Customized home lighting adaptive adjustment management method and system based on artificial intelligence

Abstract

The present invention discloses a customized home lighting adaptive adjustment management method and system based on artificial intelligence, which relates to the field of customized home adjustment technology. The system includes: a customized home data acquisition module, a lighting demand analysis module, an artificial intelligence light strip wiring analysis module and an adaptive adjustment module; the output end of the customized home data acquisition module is connected to the input end of the lighting demand analysis module; the output end of the lighting demand analysis module is connected to the input end of the artificial intelligence light strip wiring analysis module; the output end of the artificial intelligence light strip wiring analysis module is connected to the input end of the adaptive adjustment module. The present invention is based on the user uploading the apartment decoration diagram of the platform system, as well as the expected installation position of the light strip, combined with artificial intelligence and big data technology, to solve the current installation defects of low-voltage light strips and the impact of voltage drop; at the same time, according to different period monitoring, the light brightness adjustment in different environments is realized.

Description

Technical Field

[0001] The present invention relates to the field of customized home adjustment technology, and in particular to a customized home lighting adaptive adjustment management method and system based on artificial intelligence. Background Art

[0002] In the field of overall customized home furnishing, in addition to the necessary lighting equipment, users usually want to set appropriate lights in various parts inside and outside the wardrobe, cabinets, various combination cabinets, etc., for temporary lighting, decoration, atmosphere adjustment, DIY, etc. At present, this kind of light is divided into two aspects: low-voltage light strips and high-voltage light strips. Among them, low-voltage light strips are widely used in the home field with the characteristics of high safety and long life. They generally use metal home furnishing plates as conductors, which are simple and convenient; however, they also have a lot of defects. Due to their low voltage characteristics, unlike high-voltage lamps that can be directly connected to 220V, low-voltage lamps require converters (i.e. transformers or drivers) to convert voltage to form a power supply voltage of about 10V, which also leads to greater troubles in the wiring of the converter during the installation project; at the same time, due to the low voltage, its voltage drop problem is also more serious than that of high-voltage lamps. As the length of the light strip of low-voltage lamps gradually increases, the brightness of the light gradually begins to dim. For the common low-voltage LED light strips of about 5V, the voltage problem is usually very serious after the length becomes longer. Therefore, in the design and wiring process, how to reduce the number of converters as much as possible, while reducing the impact of voltage drop, and how to achieve intelligent adjustment of lighting angle, brightness and color temperature according to the environment. Summary of the invention

[0003] The purpose of the present invention is to provide a customized home lighting adaptive adjustment management method and system based on artificial intelligence to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solution: a customized home lighting adaptive adjustment management method based on artificial intelligence, the method comprising the following steps:

[0005] S1. Obtain the apartment decoration drawing uploaded by the user and the location and length of the light strip to be installed. After receiving the picture, the system proceeds to the light strip wiring analysis;

[0006] S2. Call the historical big database to build a lighting demand analysis model, analyze the areas with strong and weak lighting demands based on the apartment decoration drawings uploaded by users, and mark them;

[0007] S3. Based on the length of the light strip that needs to be installed uploaded by the user, a light strip wiring analysis model is constructed and the number of converters is output;

[0008] S4. Set up sensors to monitor data and implement adaptive adjustment in areas with strong or weak lighting demand.

[0009] According to the above technical solution, the construction of the lighting demand analysis model includes:

[0010] Calling a historical big data database, wherein the historical big data database contains apartment decoration drawings of several users, marking the locations where lamps are installed based on the apartment decoration drawings, and illumination data formed by the lamps installed at each location;

[0011] An illumination data threshold is set based on the area of ​​the lighting area of ​​the lamp, the area of ​​the lighting area of ​​the lamp is determined by the lighting area marked on the nameplate of the lamp, a fixed coefficient is set, and a linear function between the illumination data threshold and the area of ​​the lighting area of ​​the lamp is formed based on the fixed coefficient;

[0012] According to the illumination data formed by the lamps installed at each position, the illumination data threshold of the area illuminated by the lamps is compared. If it is lower than the illumination data threshold, it is marked as an area with weak lighting demand;

[0013] If there is an area where no lamps are installed, it is marked as an area with weak lighting demand;

[0014] For all the apartment decoration drawings in the historical big database, mark the areas with weak lighting requirements;

[0015] Collect characteristic factors of the area with weak lighting demand, wherein the characteristic factors include area equipment, area location, and area commonness;

[0016] Taking the center of the apartment decoration map as the origin, a three-dimensional rectangular coordinate system is formed. The regional equipment refers to the distance between the edge point of the area with weak lighting demand and the nearest home equipment; the regional position refers to the distance between the edge point of the area with weak lighting demand and the origin; the regional commonness refers to the distance between the edge point of the area with weak lighting demand and the indoor corridor on the apartment decoration map; each group of characteristic factors forms a number of distance values;

[0017] Set the area interval of the user's apartment decoration map, group the characteristic factors, set the user's initial apartment area to N, and the interval to M, to form several intervals, recorded as ; Each interval range includes the front but not the back, and n represents the total number of intervals set;

[0018] The characteristic factor data is classified and stored based on different user apartment types and areas.

[0019] According to the above technical solution, in steps S2-S3, it also includes:

[0020] Based on the apartment decoration pictures uploaded by the user, select the characteristic factor data in the interval corresponding to the user's apartment area, and form a set of data of the same characteristic factor in ascending order. The total number of elements in the set is recorded as T;

[0021] Based on the apartment decoration drawings uploaded by the user, the origin is constructed, the light strip is approximated as a straight line in a three-dimensional rectangular coordinate system, and the characteristic factor data formed by the light strip is calculated; any characteristic factor data formed by the light strip is inserted into the set, all the data in the set are numbered sequentially, and the number value of any characteristic factor data formed by the light strip is obtained, which is recorded as Y;

[0022] Set the approximate threshold U, if it exists ; then mark the current light strip position as an area with weak lighting demand;

[0023] Based on the apartment decoration drawings uploaded by users, the areas with weak lighting demand of all light strips are marked; if there is an area with both ends being areas with weak lighting demand, and the proportion of non-weak lighting demand areas in the entire area does not exceed the set threshold, the entire area is marked as an area with weak lighting demand; if there is an area with both ends being areas with non-weak lighting demand, and the proportion of weak lighting demand areas in the entire area does not exceed the set threshold, the entire area is marked as an area with non-weak lighting demand.

[0024] According to the above technical solution, it also includes:

[0025] Based on the length of the light strip that needs to be installed uploaded by the user, set the stable brightness length of the light strip. When the distance between the light strip and the converter is kept within the stable brightness length of the light strip, the brightness will not change;

[0026] Calculate the initial number of converters:

[0027]

[0028] in, Refers to the initial number of converters; Represents the result rounded; Refers to the total length of the light strip that needs to be installed uploaded by the user; Refers to the length of the light strip with stable brightness;

[0029] Based on the apartment decoration drawings uploaded by users, the origin is constructed to mark the location information data where the converter can be installed in the three-dimensional rectangular coordinate system;

[0030] From the beginning, gradually increase the number of converters until the number reaches E;

[0031] Each time the number of converters is set, the node installation is automatically selected and the uncovered area is calculated. The uncovered area refers to the total area of ​​the non-lighting demand weak area that is not within the stable length of the light strip brightness after the converter is set. The system automatically selects the minimum total area under each number of converters;

[0032] Manually set the weights of the number of converters and the total area of ​​non-lighting demand weak areas that are not within the stable length of the light strip brightness to form a light strip wiring analysis plan:

[0033]

[0034] in, Representative planning division; , The weights respectively represent the number of manually set converters and the total area of ​​the non-lighting demand weak area that is not within the stable length of the light strip brightness; Represents the total area of ​​non-lighting demand weak areas that are not within the stable length of the light strip brightness; Represents the number of converters;

[0035] The following light strip wiring analysis strategies are recommended in order from small to large planning points.

[0036] According to the above technical solution, in step S4, it also includes:

[0037] The user manually turns on the adaptive adjustment switch to output a specified brightness value;

[0038] After obtaining the specified brightness value, enter the first brightness sampling cycle, set the cycle duration, and obtain the average brightness value of the area within the first brightness sampling cycle; if the average brightness value is lower than the specified brightness value, automatically increase the brightness and enter the next brightness sampling cycle;

[0039] If the average brightness value is not lower than the specified brightness value, the brightness is automatically lowered and the next brightness sampling cycle is entered.

[0040] A customized home lighting adaptive adjustment management system based on artificial intelligence, which includes: a customized home data acquisition module, a lighting demand analysis module, an artificial intelligence light strip wiring analysis module, and an adaptive adjustment module;

[0041] The customized home data acquisition module is used to obtain the house decoration drawings uploaded by the user and the location and length of the light strip to be installed. After receiving the pictures, the system transfers them to the light strip wiring analysis; the lighting demand analysis module is used to call the historical big database, build a lighting demand analysis model, analyze the strong and weak lighting demand areas based on the house decoration drawings uploaded by the user, and mark them; the artificial intelligence light strip wiring analysis module builds a light strip wiring analysis model based on the length of the light strip to be installed uploaded by the user, and outputs the number of converters; the adaptive adjustment module is used to set sensor monitoring data and realize adaptive adjustment in the strong and weak lighting demand areas;

[0042] The output end of the customized home data acquisition module is connected to the input end of the lighting demand analysis module; the output end of the lighting demand analysis module is connected to the input end of the artificial intelligence light strip wiring analysis module; the output end of the artificial intelligence light strip wiring analysis module is connected to the input end of the adaptive adjustment module.

[0043] According to the above technical solution, the customized home data collection module includes a house decoration picture collection unit and a light strip data extraction unit;

[0044] The apartment decoration drawing acquisition unit is used to obtain the apartment decoration drawing uploaded by the user; the light strip data extraction unit is used to extract the location and length of the light strip to be installed from the apartment decoration drawing uploaded by the user;

[0045] The output end of the apartment decoration picture acquisition unit is connected to the input end of the light strip data extraction unit.

[0046] According to the above technical solution, the lighting demand analysis module includes a big data unit and a lighting demand analysis unit;

[0047] The big data unit is used to call a historical big data database, which contains several users' apartment decoration drawings, and marks the locations where lamps are installed based on the apartment decoration drawings, as well as the illumination data formed by the lamps installed at each location; the lighting demand analysis unit is used to build a lighting demand analysis model, analyze the areas with strong and weak lighting demands based on the apartment decoration drawings uploaded by the users, and mark them;

[0048] The output end of the big data unit is connected to the input end of the lighting demand analysis unit.

[0049] According to the above technical solution, the artificial intelligence light strip wiring analysis module includes a light strip wiring analysis unit and a converter processing unit;

[0050] The light strip wiring analysis unit constructs a light strip wiring analysis model based on the length of the light strip that needs to be installed uploaded by the user; the converter processing unit constructs an origin based on the apartment decoration drawing uploaded by the user, marks the location information data where the converter can be installed in the three-dimensional rectangular coordinate system, and finally outputs the number of converters based on the light strip wiring analysis model;

[0051] The output end of the light strip wiring analysis unit is connected to the input end of the converter processing unit.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: based on the apartment decoration drawings uploaded by users to the platform system and the expected installation positions of the light strips, the present invention combines artificial intelligence and big data technologies to solve several shortcomings of low-voltage light strips, reduce the number of converters as much as possible, increase the lighting area range, and solve the voltage drop problem; at the same time, according to different periodic monitoring, the light brightness adjustment in different environments can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The figure is a flow chart of the customized home lighting adaptive adjustment management method based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0054] 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.

[0055] Example: Figure 1 As shown, the present invention provides a customized home lighting adaptive adjustment management method based on artificial intelligence, the method comprising: obtaining the house decoration drawing uploaded by the user and the position and length of the light strip to be installed, and the system transfers to the light strip wiring analysis after receiving; calling the historical big database, calling the historical big database, the historical big database contains the house decoration drawings of several users, marking the installation position of the lamp based on the house decoration drawing, and the illumination data formed by the lamps installed at each position;

[0056] An illumination data threshold is set based on the area of ​​the lighting area of ​​the lamp, the area of ​​the lighting area of ​​the lamp is determined by the lighting area marked on the nameplate of the lamp, a fixed coefficient is set, and a linear function between the illumination data threshold and the area of ​​the lighting area of ​​the lamp is formed based on the fixed coefficient;

[0057] According to the illumination data formed by the lamps installed at each position, the illumination data threshold of the area illuminated by the lamps is compared. If it is lower than the illumination data threshold, it is marked as an area with weak lighting demand;

[0058] If there is an area where no lamps are installed, it is marked as an area with weak lighting demand;

[0059] For all the apartment decoration drawings in the historical big database, mark the areas with weak lighting requirements;

[0060] Collect characteristic factors of the area with weak lighting demand, wherein the characteristic factors include area equipment, area location, and area commonness;

[0061] Taking the center of the apartment decoration map as the origin, a three-dimensional rectangular coordinate system is formed. The regional equipment refers to the distance between the edge point of the area with weak lighting demand and the nearest home equipment; the regional position refers to the distance between the edge point of the area with weak lighting demand and the origin; the regional commonness refers to the distance between the edge point of the area with weak lighting demand and the indoor corridor on the apartment decoration map; each group of characteristic factors forms a number of distance values;

[0062] Set the area interval of the user's apartment decoration map, group the characteristic factors, set the user's initial apartment area to N, and the interval to M, to form several intervals, recorded as ; Each interval range includes the front but not the back, and n represents the total number of intervals set;

[0063] The characteristic factor data is classified and stored based on different user apartment types and areas.

[0064] Based on the apartment decoration pictures uploaded by the user, select the characteristic factor data in the interval corresponding to the user's apartment area, and form a set of data of the same characteristic factor in ascending order. The total number of elements in the set is recorded as T;

[0065] Based on the apartment decoration drawings uploaded by the user, the origin is constructed, the light strip is approximated as a straight line in a three-dimensional rectangular coordinate system, and the characteristic factor data formed by the light strip is calculated; any characteristic factor data formed by the light strip is inserted into the set, all the data in the set are numbered sequentially, and the number value of any characteristic factor data formed by the light strip is obtained, which is recorded as Y;

[0066] Set the approximate threshold U, if it exists ; then mark the current light strip position as an area with weak lighting demand;

[0067] Based on the apartment decoration drawings uploaded by users, the areas with weak lighting demand of all light strips are marked; if there is an area with both ends being areas with weak lighting demand, and the proportion of non-weak lighting demand areas in the entire area does not exceed the set threshold, the entire area is marked as an area with weak lighting demand; if there is an area with both ends being areas with non-weak lighting demand, and the proportion of weak lighting demand areas in the entire area does not exceed the set threshold, the entire area is marked as an area with non-weak lighting demand.

[0068] Based on the length of the light strip that needs to be installed uploaded by the user, set the stable brightness length of the light strip. When the distance between the light strip and the converter is kept within the stable brightness length of the light strip, the brightness will not change;

[0069] Calculate the initial number of converters:

[0070]

[0071] in, Refers to the initial number of converters; Represents the result rounded; Refers to the total length of the light strip that needs to be installed uploaded by the user; Refers to the length of the light strip with stable brightness;

[0072] Based on the apartment decoration drawings uploaded by users, the origin is constructed to mark the location information data where the converter can be installed in the three-dimensional rectangular coordinate system;

[0073] From the beginning, gradually increase the number of converters until the number reaches E;

[0074] Each time the number of converters is set, the node installation is automatically selected and the uncovered area is calculated. The uncovered area refers to the total area of ​​the non-lighting demand weak area that is not within the stable length of the light strip brightness after the converter is set. The system automatically selects the minimum total area under each number of converters;

[0075] Manually set the weights of the number of converters and the total area of ​​non-lighting demand weak areas that are not within the stable length of the light strip brightness to form a light strip wiring analysis plan:

[0076]

[0077] in, Representative planning division; , The weights respectively represent the number of manually set converters and the total area of ​​the non-lighting demand weak area that is not within the stable length of the light strip brightness; Represents the total area of ​​non-lighting demand weak areas that are not within the stable length of the light strip brightness; Represents the number of converters;

[0078] The following light strip wiring analysis strategies are recommended in order from small to large planning points.

[0079] The user manually turns on the adaptive adjustment switch to output a specified brightness value;

[0080] After obtaining the specified brightness value, enter the first brightness sampling cycle, set the cycle duration, and obtain the average brightness value of the area within the first brightness sampling cycle; if the average brightness value is lower than the specified brightness value, automatically increase the brightness and enter the next brightness sampling cycle;

[0081] If the average brightness value is not lower than the specified brightness value, the brightness is automatically lowered and the next brightness sampling cycle is entered.

[0082] In this embodiment, a customized home lighting adaptive adjustment management system based on artificial intelligence is also provided, which includes: a customized home data acquisition module, a lighting demand analysis module, an artificial intelligence light strip wiring analysis module and an adaptive adjustment module;

[0083] The customized home data acquisition module is used to obtain the house decoration drawings uploaded by the user and the location and length of the light strip to be installed. After receiving the pictures, the system transfers them to the light strip wiring analysis; the lighting demand analysis module is used to call the historical big database, build a lighting demand analysis model, analyze the strong and weak lighting demand areas based on the house decoration drawings uploaded by the user, and mark them; the artificial intelligence light strip wiring analysis module builds a light strip wiring analysis model based on the length of the light strip to be installed uploaded by the user, and outputs the number of converters; the adaptive adjustment module is used to set sensor monitoring data and realize adaptive adjustment in the strong and weak lighting demand areas;

[0084] The output end of the customized home data acquisition module is connected to the input end of the lighting demand analysis module; the output end of the lighting demand analysis module is connected to the input end of the artificial intelligence light strip wiring analysis module; the output end of the artificial intelligence light strip wiring analysis module is connected to the input end of the adaptive adjustment module.

[0085] The customized home data collection module includes a house decoration picture collection unit and a light strip data extraction unit;

[0086] The apartment decoration drawing acquisition unit is used to obtain the apartment decoration drawing uploaded by the user; the light strip data extraction unit is used to extract the location and length of the light strip to be installed from the apartment decoration drawing uploaded by the user;

[0087] The output end of the apartment decoration picture acquisition unit is connected to the input end of the light strip data extraction unit.

[0088] The lighting demand analysis module includes a big data unit and a lighting demand analysis unit;

[0089] The big data unit is used to call a historical big data database, which contains several users' apartment decoration drawings, and marks the locations where lamps are installed based on the apartment decoration drawings, as well as the illumination data formed by the lamps installed at each location; the lighting demand analysis unit is used to build a lighting demand analysis model, analyze the areas with strong and weak lighting demands based on the apartment decoration drawings uploaded by the users, and mark them;

[0090] The output end of the big data unit is connected to the input end of the lighting demand analysis unit.

[0091] The artificial intelligence light strip wiring analysis module includes a light strip wiring analysis unit and a converter processing unit;

[0092] The light strip wiring analysis unit constructs a light strip wiring analysis model based on the length of the light strip that needs to be installed uploaded by the user; the converter processing unit constructs an origin based on the apartment decoration drawing uploaded by the user, marks the location information data where the converter can be installed in the three-dimensional rectangular coordinate system, and finally outputs the number of converters based on the light strip wiring analysis model;

[0093] The output end of the light strip wiring analysis unit is connected to the input end of the converter processing unit.

[0094] 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 customized home lighting adaptive adjustment management method based on artificial intelligence, characterized by: The method comprises the following steps: S1. Obtain the apartment decoration drawing uploaded by the user and the location and length of the light strip to be installed. After receiving the picture, the system proceeds to the light strip wiring analysis; S2. Call the historical big database to build a lighting demand analysis model, analyze the areas with strong and weak lighting demands based on the apartment decoration drawings uploaded by users, and mark them; S3. Based on the length of the light strip that needs to be installed uploaded by the user, a light strip wiring analysis model is constructed and the number of converters is output; S4. Set up sensors to monitor data and implement adaptive adjustment in areas with strong or weak lighting demand; The constructing of the lighting demand analysis model comprises: Calling a historical big data database, wherein the historical big data database contains apartment decoration drawings of several users, marking the locations where lamps are installed based on the apartment decoration drawings, and illumination data formed by the lamps installed at each location; An illumination data threshold is set based on the area of ​​the lighting area of ​​the lamp, the area of ​​the lighting area of ​​the lamp is determined by the lighting area marked on the nameplate of the lamp, a fixed coefficient is set, and a linear function between the illumination data threshold and the area of ​​the lighting area of ​​the lamp is formed based on the fixed coefficient; According to the illumination data formed by the lamps installed at each position, the illumination data threshold of the area illuminated by the lamps is compared. If it is lower than the illumination data threshold, it is marked as an area with weak lighting demand; If there is an area where no lamps are installed, it is marked as an area with weak lighting demand; For all the apartment decoration drawings in the historical big database, mark the areas with weak lighting requirements; Collect characteristic factors of the area with weak lighting demand, wherein the characteristic factors include area equipment, area location and area commonness; Taking the center of the apartment decoration map as the origin, a three-dimensional rectangular coordinate system is formed. The regional equipment refers to the distance between the edge point of the area with weak lighting demand and the nearest home equipment; the regional position refers to the distance between the edge point of the area with weak lighting demand and the origin; the regional commonness refers to the distance between the edge point of the area with weak lighting demand and the indoor corridor on the apartment decoration map; each group of characteristic factors forms a number of distance values; Set the area interval of the user's apartment decoration map, group the characteristic factors, set the user's initial apartment area to N, and the interval to M, to form several intervals, recorded as ; Each interval range includes the front but not the back, and n represents the total number of intervals set; Classify and store characteristic factor data based on different user apartment sizes; In steps S2-S3, it also includes: Based on the apartment decoration pictures uploaded by the user, select the characteristic factor data in the interval corresponding to the user's apartment area, and form a set of data of the same characteristic factor in ascending order. The total number of elements in the set is recorded as T; Based on the apartment decoration drawings uploaded by the user, the origin is constructed, the light strip is approximated as a straight line in a three-dimensional rectangular coordinate system, and the characteristic factor data formed by the light strip is calculated; any characteristic factor data formed by the light strip is inserted into the set, all the data in the set are numbered sequentially, and the number value of any characteristic factor data formed by the light strip is obtained, which is recorded as Y; Set the approximate threshold U, if it exists ; then mark the current light strip position as an area with weak lighting demand; Based on the apartment decoration drawings uploaded by users, the areas with weak lighting demand of all light strips are marked; if there is an area with both ends being areas with weak lighting demand, and the proportion of non-weak lighting demand areas in the entire area does not exceed the set threshold, the entire area is marked as an area with weak lighting demand; if there is an area with both ends being areas with non-weak lighting demand, and the proportion of weak lighting demand areas in the entire area does not exceed the set threshold, the entire area is marked as an area with non-weak lighting demand.

2. The method for customized home lighting adaptive adjustment management based on artificial intelligence according to claim 1 is characterized in that: Also includes: Based on the length of the light strip that needs to be installed uploaded by the user, set the stable brightness length of the light strip. When the distance between the light strip and the converter is kept within the stable brightness length of the light strip, the brightness does not change; Calculate the initial number of converters: ； in, Refers to the initial number of converters; Represents the result rounded; Refers to the total length of the light strip that needs to be installed uploaded by the user; Refers to the length of the light strip with stable brightness; Based on the apartment decoration drawings uploaded by users, an origin is constructed to mark the location information data where the converter can be installed in the three-dimensional rectangular coordinate system; From the beginning, gradually increase the number of converters until the number reaches E; Each time the number of converters is set, the node installation is automatically selected and the uncovered area is calculated. The uncovered area refers to the total area of ​​the non-lighting demand weak area that is not within the stable length of the light strip brightness after the converter is set. The system automatically selects the minimum total area under each number of converters; Manually set the weights of the number of converters and the total area of ​​non-lighting demand weak areas that are not within the stable length of the light strip brightness to form a light strip wiring analysis plan: ； in, Representative planning division; , The weights respectively represent the number of manually set converters and the total area of ​​the non-lighting demand weak area that is not within the stable length of the light strip brightness; Represents the total area of ​​non-lighting demand weak areas that are not within the stable length of the light strip brightness; Represents the number of converters; The following light strip wiring analysis strategies are recommended in order from small to large planning points.

3. The method for adaptively adjusting and managing customized home lighting based on artificial intelligence according to claim 1 is characterized in that: In step S4, it also includes: The user manually turns on the adaptive adjustment switch to output a specified brightness value; After obtaining the specified brightness value, enter the first brightness sampling cycle, set the cycle duration, and obtain the average brightness value of the area within the first brightness sampling cycle; if the average brightness value is lower than the specified brightness value, automatically increase the brightness and enter the next brightness sampling cycle; If the average brightness value is not lower than the specified brightness value, the brightness is automatically lowered and the next brightness sampling cycle is entered.

4. A customized home lighting adaptive adjustment management system based on artificial intelligence, using the customized home lighting adaptive adjustment management method based on artificial intelligence as claimed in claim 1, characterized in that: The system includes: a customized home data acquisition module, a lighting demand analysis module, an artificial intelligence light strip wiring analysis module, and an adaptive adjustment module; The customized home data acquisition module is used to obtain the house decoration drawings uploaded by the user and the location and length of the light strip to be installed. After receiving the pictures, the system transfers them to the light strip wiring analysis; the lighting demand analysis module is used to call the historical big database, build a lighting demand analysis model, analyze the strong and weak lighting demand areas based on the house decoration drawings uploaded by the user, and mark them; the artificial intelligence light strip wiring analysis module builds a light strip wiring analysis model based on the length of the light strip to be installed uploaded by the user, and outputs the number of converters; the adaptive adjustment module is used to set sensor monitoring data and realize adaptive adjustment in the strong and weak lighting demand areas; The output end of the customized home data acquisition module is connected to the input end of the lighting demand analysis module; the output end of the lighting demand analysis module is connected to the input end of the artificial intelligence light strip wiring analysis module; the output end of the artificial intelligence light strip wiring analysis module is connected to the input end of the adaptive adjustment module.

5. The customized home lighting adaptive adjustment management system based on artificial intelligence according to claim 4 is characterized by: The customized home data collection module includes a house decoration picture collection unit and a light strip data extraction unit; The apartment decoration drawing acquisition unit is used to obtain the apartment decoration drawing uploaded by the user; the light strip data extraction unit is used to extract the location and length of the light strip to be installed from the apartment decoration drawing uploaded by the user; The output end of the apartment decoration picture acquisition unit is connected to the input end of the light strip data extraction unit.

6. The customized home lighting adaptive adjustment management system based on artificial intelligence according to claim 4 is characterized by: The lighting demand analysis module includes a big data unit and a lighting demand analysis unit; The big data unit is used to call a historical big data database, which contains several users' apartment decoration drawings, and marks the locations where lamps are installed based on the apartment decoration drawings, as well as the illumination data formed by the lamps installed at each location; the lighting demand analysis unit is used to build a lighting demand analysis model, analyze and mark the areas with strong and weak lighting demands based on the apartment decoration drawings uploaded by the users; The output end of the big data unit is connected to the input end of the lighting demand analysis unit.

7. The customized home lighting adaptive adjustment management system based on artificial intelligence according to claim 4 is characterized by: The artificial intelligence light strip wiring analysis module includes a light strip wiring analysis unit and a converter processing unit; The light strip wiring analysis unit constructs a light strip wiring analysis model based on the length of the light strip that needs to be installed uploaded by the user; the converter processing unit constructs an origin based on the apartment decoration drawing uploaded by the user, marks the location information data where the converter can be installed in the three-dimensional rectangular coordinate system, and finally outputs the number of converters based on the light strip wiring analysis model; The output end of the light strip wiring analysis unit is connected to the input end of the converter processing unit.

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