阅读说明：本技术 浴室喷头智能预设方法及系统 (intelligent presetting method and system for bathroom shower nozzle ) 是由 尹奇志 代黎博 陈兴 袁裕鹏 于 2019-09-03 设计创作，主要内容包括：本发明公开了一种浴室喷头智能预设方法及系统,其中方法为：通过温湿度传感器、电磁流量计、计时器分别采集环境温度、水温、浴室湿度、喷头流量、用户用水持续时间；并获取用户淋浴前输入的自身的年龄、性别、身高、体重数据；根据所采集的样本数据进行分析、处理,建立用户特征、浴室环境温、湿度和舒适水温、流量之间的模型；根据建立的模型以及当前获取的用户数据自动预设决策值,包括针对该用户的合适的用水流量、温度；根据预设决策值调节浴室喷头的出水。本发明针对大型群体用水,对不同用户做出特有的合理用水指导,优化用水体验,也相对减少了淋浴的持续时长,尤其是对于客流量大、淋浴时段集中的场所,具有显著的节水效应。(The invention discloses an intelligent presetting method and system for bathroom sprayers, wherein the method comprises the following steps: respectively collecting ambient temperature, water temperature, bathroom humidity, shower nozzle flow and user water using duration time through a temperature and humidity sensor, an electromagnetic flow meter and a timer; acquiring self age, sex, height and weight data input by a user before showering; analyzing and processing the collected sample data, and establishing a model among user characteristics, the bathroom environment temperature, the bathroom environment humidity, the comfortable water temperature and the flow; automatically presetting decision values according to the established model and the currently acquired user data, wherein the decision values comprise the appropriate water flow and temperature for the user; and adjusting the water outlet of the bathroom shower nozzle according to a preset decision value. The invention aims at the water consumption of large-scale groups, makes specific reasonable water consumption guidance for different users, optimizes water consumption experience, relatively reduces the duration of shower, and particularly has obvious water saving effect for places with large passenger flow and concentrated shower time intervals.)

1. The utility model provides a bathroom shower nozzle intelligence system of predetermineeing which characterized in that should predetermine the system and include:

The parameter acquisition module is used for respectively acquiring the ambient temperature, the water temperature, the bathroom humidity, the flow rate of the spray head and the duration time of water consumption of a user through a temperature and humidity sensor, an electromagnetic flow meter and a timer; acquiring self age, sex, height and weight data input by a user before showering;

the analysis decision module is used for analyzing and processing the collected sample data and establishing a model among user characteristics, bathroom environment temperature, bathroom environment humidity, comfortable water temperature and flow;

The automatic setting module is used for automatically presetting decision values including appropriate water flow and temperature for the user according to the established model and the currently acquired user data;

and the adjusting module is used for adjusting the water outlet of the bathroom shower nozzle according to a preset decision value.

2. the intelligent presetting system of bathroom sprinklers of claim 1, wherein the establishment process of the specific model relationship in the analysis and decision module is as follows:

Dividing sample data into two categories according to male and female, and dividing each group of sample data into input and output values, wherein the input value is as follows: 5 types of data of age (A), height (H), weight (W), ambient temperature (t) and humidity (RH); the output value is: 2 types of data of water flow (Q) and temperature (T); normalizing the input and output samples, namely mapping the input and output data between (0,1) ranges according to a certain standard; the treatment is as follows (1):

in the formula, i represents an actual input value; i.e. i_min、i_maxrespectively representing the minimum value and the maximum value of the type data of the variable i; f (i) is the result of the corresponding normalization process;

Respectively establishing models among the age (A) of a user, the height (H) of the user, the weight (W), the ambient temperature (t), the humidity (RH) and the water flow (Q), and assuming that sample data for solving the models are n groups, obtaining corresponding polynomials as shown in the following formulas (2) to (6):

Q(A_i)＝k₁A_i+k₂A_i ²+k₃A_i ³+…+k_nA_i ⁿ (2)

Q(H_i)＝a₁H_i+a₂H_i ²+a₃H_i ³+…+a_nH_i ⁿ (3)

Q(W_i)＝b₁W_i+b₂W_i ²+b₃W_i ³+…+b_nW_i ⁿ (4)

Q(t_i)＝c₁t_i+c₂t_i ²+c₃t_i ³+…+c_nt_i ⁿ (5)

Q(RH_i)＝d₁RH_i+d₂RH_i ²+d₃RH_i ³+…+d_nRH_i ⁿ (6)

in the formula, A_iRepresents the normalized user age value, Q (A)_i) A water flow rate value, k, corresponding to the age of the user₁…k_nIs an unknown coefficient; h_iRepresents the normalized height value of the user, Q (H)_i) A water flow value corresponding to the height of the user, a₁…a_nIs an unknown coefficient; w_iRepresents the normalized user weight value, Q (W)_i) A water flow value corresponding to the user's body weight, b₁…b_nis an unknown coefficient; t is t_iRepresents the normalized ambient temperature value, Q (t)_i) Representing the water flow rate value corresponding to the ambient temperature, c₁…c_nis an unknown coefficient; RH (relative humidity)_iRepresents the normalized ambient humidity value, Q (RH)_i) A water flow rate value, d, corresponding to the ambient humidity₁…d_nIs an unknown coefficient;

Respectively substituting 5 × n groups of age-flow, height-flow, weight-flow, temperature-flow, humidity-flow data values in the database into equations (2), (3), (4), (5) and (6), and solving the model to obtain 5 groups of corresponding unknown coefficients k₁…k_n,a₁…a_n,b₁…b_n,c₁…c_n,d₁…d_n；

establishing a model between the water temperature and the age, height, weight, environment temperature and humidity of a user;

T(A_i)＝k₁′A_i+k₂′A_i ²+k₃′A_i ³+…+k_n′A_i ⁿ (7)

T(H_i)＝a₁′H_i+a₂′H_i ²+a₃′H_i ³+…+a_n′H_i ⁿ (8)

T(W_i)＝b₁′W_i+b₂′W_i ²+b₃′W_i ³+…+b_n′W_i ⁿ (9)

T(t_i)＝c₁′t_i+c₂′t_i ²+c₃′t_i ³+…+c_n′t_i ⁿ (10)

T(RH_i)＝d₁′RH_i+d₂′RH_i ²+d₃′RH_i ³+…+d_n′RH_i ⁿ (11)

In the formula, T (A)_i) A water usage temperature value k representing the age of the user₁′…k_n' is the unknown coefficient; t (H)_i) A water temperature value corresponding to the height of the user, a₁′…a_n' is the unknown coefficient; t (W)_i) A water usage temperature value representing the weight of the user, b₁′…b_n' is the unknown coefficient; t (T)_i) Representing the water temperature value, c, corresponding to the ambient temperature₁′…c_n' is the unknown coefficient; t (RH)_i) Representing the water temperature value, d, corresponding to the ambient humidity₁′…d_n' is the unknown coefficient;

5 x n groups of data values of age, water temperature, height, water temperature, weight, water temperature, room temperature, water temperature, humidity and water temperature in the database are respectively substituted into equations (7), (8), (9), (10) and (11) for solving to obtain 5 groups of corresponding unknown coefficients k₁’…k_n’,a₁’…a_n’,b₁’…b_n’,c₁’…c_n’,d₁’…d_n’。

3. The intelligent presetting system of bathroom shower heads as claimed in claim 2, characterized in that the determination of the preset decision value is specifically:

according to information provided by a user and by combining with the established model, 5 groups of preset values of water flow and water temperature are respectively calculated, finally, the average value of the 5 groups of preset values is obtained to be used as a preset decision value of the water flow and the temperature, and the preset decision values of the water flow and the temperature are respectively determined according to the following formulas (12) and (13):

wherein Q (A)_i)、T(A_i) The water flow and temperature value corresponding to the age of the user, Q (H)_i)、T(H_i) The water flow and temperature value, Q (W), corresponding to the height of the user_i)、T(W_i) The water flow and temperature value, Q (t), corresponding to the weight of the user_i)、T(t_i) The water flow and temperature corresponding to the ambient temperature, Q (RH)_i)、T(RH_i) The water flow and the temperature value corresponding to the environmental humidity are obtained, and Q (i) and T (i) are the averaged water flow and temperature value;

Carrying out the inverse normalization processing of formulas (14) and (15) on the obtained preset values Q (i) and T (i), and sending the processing result to an adjusting module;

Q＝Q(i)(Q_max-Q_min)+Q_min (14)

T＝T(i)(T_max-T_min)+T_min (15)

wherein Q (i) and T (i) are water flow and temperature values calculated by a model, and Q_max、Q_minFor maximum and minimum water flow values, T, in the collected database_max、T_minthe maximum and minimum water temperature values in the collected database, Q, T are the decision values of actual water flow and water temperature。

4. The intelligent presetting system of bathroom shower head of claim 1, characterized in that, the presetting system further comprises a feedback module, which is used for analyzing the parameter value adjusted by the user in real time and the related historical data to obtain the optimal water flow and temperature of different ages, sexes, bathroom temperature and humidity based on more sample data; the more the number of samples is, the higher the polynomial degree of calculation is brought into the model, the more accurate the solved unknown coefficient is, and the preset decision value is updated and corrected in sequence.

5. The intelligent presetting system of bathroom shower nozzles as claimed in claim 4, wherein the feedback module specifically compares the actual value of water usage with a preset decision value, and if the error is greater than 5%, performs a new round of model solution, assuming that the number of initial samples is n, and the new sample to be brought into correction is p, specifically:

In the formula, p is the data characteristic of the new sample, and the normalized processing values of age, height, weight, room temperature and humidity can be taken; x is the number of₁…x_n+1,x₁’…x_n+1', unknown coefficients to be solved;

coefficient x thus determined₁…x_n+1,x₁’…x_n+1' more closely to the actual situation.

6. An intelligent presetting method for bathroom sprayers is characterized by comprising the following steps:

Respectively collecting ambient temperature, water temperature, bathroom humidity, shower nozzle flow and user water using duration time through a temperature and humidity sensor, an electromagnetic flow meter and a timer; acquiring self age, sex, height and weight data input by a user before showering;

Analyzing and processing the collected sample data, and establishing a model among user characteristics, the bathroom environment temperature, the bathroom environment humidity, the comfortable water temperature and the flow;

automatically presetting decision values according to the established model and the currently acquired user data, wherein the decision values comprise the appropriate water flow and temperature for the user;

And adjusting the water outlet of the bathroom shower nozzle according to a preset decision value.

7. The intelligent presetting method of bathroom sprinklers according to claim 6, wherein the specific model relationship establishing process is as follows:

Dividing sample data into two categories according to male and female, and dividing each group of sample data into input and output values, wherein the input value is as follows: 5 types of data of age (A), height (H), weight (W), ambient temperature (t) and humidity (RH); the output value is: 2 types of data of water flow (Q) and temperature (T); normalizing the input and output samples, namely mapping the input and output data between (0,1) ranges according to a certain standard; the treatment is as follows (1):

In the formula, i represents an actual input value; i.e. i_min、i_maxrespectively representing the minimum value and the maximum value of the type data of the variable i; f (i) is the result of the corresponding normalization process;

Respectively establishing models among the age (A) of the user, the height (H) of the user, the weight (W), the ambient temperature (t), the humidity (RH) and the water flow (Q) to obtain corresponding polynomials as shown in the following formulas (2) to (6):

Q(A_i)＝k₁A_i+k₂A_i ²+k₃A_i ³+…+k_nA_i ⁿ (2)

Q(H_i)＝a₁H_i+a₂H_i ²+a₃H_i ³+…+a_nH_i ⁿ (3)

Q(W_i)＝b₁W_i+b₂W_i ²+b₃W_i ³+…+b_nW_i ⁿ (4)

Q(t_i)＝c₁t_i+c₂t_i ²+c₃t_i ³+…+c_nt_i ⁿ (5)

Q(RH_i)＝d₁RH_i+d₂RH_i ²+d₃RH_i ³+…+d_nRH_i ⁿ (6)

in the formula, A_iRepresents the normalized user age value, Q (A)_i) A water flow rate value, k, corresponding to the age of the user₁…k_nIs an unknown coefficient; h_irepresents the normalized height value of the user, Q (H)_i) A water flow value corresponding to the height of the user, a₁…a_nIs an unknown coefficient; w_iRepresents the normalized user weight value, Q (W)_i) A water flow value corresponding to the user's body weight, b₁…b_nis an unknown coefficient; t is t_iRepresents the normalized ambient temperature value, Q (t)_i) Representing the water flow rate value corresponding to the ambient temperature, c₁…c_nIs an unknown coefficient; RH (relative humidity)_iRepresents the normalized ambient humidity value, Q (RH)_i) A water flow rate value, d, corresponding to the ambient humidity₁…d_nis an unknown coefficient;

respectively substituting 5 × n groups of age-flow, height-flow, weight-flow, temperature-flow, humidity-flow data values in the database into equations (2), (3), (4), (5) and (6), and solving the model to obtain 5 groups of corresponding unknown coefficients k₁…k_n,a₁…a_n,b₁…b_n,c₁…c_n,d₁…d_n；

Establishing a model between the water temperature and the age, height, weight, environment temperature and humidity of a user;

T(A_i)＝k₁′A_i+k₂′A_i ²+k₃′A_i ³+…+k_n′A_i ⁿ (7)

T(H_i)＝a₁′H_i+a₂′H_i ²+a₃′H_i ³+…+a_n′H_i ⁿ (8)

T(W_i)＝b₁′W_i+b₂′W_i ²+b₃′W_i ³+…+b_n′W_i ⁿ (9)

T(t_i)＝c₁′t_i+c₂′t_i ²+c₃′t_i ³+…+c_n′t_i ⁿ (10)

T(RH_i)＝d₁′RH_i+d₂′RH_i ²+d₃′RH_i ³+…+d_n′RH_i ⁿ (11)

In the formula, T (A)_i) A water usage temperature value k representing the age of the user₁′…k_n' is the unknown coefficient; t (H)_i) A water temperature value corresponding to the height of the user, a₁′…a_n' is the unknown coefficient; t (W)_i) A water usage temperature value representing the weight of the user, b₁′…b_n' is the unknown coefficient; t (T)_i) Representing the water temperature value, c, corresponding to the ambient temperature₁′…c_n' is the unknown coefficient; t (RH)_i) Representing the water temperature value, d, corresponding to the ambient humidity₁′…d_n' is the unknown coefficient;

5 x n groups of data values of age, water temperature, height, water temperature, weight, water temperature, room temperature, water temperature, humidity and water temperature in the database are respectively substituted into equations (7), (8), (9), (10) and (11) to solveSolving to obtain 5 groups of corresponding unknown coefficients k₁’…k_n’,a₁’…a_n’,b₁’…b_n’,c₁’…c_n’,d₁’…d_n’。

8. A shower head assembly, comprising:

The system comprises a sensor group, a temperature and humidity sensor, an electromagnetic flowmeter and a timer, wherein the sensor group is used for respectively collecting the ambient temperature, the water temperature, the bathroom humidity, the flow of a spray head and the duration of water consumption of a user;

The intelligent preset system is the intelligent preset system of the bathroom shower nozzle in any one of claims 1 to 5, and is connected with the sensor group and the cold and hot water regulating valve;

And the parameter input and feedback panel is connected with the intelligent preset system and is used for inputting and feeding back data by a user.

9. The shower head assembly of claim 8 further comprising:

And the remote input unit is connected with the intelligent preset system and used for inputting user data through a remote end.

Technical Field

The invention relates to the field of shower equipment, in particular to an intelligent presetting method and system for a bathroom sprayer.

Background

At present, domestic technical research on measurement and optimization of shower comfort is relatively few, and as for the prior art, only a test technology and a system of shower comfort indexes are provided, and the analysis and decision functions of setting the optimal water temperature and flow based on acquisition parameters are not provided; secondly, the existing acquisition system is aimed at parameter acquisition of the shower process of an individual user, and for the shower processes of different users in a large guest room unit, the acquisition parameters are not comprehensive enough, and the influence of the difference of the age and the sex of the users on the optimal water temperature and flow is not considered.

Disclosure of Invention

in order to reduce the time for customers such as large hotels, large mail ships and the like to use the shower nozzles to shower and adjust the flow, the water temperature and the like, optimize the water consumption experience of the customers and reduce the waste of resource and energy, the invention provides the preset system for the intelligent flow and the water temperature of the shower nozzles of the bathroom, which integrates acquisition, analysis and decision-making.

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

The utility model provides a bathroom shower nozzle intelligence system of predetermineeing, should predetermine the system and include:

The parameter acquisition module is used for acquiring the environment temperature, the water temperature, the bathroom humidity, the shower nozzle flow and the user water using duration which are respectively recorded by the temperature and humidity sensor, the electromagnetic flow meter and the timer; acquiring self age, sex, height and weight data input by a user before showering;

The analysis decision module is used for analyzing and processing the collected sample data and establishing a model relation among user characteristics, bathroom environment temperature, humidity, comfortable water temperature and flow;

The automatic setting module is used for automatically presetting decision values including appropriate water flow and temperature for the user according to the established model and the currently acquired user data;

and the adjusting module is used for adjusting the water outlet of the bathroom shower nozzle according to a preset decision value.

according to the technical scheme, the specific model relationship establishment process in the analysis decision module is as follows:

Dividing sample data into two categories according to male and female, and dividing each group of sample data into input and output values, wherein the input value is as follows: 5 types of data of age (A), height (H), weight (W), ambient temperature (t) and humidity (RH); the output value is: 2 types of data of water flow (Q) and temperature (T); normalizing the input and output samples, namely mapping the input and output data between (0,1) ranges according to a certain standard; the treatment is as follows (1):

In the formula, i represents an actual input value; i.e. i_min、i_maxrespectively representing the minimum value and the maximum value of the type data of the variable i; f (i) is the result of the corresponding normalization process;

Respectively establishing models among the age (A) of the user, the height (H) of the user, the weight (W), the ambient temperature (t), the humidity (RH) and the water flow (Q) to obtain corresponding polynomials as shown in the following formulas (2) to (6):

Q(A_i)＝k₁A_i+k₂A_i ²+k₃A_i ³+…+k_nA_i ⁿ (2)

Q(H_i)＝a₁H_i+a₂H_i ²+a₃H_i ³+…+a_nH_i ⁿ (3)

Q(W_i)＝b₁W_i+b₂W_i ²+b₃W_i ³+…+b_nW_i ⁿ (4)

Q(t_i)＝c₁t_i+c₂t_i ²+c₃t_i ³+…+c_nt_i ⁿ (5)

Q(RH_i)＝d₁RH_i+d₂RH_i ²+d₃RH_i ³+… (6)

+d_nRH_i ⁿ

in the formula, A_iRepresents the normalized user age value, Q (A)_i) A water flow rate value, k, corresponding to the age of the user₁…k_nis an unknown coefficient; h_irepresents the normalized height value of the user, Q (H)_i) A water flow value corresponding to the height of the user, a₁…a_nIs an unknown coefficient; w_iRepresents the normalized user weight value, Q (W)_i) A water flow value corresponding to the user's body weight, b₁…b_nIs an unknown coefficient; t is t_iRepresents the normalized ambient temperature value, Q (t)_i) Representing the water flow rate value corresponding to the ambient temperature, c₁…c_nis an unknown coefficient; RH (relative humidity)_iRepresents the normalized ambient humidity value, Q (RH)_i) A water flow rate value, d, corresponding to the ambient humidity₁…d_nis an unknown coefficient;

Respectively substituting 5 × n groups of age-flow, height-flow, weight-flow, temperature-flow, humidity-flow data values in the database into equations (2), (3), (4), (5) and (6), and solving the model to obtain 5 groups of corresponding unknown coefficients k₁…k_n,a₁…a_n,b₁…b_n,c₁…c_n,d₁…d_n；

Establishing a model between the water temperature and the age, height, weight, environment temperature and humidity of a user;

T(A_i)＝k₁′A_i+k₂′A_i ²+k₃′A_i ³+…+k_n′A_i ⁿ (7)

T(H_i)＝a₁′H_i+a₂′H_i ²+a₃′H_i ³+…+a_n′H_i ⁿ (8)

T(W_i)＝b₁′W_i+b₂′W_i ²+b₃′W_i ³+…+b_n′W_i ⁿ (9)

T(t_i)＝c₁′t_i+c₂′t_i ²+c₃′t_i ³+…+c_n′t_i ⁿ (10)

T(RH_i)＝d₁′RH_i+d₂′RH_i ²+d₃′RH_i ³+… (11)

+d_n′RH_i ⁿ

In the formula, T (A)_i) A water usage temperature value k representing the age of the user₁′…k_n' is the unknown coefficient; t (H)_i) A water temperature value corresponding to the height of the user, a₁′…a_n' is the unknown coefficient; t (W)_i) A water usage temperature value representing the weight of the user, b₁′…b_n' is the unknown coefficient; t (T)_i) Representing the water temperature value, c, corresponding to the ambient temperature₁′…c_n' is the unknown coefficient; t (RH)_i) Representing the water temperature value, d, corresponding to the ambient humidity₁′…d_n' is the unknown coefficient;

5 x n groups of data values of age, water temperature, height, water temperature, weight, water temperature, room temperature, water temperature, humidity and water temperature in the database are respectively substituted into equations (7), (8), (9), (10) and (11) for solving to obtain 5 groups of corresponding unknown coefficients k₁’…k_n’,a₁’…a_n’,b₁’…b_n’,c₁’…c_n’,d₁’…d_n’。

In connection with the above technical solution, the determination of the preset decision value specifically includes:

According to information provided by a user and by combining with the established model, 5 groups of preset values of water flow and water temperature are respectively calculated, finally, the average value of the 5 groups of preset values is obtained to be used as a preset decision value of the water flow and the temperature, and the preset decision values of the water flow and the temperature are respectively determined according to the following formulas (12) and (13):

Wherein Q (A)_i)、T(A_i) The water flow and temperature value corresponding to the age of the user, Q (H)_i)、T(H_i) The water flow and temperature value, Q (W), corresponding to the height of the user_i)、T(W_i) The water flow and temperature value, Q (t), corresponding to the weight of the user_i)、T(t_i) The water flow and temperature corresponding to the ambient temperature, Q (RH)_i)、T(RH_i) The water flow and the temperature value corresponding to the environmental humidity are obtained, and Q (i) and T (i) are the averaged water flow and temperature value;

carrying out the inverse normalization processing of formulas (14) and (15) on the obtained preset values Q (i) and T (i), and sending the processing result to an adjusting module;

Q＝Q(i)(Q_max-Q_min)+Q_min (14)

T＝T(i)(T_max-T_min)+T_min (15)

Wherein Q (i) and T (i) are water flow and temperature values calculated by a model, and Q_max、Q_minfor maximum and minimum water flow values, T, in the collected database_max、T_minThe maximum and minimum water temperature values in the collected database are shown, and Q, T are decision values of actual water flow and water temperature.

According to the technical scheme, the preset system further comprises a feedback module, wherein the feedback module is used for analyzing the parameter values and related historical data according to the parameter values adjusted by the user in real time to obtain the optimal water flow and temperature of different ages, sexes, bath room temperatures and humidity based on more sample data; the more the number of samples is, the higher the polynomial degree of calculation is brought into the model, the more accurate the solved unknown coefficient is, and the preset decision value is updated and corrected in sequence.

According to the technical scheme, the feedback module specifically compares the actual water use value with a preset decision value, and if the error is more than 5%, a new round of model solution is performed.

Assuming that the number of the initial samples is n, the new sample to be substituted into the correction is p, specifically:

In the formula, p is the data characteristic of the new sample, and the normalized processing values of age, height, weight, room temperature and humidity can be taken; x is the number of₁…x_n+1,x₁’…x_n+1', is the unknown coefficient to be solved for.

coefficient x thus determined₁…x_n+1,x₁’…x_n+1' more closely to the actual situation.

The invention also provides an intelligent presetting method of the bathroom shower nozzle, which comprises the following steps:

Respectively collecting ambient temperature, water temperature, bathroom humidity, shower nozzle flow and user water using duration time through a temperature and humidity sensor, an electromagnetic flow meter and a timer; acquiring self age, sex, height and weight data input by a user before showering;

Analyzing and processing the collected sample data, and establishing a model relation among user characteristics, bathroom environment temperature, humidity, comfortable water temperature and flow;

automatically presetting decision values according to the established model and the currently acquired user data, wherein the decision values comprise the appropriate water flow and temperature for the user;

And the adjusting module is used for adjusting the water outlet of the bathroom shower nozzle according to a preset decision value.

According to the technical scheme, the specific model relationship establishing process is as follows:

dividing sample data into two categories according to male and female, and dividing each group of sample data into input and output values, wherein the input value is as follows: 5 types of data of age (A), height (H), weight (W), ambient temperature (t) and humidity (RH); the output value is: 2 types of data of water flow (Q) and temperature (T); normalizing the input and output samples, namely mapping the input and output data between (0,1) ranges according to a certain standard; the treatment is as follows (1):

In the formula, i represents an actual input value; i.e. i_min、i_maxRespectively representing the minimum value and the maximum value of the type data of the variable i; f (i) is the result of the corresponding normalization process;

respectively establishing models among the age (A) of the user, the height (H) of the user, the weight (W), the ambient temperature (t), the humidity (RH) and the water flow (Q) to obtain corresponding polynomials as shown in the following formulas (2) to (6):

Q(A_i)＝k₁A_i+k₂A_i ²+k₃A_i ³+…+k_nA_i ⁿ (2)

Q(H_i)＝a₁H_i+a₂H_i ²+a₃H_i ³+…+a_nH_i ⁿ (3)

Q(W_i)＝b₁W_i+b₂W_i ²+b₃W_i ³+…+b_nW_i ⁿ (4)

Q(t_i)＝c₁t_i+c₂t_i ²+c₃t_i ³+…+c_nt_i ⁿ (5)

Q(RH_i)＝d₁RH_i+d₂RH_i ²+d₃RH_i ³+… (6)

+d_nRH_i ⁿ

in the formula, A_iRepresents the normalized user age value, Q (A)_i) A water flow rate value, k, corresponding to the age of the user₁…k_nIs an unknown coefficient; h_iRepresents the normalized height value of the user, Q (H)_i) A water flow value corresponding to the height of the user, a₁…a_nis an unknown coefficient; w_irepresents the normalized user weight value, Q (W)_i) A water flow value corresponding to the user's body weight, b₁…b_nIs an unknown coefficient; t is t_iRepresents the normalized ambient temperature value, Q (t)_i) Representing the water flow rate value corresponding to the ambient temperature, c₁…c_nis an unknown coefficient; RH (relative humidity)_iRepresents the normalized ambient humidity value, Q (RH)_i) A water flow rate value, d, corresponding to the ambient humidity₁…d_nIs an unknown coefficient;

Respectively substituting 5 × n groups of age-flow, height-flow, weight-flow, temperature-flow, humidity-flow data values in the database into equations (2), (3), (4), (5) and (6), and solving the model to obtain 5 groups of corresponding unknown coefficients k₁…k_n,a₁…a_n,b₁…b_n,c₁…c_n,d₁…d_n；

Establishing a model between the water temperature and the age, height, weight, environment temperature and humidity of a user;

T(A_i)＝k₁′A_i+k₂′A_i ²+k₃′A_i ³+…+k_n′A_i ⁿ (7)

T(H_i)＝a₁′H_i+a₂′H_i ²+a₃′H_i ³+…+a_n′H_i ⁿ (8)

T(W_i)＝b₁′W_i+b₂′W_i ²+b₃′W_i ³+…+b_n′W_i ⁿ (9)

T(t_i)＝c₁′t_i+c₂′t_i ²+c₃′t_i ³+…+c_n′t_i ⁿ (10)

T(RH_i)＝d₁′RH_i+d₂′RH_i ²+d₃′RH_i ³+… (11)

+d_n′RH_i ⁿ

In the formula, T (A)_i) A water usage temperature value k representing the age of the user₁′…k_n' is the unknown coefficient; t (H)_i) A water temperature value corresponding to the height of the user, a₁′…a_n' is the unknown coefficient; t (W)_i) A water usage temperature value representing the weight of the user, b₁′…b_n' is the unknown coefficient; t (T)_i) Representing the water temperature value, c, corresponding to the ambient temperature₁′…c_n' is the unknown coefficient; t (RH)_i) Representing the water temperature value, d, corresponding to the ambient humidity₁′…d_n' is the unknown coefficient;

5 x n groups of data values of age, water temperature, height, water temperature, weight, water temperature, room temperature, water temperature, humidity and water temperature in the database are respectively substituted into equations (7), (8), (9), (10) and (11) for solving to obtain 5 groups of corresponding unknown coefficients k₁’…k_n’,a₁’…a_n’,b₁’…b_n’,c₁’…c_n’,d₁’…d_n’。

the present invention also provides a shower head device for a bathroom, comprising:

The system comprises a sensor group, a temperature and humidity sensor, an electromagnetic flowmeter and a timer, wherein the sensor group is used for respectively collecting the ambient temperature, the water temperature, the bathroom humidity, the flow of a spray head and the duration of water consumption of a user;

The intelligent preset system is the intelligent preset system of the bathroom shower nozzle in any one of claims 1 to 5, and is connected with the sensor group and the cold and hot water regulating valve;

and the parameter input and feedback panel is connected with the intelligent preset system and is used for inputting and feeding back data by a user.

In connection with the above technical solution, the shower head device further comprises: and the remote input unit is connected with the intelligent preset system and used for inputting user data through a remote end.

the invention has the following beneficial effects:

1) The invention can set the optimal shower, water temperature and flow parameters according to the physical sign information and environmental parameters of the user, not only can provide more appropriate service for the user, but also can provide guidance for water supply management of large rooms such as luxury hotels, passenger ships and cruise ships.

2) The system is provided with a correction and feedback module which can record data adjusted by a user and analyze the new data and relevant historical data together to update and correct the preset value so as to perfect the database of the preset system.

3) The user information acquisition module is additionally provided with the acquisition of the age and gender information of the user, and the great influence of the difference of the age and the gender on the water temperature and the flow of the shower is fully considered, so that the relation between the water parameter and the user information can be more accurate in the scheme.

4) The using objects are oriented to the large-scale group, the establishment of the presetter is based on the large-scale group, the presetter serves the large-scale group, can be continuously improved and updated according to the increase of the using objects, and is particularly suitable for places with large water consumption requirements and concentrated water consumption time intervals, such as luxury mail ships.

Drawings

The invention will be further described with reference to the accompanying drawings and examples, in which:

FIG. 1 is a flow chart of a method for intelligent presetting of bathroom sprinklers in accordance with an embodiment of the present invention;

FIG. 2 is a schematic structural diagram of an intelligent presetting system of bathroom sprinklers according to an embodiment of the invention;

FIG. 3 is a schematic view showing the construction of a shower head unit according to an embodiment of the present invention;

Fig. 4 is a schematic diagram of an example of the embodiment of the present invention applied to a hotel room or a cruise ship room.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The intelligent presetting method of the bathroom shower nozzle, as shown in figure 1, comprises the following steps:

s1, collecting the ambient temperature, the water temperature, the bathroom humidity, the flow rate of the spray head and the duration of water use for a user through a temperature and humidity sensor, an electromagnetic flow meter and a timer respectively; acquiring self age, sex, height and weight data input by a user before showering; the corresponding time periods of different water parameters can be compared, and the water parameter group with the longest duration time is screened out and analyzed together with the user and the environment parameters. In addition, the flow rate and temperature of the used water and the age, sex, height, weight, ambient temperature and humidity of the user can be collected and grouped, and an original sample database is established.

S2, analyzing and processing the collected sample data, and establishing a model relation among user characteristics, bathroom environment temperature, humidity, comfortable water temperature and flow;

S3, automatically presetting decision values according to the established model and the currently acquired user data, wherein the decision values comprise the appropriate water flow and temperature for the user;

And S4, adjusting the water outlet of the bathroom shower according to the preset decision value.

further, the specific model relationship establishing process is as follows:

Dividing sample data into two categories according to male and female, and dividing each group of sample data into input and output values, wherein the input value is as follows: 5 types of data of age (A), height (H), weight (W), ambient temperature (t) and humidity (RH); the output value is: 2 types of data of water flow (Q) and temperature (T); normalizing the input and output samples, namely mapping the input and output data between (0,1) ranges according to a certain standard; the treatment is as follows (1):

in the formula, i represents an actual input value; i.e. i_min、i_maxRespectively representing the minimum value and the maximum value of the type data of the variable i; f (i) is the result of the corresponding normalization process;

Respectively establishing models among the age (A) of the user, the height (H) of the user, the weight (W), the ambient temperature (t), the humidity (RH) and the water flow (Q) to obtain corresponding polynomials as shown in the following formulas (2) to (6):

Q(A_i)＝k₁A_i+k₂A_i ²+k₃A_i ³+…+k_nA_i ⁿ (2)

Q(H_i)＝a₁H_i+a₂H_i ²+a₃H_i ³+…+a_nH_i ⁿ (3)

Q(W_i)＝b₁W_i+b₂W_i ²+b₃W_i ³+…+b_nW_i ⁿ (4)

Q(t_i)＝c₁t_i+c₂t_i ²+c₃t_i ³+…+c_nt_i ⁿ (5)

Q(RH_i)＝d₁RH_i+d₂RH_i ²+d₃RH_i ³+… (6)

+d_nRH_i ⁿ

In the formula, A_irepresents the normalized user age value, Q (A)_i) A water flow rate value, k, corresponding to the age of the user₁…k_nIs an unknown coefficient; h_iRepresents the normalized height value of the user, Q (H)_i) Indicating correspondence of height of the userflow rate of water, a₁…a_nIs an unknown coefficient; w_iRepresents the normalized user weight value, Q (W)_i) A water flow value corresponding to the user's body weight, b₁…b_nIs an unknown coefficient; t is t_iRepresents the normalized ambient temperature value, Q (t)_i) Representing the water flow rate value corresponding to the ambient temperature, c₁…c_nis an unknown coefficient; RH (relative humidity)_iRepresents the normalized ambient humidity value, Q (RH)_i) A water flow rate value, d, corresponding to the ambient humidity₁…d_nIs an unknown coefficient;

Respectively substituting 5 × n groups of age-flow, height-flow, weight-flow, temperature-flow, humidity-flow data values in the database into equations (2), (3), (4), (5) and (6), and solving the model to obtain 5 groups of corresponding unknown coefficients k₁…k_n,a₁…a_n,b₁…b_n,c₁…c_n,d₁…d_n；

establishing a model between the water temperature and the age, height, weight, environment temperature and humidity of a user;

T(A_i)＝k₁′A_i+k₂′A_i ²+k₃′A_i ³+…+k_n′A_i ⁿ (7)

T(H_i)＝a₁′H_i+a₂′H_i ²+a₃′H_i ³+…+a_n′H_i ⁿ (8)

T(W_i)＝b₁′W_i+b₂′W_i ²+b₃′W_i ³+…+b_n′W_i ⁿ (9)

T(t_i)＝c₁′t_i+c₂′t_i ²+c₃′t_i ³+…+c_n′t_i ⁿ (10)

T(RH_i)＝d₁′RH_i+d₂′RH_i ²+d₃′RH_i ³+… (11)

+d_n′RH_i ⁿ

in the formula, T (A)_i) A water usage temperature value k representing the age of the user₁′…k_n' is the unknown coefficient; t (H)_i) A water temperature value corresponding to the height of the user, a₁′…a_n' is the unknown coefficient; t (W)_i) A water usage temperature value representing the weight of the user, b₁′…b_n' is the unknown coefficient; t (T)_i) Representing the water temperature value, c, corresponding to the ambient temperature₁′…c_n' is the unknown coefficient; t (RH)_i) Representing the water temperature value, d, corresponding to the ambient humidity₁′…d_n' is the unknown coefficient;

5 x n groups of data values of age, water temperature, height, water temperature, weight, water temperature, room temperature, water temperature, humidity and water temperature in the database are respectively substituted into equations (7), (8), (9), (10) and (11) for solving to obtain 5 groups of corresponding unknown coefficients k₁’…k_n’,a₁’…a_n’,b₁’…b_n’,c₁’…c_n’,d₁’…d_n’。

as shown in fig. 2, the intelligent presetting system for bathroom sprinklers of the embodiment of the invention comprises:

the parameter acquisition module is used for acquiring the environment temperature, the water temperature, the bathroom humidity, the shower nozzle flow and the user water using duration which are respectively recorded by the temperature and humidity sensor, the electromagnetic flow meter and the timer; acquiring self age, sex, height and weight data input by a user before showering;

The analysis decision module is used for analyzing and processing the collected sample data and establishing a model relation among user characteristics, bathroom environment temperature, humidity, comfortable water temperature and flow;

The automatic setting module is used for automatically presetting decision values including appropriate water flow and temperature for the user according to the established model and the currently acquired user data;

and the adjusting module is used for adjusting the water outlet of the bathroom shower nozzle according to a preset decision value. The adjusting module is mainly used for adjusting a cold and hot water flow adjusting valve of a bathroom sprayer, adjusting the mixing proportion of cold and hot water flows to a preset temperature value and controlling the total flow of mixed water. The water flow and the temperature are pre-adjusted to preset values according to the adjusting module, so that the process that a user tries water continuously and seeks an optimal experience point can be omitted.

According to the technical scheme, the specific model relationship establishment process in the analysis decision module is as follows:

Dividing sample data into two categories according to male and female, and dividing each group of sample data into input and output values, wherein the input value is as follows: 5 types of data of age (A), height (H), weight (W), ambient temperature (t) and humidity (RH); the output value is: 2 types of data of water flow (Q) and temperature (T); normalizing the input and output samples, namely mapping the input and output data between (0,1) ranges according to a certain standard; the treatment is as follows (1):

In the formula, i represents an actual input value; i.e. i_min、i_maxrespectively representing the minimum value and the maximum value of the type data of the variable i; f (i) is the result of the corresponding normalization process;

respectively establishing models among the age (A) of the user, the height (H) of the user, the weight (W), the ambient temperature (t), the humidity (RH) and the water flow (Q) to obtain corresponding polynomials as shown in the following formulas (2) to (6):

Q(A_i)＝k₁A_i+k₂A_i ²+k₃A_i ³+…+k_nA_i ⁿ (2)

Q(H_i)＝a₁H_i+a₂H_i ²+a₃H_i ³+…+a_nH_i ⁿ (3)

Q(W_i)＝b₁W_i+b₂W_i ²+b₃W_i ³+…+b_nW_i ⁿ (4)

Q(t_i)＝c₁t_i+c₂t_i ²+c₃t_i ³+…+c_nt_i ⁿ (5)

Q(RH_i)＝d₁RH_i+d₂RH_i ²+d₃RH_i ³+… (6)

+d_nRH_i ⁿ

In the formula, A_iRepresents the normalized user age value, Q (A)_i) A water flow rate value, k, corresponding to the age of the user₁…k_nis an unknown coefficient; h_iRepresents the normalized height value of the user, Q (H)_i) A water flow value corresponding to the height of the user, a₁…a_nis an unknown coefficient; w_irepresents the normalized user weight value, Q (W)_i) A water flow value corresponding to the user's body weight, b₁…b_nIs an unknown coefficient; t is t_iRepresents the normalized ambient temperature value, Q (t)_i) Representing the water flow rate value corresponding to the ambient temperature, c₁…c_nis an unknown coefficient; RH (relative humidity)_iRepresents the normalized ambient humidity value, Q (RH)_i) A water flow rate value, d, corresponding to the ambient humidity₁…d_nIs an unknown coefficient;

Respectively substituting 5 × n groups of age-flow, height-flow, weight-flow, temperature-flow, humidity-flow data values in the database into equations (2), (3), (4), (5) and (6), and solving the model to obtain 5 groups of corresponding unknown coefficients k₁…k_n,a₁…a_n,b₁…b_n,c₁…c_n,d₁…d_n；

Establishing a model between the water temperature and the age, height, weight, environment temperature and humidity of a user;

T(A_i)＝k₁′A_i+k₂′A_i ²+k₃′A_i ³+…+k_n′A_i ⁿ (7)

T(H_i)＝a₁′H_i+a₂′H_i ²+a₃′H_i ³+…+a_n′H_i ⁿ (8)

T(W_i)＝b₁′W_i+b₂′W_i ²+b₃′W_i ³+…+b_n′W_i ⁿ (9)

T(t_i)＝c₁′t_i+c₂′t_i ²+c₃′t_i ³+…+c_n′t_i ⁿ (10)

T(RH_i)＝d₁′RH_i+d₂′RH_i ²+d₃′RH_i ³+… (11)

+d_n′RH_i ⁿ

In the formula, T (A)_i) A water usage temperature value k representing the age of the user₁′…k_n' is the unknown coefficient; t (H)_i) A water temperature value corresponding to the height of the user, a₁′…a_n' is the unknown coefficient; t (W)_i) A water usage temperature value representing the weight of the user, b₁′…b_n' is the unknown coefficient; t (T)_i) Representing the water temperature value, c, corresponding to the ambient temperature₁′…c_n' is the unknown coefficient; t (RH)_i) Representing the water temperature value, d, corresponding to the ambient humidity₁′…d_n' is the unknown coefficient;

5 x n groups of data values of age, water temperature, height, water temperature, weight, water temperature, room temperature, water temperature, humidity and water temperature in the database are respectively substituted into equations (7), (8), (9), (10) and (11)Solving to obtain 5 groups of corresponding unknown coefficients k₁’…k_n’,a₁’…a_n’,b₁’…b_n’,c₁’…c_n’,d₁’…d_n’；

Further, the determination of the preset decision value specifically includes:

According to information provided by a user and by combining with the established model, 5 groups of preset values of water flow and water temperature are respectively calculated, finally, the average value of the 5 groups of preset values is obtained to be used as a preset decision value of the water flow and the temperature, and the preset decision values of the water flow and the temperature are respectively determined according to the following formulas (12) and (13):

Wherein Q (A)_i)、T(A_i) The water flow and temperature value corresponding to the age of the user, Q (H)_i)、T(H_i) The water flow and temperature value, Q (W), corresponding to the height of the user_i)、T(W_i) The water flow and temperature value, Q (t), corresponding to the weight of the user_i)、T(t_i) The water flow and temperature corresponding to the ambient temperature, Q (RH)_i)、T(RH_i) The water flow and the temperature value corresponding to the environmental humidity are obtained, and Q (i) and T (i) are the averaged water flow and temperature value;

carrying out the inverse normalization processing of formulas (14) and (15) on the obtained preset values Q (i) and T (i), and sending the processing result to an adjusting module;

Q＝Q(i)(Q_max-Q_min)+Q_min (14)

T＝T(i)(T_max-T_min)+T_min (15)

Wherein Q (i) and T (i) are water flow and temperature values calculated by a model, and Q_max、Q_minFor maximum and minimum water flow in the collected databaseValue, T_max、T_minThe maximum and minimum water temperature values in the collected database are shown, and Q, T are decision values of actual water flow and water temperature.

further, the preset system also comprises a feedback module, which is used for analyzing the parameter values adjusted by the user in real time and related historical data according to the parameter values adjusted by the user in real time to obtain the optimal water flow and temperature of different ages, sexes, bath room temperatures and humidity based on more sample data; the more the number of samples is, the higher the polynomial degree of calculation is brought into the model, the more accurate the solved unknown coefficient is, and the preset decision value is updated and corrected in sequence.

further, the feedback module compares the actual water use value with a preset decision value, and performs a new round of model solution if the error is greater than 5%.

The shower head device of the embodiment of the invention comprises:

The system comprises a sensor group, a temperature and humidity sensor, a temperature sensor, an electromagnetic flowmeter and a timer, wherein the sensor group is used for respectively collecting the ambient temperature, the water temperature, the bathroom humidity, the flow rate of a spray head and the duration time of water consumption of a user; the electromagnetic flowmeter can be arranged on the mixed water pipe; the temperature and humidity sensor can be installed in a bathroom to collect the temperature and humidity of the environment. Before a user showers, the ambient temperature and humidity in the bathroom are collected and synchronously input into the intelligent preset system together with the user parameter information.

The intelligent preset system is the bathroom shower nozzle intelligent preset system of the embodiment and is connected with the sensor group and the cold and hot water regulating valve;

and the parameter input and feedback panel is connected with the intelligent preset system and is used for inputting and feeding back data by a user. The parameter input and feedback panel may be installed in a bathroom or in a room. Before the user showers, the age, sex, height and weight of the user are input according to the operation prompt.

The shower head device further includes: and the remote input unit is connected with the intelligent preset system and used for inputting user data through a remote end. The remote input device can be installed at a remote end, such as a hotel lobby, and when a customer logs in and registers, the corresponding age and sex information of the customer is sent to the intelligent preset system installed in the corresponding guest room.

In one embodiment of the present invention, as shown in FIG. 3, a shower head apparatus includes: the system comprises 1-a shower head, 2-a sensing acquisition module for flow, temperature and time, 3-a cold and hot water flow regulating valve, 4-a cold water branch pipe, 5-a hot water main pipe, 6-a cold water main pipe, 7-a hot water branch pipe, 8-a discharge valve, 9-a shower head hose, 10-a temperature and humidity sensor, 11-acquired data transmission, 12-a parameter input and feedback panel, 13-a preset value output line, 14-an actual value feedback line and 15-an analysis decision module.

The flow, temperature and time sensing and collecting module 2 is connected to a shower hose 9 in a threaded manner, a temperature and humidity sensor 10 is installed at a proper position of a bathroom, a parameter input and feedback panel 12 is installed at an inlet and an outlet of the bathroom and used for user self information input and water comfort index display and feedback functions, and a cold and hot water flow regulating valve 3 is in signal transmission connection with the parameter input and feedback panel 12 through a signal line and used for indoor temperature and humidity collection before showering; the sensing acquisition module 2, the temperature and humidity sensor 10 and the parameter input and feedback panel 12 are in transmission connection through signal lines and used for data feedback of water temperature and flow in the shower process, and the analysis and decision module 15 is arranged between the water supply management control rooms of the corresponding floor units, is connected with the parameter input and feedback panel 12 through signal lines and used for outputting preset decision values and feeding back actual measurement water use indexes.

when a shower starts, mixed water is limited by a cold and hot water flow regulating valve, the mixed water respectively records the water temperature and the flow at the moment by a temperature sensor and an electromagnetic flow meter, and a timer records the duration time of the water use parameters; if the user adjusts the cold and hot water adjusting valve according to the needs, the timer, the temperature sensor and the electromagnetic flowmeter send the collected temperature, flow and continuous time values to the intelligent preset system for storage, the sensor group is reset, and the next group of collection is carried out.

As shown in fig. 4, an example of the present invention applied to hotel rooms or cruise ship rooms specifically includes:

The user objects of the large-scale cruise ship are generally large in number, long in residence time and small in passenger flow, so that water use optimization guidance can be conducted on the large-scale and relatively stable group. Firstly, before passengers board and live in a ship, the foreground synchronizes basic information provided by the passengers voluntarily to the parameter input panel 12 of a specific room, when the passengers live in a shower for the first time, the sensing and collecting module 2 can collect the environmental temperature and humidity before the shower, the regulated water flow and temperature in the shower process, and transmit the collected signals to the analysis and decision module 15. The analysis decision module establishes a model relation among the information parameters and the environmental parameters of the group of users, the water flow and the water temperature, and obtains a corresponding unknown coefficient.

The invention aims at the collection and analysis of water for large-scale groups, makes full use of the effective value of big data, makes special reasonable water use guidance for different users, optimizes water use experience, relatively reduces the duration of showering, and has obvious water saving effect especially for places with large passenger flow and concentrated showering time.

It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.