CN107860076B - Multi-user dynamic temperature-regulating central air-conditioning system and method based on artificial intelligence - Google Patents
Multi-user dynamic temperature-regulating central air-conditioning system and method based on artificial intelligence Download PDFInfo
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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
- F24F3/00—Air-conditioning systems in which conditioned primary air is supplied from one or more central stations to distributing units in the rooms or spaces where it may receive secondary treatment; Apparatus specially designed for such systems
Abstract
The multi-user dynamic temperature-regulating central air-conditioning system based on artificial intelligence obtains temperature curve models which accord with different age groups, different professions, different departments and sexes through an information retrieval unit according to conditions that the sexes, the age groups, the professions and the working departments are used as retrieval temperature curve models, and then carries out weighted average on the temperature curve models of the different age groups, the different professions, the different departments and the sexes retrieved by the retrieval unit according to the personal information conditions of all office workers in an office area to obtain an optimal temperature curve model; the temperature controller controls the water flow or air quantity of the fan coil according to the current optimal temperature curve model, so that the office area temperature in different time periods in one day can be optimally adjusted through a large amount of data, and the comfort level of the office environment temperature is improved; the invention also provides a multi-user dynamic temperature adjustment central air-conditioning method based on artificial intelligence.
Description
Technical Field
The invention relates to the field of central air conditioners, in particular to a multi-user dynamic temperature adjustment central air conditioning system and a method based on artificial intelligence.
Background
A survey conducted 2015 on 129 office workers found that 42% thought themselves to be too hot in their office building and 56% thought to be too cold. We can now adjust the office to any temperature at will, but cannot agree on a specific temperature.
Setting the appropriate room temperature can improve the work satisfaction and the work and cooperation efficiency. If the temperature is not properly set, the efficiency of the staff is low, the body is fat, and the prevalence rate of metabolic disorder diseases such as diabetes is increased.
In offices around the world, temperature settings in offices are often debated and data suggests that about 2% of the working time of an office in the uk is wasted in the debate related to air conditioning temperature, the resulting economic waste exceeds 130 billion pounds per year and the resulting economic loss in australia amounts to $ 62 billion.
In fact, ambient temperature can not only affect work efficiency, but also change thinking patterns. The warm environment is more suitable for creative thinking, and the environment with lower temperature can help people to improve attention in repeated and monotonous tasks; our mathematical power is reduced when the ambient temperature exceeds 27 degrees celsius (80.6 degrees fahrenheit).
The ambient temperature has a direct influence on the cooperation ability, and people are more likely to feel that surrounding people are well-intentioned and feel that the attitude is cool in a warm room.
Those skilled in the art have devised a set of criteria for calculating the number of employees who feel Dissatisfied at each temperature, i.e., a Predicted dissatisfaction ratio (hereinafter referred to as "PPD"). To calculate PPD, most property managers use a standard formula in the 60's of the 20 th century that considers such factors as the clothing of the building user and the metabolic rate (rate at which we generate heat) calculated from a 40 year old male weighing 70 kg. The optimum office temperature is approximately between 22 and 24 degrees celsius (71.6 to 75.2 degrees fahrenheit).
The metabolic rate of each person is different at different time periods of the day, so the perception of temperature is different at different time periods of the day, the metabolic rate of women is much slower than men, and the suitable office temperature is 3 degrees celsius (5.4 degrees fahrenheit) higher than men, however, the metabolic rate currently used in controlling office temperature is calculated from 40 years old men weighing 70 kg, and is therefore not suitable for the current environment of a man-woman co-office.
There are many office workers in each office area of public office space, and the perception condition of each individual to the temperature in each office area is different in different time slots in a day, however, there is often only one temperature regulator in each office area, and after setting the temperature, the temperature in the office area is basically unchanged in a day, and can not be adjusted to a certain extent with the metabolism rate of the human body, and the setting of the temperature is relatively rigid. At the same time, who exercises control over temperature in each area is always at a debate, and we often hear colleagues 'complaints and disputes about temperature settings, giving rise to a series of topics about colleagues' co-mood and co-ordinance.
At present, the temperature of an office air conditioner is generally about 20-24 ℃, most people can accept the temperature when going to work, after several hours, within two hours before lunch and off-duty, the energy consumption in the body is more, and many people feel cold, especially in the afternoon break time. At low temperature, when the body feels hungry, most energy is used for keeping the body temperature, and the working efficiency of the body is seriously influenced.
The invention fully considers the conditions of sex, age, metabolism, occupation and working department of office staff, obtains temperature curve models of different age groups, different occupations, different departments and sex through a large amount of data, weights according to the staff proportion of an office area, and fuses to obtain an optimal temperature curve model.
The fan coil is an ideal end product of a central air conditioner, and is widely applied to places such as hotels, office buildings, hospitals, business and residential institutions, scientific research institutions and the like. The working principle is as follows: the forced action of the fan is relied on to lead the air to pass through the coil pipe, the air in the room in which the unit is continuously recycled is cooled (heated) after passing through the cold water (hot water) coil pipe, and the indoor temperature is reduced or increased, so as to meet the requirement of comfort of people.
The control mode is as follows: the STC series temperature controller automatically controls the opening and closing of the STV series electric two/three-way valve according to the comparison and calculation of the set temperature and the actual detection temperature; the three-speed conversion and the start and stop of the fan are directly controlled, so that the purpose of constant temperature is achieved by controlling the water flow or the air quantity of the system.
The adjusting mode is as follows: generally, air volume regulation (regulating the input voltage of a unidirectional capacitance speed regulating motor) is also adopted.
Disclosure of Invention
Aiming at the defects in the prior art, one problem to be solved by the invention is that personal information of all people in an office area is input into an information retrieval unit, the information retrieval unit obtains temperature curve models which accord with different age groups, different occupations, different departments and sexes through the information retrieval unit according to the conditions that the sexes, the age groups, the occupations and the working departments are used as retrieval temperature curve models, and then the temperature curve models of the different age groups, the different occupations, the different departments and the sexes retrieved by the retrieval unit are weighted and averaged according to the personal information conditions of all office staff in the office area to obtain an optimal temperature curve model; the temperature controller controls the water flow or the air volume of the fan coil according to the current optimal temperature curve model, so that the office area temperature in different time periods in one day can be optimally adjusted through a large amount of data, the comfort level of the office environment temperature is improved, and the dispute of office staff on the office area set temperature is reduced.
The invention is realized by adopting the following technical scheme, and the multi-user dynamic temperature-adjusting central air-conditioning system based on artificial intelligence, which is designed according to the purpose, comprises: the device comprises an individual set temperature acquisition unit, an individual set temperature processing unit, a temperature curve model library, an information retrieval unit, an artificial intelligence unit, an updating unit and a temperature controller.
The personal information includes: name, gender, age group, occupation, and work department.
Personal setting temperature acquisition unit: the temperature set by the individual in different time periods in one day is collected, and the temperature set by the individual is transmitted to the server in a wired transmission or wireless transmission mode.
The personal temperature acquisition unit can be a wearable device such as a bracelet and a watch, or a mobile phone, a tablet and a computer.
Individual setting temperature processing unit: the server carries out average value processing on the temperature set by the individual in different time periods every day to obtain an individual temperature curve model.
Temperature curve model library: the server classifies and averages the stored various personal temperature curve models according to gender, age groups, occupation and working departments to obtain temperature curve models of different age groups, different occupation, different working departments and genders.
An information retrieval unit: and obtaining the temperature curve model meeting the conditions through the retrieval unit according to one or more combinations of gender, age group, work department and occupation as the conditions for retrieving the temperature curve model.
An artificial intelligence unit: the method comprises the steps of intelligently simulating temperature curve models corresponding to emerging careers which do not exist in a temperature curve model base, extracting features of the emerging careers to obtain temperature curve models corresponding to the careers with the largest correlation, and then fusing the temperature curve models corresponding to the careers with the largest correlation to obtain the temperature curve models corresponding to the emerging careers.
An update unit: the personal set temperature acquisition unit continuously acquires the temperatures set by the person in different time periods in one day and transmits the acquired temperatures to the server, and the server updates the data of the temperatures set by the person in different time periods every other time period and then performs mean value processing to obtain an updated personal temperature curve model; and the server classifies and averages the updated various personal temperature curve models again according to the gender, the age group, the occupation and the working department to obtain the updated temperature curve models of different age groups, different occupations, different working departments and the gender.
The temperature set by the individual in different time periods in one day is collected through the individual temperature setting collection unit, then the temperature data set by the individual is transmitted to the server, the server performs mean value processing on the temperature set by the individual in different time periods every day to obtain an individual temperature curve model, and then the individual temperature curve models are classified and averaged according to gender, age range, occupation and working department to obtain temperature curve models of different age ranges, different occupations, different departments and gender.
And obtaining the temperature curve model meeting the conditions through the information retrieval unit.
Firstly, personal information of all persons in an office area is input into an information retrieval unit, the information retrieval unit obtains temperature curve models which accord with different age groups, different occupations, different departments and genders through the information retrieval unit according to conditions of gender, age groups, occupation and working departments for retrieving the temperature curve models, and then the temperature curve models of different age groups, different occupations, different departments and genders retrieved by the retrieval unit are weighted and averaged according to the personal information conditions of all office staff in the office area to obtain an optimal temperature curve model.
The temperature controller controls the water flow or the air volume of the fan coil according to the current optimal temperature curve model, so that the temperature of an office area covered by the fan coil is adjusted in time, and the energy high-efficiency use state with supply according to needs and reasonable resource allocation is achieved.
The optimal temperature curve model is as follows:
the multi-user dynamic temperature-adjusting central air-conditioning method based on artificial intelligence designed according to the purpose comprises the following steps: the temperature set by the individual in different time periods in one day is collected through the individual temperature setting collection unit, the temperature data set by the individual is transmitted to the server, the server performs mean value processing on the temperature set by the individual in different time periods every day to obtain an individual temperature curve model, and then the individual temperature curve models are classified and averaged according to gender, age range, occupation and working department to obtain temperature curve models of different age ranges, different occupations, different departments and gender.
The method comprises the steps that personal information of all persons in an office area is input into an information retrieval unit, the information retrieval unit obtains temperature curve models which accord with different age groups, different occupations, different departments and genders through the information retrieval unit according to conditions of gender, age groups, occupation and working departments for retrieving the temperature curve models, and then weighted averaging is conducted on the temperature curve models which are retrieved by the retrieval unit and obtained in different age groups, different occupations, different departments and genders according to personal information conditions of all office staff in the office area to obtain an optimal temperature curve model.
The temperature controller controls the water flow or the air volume of the fan coil according to the current optimal temperature curve model, so that the temperature of an office area covered by the fan coil is adjusted in time, and the energy high-efficiency use state with supply according to needs and reasonable resource allocation is achieved.
The optimal temperature curve model is as follows:
drawings
FIG. 1 is a schematic diagram of the system of the present invention.
FIG. 2 shows the steps of establishing temperature profile models for different age groups, different occupations, different departments, and genders.
FIG. 3 is a step of obtaining an optimal temperature curve model meeting the conditions by the information retrieval unit.
Fig. 4 shows part of employee information of a certain original technology company.
FIG. 5 is a temperature profile model for a male software engineer in the development department between 20 and 30 years of age.
FIG. 6 is a temperature profile model for a female software engineer developing a department age between 20-30 years old.
FIG. 7 is an optimized temperature profile model for 10 software engineers in the development department between 20 and 30 years old in the same office area.
In fig. 1, 1 is a wearable device such as a bracelet and a watch, or a mobile phone and a tablet, 2 is a computer, 3 is a server, 4 is a thermostat, and 5 is a fan coil.
Detailed Description
The invention is further illustrated with reference to the accompanying drawings and specific examples.
First embodiment
Referring to fig. 2, the temperature curve models of different ages, different professions, different departments and sexes are established.
Step S11: the temperature set by the individual at different time periods in one day is collected through the individual setting temperature collecting unit, and the temperature data set by the individual is transmitted to the server.
Step S12: the server carries out average value processing on the temperature set by the individual in different time periods every day to obtain an individual temperature curve model.
Step S13: and the server classifies and averages the individual temperature curve models according to gender, age group, occupation and working department to obtain temperature curve models of different age groups, different occupations, different departments and genders.
Second embodiment
And (3) acquiring the optimal temperature curve model meeting the conditions through the information retrieval unit.
Step S21: personal information of all persons in the office area is input to the information retrieval unit.
Step S22: the information retrieval unit is used for obtaining temperature curve models which accord with different age groups, different professions, different departments and sexes through the information retrieval unit according to the conditions that the sexes, the age groups, the professions and the working departments serve as retrieval temperature curve models.
Step S23: and the server carries out weighted average on the temperature curve models of different age groups, different professions, different departments and genders retrieved by the retrieval unit according to the personal information conditions of all office workers in the office area to obtain an optimal temperature curve model.
Step S24: the temperature controller controls the water flow or the air quantity of the fan coil according to the current optimal temperature curve model, so that the temperature of an office area covered by the fan coil is adjusted in time.
The optimal temperature curve model is as follows:
third embodiment
Fig. 4 shows part of employee information of a certain original technology company.
If 6 software engineers of male research and development department exist in one office area, the age range is between 20 and 30 years, and 4 software engineers of female research and development department exist, the age range is between 20 and 30 years; the weights of male software engineers in the development department between 20-30 years of age are: 6/10=60%, the weights of the female software engineers in the development department between the ages of 20-30 are: 4/10= 40%.
Referring to FIG. 5, a temperature profile model was developed for a male software engineer aged 20-30 years. The temperature curve model corresponding to the male software engineer with the age range of 20-30 years in the development department is obtained by averaging all personal temperature curve models which are in line with the working department, occupation, age range and gender in a temperature curve model library.
Referring to fig. 6, a temperature profile model corresponding to a female software engineer aged between 20-30 years was developed. The temperature curve model corresponding to the female software engineer with the age range of 20-30 years in the development department is obtained by averaging all personal temperature curve models which are in line with the working department, occupation, age range and gender in a temperature curve model library.
Referring to fig. 7, the temperature curve models corresponding to the software engineers in the 10 research and development department ages ranging from 20 to 30 years are weighted and averaged in the office area to obtain the optimal temperature curve models corresponding to the software engineers in the 10 research and development department ages ranging from 20 to 30 years.
The temperature controller controls the water flow or the air volume of the fan coil according to the current optimal temperature curve model, so that the office area temperature in different time periods in one day can be optimally adjusted through a large amount of data, the dispute of office staff on the office area set temperature is reduced, the office environment and the working efficiency of the office staff are improved, the flexibility and the adaptability of the system are improved, and the system is more humanized.
The optimal temperature curve model of each office area can be set by each office area by the method, so that the office areas are independent from each other.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention; all equivalent changes and modifications made according to the present invention are covered by the scope of the claims of the present invention.
Claims (5)
1. The utility model provides a multi-user developments central air conditioning system that adjusts temperature based on artificial intelligence which characterized in that, multi-user developments central air conditioning system that adjusts temperature based on artificial intelligence includes: the system comprises an individual set temperature acquisition unit, an individual set temperature processing unit, a temperature curve model library, an information retrieval unit, an artificial intelligence unit, an updating unit and a temperature controller;
the personal information includes: name, gender, age group, occupation, and work department;
personal setting temperature acquisition unit: collecting the temperatures set by an individual at different time periods in one day, and transmitting the temperatures set by the individual to a server in a wired transmission or wireless transmission mode;
individual setting temperature processing unit: the server performs mean value processing on the temperatures set by the individual in different time periods every day to obtain an individual temperature curve model;
temperature curve model library: the server classifies and averages various stored personal temperature curve models according to gender, age groups, occupation and working departments to obtain temperature curve models of different age groups, different occupations, different working departments and genders;
an information retrieval unit: obtaining a temperature curve model meeting the conditions through a retrieval unit according to one or more combinations of gender, age group, work department and occupation as the conditions for retrieving the temperature curve model;
an artificial intelligence unit: intelligently simulating temperature curve models corresponding to emerging careers which do not exist in a temperature curve model library, extracting the characteristics of the emerging careers to obtain temperature curve models corresponding to the careers with the maximum correlation, and then fusing the temperature curve models corresponding to the careers with the maximum correlation to obtain temperature curve models corresponding to the emerging careers;
an update unit: the personal set temperature acquisition unit continuously acquires the temperatures set by the person in different time periods in one day and transmits the acquired temperatures to the server, and the server updates the data of the temperatures set by the person in different time periods every other time period and then performs mean value processing to obtain an updated personal temperature curve model; the server classifies and averages the updated various personal temperature curve models again according to the gender, the age group, the occupation and the working department to obtain updated temperature curve models of different age groups, different occupations, different working departments and the gender;
the optimal temperature curve model is as follows:
the personal setting temperature acquisition unit is a bracelet, a watch, a mobile phone or a computer;
the temperature controller controls the water flow or air volume of the fan coil according to the current optimal temperature curve model, and timely adjusts the temperature of an office area covered by the fan coil;
establishing temperature curve models of different age groups, different occupations, different departments and genders:
firstly, acquiring the temperature set by an individual at different time periods in one day through an individual setting temperature acquisition unit, and transmitting the temperature data set by the individual to a server;
then, the server performs mean value processing on the temperatures set by the individual in different time periods every day to obtain an individual temperature curve model;
finally, the server classifies and averages the individual temperature curve models according to gender, age group, occupation and working department to obtain temperature curve models of different age groups, different occupations, different departments and genders;
the method comprises the following steps of obtaining an optimal temperature curve model meeting conditions through an information retrieval unit:
firstly, inputting personal information of all persons in an office area into an information retrieval unit;
then, the information retrieval unit obtains temperature curve models which accord with different age groups, different professions, different departments and sexes through the information retrieval unit according to the conditions that the sexes, the age groups, the professions and the working departments serve as retrieval temperature curve models;
then, the server averages the temperature curve models of different age groups, different professions, different departments and genders, which are obtained by the retrieval unit, according to the personal information conditions of all office workers in the office area to obtain an optimal temperature curve model;
and finally, the temperature controller controls the water flow or the air volume of the fan coil according to the current optimal temperature curve model, so that the temperature of the office area covered by the fan coil is adjusted in time.
2. The central air-conditioning method of a central air-conditioning system according to claim 1, wherein: according to conditions that gender, age group, occupation and working departments are used as retrieval temperature curve models, a multi-user dynamic temperature adjustment central air-conditioning method based on artificial intelligence obtains temperature curve models which accord with different age groups, different occupations, different departments and genders through an information retrieval unit, and then averages the temperature curve models of different age groups, different occupations, different departments and genders retrieved by the retrieval unit according to personal information conditions of all office staff in an office area to obtain an optimal temperature curve model; the temperature controller controls the water flow or air volume of the fan coil according to the current optimal temperature curve model, so that the office area temperature in different time periods in one day can be optimally adjusted through a large amount of data;
the optimal temperature curve model is as follows:
3. the central air-conditioning method of a central air-conditioning system according to claim 1,
establishing temperature curve models of different age groups, different occupations, different departments and genders:
firstly, acquiring the temperature set by an individual at different time periods in one day through an individual setting temperature acquisition unit, and transmitting the temperature data set by the individual to a server;
then, the server performs mean value processing on the temperatures set by the individual in different time periods every day to obtain an individual temperature curve model;
and finally, the server classifies and averages the individual temperature curve models according to the gender, the age group, the occupation and the working department to obtain the temperature curve models of different age groups, different occupations, different departments and genders.
4. The central air-conditioning method of a central air-conditioning system according to claim 1,
the method comprises the following steps of obtaining an optimal temperature curve model meeting conditions through an information retrieval unit:
firstly, inputting personal information of all persons in an office area into an information retrieval unit;
then, the information retrieval unit obtains temperature curve models which accord with different age groups, different professions, different departments and sexes through the information retrieval unit according to the conditions that the sexes, the age groups, the professions and the working departments serve as retrieval temperature curve models;
then, the server averages the temperature curve models of different age groups, different professions, different departments and genders, which are obtained by the retrieval unit, according to the personal information conditions of all office workers in the office area to obtain an optimal temperature curve model;
and finally, the temperature controller controls the water flow or the air volume of the fan coil according to the current optimal temperature curve model, so that the temperature of the office area covered by the fan coil is adjusted in time.
5. The central air-conditioning method of a central air-conditioning system according to claim 1, wherein: the personal set temperature acquisition unit continuously acquires the temperatures set by the person in different time periods in one day and transmits the acquired temperatures to the server, and the server updates the data of the temperatures set by the person in different time periods every other time period and then performs mean value processing to obtain an updated personal temperature curve model; and the server classifies and averages the updated various personal temperature curve models again according to the gender, the age group, the occupation and the working department to obtain the updated temperature curve models of different age groups, different occupations, different working departments and the gender.
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