CN112734600B - A personalized smart lighting method for hotels based on mobile phone APP - Google Patents

Abstract

The invention provides a hotel personalized intelligent lighting method based on a mobile phone APP. After check-in registration and information collection, the user's personal characteristic data is fuzzy classified to obtain the characteristic membership value, and the dimming instructions are analyzed to obtain the modulation value of each lamp. Based on the comparison of the user characteristic membership value vector and the light modulation vector with the historical occupancy user data in the database, the user's preferred light modulation vector is inferred based on the similarity between the vectors. The inference results are pushed to the user and after confirmation, the user's preferred light modulation vector is The output module sends dimming instructions to the dimmable light group. This invention infers the dimming preferences of unoperated lamps in the guest rooms based on the user's individual characteristics and similarities in dimming operations, and can quickly push the lighting effects that the user may be interested in, thereby realizing personalized intelligent lighting recommendations. and decision-making.

Description

A personalized smart lighting method for hotels based on mobile phone APP

This application is a divisional application with application number 201910287080.6, application date April 3, 2019, and invention title "A hotel personalized intelligent lighting device, system and method based on mobile phone APP".

Technical field

The patent of this invention relates to a hotel personalized intelligent lighting method based on a mobile phone APP, which belongs to the field of intelligent lighting.

Background technique

As LED smart lighting technology matures, the popularity of LED smart lighting is getting higher and higher. Its advantages are that it is efficient, energy-saving and has more diverse lighting methods. It can switch lighting effects in real time according to the needs of different scenes, making it more humane. As a representative service industry, the hotel industry attaches great importance to the customer's stay experience. Since the main usage areas of customers are concentrated indoors and the usage time is concentrated at night, the industry has a stronger interest in efficient, practical, and personalized smart lighting equipment.

At the same time, with the continuous development of the Internet industry and the advent of the big data era, the personalized recommendation function, as a product and service recommendation function that can independently eliminate redundant information and find optimal results based on user characteristics, has an increasingly wide range of applications. widely.

However, for the hotel industry, the smart lighting equipment currently used in the industry is often limited to simple infrared, voice-activated switch equipment, or wireless remote control equipment, etc., which are not highly targeted and cannot be based on the personalized lighting of the staying users. Provide a more applicable lighting experience for the residents.

Invention patent content

The purpose of the patent of this invention is to provide a hotel personalized intelligent lighting device, system and method based on a mobile phone APP, integrating the advantages of intelligent lighting equipment and personalized recommendation functions to provide hotel users with a more personalized lighting experience .

One of the technical solutions of the present invention is to provide a hotel personalized intelligent lighting device based on a mobile phone APP with the following structure, which includes a host unit and a user interface unit. The host unit includes an input module, a user characteristic analysis module, and a user interface unit. Modulation analysis module, lighting recommendation module, output module and storage module, the user interface unit is provided with an operation panel;

The host unit is configured as:

The user inputs registration information such as user personal characteristic parameters from the mobile APP terminal, and also inputs dimming instructions to modulate the color temperature and brightness of the dimmable lamp group in the hotel room through the mobile APP terminal. The information and instructions are all passed through the input module transmitted to the host unit and transferred by the host unit to the database of the external server,

The user's personal characteristic parameters include age, gender, region, occupation, preferred color temperature, preferred brightness, number of guests and travel purpose, etc.

The user characteristic analysis module processes and analyzes the user's personal characteristic data by category. First, it establishes a fuzzy variable set for each category in its domain of discussion and establishes a membership function for each fuzzy variable in the set; then, based on the required Describe the membership function, calculate the membership value of each fuzzy variable in the category corresponding to each data in the user's personal characteristic data, and arrange all the membership values in order into a characteristic membership value vector; then, the characteristic membership value The vectors are saved to the storage module and external database respectively.

The user modulation analysis module processes the dimming signals corresponding to each lamp in the dimmable lamp group in the dimming instructions input by the user through the APP terminal to obtain the corresponding modulation values, and arranges the modulation values in order into a The lamp modulation vector is then saved to the storage module and external database respectively.

The lighting recommendation module compares the user's characteristic membership value vector and light modulation vector with other historical occupancy user data in the external database, and infers the user's final light modulation vector based on the similarity between the vectors. Mobile APP terminal is recommended to users,

After the user confirms the recommendation, a dimming instruction is sent to the dimmable lamp group through the output module.

As a preferred option, when a user checks in at the front desk, the necessary identity information for the registration, including ID number, room number, etc., is stored in the database and transmitted from the database to the host unit in the guest room.

The mobile APP terminal is equipped with three interfaces - login interface, registration interface and lamp modulation interface. When a check-in user logs in through the login interface of the mobile APP terminal, the host unit compares the identity information input by the check-in user with the front desk registration information to see whether it matches. To determine whether the check-in user is allowed to log in and enter the subsequent interface,

When the user logs in successfully, he or she can send information to the host unit through the registration interface and lamp modulation interface, improve the user's personal characteristics parameters, and perform switching and brightness and color temperature modulation operations on the lamps in the guest room.

Preferably, the host unit is further configured to:

When a new check-in user u checks into the hotel for the first time and opens the mobile APP terminal to register, the lighting recommendation module combines the user's characteristic membership value vector with the characteristic membership value vector of other historical check-in users v in the database based on the user registration information. Compare and calculate the similarity between the two vectors,

Among them, sim _i (u, v) is the characteristic similarity of users u and v in the i-th parameter category dimension, I is the set of categories such as age, and j is the fuzzy in-set fuzzy variable of the i-th parameter category. The number of quantities, μ _ik (u _i ) and μ _ik (v _i ) are respectively the parameter values of users u and v on the i-th category corresponding to the membership value of the k-th fuzzy variable in the category, k= 1,2,...,j.

For example, for the age parameter category, you can set four fuzzy variables such as juvenile, young, middle-aged, and old; for the color temperature parameter category, you can set three fuzzy variables such as low color temperature, medium color temperature, and high color temperature.

Preferably, the preferred color temperature can be a list of color temperatures based on time periods, with the time periods being morning, afternoon and night; the purpose of travel can be travel, work, meeting friends, etc., and work can be subdivided into business, engineering, and technical services. and other activities.

Preferably, the host unit is further configured to:

For non-first-time users, when the user u modulates the color temperature and brightness of the lamp through the mobile APP terminal, its modulation information is recorded. At the same time, the lighting recommendation module establishes the lamp modulation vector [X _u , Y _u ] based on its modulation value:

_{Among them , _} Color temperature modulation value, the corresponding values are y ₁ to y _m respectively,

Then, based on the light modulation vector, compare it with the light modulation vectors of historical occupancy users stored in the database, and calculate the vector similarity one by one:

sim(u,v)=α·sim _x +(1-α)·sim _y

Among them, m represents the number of lamps modulated by the occupancy users, xi _{, u} , y _{i, u} , xi _{, v} , y _{i, v} respectively represent the brightness and color temperature modulation values of the lamps by occupancy users u and v. Represents the average value of the corresponding modulation values,/> is the arithmetic mean of the brightness modulation values of m modulated lights by user u,

simx (u, v), sim _y (u, v), sim (u, v) respectively represent the brightness similarity, color temperature similarity and overall vector similarity of u and v. The overall similarity is the brightness similarity and color temperature similarity. Degree weighted sum, α is the weight of brightness similarity.

Preferably, the host unit is further configured to:

After the vector similarity calculation is completed, the K historical occupancy users with the highest similarity are taken as neighbors of u and form u's neighbor set. Then, based on the historical modulation information of the occupancy users in the neighbor set, the lamp i in the guest room of user u is inferred. Recommended brightness modulation value x′ _u,i and color temperature modulation value y' _u,i :

Among them, i=1, 2, ..., n, n is the number of lamps in the guest room for which user u has not yet determined the modulation value;

The output module sends the brightness modulation value x′ _{u, i} and the color temperature modulation value y′ _{u, i} to the mobile phone APP terminal, and stores the confirmed modulation data returned by the mobile phone APP terminal into the database and sends it to the lamp through the output module. Group of drives.

Preferably, the lamps i are all lamps in the guest room that have not been modulated and confirmed to be operated by the user u.

In another embodiment of the present invention, the host unit may also be configured as:

When a new check-in user u checks in for the first time and opens the mobile APP terminal to register, first use the fuzzy classification method to classify the user based on the user registration information and calculate its category:

F＝S+N+Y+D

Among them, S is the gender of the checking-in user. For example, male is represented by 0 and female is represented by 1; N is the number of guests, usually with values 1, 2, 3, etc.; The number and cut-off range is 0 to 4; D is the purpose of travel, and its value ranges from 0 to 6, corresponding to business, tourism, reception, leisure, conference, office and long-term rental respectively. F is the classification value of check-in users. Users with the same score are classified into the same category, and their values may range from 1 to 14, that is, all historical check-in users are divided into 14 categories.

After the user enters the guest room, the lighting recommendation module compares its F value with the F value of other historical check-in users in the external database, combines users with the same value into the user's neighbor set, and determines the user's final light modulation vector according to Each user in the neighbor set calculates the average of the modulation values of each lamp as a guess value and combines it into a modulation vector, and then recommends the modulation vector to the user through the mobile APP terminal.

In another embodiment of the present invention, a hotel personalized intelligent lighting system based on a mobile phone APP is also provided, which includes an adjustable light color lamp group with adjustable brightness and color temperature, and is used for parameter input. and the user interface unit, mobile phone APP terminal and server for dimming operation, as well as the host unit respectively connected to the lamp group, user interface unit, mobile phone APP terminal and server,

The host unit includes an input module, a user characteristic analysis module, a user modulation analysis module, a lighting recommendation module, an output module and a storage module, and is configured as:

The user inputs registration information such as user personal characteristic parameters from the mobile APP terminal, and also inputs dimming instructions to modulate the color temperature and brightness of the lamp group in the hotel room through the mobile APP terminal. The information and instructions are all passed through the control unit. The input module is transmitted to the host unit and dumped by the host unit into the database of the server,

The user's personal characteristic parameters include age, gender, region, occupation, preferred color temperature, preferred brightness, number of guests and travel purpose, etc.

When a new user u checks into the hotel for the first time, the user characteristic analysis module processes and analyzes the user's personal characteristic data by category. First, a fuzzy variable set is established for each category in its domain of discussion and each fuzzy variable set in the set is Variables establish a membership function; then, according to the membership function, calculate the membership value of each data in the user's personal characteristic data corresponding to each fuzzy variable in its category, and arrange all the membership values in order into a characteristic membership degree value vector; then, save the characteristic membership value vector to the storage module and server database respectively.

The user modulation analysis module processes the dimming signals corresponding to each lamp in the dimmable lamp group in the dimming instructions input by the user through the APP terminal to obtain the corresponding modulation values, and arranges the modulation values in order into a The lamp modulation vector is then saved to the storage module and server database respectively.

The lighting recommendation module compares the user's characteristic membership value vector and light modulation vector with other historical occupancy user data in the server database, and infers the user's final light modulation vector based on the similarity between the vectors. Mobile APP terminal is recommended to users,

After the user confirms the recommendation, a dimming instruction is sent to the dimmable lamp group through the output module.

Among them, the similarity of the feature membership value vector is calculated as follows:

in,

sim _i (u, v) is the characteristic similarity between the new check-in user u and the historical check-in user v in the database in the i-th parameter category dimension, I is the set of categories such as age, and j is the i-th parameter category The number of fuzzy quantities in the fuzzy variable set, μ _ik (u _i ), μ _ik (vi) are the parameter values of users u and v on the i-th category corresponding to the membership of the k-th fuzzy variable in the category. Degree value, k=1, 2,...,j.

Preferably, the lamp group is composed of multiple dimmable LED lamps and is arranged on the ceiling of the hotel. The driver of the LED lamp is connected to the host unit through the communication interface. The host unit changes through the driver according to the instructions sent by the mobile phone APP terminal. The driving current of each driving channel in the LED lamp realizes the modulation of its brightness and color temperature.

The modulation value is the PWM wave duty cycle value of the drive current of each drive channel,

The mobile APP terminal can be connected to the host unit through the wireless router in the guest room. At the same time, the host unit is also connected to the dimmable color LED light group and the server through wired connections.

In yet another embodiment of the present invention, a hotel personalized intelligent lighting method based on a mobile phone APP is also provided, including the following steps:

S1. Initialization, user check-in registration and information collection,

The system accepts registration information such as user personal characteristics parameters through the mobile APP terminal, and transfers them to the database of the server by the host unit. The user personal characteristics parameters include age, gender, region, occupation, favorite color temperature, favorite brightness, and number of guests. and travel purpose and other categories,

For each user's personal characteristic parameter category, a fuzzy variable set is established within its domain of discussion, and a membership function is established for each fuzzy variable in the set.

S2. The host unit in the hotel room determines whether the user is checking in for the first time and builds a user personality vector. If it is the first time, it goes to S3. Otherwise, the user modulates the color temperature and brightness of the dimmable light group in the hotel room through the mobile APP terminal. A dimming instruction, process the dimming signal corresponding to each lamp in the dimmable lamp group in the dimming instruction to obtain the corresponding modulation value, and arrange the modulation values in sequence into a lamp modulation vector As the user personality vector, then, go to S4,

S3. For first-time check-in users, first, for each personal characteristic parameter, based on the membership function of each fuzzy variable in the category of the parameter, calculate the membership value corresponding to the user's personal characteristic parameter value, and combine all the The membership values are arranged in order into a characteristic membership value vector as the user personality vector,

S4. Save the user personality vector to the storage module of the host unit and the server database respectively.

S5. Compare the user's user personality vector with other historical occupancy user data in the server database, and infer the user's preferred light modulation vector based on the similarity between the vectors.

S6. Recommend the prediction results to the user through the mobile APP terminal. After the user confirms or adjusts the recommendation, the confirmed lamp modulation vector is transmitted to the dimmable lamp group through the output module of the host unit for dimming. At the same time, The lamp modulation vector is stored in the server database.

Preferably, step S1 also includes:

When a user checks in at the front desk, the registered necessary identity information, including ID number, room number, etc., is stored in the server database and transmitted from the database to the host unit in the guest room.

Three interfaces are set up on the mobile APP terminal - login interface, registration interface and lamp modulation interface. When a check-in user logs in through the login interface of the mobile APP terminal, the host unit compares the identity information input by the check-in user with the front desk registration information. Comply, to determine whether the check-in user is allowed to log in and enter the subsequent interface.

When the user logs in successfully, he or she can send information to the host unit through the registration interface and lamp modulation interface to improve the user's personal characteristics and adjust the brightness and color temperature of the lamps in the guest room.

Preferably, in step S1, the server adopts a cloud server. After the system receives the user's personal characteristic parameters and other registration information through the mobile APP terminal, the mobile APP terminal directly saves the information to the database of the cloud server.

Preferably, step S2 also includes:

For non-first-time users, when the user u modulates the color temperature and brightness of the lamp through the mobile APP terminal, the modulation information is recorded, and the lamp modulation vector [X _u , Y _u ] is established based on the modulation value:

_{Among them , _} Modulation value, its corresponding values are y ₁ to y _m respectively;

The step S5 also includes:

Based on the lamp modulation vectors of the m lamps, compare them with the lamp modulation vectors of historical occupancy users stored in the database, and calculate the vector similarity one by one:

sim(u,v)=α·sim _x +(1-α)·sim _y

Among them, m represents the number of lamps modulated by the occupancy users, xi _{, u} , y _{i, u} , xi _{, v} , y _{i, v} respectively represent the brightness and color temperature modulation values of the lamps by occupancy users u and v. Represents the average value of the corresponding modulation values,/> is the arithmetic mean of the brightness modulation values of m modulated lights by user u,

sim _x (u, v), sim _y (u, v), sim (u, v) respectively represent the brightness similarity, color temperature similarity and overall vector similarity of u and v. The overall similarity is the brightness similarity and color temperature. The weighted sum of similarities, α is the weight of brightness similarity.

Preferably, step S5 also includes:

For the first-time check-in user u, compare the characteristic membership value vector of the user with the characteristic membership value vector of other historical check-in users v in the database, and calculate the similarity between the two vectors,

Among them, sim _i (u, v) is the characteristic similarity of users u and v in the i-th parameter category dimension, I is the set of categories such as age, and j is the fuzzy in-set fuzzy variable of the i-th parameter category. The number of quantities, μ _ik (u _i ) and μ _ik (vi) are the parameter values of users u and v on the i-th category corresponding to the membership value of the k-th fuzzy variable in the category, k=1 ,2,...,j.

Preferably, step S5 also includes:

After the vector similarity calculation is completed, the K historical occupancy users with the highest similarity are taken as neighbors of u and form u's neighbor set. Then, based on the historical modulation information of the occupancy users in the neighbor set, the lamp i in the guest room of user u is inferred. Recommended brightness modulation value x′ _u,i and color temperature modulation value y′ _u,i :

Among them, i=1, 2,..., n;

The step S6 includes:

The host unit sends the brightness modulation value x′ _u,i and the color temperature modulation value y′ _u,i to the mobile phone APP terminal, stores the confirmed modulation data returned by the mobile phone APP terminal into the database and sends it to the user through the output module. Driver for dimming light sets.

Preferably, the lamps i are all lamps in the guest room that have not been modulated and confirmed to be operated by the user u.

Preferably, the following steps are also included:

Use the PWM wave duty cycle value of the drive current of each drive channel in the dimmable lamp group as the modulation value, and transmit the modulation value in the dimming command,

An operation panel is set up in the hotel room for parameter input, and the display screen is used for auxiliary display. After the user confirms the light modulation vector, the vector is displayed on the display screen in the form of a list.

In another embodiment of the present invention, step S3 adopts the following processing:

When a new check-in user u checks in for the first time and opens the mobile APP terminal to register, first use the fuzzy classification method to classify the user based on the user registration information and calculate its category:

F＝S+N+Y+D

Among them, S is the gender of the checking-in user. For example, male is represented by 0 and female is represented by 1; N is the number of guests, usually with values 1, 2, 3, etc.; The number and cut-off range is 0 to 4; D is the purpose of travel, and its value ranges from 0 to 6, corresponding to business, tourism, reception, leisure, conference, office and long-term rental respectively. F is the classification value of check-in users. Users with the same score are classified into the same category, and their values may range from 1 to 14, that is, all historical check-in users are divided into 14 categories;

Step S5 adopts the following processing:

After the first-time check-in user enters the room, compare its F value with the F value of other historical check-in users in the server database, combine users with the same value into the user's neighbor set, and use the user's preferred light modulation vector according to the neighbor set. Each user calculates the average of the modulation values of each lamp as the estimated value and combines it into a modulation vector, and then recommends the modulation vector to the user through the mobile APP terminal.

In the lighting system adopting the present invention, the mobile phone APP terminal is connected to the host unit through wireless communication such as a WIFI interface. The mobile phone APP terminal can send user registration information and control instructions to the host unit through the WIFI network of the hotel room. The host unit is connected to the LED string of the dimmable color LED lamp through the driver. According to the control instructions of the mobile APP terminal, the brightness and color temperature of the dimmable color LED lamp group are modulated. At the same time, the user input information and control instructions are entered into the database, and the Comparison with other historical occupancy users, based on the user characteristics and the similarity of each lamp modulation value in the control instructions, the user control parameters are inferred and recommended to the user through the mobile APP terminal to meet the user's personalized lighting needs.

The dimmable color LED lamp set is composed of multiple LED lamps with adjustable brightness and color temperature. For renovated hotels, it can be placed on the ceiling of the hotel according to the original lighting layout of the hotel and connected to the host unit. The host unit contains a WIFI communication module , the module can be connected to the wireless router in the guest room environment to communicate with the mobile APP terminal, and at the same time, it can be connected to the dimmable color LED lights and the server through wired connections. After the host unit recommends the guessed lamp modulation vector to the user, it controls the driver to correspondingly change the drive current of each drive channel of the dimmable color LED lamp by changing the PWM wave duty cycle according to the confirmation command sent by the mobile APP terminal to realize the control of the interior of the guest room. The brightness and color temperature of each LED light can be modulated.

After the recommended brightness modulation values x′ _{u, i} and color temperature modulation values y′ _{u, i} are calculated, the combination is a lamp modulation vector and sent to the mobile APP terminal and displayed on its interface. Users can directly confirm the modulation parameter list corresponding to this vector, or modify the modulation parameters of some LED lights. After the user clicks to confirm on the APP interface, the mobile APP terminal sends lamp modulation instructions to the host unit according to various modulation parameters on the interface; then, the host unit stores the lamp modulation vector in the database of the server, and at the same time sends it to the LED lamp through the output module Each driver sends the corresponding PWM duty cycle modulation value to realize the scene lighting in the guest room.

Advantages of the patent of this invention:

1. Perform similarity calculations by analyzing user characteristics by category, infer the lighting needs of first-time users, and solve the cold start problem of recommended applications;

2. After the user modulates some lights, the system can use the similar modulations of historical occupancy users to infer their modulation needs for other lights, so that the lighting effect is more targeted;

3. The present invention is controlled by mobile phone APP, which can realize remote control, single lamp dimming and one-key switch of recommended scenes, which is simple and convenient.

Description of the drawings

Figure 1 is a work flow chart of the personalized smart lighting method for hotels based on mobile APP;

Figure 2 is a structural diagram of a hotel personalized intelligent lighting system based on a mobile phone APP using the lighting method of the present invention;

Figure 3 is a schematic diagram of the application environment of the hotel's personalized intelligent lighting system;

Figure 4 is a schematic diagram of the attribute membership function;

Figure 5 is a signal flow diagram for personalized recommendation of lamp modulation parameters.

in:

1000 hotel personalized smart lighting systems based on mobile APP, 100 host units, 200 dimmable light groups, 300 user interface units, 400 mobile APP terminals, 500 servers, 800 hotel personalized smart lighting devices based on mobile APP,

110 input module, 120 user characteristic analysis module, 130 lighting recommendation module, 140 output module, 150 storage module, 160 user modulation analysis module,

210 drivers, 220LED lights,

310 display screen, 320 operation panel.

Detailed ways

Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention is not limited only to these embodiments. The present invention covers any alternatives, modifications, equivalent methods and solutions within the spirit and scope of the invention.

In order to provide the public with a thorough understanding of the present invention, specific details are described in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without the description of these details.

The invention is described in more detail by way of example in the following paragraphs with reference to the accompanying drawings. It should be noted that the drawings are in a relatively simplified form and use imprecise proportions, and are only used to conveniently and clearly assist in explaining the embodiments of the present invention.

Example 1

As shown in Figure 1, the hotel personalized intelligent lighting method based on mobile phone APP of the present invention includes the following steps:

S1. Initialization, user check-in registration and information collection,

The system accepts registration information such as user personal characteristics parameters through the mobile APP terminal, and transfers them to the database of the server by the host unit. The user personal characteristics parameters include age, gender, region, occupation, favorite color temperature, favorite brightness, and number of guests. and travel purpose and other categories,

For each user's personal characteristic parameter category, a fuzzy variable set is established within its domain of discussion, and a membership function is established for each fuzzy variable in the set.

S2. The host unit in the hotel room determines whether the user is checking in for the first time and builds a user personality vector. If it is the first time, it goes to S3. Otherwise, the user modulates the color temperature and brightness of the dimmable light group in the hotel room through the mobile APP terminal. A dimming instruction, process the dimming signal corresponding to each lamp in the dimmable lamp group in the dimming instruction to obtain the corresponding modulation value, and arrange the modulation values in sequence into a lamp modulation vector As the user personality vector, then, go to S4,

S3. For first-time check-in users, first, for each personal characteristic parameter, based on the membership function of each fuzzy variable in the category of the parameter, calculate the membership value corresponding to the user's personal characteristic parameter value, and combine all the The membership values are arranged in order into a characteristic membership value vector as the user personality vector,

S4. Save the user personality vector to the storage module of the host unit and the server database respectively.

S5. Compare the user's user personality vector with other historical occupancy user data in the server database, and infer the user's preferred light modulation vector based on the similarity between the vectors.

S6. Recommend the prediction results to the user through the mobile APP terminal. After the user confirms or adjusts the recommendation, the confirmed lamp modulation vector is transmitted to the dimmable lamp group through the output module of the host unit for dimming. At the same time, The lamp modulation vector is stored in the server database.

The processing process and application of the present invention will be described in detail below.

As shown in Figure 2, a hotel personalized intelligent lighting system 1000 based on a mobile phone APP using the lighting method of the present invention includes a hotel personalized intelligent lighting device 800 based on a mobile phone APP, and a dimmable lamp group 200 with adjustable brightness and color temperature. , server 500 and mobile APP terminal 400.

Among them, the hotel personalized smart lighting device 800 based on the mobile phone APP includes a host unit 100 and a user interface unit 300. The host unit 100 is provided with an input module 110, a user characteristic analysis module 120, a lighting recommendation module 130, an output module 140, a storage module 150 and a user modulation analysis module 160.

In the hotel guest room lighting system, the hotel personalized intelligent lighting device 800 based on the mobile phone APP is connected to the dimmable light group 200 with adjustable brightness and color temperature, the server 500 and the mobile phone APP terminal 400 respectively. Both the user interface unit 300 and the mobile APP terminal 400 can be used to input registration information such as the user's personal characteristic parameters, and to perform dimming operations on the dimmable lamp group 200 in the hotel room. And the registration information and dimming instructions are transmitted to the host unit 100 through the input module 110 and transferred to the database in the server 500 by the host unit.

As shown in FIG. 3 , the dimmable color lamp group 200 adopts an LED lamp group, which is composed of a plurality of LED lamps with adjustable brightness and color temperature. LED lamps are arranged according to functional zones according to the structure of the guest room. For converted hotels that replace traditional lamps with LED lamps, LED lamps can be placed on the hotel ceiling according to the original lamp layout. The LED lights are connected to the host unit 100 through wired communication. For example, when a DALI bus is used, one host unit can connect up to 64 LED lights. The host unit includes a WIFI communication module, which can be connected to the wireless router in the guest room environment to achieve communication with the mobile APP terminal 400. At the same time, the host unit 100 is also connected to the user interface unit 300 and the database of the server 500 through wired connections. The host unit 100 changes the PWM wave duty cycle value transmitted to the LED lamp driver through the output module according to the operation signal sent by the user interface unit 300 or the mobile APP terminal 400, so that the driver changes the driving current sent to each driving channel to realize each LED Lamp brightness and color temperature modulation.

As shown in Figure 1, Figure 2, and Figure 5, the present invention modulates the lighting in the guest room and realizes various scene lighting by analyzing the user's preference for the light output of each lamp in the light group in the guest room. The basis for the analysis of preferences mainly comes from the dimming data of some lamps by current users. Based on the brightness, color temperature and other modulation data of the operated dimming lamps, these data are compared with the modulation data of the same lamps by historical occupancy users recorded in the server database. Compare and analyze the degree of similarity, and then based on the modulation data of historical occupancy users on the current user's lamps that have not yet been dimmed, the dimming preference of the current user can be inferred and the inference results are presented to the user for decision-making. Based on the accumulation of data, when users check into a new room type of the hotel or other hotels in the chain hotel group, they can also speculate on the dimming preferences in the new hotel rooms based on their existing modulation data of lamps and lights, thereby achieving personalization. Intelligent lighting recommendations and decisions.

To this end, the user modulation analysis module 160 processes the dimming signal corresponding to each lamp in the dimmable lamp group 200 in the dimming instruction input by the user through the APP terminal 400 to obtain the corresponding modulation value, and converts the modulation The values are arranged in sequence into a light modulation vector and then saved to the storage module and server database respectively.

As shown in Figures 1, 2, and 3, in a hotel room, the dimmable LED light group 200 consists of 11 LED lights with adjustable brightness and color temperature arranged on the ceiling of the hotel. The host unit 100 and the driver 210 are placed in the ceiling of the guest room bathroom, and the server 500 is placed in the hotel computer room.

When a check-in user registers at the front desk, according to regulations, the ID card information, contact information and room number need to be registered. This information can be operated by the hotel front desk as a basic information table and written into the server database, where the ID number is used as the primary key of the user. ID is a unique identifier.

In the database of the server, there is also a table of dimming information of LED lights used by users in various guest rooms. The table stores their dimming preferences in the format of lamp modulation vectors. Each record in the table includes the user name, ID number, and modulation parameters of each drive channel of the 11 LED lights in the guest room.

After the check-in user completes registration at the front desk, he enters the guest room and connects to the host unit 100 through the mobile APP terminal 400. The mobile APP terminal 400 includes at least three interfaces - a login interface, a registration interface and a lamp modulation interface. In the login interface, the information entered by the check-in user is compared with the information recorded in the server 500 database. If it matches, it is determined to be a compliant user and allowed to enter the subsequent interface; when the check-in user successfully logs in, he can enter the registration interface and lamp modulation interface, and complete personal information through the registration interface; at the same time, when the user enters the lamp modulation interface and operates the slider to modulate the lamp parameters, the sliderRelease() event of the mobile APP terminal 400 will be triggered. In response to this event, the APP terminal 400 will The dimming signal is sent to the host unit 100.

Among them, the APP terminal 400 has a dimming interface for each lamp. Typically, the brightness of each lamp can be represented by a slide bar. The user moves the cursor of the slide bar up and down to adjust it between 0 and 100%. Brightness; for color temperature, a sector ring similar to a pointer-type multimeter with a width of 120 to 150 degrees can be used to represent the adjustable color temperature range. The user can adjust the color temperature by moving the cursor. After the user setting is completed, the APP terminal 400 sends the brightness and color temperature setting values to the host unit 100 through the dimming signal.

The user modulation analysis module 160 in the host unit 100 analyzes the dimming signal, and converts the brightness setting value and color temperature setting value into corresponding driving channels according to the preset brightness-driving current and color temperature-driving current correspondence relationships. The PWM duty cycle value of the current is the modulation value, and these modulation values are arranged in sequence into a lamp modulation vector and then stored in the database of the storage module 150 and the server 500 respectively. At the same time, these PWM duty cycle values are sent to the driver 210 of the corresponding LED lamp 220 in the dimmable lamp group 200 through the output module 140. The driver 210 changes the driving current accordingly, thereby adjusting the brightness and color temperature of the corresponding LED lamp 220 to Setting value of APP terminal 400.

Then, the lighting recommendation module 130 compares the light modulation vector of the occupancy user with other historical occupancy user data in the database of the server 500, and infers the user's preferred light modulation vector based on the similarity between the vectors, and uses the mobile APP terminal to 400 is recommended to the user; then, after the user confirms the recommendation, a dimming instruction including the modulation value of each drive channel or the drive current PWM duty cycle value of all LED lights in the guest room is sent to the dimmable lamp group 200 through the output module 140. Implement scene lighting.

For non-first-time users, when the user u modulates the color temperature and brightness of the lamp through the mobile APP terminal, the user modulation analysis module records the modulation information, and the lighting recommendation module establishes the lamp modulation vector [X _u , Y _u ]:

_{Among them , _} Color temperature modulation value, the corresponding values are y ₁ to y _m respectively.

Among them, the m lamps in the guest room that have been modulated can belong to the current guest room, or other lamps whose lighting parameters have been modulated by the user in his previous stays. For example, they can be within the data range that the hotel can obtain. Lighting fixtures in guest rooms in other hotels, or in other guest rooms of different room types in the current hotel, or in guest rooms of the same room type but different room numbers in the current hotel.

Then, based on the light modulation vector, the lighting recommendation module compares it with the light modulation vectors of other historical users v stored in the server database, and calculates the vector similarity one by one:

sim(u,v)=α·sim _x +(1-α)·sim _y

Among them, m represents the number of lamps modulated by the occupancy users u and v, xi _{, u} , y _{i, u} , xi _{, v} , y _{i, v} respectively represent the brightness and color temperature of the lamps by the occupancy users u and v. modulation value, respectively represent the average value of the corresponding modulation values, are the arithmetic average of the brightness and color temperature modulation values of m modulated lights by user u respectively,

sim _x (u, v), sim _y (u, v), sim (u, v) respectively represent the brightness similarity, color temperature similarity and overall vector similarity of u and v. The overall similarity is the brightness similarity and color temperature. The weighted sum of similarities, α is the weight of the brightness similarity between 0 and 1.

Preferably, when the number of historical occupancy users who have jointly modulated m lamps with occupancy user u is not large enough, the number of lamps that have been jointly modulated can be reduced.

As a preference, when the number of historical occupancy users who have jointly modulated m lamps with the occupancy user u is not large enough, one or several of the lamps can be replaced. The occupancy user u has not modulated but the selected historical occupancy users all have the replaced The lamp has a modulation record of the lamp, and when calculating the vector similarity, the modulation value of the replaced lamp by the resident user u is set as the default initial value of the lamp, such as 80%.

Afterwards, after the vector similarity calculation is completed, the K historical occupancy users with the highest similarity are taken as u's neighbors and form u's neighbor set. Then, based on the historical modulation information of the occupancy users in the neighbor set, it is inferred that the room of the occupancy user u Recommended brightness modulation value x′ _u,i and color temperature modulation value y′ _u,i of lamp i:

Among them, i=1, 2, ..., n, n is the number of lamps in the guest room for which user u has not yet determined the modulation value;

The output module sends the brightness modulation value x′ _{u, i} and the color temperature modulation value y′ _{u, i} to the mobile phone APP terminal, and stores the confirmed modulation data returned by the mobile phone APP terminal into the database and sends it to the lamp through the output module. Group of drives.

K is optimized based on the richness of the data records. For example, it is initially set to a smaller value, and when more and more data records are collected, its value is initially increased to a maximum value.

Some lamps have unique functions, such as mirror headlights. As a first choice, if all historical occupancy users in u's neighbor set have not modulated a certain lamp i, then it is considered that all occupancy users in the set have the modulation value of the lamp i. If you are satisfied with the initial value, or think that the brightness and color temperature of the lamp will not affect your occupancy experience, use the default modulation value of the lamp as the recommended value instead of the calculated value.

After the recommended brightness modulation values x′ _{u, i} and color temperature modulation values y′ _{u, i} are calculated, the combination is a lamp modulation vector and sent to the mobile APP terminal and displayed on its interface. Users can directly confirm the modulation parameter list corresponding to this vector, or modify the modulation parameters of some LED lights. After the user clicks to confirm on the APP interface, the mobile APP terminal sends lamp modulation instructions to the host unit according to various modulation parameters on the interface; then, the host unit stores the lamp modulation vector in the database of the server, and at the same time sends it to the LED lamp through the output module Each driver sends the corresponding PWM duty cycle modulation value to realize the scene lighting in the guest room.

The above achieves personalized lighting parameter recommendations for non-first-time users. However, for first-time users, the above method cannot implement recommendations due to the lack of data for modulating lamp parameters. For this cold start problem, the present invention solves it based on the analysis of user characteristics.

As shown in Figure 1, Figure 2, and Figure 4, a first-time user, after completing the login in the mobile APP terminal 400, enters his registration information in the registration interface, including personal characteristics parameters, such as age, gender, region, and occupation. , favorite color temperature, favorite brightness, number of guests, and travel purposes. In the database of the server 500, there is a user characteristic data table that records the user primary key ID and these user personal characteristic parameters.

The user characteristic analysis module 120 processes and analyzes user personal characteristic data by category. First, a fuzzy variable set is established for each category within its domain of discourse and a membership function is established for each fuzzy variable in the set.

Referring to Figure 4, in Figure 4a, for the age parameter category, four fuzzy variables such as juvenile, young, middle-aged, and old are set; in Figure 4b, for the color temperature parameter category, low color temperature, medium color temperature, and 3 fuzzy variables including high color temperature.

Then, according to the membership function established in Figure 4, the membership value of each data in the user's personal characteristic data corresponding to each fuzzy variable in its category is calculated, and all the membership values are arranged in order into a characteristic membership value vector. At the same time, the characteristic membership value vector is saved to the storage module and server database respectively.

Then, the lighting recommendation module 130 compares the characteristic membership value vector of the first-time check-in user u with the characteristic membership value vector of other historical check-in users v in the database, and calculates the similarity between the two vectors,

Among them, sim _i (u, v) is the characteristic similarity of users u and v in the i-th parameter category dimension, I is the set of characteristic parameter categories such as age, and j is the fuzzy variable of the i-th parameter category. The number of fuzzy variables in the set, μ _ik (u _i ) and μ _ik (v _i ) are respectively the parameter values of users u and v on the i-th category corresponding to the membership value of the k-th fuzzy variable in the category. ,k=1,2,...,j.

After that, in a similar manner to non-first-time users, the recommended values of personalized lighting parameters are calculated and pushed to the first-time user u.

Example 2

Different from Embodiment 1, in this embodiment, a method of fuzzy classification of users is used to recommend lighting parameters to first-time users.

When a new check-in user u checks in for the first time and opens the mobile APP terminal 400 to register, first use the fuzzy classification method to classify the user according to the user registration information and calculate its category:

F＝S+N+Y+D

Among them, S is the gender of the checking-in user. For example, male is represented by 0 and female is represented by 1; N is the number of guests, usually with values 1, 2, 3, etc.; The number and cut-off range is 0 to 4; D is the purpose of travel, and its value ranges from 0 to 6, corresponding to business, tourism, reception, leisure, conference, office and long-term rental respectively. F is the classification value of check-in users. Users with the same score are classified into the same category, and their values may range from 1 to 14, that is, all historical check-in users are divided into 14 categories.

Then, the lighting recommendation module takes all historical occupancy users with the same classification value in the server database as u's neighbors and forms u's neighbor set. Then, based on the historical modulation information of the occupancy users in the above neighbor set, it is inferred by calculating the arithmetic mean. The recommended brightness modulation value x′ _{u, i} and color temperature modulation value y′ _{u, i} of the lamp i in the guest room of user u are pushed to the new user u.

Example 3

This embodiment provides a hotel personalized intelligent lighting system based on a mobile phone APP. As shown in Figures 1 to 4, the hotel personalized intelligent lighting system 1000 based on a mobile phone APP includes a hotel personalized intelligent lighting device 800 based on a mobile phone APP, brightness and a dimmable light set 200 with adjustable color temperature, a server 500 and a mobile APP terminal 400. The hotel personalized smart lighting device 800 based on the mobile phone APP includes a host unit 100 and a user interface unit 300 .

The host unit 100, which is respectively connected to the lamp set, the user interface unit, the mobile APP terminal and the server, includes an input module 110, a user characteristic analysis module 120, a lighting recommendation module 130, an output module 140, a storage module 150 and a user modulation analysis module 160.

The user inputs registration information such as user personal characteristic parameters from the user interface unit 300 or mobile APP terminal 400, and also inputs dimming instructions for modulating the color temperature and brightness of the dimmable lamp group 200 in the hotel room through the mobile APP terminal 400. These information and instructions are transmitted to the host unit through the input module 110 in the host unit 100 and transferred to the database of the server 500 by the host unit.

The user's personal characteristic parameters include categories such as age, gender, region, occupation, preferred color temperature, preferred brightness, number of guests, and travel purpose.

When a new check-in user u checks into the hotel for the first time, the user characteristic analysis module 120 processes and analyzes the user's personal characteristic data by category:

First, a fuzzy variable set is established for each category within its domain of discourse and a membership function is established for each fuzzy variable in the set;

Then, according to the membership function, calculate the membership value of each fuzzy variable in the category corresponding to each data in the user's personal characteristic data, and arrange all the membership values in order into a characteristic membership value vector;

Then, save the characteristic membership value vector to the storage module and server database respectively.

The user modulation analysis module 160 processes the dimming signals corresponding to each lamp in the dimmable lamp group in the dimming instructions input by the user through the APP terminal 400 to obtain corresponding modulation values, and arranges the modulation values in order. The modulation vector for a lamp is then saved in the database of the storage module 150 and the server 500 respectively.

The lighting recommendation module 130 compares the user's characteristic membership value vector and lamp modulation vector with other historical occupancy user data in the server database, and infers the user's final lamp modulation vector based on the similarity between the vectors, and Recommended to users through mobile APP terminal 400,

After the user confirms the recommendation, a dimming instruction is sent to the dimmable lamp group 200 through the output module 140 .

Among them, the similarity of the feature membership value vector is calculated as follows:

in,

sim _i (u, v) is the characteristic similarity between the new check-in user u and the historical check-in user v in the database in the i-th parameter category dimension, I is the set of categories such as age, and j is the i-th parameter category The number of fuzzy variables in the fuzzy variable set, μ _ik (u _i ) and μ _ik (v _i ) are respectively the parameter values of users u and v on the i-th category corresponding to the k-th fuzzy variable in the category. Membership value, k=1, 2,..., j.

Preferably, the user interface unit 300 is provided with an operation panel 320 for inputting parameters, and is also provided with a display screen 310 for assisting input of parameters and displaying the processing results of the lighting recommendation module in the form of a list.

Several embodiments of the present invention have been described above. However, these embodiments are presented as examples and do not limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the spirit of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are also included in the scope of the invention described in the claims and their equivalents.

Claims (8)

1. A hotel individuation intelligent lighting method based on a mobile phone APP comprises the following steps:

s1, initializing, registering and information collecting the user,

the system receives registration information such as personal characteristic parameters of a user through a mobile phone APP terminal, and the registration information is transferred to a database of a server by a host unit, wherein the personal characteristic parameters of the user comprise categories such as age, gender, area, occupation, favorite color temperature, favorite brightness, number of people in the house, trip purpose and the like,

Establishing fuzzy variable sets in the range of the discourse domain for each user personal characteristic parameter category, establishing membership functions for each fuzzy variable in the sets,

s2, a host unit in the hotel room judges whether a user first check-in and builds a user personality vector, if the user first check-in, the process goes to S3, otherwise, a mobile phone APP terminal receives a dimming instruction for modulating the color temperature and the brightness of a dimmable lamp group in the hotel room by the user, processes dimming signals of lamps in the corresponding dimmable lamp group in the dimming instruction to obtain corresponding modulation values, and sequentially arranges the modulation values into a lamp modulation vector serving as the user personality vector, then goes to S4,

s3, for the first-time user, firstly, calculating membership value corresponding to the personal characteristic parameter value of the user based on membership function of each fuzzy variable of the category of the parameter for each personal characteristic parameter, and sequentially arranging all the membership values into a characteristic membership value vector as a user personality vector,

s4, respectively storing the user personality vectors into a storage module of the host unit and a server database,

S5, comparing the user personality vector of the user with other historical check-in user data in a server database, according to the similarity between the vectors, presuming the lamp modulation vector preferred by the user,

if the user u is the first user:

comparing the characteristic membership value vector of the user with the characteristic membership value vectors of other historical living users v in the database, calculating the similarity between the two vectors,

wherein sim is _i (u, v) is the characteristic similarity of the users u, v in the dimension of the ith parameter category, I is the category set of the ages and the like, j is the number of fuzzy variable sets in the ith parameter category, mu _ik (u _i )、μ _ik (v _i ) The parameter values of the users u, v on the i-th category correspond to the membership value of the k-th fuzzy variable in the category, k=1, 2, j;

if the user u is a non-first-time user: based on the lamp modulation vectors of m lamps, comparing the lamp modulation vectors with the lamp modulation vectors of the historical entering users stored in the database, calculating vector similarity one by one,

sim(u，v)＝α·sim _x +(1-α)·sim _y

wherein m represents the number of lamps commonly modulated by the users, and x _i，u 、y _i，u 、x _i，v 、y _i，v Representing the luminance and color temperature modulation values of the luminaire by the incumbent users u and v respectively, Respectively represent the average value of the corresponding modulation values, +.>For the arithmetic mean of the luminance modulation values of user u for m modulated lamps,

sim _x (u，v)、sim _y (u, v), sim (u, v) represent the luminance similarity, the color temperature similarity and the overall vector similarity of u and v, respectively, the overall similarity is a weighted sum of the luminance similarity and the color temperature similarity, alpha is a weight of the luminance similarity,

s6, recommending the presumption result to a user through the mobile phone APP terminal, after the user confirms or adjusts the recommendation, transmitting the confirmed lamp modulation vector to the dimmable lamp group through an output module of the host unit to dim, and simultaneously storing the lamp modulation vector into a server database.

2. The hotel personalized intelligent lighting method based on the mobile phone APP as set forth in claim 1, wherein the step S1 further comprises:

when the user foreground registers in the living, the registered necessary identity information including the ID card number, the guest room number and the like is stored in the server database and transmitted to the host unit in the living room by the database,

setting three interfaces, namely a login interface, a registration interface and a lamp modulation interface, on the mobile phone APP terminal, when a user logs in through the login interface of the mobile phone APP terminal, the host unit judges whether to allow the user to log in and enter a subsequent interface by comparing whether the identity information input by the user is in accordance with the foreground registration information,

After the login of the user is successful, information can be sent to the host unit through the registration interface and the lamp modulation interface, so that personal characteristic parameters of the user and the modulation operation of brightness and color temperature on lamps in a guest room are perfected.

3. The hotel personalized intelligent lighting method based on the mobile phone APP, which is claim 1, is characterized in that,

in the step S1, the server adopts a cloud server, and after the system receives registration information such as personal characteristic parameters of a user through the mobile phone APP terminal, the mobile phone APP terminal directly stores the information into a database of the cloud server.

4. The hotel personalized intelligent lighting method according to claim 1, wherein the step S2 further comprises:

for a non-first-time user, when the user u modulates the color temperature and brightness of the lamp through the mobile phone APP terminal, the modulation information is recorded, and a lamp modulation vector [ X ] is established according to the modulation value _u ，Y _u ]：

Wherein X is _u The brightness modulation value of the user u to part of m lamps in the adjustable lamp group in the hotel room is represented by x ₁ To x _m ，Y _u Representing the color temperature modulation value of the user u to the m lamps, wherein the corresponding value is y ₁ To y _m 。

5. The hotel personalized intelligent lighting method according to claim 1, wherein the step S5 further comprises:

after vector similarity calculation is completed, taking K historical check-in users with highest similarity as neighbors of u and forming a neighbor set of u, and then presuming a recommended brightness modulation value x 'of a lamp i in a guest room of the check-in user u according to historical modulation information of the check-in users in the neighbor set' _u，i And a color temperature modulation value y' _u，i ：

Wherein i=1, 2, n;

the step S6 includes:

the host unit modulates the brightness modulation value x' _u，i And a color temperature modulation value y' _u，i And sending the modulation data to the mobile phone APP terminal, storing the modulation data returned by the mobile phone APP terminal after confirmation into a database, and sending the modulation data to a driver of the adjustable light group through an output module.

6. The hotel personalized intelligent lighting method based on the mobile phone APP as claimed in claim 1, further comprising the steps of:

the PWM wave duty ratio value of the driving current of each driving channel in the adjustable light lamp group is used as a modulation value, the modulation value is transmitted in the dimming command,

an operation panel is arranged in a hotel room to input parameters, meanwhile, the parameters are displayed in an auxiliary mode through a display screen, and after a user confirms the light modulation vector, the vector is displayed on the display screen in a list mode.

7. The hotel personalized intelligent lighting method according to claim 1, wherein the step S3 is performed by:

when a new check-in user u checks in for the first time and opens a mobile phone APP terminal to register, firstly classifying the new check-in user u by adopting a fuzzy classification method according to user registration information, and calculating the classification of the new check-in user u:

F＝S+N+Y+D

wherein S is the sex of the living user, such as 0 for men and 1 for women; n is the number of people in the house, and the general values are 1, 2, 3 and the like; y is the age of the user, ten digits of the actual age value minus 20 are taken and cut off to be 0 to 4; d is a trip purpose, the value of which is from 0 to 6, corresponds to business, travel, reception, leisure, conference, office and long lease respectively, F is a classification value of the living users, the users who obtain the same score are classified into the same class, the value of which may be from 1 to 14, namely, all the historical living users are classified into 14 classes.

8. The hotel personalized intelligent lighting method according to claim 1, wherein the step S5 is performed by:

after the first user enters the guest room, comparing the F value with F values of other historical user entering in a server database, combining the users with the same value into a neighbor set of the user, calculating average numbers of lamp modulation vectors preferred by the user according to the lamp modulation values of all users in the neighbor set, then using the average numbers as the estimated values, combining the estimated values into a modulation vector, and recommending the modulation vector to the user through a mobile phone APP terminal.