A clothes drying machine light automatic switching method, device, equipment and medium

Abstract

The embodiment of the application discloses a clothes airing machine light automatic switching method, device, equipment and medium, relates to the clothes airing machine light control technical field, and comprises the following steps: acquiring geographical position positioning information of a clothes airing machine, determining corresponding weather environment data and time dimension data based on the geographical position positioning information; based on the weather environment data and the time dimension data, a core feature parameter set is extracted; based on the core feature parameter set, a target light mode is determined; and the light module of the clothes airing machine is controlled to switch to the target light mode. The clothes airing machine light control solution provided by the application can realize automatic and accurate switching of the light mode.

Description

Technical Field

[0001] This invention relates to the field of clothes drying rack lighting control technology, and in particular to a method, device, equipment and medium for automatic switching of clothes drying rack lighting. Background Technology

[0002] With the development of the smart home industry, clothes drying racks have been upgraded from a single function of drying clothes to creating aesthetic and atmospheric effects on balconies. Some products have begun to be equipped with multi-spectral systems to support multiple ambient lighting modes.

[0003] However, current control technology for ambient lights on clothes drying racks still has significant shortcomings, specifically: Mainstream solutions rely on users manually switching light modes via physical buttons, mobile apps, or voice commands, which is cumbersome and lacks environmental adaptability. While a few products attempt to incorporate local light sensors or real-time clock modules, these only achieve simple brightness and color temperature adjustments based on a single light intensity or fixed time, making them susceptible to curtain obstructions, ambient light interference, and seasonal changes, leading to false triggering or mode mismatch.

[0004] In conclusion, how to provide a clothes drying rack lighting control solution that can achieve automatic and precise switching of lighting modes is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] This invention provides a method, device, equipment, and medium for automatically switching the lights on a clothes drying rack. The technical problem it aims to solve is: how to provide a clothes drying rack lighting control solution that can achieve automatic and precise switching of lighting modes.

[0006] In a first aspect, embodiments of the present invention provide a method for automatically switching the lights on a clothes drying rack, comprising: Obtain the geographical location information of the clothes drying rack, and determine the corresponding weather and time data based on the geographical location information; Based on the weather and environmental data and the time dimension data, extract the core feature parameter set; The target lighting pattern is determined based on the core feature parameter set; Control the light module of the clothes dryer to switch to the target light mode.

[0007] Optionally, the step of extracting a core feature parameter set based on the weather environment data and the time dimension data includes: The weather and environmental data and the time dimension data are cleaned to obtain cleaned data; The current living time period is dynamically divided based on the sunrise and sunset times of the day in the cleaned data; The core feature parameter set is extracted from the cleaned data, including at least real-time weather conditions, real-time light intensity, and the current time of day.

[0008] Optionally, the step of dynamically dividing the current living time period based on the sunrise and sunset times of the current day in the cleaned data includes: The period from the first preset duration after sunrise to the second preset duration before sunset is divided into daytime periods. The period from the second preset duration before sunset to the third preset duration after sunset is defined as the twilight period. The period from the third preset time after sunset to the preset time of the day is divided into the nighttime period; The period from the preset time of the day to the fourth preset time before sunrise the next day is divided into the late night period.

[0009] Optionally, determining the target lighting pattern based on the core feature parameter set includes: The core feature parameter set is matched with the trigger conditions of a variety of preset lighting modes to determine the target lighting mode.

[0010] Optionally, the lighting modes include two or more of the following: clear sky mode, sunset mode, aurora mode, and tipsy mode; the step of matching the core feature parameter set with the trigger conditions of multiple preset lighting modes to determine the target lighting mode includes: When the real-time weather conditions in the core feature parameter set are sunny or cloudy, the real-time light intensity is greater than or equal to the first light threshold, and the current living time is daytime, it is matched as the clear sky mode. When the real-time weather conditions in the core feature parameter set are sunny or partly cloudy, the real-time light intensity is greater than or equal to the second light threshold and less than the first light threshold, and the current living time is dusk, the matching is sunset mode. When the real-time weather conditions in the core feature parameter set are no rain and no snow, the real-time light intensity is less than or equal to the third light threshold, and the current living time is late at night, the matching is aurora mode; When the real-time weather conditions in the core feature parameter set are no rain and no snow, the real-time light intensity is less than or equal to the second light threshold, and the current living time is nighttime, the matching is the slightly tipsy mode; Among them, the first illumination threshold is greater than the second illumination threshold, and the second illumination threshold is greater than the third illumination threshold.

[0011] Optionally, determining the target lighting pattern based on the core feature parameter set further includes: When the set of core feature parameters does not meet the matching conditions of any of the modes, namely Clear Sky Mode, Sunset Mode, Aurora Mode, and Tipsy Mode, the matching mode is the basic lighting mode.

[0012] Optionally, the step of controlling the lighting module of the clothes dryer to switch to the target lighting mode includes: Load the target lighting parameters corresponding to the target lighting mode, and adjust the lighting parameters of the clothes drying rack's lighting module to the target lighting parameters.

[0013] Optionally, the step of cleaning the weather and environmental data and the time dimension data to obtain cleaned data includes: Missing values ​​and anomalous jump values ​​are removed from the weather and time data to obtain intermediate data. The real-time light intensity in the intermediate data is cross-validated with the light data collected by the local light sensor. The real-time light intensity is then corrected or retained based on the cross-validation result to obtain the cleaned data.

[0014] Optionally, the method further includes: If a user's manual operation command is detected, the corresponding lighting mode switch is executed first, and the user's operation data is recorded. The user's operation data includes at least the operation time, the weather environment data at the time of operation, the current time of day at the time of operation, and the target lighting mode to be switched by the manual operation command. The user operation data is uploaded to the cloud server.

[0015] Optionally, the method further includes: The system receives optimized trigger conditions or lighting parameters from the cloud server and updates the corresponding parameters stored locally on the clothes drying rack. The optimized trigger conditions or lighting parameters are generated by the cloud server based on the received user operation data.

[0016] Optionally, the optimized triggering conditions or lighting parameters can be generated in the following ways: When the same clothes drying rack is manually switched to the same lighting mode by the user a preset number of times under the same weather conditions and current time of day, the combination of features will be generated as a personalized trigger condition for the same clothes drying rack. Based on the operating data of multiple clothes drying racks in the same geographical area during the same season, optimized parameters are generated for the time period division rules and light intensity thresholds of the same geographical area during the same season. Based on custom parameter data from multiple users, optimized values ​​for lighting parameters of each lighting mode are generated.

[0017] Optionally, the method further includes: When a network disconnection is detected, the clothes drying rack's lighting mode is determined based on the time from the local real-time clock module and the illumination data from the local light sensor.

[0018] Secondly, embodiments of the present invention also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0019] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the above-described method.

[0020] This invention provides a method, apparatus, device, and medium for automatically switching the lights on a clothes drying rack. The method includes: acquiring the geographical location information of the clothes drying rack; determining corresponding weather and time data based on the geographical location information; extracting a set of core feature parameters based on the weather and time data; determining a target lighting mode based on the core feature parameter set; and controlling the clothes drying rack's lighting module to switch to the target lighting mode. This invention replaces traditional local sensors or single data sources with fixed time parameters by acquiring the geographical location information of the clothes drying rack and determining the corresponding weather and time data from the cloud based on this location information. Based on this, a set of core feature parameters is extracted from the aforementioned big data, and the target lighting mode is automatically determined based on this parameter set, ultimately controlling the lighting module to switch. Therefore, the clothes drying rack can automatically make decisions and switch lighting modes based on real-time, accurate weather and time information, without any manual operation by the user. This eliminates the problems of false triggering or mode mismatch caused by curtains, ambient light interference, seasonal changes, and regional differences, achieving accurate adaptation of the lighting mode to the real natural environment and time of day. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an automatic light switching method for a clothes drying rack, as provided in an embodiment of the present invention. Figure 2 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0027] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0028] Please see Figure 1 This invention provides a method for automatically switching the lights on a clothes drying rack, the method comprising the following steps: S1, obtain the geographical location information of the clothes drying rack, and determine the corresponding weather environment data and time dimension data based on the geographical location information.

[0029] In practice, the clothes drying rack first obtains its own geographic location information. For example, the clothes drying rack connects to the Internet through its built-in wireless communication module. After the device is activated, it uses the network protocol stack to obtain the public IP address of the current network access point, and obtains the precise latitude and longitude coordinates of the province, city, district and county where the clothes drying rack is installed, thus forming geographic location information.

[0030] Furthermore, based on the acquired geographic location information, the clothes drying rack determines the corresponding weather and time-related data. For example, the clothes drying rack encapsulates the geographic location information in a data request command and sends it to a cloud server via a wireless network. The cloud server, based on this location information, obtains real-time weather data corresponding to that geographic location from a third-party meteorological data platform (such as the China Weather Network or the official application programming interface of the National Meteorological Information Center). This includes real-time weather conditions (sunny, cloudy, rainy, blizzard, etc.), real-time light intensity, atmospheric visibility, sunrise time, sunset time, and short-term weather warnings, etc., which are not specifically limited in this invention. Simultaneously, the cloud server obtains standard Beijing time, seasonal indicators, and holiday indicators, etc., from the National Time Service Center or a network time protocol server, which are also not specifically limited in this invention. The cloud server packages and encrypts the aforementioned weather and time-related data before sending it to the clothes drying rack. The clothes drying rack receives and parses this data packet via its wireless communication module to obtain the weather and time-related data.

[0031] S2, Based on the weather and environmental data and the time dimension data, extract the core feature parameter set.

[0032] In practice, the clothes drying rack extracts a set of core feature parameters based on the aforementioned weather and time-related data. First, the clothes drying rack cleans the received raw data, removing null values ​​or data with incorrect timestamps caused by network transmission delays, as well as abnormal jump values ​​exceeding reasonable ranges in the meteorological data. Further, after data cleaning, the clothes drying rack extracts parameters that have a key impact on the determination of lighting patterns from the cleaned data, including real-time weather conditions (represented by standardized codes, such as code 00 for sunny days), real-time light intensity (in lux), and the current time of day dynamically divided according to sunrise and sunset times (such as daytime, dusk, nighttime, or late night). The clothes drying rack then normalizes these parameters to form a set of core feature parameters.

[0033] In some preferred embodiments, the step of extracting a core feature parameter set based on the weather environment data and the time dimension data includes: cleaning the weather environment data and the time dimension data to obtain cleaned data; dynamically dividing the current living time period according to the sunrise and sunset times of the day in the cleaned data; and extracting parameters from the cleaned data, including at least real-time weather conditions, real-time light intensity, and the current living time period, as the core feature parameter set.

[0034] In practice, when the clothes drying rack extracts the core feature parameter set based on weather and time-related data, it first performs data cleaning. The clothes drying rack receives raw data packets from the cloud server, which contain multiple fields: real-time weather condition code, real-time light intensity value, sunrise time string, sunset time string, standard Beijing time string, and season identifier. The clothes drying rack iterates through the values ​​of each field, checking for null values ​​or values ​​exceeding a preset reasonable range (e.g., negative real-time light intensity or values ​​exceeding the maximum solar irradiance on the Earth's surface). For detected missing or anomalous values, the clothes drying rack fills in the missing value with a valid value from the previous moment, or discards the anomalous record and waits for the next data delivery from the cloud server. After completing the removal of missing and anomalous values, the clothes drying rack obtains the cleaned data.

[0035] Furthermore, the clothes drying rack dynamically divides the current living time into time periods based on the sunrise and sunset times of the day in the data after washing. The clothes drying rack parses the sunrise and sunset time strings of the day and converts them into seconds or timestamps starting from midnight of the day. Using the current standard Beijing time as a reference, the clothes drying rack determines which of the multiple time periods defined by the sunrise and sunset times the current moment falls within.

[0036] Furthermore, the clothes dryer extracts parameters from the washed data, including at least real-time weather conditions, real-time light intensity, and the current time of day, as a core feature parameter set. Specifically, the clothes dryer reads the standardized code of the real-time weather conditions and the floating-point value of the real-time light intensity from the washed data. It then combines the current time of day dynamically segmented in the previous step (e.g., using enumerated values ​​0 for daytime, 1 for dusk, 2 for night, and 3 for late night) with the above two parameters to form a structured feature vector, i.e., the core feature parameter set. This feature vector is subsequently used to compare with preset lighting mode triggering conditions.

[0037] This embodiment ensures the validity and stability of input data by removing missing and anomalous values ​​from the big data on the cloud server, avoiding control decision errors caused by network transmission quality issues. Furthermore, it dynamically divides living time periods based on real-time sunrise and sunset times, ensuring that the definitions of daytime, dusk, night, and late night strictly correspond to the actual astronomical time of the local day, completely eliminating the misalignment defects of fixed time threshold schemes in different regions and seasons. Moreover, the extracted core feature parameters from three dimensions—real-time weather conditions, real-time light intensity, and dynamic living time periods—provide a comprehensive and standardized decision-making basis for subsequent precise matching of lighting patterns, thereby achieving adaptive lighting control nationwide and in all seasons.

[0038] In some preferred embodiments, the step of cleaning the weather environment data and the time dimension data to obtain cleaned data includes: performing missing value removal and abnormal jump value removal on the weather environment data and the time dimension data to obtain intermediate data; performing cross-validation between the real-time light intensity in the intermediate data and the light data collected by the local light sensor; and correcting or retaining the real-time light intensity based on the cross-validation result to obtain the cleaned data.

[0039] In practice, the clothes drying machine performs missing value removal and abnormal jump value removal on the received weather and time dimension data. The machine iterates through each field in the weather and time dimension data. For missing values, such as when the real-time light intensity field in a data packet is empty or null, the machine discards the current record for that field or completes it using the value of the corresponding field from the previously successfully received data packet. For abnormal jump values, the machine determines whether the real-time light intensity value exceeds a preset reasonable range (e.g., less than a preset lower limit, greater than a preset upper limit, or the difference from the previous data is greater than a preset difference threshold). If it does, it is considered an abnormal jump value and is removed. After the removal operation, the machine obtains intermediate data. All fields in this intermediate data have valid values, and the values ​​are within a reasonable range.

[0040] Furthermore, the clothes drying rack cross-validates the real-time light intensity data from the intermediate data with the light intensity data collected by the local light sensor. The clothes drying rack is equipped with at least one local light sensor to sense the actual ambient light intensity around it. The local light sensor collects light data at a fixed sampling frequency (e.g., once per second) and temporarily stores the collected values ​​in a local cache. The real-time light intensity obtained by the clothes drying rack from the cloud server reflects the large-scale ambient light intensity measured by a meteorological station for that geographical location, while the local light sensor collects the local light intensity at the clothes drying rack's installation location. The clothes drying rack calculates the difference or ratio between the two. If the deviation exceeds a preset tolerance range (e.g., the cloud server value is 5000 lux while the local value is only 200 lux, a deviation exceeding 90%), it is determined that the local sensor may be affected by curtains, clothing, or foreign objects. In this case, the clothes drying rack prioritizes the cloud server data, retains the real-time light intensity from the cloud server as the valid value, and ignores the abnormal value from the local sensor. Conversely, if the deviation between the two values ​​is within the tolerance range, the clothes dryer can retain the light intensity value from the cloud server, take the weighted average of the two values, or take the light intensity value from the local sensor. After cross-validation and correction, the clothes dryer obtains the data after washing, and the real-time light intensity field in this data is a verified and reliable value.

[0041] In some preferred embodiments, the step of dynamically dividing the current living time period based on the sunrise and sunset times of the day in the cleaned data includes: dividing the period from a first preset time after sunrise to a second preset time before sunset into the daytime period; dividing the period from the second preset time before sunset to a third preset time after sunset into the twilight period; dividing the period from the third preset time after sunset to a preset time of the day into the nighttime period; and dividing the period from the preset time of the day to a fourth preset time before sunrise of the next day into the late night period.

[0042] In practice, when the clothes drying machine dynamically divides the current living time period based on the sunrise and sunset times of the day in the data after washing, the following specific time interval division rules are adopted.

[0043] The clothes drying rack reads the sunrise and sunset times of the current day from the data after washing. Assuming the sunrise time is T_sunrise and the sunset time is T_sunset, both are expressed as the number of minutes elapsed since midnight. The drying rack has four preset duration parameters: the first preset duration is denoted as Delta1, with a value of 30 minutes; the second preset duration is denoted as Delta2, with a value of 60 minutes; the third preset duration is denoted as Delta3, with a value of 30 minutes; and the fourth preset duration is denoted as Delta4, with a value of 30 minutes. The preset time of the day is denoted as T_night_start, with a value of 23:00.

[0044] The clothes drying rack is divided into four time periods according to the following rules: The daytime period is defined as the time interval from T_sunrise + Delta1 to T_sunset - Delta2, that is, 30 minutes after sunrise to 60 minutes before sunset.

[0045] The twilight period is defined as the time interval from T_sunset - Delta2 to T_sunset + Delta3, that is, 60 minutes before sunset to 30 minutes after sunset.

[0046] The nighttime period is defined as the time interval from T_sunset + Delta3 to T_night_start, that is, 30 minutes after sunset to 23:00 on the same day.

[0047] The late-night period is defined as the time interval from T_night_start to T_sunrise - Delta4 the following day. That is, from 23:00 on the current day to 30 minutes before sunrise the next day.

[0048] The clothes drying rack obtains the current standard Beijing time and converts it to minutes since midnight, denoted as T_current. The drying rack then sequentially checks if T_current falls within one of the four time intervals mentioned above. If T_current falls within the daytime interval, the current living time is determined to be daytime; if it falls within the dusk interval, it is determined to be dusk; if it falls within the nighttime interval, it is determined to be nighttime; and if it falls within the late night interval, it is determined to be late night.

[0049] S3, determine the target lighting pattern based on the core feature parameter set.

[0050] In practice, the clothes drying rack determines the target lighting mode based on a set of core feature parameters. The drying rack has pre-set trigger conditions for various lighting modes, which are determined by the range of real-time weather conditions, the threshold range of real-time light intensity, and the current time of day. The drying rack compares each parameter in the core feature parameter set with the pre-set trigger conditions. When all parameters meet the trigger conditions for a particular lighting mode, that mode is designated as the target lighting mode. For example, if the real-time weather is sunny, the real-time light intensity is greater than or equal to the high illuminance threshold, and the current time of day is daytime, then the target lighting mode is determined to be the clear sky mode.

[0051] In some preferred embodiments, determining the target lighting mode based on the core feature parameter set includes: matching the core feature parameter set with the triggering conditions of a variety of preset lighting modes to determine the target lighting mode.

[0052] In practice, when the clothes drying rack determines the target lighting mode based on the core feature parameter set, it performs a matching operation. The clothes drying rack's internal memory contains a pre-set matching rule table, which includes trigger conditions for various lighting modes. Each trigger condition consists of requirements for the range of real-time weather conditions, the threshold range of real-time light intensity, and the type of the current living time. The clothes drying rack compares the three parameters (real-time weather conditions, real-time light intensity, and current living time) from the core feature parameter set with each trigger condition in the matching rule table. During the comparison, it first checks whether the real-time weather conditions meet the weather type set specified by the trigger condition, then checks whether the real-time light intensity falls within the specified threshold range, and finally checks whether the current living time is equal to the specified time type. When all three conditions are met, the clothes drying rack determines the lighting mode corresponding to that trigger condition as the target lighting mode. If none of the trigger conditions are met, the clothes drying rack determines the preset default lighting mode (e.g., basic lighting mode) as the target lighting mode. The clothes drying rack outputs the identifier of the determined target lighting mode (e.g., mode number or mode name string) for use in subsequent control steps.

[0053] This embodiment determines the target lighting mode by matching a set of core feature parameters with preset trigger conditions for various lighting modes, achieving automated decision-making based on multi-dimensional environmental parameters. This matching mechanism employs logic that requires multiple conditions to be met simultaneously, ensuring that the switching of lighting modes has a clear causal basis and avoiding the uncertainty caused by decisions based on a single parameter. Furthermore, since the matching rule table can be flexibly configured with various lighting modes and their trigger conditions, the clothes dryer can select the most suitable scene atmosphere according to different weather and time of day combinations, thereby significantly improving the environmental adaptability of lighting control and the coverage of user scenarios.

[0054] In some preferred embodiments, the lighting mode includes two or more of the following: clear sky mode, sunset mode, aurora mode, and tipsy mode; the step of matching the core feature parameter set with the triggering conditions of multiple preset lighting modes to determine the target lighting mode includes: when the real-time weather conditions in the core feature parameter set are sunny or cloudy, the real-time light intensity is greater than or equal to a first light threshold, and the current living time is daytime, the mode is matched as clear sky mode; when the real-time weather conditions in the core feature parameter set are sunny or partly cloudy, and the real-time light intensity is greater than or equal to a second light threshold... When the light intensity is less than the first light threshold and the current living time is dusk, the matching mode is sunset; when the real-time weather conditions in the core feature parameter set are no rain and no snow, the real-time light intensity is less than or equal to the third light threshold, and the current living time is late night, the matching mode is aurora; when the real-time weather conditions in the core feature parameter set are no rain and no snow, the real-time light intensity is less than or equal to the second light threshold, and the current living time is night, the matching mode is tipsy; wherein, the first light threshold is greater than the second light threshold, and the second light threshold is greater than the third light threshold.

[0055] In practice, the clothes drying rack has four preset lighting modes: clear sky mode, sunset mode, aurora mode, and tipsy mode. The clothes drying rack matches the core feature parameter set with the trigger conditions of these four modes and determines the target lighting mode according to the following specific rules.

[0056] Furthermore, the clothes drying rack first reads the real-time weather condition field from the core feature parameter set. This field uses standardized codes, such as 00 for sunny, 01 for cloudy, 02 for partly cloudy, 03 for overcast, 10 for rain, and 11 for snow. The clothes drying rack also reads the real-time light intensity value L (in lux) and the current time of day P (values ​​include daytime, dusk, night, or late night).

[0057] Furthermore, the clothes drying racks are matched according to the following priority order: The first rule: If the real-time weather condition is sunny or cloudy (i.e., the code is 00 or 01), and the real-time light intensity L is greater than or equal to the first light threshold L1 (for example, L1 is set to 3000 lux), and the current living period P is the daytime period, then the clothes dryer is matched to the clear sky mode.

[0058] The second rule: If the real-time weather condition is sunny or partly cloudy (i.e., the code is 00 or 02), and the real-time light intensity L is between the second light threshold L2 and the first light threshold L1, i.e., L2 ≤ L < L1 (for example, L2 is set to 500 lux), and the current living period P is the dusk period, then the clothes dryer is matched to the sunset mode.

[0059] The third rule: If the real-time weather condition is rainless and snowless (i.e., the real-time weather condition code does not belong to the rain or snow category, for example, the code is not 10, 11), and the real-time light intensity L is less than or equal to the third light threshold L3 (for example, L3 is set to 100 lux), and the current living period P is the late night period, then the clothes dryer is matched to the aurora mode.

[0060] The fourth rule: If the real-time weather condition is rainless and snowless, and the real-time light intensity L is less than or equal to the second light threshold L2 (i.e., L ≤ 500 lux), and the current living period P is the night period, then the clothes dryer is matched to the tipsy mode.

[0061] Among them, the first light threshold L1, the second light threshold L2, and the third light threshold L3 satisfy the relationship of L1 > L2 > L3. The clothes dryer checks each rule in the above order in sequence. Once all the conditions of a certain rule are satisfied, the subsequent rule checks are stopped, and the corresponding light mode is determined as the target light mode.

[0062] It should be noted that the specific values of the above first light threshold, second light threshold, and third light threshold can be configured and optimized according to the average ambient light intensity of the clothes dryer installation environment, user usage habits, or through the cloud server. For example, for southern regions or high-rise buildings with better lighting conditions, the thresholds can be appropriately increased; for northern regions or low-rise buildings with poor lighting conditions, the thresholds can be appropriately decreased. These adjustments of the thresholds are conventional selections made by those skilled in the art based on the core idea of this invention and should not be construed as a limitation on the protection scope of this invention.

[0063] In some preferred embodiments, determining the target light mode based on the core feature parameter set further includes: when the core feature parameter set does not meet the matching conditions of any of the clear sky mode, sunset mode, aurora mode, and tipsy mode, it is matched to the basic lighting mode.

[0064] In practice, when determining the target lighting mode based on the core feature parameter set, the clothes drying rack also includes a fallback matching rule. After sequentially checking the trigger conditions for clear sky mode, sunset mode, aurora mode, and tipsy mode, if the clothes drying rack finds that the core feature parameter set does not meet all the matching conditions for any of the above four modes, it performs a fallback matching and determines the target lighting mode as the basic lighting mode. For example, when the real-time weather conditions are cloudy / rainy or heavy rain / snow, or when the real-time light intensity value is within the range that cannot be classified into any of the above mode thresholds, or when the current time of day does not match the time period required by the above modes, the clothes drying rack will not attempt to match any atmosphere mode, but will directly adopt the basic lighting mode. The basic lighting mode is a general mode that ensures the basic lighting needs of the balcony, and its lighting parameters are preset independently of the above four atmosphere modes.

[0065] S4, control the light module of the clothes drying rack to switch to the target light mode.

[0066] In practice, the clothes drying rack controls its own lighting module to switch to the target lighting mode. The clothes drying rack stores the lighting parameters corresponding to each lighting mode, including color temperature (Kelvin), brightness (percentage), and dynamic effect parameters (such as static, linear gradient, cyclic gradient, etc.).

[0067] Furthermore, based on the determined target lighting mode, the clothes drying rack reads the corresponding lighting parameters from its local memory, generates a pulse width modulation (PWM) control signal, and outputs this signal to the drive circuit of the multi-mode spectral LED lighting module. The drive circuit adjusts the current and on / off timing of the LED chips according to the duty cycle and frequency of the PWM signal, enabling the lighting module to display the color temperature, brightness, and dynamic visual effects defined by the target lighting mode, thus completing the automatic switching of the lighting mode.

[0068] This embodiment obtains geographic location information and uses it to determine the weather environment and time dimension data on the cloud server, replacing the traditional single data source solution of local light sensors and fixed clocks. This ensures the accuracy and reliability of environmental perception from the data source. The clothes drying rack extracts core feature parameter sets based on post-wash data and automatically matches the target lighting mode. Automatic switching of lighting modes can be achieved without any manual operation by the user, significantly improving the convenience and intelligence of the smart clothes drying rack. This method avoids false triggering or mode mismatch problems caused by curtains, ambient light interference, seasonal changes, and regional differences. It achieves precise adaptation of lighting modes to the real natural environment and time of day, providing users with a seamless smart lighting experience.

[0069] In some preferred embodiments, controlling the light module of the clothes dryer to switch to the target light mode includes: loading the target light parameters corresponding to the target light mode, and adjusting the light parameters of the light module of the clothes dryer to the target light parameters.

[0070] In practice, when the clothes drying rack controls its own lighting module to switch to the target lighting mode, it performs parameter loading and adjustment operations. The clothes drying rack's local memory pre-stores a table of lighting parameters corresponding to each lighting mode. For example, in this table, the lighting parameters for the "Clear Sky" mode are: a fixed color temperature value within the range of 5500 Kelvin to 6500 Kelvin (e.g., 6000 Kelvin), a fixed brightness value within the range of 80% to 100% (e.g., 90%), and the dynamic effect parameter is set to static lighting. The lighting parameters for the "Sunset" mode are: a fixed color temperature value within the range of 2000 Kelvin to 3000 Kelvin (e.g., 2500 Kelvin), a fixed brightness value within the range of 50% to 70% (e.g., 60%), and the dynamic effect parameter is set to warm orange linear gradient dynamic lighting, meaning the color temperature and brightness of the LED beads change linearly from the initial value to the end value according to a preset time curve, simulating the flowing effect of the sunset's afterglow. The lighting parameters for Aurora Mode are: a fixed color temperature value within the range of 4000 Kelvin to 5000 Kelvin (e.g., 4500 Kelvin), a fixed brightness value within the range of 30% to 50% (e.g., 40%), and a dynamic effect parameter set to blue-purple gradient cycle dynamic lighting, meaning the LED color cycles between blue and purple according to a preset period, accompanied by brightness fluctuations. The lighting parameters for Twilight Mode are: a fixed color temperature value within the range of 2500 Kelvin to 3500 Kelvin (e.g., 3000 Kelvin), a fixed brightness value within the range of 20% to 40% (e.g., 30%), and a dynamic effect parameter set to warm yellow soft light static lighting, meaning it emits a stable warm yellow light without dynamic changes. After determining the target lighting mode, the clothes drying rack reads the corresponding color temperature value, brightness value, and dynamic effect parameters from the parameter table. Then, it controls the driving current of different colored LED beads such as cool white, warm white, blue, and purple in the lighting module through pulse width modulation signals, so that the overall output light effect meets the above parameter definitions, and completes a smooth or instantaneous switch from the current lighting mode to the target lighting mode.

[0071] It should be noted that the parameter settings for each of the above modes are merely examples, and those skilled in the art can adjust them according to actual needs without exceeding the scope of protection of this invention.

[0072] In some preferred embodiments, the method further includes: if a user's manual operation command is detected, prioritizing the switching of the lighting mode corresponding to the manual operation command, and recording the user operation data; the user operation data includes at least the operation time, weather environment data at the time of operation, the current living time at the time of operation, and the target lighting mode switched by the manual operation command; and uploading the user operation data to a cloud server.

[0073] In practice, the clothes drying rack also performs detection and response operations for user manual intervention. The drying rack detects user manual operation commands in real time through a human-machine interface. This interface includes a physical touch button module, a voice interaction module, and a mobile application communication interface. The physical touch button module is located on the drying rack body or remote control; when a user presses a button, an interrupt signal is generated, which the drying rack's main control chip captures and parses the button code. The voice interaction module picks up user voice commands through a microphone, which are then converted into control commands after local or cloud server voice recognition. The mobile application establishes a connection with the drying rack via a wireless network; when the user clicks the light mode icon in the application interface, the application sends a mode switching command to the drying rack.

[0074] Furthermore, if the clothes dryer detects a manual operation command from any of the aforementioned sources, it immediately interrupts the ongoing automatic switching process and prioritizes the switching of the light mode corresponding to the manual operation command. Based on the target light mode identifier carried in the command, the clothes dryer directly controls the light module to switch to the user-specified mode and sets the pause flag for the automatic switching function to an active state, preventing the automatic switching function from triggering again within a preset pause duration. In one embodiment, the pause duration defaults to 24:00 on the current day, and the automatic switching function automatically resumes at 00:00 the following day. Users can also customize the pause duration through the application.

[0075] Furthermore, while responding to user manual operation commands, the clothes dryer records user operation data. This user operation data includes at least the following fields: operation time (standard Beijing time accurate to the second), weather environment data at the time of operation (including real-time weather conditions, real-time light intensity, and sunrise and sunset times for the day), the current time of day (daytime, dusk, nighttime, or late night), and the target lighting mode identifier for the manual operation command. The clothes dryer assembles this data into a structured record and temporarily stores it in local memory.

[0076] Furthermore, the clothes drying rack uploads the aforementioned user operation data to a cloud server via a wireless network. The clothes drying rack can choose to upload in real-time (i.e., send data immediately after each user operation) or in batches (e.g., send data at fixed intervals or after accumulating a certain number of records). During upload, the clothes drying rack encrypts the data to ensure privacy and security during transmission.

[0077] This embodiment ensures user control over the clothes drying rack's lighting by real-time detection of user manual commands and prioritizing their execution. This avoids situations where the fully automatic mode doesn't meet the user's immediate needs in certain scenarios. Furthermore, the clothes drying rack records and uploads user operation data, providing valuable personalized behavior samples for the cloud server's self-learning model. The user's manually switched modes, the environmental parameters at the time of switching, and the time of day reflect the user's lighting preferences for specific weather and time combinations. This data collection enables the clothes drying rack to learn from user behavior, laying the data foundation for subsequent personalized adaptation and achieving a balance between automation and personalization.

[0078] In some preferred embodiments, the method further includes: receiving optimized trigger conditions or lighting parameters from a cloud server and updating the corresponding parameters stored locally on the clothes drying rack; the optimized trigger conditions or lighting parameters are generated by the cloud server based on the received user operation data.

[0079] In practice, the clothes drying rack also receives parameters from the cloud server and updates them locally. The drying rack continuously monitors the optimization data sent by the cloud server via a wireless network. Based on the massive amounts of user operation data collected, the cloud server processes the data using big data analytics algorithms to generate optimized trigger conditions or optimized lighting parameters. The cloud server packages and encrypts this optimized data and pushes it to the corresponding clothes drying rack device via the wireless network.

[0080] Furthermore, after receiving the optimized data packet from the cloud server, the clothes dryer's communication module parses the packet content. The optimized data packet may contain one or more of the following information: updated trigger conditions (e.g., modifying the light threshold for clear sky mode, modifying the duration of dusk time periods), updated lighting parameters (e.g., adjusting the color temperature or brightness percentage for sunset mode), or newly added lighting mode definitions. After verifying the integrity and validity of the data packet, the clothes dryer writes the optimized trigger conditions or lighting parameters to its local storage, overwriting the original corresponding parameters. Specifically, the clothes dryer internally stores a configuration table that records the trigger condition parameters for each lighting mode (including weather type set, upper and lower limits of light threshold, applicable time period type) and the lighting parameters (color temperature, brightness, dynamic effects) for each lighting mode. Based on the field identifiers in the optimized data packet, the clothes dryer locates the corresponding entry in the configuration table and performs the update operation. After the update is complete, the clothes dryer will use the updated parameters when performing subsequent lighting mode matching and control.

[0081] This embodiment receives optimized trigger conditions or lighting parameters from the cloud server and updates the local storage, enabling the clothes dryer to continuously improve its control strategy throughout its product lifecycle without requiring users to replace hardware or manually upgrade firmware. Furthermore, the optimized parameters generated by the cloud server based on massive amounts of user data can correct unreasonable aspects of the initial preset values ​​(e.g., a certain light threshold being frequently manually overridden by a large number of users), and can also be dynamically adjusted according to seasonal changes or regional differences. This cloud server-to-local parameter synchronization mechanism endows the clothes dryer with self-evolution capabilities, allowing its control effect to continuously improve with the accumulation of usage data, thus always adapting to users' actual usage habits and environmental changes, significantly enhancing the product's intelligence level and user satisfaction.

[0082] In some preferred embodiments, the method of generating the optimized triggering conditions or lighting parameters includes: when the same clothes drying rack is manually switched to the same lighting mode by the user a preset number of times under the same weather environment and current living time feature combination, the feature combination is generated as the personalized triggering condition for the same clothes drying rack; based on the operating data of multiple clothes drying racks in the same geographical area in the same season, optimized parameters of the time period division rules and light intensity threshold of the same geographical area in the same season are generated; based on the custom parameter data of multiple users, optimized values ​​of lighting parameters for each lighting mode are generated.

[0083] In practice, the cloud server generates optimized trigger conditions or lighting parameters using the following three methods.

[0084] The first method involves generating personalized trigger conditions. The cloud server receives multiple user operation data records from the same clothes drying rack. The cloud server extracts features from the weather and current time of day in each record, forming a feature combination vector, for example (real-time weather: sunny; real-time light intensity: 2800 lux; current time of day: dusk). The cloud server counts the number of times the user manually switches the clothes drying rack to the same lighting mode under the same feature combination. When a preset number of consecutive times (e.g., 3 times) are met—that the user manually switches the lights to the same mode (e.g., sunset mode) when the feature combination occurs—the cloud server determines that the user has a stable personalized preference for that feature combination. The cloud server generates a personalized trigger condition for the clothes drying rack based on this feature combination and binds it to the corresponding target lighting mode. The cloud server sends the generated personalized trigger condition to the clothes drying rack, which prioritizes using this personalized trigger condition in subsequent matching processes, rather than the general preset trigger condition.

[0085] Furthermore, the second approach involves generating optimized parameters based on region and season. The cloud server groups the operational data of a massive amount of clothes drying equipment according to geographical region (e.g., provincial or municipal administrative divisions) and season (spring, summer, autumn, winter). Operational data includes environmental parameters during automatic equipment switching, the distribution of manual user operations, and user-defined time-segmentation parameters. The cloud server analyzes data from all equipment within the same geographical region and season, calculating statistics such as the average sunset time offset for actually triggering dusk mode in that region and season, and the median of user-preferred light thresholds. Based on these statistics, the cloud server generates optimized time-segmentation rules for that geographical region and season (e.g., adjusting the start time of dusk from 60 minutes before sunset to 45 minutes before sunset) and optimized light intensity thresholds (e.g., adjusting the first light threshold from 3000 lux to 2800 lux). These optimized parameters are only applicable to equipment in that region and season, reflecting regional and seasonal differences.

[0086] Furthermore, the third approach involves optimizing universal lighting parameters. The cloud server collects all user-defined lighting parameter data via the mobile application. For example, a user might adjust the brightness of the clear sky mode from the default 90% to 80%, or the color temperature of the sunset mode from 2500 Kelvin to 2200 Kelvin. The cloud server calculates the statistical distribution for each parameter of each lighting mode, for example, taking the average or median of all user-defined values ​​as the optimized value. If this optimized value differs significantly from the factory default value (e.g., exceeding a preset change threshold), the cloud server generates new universal lighting parameter default values ​​and gradually pushes them to all devices or newly activated devices.

[0087] This embodiment achieves comprehensive parameter evolution from individual to group, and from regional to universal, through a three-tiered cloud server optimization mechanism. The generation of personalized trigger conditions allows the clothes dryer to learn and remember users' unique preferences. For example, if a user prefers to switch to sunset mode early at dusk even when the sunlight is still strong, the device will automatically adapt to this behavior, providing a personalized experience for each user. Furthermore, regional and seasonal parameter optimization allows users in the same city or season to enjoy control rules that better suit local climate and sunlight patterns, overcoming the poor applicability of nationally unified parameters in different regions. Moreover, the optimization of universal lighting parameters is based on massive user-defined behaviors, ensuring that default parameters continuously approach the most popular values, improving the rationality of the product's factory settings and user satisfaction. These three optimization methods together form a complete self-learning closed loop, making the clothes dryer's lighting control increasingly precise and user-friendly.

[0088] In some preferred embodiments, the method further includes: when a network disconnection is detected, determining the clothes drying rack's lighting mode based on the time of the clothes drying rack's local real-time clock module and the illumination data from the local light sensor.

[0089] In practice, the clothes drying rack also performs fallback control operations in case of network disconnection. The main control program of the clothes drying rack continuously monitors the wireless network connection status. Specifically, the clothes drying rack determines network availability by periodically sending heartbeat packets to the cloud server or checking the connection status of the network interface. When the clothes drying rack detects a network disconnection (e.g., multiple consecutive unresponsive heartbeat packets, the WiFi module failing to connect to the access point, or the cloud server not responding), the clothes drying rack automatically switches from the cloud server big data control mode to the local control mode.

[0090] Furthermore, in local control mode, the clothes dryer no longer relies on weather and time data obtained from a cloud server. Instead, it uses limited data provided by local hardware modules for simplified lighting control. The clothes dryer has a built-in local real-time clock module and a local light sensor. The real-time clock module is battery-powered and can continue counting time even when power is off, providing current year, month, day, hour, minute, and second information. The local light sensor is installed on the main body of the clothes dryer and continuously collects the ambient light intensity.

[0091] Furthermore, the clothes dryer determines the approximate time of day based on time information provided by the local real-time clock module, combined with preset fixed time period division rules (such as hard-coded approximate sunrise and sunset times or simple morning and evening time divisions). Simultaneously, the clothes dryer judges the ambient light level based on light data collected by the local light sensor. Based on this limited information, the clothes dryer executes simplified lighting control logic: for example, if the local clock indicates it is daytime and the local light sensor detects a dark environment (possibly due to cloudy skies or obstructions), basic lighting is turned on; if the local clock indicates nighttime, a dim lighting mode or basic lighting mode is automatically activated; if the local clock indicates late night, the lights are turned off or switched to an extremely low brightness mode. This simplified logic does not involve precise multi-mode matching; it only ensures that the clothes dryer can still provide basic lighting functionality when the network is interrupted.

[0092] Furthermore, the clothes dryer continuously monitors the network recovery status in local control mode. Once a successful reconnection of the wireless network is detected (e.g., the heartbeat packet returns to normal response), the clothes dryer automatically switches from local control mode to cloud server big data control mode, re-executes the entire process from obtaining geographic location information to controlling the switching of the lighting module.

[0093] This embodiment significantly enhances the robustness and availability of the system by introducing a fallback control mechanism for network disconnection. When a network failure occurs or cloud server services become unavailable, the clothes dryer will not become functionally paralyzed or unresponsive. Instead, it automatically degrades to a simplified control mode based on a local real-time clock and light sensors, continuing to provide basic lighting functions to the user. This design ensures that users still have access to usable clothes dryer lighting even in abnormal network conditions. Furthermore, once the network is restored, the clothes dryer can seamlessly switch back to the high-precision cloud server big data control mode without manual user intervention. This mechanism balances the high dependence of intelligent functions with the requirements of system reliability, enabling the product to operate stably under various network conditions, improving the continuity of the user experience and the product's market adaptability.

[0094] Further, please refer to Figure 2 , Figure 2 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0095] The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0096] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, it causes the processor 502 to execute a method for automatically switching the lights on a clothes drying rack.

[0097] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0098] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a method for automatically switching the lights of a clothes drying rack.

[0099] The network interface 505 is used for network communication with other devices. Those skilled in the art will understand that the above structure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. A specific computer device 500 may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements.

[0100] The processor 502 is used to run a computer program 5032 stored in a memory to implement the steps of the automatic switching method for clothes drying rack lights provided in any of the above embodiments.

[0101] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0102] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program may be stored in a storage medium, which is a computer-readable storage medium. The computer program is executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0103] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program. When executed by a processor, the computer program causes the processor to perform the steps of the automatic switching method for the lights of a clothes drying rack provided in any of the above embodiments.

[0104] The storage medium is a physical, non-transient storage medium, such as a USB flash drive, external hard drive, read-only memory (ROM), magnetic disk, or optical disk, or any other physical storage medium capable of storing program code. The computer-readable storage medium can be non-volatile or volatile.

[0105] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0106] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0107] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0108] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0109] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0110] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Since these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0111] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for automatically switching the lights on a clothes drying rack, characterized in that, include: Obtain the geographical location information of the clothes drying rack, and determine the corresponding weather and time data based on the geographical location information; Based on the weather and environmental data and the time dimension data, extract the core feature parameter set; The target lighting pattern is determined based on the core feature parameter set; Control the light module of the clothes dryer to switch to the target light mode.

2. The automatic switching method for clothes drying rack lights according to claim 1, characterized in that, The extraction of a core feature parameter set based on the weather and environmental data and the time dimension data includes: The weather and environmental data and the time dimension data are cleaned to obtain cleaned data; The current living time period is dynamically divided based on the sunrise and sunset times of the day in the cleaned data; The core feature parameter set is extracted from the cleaned data, including at least real-time weather conditions, real-time light intensity, and the current time of day.

3. The automatic switching method for clothes drying rack lights according to claim 2, characterized in that, The step of dynamically dividing the current living time period based on the sunrise and sunset times of the day in the cleaned data includes: The period from the first preset duration after sunrise to the second preset duration before sunset is divided into daytime periods. The period from the second preset duration before sunset to the third preset duration after sunset is defined as the twilight period. The period from the third preset time after sunset to the preset time of the day is divided into the nighttime period; The period from the preset time of the day to the fourth preset time before sunrise the next day is divided into the late night period; And / or, determining the target lighting pattern based on the core feature parameter set includes: The core feature parameter set is matched with the trigger conditions of a variety of preset lighting modes to determine the target lighting mode.

4. The automatic switching method for clothes drying rack lights according to claim 3, characterized in that, The lighting modes include two or more of the following: clear sky mode, sunset mode, aurora mode, and tipsy mode; the process of matching the core feature parameter set with the preset trigger conditions of multiple lighting modes to determine the target lighting mode includes: When the real-time weather conditions in the core feature parameter set are sunny or cloudy, the real-time light intensity is greater than or equal to the first light threshold, and the current living time is daytime, it is matched as the clear sky mode. When the real-time weather conditions in the core feature parameter set are sunny or partly cloudy, the real-time light intensity is greater than or equal to the second light threshold and less than the first light threshold, and the current living time is dusk, the matching is sunset mode. When the real-time weather conditions in the core feature parameter set are no rain and no snow, the real-time light intensity is less than or equal to the third light threshold, and the current living time is late at night, the matching is aurora mode; When the real-time weather conditions in the core feature parameter set are no rain and no snow, the real-time light intensity is less than or equal to the second light threshold, and the current living time is nighttime, the matching mode is "slightly tipsy"; wherein, the first light threshold is greater than the second light threshold, and the second light threshold is greater than the third light threshold. When the set of core feature parameters does not meet the matching conditions of any of the modes, namely Clear Sky Mode, Sunset Mode, Aurora Mode, and Tipsy Mode, the matching mode is the basic lighting mode. And / or, the control of the clothes drying rack's lighting module to switch to the target lighting mode includes: Load the target lighting parameters corresponding to the target lighting mode, and adjust the lighting parameters of the clothes drying rack's lighting module to the target lighting parameters.

5. The automatic switching method for clothes drying rack lights according to claim 2, characterized in that, The step of cleaning the weather and environmental data and the time-dimension data to obtain cleaned data includes: Missing values ​​and anomalous jump values ​​are removed from the weather and time data to obtain intermediate data. The real-time light intensity in the intermediate data is cross-validated with the light data collected by the local light sensor. The real-time light intensity is then corrected or retained based on the cross-validation result to obtain the cleaned data.

6. The automatic switching method for clothes drying rack lights according to claim 1, characterized in that, The method further includes: If a user's manual operation command is detected, the corresponding lighting mode switch is executed first, and the user's operation data is recorded. The user's operation data includes at least the operation time, the weather environment data at the time of operation, the current time of day at the time of operation, and the target lighting mode to be switched by the manual operation command. And / or, the method further includes: The system receives optimized trigger conditions or lighting parameters from the cloud server and updates the corresponding parameters stored locally on the clothes drying rack. The optimized trigger conditions or lighting parameters are generated by the cloud server based on the received user operation data.

7. The automatic switching method for clothes drying rack lights according to claim 6, characterized in that, The methods for generating the optimized triggering conditions or lighting parameters include: When the same clothes drying rack is manually switched to the same lighting mode by the user a preset number of times under the same weather conditions and current time of day, the combination of features will be generated as a personalized trigger condition for the same clothes drying rack. Based on the operating data of multiple clothes drying racks in the same geographical area during the same season, optimized parameters are generated for the time period division rules and light intensity thresholds of the same geographical area during the same season. Based on custom parameter data from multiple users, optimized values ​​for lighting parameters of each lighting mode are generated.

8. The automatic switching method for clothes drying rack lights according to claim 1, characterized in that, The method further includes: When a network disconnection is detected, the clothes drying rack's lighting mode is determined based on the time from the local real-time clock module and the illumination data from the local light sensor.

9. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, can implement the method as described in any one of claims 1-8.

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