Smart home illumination control system and method based on Internet of Things

Abstract

The invention discloses a smart home illumination control system and method based on the Internet of Things, and relates to the technical field of home illumination control, and the method comprises the steps: constructing a smart home illumination cloud platform, analyzing the illumination correlation degree between family members and home lamps, and obtaining feature home lamps; analyzing the illumination influence degree between the household lamps with different characteristics in the use process of the family members to obtain mark influence data; acquiring historical illumination records of the characteristic home lamps, acquiring historical illumination data from the historical illumination records, and constructing a home lamp illumination prediction model of the characteristic home lamps; and acquiring illumination data of the family members and the home lamps turned on in the home environment in the current period, acquiring mark influence data of the family members, and performing intelligent control on illumination of the home lamps in the home environment in combination with the home lamp illumination prediction model of the home lamps.

Description

Technical Field

[0001] The present invention relates to the field of home lighting control technology, and in particular to a smart home lighting control system and method based on the Internet of Things. Background Art

[0002] With the continuous development of science and technology, the Internet of Things technology is becoming more and more widespread in the field of smart home lighting control. The use of Internet of Things technology brings the following benefits to smart home lighting control: 1. Remote lighting control. Internet of Things technology allows users to remotely control the lighting system in their homes through smartphones, tablets or computers. No matter where the user is, they can adjust the lighting of lamps anytime and anywhere; 2. Device integration. Internet of Things technology can enable various smart devices to be seamlessly integrated. Users can link the lighting system with other smart homes to achieve more complex and intelligent operations, making the home environment more comfortable.

[0003] At present, the intelligent control of home lamps is mainly achieved by remotely controlling and adjusting home lamps through smartphone applications, or controlling home lamps through smart voice assistants. However, in real life, there is generally more than one user of home lamps in a home environment. Home lamps need to be used by multiple users, and the usage habits of different users are different. If home lamps are still turned on and off according to pre-set data, then it is possible that the home lamps are frequently adjusted manually, which will not only greatly reduce the user experience, but also cause damage to the home lamps due to improper human operation, and even cause safety accidents. Summary of the invention

[0004] The purpose of the present invention is to provide a smart home lighting control system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a smart home lighting control method based on the Internet of Things, the method comprising: Step S100: construct a smart home lighting cloud platform, obtain the residence location information of family members from the cloud platform, obtain the location information of home lamps in the home environment, obtain the historical usage records of home lamps, analyze the lighting correlation between family members and home lamps, and obtain characteristic home lamps; Step S200: Obtain historical usage records of family members on characteristic home lamps, extract historical usage data from the historical usage records, analyze the degree of lighting impact between different characteristic home lamps during the use of family members, and obtain marked impact data; Step S300: obtaining historical lighting records of characteristic home lamps, obtaining historical lighting data from the historical lighting records, and constructing a home lamp lighting prediction model for the characteristic home lamps; Step S400: Obtain lighting data of household lamps turned on by family members in the home environment during the current cycle, obtain tag impact data of family members, and combine the lighting prediction model of household lamps to intelligently control the lighting of household lamps in the home environment. Furthermore, step S100 includes: Step S101: Acquire residence location information in a home environment, where the residence location information includes the location coordinates of the bedroom recorded by the household members in the cloud platform; Step S102: Acquire location information of household lamps in a home environment, where the location information includes location coordinates of the household lamps; Step S103: Calculate the Euclidean distance between each home lamp in the home environment, where the Euclidean distance L between the αth home lamp and the βth home lamp is α,β : , Among them, x α ,y α 、z α are the corresponding values ​​of the αth home lamp on the x-axis, y-axis and z-axis in the preset three-dimensional coordinate system of the furniture environment; β ,y β 、z β are the corresponding values ​​of the β-th household lamp on the x-axis, y-axis and z-axis in the three-dimensional coordinate system; Step S104: analyzing the lighting correlation between the family members and each home lamp, wherein the specific process of analyzing the lighting correlation between the family members and the ath home lamp is as follows: Get the total number of historical usage records of the a-th home lamp M a,sum , get the total number M of historical usage records of family members on the ath home lamp a , get the maximum value L of the Euclidean distance between the bedroom of a family member and each household lamp max and the minimum value L min , calculate the lighting correlation degree D between family members and the ath home lamp a : , Among them, L a It is represented as the Euclidean distance between the family member and the ath household lamp; γ1 and γ2 are respectively represented as the preset first lighting correlation coefficient and the second lighting correlation coefficient, γ2>γ1>0, γ1+γ2=1; Step S105: When the lighting correlation level D a If the value is greater than a preset lighting-related threshold, it is determined that there is lighting correlation between the family member and the a-th home lamp, and the a-th home lamp is recorded as a characteristic home lamp of the family member.

[0006] Further, step S200 includes: Step S201: Acquire each characteristic home lamp of a family member, obtain the historical usage records of the family member on the characteristic home lamp, and extract historical usage data from the historical usage records, the historical usage data including the timestamp of turning on the characteristic home lamp and the historical lighting period; Step S202: Analyze the lighting influence degree between each characteristic household lamp during the use of the family members, wherein the specific process of analyzing the lighting influence degree between the cth characteristic household lamp and the eth characteristic household lamp during the use of the family members is as follows: Obtain a certain historical usage record of a family member on the cth characteristic home lamp, and obtain another historical usage record of a family member on the eth characteristic home lamp; Step S203: when the time point corresponding to the timestamp of the opening of the e-th characteristic household lamp in another historical usage record is within the lighting history period of the c-th characteristic household lamp in a certain historical usage record, it is determined that a certain historical usage record of the c-th characteristic household lamp has a lighting impact on another historical usage record of the e-th characteristic household lamp, and the certain historical usage record is aggregated with the other historical usage record to obtain a lighting impact record set of the c-th characteristic household lamp on the e-th characteristic household lamp; Step S204: Obtain the characteristic impact duration of each lighting impact record set of the cth characteristic household lamp on the eth characteristic household lamp, wherein the characteristic impact duration T of the cth characteristic household lamp on the zth lighting impact record set of the eth characteristic household lamp is ζ : , Among them, t e,ζ is the timestamp of the start of the e-th characteristic household lamp in the corresponding historical usage record in the ζ-th lighting impact record set; t c,ζ is the timestamp of the start of the cth characteristic household lamp in the corresponding historical usage record in the ζth lighting impact record set; t △ It is the preset characteristic time conversion value; Step S205: Calculate the lighting impact value G of the cth characteristic home lamp on the eth characteristic home lamp during use by family members. c,e : , Where j is the total number of lighting impact record sets of the cth characteristic household lamp on the eth characteristic household lamp; T i is the characteristic impact duration of the i-th lighting impact record set in each lighting impact record set; T max It is the maximum value of the characteristic impact duration of each lighting impact record set; Step S206: When the lighting impact value G c,e If the value is greater than a preset lighting influence threshold, it is determined that the cth characteristic home lamp has an influence on the lighting of the eth characteristic home lamp during use by family members, and the cth characteristic home lamp is recorded as the characteristic influencing home lamp of the eth characteristic home lamp; Step S207: obtaining and aggregating several characteristics of several characteristic home lamps that affect the home lamps during use by family members to obtain the marked influence data of the family members; The above steps analyze the degree of lighting influence of the cth characteristic home lamp on the eth characteristic home lamp through the characteristic influence duration. This is because in the actual process, it takes time for family members to move from the location of the cth characteristic home lamp to the area where the eth characteristic home lamp is located and turn it on. If the time consumed in the middle is too long, it means that the user does not have a strong demand for lighting the eth characteristic home lamp after lighting the cth characteristic home lamp. It also shows that the cth characteristic home lamp has little influence on the lighting of the eth characteristic home lamp. Through this analysis method, the degree of influence between different characteristic home lamps can be accurately and scientifically obtained, providing effective support for the subsequent intelligent control of home lamps.

[0007] Furthermore, step S300 includes: Step S301: Obtain historical lighting records of characteristic home lamps of family members, and obtain historical lighting data from the historical lighting records, where the historical lighting data includes data corresponding to various lighting parameters of the characteristic home lamps; Step S302: acquiring historical lighting impact data of the home environment to which the characteristic home lamp belongs from the historical lighting records, wherein the historical lighting impact data includes data corresponding to various lighting impact parameters in the home environment within the historical period; Step S303: taking the historical lighting data of the characteristic household lamps as output data and the historical lighting impact data as input data, preprocessing the output data and the input and output data, and constructing a household lamp lighting prediction model of the characteristic household lamps. The specific construction process is as follows: Get the output vector H=[h1,h2,...,h m ], where h1,h2,...,hm are the 1st, 2nd, ..., mth lighting parameters of the characteristic home lamps, respectively, to obtain the home lamp lighting prediction model of the characteristic home lamps: , Among them, μ0 is the characteristic intercept; μ1, μ2, ..., μ n is the regression coefficient of the 1st, 2nd, ..., nth lighting influencing parameters in the home environment; ε is the error term; K1, X2, ..., X n are the 1st, 2nd, ..., nth lighting influencing parameters in the home environment; Step S304: Obtain each historical lighting record of the characteristic home lamps, divide each historical lighting record into a training set and a test set, use the training set to fit the home lamp lighting prediction model, and obtain the fitted home lamp lighting prediction model: Step S305: Calculate the home lamp lighting prediction model, and calculate the characteristic model error P of the vth lighting parameter of the characteristic home lamp. v : , Where s is the total number of historical lighting records of characteristic household lamps in the test set; h v,q is the actual value of the vth lighting parameter of the characteristic household lamp in the qth historical lighting record among several historical lighting records; h´ v,q is the household lighting prediction model, the predicted value of the vth lighting parameter in the qth historical lighting record; When the feature model errors of the home lamp lighting prediction model for each lighting parameter of the characteristic home lamp are less than the preset feature model error threshold, it is determined that the home lamp lighting prediction model of the characteristic home lamp is completed.

[0008] Furthermore, step S400 includes: Step S401: monitoring the home environment of the family members, obtaining lighting data of the home lamps turned on by the family members in the home environment in the current cycle, the lighting data including data corresponding to various lighting parameters of the home lamps in the current cycle; Step S402: Obtain the marked influence data of family members, obtain the home lamp lighting prediction model of each home lamp in the home environment, set various lighting parameters of each home lamp in the home environment, and intelligently control the lighting of the home lamps in the home environment.

[0009] In order to better implement the above steps, a smart home lighting control system based on the Internet of Things is also proposed, the system includes a lighting related analysis module, a tag impact data module, a prediction model building module, and a lighting intelligent control module; A lighting correlation analysis module is used to analyze the lighting correlation between family members and home lamps to obtain characteristic home lamps; The tag impact data module is used to analyze the lighting impact between household lamps with different characteristics during use by family members and obtain tag impact data; A prediction model building module, used to build a home lamp lighting prediction model for characteristic home lamps; The lighting intelligent control module is used to intelligently control the lighting of home lamps in a home environment.

[0010] Further, the lighting related analysis module includes a lighting related degree unit and a lighting related analysis unit; A lighting correlation unit, used to calculate the lighting correlation between family members and home lamps; The lighting correlation analysis unit is used to determine the lighting correlation degree between family members and home lamps according to the lighting correlation degree, and obtain characteristic home lamps.

[0011] Further, the marking impact data module includes a lighting impact degree value unit and a marking impact data unit; A lighting impact value unit is used to calculate the lighting impact values ​​between household lamps with different characteristics when used by family members; The marking influence data unit is used to analyze the lighting influence degree between various characteristic home lamps during use by family members according to the lighting influence degree value, and obtain the marking influence data.

[0012] Furthermore, the prediction model building module includes a data acquisition unit and a model building unit; A data acquisition unit, used to acquire historical lighting records of characteristic home lamps of family members; The model building unit is used to build a home lamp lighting prediction model of the characteristic home lamp according to the historical lighting records of the characteristic home lamp.

[0013] Further, the lighting intelligent control module includes a lighting intelligent control unit; The lighting intelligent control unit is used to obtain the lighting data of household lamps turned on by family members in the home environment during the current cycle, obtain the marking influence data of family members, and combine the home lamp lighting prediction model of the home lamps to intelligently control the lighting of home lamps in the home environment.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes the intelligent control of smart home lighting, and taking into account the situation that there may be several family members in the home environment in actual process, starting from the family members, the usage of different home lamps by the family members is analyzed, so as to obtain the home lamps with higher usage frequency and usage demand of the family members, and record them as characteristic home lamps. By acquiring the characteristic home lamps, analyzing the mutual influence relationship between the characteristic home lamps among family members, and constructing a lighting prediction model for the characteristic home lamps, the intelligent control of home lamps is further realized, which not only greatly improves the experience of family members, but also avoids the losses caused by frequent improper human operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a method flow chart of a smart home lighting control system and method based on the Internet of Things of the present invention; Figure 2 It is a module schematic diagram of the smart home lighting control system and method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, a smart home lighting control method based on the Internet of Things, the method comprising: Step S100: construct a smart home lighting cloud platform, obtain the residence location information of family members from the cloud platform, obtain the location information of home lamps in the home environment, obtain the historical usage records of home lamps, analyze the lighting correlation between family members and home lamps, and obtain characteristic home lamps; Wherein, step S100 includes: Step S101: Acquire residence location information in a home environment, where the residence location information includes the location coordinates of the bedroom recorded by the household members in the cloud platform; Step S102: Acquire location information of household lamps in a home environment, where the location information includes location coordinates of the household lamps; Step S103: Calculate the Euclidean distance between each home lamp in the home environment, where the Euclidean distance L between the αth home lamp and the βth home lamp is α,β : , Among them, x α ,y α 、z α are the corresponding values ​​of the αth home lamp on the x-axis, y-axis and z-axis in the preset three-dimensional coordinate system of the furniture environment; β ,y β 、z β are the corresponding values ​​of the β-th household lamp on the x-axis, y-axis and z-axis in the three-dimensional coordinate system; Step S104: analyzing the lighting correlation between the family members and each home lamp, wherein the specific process of analyzing the lighting correlation between the family members and the ath home lamp is as follows: Get the total number of historical usage records of the a-th home lamp M a,sum , get the total number M of historical usage records of family members on the ath home lamp a , get the maximum value L of the Euclidean distance between the bedroom of a family member and each household lamp max and the minimum value L min , calculate the lighting correlation degree D between family members and the ath home lamp a : , Among them, L a It is represented as the Euclidean distance between the family member and the ath household lamp; γ1 and γ2 are respectively represented as the preset first lighting correlation coefficient and the second lighting correlation coefficient, γ2>γ1>0, γ1+γ2=1; Step S105: When the lighting correlation level D a If the value is greater than a preset lighting-related threshold, it is determined that there is lighting correlation between the family member and the a-th home lamp, and the a-th home lamp is recorded as a characteristic home lamp of the family member; Step S200: Obtain historical usage records of family members on characteristic home lamps, extract historical usage data from the historical usage records, analyze the degree of lighting impact between different characteristic home lamps during the use of family members, and obtain marked impact data; Wherein, step S200 includes: Step S201: Acquire each characteristic home lamp of a family member, obtain the historical usage records of the family member on the characteristic home lamp, and extract historical usage data from the historical usage records, the historical usage data including the timestamp of turning on the characteristic home lamp and the historical lighting period; Step S202: Analyze the lighting influence degree between each characteristic household lamp during the use of the family members, wherein the specific process of analyzing the lighting influence degree between the cth characteristic household lamp and the eth characteristic household lamp during the use of the family members is as follows: Obtain a certain historical usage record of a family member on the cth characteristic home lamp, and obtain another historical usage record of a family member on the eth characteristic home lamp; Step S203: when the time point corresponding to the timestamp of the opening of the e-th characteristic household lamp in another historical usage record is within the lighting history period of the c-th characteristic household lamp in a certain historical usage record, it is determined that a certain historical usage record of the c-th characteristic household lamp has a lighting impact on another historical usage record of the e-th characteristic household lamp, and the certain historical usage record is aggregated with the other historical usage record to obtain a lighting impact record set of the c-th characteristic household lamp on the e-th characteristic household lamp; Step S204: Obtain the characteristic impact duration of each lighting impact record set of the cth characteristic household lamp on the eth characteristic household lamp, wherein the characteristic impact duration T of the cth characteristic household lamp on the zth lighting impact record set of the eth characteristic household lamp is ζ : , Among them, t e,ζ is the timestamp of the start of the e-th characteristic household lamp in the corresponding historical usage record in the ζ-th lighting impact record set; t c,ζ is the timestamp of the start of the cth characteristic household lamp in the corresponding historical usage record in the ζth lighting impact record set; t △ It is the preset characteristic time conversion value; For example, when the feature impact duration needs to be converted into minutes, t △ is 60. When the feature impact duration needs to be converted into hours, t △ is 3600; Step S205: Calculate the lighting impact value G of the cth characteristic home lamp on the eth characteristic home lamp during use by family members. c,e : , Where j is the total number of lighting impact record sets of the cth characteristic household lamp on the eth characteristic household lamp; T i is the characteristic impact duration of the i-th lighting impact record set in each lighting impact record set; T max It is the maximum value of the characteristic impact duration of each lighting impact record set; For example, for the first characteristic household lamp, the total number j of each lighting impact record set for the second characteristic household lamp is 4; the characteristic impact duration T1 of the first lighting impact record set in each lighting impact record set is 30s; the characteristic impact duration T2 of the second lighting impact record set in each lighting impact record set is 40s; the characteristic impact duration T3 of the third lighting impact record set in each lighting impact record set is 50s; the characteristic impact duration T4 of the first lighting impact record set in each lighting impact record set is 100s; the maximum value T of the characteristic impact duration of each lighting impact record set is max is 100s; Calculate the lighting impact value G of the first characteristic home lamp on the second characteristic home lamp during use by family members 1,2 : , Step S206: When the lighting impact value G c,e If the value is greater than a preset lighting influence threshold, it is determined that the cth characteristic home lamp has an influence on the lighting of the eth characteristic home lamp during use by family members, and the cth characteristic home lamp is recorded as the characteristic influencing home lamp of the eth characteristic home lamp; Step S207: obtaining and aggregating several characteristics of several characteristic home lamps that affect the home lamps during use by family members to obtain the marked influence data of the family members; Step S300: obtaining historical lighting records of characteristic home lamps, obtaining historical lighting data from the historical lighting records, and constructing a home lamp lighting prediction model for the characteristic home lamps; Wherein, step S300 includes: Step S301: Obtain historical lighting records of characteristic home lamps of family members, and obtain historical lighting data from the historical lighting records, where the historical lighting data includes data corresponding to various lighting parameters of the characteristic home lamps; For example, various lighting parameters include lighting intensity, lighting color, etc.; Step S302: acquiring historical lighting impact data of the home environment to which the characteristic home lamp belongs from the historical lighting records, wherein the historical lighting impact data includes data corresponding to various lighting impact parameters in the home environment within the historical period; For example, various lighting influencing parameters include temperature, humidity, season, sunshine hours, sunrise time, etc.; Step S303: taking the historical lighting data of the characteristic household lamps as output data and the historical lighting impact data as input data, preprocessing the output data and the input and output data, and constructing a household lamp lighting prediction model of the characteristic household lamps. The specific construction process is as follows: Get the output vector H=[h1,h2,...,h m ], where h1,h2,...,h m are the 1st, 2nd, ..., mth lighting parameters of the characteristic home lamps, respectively, to obtain the home lamp lighting prediction model of the characteristic home lamps: , Among them, μ0 is the characteristic intercept; μ1, μ2, ..., μ n is the regression coefficient of the 1st, 2nd, ..., nth lighting influencing parameters in the home environment; ε is the error term; K1, X2, ..., X n are the 1st, 2nd, ..., nth lighting influencing parameters in the home environment; For example, preprocessing includes removing missing values ​​or noise data, unique-hot encoding of data, normalization, etc. Step S304: Obtain each historical lighting record of the characteristic home lamps, divide each historical lighting record into a training set and a test set, use the training set to fit the home lamp lighting prediction model, and obtain the fitted home lamp lighting prediction model: Step S305: Calculate the home lamp lighting prediction model, and calculate the characteristic model error P of the vth lighting parameter of the characteristic home lamp. v : , Where s is the total number of historical lighting records of characteristic household lamps in the test set; h v,q is the actual value of the vth lighting parameter of the characteristic household lamp in the qth historical lighting record among several historical lighting records; h´ v,q is the household lighting prediction model, the predicted value of the vth lighting parameter in the qth historical lighting record; When the characteristic model errors of the home lamp lighting prediction model for each lighting parameter of the characteristic home lamp are all less than the preset characteristic model error threshold, it is determined that the home lamp lighting prediction model of the characteristic home lamp is completed; Step S400: obtaining lighting data of household lamps turned on by family members in the home environment during the current period, obtaining the marking influence data of the family members, and combining the lighting prediction model of the household lamps to intelligently control the lighting of the household lamps in the home environment; Wherein, step S400 includes: Step S401: monitoring the home environment of the family members, obtaining lighting data of the home lamps turned on by the family members in the home environment in the current cycle, the lighting data including data corresponding to various lighting parameters of the home lamps in the current cycle; Step S402: obtaining the marked influence data of the family members, obtaining the lighting prediction model of each home lamp in the home environment, setting various lighting parameters of each home lamp in the home environment, and intelligently controlling the lighting of the home lamps in the home environment; In order to better implement the above steps, a smart home lighting control system based on the Internet of Things is also proposed, the system includes a lighting related analysis module, a tag impact data module, a prediction model building module, and a lighting intelligent control module; A lighting correlation analysis module is used to analyze the lighting correlation between family members and home lamps to obtain characteristic home lamps; The tag impact data module is used to analyze the lighting impact between household lamps with different characteristics during use by family members and obtain tag impact data; A prediction model building module, used to build a home lamp lighting prediction model for characteristic home lamps; Lighting intelligent control module, used for intelligent control of home lighting in home environment; Wherein, the lighting related analysis module includes a lighting related degree unit and a lighting related analysis unit; A lighting correlation unit, used to calculate the lighting correlation between family members and home lamps; A lighting correlation analysis unit, used to determine the lighting correlation degree between family members and home lamps according to the lighting correlation degree, and obtain characteristic home lamps; Wherein, the marking influence data module includes a lighting influence degree value unit and a marking influence data unit; A lighting impact value unit is used to calculate the lighting impact values ​​between household lamps with different characteristics when used by family members; The marking influence data unit is used to analyze the lighting influence degree between various characteristic household lamps during use by family members according to the lighting influence degree value, and obtain the marking influence data; Among them, the prediction model building module includes a data acquisition unit and a model building unit; A data acquisition unit, used to acquire historical lighting records of characteristic home lamps of family members; A model building unit, used to build a home lamp lighting prediction model of the characteristic home lamp according to the historical lighting records of the characteristic home lamp; Wherein, the lighting intelligent control module includes a lighting intelligent control unit; The lighting intelligent control unit is used to obtain the lighting data of household lamps turned on by family members in the home environment during the current cycle, obtain the marking influence data of family members, and combine the home lamp lighting prediction model of the home lamps to intelligently control the lighting of home lamps in the home environment.

[0018] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A smart home lighting control method based on the Internet of Things, characterized in that: The method comprises: Step S100: constructing a smart home lighting cloud platform, obtaining the residence location information of family members from the cloud platform, obtaining the location information of home lamps in the home environment, obtaining the historical usage records of the home lamps, analyzing the lighting correlation between the family members and the home lamps, and obtaining characteristic home lamps; Step S200: obtaining the historical usage records of the family members on the characteristic household lamps, extracting the historical usage data from the historical usage records, analyzing the lighting influence degree between the different characteristic household lamps during the use of the family members, and obtaining the marked influence data; Step S300: obtaining historical lighting records of the characteristic home lamps, obtaining historical lighting data from the historical lighting records, and constructing a home lamp lighting prediction model for the characteristic home lamps; Step S400: Obtain lighting data of household lamps turned on by the family members in the home environment during the current cycle, obtain tag impact data of the family members, and combine the lighting prediction model of the household lamps to intelligently control the lighting of the household lamps in the home environment.

2. The smart home lighting control method based on the Internet of Things according to claim 1 is characterized in that: The step S100 includes: Step S101: Acquire residence location information in a home environment, wherein the residence location information includes the location coordinates of the bedroom recorded by the household member in the cloud platform; Step S102: Acquire location information of household lamps in the household environment, wherein the location information includes location coordinates of the household lamps; Step S103: Calculate the Euclidean distance between each home lamp in the home environment, wherein the Euclidean distance L between the αth home lamp and the βth home lamp is α,β : ， Among them, x α ,y α 、z α are the corresponding values ​​of the αth home lamp on the x-axis, y-axis and z-axis in the preset three-dimensional coordinate system of the furniture environment; β ,y β 、z β are the corresponding values ​​of the β-th household lamp on the x-axis, y-axis and z-axis in the three-dimensional coordinate system; Step S104: analyzing the lighting correlation between the family members and the respective household lamps, wherein the specific process of analyzing the lighting correlation between the family members and the ath household lamp is as follows: Get the total number M of each historical usage record of the ath household lamp a,sum , obtain the total number M of several historical usage records of the family members on the ath home lamp a , obtain the maximum value L of the Euclidean distance between the bedroom of the family member and each household lamp max and the minimum value L min , calculate the lighting correlation degree D between the family member and the ath home lamp a : ， Among them, L a It is represented as the Euclidean distance between the family member and the a-th household lamp; γ1 and γ2 are respectively represented as the preset first lighting correlation coefficient and the second lighting correlation coefficient, γ2>γ1>0, γ1+γ2=1; Step S105: When the lighting correlation degree D a If the value of the a-th household lamp is greater than a preset lighting correlation threshold, it is determined that there is lighting correlation between the family member and the a-th household lamp, and the a-th household lamp is recorded as a characteristic household lamp of the family member.

3. The smart home lighting control method based on the Internet of Things according to claim 1 is characterized in that: The step S200 includes: Step S201: Acquire each characteristic home lamp of the family member, acquire the historical usage record of the family member on the characteristic home lamp, extract historical usage data from the historical usage record, the historical usage data including the timestamp of turning on the characteristic home lamp and the historical lighting period; Step S202: analyzing the lighting influence degree between the characteristic household lamps during the use of the family members, wherein the specific process of analyzing the lighting influence degree between the c-th characteristic household lamps on the e-th characteristic household lamps during the use of the family members is as follows: Obtain a certain historical usage record of the family member on the c-th characteristic home lamp, and obtain another historical usage record of the family member on the e-th characteristic home lamp; Step S203: When the time point corresponding to the timestamp of the start of the e-th characteristic household lamp in the other historical usage record is within the lighting history period of the c-th characteristic household lamp in the one historical usage record, it is determined that the one historical usage record of the c-th characteristic household lamp has a lighting impact on the other historical usage record of the e-th characteristic household lamp, and the one historical usage record is aggregated with the other historical usage record to obtain a lighting impact record set of the c-th characteristic household lamp on the e-th characteristic household lamp; Step S204: Obtain the characteristic impact duration of each lighting impact record set of the c-th characteristic home lamp on the e-th characteristic home lamp, wherein the characteristic impact duration T of the z-th lighting impact record set of the e-th characteristic home lamp by the c-th characteristic home lamp is ζ : ， Among them, t e,ζ is the timestamp of when the e-th characteristic household lamp is turned on in the corresponding historical usage record in the z-th lighting impact record set; c,ζ is the timestamp of when the cth characteristic household lamp is turned on in the corresponding historical usage record in the zth lighting impact record set; △ It is the preset characteristic time conversion value; Step S205: Calculate the lighting impact value G of the cth characteristic home lamp on the eth characteristic home lamp during use by the family members. c,e : ， Wherein, j is the total number of the lighting impact record sets of the c-th characteristic household lamp on the e-th characteristic household lamp; T i is the characteristic impact duration of the i-th lighting impact record set in each lighting impact record set; T max is the maximum value of the characteristic impact duration of each lighting impact record set; Step S206: When the lighting impact value G c,e is greater than a preset lighting influence degree threshold, it is determined that during the use of the family members, the c-th characteristic home lamp has an influence on the lighting of the e-th characteristic home lamp, and the c-th characteristic home lamp is recorded as the characteristic influencing home lamp of the e-th characteristic home lamp; Step S207: obtaining and aggregating several characteristic household lamps that affect the household lamps during use by the family members to obtain the marked influence data of the family members.

4. The smart home lighting control method based on the Internet of Things according to claim 3 is characterized in that: The step S300 includes: Step S301: Acquire historical lighting records of characteristic home lamps of the family members, and acquire historical lighting data from the historical lighting records, wherein the historical lighting data includes data corresponding to various lighting parameters of the characteristic home lamps; Step S302: acquiring historical lighting impact data of the home environment to which the characteristic home lamp belongs from the historical lighting record, wherein the historical lighting impact data includes data corresponding to various lighting impact parameters in the home environment within the historical period; Step S303: taking the historical lighting data of the characteristic household lamp as output data and the historical lighting impact data as input data, preprocessing the output data and the input and output data, and constructing a household lamp lighting prediction model for the characteristic household lamp. The specific construction process is as follows: Get the output vector H=[h1,h2,...,h m ], where h1,h2,...,h m The first, second, ..., m lighting parameters of the characteristic household lamps are respectively used to obtain a household lamp lighting prediction model of the characteristic household lamps: ， Among them, μ0 is the characteristic intercept; μ1, μ2, ..., μ n is the regression coefficient of the 1st, 2nd, ..., nth lighting influencing parameter in the home environment; ε is the error term; K1, X2, ..., X n are the 1st, 2nd, ..., nth lighting influencing parameters in the home environment; Step S304: Obtain each historical lighting record of the characteristic household lamp, divide each historical lighting record into a training set and a test set, use the training set to fit the household lamp lighting prediction model, and obtain the fitted household lamp lighting prediction model: Step S305: Calculate the household lamp lighting prediction model, and calculate the characteristic model error P of the vth lighting parameter of the characteristic household lamp. v : ， Wherein, s is the total number of historical lighting records of the characteristic household lamps in the test set; h v,q is the actual value of the vth lighting parameter of the characteristic household lamp in the qth historical lighting record among the plurality of historical lighting records; v,q is the predicted value of the vth lighting parameter in the qth historical lighting record by the household lamp lighting prediction model; When the characteristic model errors of the household lamp lighting prediction model for each lighting parameter of the characteristic household lamp are all less than a preset characteristic model error threshold, it is determined that the construction of the household lamp lighting prediction model of the characteristic household lamp is completed.

5. The smart home lighting control method based on the Internet of Things according to claim 4 is characterized in that: The step S400 includes: Step S401: monitoring the home environment where the family members are located, and obtaining lighting data of home lamps turned on by the family members in the home environment in the current cycle, wherein the lighting data includes data corresponding to various lighting parameters of the home lamps in the current cycle; Step S402: Obtain the marked influence data of the family members, obtain the home lamp lighting prediction model of each home lamp in the home environment, set various lighting parameters of each home lamp in the home environment, and intelligently control the lighting of the home lamps in the home environment.

6. A smart home lighting control system based on the Internet of Things, used to execute the smart home lighting control method based on the Internet of Things according to any one of claims 1 to 5, characterized in that: The system includes a lighting related analysis module, a marking impact data module, a prediction model building module, and a lighting intelligent control module; The lighting correlation analysis module is used to analyze the lighting correlation between the family members and the home lamps to obtain characteristic home lamps; The marking influence data module is used to analyze the lighting influence degree between household lamps with different characteristics during use by the family members to obtain marking influence data; The prediction model building module is used to build a home lamp lighting prediction model for the characteristic home lamp; The lighting intelligent control module is used to intelligently control the lighting of home lamps in the home environment.

7. The smart home lighting control system based on the Internet of Things according to claim 6 is characterized in that: The lighting related analysis module includes a lighting related degree unit and a lighting related analysis unit; The lighting correlation unit is used to calculate the lighting correlation between the family members and the home lamps; The lighting correlation analysis unit is used to determine the lighting correlation degree between the family members and the home lamps according to the lighting correlation degree, Get Feature Home Lighting.

8. The smart home lighting control system based on the Internet of Things according to claim 6, characterized in that: The marking impact data module includes a lighting impact degree value unit and a marking impact data unit; The lighting impact degree value unit is used to calculate the lighting impact degree values ​​between household lamps with different characteristics during use by the family members; The marked impact data unit is used to analyze the lighting impact degree between each characteristic household lamp during use by the family members according to the lighting impact degree value to obtain marked impact data.

9. The smart home lighting control system based on the Internet of Things according to claim 6, characterized in that: The prediction model building module includes a data acquisition unit and a model building unit; The data acquisition unit is used to acquire the historical lighting records of the characteristic home lamps of the family members; The model building unit is used to build a home lamp lighting prediction model of the characteristic home lamp according to the historical lighting records of the characteristic home lamp.

10. The smart home lighting control system based on the Internet of Things according to claim 6, characterized in that: The lighting intelligent control module includes a lighting intelligent control unit; The lighting intelligent control unit is used to obtain the lighting data of the household lamps turned on by the family members in the home environment during the current cycle, obtain the marking influence data of the family members, and combine the household lamp lighting prediction model of the household lamps to intelligently control the lighting of the household lamps in the home environment.

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