Environment modeling method and system applied to sunshade of intelligent sun shield

Abstract

The invention relates to the technical field of sun visors, in particular to an environment modeling method and system applied to sun shading of an intelligent sun visor, and the method comprises the steps: obtaining a target adjustment moment, an environment monitoring unit and the intelligent sun visor, obtaining a historical environment data set based on the environment monitoring unit, obtaining a historical data sequence according to the historical environment data set, and obtaining a target adjustment moment; the weather forecast interface unit receives the prediction data sequence, the real-time monitoring unit obtains real-time environment data, the environment modal is analyzed based on the historical data sequence, the prediction data sequence and the real-time environment data, and a sun shield control instruction of the intelligent sun shield is generated according to the environment modal. And according to the sun shield control instruction, controlling the pose of the sun shield, and completing environment modeling applied to the intelligent sun shield under sun shading. According to the invention, the sun shield can be intelligently controlled according to the environmental data.

Description

Technical Field

[0001] The present invention relates to the technical field of sun visors, and in particular to an environment modeling method and system applied to sun shading by intelligent sun visors. Background Art

[0002] With the development of science and technology, the field of visor technology has continued to advance, especially in the design and application of smart visors. Smart visors can automatically adjust according to changes in ambient light to provide the best shading effect and enhance user comfort and safety. Existing sun visors include fixed sun visors, mechanical sun visors, intelligent pneumatic sun visors, etc. Fixed sun visors are fixed sun visors, mechanical sun visors are sun visors that can be retracted through a mechanical structure, and intelligent pneumatic sun visors can set the position of the sun visor by the user pressing a button or using a remote control device.

[0003] Although the above-mentioned prior arts can realize the function of the sun visor, they are unable to adaptively generate instructions and control the sun visor according to environmental data. Summary of the invention

[0004] The present invention provides an environment modeling method applied to sunshade of an intelligent sunshade, and a computer can realize intelligent control of the sunshade according to environmental data.

[0005] To achieve the above object, the present invention provides an environment modeling method for sunshade of an intelligent sun visor, comprising: Obtaining the target adjustment time, the environment monitoring unit and the intelligent sun visor, wherein the environment monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the intelligent sun visor includes: a sun visor, a control unit and a limit sensor; Based on the environmental monitoring unit, the data storage unit is accessed, and a historical environmental data set is obtained from the data storage unit. The historical environmental data set is divided according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence. Based on the weather forecast interface unit, the predicted environmental data at the target adjustment time within the preset influence interval span is received to obtain a predicted data sequence, and the real-time environmental data is obtained based on the real-time monitoring unit; Analyze the environmental modality at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environmental data, and generate the sun visor control instruction of the intelligent sun visor according to the environmental modality; Sending a sun visor control instruction to a control unit and a limit sensor, and using the control unit to control the posture of the sun visor, wherein the posture includes a tilt angle and a structural level of the sun visor; The limit sensor is used to detect in real time whether the sun visor has reached the preset position. After confirming that the sun visor has reached the preset position, the environmental modeling applied to the sun visor under the shading of the intelligent sun visor is completed.

[0006] Optionally, the acquiring of the historical environment data set from the data storage unit, dividing the historical environment data set according to the pre-constructed time sequence and the data processing unit to obtain the historical data sequence, includes: Obtaining a preset number of preceding environmental days, and extracting a historical environmental data set from a data storage unit according to the preceding environmental days, wherein the historical environmental data set includes a plurality of historical environmental data, and the number of the historical environmental data is equal to the number of preceding environmental days, the historical environmental data includes a plurality of historical daily environmental data, and the plurality of historical daily environmental data are respectively a temperature sequence, a humidity sequence, a radiation intensity sequence, and a scattered radiation sequence, the historical daily environmental data includes a plurality of historical time environmental data, and the plurality of historical time environmental data are all marked; Based on the time series, multiple time environment data matrices are constructed, and the following operations are performed on the multiple time environment data matrices: Extract the elements to be filled in from the time environment data matrix in sequence, identify the data labels and time labels of the elements to be filled in, obtain the target data labels, and retrieve the target data labels in the historical environment data set; If the target data tag does not exist in the historical environment data set, the historical environment data set is used to perform a completion operation on the to-be-filled element corresponding to the target data tag; otherwise, the to-be-filled element is filled based on the historical environment data corresponding to the target data tag in the historical environment data set; After confirming that the filled multiple time environment data matrices are not the preset blank matrices, the multiple time environment data matrices are spliced ​​to obtain a historical data sequence.

[0007] Optionally, the weather forecast interface unit receives the forecast environment data within a preset influence interval span at the target adjustment time to obtain the forecast data sequence, including: Acquire multiple impact moments based on the impact interval span and the target adjustment moment, and extract a forecast environment data set from the weather forecast interface unit according to the multiple impact moments, wherein the forecast environment data set includes multiple forecast environment data, and the forecast environment data corresponds to the impact moment one by one, and the forecast environment data includes: temperature, humidity, radiation intensity and scattered radiation; A plurality of prediction environment number matrices are constructed based on the time series, and after the prediction environment data set is mapped to the plurality of prediction environment number matrices, the plurality of prediction environment number matrices are spliced ​​to obtain a prediction data sequence.

[0008] Optionally, the environmental modality of the target adjustment time is analyzed based on the historical data sequence, the predicted data sequence and the real-time environmental data, including: Map the target adjustment time to the time sequence, confirm the approximate time based on the time sequence after mapping the target adjustment time, obtain the time label of the approximate time, obtain the target time label, extract the historical modal data and the predicted modal data from the historical data sequence and the predicted data sequence according to the target time label, associate the real-time environmental data with the historical modal data and the predicted modal data, and obtain the environmental data set to be analyzed; Parsing the environmental data set to be analyzed, and obtaining the target environmental modal data set based on the parsed environmental data set to be analyzed; The target environmental modal data set is imported into the pre-built environmental analysis model, and the environmental analysis model after receiving the target environmental modal data set is used to confirm the environmental modality corresponding to the target environmental modal data set, where the environmental modalities include: sunny, cloudy and others.

[0009] Optionally, the determining the approximate time based on the time sequence of the mapped target adjustment time includes: If there is a time corresponding to the target adjustment time in the time sequence, the target adjustment time is confirmed as the approximate time; Otherwise, extract two moments adjacent to the target adjustment moment from the moment sequence to obtain the preceding moment and the following moment, wherein the preceding moment is the first moment before the target adjustment moment, and the following moment is the first moment after the target adjustment moment; The absolute values ​​of the differences between the preceding moment, the following moment and the target adjustment moment are calculated respectively to obtain the preceding absolute time difference and the following absolute time difference. If the preceding absolute time difference is less than the following absolute time difference, the preceding moment is confirmed as the approximate moment; otherwise, the following moment is confirmed as the approximate moment.

[0010] Optionally, parsing the environmental data set to be analyzed, and acquiring the target environmental modal data set based on the parsed environmental data set to be analyzed, includes: Extract the environmental data to be analyzed from the environmental data set to be analyzed in sequence, and perform the following operations on the extracted environmental data to be analyzed: From the environmental data to be analyzed, historical modal data, predicted modal data and real-time detection data are sequentially selected; Obtaining historical initial data and predicted initial data based on historical modal data and predicted modal data respectively; Get the target project and perform the following operations on each target project: Extract the parameters corresponding to the target project from the historical initial data, predicted initial data and real-time detection data respectively to obtain historical parameters, predicted parameters and real-time parameters. Calculate the absolute value of the difference between the historical parameter and the real-time parameter, the absolute value of the difference between the historical parameter and the predicted parameter, and the absolute value of the difference between the real-time parameter and the predicted parameter, respectively, to obtain the historical-real-time difference, the historical-prediction difference, and the real-time-prediction difference; If the historical-real-time difference, historical-forecast difference and real-time-forecast difference are all less than or equal to the preset difference threshold, the real-time parameter is confirmed as the target project unit data; If there is a value greater than the difference threshold among the historical-real-time difference, the historical-prediction difference, and the real-time-prediction difference, the detection unit corresponding to the target item is obtained based on the real-time monitoring unit, and an integrity check is performed on the detection unit to obtain a test result; If the test result shows that the detection unit is a preset abnormal unit, the prediction parameter is confirmed as the target item unit data; if the test result shows that the detection unit is a preset normal unit, a plurality of re-detection values ​​are obtained by using the detection unit, the average of the plurality of re-detection values ​​is calculated, and the average of the plurality of re-detection values ​​is confirmed as the target item unit data; Summarize the target project unit data to obtain the target environment modal dataset.

[0011] Optionally, generating a sun visor control instruction of the smart sun visor according to the environmental modality includes: If the ambient mode is sunny, the target radiation intensity and target scattered radiation are extracted from the target ambient mode data set based on the ambient mode. If the target radiation intensity is greater than or equal to the preset radiation intensity threshold, or the target scattered radiation is greater than or equal to the preset scattered radiation threshold, a sunshade instruction is generated to obtain an initial control instruction. If the environmental modality is cloudy, the environmental modality extracts the target humidity and target temperature from the target environmental modality data set. If the target temperature is less than or equal to the preset temperature threshold, or the target humidity is greater than or equal to the preset humidity threshold, a transparent shielding instruction is generated to obtain the initial control instruction. Otherwise, a sun visor retraction instruction is generated to obtain the initial control instruction. If the environmental mode is other, a transparent masking instruction is generated to obtain the initial control instruction; The initial control instruction is sent to the pre-built sun visor control terminal, and the control terminal based on the initial control instruction confirms the sun visor control instruction.

[0012] Optionally, the control terminal confirming the sun visor control instruction based on the initial control instruction includes: determining whether a user control instruction from a pre-built sun visor control terminal is received based on a preset response time; If a user control instruction is received within the response time, the user control instruction is parsed, and if the user control instruction is a confirmation instruction, whether the initial control instruction is a sunshade instruction is determined; If the initial control instruction is a sunshade instruction, the target environment geographic data is obtained, the direct solar angle corresponding to the target adjustment time is calculated based on the target environment geographic data, the inclination angle of the sun visor is calculated according to the direct solar angle, and the sun visor control instruction is generated based on the inclination angle of the sun visor; otherwise, the initial control instruction is confirmed as the sun visor control instruction; if the user control instruction is not confirmed, a custom instruction is generated based on the user control instruction to obtain the sun visor control instruction. If the user control instruction is not received within the response time, the delayed response time, the starting time of the response time and the current time are obtained, the total response length is calculated based on the current time and the starting time, the delayed response time is taken as the response time, and the step of judging whether the user control instruction from the sun visor control terminal is received based on the preset response time is returned until the sun visor control instruction is obtained or the total response length is greater than or equal to the preset response time threshold; If the total response length is greater than or equal to the preset response time threshold, the control unit determines whether there is a previous historical sun visor control instruction adjacent to the response time. If there is a previous historical sun visor control instruction adjacent to the response time, the historical sun visor control instruction is confirmed as the sun visor control instruction; otherwise, a transparent shielding instruction is generated to obtain the sun visor control instruction.

[0013] Optionally, the step of calculating the solar direct angle corresponding to the target adjustment time based on the target environment geographic data, and calculating the inclination angle of the sun visor according to the solar direct angle includes: The solar declination is calculated using the target geographic environment data and the pre-built solar red tail angle calculation formula, where the solar red tail angle calculation formula is as follows:

[0014] in, Indicates the solar declination corresponding to the day sequence of the target adjustment time, The date sequence number corresponding to the date sequence of the target adjustment time, Indicates the angle unit, The date sequence indicating the time of target adjustment; The time difference is calculated using the target geographic environment data and the pre-built time difference formula, where the time difference formula is as follows:

[0015] in, Indicates the time difference, Indicates the longitude in the target geographic environment data; The solar altitude angle is calculated based on the solar declination angle, and the complementary angle of the solar altitude angle is obtained to obtain the inclination angle of the sunshade. The calculation formula of the solar altitude angle is:

[0016] in, represents the solar altitude angle, Indicates the latitude in the target geographic environment data. represents the solar declination, Indicates time difference.

[0017] To achieve the above object, the present invention also provides an environment modeling system for sunshade of an intelligent sun visor, comprising: The intelligent sun visor acquisition module is used to obtain the target adjustment time, the environment monitoring unit and the intelligent sun visor, wherein the environment monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the intelligent sun visor includes: a sun visor, a control unit and a limit sensor; A sun visor control instruction acquisition module is used to access the data storage unit based on the environment monitoring unit, and obtain a historical environment data set from the data storage unit, divide the historical environment data set according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence, receive the predicted environment data within the preset influence interval span at the target adjustment time based on the weather forecast interface unit, obtain the predicted data sequence, obtain the real-time environment data based on the real-time monitoring unit, analyze the environment mode at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environment data, and generate the sun visor control instruction of the smart sun visor according to the environment mode; A posture control module, used for sending a sun visor control instruction to a control unit and a limit sensor, and controlling the posture of the sun visor by using the control unit, wherein the posture includes a tilt angle and a structural level of the sun visor; The posture confirmation module is used to use the limit sensor to detect in real time whether the sun visor has reached a preset posture. After confirming that the sun visor has reached the preset posture, the environment modeling applied to the intelligent sun visor under the shading is completed.

[0018] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising: A memory storing at least one instruction; The processor executes the instructions stored in the memory to implement the above-mentioned environmental modeling method applied to the sun shading of the smart sun visor.

[0019] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned environmental modeling method applied to the sun shading of a smart sun visor.

[0020] The present invention solves the problems described in the background technology. First, the data storage unit is accessed based on the environmental monitoring unit, and a historical environmental data set is obtained from the data storage unit. The historical environmental data set is divided according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence. The predicted environmental data at the target adjustment time within the preset influence interval span is received based on the weather forecast interface unit to obtain a predicted data sequence. The real-time environmental data is obtained based on the real-time monitoring unit. The environmental mode at the target adjustment time is analyzed based on the historical data sequence, the predicted data sequence and the real-time environmental data. The sun visor control instruction of the intelligent sun visor is generated according to the environmental mode. The present invention classifies the environmental data using the time sequence, thereby reducing the amount of calculation under the premise of ensuring the accuracy of the environmental data. Since the target adjustment time may occur at any time of the day, it is very cumbersome to retrieve the historical mode data and the predicted mode data according to the target adjustment time, and there is a huge amount of calculation. Since the weather is not changing rapidly, the accuracy is appropriately reduced, and the approximate time is used as the reference data of the target adjustment time, which can reduce the amount of calculation while ensuring the accuracy of the data. And when generating the sun visor control instruction, if the real-time monitoring unit is damaged, the prediction parameter is used as the target project unit data. If the real-time monitoring unit is not damaged, the data that the real-time monitoring unit can monitor is still used as the standard. The present invention enables the intelligent sun visor to obtain the initial control instruction according to the parsed environmental data, but the priority is to meet the user's needs. Therefore, it is necessary to accept the user's control instruction to let the user confirm. If there is no way to confirm, in order to maintain the safety of the place covered by the sun visor, the transparent shielding instruction is used as a common instruction. The sun visor control instruction is sent to the control unit and the limit sensor, and the control unit is used to control the posture of the sun visor, wherein the posture includes the inclination angle and structural level of the sun visor. The limit sensor is used to detect in real time whether the sun visor reaches the preset posture. After confirming that the sun visor reaches the preset posture, the environmental modeling applied to the sun visor under the sunshade is completed. The present invention combines the environmental data stored historically in the data storage unit, the environmental data detected in real time by the real-time monitoring unit, and the environmental data called by the weather forecast interface unit to evaluate the current environment, thereby adjusting the posture of the intelligent sun visor. Therefore, the present invention can realize intelligent control of the sun visor according to environmental data. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic flow chart of an environment modeling method applied to a smart sun visor under sunshade provided by an embodiment of the present invention; Figure 2 A functional module diagram of an environment modeling system applied to sunshade under intelligent sun visor provided by an embodiment of the present invention; Figure 3A schematic diagram of the structure of an electronic device for implementing the method for modeling an environment under sun shading of an intelligent sun visor provided in an embodiment of the present invention; Description of reference numerals: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0022] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0023] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0024] The embodiment of the present application provides an environmental modeling method for use under the sunshade of a smart sun visor. The execution subject of the environmental modeling method for use under the sunshade of a smart sun visor includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the environmental modeling method for use under the sunshade of a smart sun visor can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0025] Reference Figure 1 FIG. 1 is a flow chart of an environment modeling method applied to a smart sun visor under sunshade provided by an embodiment of the present invention. In this embodiment, the environment modeling method applied to a smart sun visor under sunshade includes: S1. Obtain target adjustment time, environmental monitoring unit and smart sun visor.

[0026] Specifically, the environmental monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the intelligent sun visor includes: a sun visor, a control unit and a limit sensor.

[0027] It is understandable that the target adjustment moment is the moment when the smart sun visor needs to be adjusted according to the environmental data. The real-time monitoring unit is a unit that monitors the environment around the smart sun visor, including multiple sensors that can continuously monitor the temperature, humidity, scattered radiation and radiation intensity of the environment around the smart sun visor. The radiation intensity in the embodiment of the present invention is the radiation intensity when the sun is directly shining. If there is no direct sunlight, the radiation intensity is 0. The data storage unit is a unit that stores the data collected by the environmental monitoring unit, the data processing unit is a unit that processes the data, and the weather forecast interface unit is a unit that calls the weather forecast data of the local area from the existing networked database. For example, when the area is District B of City A, the weather forecast data of District B of City A can be called from the weather forecast interface unit.

[0028] Furthermore, the smart sun visor is a sun visor that can be intelligently adjusted according to environmental data. In the embodiment of the present invention, the smart sun visor responds to instructions through different structural levels. Please refer to the subsequent embodiments for specific response methods. The control unit is a unit that controls the position of the smart sun visor according to the instructions, and the limit sensor is a unit for detecting the position of the smart sun visor.

[0029] It should be noted that the embodiment of the present invention combines the environmental data historically stored in the data storage unit, the environmental data detected in real time by the real-time monitoring unit, and the environmental data called by the weather forecast interface unit to evaluate the current environment, thereby adjusting the position of the smart sun visor. The sun visor in the embodiment of the present invention is set in a fixed position.

[0030] S2. Based on the environmental monitoring unit, the data storage unit is accessed, and a historical environmental data set is obtained from the data storage unit. The historical environmental data set is divided according to a pre-constructed time sequence and a data processing unit to obtain a historical data sequence. Based on the weather forecast interface unit, the predicted environmental data at the target adjustment time within the preset influence interval span is received to obtain a predicted data sequence. The real-time environmental data is obtained based on the real-time monitoring unit.

[0031] It should be noted that the historical environmental data set is data extracted from the data storage unit, and specifically refers to a data set detected by the real-time monitoring unit within a period of time before the target adjustment time. The moment series is a sequence constructed by some specific moments, for example: [0:00, 1:00, ..., 23:00]. Since the real-time monitoring unit collects environmental data according to the monitoring frequency preset in the real-time monitoring unit, the frequency of collection for different environmental data is different, and the amount of environmental data collected is huge and relatively stable in the same time period. Therefore, the embodiment of the present invention uses the moment series to classify the environmental data, thereby reducing the amount of calculation while ensuring the accuracy of the environmental data.

[0032] Specifically, the real-time environmental data is data obtained by detection by a real-time monitoring unit.

[0033] Furthermore, the historical environment data set is obtained from the data storage unit, and the historical environment data set is divided according to the pre-constructed time sequence and the data processing unit to obtain the historical data sequence, including: Obtaining a preset number of preceding environmental days, and extracting a historical environmental data set from a data storage unit according to the preceding environmental days, wherein the historical environmental data set includes a plurality of historical environmental data, and the number of the historical environmental data is equal to the number of preceding environmental days, the historical environmental data includes a plurality of historical daily environmental data, and the plurality of historical daily environmental data are respectively a temperature sequence, a humidity sequence, a radiation intensity sequence, and a scattered radiation sequence, the historical daily environmental data includes a plurality of historical time environmental data, and the plurality of historical time environmental data are all marked; Based on the time series, multiple time environment data matrices are constructed, and the following operations are performed on the multiple time environment data matrices: Extract the elements to be filled in from the time environment data matrix in sequence, identify the data labels and time labels of the elements to be filled in, obtain the target data labels, and retrieve the target data labels in the historical environment data set; If the target data tag does not exist in the historical environment data set, the historical environment data set is used to perform a completion operation on the to-be-filled element corresponding to the target data tag; otherwise, the to-be-filled element is filled based on the historical environment data corresponding to the target data tag in the historical environment data set; After confirming that the filled multiple time environment data matrices are not the preset blank matrices, the multiple time environment data matrices are spliced ​​to obtain a historical data sequence.

[0034] It is understandable that the preceding environmental days are the days before the target adjustment time that are artificially selected, and are used to limit the length of the extracted historical environmental data set. For example, if the preceding environmental days are 3, and the date corresponding to the target adjustment time is January 4, then the data sets detected by the real-time monitoring unit on January 3, January 2, and January 1 are extracted from the data storage unit to obtain the historical environmental data set. The temperature sequence, humidity sequence, radiation intensity sequence, and scattered radiation sequence are respectively multiple detected by a temperature sensor, a humidity sensor, a radiation intensity sensor, and a scattered radiation intensity sensor, and are arranged in chronological order. This technology is a prior art and will not be repeated here. The historical time environmental data is the data in the historical daily environmental data. For example, the historical daily environmental data is a temperature sequence, and the temperature sequence is [a, b, c], then each element in the temperature sequence is the historical time environmental data. The meaning that the multiple historical time environmental data have been marked is that each of the multiple historical time environmental data is marked using a time tag and a data tag. For example, if the temperature sequence is [a, b, c], then the time label corresponding to a is the label generated according to the time corresponding to the historical environmental data a, such as d-1-1-0:00, which means 0:00 on January 1, year d. The data label is the label generated by the detection sensor corresponding to the historical environmental data a. Therefore, the data label at this time is Temp (abbreviation for temperature). It can be obtained that the label corresponding to the historical environmental data a is: d-1-1-0:00-Temp.

[0035] Furthermore, the time environment data matrix is ​​a blank matrix constructed based on the historical environment data set, and the number of the time environment data matrices is equal to the types of detection sensors.

[0036] In an embodiment of the present invention, a historical environmental data set is extracted from the past few days, each day corresponds to a historical environmental data, and each historical environmental data contains sequences (including temperature, humidity, radiation intensity and scattering intensity) detected by multiple different detection sensors. The sequences detected by each detection sensor in the past few days are used to generate a time environment data matrix. That is to say, in the embodiment of the present invention, the multiple time environment data matrices are respectively a time environment data matrix corresponding to temperature, a time environment data matrix corresponding to humidity, a time environment data matrix corresponding to radiation intensity, and a time environment data matrix corresponding to scattered radiation intensity, and each time environment data matrix summarizes the environmental data of the past few days in units of time in the moment series, and the same data processing is performed on each of the multiple time environment data matrices.

[0037] Specifically, the time environment data matrix is ​​as follows:

[0038] in, represents the environment data matrix, Indicates Historical environmental data The historical environment data corresponding to the moment, Indicates Historical environmental data The historical environment data corresponding to the moment, Indicates Historical environmental data The historical environment data corresponding to the moment, Indicates Historical environmental data The historical environmental data corresponding to the moment.

[0039] For example, if the time environment data matrix is ​​a time environment data matrix corresponding to temperature, then in the time environment data matrix, the row vector represents the temperature sequence of the historical environment data, the first row is the temperature sequence corresponding to the first historical environment data, and Indicates the temperature sequence of the first historical environmental data. The historical environmental data corresponding to the moment.

[0040] Further, the elements to be filled are elements in the environment data matrix. For example, according to the above embodiment, if is an element to be filled, and the above example of the embodiment of the present invention is used. If The time label of is d-1-1-0:00, then the time label of the moment is 0:00, that is, the time label contains the year, month, day and moment, while the moment label only contains the moment. The target data label is a collection of data labels and moment time labels. For example, the time environment data matrix is ​​the time environment data matrix corresponding to temperature, then the target data label is: 0:00-Temp.

[0041] For example, the historical environmental data set includes a temperature sequence of [a, b, c], and the label corresponding to the historical environmental data a is: d-1-1-0:00-Temp, and the element to be filled is The target data label is: 0:00-Temp, then the target data label exists in the historical environment data set, and the historical environment data a is filled to ,but To fill in the data. Elements to be filled in The target data label is: 0:30-Temp, the label corresponding to the historical environmental data b is d-1-1-1:00-Temp, and the label corresponding to the historical environmental data c is d-1-1-2:00-Temp. That is to say, if the target data label does not exist in the historical environmental data set, the element to be filled is filled according to the temperature sequence. For example, the mean of the historical environmental data b and the historical environmental data a is calculated to fill the element to be filled. It should be noted that there are a variety of existing technologies that can complete the elements to be filled, and the embodiments of the present invention are not limited to this.

[0042] It is understandable that if there are unfilled elements in the time environment data matrix, it is the preset blank matrix. The splicing of the multiple time environment data matrices is to put the filled multiple time environment data matrices into the same matrix according to the data of temperature, humidity radiation intensity and scattered radiation intensity, and the obtained block matrix is ​​the historical data sequence.

[0043] Understandably, the historical data series looks like this:

[0044] in, represents the historical data sequence, , , and Respectively represent the time environment data matrix corresponding to temperature, humidity, radiation intensity and scattered radiation intensity, Represents the transpose symbol.

[0045] Furthermore, the weather forecast interface unit receives the forecast environment data within the preset influence interval span at the target adjustment time to obtain the forecast data sequence, including: Acquire multiple impact moments based on the impact interval span and the target adjustment moment, and extract a forecast environment data set from the weather forecast interface unit according to the multiple impact moments, wherein the forecast environment data set includes multiple forecast environment data, and the forecast environment data corresponds to the impact moment one by one, and the forecast environment data includes: temperature, humidity, radiation intensity and scattered radiation; A plurality of prediction environment number matrices are constructed based on the time series, and after the prediction environment data set is mapped to the plurality of prediction environment number matrices, the plurality of prediction environment number matrices are spliced ​​to obtain a prediction data sequence.

[0046] It can be understood that the impact interval span is the length of an artificially preset time interval, especially referring to the total length of a period of time before and after the target adjustment time, and the impact interval span is symmetrically set according to the target adjustment time. For example, the target adjustment time is 14:00, and the impact interval span is 4h, then the prediction data sequence should extract the prediction environment data from 12:00 until the prediction environment data at 16:00 is extracted. At this time, the multiple impact times are: 12:00, 13:00, 14:00, 15:00 and 16:00. All the extracted prediction environment data are aggregated to obtain the prediction environment data set.

[0047] It should be noted that the method of constructing multiple prediction environment number matrices based on the moment series is consistent with the method of constructing the time environment data matrix, mapping the prediction environment data set to the multiple prediction environment number matrices is consistent with the operation of performing the following operations on the multiple time environment data matrices, and the method of splicing the multiple prediction environment number matrices to obtain a prediction data sequence is consistent with the method of splicing the multiple time environment data matrices to obtain a historical data sequence, and can achieve the same effect, which will not be repeated here.

[0048] It is understandable that there is a difference between the historical data series and the predicted data series. The historical environmental data set is extracted from the data storage unit based on the preceding environmental days, while the predicted environmental data set is the environmental data corresponding to the day of the target adjustment moment.

[0049] It should be noted that in order to simplify the calculation, the time sequence can be simplified according to the span of the impact interval, so that the start time of the time sequence is consistent with the start time calculated based on the span of the impact interval and the target adjustment time, and the end time of the time sequence is consistent with the end time calculated based on the span of the impact interval and the target adjustment time.

[0050] S3. Analyze the environmental modality at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environmental data, and generate a sun visor control instruction for the smart sun visor according to the environmental modality.

[0051] Furthermore, the environmental modality of the target adjustment time analyzed based on the historical data sequence, the predicted data sequence and the real-time environmental data includes: Map the target adjustment time to the time sequence, confirm the approximate time based on the time sequence after mapping the target adjustment time, obtain the time label of the approximate time, obtain the target time label, extract the historical modal data and the predicted modal data from the historical data sequence and the predicted data sequence according to the target time label, associate the real-time environmental data with the historical modal data and the predicted modal data, and obtain the environmental data set to be analyzed; Parsing the environmental data set to be analyzed, and obtaining the target environmental modal data set based on the parsed environmental data set to be analyzed; The target environmental modal data set is imported into the pre-built environmental analysis model, and the environmental analysis model after receiving the target environmental modal data set is used to confirm the environmental modality corresponding to the target environmental modal data set, where the environmental modalities include: sunny, cloudy and others.

[0052] It is understandable that the time span corresponding to the moment sequence should include the target adjustment moment. For example, if the target adjustment moment is 0:30, the moment sequence should at least include the moment before 0:30 and the moment after 0:30. Therefore, after the target adjustment moment is mapped to the moment sequence, the approximate moment can be confirmed in the moment sequence after the target adjustment moment is mapped. The approximate moment is the moment in the moment sequence that is close to the target adjustment moment. Please refer to the subsequent embodiments for the specific confirmation method.

[0053] Furthermore, the historical modal data and the predicted modal data are extracted from the historical data sequence and the predicted data sequence according to the target time tag: all data corresponding to the target time tag are found in the historical data sequence according to the target time tag, and all data corresponding to the target time tag are found in the predicted data sequence, and the historical modal data and the predicted modal data are obtained. It should also be noted that the target time tag here is a time tag of an approximate moment.

[0054] For example, the target time tag is 0:00, and the target adjustment time is January 4. When the historical data sequence is a data set detected by the real-time monitoring unit on January 3, January 2, and January 1, the temperature, humidity, radiation intensity, and scattered radiation intensity corresponding to 0:00 on January 3, January 2, and January 1 are extracted from the data storage unit, and then summarized to obtain historical modal data. The predicted data sequence includes 23:00 on January 3, 0:00 on January 3, 0:00 on January 4, and 1:00 on January 4. Then, the temperature, humidity, radiation intensity, and scattered radiation intensity corresponding to 23:00 on January 3, 0:00 on January 3, 0:00 on January 4, and January 4 are extracted, and then summarized to obtain predicted modal data.

[0055] Furthermore, the real-time environmental data is associated with the historical modal data and the predicted modal data by using the same index to set identifiers for the real-time environmental data, the historical modal data and the predicted modal data, so that the real-time environmental data, the historical modal data and the predicted modal data are integrated into the environmental data set to be analyzed.

[0056] It should be noted that the target environmental modal data set is a data set obtained after parsing the environmental data to be analyzed, which is used to adjust the smart sun visor at the target adjustment time. The environmental analysis model is a model composed of a neural network that can identify the corresponding weather according to the target environmental modal data set.

[0057] It is understandable that the environmental analysis model includes: Obtain a pre-built neural network model and a preset training date set, and obtain a training environment data set based on the training date set, wherein the training date corresponds to the training environment data one by one, and the training environment data includes a plurality of training time data; Divide the training environment data set by using the pre-constructed training time sequence to obtain the time environment data set, extract the time environment data from the time environment data set in sequence, identify the weather corresponding to the time environment data, and associate the weather corresponding to the time environment data with the time environment data to obtain training samples, summarize the training samples, and obtain a training sample set; The neural network model is trained using the training sample set to obtain an environmental analysis model, wherein the input of the environmental analysis model is the real-time environmental data and the output is the weather.

[0058] It should be noted that the training date set is a plurality of training dates, for example, each day in the past three years is selected. The training environment data set is a data set obtained according to the training date set, and has the same composition as the historical environment data set. The method for obtaining the training environment data set is not limited in the embodiment of the present invention. The training time can be a sequence consisting of recording every 30 minutes from 0:00 to 23:30. The embodiment of the present invention does not limit the time interval. The method of dividing the training environment data set using a pre-constructed training moment sequence is as follows: extracting the data set corresponding to the training moment from the training environment data set according to the time in the training moment, and aggregating the data at the same moment together. The resulting set is the time environment data set.

[0059] It is understandable that when training the neural network model, a training sample should include: temperature, humidity, radiation intensity, scattered radiation intensity and weather at a certain moment, and use: temperature, humidity, radiation intensity and scattered radiation intensity as inputs of the neural network model, and weather as outputs of the neural network model, so that the trained neural network model can judge the weather conditions based on temperature, humidity, radiation intensity and scattered radiation intensity. There are many existing technologies that can achieve the acquisition and training of neural networks, and the embodiments of the present invention will not be repeated here.

[0060] Further, the step of determining the approximate time based on the time sequence of the mapped target adjustment time includes: If there is a time corresponding to the target adjustment time in the time sequence, the target adjustment time is confirmed as the approximate time; Otherwise, extract two moments adjacent to the target adjustment moment from the moment sequence to obtain the preceding moment and the following moment, wherein the preceding moment is the first moment before the target adjustment moment, and the following moment is the first moment after the target adjustment moment; The absolute values ​​of the differences between the preceding moment, the following moment and the target adjustment moment are calculated respectively to obtain the preceding absolute time difference and the following absolute time difference. If the preceding absolute time difference is less than the following absolute time difference, the preceding moment is confirmed as the approximate moment; otherwise, the following moment is confirmed as the approximate moment.

[0061] Exemplarily, the target adjustment time is 1:30, and the time sequence is 0:00, 1:00, and 2:00. There is no time corresponding to the target adjustment time in the time sequence. Therefore, the first time before the target adjustment time is extracted from the time sequence, which is 1:00 (although 0:00 is also before 1:30, it is not the first time before the target adjustment time, but the second time before the target adjustment time). The first time after the target adjustment time, 2:00, is extracted, and the absolute value of the difference between the preceding time and the target adjustment time is calculated to obtain the preceding absolute time difference, that is, the preceding absolute time difference is 30 minutes. Similarly, the following absolute time difference is also 30 minutes. At this time, the preceding absolute time difference is equal to the following absolute time difference, and the following time is confirmed as an approximate time.

[0062] It is understandable that the target adjustment time may occur at any time of the day. It is very cumbersome to retrieve the historical modal data and the predicted modal data based on the target adjustment time, and it requires a huge amount of calculation. Since the weather is not changing rapidly, it is appropriate to reduce the accuracy and use the approximate time as the reference data for the target adjustment time. This can reduce the amount of calculation while ensuring data accuracy.

[0063] Furthermore, the step of parsing the environmental data set to be analyzed and acquiring the target environmental modal data set based on the parsed environmental data set to be analyzed includes: Extract the environmental data to be analyzed from the environmental data set to be analyzed in sequence, and perform the following operations on the extracted environmental data to be analyzed: From the environmental data to be analyzed, historical modal data, predicted modal data and real-time detection data are sequentially selected; Obtaining historical initial data and predicted initial data based on historical modal data and predicted modal data respectively; Get the target project and perform the following operations on each target project: Extract the parameters corresponding to the target project from the historical initial data, predicted initial data and real-time detection data respectively to obtain historical parameters, predicted parameters and real-time parameters. Calculate the absolute value of the difference between the historical parameter and the real-time parameter, the absolute value of the difference between the historical parameter and the predicted parameter, and the absolute value of the difference between the real-time parameter and the predicted parameter, respectively, to obtain the historical-real-time difference, the historical-prediction difference, and the real-time-prediction difference; If the historical-real-time difference, historical-forecast difference and real-time-forecast difference are all less than or equal to the preset difference threshold, the real-time parameter is confirmed as the target project unit data; If there is a value greater than the difference threshold among the historical-real-time difference, the historical-prediction difference, and the real-time-prediction difference, the detection unit corresponding to the target item is obtained based on the real-time monitoring unit, and an integrity check is performed on the detection unit to obtain a test result; If the test result shows that the detection unit is a preset abnormal unit, the prediction parameter is confirmed as the target item unit data; if the test result shows that the detection unit is a preset normal unit, a plurality of re-detection values ​​are obtained by using the detection unit, the average of the plurality of re-detection values ​​is calculated, and the average of the plurality of re-detection values ​​is confirmed as the target item unit data; Summarize the target project unit data to obtain the target environment modal dataset.

[0064] It should be noted that the target items are items corresponding to the sensors in the real-time monitoring unit, including temperature, humidity, radiation intensity, and scattered radiation intensity. For example, if the target item is temperature, the historical parameters, predicted parameters, and real-time parameters correspond to the historical temperature, predicted temperature, and real-time temperature. If the historical-real-time difference, the historical-predicted difference, and the real-time-predicted difference are all less than or equal to the preset difference threshold, it means that the temperatures calculated by the three methods are approximate, and it also means that the real-time parameter detection is basically accurate, so the real-time parameter can be confirmed as the unit data of the target item.

[0065] Furthermore, if there are values ​​greater than the difference threshold in the historical-real-time difference, the historical-prediction difference, and the real-time-prediction difference, several situations may occur. First, if the historical parameters are inaccurate, then the real-time parameters monitored by the real-time monitoring unit are more convincing. Second, if the real-time parameters are inaccurate, it may be that the sensor corresponding to the real-time parameters needs to be calibrated, or abnormal damage has occurred. The predicted parameters can be used as a reference. Third, if the predicted parameters are inaccurate, the predicted parameters are mainly obtained through the weather forecast interface. At present, they are basically accurate to the region, and may differ for specific locations. The real-time parameters monitored by the real-time monitoring unit are more convincing. Fourth, if the historical parameters and real-time parameters are inaccurate, the real-time monitoring unit still needs to be repaired. At this time, the predicted parameters become relatively accurate parameters, which can meet the needs of environmental reference to a certain extent. ..., and so on, which will not be enumerated here. In short, the key point is that if the real-time monitoring unit is damaged, the predicted parameters are used as the target project unit data. If the real-time monitoring unit is not damaged, the data that can be monitored by the real-time monitoring unit shall still prevail.

[0066] It is understandable that historical parameters and predicted parameters can be used as a reference for checking whether there are abnormalities in real-time parameters.

[0067] Furthermore, the detection unit corresponding to the target item is a sensor in the real-time monitoring unit. The integrity check is a method for checking whether the sensor can operate normally and perform detection, for example, whether the sensor is normally connected, whether it meets the corresponding electrical parameter standards, etc. There are many existing technologies that can be implemented, which will not be repeated here. The abnormal unit is a sensor in the detection unit that cannot be used normally. The normal unit is a sensor in the detection unit that can be used normally. The re-detection value is the value obtained after several more tests using the detection unit.

[0068] It should be noted that when there is a value greater than the difference threshold in the historical-real-time difference, the historical-prediction difference and the real-time-prediction difference, the embodiment of the present invention first determines whether the sensor is damaged. If the sensor is not damaged, but there is still a value greater than the difference threshold in the historical-real-time difference, the historical-prediction difference and the real-time-prediction difference, it may be an error caused by accidental errors, or an error caused by inaccurate historical parameter prediction parameters. In this case, taking the average value of multiple detections can solve this problem, so that the obtained target project unit data is more representative. If the sensor is damaged, although there may be differences between specific locations, the prediction parameters can still relatively accurately represent the environmental status, and thus the prediction parameters are confirmed as the target project unit data.

[0069] It is understandable that the obtaining of historical initial data and predicted initial data based on historical modal data and predicted modal data respectively includes: Obtaining a weather data prediction model, wherein the weather prediction data model is composed of a neural network model, importing historical modal data into the weather prediction model, and obtaining historical initial data of the target adjustment time according to the weather prediction model after importing the historical modal data; The prediction data of the target detector are extracted from the prediction modal data in sequence, and the mean of the prediction data of the target detector is calculated to obtain the unit prediction data, and the unit prediction data are summarized to obtain the prediction initial data.

[0070] It should be noted that the weather data prediction model is an existing technology. After the historical modal data is imported into the weather prediction model, the historical initial data of the target adjustment time can be calculated based on the historical modal data.

[0071] Furthermore, the target detector is a sensor in the real-time monitoring unit, and if the target sensor is a temperature sensor, all temperatures are extracted from the predicted modal data, and the mean of the temperatures is calculated, and the mean of the temperatures is confirmed as the unit predicted data. And so on, no further details are given here.

[0072] Furthermore, the generating of the sun visor control instruction of the intelligent sun visor according to the environmental modality includes: If the ambient mode is sunny, the target radiation intensity and target scattered radiation are extracted from the target ambient mode data set based on the ambient mode. If the target radiation intensity is greater than or equal to the preset radiation intensity threshold, or the target scattered radiation is greater than or equal to the preset scattered radiation threshold, a sunshade instruction is generated to obtain an initial control instruction. If the environmental modality is cloudy, the environmental modality extracts the target humidity and target temperature from the target environmental modality data set. If the target temperature is less than or equal to the preset temperature threshold, or the target humidity is greater than or equal to the preset humidity threshold, a transparent shielding instruction is generated to obtain the initial control instruction. Otherwise, a sun visor retraction instruction is generated to obtain the initial control instruction. If the environmental mode is other, a transparent masking instruction is generated to obtain the initial control instruction; The initial control instruction is sent to the pre-built sun visor control terminal, and the control terminal based on the initial control instruction confirms the sun visor control instruction.

[0073] It is understandable that when the environmental mode is sunny, there may be a situation where shading is not required, for example, in winter when the sun is relatively weak. Therefore, the embodiment of the present invention considers that the target radiation intensity is greater than or equal to the preset radiation intensity threshold, or the target scattered radiation is greater than or equal to the preset scattered radiation threshold, and then generates a shading instruction for the sun visor. The radiation intensity threshold and the scattered radiation threshold are both preset values, which are used to limit whether it is necessary to generate a shading instruction. The shading instruction is an instruction to control the sun visor to block the sun, and the transparent shielding instruction is an instruction to use a layer of transparent material to achieve light shielding.

[0074] Furthermore, when the environmental mode is cloudy, if it is too cold or too humid, the transparent part of the sun visor can be used for shielding, which can not only block the wind and keep warm, but also ensure the transparency of light. Therefore, the embodiment of the present invention is set to generate a transparent shielding instruction when the target temperature is less than or equal to the preset temperature threshold, or the target humidity is greater than or equal to the preset humidity threshold.

[0075] It should be noted that a sunny day is a day with direct sunlight, a cloudy day is a day without direct sunlight, but not an extreme weather day, and other days are weather other than sunny and cloudy days, including extreme weather conditions, rain, snow, etc. Therefore, transparent shielding instructions are also generated in other weather conditions. The initial control instruction is an instruction generated by the computer according to the environmental modality. The sun visor control instruction is the instruction obtained after the final confirmation.

[0076] Further, the control end confirming the sun visor control instruction based on the initial control instruction includes: determining whether a user control instruction from a pre-built sun visor control terminal is received based on a preset response time; If a user control instruction is received within the response time, the user control instruction is parsed, and if the user control instruction is a confirmation instruction, whether the initial control instruction is a sunshade instruction is determined; If the initial control instruction is a sunshade instruction, the target environment geographic data is obtained, the direct solar angle corresponding to the target adjustment time is calculated based on the target environment geographic data, the inclination angle of the sun visor is calculated according to the direct solar angle, and the sun visor control instruction is generated based on the inclination angle of the sun visor; otherwise, the initial control instruction is confirmed as the sun visor control instruction; if the user control instruction is not confirmed, a custom instruction is generated based on the user control instruction to obtain the sun visor control instruction. If the user control instruction is not received within the response time, the delayed response time, the starting time of the response time and the current time are obtained, the total response length is calculated based on the current time and the starting time, the delayed response time is taken as the response time, and the step of judging whether the user control instruction from the sun visor control terminal is received based on the preset response time is returned until the sun visor control instruction is obtained or the total response length is greater than or equal to the preset response time threshold; If the total response length is greater than or equal to the preset response time threshold, the control unit determines whether there is a previous historical sun visor control instruction adjacent to the response time. If there is a previous historical sun visor control instruction adjacent to the response time, the historical sun visor control instruction is confirmed as the sun visor control instruction; otherwise, a transparent shielding instruction is generated to obtain the sun visor control instruction.

[0077] It is understandable that the user control instruction is an instruction issued by the user in the pre-built sun visor control terminal. In the embodiment of the present invention, the user control instruction has the highest priority, and after the initial control instruction is generated, the initial control instruction is sent to the user to let the user confirm whether it needs to be executed according to the initial control instruction. The target environment geographic data is the longitude, latitude and other data of the location of the sun visor for calculating the direct angle of the sun. The confirmation instruction is an instruction issued by the user to accept the decision of the initial control instruction. The sun visor control instruction is an instruction for controlling the sun visor to block the sun based on the direct angle of the sun, the tilt angle of the sun visor and the sun shading instruction.

[0078] It is understandable that if a confirmation instruction is received from a user, and the initial control instruction corresponding to the confirmation instruction is a sunshade instruction, the embodiment of the present invention calculates the direct angle of the sun, and adjusts the position of the sun visor according to the direct angle of the sun, so that the adjusted sun visor reflects sunlight, increases the shielding area, and achieves an optimized sunshade effect. If the initial control instruction is not a sunshade instruction, that is, there is no direct sunlight or there is no need to shield the direct sunlight, the initial control instruction is confirmed as a sunshade control instruction.

[0079] Furthermore, if no user control instruction is received within the response time, the initial control instruction is repeatedly sent to the user. If the user control instruction cannot be received, the number of repetitions is not infinite. When the total response length is greater than or equal to the preset response time threshold, the user control instruction will be stopped from being obtained, and at the target adjustment time, the previous historical sun visor control instruction adjacent to the response time is used as the sun visor control instruction. If there is no previous historical sun visor control instruction adjacent to the response time, the transparent shading instruction is confirmed as the initial control instruction.

[0080] It is understandable that although the embodiment of the present invention is that the smart sun visor obtains the initial control instructions based on the analyzed environmental data, satisfying the user's needs takes priority. Therefore, it is necessary to accept the user's control instructions for user confirmation. If there is no way to confirm, in order to maintain the safety of the area shielded by the sun visor, the transparent shielding instruction is used as a common instruction.

[0081] Furthermore, the step of calculating the solar direct angle corresponding to the target adjustment time based on the target environment geographic data, and calculating the inclination angle of the sun visor according to the solar direct angle, includes: The solar declination is calculated using the target geographic environment data and the pre-built solar red tail angle calculation formula, where the solar red tail angle calculation formula is as follows:

[0082] in, Indicates the solar declination corresponding to the day sequence of the target adjustment time, The date sequence number corresponding to the date sequence of the target adjustment time, Indicates the angle unit, The date sequence indicating the time of target adjustment; The time difference is calculated using the target geographic environment data and the pre-built time difference formula, where the time difference formula is as follows:

[0083] in, Indicates the time difference, Indicates the longitude in the target geographic environment data; The solar altitude angle is calculated based on the solar declination angle, and the complementary angle of the solar altitude angle is obtained to obtain the inclination angle of the sunshade. The calculation formula of the solar altitude angle is:

[0084] in, represents the solar altitude angle, Indicates the latitude in the target geographic environment data. represents the solar declination, Indicates the time difference.

[0085] S4, sending a sun visor control instruction to a control unit and a limit sensor, and using the control unit to control the posture of the sun visor, wherein the posture includes a tilt angle and a structural level of the sun visor; It is understandable that in the sunshade instruction, the inclination angle of the sun visor has been calculated, so the position of the sun visor needs to be adjusted to the corresponding inclination angle. The shading opening and closing degree is the degree to which the sun visor is opened. The sun visor in the embodiment of the present invention can be retracted or folded to make the shaded area completely ventilated or closed. Furthermore, the structural hierarchy is the material layer exposed by the sun visor. For example, in the sunshade instruction, the sun visor exposes a layer of shading material and is tilted according to the inclination angle of the sun visor, so that the sun visor can be perpendicular to the sunlight and better achieve sunshade. If the sun visor control instruction is a transparent shielding instruction, a layer of transparent material is used for shielding, such as tempered glass, soft glass, etc., so that light can pass through the sun visor.

[0086] Furthermore, if the sun visor control instruction is a custom instruction, the sun visor is adjusted according to the custom instruction. The custom instruction is an instruction for the user to adjust the sun visor according to the user's purpose, such as sunshade, transparent shielding, and folding the sun visor.

[0087] S5. Use a limit sensor to detect in real time whether the sun visor has reached a preset position. After confirming that the sun visor has reached the preset position, complete the environmental modeling applied to the sun visor under the sunshade of the smart sun visor.

[0088] It is understandable that the preset posture is the state of the standard sun visor obtained according to the sun visor control instruction.

[0089] The present invention solves the problems described in the background technology. First, the data storage unit is accessed based on the environmental monitoring unit, and a historical environmental data set is obtained from the data storage unit. The historical environmental data set is divided according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence. The predicted environmental data at the target adjustment time within the preset influence interval span is received based on the weather forecast interface unit to obtain a predicted data sequence. The real-time environmental data is obtained based on the real-time monitoring unit. The environmental mode at the target adjustment time is analyzed based on the historical data sequence, the predicted data sequence and the real-time environmental data. The sun visor control instruction of the intelligent sun visor is generated according to the environmental mode. The present invention classifies the environmental data using the time sequence, thereby reducing the amount of calculation under the premise of ensuring the accuracy of the environmental data. Since the target adjustment time may occur at any time of the day, it is very cumbersome to retrieve the historical mode data and the predicted mode data according to the target adjustment time, and there is a huge amount of calculation. Since the weather is not changing rapidly, the accuracy is appropriately reduced, and the approximate time is used as the reference data of the target adjustment time, which can reduce the amount of calculation while ensuring the accuracy of the data. And when generating the sun visor control instruction, if the real-time monitoring unit is damaged, the prediction parameter is used as the target project unit data. If the real-time monitoring unit is not damaged, the data that the real-time monitoring unit can monitor is still used as the standard. The present invention enables the intelligent sun visor to obtain the initial control instruction according to the parsed environmental data, but the priority is to meet the user's needs. Therefore, it is necessary to accept the user's control instruction to let the user confirm. If there is no way to confirm, in order to maintain the safety of the place covered by the sun visor, the transparent shielding instruction is used as a common instruction. The sun visor control instruction is sent to the control unit and the limit sensor, and the control unit is used to control the posture of the sun visor, wherein the posture includes the inclination angle and structural level of the sun visor. The limit sensor is used to detect in real time whether the sun visor reaches the preset posture. After confirming that the sun visor reaches the preset posture, the environmental modeling applied to the sun visor under the sunshade is completed. The present invention combines the environmental data stored historically in the data storage unit, the environmental data detected in real time by the real-time monitoring unit, and the environmental data called by the weather forecast interface unit to evaluate the current environment, thereby adjusting the posture of the intelligent sun visor. Therefore, the present invention can realize intelligent control of the sun visor according to environmental data.

[0090] like Figure 2 1 is a functional module diagram of an environment modeling system applied to sunshade by an intelligent sun visor provided by an embodiment of the present invention.

[0091] The environment modeling system 100 applied to the sunshade of the smart sun visor of the present invention can be installed in an electronic device. According to the functions to be implemented, the environment modeling system 100 applied to the sunshade of the smart sun visor may include a smart sun visor acquisition module 101, a sun visor control instruction acquisition module 102, a posture control module 103 and a posture confirmation module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0092] The smart sun visor acquisition module 101 is used to obtain the target adjustment time, the environment monitoring unit and the smart sun visor, wherein the environment monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the smart sun visor includes: a sun visor, a control unit and a limit sensor; The sun visor control instruction acquisition module 102 is used to access the data storage unit based on the environment monitoring unit, and obtain the historical environment data set from the data storage unit, divide the historical environment data set according to the pre-constructed time sequence and the data processing unit to obtain the historical data sequence, receive the predicted environment data within the preset influence interval span at the target adjustment time based on the weather forecast interface unit, obtain the predicted data sequence, obtain the real-time environment data based on the real-time monitoring unit, analyze the environment mode at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environment data, and generate the sun visor control instruction of the smart sun visor according to the environment mode; The posture control module 103 is used to send the sun visor control instruction to the control unit and the limit sensor, and use the control unit to control the posture of the sun visor, wherein the posture includes the tilt angle and structural level of the sun visor; The posture confirmation module 104 is used to use a limit sensor to detect in real time whether the sun visor has reached a preset posture. After confirming that the sun visor has reached the preset posture, the environment modeling applied to the sun visor under the shading of the smart sun visor is completed.

[0093] In detail, the modules in the environment modeling system 100 applied to the sunshade of the smart sun visor in the embodiment of the present invention are used in the same manner as above. Figure 1 The same technical means as the environmental modeling method applied to the smart sun visor under shading described in , and can produce the same technical effects, will not be repeated here.

[0094] like Figure 3 1 is a schematic diagram of the structure of an electronic device for implementing an environment modeling method applied to a smart sun visor under sun shading, provided by an embodiment of the present invention.

[0095] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a method program for modeling an environment under sun protection of a smart sun visor.

[0096] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the environment modeling method program under the sunshade of the smart sun visor, but also can be used to temporarily store data that has been output or is to be output.

[0097] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a program for modeling the environment under the sunshade of an intelligent sun visor, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0098] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0099] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0100] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0101] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0102] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0103] The program of the method for modeling the environment under the sunshade of the smart sun visor stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: Obtaining the target adjustment time, the environment monitoring unit and the intelligent sun visor, wherein the environment monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the intelligent sun visor includes: a sun visor, a control unit and a limit sensor; Based on the environmental monitoring unit, the data storage unit is accessed, and a historical environmental data set is obtained from the data storage unit. The historical environmental data set is divided according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence. Based on the weather forecast interface unit, the predicted environmental data at the target adjustment time within the preset influence interval span is received to obtain a predicted data sequence, and the real-time environmental data is obtained based on the real-time monitoring unit; Analyze the environmental modality at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environmental data, and generate the sun visor control instruction of the intelligent sun visor according to the environmental modality; Sending a sun visor control instruction to a control unit and a limit sensor, and using the control unit to control the posture of the sun visor, wherein the posture includes a tilt angle and a structural level of the sun visor; The limit sensor is used to detect in real time whether the sun visor has reached the preset position. After confirming that the sun visor has reached the preset position, the environmental modeling applied to the sun visor under the shading of the intelligent sun visor is completed.

[0104] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0105] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0106] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement: Obtaining the target adjustment time, the environment monitoring unit and the intelligent sun visor, wherein the environment monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the intelligent sun visor includes: a sun visor, a control unit and a limit sensor; Based on the environmental monitoring unit, the data storage unit is accessed, and a historical environmental data set is obtained from the data storage unit. The historical environmental data set is divided according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence. Based on the weather forecast interface unit, the predicted environmental data at the target adjustment time within the preset influence interval span is received to obtain a predicted data sequence, and the real-time environmental data is obtained based on the real-time monitoring unit; Analyze the environmental modality at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environmental data, and generate the sun visor control instruction of the intelligent sun visor according to the environmental modality; Sending a sun visor control instruction to a control unit and a limit sensor, and using the control unit to control the posture of the sun visor, wherein the posture includes a tilt angle and a structural level of the sun visor; The limit sensor is used to detect in real time whether the sun visor has reached the preset position. After confirming that the sun visor has reached the preset position, the environmental modeling applied to the sun visor under the shading of the intelligent sun visor is completed.

[0107] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0108] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0110] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for modeling an environment under the sunshade of an intelligent sun visor, characterized in that: The method comprises: Obtaining the target adjustment time, the environment monitoring unit and the intelligent sun visor, wherein the environment monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the intelligent sun visor includes: a sun visor, a control unit and a limit sensor; Based on the environmental monitoring unit, the data storage unit is accessed, and a historical environmental data set is obtained from the data storage unit. The historical environmental data set is divided according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence. Based on the weather forecast interface unit, the predicted environmental data at the target adjustment time within the preset influence interval span is received to obtain a predicted data sequence, and the real-time environmental data is obtained based on the real-time monitoring unit; Analyze the environmental modality at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environmental data, and generate the sun visor control instruction of the intelligent sun visor according to the environmental modality; Sending a sun visor control instruction to a control unit and a limit sensor, and using the control unit to control the posture of the sun visor, wherein the posture includes a tilt angle and a structural level of the sun visor; The limit sensor is used to detect in real time whether the sun visor has reached the preset position. After confirming that the sun visor has reached the preset position, the environmental modeling applied to the sun visor under the shading of the intelligent sun visor is completed.

2. The method for modeling an environment under sunshade of an intelligent sun visor according to claim 1, characterized in that: The method of obtaining a historical environment data set from a data storage unit and dividing the historical environment data set according to a pre-constructed time sequence and a data processing unit to obtain a historical data sequence includes: Obtaining a preset number of preceding environmental days, and extracting a historical environmental data set from a data storage unit according to the preceding environmental days, wherein the historical environmental data set includes a plurality of historical environmental data, and the number of the historical environmental data is equal to the number of preceding environmental days, the historical environmental data includes a plurality of historical daily environmental data, and the plurality of historical daily environmental data are respectively a temperature sequence, a humidity sequence, a radiation intensity sequence, and a scattered radiation sequence, the historical daily environmental data includes a plurality of historical time environmental data, and the plurality of historical time environmental data are all marked; Based on the time series, multiple time environment data matrices are constructed, and the following operations are performed on the multiple time environment data matrices: Extract the elements to be filled in from the time environment data matrix in sequence, identify the data labels and time labels of the elements to be filled in, obtain the target data labels, and retrieve the target data labels in the historical environment data set; If the target data tag does not exist in the historical environment data set, the historical environment data set is used to perform a completion operation on the to-be-filled element corresponding to the target data tag; otherwise, the to-be-filled element is filled based on the historical environment data corresponding to the target data tag in the historical environment data set; After confirming that the filled multiple time environment data matrices are not the preset blank matrices, the multiple time environment data matrices are spliced ​​to obtain a historical data sequence.

3. The method for modeling an environment under sunshade of an intelligent sun visor according to claim 2, characterized in that: The step of receiving the forecast environment data within the preset influence interval span at the target adjustment time based on the weather forecast interface unit to obtain the forecast data sequence includes: Acquire multiple impact moments based on the impact interval span and the target adjustment moment, and extract a forecast environment data set from the weather forecast interface unit according to the multiple impact moments, wherein the forecast environment data set includes multiple forecast environment data, and the forecast environment data corresponds to the impact moment one by one, and the forecast environment data includes: temperature, humidity, radiation intensity and scattered radiation; A plurality of prediction environment number matrices are constructed based on the time series, and after the prediction environment data set is mapped to the plurality of prediction environment number matrices, the plurality of prediction environment number matrices are spliced ​​to obtain a prediction data sequence.

4. The method for modeling an environment under sunshade of an intelligent sun visor according to claim 3, characterized in that: The environmental modality of the target adjustment time is analyzed based on the historical data sequence, the predicted data sequence and the real-time environmental data, including: Map the target adjustment time to the time sequence, confirm the approximate time based on the time sequence after mapping the target adjustment time, obtain the time label of the approximate time, obtain the target time label, extract the historical modal data and the predicted modal data from the historical data sequence and the predicted data sequence according to the target time label, associate the real-time environmental data with the historical modal data and the predicted modal data, and obtain the environmental data set to be analyzed; Parsing the environmental data set to be analyzed, and obtaining the target environmental modal data set based on the parsed environmental data set to be analyzed; The target environmental modal data set is imported into the pre-built environmental analysis model, and the environmental analysis model after receiving the target environmental modal data set is used to confirm the environmental modality corresponding to the target environmental modal data set, where the environmental modalities include: sunny, cloudy and others.

5. The method for modeling an environment under shading by a smart sun visor as claimed in claim 4, characterized in that: The step of determining the approximate time based on the time sequence of the mapped target adjustment time comprises: If there is a time corresponding to the target adjustment time in the time sequence, the target adjustment time is confirmed as the approximate time; Otherwise, extract two moments adjacent to the target adjustment moment from the moment sequence to obtain the preceding moment and the following moment, wherein the preceding moment is the first moment before the target adjustment moment, and the following moment is the first moment after the target adjustment moment; The absolute values ​​of the differences between the preceding moment, the following moment and the target adjustment moment are calculated respectively to obtain the preceding absolute time difference and the following absolute time difference. If the preceding absolute time difference is less than the following absolute time difference, the preceding moment is confirmed as the approximate moment; otherwise, the following moment is confirmed as the approximate moment.

6. The method for modeling an environment under sunshade of an intelligent sunshade according to claim 5, characterized in that: The step of parsing the environmental data set to be analyzed and acquiring the target environmental modal data set based on the parsed environmental data set to be analyzed includes: Extract the environmental data to be analyzed from the environmental data set to be analyzed in sequence, and perform the following operations on the extracted environmental data to be analyzed: From the environmental data to be analyzed, historical modal data, predicted modal data and real-time detection data are sequentially selected; Obtaining historical initial data and predicted initial data based on historical modal data and predicted modal data respectively; Get the target project and perform the following operations on each target project: Extract the parameters corresponding to the target project from the historical initial data, predicted initial data and real-time detection data respectively to obtain historical parameters, predicted parameters and real-time parameters. Calculate the absolute value of the difference between the historical parameter and the real-time parameter, the absolute value of the difference between the historical parameter and the predicted parameter, and the absolute value of the difference between the real-time parameter and the predicted parameter, respectively, to obtain the historical-real-time difference, the historical-prediction difference, and the real-time-prediction difference; If the historical-real-time difference, historical-forecast difference and real-time-forecast difference are all less than or equal to the preset difference threshold, the real-time parameter is confirmed as the target project unit data; If there is a value greater than the difference threshold among the historical-real-time difference, the historical-prediction difference, and the real-time-prediction difference, the detection unit corresponding to the target item is obtained based on the real-time monitoring unit, and an integrity check is performed on the detection unit to obtain a test result; If the test result shows that the detection unit is a preset abnormal unit, the prediction parameter is confirmed as the target item unit data; if the test result shows that the detection unit is a preset normal unit, a plurality of re-detection values ​​are obtained by using the detection unit, the average of the plurality of re-detection values ​​is calculated, and the average of the plurality of re-detection values ​​is confirmed as the target item unit data; Summarize the target project unit data to obtain the target environment modal dataset.

7. The method for modeling an environment under sunshade of an intelligent sunshade as claimed in claim 6, characterized in that: The generating of the sun visor control instruction of the intelligent sun visor according to the environmental modality comprises: If the ambient mode is sunny, the target radiation intensity and target scattered radiation are extracted from the target ambient mode data set based on the ambient mode. If the target radiation intensity is greater than or equal to the preset radiation intensity threshold, or the target scattered radiation is greater than or equal to the preset scattered radiation threshold, a sunshade instruction is generated to obtain an initial control instruction. If the environmental modality is cloudy, the environmental modality extracts the target humidity and target temperature from the target environmental modality data set. If the target temperature is less than or equal to the preset temperature threshold, or the target humidity is greater than or equal to the preset humidity threshold, a transparent shielding instruction is generated to obtain the initial control instruction. Otherwise, a sun visor retraction instruction is generated to obtain the initial control instruction. If the environmental mode is other, a transparent masking instruction is generated to obtain the initial control instruction; The initial control instruction is sent to the pre-built sun visor control terminal, and the control terminal based on the initial control instruction confirms the sun visor control instruction.

8. The method for modeling an environment under sunshade of an intelligent sunshade as claimed in claim 7, characterized in that: The control end confirming the sun visor control instruction based on the initial control instruction includes: determining whether a user control instruction from a pre-built sun visor control terminal is received based on a preset response time; If a user control instruction is received within the response time, the user control instruction is parsed, and if the user control instruction is a confirmation instruction, whether the initial control instruction is a sunshade instruction is determined; If the initial control instruction is a sunshade instruction, the target environment geographic data is obtained, the direct solar angle corresponding to the target adjustment time is calculated based on the target environment geographic data, the inclination angle of the sun visor is calculated according to the direct solar angle, and the sun visor control instruction is generated based on the inclination angle of the sun visor; otherwise, the initial control instruction is confirmed as the sun visor control instruction; if the user control instruction is not confirmed, a custom instruction is generated based on the user control instruction to obtain the sun visor control instruction. If the user control instruction is not received within the response time, the delayed response time, the starting time of the response time and the current time are obtained, the total response length is calculated based on the current time and the starting time, the delayed response time is taken as the response time, and the step of judging whether the user control instruction from the sun visor control terminal is received based on the preset response time is returned until the sun visor control instruction is obtained or the total response length is greater than or equal to the preset response time threshold; If the total response length is greater than or equal to the preset response time threshold, the control unit determines whether there is a previous historical sun visor control instruction adjacent to the response time. If there is a previous historical sun visor control instruction adjacent to the response time, the historical sun visor control instruction is confirmed as the sun visor control instruction; otherwise, a transparent shielding instruction is generated to obtain the sun visor control instruction.

9. The method for modeling an environment under sunshade of a smart sunshade as claimed in claim 8, characterized in that: The method of calculating the solar direct angle corresponding to the target adjustment time based on the target environment geographic data, and calculating the inclination angle of the sun visor according to the solar direct angle, comprises: The solar declination is calculated using the target geographic environment data and the pre-built solar red tail angle calculation formula, where the solar red tail angle calculation formula is as follows: in, Indicates the solar declination corresponding to the day sequence of the target adjustment time, The date sequence number corresponding to the date sequence of the target adjustment time, Indicates the angle unit, The date sequence indicating the time of target adjustment; The time difference is calculated using the target geographic environment data and the pre-built time difference formula, where the time difference formula is as follows: in, Indicates the time difference, Indicates the longitude in the target geographic environment data; The solar altitude angle is calculated based on the solar declination angle, and the complementary angle of the solar altitude angle is obtained to obtain the inclination angle of the sunshade. The calculation formula of the solar altitude angle is: in, represents the solar altitude angle, Indicates the latitude in the target geographic environment data. represents the solar declination, Indicates time difference.

10. An environmental modeling system for sunshade under intelligent sun visor, characterized in that: The system comprises: The intelligent sun visor acquisition module is used to obtain the target adjustment time, the environment monitoring unit and the intelligent sun visor, wherein the environment monitoring unit includes: a real-time monitoring unit, a data storage unit, a data processing unit and a weather forecast interface unit, and the intelligent sun visor includes: a sun visor, a control unit and a limit sensor; A sun visor control instruction acquisition module is used to access the data storage unit based on the environment monitoring unit, and obtain a historical environment data set from the data storage unit, divide the historical environment data set according to the pre-constructed time sequence and the data processing unit to obtain a historical data sequence, receive the predicted environment data within the preset influence interval span at the target adjustment time based on the weather forecast interface unit, obtain the predicted data sequence, obtain the real-time environment data based on the real-time monitoring unit, analyze the environment mode at the target adjustment time based on the historical data sequence, the predicted data sequence and the real-time environment data, and generate the sun visor control instruction of the smart sun visor according to the environment mode; A posture control module, used for sending a sun visor control instruction to a control unit and a limit sensor, and controlling the posture of the sun visor by using the control unit, wherein the posture includes a tilt angle and a structural level of the sun visor; The posture confirmation module is used to use the limit sensor to detect in real time whether the sun visor has reached a preset posture. After confirming that the sun visor has reached the preset posture, the environment modeling applied to the intelligent sun visor under the shading is completed.

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