A UV smoke hood safety monitoring method and system based on the Internet of Things

Abstract

The invention relates to the technical field of safety monitoring, and specifically discloses a UV smoke hood safety monitoring method and system based on the Internet of Things, comprising: a spectral sample acquisition module: a collection point is set under UV smoke hoods of multiple households through the spectral sample acquisition module, high-spectral imaging and shooting images are regularly obtained, and a smoke spectrum database is established; an absorption ratio determination module: a collection point is set at the filter screen and the air inlet of the UV smoke hood through the absorption ratio determination module, and the intake and suction high-spectral imaging is obtained, the number of smoke pixels and the volume index are obtained, and the absorption ratio is obtained; a real-time monitoring module: the absorption ratio is obtained in real time through the real-time monitoring module and a change curve is established, and the filter screen blockage condition is prompted according to the change rate; the invention can monitor the use status of the UV smoke hood in the kitchen in real time, effectively prevent the safety problems caused by the filter screen blockage, and improve the kitchen safety.

Description

Technical Field

[0001] The present invention relates to the technical field of safety monitoring, and in particular to a UV smoke hood safety monitoring method and system based on the Internet of Things. Background Art

[0002] UV hood is a device that uses ultraviolet radiation technology to purify the air. It is mainly used in commercial kitchens and industrial sites to reduce the impact of oil smoke and harmful gases on the environment and human health. UV hood consists of multiple parts, including a filtration system, a UV light box, an ozone generator, etc. These parts together constitute its complete structure and ensure the efficient operation of the equipment.

[0003] The safety monitoring of UV fume hoods is used to ensure the efficient and safe operation of the equipment. The contents of UV fume hood safety monitoring mainly include ultraviolet lamp status monitoring, ozone concentration monitoring, filtration system status monitoring and temperature monitoring.

[0004] In the prior art, the monitoring of the filter blockage of the UV hood is often based on the air pressure measurement method, and an air pressure monitoring module is used to detect the air pressure value after the air passes through the filter of the UV hood. The more serious the filter blockage is, the greater the obstruction to the air circulation is, which reduces the air flow rate behind the filter, reduces the dynamic pressure, and increases the static pressure, thereby causing the air pressure value behind the filter to decrease accordingly; but in actual situations, the ambient temperature in the kitchen is often high, and the humidity is also changing all the time, the monitoring result of the air pressure monitoring module will be inaccurate, which will cause inaccurate judgment of the filter blockage. Summary of the invention

[0005] The purpose of the present invention is to provide a UV smoke hood safety monitoring method and system based on the Internet of Things to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A UV smoke hood safety monitoring system based on the Internet of Things, comprising:

[0008] Spectral sample acquisition module: select several sample households and set the acquisition cycle; set acquisition points under the UV hoods of the sample households, and the acquisition points acquire the hyperspectral imaging and photographic images under the UV hoods during the operation period; obtain the spectral database of oil smoke based on the hyperspectral imaging and photographic images of each sample household in all operation periods during the acquisition cycle;

[0009] Absorption ratio determination module: a first collection point is set at the filter of the UV hood, the first collection point is directly opposite to the stove, and a second collection point is set in the air inlet of the UV hood, the second collection point is directly opposite to the filter;

[0010] During the operation period, the first acquisition point obtains the hyperspectral imaging of the intake air above the stove, and according to the spectral database, obtains the number of pixels n0 occupied by the oil smoke in the hyperspectral imaging of the intake air, and obtains the intake volume index V0=n0*H0, where H is the height of the UV hood compared to the stove;

[0011] The second acquisition point obtains the hyperspectral imaging of the air intake at the filter, and according to the spectral database, obtains the number of pixels n1 occupied by the oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V1=n1*H1, where H1 is the height of the second acquisition point compared to the filter; and obtains the absorption ratio Ar=V1 / V0;

[0012] Real-time monitoring module: acquires the absorption ratio in real time during the operation period of the UV hood, establishes an absorption ratio change curve, and prompts the clogging condition of the filter according to the change rate of the absorption ratio change curve.

[0013] As a further solution of the present invention: the process of obtaining the spectral database includes:

[0014] The hyperspectral imaging and the captured image are rasterized and divided into a number of pixels, and each pixel is numbered; the numbers of the pixels occupied by the oil smoke are marked in the captured image to obtain a numbering sequence, and the corresponding pixels are selected in the hyperspectral imaging according to the numbering sequence to obtain the spectral information in each pixel; a spectral database is obtained based on all the collected spectral information.

[0015] As a further solution of the present invention: the setting range of the collection period is [1month, 1years].

[0016] As a further solution of the present invention: the process of determining the operating time period includes obtaining the opening time and closing time of the UV hood, and recording the time period between each opening time and closing time as the operating time period.

[0017] As a further solution of the present invention: the process of establishing the absorption ratio change curve includes:

[0018] The moment when the user cleans the filter is obtained and recorded as the starting moment, and the starting moment includes the moment when the UV hood is first put into use; the time period formed by every two adjacent starting moments is recorded as the operation cycle, and within the operation cycle, the absorption ratio within each operation time period is obtained in real time according to a preset time interval; based on the absorption ratio obtained each time within the operation time period, an absorption ratio change curve of the operation time period is established.

[0019] As a further solution of the present invention: the process of indicating the clogging condition of the filter screen according to the change rate of the absorption ratio change curve includes:

[0020] At the end of the operation period, the absorption ratio variation curve {f1, f2, ..., f m}, where f1 is the first running period, and m is the total number of running periods; the change rate of the absorption ratio is obtained , where f´ ei is the absorption ratio obtained for the i-th time in the e-th operating period, N e is the total number of absorption ratios obtained in the e-th operating period; and the clogging condition of the filter is predicted based on the change rate.

[0021] As a further solution of the present invention: the process of predicting the clogging condition of the filter screen according to the change rate includes:

[0022] Obtain the duration of all operating time periods collected in the spectrum sample acquisition module, select the minimum value Tmin and the average value Tave of the duration; and obtain the absorption ratio Ar0 at the closing time of the latest operating time period; according to the change rate, obtain the predicted time t=(Ar0-Ar´) / Cr before the filter is blocked, where Ar´ is the preset blockage absorption ratio threshold;

[0023] If t≤Tmin, the user is reminded that the filter of the UV hood needs to be cleaned; if Tmin<t<Tave, the user is reminded that the filter of the UV hood may be clogged during the next operation period; if t≥Tave, no reminder is given.

[0024] As a further solution of the present invention: a UV smoke hood safety monitoring method based on the Internet of Things, comprising the following steps:

[0025] Step S1: Select several sample households and set a collection cycle; set a collection point under the UV hood of the sample household, and the collection point obtains the hyperspectral imaging and shooting images under the UV hood during the operation period; obtain the spectral database of oil smoke according to the hyperspectral imaging and shooting images of each sample household in all operation periods during the collection cycle;

[0026] Step S2: a first collection point is set at the filter of the UV hood, the first collection point is directly opposite to the stove, and a second collection point is set in the air inlet of the UV hood, the second collection point is directly opposite to the filter;

[0027] During the operation period, the first acquisition point obtains the hyperspectral imaging of the intake air above the stove, and according to the spectral database, obtains the number of pixels n0 occupied by the oil smoke in the hyperspectral imaging of the intake air, and obtains the intake volume index V0=n0*H0, where H is the height of the UV hood compared to the stove;

[0028] The second acquisition point obtains the hyperspectral imaging of the air intake at the filter, and according to the spectral database, obtains the number of pixels n1 occupied by the oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V1=n1*H1, where H1 is the height of the second acquisition point compared to the filter; and obtains the absorption ratio Ar=V1 / V0;

[0029] Step S3: acquiring the absorption ratio in real time during the operation period of the UV hood, establishing an absorption ratio variation curve, and indicating the clogging condition of the filter according to the variation rate of the absorption ratio variation curve.

[0030] Beneficial effects of the present invention:

[0031] In the prior art, the monitoring of the filter blockage of the UV hood is often based on the air pressure measurement method, and an air pressure monitoring module is used to detect the air pressure value after the air passes through the filter of the UV hood. The more serious the filter blockage is, the greater the obstruction to the air circulation is, which reduces the air flow rate behind the filter, reduces the dynamic pressure, and increases the static pressure, thereby causing the air pressure value behind the filter to decrease accordingly; but in actual situations, the ambient temperature in the kitchen is often high, and the humidity is also changing all the time, the monitoring result of the air pressure monitoring module will be inaccurate, which will cause inaccurate judgment of the filter blockage.

[0032] Compared with the prior art, the present invention establishes an oil fume spectrum database and an oil fume spectrum information database through a spectral sample acquisition module to provide data support for monitoring; when the UV hood is in operation, the volume index of oil fume generated by the stove is captured by collecting high-spectral imaging below the UV hood, and high-spectral imaging of the oil fume inhaled in real time inside the filter of the UV hood is collected to obtain the volume index of the oil fume inhaled in real time inside the filter, and the two volume indexes are compared to obtain the real-time absorption ratio of the filter of the UV hood; the smaller the value of the absorption ratio, the more serious the blockage of the filter; and then the blockage of the filter is judged by real-time analysis of the absorption ratio of the filter during the operation cycle; this method avoids the problem in the prior art that the monitoring result is easily affected by temperature and humidity in judging the blockage of the filter by monitoring the air pressure; the absorption ratio is obtained in real time and a change curve is established, and the blockage of the filter is prompted according to the change rate, thereby improving kitchen safety; and effectively preventing safety problems caused by filter blockage. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below in conjunction with the accompanying drawings.

[0034] Figure 1 The invention discloses a UV smoke hood safety monitoring method and system based on the Internet of Things. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] See also Figure 1 As shown, the present invention is a UV smoke hood safety monitoring system based on the Internet of Things, comprising:

[0037] Spectral sample acquisition module: select several sample households and set the acquisition cycle; set acquisition points under the UV hoods of the sample households, and the acquisition points acquire the hyperspectral imaging and photographic images under the UV hoods during the operation period; obtain the spectral database of oil smoke based on the hyperspectral imaging and photographic images of each sample household in all operation periods during the acquisition cycle;

[0038] It can be understood that by setting a collection point under the UV hood in the selected sample households, the hyperspectral imaging and photographed images under the UV hood during the operation period are acquired in real time; the hyperspectral imaging and photographed images are accumulated over time to form a spectral database of oil smoke;

[0039] As a preferred embodiment of the present invention, the process of obtaining the spectrum database includes:

[0040] The hyperspectral imaging and the captured image are rasterized and divided into a number of pixels, and each pixel is numbered; the numbers of the pixels occupied by the oil smoke are marked in the captured image to obtain a number sequence, and the corresponding pixels are selected in the hyperspectral imaging according to the number sequence to obtain the spectral information in each pixel; a spectral database is obtained according to all the collected spectral information;

[0041] It can be understood that the hyperspectral imaging is a method of imaging using hyperspectral technology, which can capture the spectral information of oil smoke in different bands; however, oil smoke is a complex mixture composed of multiple compounds, which are produced by thermal decomposition and oxidation reactions during the cooking process. The oil smoke produced by using different cooking oils, different cooking foods and cooking methods contains different organic and inorganic compounds, and thus the spectral information shown in hyperspectral imaging is different; therefore, several sample families are selected, and the spectral information of the oil smoke generated by the sample families in the kitchen is collected within the collection period to form a spectral database of oil smoke; the spectral database of oil smoke is used to subsequently identify the pixels occupied by oil smoke in the hyperspectral imaging directly under the UV hood;

[0042] As a preferred embodiment of the present invention, the setting range of the collection period is [1month, 1years];

[0043] It is understandable that the setting range of the collection period needs to be long enough to cover the diet of most of the sample families; the sample families need to be different, such as different types of edible oils, different dietary tastes, different economic conditions, etc.; in this way, the spectral information of the collected oil smoke contains more types and the spectral database is more substantial;

[0044] As a preferred embodiment of the present invention, the process of determining the operating time period includes obtaining the opening time and closing time of the UV hood, and recording the time period between each opening time and closing time as the operating time period;

[0045] Absorption ratio determination module: a first collection point is set at the filter of the UV hood, the first collection point is directly opposite to the stove, and a second collection point is set in the air inlet of the UV hood, the second collection point is directly opposite to the filter;

[0046] During the operation period, the first acquisition point obtains the hyperspectral imaging of the intake air above the stove, and according to the spectral database, obtains the number of pixels n0 occupied by the oil smoke in the hyperspectral imaging of the intake air, and obtains the intake volume index V0=n0*H0, where H is the height of the UV hood compared to the stove;

[0047] The second acquisition point obtains the hyperspectral imaging of the air intake at the filter, and according to the spectral database, obtains the number of pixels n1 occupied by the oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V1=n1*H1, where H1 is the height of the second acquisition point compared to the filter; and obtains the absorption ratio Ar=V1 / V0;

[0048] It should be noted that, according to the common knowledge in the art, each pixel in the hyperspectral imaging not only represents an image point, but also contains the spectral information of the point; according to the spectral database of oil smoke, it is judged whether the spectral information of each pixel in the hyperspectral imaging belongs to the spectral database, if so, the substance corresponding to the pixel point is oil smoke; otherwise, the substance corresponding to the pixel point is not oil smoke;

[0049] It is understandable that when the UV hood is in operation, the oil smoke rises vertically upward, so the volume index of the oil smoke generated by the stove in real time is estimated based on the product of n0 and H0; and the volume index of the oil smoke passing through the filter when the UV hood absorbs the oil smoke is estimated based on n1 and H1; in the process of obtaining the absorption ratio, H1 and H0 are both fixed values. When the amount of oil smoke generated by the stove is greater, the value of n0 is greater, and the proportion of oil smoke in the gas absorbed by the UV hood is also greater, that is, the greater n1; therefore, the smaller the absorption ratio, the more serious the blockage of the filter;

[0050] Real-time monitoring module: obtains the absorption ratio in real time during the operation period of the UV hood, establishes an absorption ratio change curve, and prompts the blockage of the filter according to the change rate of the absorption ratio change curve;

[0051] It can be understood that during the operation of the UV hood, the absorption ratio of the oil smoke passing through the filter is obtained in real time, and the absorption ratio reflects the efficiency of the particles in the oil smoke being absorbed by the filter; with the use of the UV hood, grease and particles will gradually accumulate on the filter, affecting its filtering efficiency; the real-time monitoring module will establish an absorption ratio curve that changes with time based on the acquired absorption ratio data to describe the change trend of the adsorption capacity of the filter; by analyzing the rate of change of the absorption ratio curve, that is, the slope of the curve, the blockage degree of the filter can be evaluated;

[0052] As a preferred embodiment of the present invention, the process of establishing the absorption ratio variation curve includes:

[0053] The time when the user cleans the filter is obtained and recorded as the starting time, and the starting time includes the time when the UV hood is put into use for the first time; the time period formed by each two adjacent starting times is recorded as the operation cycle, and within the operation cycle, the absorption ratio in each operation time period is obtained in real time according to the preset time interval; according to the absorption ratio obtained each time during the operation time period, the absorption ratio change curve of the operation time period is established;

[0054] The process of indicating the clogging condition of the filter screen according to the change rate of the absorption ratio change curve includes:

[0055] At the end of the operation period, the absorption ratio variation curve {f1, f2, ..., f m}, where f1 is the first running period, and m is the total number of running periods; the change rate of the absorption ratio is obtained , where f´ ei is the absorption ratio obtained for the i-th time in the e-th operating period, N e is the total number of absorption ratios obtained in the e-th operating period; predicting the clogging of the filter according to the change rate;

[0056] The process of predicting the clogging condition of the filter screen according to the change rate includes:

[0057] Obtain the duration of all operating time periods collected in the spectrum sample acquisition module, select the minimum value Tmin and the average value Tave of the duration; and obtain the absorption ratio Ar0 at the closing time of the latest operating time period; according to the change rate, obtain the predicted time t=(Ar0-Ar´) / Cr before the filter is blocked, where Ar´ is the preset blockage absorption ratio threshold;

[0058] If t≤Tmin, the user is reminded that the filter of the UV hood needs to be cleaned; if Tmin<t<Tave, the user is reminded that the filter of the UV hood may be blocked during the next operation period; if t≥Tave, no reminder is given;

[0059] It can be understood that the process of establishing the absorption ratio change curve includes:

[0060] During the operation period, every time interval is recorded as a time node, and each time node is numbered; a rectangular coordinate system is established with the number of the time node as the horizontal coordinate and the absorption ratio as the vertical coordinate; each time node and the absorption ratio obtained at each time node are converted into coordinate points on the rectangular coordinate system, and each coordinate point is connected with a smooth curve, and the curve is recorded as the absorption ratio change curve.

[0061] A UV smoke hood safety monitoring method based on the Internet of Things includes the following steps:

[0062] Step S1: Select several sample households and set a collection cycle; set a collection point under the UV hood of the sample household, and the collection point obtains the hyperspectral imaging and shooting images under the UV hood during the operation period; obtain the spectral database of oil smoke according to the hyperspectral imaging and shooting images of each sample household in all operation periods during the collection cycle;

[0063] Step S2: a first collection point is set at the filter of the UV hood, the first collection point is directly opposite to the stove, and a second collection point is set in the air inlet of the UV hood, the second collection point is directly opposite to the filter;

[0064] During the operation period, the first acquisition point obtains the hyperspectral imaging of the intake air above the stove, and according to the spectral database, obtains the number of pixels n0 occupied by the oil smoke in the hyperspectral imaging of the intake air, and obtains the intake volume index V0=n0*H0, where H is the height of the UV hood compared to the stove;

[0065] The second acquisition point obtains the hyperspectral imaging of the air intake at the filter, and according to the spectral database, obtains the number of pixels n1 occupied by the oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V1=n1*H1, where H1 is the height of the second acquisition point compared to the filter; and obtains the absorption ratio Ar=V1 / V0;

[0066] Step S3: acquiring the absorption ratio in real time during the operation period of the UV hood, establishing an absorption ratio variation curve, and indicating the clogging condition of the filter according to the variation rate of the absorption ratio variation curve.

[0067] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A UV smoke hood safety monitoring system based on the Internet of Things, characterized in that: include: Spectral sample collection module: select several sample families and set the collection cycle; A collection point is set under the UV smoke hood of the sample household, and the collection point obtains the hyperspectral imaging and shooting images under the UV smoke hood during the operation period; according to the hyperspectral imaging and shooting images of each sample household in all the operation periods during the collection period, a spectral database of oil smoke is obtained; Absorption ratio determination module: a first collection point is set at the filter of the UV hood, the first collection point is directly opposite to the stove, and a second collection point is set in the air inlet of the UV hood, the second collection point is directly opposite to the filter; During the operation period, the first acquisition point obtains the hyperspectral imaging of the air intake above the stove, and according to the spectral database, obtains the number of pixels n0 occupied by oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V0=n0*H0, where H0 is the height of the UV hood compared to the stove; The second acquisition point obtains the hyperspectral imaging of the air intake at the filter, and according to the spectral database, obtains the number of pixels n1 occupied by the oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V1=n1*H1, where H1 is the height of the second acquisition point compared to the filter; and obtains the absorption ratio Ar=V1 / V0; Real-time monitoring module: acquires the absorption ratio in real time during the operation period of the UV hood, establishes an absorption ratio change curve, and prompts the clogging of the filter according to the change rate of the absorption ratio change curve.

2. The UV smoke hood safety monitoring system based on the Internet of Things according to claim 1 is characterized in that: The process of obtaining the spectral database includes: The hyperspectral imaging and the captured image are rasterized and divided into a number of pixels, and each pixel is numbered; the numbers of the pixels occupied by the oil smoke are marked in the captured image to obtain a numbering sequence, and the corresponding pixels are selected in the hyperspectral imaging according to the numbering sequence to obtain the spectral information in each pixel; a spectral database is obtained based on all the collected spectral information.

3. The UV smoke hood safety monitoring system based on the Internet of Things according to claim 1 is characterized in that: The setting range of the collection period is [1month, 1years].

4. The UV smoke hood safety monitoring system based on the Internet of Things according to claim 1 is characterized in that: The process of determining the operating time period includes obtaining the opening time and closing time of the UV hood, and recording the time period between each opening time and closing time as the operating time period.

5. The UV smoke hood safety monitoring system based on the Internet of Things according to claim 1 is characterized in that: The process of establishing the absorption ratio variation curve includes: The moment when the user cleans the filter is obtained and recorded as the starting moment, and the starting moment includes the moment when the UV hood is first put into use; the time period formed by every two adjacent starting moments is recorded as the operation cycle, and within the operation cycle, the absorption ratio within each operation time period is obtained in real time according to a preset time interval; based on the absorption ratio obtained each time within the operation time period, an absorption ratio change curve of the operation time period is established.

6. The Internet of Things-based UV fume hood safety monitoring system according to claim 5, characterized in that: The process of indicating the clogging condition of the filter screen according to the change rate of the absorption ratio change curve includes: At the end of the operation period, the absorption ratio variation curve {f1, f2, ..., f m }, where f1 is the first running period, and m is the total number of running periods; the change rate of the absorption ratio is obtained , where f´ ei is the absorption ratio obtained for the i-th time in the e-th operating period, N e is the total number of absorption ratios obtained in the e-th operating period; and the clogging condition of the filter is predicted based on the change rate.

7. The Internet of Things-based UV fume hood safety monitoring system according to claim 6, characterized in that: The process of predicting the clogging condition of the filter screen according to the change rate includes: Obtain the duration of all operating time periods collected in the spectrum sample acquisition module, select the minimum value Tmin and the average value Tave of the duration; and obtain the absorption ratio Ar0 at the closing time of the latest operating time period; according to the change rate, obtain the predicted time t=(Ar0-Ar´) / Cr before the filter is blocked, where Ar´ is the preset blockage absorption ratio threshold; If t≤Tmin, the user is reminded that the filter of the UV hood needs to be cleaned; if Tmin<t<Tave, the user is reminded that the filter of the UV hood may be clogged during the next operation period; if t≥Tave, no reminder is given.

8. A UV smoke hood safety monitoring method based on the Internet of Things, characterized in that: The following steps are involved: Step S1: Select several sample households and set a collection cycle; set a collection point under the UV hood of the sample household, and the collection point obtains the hyperspectral imaging and shooting images under the UV hood during the operation period; obtain the spectral database of oil smoke according to the hyperspectral imaging and shooting images of each sample household in all operation periods during the collection cycle; Step S2: a first collection point is set at the filter of the UV hood, the first collection point is directly opposite to the stove, and a second collection point is set in the air inlet of the UV hood, the second collection point is directly opposite to the filter; During the operation period, the first acquisition point obtains the hyperspectral imaging of the air intake above the stove, and according to the spectral database, obtains the number of pixels n0 occupied by oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V0=n0*H0, where H0 is the height of the UV hood compared to the stove; The second acquisition point obtains the hyperspectral imaging of the air intake at the filter, and according to the spectral database, obtains the number of pixels n1 occupied by the oil smoke in the hyperspectral imaging of the air intake, and obtains the air intake volume index V1=n1*H1, where H1 is the height of the second acquisition point compared to the filter; and obtains the absorption ratio Ar=V1 / V0; Step S3: acquiring the absorption ratio in real time during the operation period of the UV hood, establishing an absorption ratio variation curve, and indicating the clogging condition of the filter according to the variation rate of the absorption ratio variation curve.

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