at a plurality of different sterilized positions, and calculating the average bacterial concentration of the different types of bacteria as the average bacterial concentration of the sterilization program; wherein the area of the plurality of different sterilized sites should be the same. Of course, in other embodiments, not only the bacterial concentration of the bacteria, but also the concentration of microorganisms, such as fungi, etc., may be calculated.

Specifically, the first device parameter includes at least one of: a temperature value of the temperature and humidity control system 11 and a humidity value of the temperature and humidity control system 11; the second device parameter includes at least one of: the light intensity of the ultraviolet lamp, the wavelength of the ultraviolet lamp and the irradiation time of the ultraviolet lamp.

It should be noted that, if the sterilization system sterilizes the living environment, the temperature value range and the humidity value range of the temperature and humidity control system 11 should meet the requirement of comfort of people, and change with weather and seasons. If the sterilization system sterilizes the storage environment, the temperature value range and the humidity value range of the temperature and humidity control system 11 should meet the storage environment requirement of the storage.

The light intensity of the ultraviolet lamp, the wavelength of the ultraviolet lamp and the irradiation time of the ultraviolet lamp all influence bacteria, and the average bacteria concentration of different types of bacteria is changed.

Because various bacteria exist in the environment, after the sterilization system continuously feeds back and optimizes the parameters of the first device and the second device for sterilization, the average bacterial concentration of the specified strains obtained by detection should meet the qualified range specified by the sanitary conditions. The specified species may be one species of bacteria or a plurality of species of bacteria. If the specified species is a plurality of bacterial species, the plurality of bacterial species is prioritized. For the bacteria with high priority, the corresponding average bacteria concentration should be allowed to meet the qualified range preferentially in the feedback and optimization process of the first device parameter and the second device parameter.

Further, the first and second correlations may be determined by mathematical modeling, may be determined as a functional relationship, or may be determined by machine learning. In this embodiment, the first neural network and the second neural network are each a multi-layer neural network, each comprising an input layer, an intermediate layer, and an output layer. The middle layer may be composed of a plurality of machine learning function layers, and the function layers may be convolutional layers, pooling layers, and the like. For the first neural network, training the first neural network by taking the historical average bacterial concentration and the historical first device parameters as training data until the first neural network converges, wherein the calculation function of each layer of the first neural network is a first incidence relation; and for the second neural network, training the second neural network by taking the historical average bacterial concentration and the historical second device parameters as training data until the second neural network converges, wherein the calculation function of each layer of the second neural network is the second incidence relation.

Referring to fig. 3, some embodiments of the present invention provide a sterilization system, including:

the temperature and humidity control system 11 comprises a temperature adjusting device and a humidity adjusting device; of course, the temperature adjusting device and the humidity adjusting device may be integrated into one device, such as an air conditioner;

an ultraviolet lamp control system 12 comprising a plurality of ultraviolet lamps; the ultraviolet lamps can be fixedly arranged on a ceiling or a wall, or can be arranged on a mobile robot;

a detection robot 21 for collecting and detecting average bacterial concentrations of different types of bacteria at a plurality of sterilized positions;

the first optimization module 22 is configured to feed back and optimize a first device parameter of the temperature and humidity control system 11 according to the average bacteria concentration and a first association relationship between the first device parameter of the temperature and humidity control system 11 and the average bacteria concentration;

a second optimization module 24 for feeding back and optimizing the second device parameter of the ultraviolet lamp control system 12 based on the average bacterial concentration and a second correlation between the second device parameter of the ultraviolet lamp control system 12 and the average bacterial concentration;

the controller 23 is used for controlling the sterilization system to sterilize according to the optimized first device parameter and the optimized second device parameter;

wherein the first incidence relation is determined by training the first neural network through historical average bacterial concentration and historical first device parameters; the second correlation is determined by training a second neural network with the historical mean bacterial concentration and the historical second device parameters.

It should be noted that the uv lamp control system 12 further includes a remote control module 121, a circuit diagram of the remote control module 121 is shown in fig. 4, and the remote control module 121 can control the on and off of the plurality of uv lamps. Specifically, remote control module is equipped with wifi chip 122, and remote control module 121 passes through wifi and a plurality of ultraviolet lamp communication connection. Of course, in other embodiments, the wireless communication connection may be implemented by bluetooth, zigbee, or the like.

Further, the inspection robot 21 includes:

a microscopic electron microscope for taking an image of the sterilized position;

the image processing module is used for counting the number of different types of bacteria in the image through an image recognition technology;

and the statistical module is used for calculating the bacterial concentration.

The inspection robot 21 has moving members such as wheels, and performs

Of course, the image processing module and the statistical module can be realized by adopting an algorithm and are positioned in the control background. The detection robot 21 transmits the image of the sterilized position shot by the microscopic electron microscope to the image processing module of the control background for processing through network communication.

Further, the first device parameter includes at least one of: the temperature value of the temperature adjusting device and the humidity value of the humidity adjusting device; the second device parameter includes at least one of: the light intensity of the ultraviolet lamp, the wavelength of the ultraviolet lamp and the irradiation time of the ultraviolet lamp.

Further, the first and second correlations may be determined by mathematical modeling, may be determined as a functional relationship, or may be determined by machine learning. In this embodiment, the first neural network and the second neural network are each a multi-layer neural network, each comprising an input layer, an intermediate layer, and an output layer. The middle layer may be composed of a plurality of machine learning function layers, and the function layers may be convolutional layers, pooling layers, and the like. For the first neural network, training the first neural network by taking the historical average bacterial concentration and the historical first device parameters as training data until the first neural network converges, wherein the calculation function of each layer of the first neural network is a first incidence relation; and for the second neural network, training the second neural network by taking the historical average bacterial concentration and the historical second device parameters as training data until the second neural network converges, wherein the calculation function of each layer of the second neural network is the second incidence relation.

In the sterilization system, each module corresponds to the steps of the control method, and the sterilization system has the same beneficial effects as the control method, and therefore, the detailed description is omitted.

Some embodiments of the present invention provide a storage medium storing executable instructions that can be executed by a device to cause the device to perform the control method as described above.

Examples of storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and the present invention shall fall within the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means.