CN117490206B - Deep learning-based epidemic prevention switching system and method for air conditioning unit - Google Patents
Deep learning-based epidemic prevention switching system and method for air conditioning unit Download PDFInfo
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- 238000004378 air conditioning Methods 0.000 title claims abstract description 88
- 230000002265 prevention Effects 0.000 title claims abstract description 47
- 238000013135 deep learning Methods 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000004659 sterilization and disinfection Methods 0.000 claims abstract description 127
- 241000700605 Viruses Species 0.000 claims abstract description 115
- 230000036760 body temperature Effects 0.000 claims abstract description 66
- 208000015181 infectious disease Diseases 0.000 claims abstract description 36
- 238000001514 detection method Methods 0.000 claims abstract description 20
- 239000003570 air Substances 0.000 claims description 135
- 230000001954 sterilising effect Effects 0.000 claims description 57
- 230000005764 inhibitory process Effects 0.000 claims description 21
- 230000008859 change Effects 0.000 claims description 18
- 239000011159 matrix material Substances 0.000 claims description 13
- 230000000249 desinfective effect Effects 0.000 claims description 11
- 238000013507 mapping Methods 0.000 claims description 4
- 239000012080 ambient air Substances 0.000 claims description 3
- 230000002401 inhibitory effect Effects 0.000 claims 2
- 230000036541 health Effects 0.000 abstract description 13
- 238000011217 control strategy Methods 0.000 abstract description 3
- 101100100125 Mus musculus Traip gene Proteins 0.000 description 23
- 230000009471 action Effects 0.000 description 5
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- 125000004122 cyclic group Chemical group 0.000 description 3
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- 230000003287 optical effect Effects 0.000 description 3
- 231100000481 chemical toxicant Toxicity 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 210000002249 digestive system Anatomy 0.000 description 2
- 210000003128 head Anatomy 0.000 description 2
- 238000011534 incubation Methods 0.000 description 2
- 230000002458 infectious effect Effects 0.000 description 2
- 230000002427 irreversible effect Effects 0.000 description 2
- 210000002345 respiratory system Anatomy 0.000 description 2
- 208000024891 symptom Diseases 0.000 description 2
- 239000003440 toxic substance Substances 0.000 description 2
- 230000009385 viral infection Effects 0.000 description 2
- 206010065369 Burnout syndrome Diseases 0.000 description 1
- 208000000059 Dyspnea Diseases 0.000 description 1
- 206010013975 Dyspnoeas Diseases 0.000 description 1
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- 230000005540 biological transmission Effects 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/65—Electronic processing for selecting an operating mode
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Abstract
The embodiment of the application relates to the technical field of epidemic prevention and discloses an epidemic prevention switching system and method of an air conditioning unit based on deep learning, wherein the system comprises a user travel detection module, a epidemic risk identification module, a user body temperature parameter identification module, a virus killing switching module and a control module; then, performing deep learning according to the acquired travel data of the user to obtain a travel journey of the user; judging and identifying the magnitude of the risk value of epidemic infection of the user according to the travel journey of the user and the demarcation range of the epidemic situation risk area; finally, different epidemic prevention switching modes are carried out according to the risk value of user epidemic prevention. Therefore, different circulation control strategies of virus disinfection and fresh air introduction of the indoor environment can be carried out according to the epidemic risk of the user, so that the physical health of the patient in the indoor environment can be comprehensively ensured.
Description
Technical Field
The invention relates to the technical field of epidemic prevention, in particular to an air conditioning unit epidemic prevention switching system and method based on deep learning.
Background
Currently, there are a large number of infectious viruses, toxic chemicals, etc. in the world, and their existence cannot be captured in the form of naked eyes, and they can enter the human body through the respiratory system, the digestive system, etc. to cause irreversible damage to the human body.
In the related art, when the epidemic prevention shelter hospital receives and treats patients, as the viruses have a longer incubation period, a plurality of patients can not accurately determine whether the patients really infect the viruses when the patients are admitted, so that the hospital generally closes the ward door to achieve the aim of cutting off the virus infection, but for the patients sealed in a narrow space, the patients still face a severe health problem, and the health of the patients still cannot be effectively guaranteed.
Disclosure of Invention
The invention mainly aims to provide an epidemic prevention switching system and method for an air conditioning unit based on deep learning, and aims to solve the technical problem that the physical health of patients suffering from epidemic disease in a narrow space is not effectively guaranteed in the prior art.
In order to achieve the above object, in a first aspect, an embodiment of the present application provides an air conditioning unit epidemic prevention switching system based on deep learning, including:
the user travel detection module comprises a user identity identification unit and a first deep learning unit, wherein the user identity identification unit is used for identifying user identity information in an indoor work environment of the air conditioner, and the first deep learning unit is used for performing deep learning on travel data of a user according to the acquired user identity information to obtain travel of the user;
the epidemic risk infection identification module comprises a risk identification unit and an epidemic risk area updating unit, wherein the risk identification unit is used for judging and identifying the risk of the epidemic of the user according to the travel journey of the user and the demarcation range of the epidemic risk area, and the epidemic risk area updating unit is used for updating the demarcation range of the epidemic risk area in real time;
the body temperature parameter identification module comprises a body temperature matrix detection unit and a body temperature correction unit, wherein the body temperature matrix detection unit is used for detecting temperature parameters of different head positions of a user, and the body temperature correction unit is used for correcting the temperature parameters according to the temperature parameters of air output by an air conditioning unit so as to obtain the body temperature parameters of the user;
The virus disinfection and sterilization switching module comprises an air conditioning unit circulation switching unit, a first sterilization unit and a second sterilization unit, wherein the air conditioning unit circulation switching unit is used for switching an inner circulation mode and an outer circulation mode of an air conditioning indoor unit, the first sterilization unit is used for sterilizing and disinfecting indoor air in the inner circulation mode of the air conditioning indoor unit, and the second sterilization unit is used for sterilizing and disinfecting air entering a room in the outer circulation mode of the air conditioning indoor unit;
And the control module is used for performing disinfection and switching control of the virus disinfection and switching module according to the magnitude of the risk value of user epidemic infection.
Further, the trip data at least comprises one of access control traffic data, traffic payment data, shopping payment data, trip map data and mobile phone roaming place data in the trip process.
Further, the epidemic prevention switching system of the air conditioning unit further comprises:
The virus habit learning module comprises a second deep learning unit, wherein the second deep learning unit is used for performing deep learning according to internet information to obtain virus habit data, and the virus habit data comprises virus propagation inhibition temperature range data and virus propagation inhibition humidity range data;
The temperature and humidity adjusting module comprises a temperature adjusting unit and a humidity adjusting unit, wherein the temperature adjusting unit is used for adjusting the temperature of indoor air according to the data of the temperature range of virus propagation inhibition, and the humidity adjusting unit is used for adjusting the humidity of the indoor air according to the data of the humidity range of virus propagation inhibition.
In a second aspect, an embodiment of the present application further provides an air conditioning unit epidemic prevention switching method based on deep learning, which is applied to the air conditioning unit epidemic prevention switching system, where the air conditioning unit epidemic prevention switching system includes a user trip detection module, a epidemic risk identification module, a virus disinfection switching module, a virus habit learning module, and a temperature and humidity adjustment module, and the method includes:
acquiring user travel data of a user in an indoor work environment of the air conditioning unit;
deep learning is carried out according to the acquired user travel data so as to obtain a travel journey of the user;
Judging and identifying the magnitude of a risk value of epidemic infection of a user according to the travel journey of the user and the demarcation range of the epidemic situation risk area;
Determining that the risk value of user epidemic infection is smaller than a first preset risk threshold value, and controlling the air conditioning unit to execute a first disinfection switching mode;
Determining that the risk value of user epidemic infection is larger than a first preset risk threshold value and smaller than a second preset risk value, and controlling the air conditioning unit to execute a second disinfection switching mode;
Determining that the risk value of the user epidemic is larger than a second preset risk threshold value, controlling the air conditioning unit to execute a third disinfection switching mode, wherein,
The second preset risk threshold is larger than the first preset risk threshold, the duration of killing indoor air in the first killing switching mode is smaller than the duration of killing indoor air in the second killing switching mode, and the duration of killing indoor air in the third killing switching mode is longer than the duration of killing indoor air in the second killing switching mode.
Further, the first sterilizing switching mode comprises a cyclic switching control step of sterilizing the indoor air for a first time period and introducing the outdoor fresh air into the indoor environment for a second time period; the second disinfection switching mode comprises a circulation switching control step of disinfecting the indoor air for a second time period and introducing outdoor fresh air into the indoor environment for a first time period, wherein the first time period is smaller than the second time period; the third sterilization switching mode includes a control step of continuously sterilizing the indoor air for viruses.
Further, the acquiring trip data of the user in the indoor unit working environment of the air conditioning unit includes:
Identifying user identity information of a user in an indoor work environment of the air conditioner;
and acquiring travel data of the user according to the user identity information.
Further, after the step of controlling the air conditioning unit to execute the third disinfection and sterilization switching mode, the method further includes:
virus killing is carried out for a first preset time period based on a preset standard killing temperature and a preset killing humidity;
analyzing the virus content of indoor environment air to obtain the change rate of the virus content in the air within the first preset duration;
Determining that the change rate of the virus content is smaller than or equal to a change rate threshold value, and acquiring virus habit parameters of the current virus, wherein the virus habit parameters of the current virus comprise virus propagation inhibition temperature range data and virus propagation inhibition humidity range data;
Adjusting the standard disinfection temperature and the disinfection humidity according to the virus habit parameters to obtain the current disinfection temperature and the current disinfection humidity;
and virus sterilization is carried out on the indoor environment air at the current sterilization temperature and the current sterilization humidity.
Further, after the analyzing the virus content of the indoor ambient air to obtain the change rate of the virus content in the air within the first preset duration, the method further includes:
and determining that the change rate of the virus content is larger than a change rate threshold value, and maintaining the standard disinfection temperature and the disinfection humidity to carry out virus disinfection.
Further, after the step of controlling the air conditioning unit to execute the first disinfection switching mode, the method further includes:
after virus killing is carried out for a second preset time period, acquiring a body temperature value of a user;
And determining that the body temperature value of the user is in a normal body temperature range, and adjusting the first sterilization switching mode to a fourth sterilization switching mode, wherein the fourth sterilization switching mode comprises a control step of continuously introducing outdoor fresh air into an indoor environment.
Further, the acquiring the body temperature value of the user includes:
acquiring a temperature parameter Ti output by a body temperature matrix detection unit and the distance between an air outlet of an air conditioner and a user;
According to the distance between the air outlet of the air conditioner and a user, inquiring a mapping table to obtain an influence coefficient P of the air outlet of the air conditioner on the body temperature of the user;
Inputting the temperature parameter Ti and the influence coefficient P of air conditioner air outlet on the body temperature of the user into a pre-trained body temperature correction model to obtain the body temperature value of the user, wherein the mathematical expression of the body temperature correction model is as follows:
-T 10 x P (air conditioner outlet temperature greater than 30 degrees celsius),/> +T 20.P (air-conditioning outlet temperature is less than 5 ℃), T 10 is a temperature correction reference value when the air-conditioning outlet temperature exceeds 30 ℃, and T 20 is a temperature correction reference value when the air-conditioning outlet temperature is less than 5 ℃.
Compared with the prior art, the deep learning-based epidemic prevention switching system of the air conditioning unit provided by the embodiment of the application comprises a user travel detection module, a epidemic risk identification module, a user body temperature parameter identification module, a virus killing switching module and a control module, wherein when the air conditioning epidemic prevention switching is carried out, user travel data of a user in an indoor working environment of the air conditioning unit is firstly obtained; then, performing deep learning according to the acquired travel data of the user to obtain a travel journey of the user; judging and identifying the magnitude of the risk value of epidemic infection of the user according to the travel journey of the user and the demarcation range of the epidemic situation risk area; finally, different epidemic prevention switching modes are carried out according to the risk value of user epidemic, namely, the risk value of user epidemic is determined to be smaller than a first preset risk threshold value, and the air conditioning unit is controlled to execute a first disinfection switching mode; determining that the risk value of user epidemic infection is larger than a first preset risk threshold value and smaller than a second preset risk value, and controlling the air conditioning unit to execute a second disinfection switching mode; and determining that the risk value of the user epidemic infection is larger than a second preset risk threshold value, and controlling the air conditioning unit to execute a third disinfection switching mode.
Therefore, the application can carry out different circulation control strategies of sterilizing the indoor environment viruses and introducing fresh air according to the epidemic risk of the user, so that the fresh degree of the indoor environment air can be ensured while the viruses are sterilized, and the physical health of the patient in the indoor environment can be comprehensively ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic block diagram of an air conditioning unit epidemic prevention switching system in some embodiments of the present application;
FIG. 2 is a flow chart of an air conditioning unit epidemic prevention switching method according to some embodiments of the present application;
fig. 3 is a schematic diagram of a hardware structure of an electronic device according to another embodiment of the application.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, "and/or" throughout this document includes three schemes, taking a and/or B as an example, including a technical scheme, a technical scheme B, and a technical scheme that both a and B satisfy; in addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Currently, there are a large number of infectious viruses, toxic chemicals, etc. in the world, and their existence cannot be captured in the form of naked eyes, and they can enter the human body through the respiratory system, the digestive system, etc. to cause irreversible damage to the human body.
In the related art, when the epidemic prevention shelter hospital receives and treats patients, as the viruses have a longer incubation period, a plurality of patients can not accurately determine whether the patients really infect the viruses when the patients are admitted, therefore, the general practice of the hospital is to tightly close the ward door to achieve the aim of cutting off the virus infection, but for the patients sealed in a narrow space, the patients still face a severe health problem, and the health of the patients still can not be effectively guaranteed; for example, patients in a small space have health problems such as dyspnea and physical and mental exhaustion due to the inability to breathe fresh air for a long time.
In order to solve the above problems, the present application provides an air conditioning unit epidemic prevention switching system based on deep learning, as shown in fig. 1, the air conditioning unit epidemic prevention switching system includes:
the user trip detection module 100 comprises a user identity identification unit 110 and a first deep learning unit 120, wherein the user identity identification unit 110 is used for identifying user identity information in an indoor working environment of an air conditioner, and the first deep learning unit 120 is used for performing deep learning on trip data of a user according to the acquired user identity information to obtain a trip of the user;
Specifically, the trip data at least comprises one of access control traffic data, traffic payment data, shopping payment data, trip map data and mobile phone roaming place data in the trip process.
The epidemic risk identification module 200 comprises a risk identification unit 210 and an epidemic risk area updating unit 220, wherein the risk identification unit 210 is used for judging and identifying the risk of the epidemic of the user according to the travel journey of the user and the demarcation range of the epidemic risk area, and the epidemic risk area updating unit 220 is used for updating the demarcation range of the epidemic risk area in real time;
The user body temperature parameter identification module 300 comprises a body temperature matrix detection unit 310 and a body temperature correction unit 320, wherein the body temperature matrix detection unit 310 is used for detecting temperature parameters of different head positions of a user, and the body temperature correction unit 320 is used for correcting the temperature parameters according to the temperature parameters of air output by an air conditioning unit so as to obtain the body temperature parameters of the user;
The virus disinfection and sterilization switching module 400 comprises an air conditioning unit circulation switching unit 410, a first sterilization unit 420 and a second sterilization unit 430, wherein the air conditioning unit circulation switching unit 410 is used for switching an internal and external circulation mode of an air conditioning indoor unit, the first sterilization unit 420 is used for sterilizing and disinfecting indoor air in the internal circulation mode of the air conditioning indoor unit, and the second sterilization unit 430 is used for sterilizing and disinfecting air entering a room in the external circulation mode of the air conditioning indoor unit;
And the control module 500 is used for performing disinfection switching control of the virus disinfection switching module according to the magnitude of the risk value of user epidemic infection by the control module 500.
In other embodiments, the air conditioning unit epidemic prevention switching system further includes:
the virus habit learning module 600 includes a second deep learning unit 610, where the second deep learning unit 610 is configured to perform deep learning according to internet information to obtain virus habit data, where the virus habit data includes virus propagation inhibition temperature range data and virus propagation inhibition humidity range data;
the temperature and humidity adjusting module 700 comprises a temperature adjusting unit 710 and a humidity adjusting unit 720, wherein the temperature adjusting unit 710 is used for adjusting the temperature of indoor air according to the data of the temperature range of virus propagation inhibition, and the humidity adjusting unit 720 is used for adjusting the humidity of the indoor air according to the data of the humidity range of virus propagation inhibition.
It should be noted that, the indoor unit of the air conditioning unit according to the present application includes two circulation modes, i.e., a first circulation mode (internal circulation) and a second circulation mode (external circulation), and the two circulation modes can be controlled by a switching valve (not shown), where the first circulation mode is to kill viruses in the air of the indoor environment, and the air of the indoor environment circulates only inside, and in this mode, the first sterilization unit 420 is started, for example, the first ultraviolet module is turned on to kill viruses in the air of the indoor environment; the second circulation mode is to introduce fresh air into the indoor environment, i.e., to introduce fresh air from the outdoor environment into the indoor environment, and the second sterilizing unit 430 is activated, for example, to turn on the second ultraviolet module to sterilize the air entering the indoor environment.
The present application also proposes an air conditioning unit epidemic prevention switching method, which is applied to the above air conditioning unit epidemic prevention switching system, and the specific steps of the air conditioning unit epidemic prevention switching method will be mainly described below, and it should be noted that although a logic sequence is shown in the flowchart, in some cases, the steps shown or described may be performed in a sequence different from that herein. Referring to fig. 2, the method includes:
s100, acquiring user travel data of a user in an indoor unit working environment of the air conditioning unit;
in one embodiment, the step S100 of acquiring the user travel data of the user in the indoor working environment of the air conditioning unit comprises:
T100: identifying user identity information of a user in an indoor work environment of the air conditioner;
Specifically, the manner of identifying the user identity information may be by facial image recognition, or may be by user fingerprint recognition, or the like, which is not limited herein.
T200: and acquiring travel data of the user according to the user identity information.
Specifically, after the identity information of the user is determined, the system determines travel data of the user before the user enters the indoor working environment of the air conditioner based on the identity information, wherein the travel data at least comprises one of access control traffic data, traffic payment data, shopping payment data, travel map data and mobile phone roaming place data in the travel process.
S200, performing deep learning according to the acquired user travel data to obtain a travel journey of the user;
Specifically, after obtaining the trip data of the user, the first deep learning unit 120 first analyzes a single-point trip of the trip data of the user, where the single-point trip is a trip of the user at a certain place, and then connects each single-point trip in series and performs deep learning analysis in combination with the roaming place data of the mobile phone to obtain the trip activity trip of the user.
For example, when a payment account bound by the user identity information has a consumption record of consumption by merchants at different places, the system can identify a travel route according to the consumption record, for example, the system can pay consumption in a XX supermarket in XX time, and can identify that the travel route of the user includes an administrative area, a street, and the like where the supermarket is located according to the consumption record.
The system can query the roaming place data of the mobile phone according to the mobile phone card binding the identity information of the user, and can know that the travel route of the user comprises administrative areas and the like in the roaming place data of the mobile phone according to the roaming data.
For example, when a first single-point trip of a user is a XX supermarket of a first administrative region, a second single-point trip is a XX restaurant of a second administrative region, and the mobile phone roaming place data all include the first administrative region and the second administrative region, after deep learning, the user is judged to have an activity trip moving from the first administrative region to the second administrative region.
S300, judging and identifying the magnitude of a risk value of epidemic infection of the user according to the travel journey of the user and the demarcation range of the epidemic situation risk area;
In an embodiment, the system may evaluate the risk value of the epidemic according to whether the trip is in risk areas with different risk degrees.
For example, when the travel distance of the user is within the range of the epidemic situation high risk area, the risk value of the user for epidemic infection is higher, for example, the risk value of epidemic infection is 80%; when the travel journey of the user is in the range of the risk area in the epidemic situation, the risk value of the user for epidemic infection is moderate, for example, the risk value of epidemic infection is 50%; when the travel distance of the user is within the range of the epidemic situation low-risk area, the epidemic risk value of the user is lower, for example, 20%, and when the travel distance of the user is not within the range of the epidemic situation risk area, the epidemic risk value of the user is further reduced, for example, 6%.
In another embodiment, the system may evaluate risk values of epidemic infection according to a duration of travel within a defined range of an epidemic risk area, and perform calibration of different risk values according to different durations, which is not described in detail herein.
It can be understood that the epidemic risk values of different users are different, the actual epidemic probability is also different, if all patients are subjected to epidemic prevention treatment according to the same epidemic prevention treatment mode (the closed users are all closed to cut off virus diffusion transmission), the health requirements of all users/patients cannot be completely ensured.
Specifically, after determining the magnitude of the risk value for identifying the user's epidemic, the method comprises the following steps:
s410, determining that the risk value of user epidemic infection is smaller than a first preset risk threshold value, and controlling the air conditioning unit to execute a first disinfection switching mode;
in an embodiment, the first disinfection switching mode includes a cyclic switching control step of disinfecting the indoor air for a first period of time and introducing the outdoor fresh air to the indoor environment for a second period of time, wherein the first period of time is less than the second period of time. Specifically, when the risk value of user epidemic infection is identified to be smaller than a first preset risk threshold, the probability of user/patient epidemic infection is lower, at the moment, the control is performed on the indoor air for a first time period, then the circulation switching control of the outdoor fresh air for a second time period is introduced into the indoor environment, namely, the first time period is performed on the indoor air for the first time period, then the fresh air for the second time period is introduced, and then the first time period is performed on the indoor air for the first time period for the second time period, so that the circulation is performed.
For example, under the condition that the risk of user epidemic infection is low, the indoor environment is killed for 5 minutes, the fresh air is introduced for 10 minutes (regarded as a circulation action), and then the circulation is carried out all the time (the circulation action is repeated). Therefore, the total killing duration is smaller than the total fresh air duration, so that a user can breathe fresh air for a long time under the condition of low epidemic risk, and the health and safety of the user are guaranteed.
S420, determining that the risk value of user epidemic infection is larger than a first preset risk threshold value and smaller than a second preset risk value, and controlling the air conditioning unit to execute a second disinfection switching mode, wherein the second preset risk threshold value is larger than the first preset risk threshold value.
In an embodiment, the second sterilizing switching mode includes a cyclic switching control step of sterilizing the indoor air for a second period of time and introducing the outdoor fresh air to the indoor environment for a first period of time, wherein the first period of time is less than the second period of time; specifically, when the risk value of user epidemic is identified to be larger than the first preset risk threshold value and smaller than the second preset risk value, the probability of user/patient epidemic is indicated to be slightly high, at the moment, the control is performed on the indoor air for a second time period, then the circulation switching control of the first time period of outdoor fresh air is introduced into the indoor environment, namely, the second time period is firstly performed on the indoor air, the first time period of the outdoor fresh air is introduced, and then the second time period is performed on the indoor air, so that the circulation is performed.
For example, in the case of a slightly high risk of epidemic infection of the user, the indoor environment is killed for 10 minutes, then the fresh air is introduced for 5 minutes (regarded as a circulation action), and then the circulation is performed all the time (the circulation action is repeated). Therefore, the total sterilizing duration is longer than the total fresh air duration, but at least the action of introducing fresh air exists, so that a user can not only sterilize viruses under the condition of medium epidemic risk, but also ensure that the user/patient breathes fresh air, and the health and safety of the user are comprehensively ensured.
S430, determining that the risk value of the user epidemic infection is larger than a second preset risk threshold value, and controlling the air conditioning unit to execute a third disinfection switching mode.
In one embodiment, the third sterilization switch mode includes a control step of continuously sterilizing the indoor air with virus. Specifically, when the risk value of user epidemic infection is determined to be larger than a second preset risk threshold, the probability of user/patient epidemic infection is high, and the life safety of the user/patient is guaranteed to the greatest extent.
It can be understood that when the risk value of the user's epidemic is smaller than the first preset risk threshold, it is indicated that the probability of the user/patient's epidemic is lower, and if there is no symptom after the user observes for a period of time in the ward, the disinfection mode can be closed or the user can release from observing and leaving the ward, so that the user can fully enjoy fresh air, and the user health index is improved.
Based on this, in an embodiment, after the step of controlling the air conditioning unit to execute the first disinfection switching mode, the method further includes:
after virus killing is carried out for a second preset time period, acquiring a body temperature value of a user;
Wherein, in an embodiment, the acquiring the body temperature value of the user includes:
t300, acquiring a temperature parameter Ti output by the body temperature matrix detection unit and the distance between an air outlet of the air conditioner and a user;
t400, inquiring a mapping table according to the distance between the air outlet of the air conditioner and a user to obtain an influence coefficient P of the air outlet of the air conditioner on the body temperature of the user;
T500, inputting the temperature parameter Ti and the influence coefficient P of air conditioner air outlet on the body temperature of the user into a pre-trained body temperature correction model to obtain the body temperature value of the user, wherein the mathematical expression of the body temperature correction model is as follows:
-T 10 x P (air conditioner outlet temperature greater than 30 degrees celsius),/> +T 20.P (air-conditioning outlet temperature is less than 5 ℃), T 10 is a temperature correction reference value when the air-conditioning outlet temperature exceeds 30 ℃, and T 20 is a temperature correction reference value when the air-conditioning outlet temperature is less than 5 ℃.
Specifically, in the normal air-out temperature range of the air conditioner, the body temperature parameter of the human body is approximately close to the average value of the temperature parameter Ti output by the body temperature matrix detection unit, but the air-out temperature of the air conditioner has a certain influence on the body temperature detection of the user in extreme cases, for example, when the air-out temperature of the air conditioner exceeds 30 ℃, the temperature value detected by the body temperature matrix detection unit is higher than the real body temperature value of the human body, and when the air-out temperature of the air conditioner is lower than 5 ℃, the temperature value detected by the body temperature matrix detection unit is lower than the real body temperature value of the human body, and the distance between the air-out opening of the air conditioner and the user influences the deviation value, and the larger the distance between the air-out opening of the air conditioner and the user is, the smaller the influence of the air-out temperature of the air conditioner on the body temperature of the user is.
Therefore, the temperature parameter Ti output by the body temperature matrix detection unit and the distance between the air conditioner air outlet and the user are firstly obtained, then the mapping table inquiry is carried out according to the distance between the air conditioner air outlet and the user to obtain the influence coefficient P of the air conditioner air outlet on the body temperature of the user, and finally the influence coefficient P of the temperature parameter Ti and the air conditioner air outlet on the body temperature of the user is input into a pre-trained body temperature correction model to obtain the body temperature value of the user.
Therefore, the real body temperature value of the user can be accurately obtained, so that whether the user has symptoms or not can be accurately judged, and the switching of the disinfection mode can be conveniently further carried out.
And determining that the body temperature value of the user is in a normal body temperature range, and adjusting the first sterilization switching mode to a fourth sterilization switching mode, wherein the fourth sterilization switching mode comprises a control step of continuously introducing outdoor fresh air into an indoor environment.
Specifically, after virus disinfection for a certain period of time, if the body temperature of the user is in the normal body temperature range, the user/patient is not infected with epidemic situation, at the moment, the first disinfection switching mode is directly adjusted to the fourth disinfection switching mode, so that the user can fully enjoy fresh air, and medical observation of the user can be directly relieved after the fourth disinfection switching mode for a certain period of time is carried out, so that health and safety of the user are further improved.
It can be understood that when the risk value of the user epidemic is greater than the second preset risk threshold, the probability of the user/patient epidemic is higher, and virus disinfection of the indoor environment needs to be rapidly performed to reduce the virus content in the indoor environment air, so that the health and safety of the user are further improved.
Based on this, in an embodiment, after the step of controlling the air conditioning unit to execute the third disinfection switching mode, the method further includes:
T600, virus killing is carried out for a first preset time period based on a preset standard killing temperature and a preset killing humidity;
t700, analyzing the virus content of indoor environment air to obtain the change rate of the virus content in the air within the first preset duration;
T800, determining that the change rate of the virus content is smaller than or equal to a change rate threshold value, and acquiring virus habit parameters of the current virus, wherein the virus habit parameters of the current virus comprise virus propagation inhibition temperature range data and virus propagation inhibition humidity range data;
T900, adjusting the standard disinfection temperature and the disinfection humidity according to the virus habit parameters to obtain the current disinfection temperature and the current disinfection humidity;
And T1000, virus killing is carried out on the indoor environment air at the current killing temperature and the current killing humidity.
Specifically, after the air conditioning unit is controlled to execute the third disinfection switching mode, virus disinfection is performed for a period of time at a preset standard disinfection temperature and disinfection humidity, the virus content variation condition in the disinfection period is analyzed, if the virus content variation in the disinfection period is not changed or is smaller, the preset standard disinfection temperature and disinfection humidity indicate that virus cannot be disinfected or virus propagation cannot be inhibited, at this time, the current popular viruses are subjected to deep learning through the second deep learning unit 610 to obtain virus propagation inhibition temperature range data and virus propagation inhibition humidity range data, and then the preset standard disinfection temperature and disinfection humidity are adjusted to be close to the virus propagation inhibition temperature range and the virus propagation inhibition humidity range, so that the viruses are killed rapidly at the disinfection temperature and the disinfection humidity.
In other embodiments, after the analysis of the virus content of the indoor ambient air to obtain the change rate of the virus content in the air within the first preset duration, if the change rate of the virus content is determined to be greater than a change rate threshold, it is indicated that the preset standard disinfection temperature and disinfection humidity have a larger disinfection effect on the virus, and at this time, the standard disinfection temperature and disinfection humidity are maintained to carry out virus disinfection.
Therefore, the application can carry out the disinfection of the indoor environment virus and introduce different circulation control strategies of fresh air according to the epidemic risk of the user, so that the fresh degree of the indoor environment air can be ensured while the virus is disinfected, the physical health of the patient in the indoor environment can be comprehensively ensured, and the epidemic prevention parameters or modes of the air conditioning unit can be intelligently adjusted under special conditions, so that the health and safety of the user can be further improved.
The embodiment of the application further provides an electronic device 800, referring to fig. 3, fig. 3 is a schematic hardware structure of the electronic device according to the embodiment of the application.
The processor 801 is configured to provide computing and control capabilities to control the air conditioning unit epidemic prevention switching system to perform corresponding tasks, for example, control the air conditioning unit epidemic prevention switching system to perform the air conditioning unit epidemic prevention switching method in any of the method embodiments described above, where the method includes: acquiring user travel data of a user in an indoor work environment of the air conditioning unit; deep learning is carried out according to the acquired user travel data so as to obtain a travel journey of the user; judging and identifying the magnitude of a risk value of epidemic infection of a user according to the travel journey of the user and the demarcation range of the epidemic situation risk area; determining that the risk value of user epidemic infection is smaller than a first preset risk threshold value, and controlling the air conditioning unit to execute a first disinfection switching mode; determining that the risk value of user epidemic infection is larger than a first preset risk threshold value and smaller than a second preset risk value, and controlling the air conditioning unit to execute a second disinfection switching mode; determining that the risk value of user epidemic infection is larger than a second preset risk threshold value, and controlling the air conditioning unit to execute a third disinfection and sterilization switching mode, wherein the second preset risk threshold value is larger than the first preset risk threshold value, the duration of killing indoor air in the first disinfection and sterilization switching mode is smaller than the duration of killing indoor air in the second disinfection and sterilization switching mode, and the duration of killing indoor air in the third disinfection and sterilization switching mode is longer than the duration of killing indoor air in the second disinfection and sterilization switching mode.
The processor 801 may be a general purpose processor including a central processing unit (CentralProcessingUnit, CPU), a network processor (Network Processor, NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), programmable logic device (programmable logic device, PLD), or a combination thereof. The PLD may be a complex programmable logic device (complex programmable logic device, CPLD), a field-programmable gate array (FPGA) GATE ARRAY, generic array logic (GENERIC ARRAY logic, GAL), or any combination thereof.
The memory 802, as a non-transitory computer readable storage medium, may be used to store a non-transitory software program, a non-transitory computer executable program, and a module, such as a program instruction/module corresponding to an air conditioning unit epidemic prevention switching method in an embodiment of the present application. The processor 801 may implement the epidemic prevention switching method of the air conditioning unit in any of the method embodiments described above by running non-transitory software programs, instructions and modules stored in the memory 802.
In particular, the memory 802 may include Volatile Memory (VM), such as random access memory (random access memory, RAM); the memory 802 may also include non-volatile memory (NVM), such as read-only memory (ROM), flash memory (flash memory), hard disk (HARD DISK DRIVE, HDD) or solid-state disk (solid-state drive-STATE DRIVE, SSD) or other non-transitory solid state storage device; memory 802 may also include combinations of the above types of memory.
In summary, the epidemic prevention switching system of air conditioning units adopts the technical scheme of any one of the embodiments of the epidemic prevention switching method of air conditioning units, so that the epidemic prevention switching system at least has the beneficial effects brought by the technical scheme of the embodiments, and the description is omitted herein.
The embodiment of the application also provides a computer readable storage medium, such as a memory including program codes, which can be executed by a processor to complete the air crew epidemic prevention switching method in the embodiment. For example, the computer readable storage medium may be Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), compact disc Read-Only Memory (CDROM), magnetic tape, floppy disk, optical data storage device, and the like.
Embodiments of the present application also provide a computer program product comprising one or more program codes stored in a computer-readable storage medium. The processor of the electronic device reads the program code from the computer readable storage medium, and the processor executes the program code to complete the steps of the epidemic prevention switching method of the air conditioning unit provided in the above embodiment.
It will be appreciated by those of ordinary skill in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by program code related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
From the above description of embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus a general purpose hardware platform, or may be implemented by hardware. Those skilled in the art will appreciate that all or part of the processes implementing the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and where the program may include processes implementing the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random-access Memory (Random Access Memory, RAM), or the like.
The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structural changes made by the description of the present invention and the accompanying drawings or direct/indirect application in other related technical fields are included in the scope of the invention.
Claims (5)
1. An air conditioning unit epidemic prevention switching system based on deep learning, which is characterized by comprising:
The user travel detection module comprises a user identity identification unit and a first deep learning unit, wherein the user identity identification unit is used for identifying user identity information of a user in an indoor work environment of the air conditioner, and the first deep learning unit is used for performing deep learning on travel data of the user according to the acquired user identity information to obtain travel of the user;
the epidemic risk identification module comprises a risk identification unit and an epidemic risk area updating unit, wherein the risk identification unit is used for judging and identifying the magnitude of a risk value of the epidemic of a user according to the travel journey of the user and the demarcation range of the epidemic risk area, and the epidemic risk area updating unit is used for updating the demarcation range of the epidemic risk area in real time;
the body temperature parameter identification module comprises a body temperature matrix detection unit and a body temperature correction unit, wherein the body temperature matrix detection unit is used for detecting temperature parameters of different head positions of a user, and the body temperature correction unit is used for correcting the temperature parameters according to the temperature of air output by an air conditioning unit so as to obtain the body temperature parameters of the user;
The virus disinfection and sterilization switching module comprises an air conditioning unit circulation switching unit, a first sterilization unit and a second sterilization unit, wherein the air conditioning unit circulation switching unit is used for switching an inner circulation mode and an outer circulation mode of an air conditioning indoor unit, the first sterilization unit is used for sterilizing and disinfecting indoor air in the inner circulation mode of the air conditioning indoor unit, and the second sterilization unit is used for sterilizing and disinfecting air entering a room in the outer circulation mode of the air conditioning indoor unit;
The control module is used for performing disinfection and switching control of the virus disinfection and switching module according to the magnitude of the risk value of user epidemic infection;
The virus habit learning module comprises a second deep learning unit, wherein the second deep learning unit is used for performing deep learning according to internet information to obtain virus habit data, and the virus habit data comprises virus propagation inhibition temperature range data and virus propagation inhibition humidity range data;
The temperature and humidity adjusting module comprises a temperature adjusting unit and a humidity adjusting unit, wherein the temperature adjusting unit is used for adjusting the temperature of indoor air according to the data of the temperature range for inhibiting virus propagation, and the humidity adjusting unit is used for adjusting the humidity of the indoor air according to the data of the humidity range for inhibiting virus propagation;
an epidemic prevention switching method of an air conditioning unit based on deep learning comprises the following steps:
acquiring user travel data of a user in the indoor work environment of the air conditioner;
Deep learning is carried out according to the acquired travel data of the user so as to obtain a travel journey of the user;
Judging and identifying the magnitude of a risk value of epidemic infection of a user according to the travel journey of the user and the demarcation range of the epidemic situation risk area;
Determining that the risk value of user epidemic infection is smaller than a first preset risk threshold value, and controlling the air conditioning unit to execute a first disinfection switching mode;
Determining that the risk value of user epidemic infection is larger than a first preset risk threshold value and smaller than a second preset risk value, and controlling the air conditioning unit to execute a second disinfection switching mode;
Determining that the risk value of user epidemic infection is larger than a second preset risk threshold value, and controlling the air conditioning unit to execute a third disinfection and sterilization switching mode, wherein the second preset risk threshold value is larger than the first preset risk threshold value, and the first disinfection and sterilization switching mode comprises a circulation switching control step of disinfecting indoor air for a first duration and introducing outdoor fresh air into an indoor environment for a second duration; the second disinfection switching mode comprises a circulation switching control step of disinfecting the indoor air for a second time period and introducing outdoor fresh air into the indoor environment for a first time period, wherein the first time period is smaller than the second time period; the third disinfection switching mode comprises a control step of continuously disinfecting the indoor air by viruses;
After the step of controlling the air conditioning unit to execute the third disinfection and sterilization switching mode, the method further comprises the following steps:
virus killing is carried out for a first preset time period based on a preset standard killing temperature and a preset killing humidity;
analyzing the virus content of indoor environment air to obtain the change rate of the virus content in the air within the first preset duration;
Determining that the change rate of the virus content is smaller than or equal to a change rate threshold value, and acquiring virus habit parameters of the current virus, wherein the virus habit parameters of the current virus comprise virus propagation inhibition temperature range data and virus propagation inhibition humidity range data;
Adjusting the standard disinfection temperature and the disinfection humidity according to the virus habit parameters to obtain the current disinfection temperature and the current disinfection humidity;
and virus sterilization is carried out on the indoor environment air at the current sterilization temperature and the current sterilization humidity.
2. The deep learning-based air conditioning unit epidemic prevention switching system according to claim 1, wherein the trip data at least comprises one of gate inhibition traffic data, traffic payment data, shopping payment data, trip map data, and mobile phone roaming place data during trip.
3. The deep learning-based epidemic prevention switching system for air conditioning units according to claim 1, wherein after the virus content analysis is performed on the indoor ambient air to obtain the change rate of the virus content in the air within the first preset duration, the deep learning-based epidemic prevention switching system further comprises:
and determining that the change rate of the virus content is larger than a change rate threshold value, and maintaining the standard disinfection temperature and the disinfection humidity to carry out virus disinfection.
4. The deep learning based air conditioning unit epidemic prevention switching system of claim 1, wherein after the step of controlling the air conditioning unit to perform the first disinfection switching mode, further comprising:
after virus killing is carried out for a second preset time period, acquiring a body temperature value of a user;
And determining that the body temperature value of the user is in a normal body temperature range, and adjusting the first sterilization switching mode to a fourth sterilization switching mode, wherein the fourth sterilization switching mode comprises a control step of continuously introducing outdoor fresh air into an indoor environment.
5. The deep learning-based air conditioning unit epidemic prevention switching system according to claim 4, wherein the acquiring the body temperature value of the user comprises:
acquiring a temperature parameter Ti output by a body temperature matrix detection unit and the distance between an air outlet of an air conditioner and a user;
According to the distance between the air outlet of the air conditioner and a user, inquiring a mapping table to obtain an influence coefficient P of the air outlet of the air conditioner on the body temperature of the user;
Inputting the temperature parameter Ti and the influence coefficient P of air conditioner air outlet on the body temperature of the user into a pre-trained body temperature correction model to obtain the body temperature value of the user, wherein the mathematical expression of the body temperature correction model is as follows:
when the air-conditioner outlet temperature is greater than 30 degrees celsius, ，
When the air-conditioner outlet temperature is less than 5 ℃,，
T 10 is the temperature correction reference value when the air-conditioner air outlet temperature is greater than 30 ℃, and T 20 is the temperature correction reference value when the air-conditioner air outlet temperature is less than 5 ℃.
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