Intelligent door and window adaptive control method and system based on internet of things

Abstract

The present application relates to the field of control adjustment technology, in particular to a smart door and window adaptive control method and system based on Internet of Things, which comprises the following steps: obtaining indoor dimension data and outdoor dimension data of each preset dimension in a current time period, and obtaining a dimension comfort range under each preset dimension; determining an indoor comfort coefficient, an outdoor comfort coefficient and an indoor discomfort influence index corresponding to each preset dimension; screening out a current main dimension and determining a current door and window opening degree; clustering a main dimension group; determining a current window opening increment based on a historical door and window opening degree and a historical window opening degree corresponding to a historical manual window opening corresponding to all historical main dimension groups in a target cluster to which a group formed by all current main dimensions belongs; and intelligently controlling the door and window according to the current door and window opening degree and the current window opening increment. The present application realizes intelligent door and window adaptive control by analyzing data under different preset dimensions, and improves the rationality of door and window control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of control adjustment, in particular to an intelligent door and window adaptive control method and system based on Internet of Things. BACKGROUND

[0002] With the rapid development of science and technology, the Internet of Things (IoT) technology is increasingly widely used in various industries, especially in the fields of home automation and intelligent buildings. As an important application direction of the Internet of Things technology, smart home has gradually entered the daily life of ordinary families. At the same time, as doors and windows are an important part of the structure of a family building, the first thing they affect is the comfort of the living environment. For example, in order to improve the indoor suitability, it is often necessary to properly ventilate the doors and windows to achieve air circulation between the indoor and outdoor. At this time, doors and windows often refer to windows. Therefore, it is crucial to control intelligent doors and windows. At present, when controlling intelligent doors and windows, the commonly used method is to control doors and windows based on temperature, for example, if the outdoor temperature is suitable, the windows are opened for ventilation.

[0003] However, when controlling doors and windows based on temperature, the following technical problems often exist:

[0004] In actual situations, environmental suitability often relates to more than just temperature. Therefore, if doors and windows are controlled based only on temperature, the rationality of door and window control may be poor due to the single consideration factor. SUMMARY

[0005] In order to solve the technical problem of poor rationality of door and window control, the present application provides an intelligent door and window adaptive control method and system based on Internet of Things.

[0006] In a first aspect, the present application provides an intelligent door and window adaptive control method based on Internet of Things, which comprises:

[0007] Obtaining indoor dimension data and outdoor dimension data of each preset dimension in a current time period, and obtaining a dimension comfort range under each preset dimension;

[0008] According to the distribution change of the indoor dimension data and the outdoor dimension data of each preset dimension in the current time period, and the comparison between the indoor dimension data and the outdoor dimension data and the dimension comfort range respectively, determining the indoor comfort coefficient, the outdoor comfort coefficient and the indoor discomfort influence index corresponding to each preset dimension;

[0009] According to the indoor discomfort influence index corresponding to all preset dimensions, screening out the current main dimensions from all preset dimensions, and according to the indoor comfort coefficient, the outdoor comfort coefficient and the indoor discomfort influence index corresponding to all current main dimensions, determining the current door and window opening degree;

[0010] obtain a historical main dimension group corresponding to each historical manual window opening, a historical door and window opening degree and a historical window opening degree corresponding to the historical manual window opening, and perform clustering on the historical main dimension group corresponding to all historical manual window openings and a group formed by all current main dimensions to obtain a target cluster;

[0011] determine a current window opening increment based on the historical door and window opening degree and the historical window opening degree corresponding to the historical manual window opening corresponding to all historical main dimension groups in the target cluster to which the group formed by all current main dimensions belongs;

[0012] perform intelligent door and window control according to the current door and window opening degree and the current window opening increment.

[0013] In a possible implementation manner of the first aspect, the determining of the indoor comfort coefficient, the outdoor comfort coefficient and the indoor discomfort influence index corresponding to each preset dimension according to the distribution change of the indoor dimension data and the outdoor dimension data of each preset dimension in the current time period and the comparison between the indoor dimension data and the dimension comfort range of each preset dimension in the current time period comprises:

[0014] determining a current indoor dimension change distribution index corresponding to each preset dimension according to a standard deviation of all indoor dimension data of each preset dimension in the current time period and an absolute value of a slope of a fitting straight line formed by all indoor dimension data of each preset dimension in the current time period;

[0015] determining a current indoor relative comfort degree corresponding to each preset dimension according to the comparison between the indoor dimension data of each preset dimension in the current time period and the dimension comfort range of each preset dimension;

[0016] determining the indoor comfort coefficient corresponding to each preset dimension as a product of the current indoor dimension change distribution index corresponding to each preset dimension and the current indoor relative comfort degree;

[0017] Similarly, the outdoor comfort coefficient corresponding to each preset dimension is determined according to the distribution change of the outdoor dimension data of each preset dimension in the current time period and the comparison between the outdoor dimension data of each preset dimension in the current time period and the dimension comfort range of each preset dimension;

[0018] determining the indoor discomfort influence index corresponding to each preset dimension according to the indoor comfort coefficient corresponding to each preset dimension.

[0019] In a possible implementation manner of the first aspect, the determining of the current indoor relative comfort degree corresponding to each preset dimension according to the comparison between the indoor dimension data of each preset dimension in the current time period and the dimension comfort range of each preset dimension comprises:

[0020] determine half of the difference between the maximum value and the minimum value of the dimension comfort range of each preset dimension as a comfort radius of each preset dimension;

[0021] determine half of the difference between the maximum value and the minimum value of the dimension comfort range of each preset dimension as a comfort radius of each preset dimension;

[0022] determine the absolute value of the difference between the mean value of all indoor dimension data of each preset dimension in the current time period and the comfort median value of each preset dimension as a comfort deviation of each preset dimension;

[0023] determine the current indoor relative comfort degree corresponding to each preset dimension according to the difference between the comfort deviation and the comfort radius of each preset dimension, and the number of indoor dimension data belonging to the dimension comfort range of each preset dimension in the current time period.

[0024] In combination with the first aspect, in a possible implementation manner, the determining, according to the indoor comfort coefficient corresponding to each preset dimension, of the indoor non-comfort influence indicator corresponding to each preset dimension comprises:

[0025] determining, according to the indoor comfort coefficient corresponding to each preset dimension, an initial non-comfort factor corresponding to each preset dimension, wherein the indoor comfort coefficient and the initial non-comfort factor are in a negative correlation relationship;

[0026] determining, as the indoor non-comfort influence indicator corresponding to each preset dimension, a proportion of the initial non-comfort factor corresponding to each preset dimension in a total sum of the initial non-comfort factors corresponding to all preset dimensions.

[0027] In combination with the first aspect, in a possible implementation manner, the screening, from all preset dimensions, of a current main dimension according to the indoor non-comfort influence indicators corresponding to all preset dimensions comprises:

[0028] sorting, according to the indoor non-comfort influence indicators corresponding to all preset dimensions in descending order, all preset dimensions to obtain a preset dimension sequence;

[0029] determining, as a non-comfort representative indicator corresponding to each preset dimension in the preset dimension sequence, an accumulated value of the indoor non-comfort influence indicators corresponding to each preset dimension and a preset dimension before the preset dimension in the preset dimension sequence;

[0030] screening, from the preset dimension sequence, a preset dimension corresponding to a non-comfort representative indicator greater than a preset non-comfort influence threshold as a candidate dimension;

[0031] screening, from all candidate dimensions, a candidate dimension corresponding to a minimum non-comfort representative indicator as a calibration dimension;

[0032] The calibrated dimension and each preset dimension before the calibrated dimension in the preset dimension sequence are recorded as a current main dimension.

[0033] In combination with the first aspect, in a possible implementation, the current window opening degree is determined according to the indoor comfort coefficient, the outdoor comfort coefficient and the indoor non-comfort influence index corresponding to all current main dimensions, including:

[0034] If the indoor comfort coefficient corresponding to the current main dimension is greater than the outdoor comfort coefficient corresponding to the current main dimension, the expected state value corresponding to the current main dimension is set as a constant -1.

[0035] If the indoor comfort coefficient corresponding to the current main dimension is equal to the outdoor comfort coefficient corresponding to the current main dimension, the expected state value corresponding to the current main dimension is set as a constant 0.

[0036] If the indoor comfort coefficient corresponding to the current main dimension is less than the outdoor comfort coefficient corresponding to the current main dimension, the expected state value corresponding to the current main dimension is set as a constant 1.

[0037] The current window opening degree is determined according to the expected state value corresponding to all current main dimensions and the indoor non-comfort influence index, wherein the expected state value and the indoor non-comfort influence index are positively correlated with the current window opening degree.

[0038] In combination with the first aspect, in a possible implementation, the method for obtaining the historical window opening degree corresponding to each historical manual window opening includes:

[0039] Any historical manual window opening is determined as a marked manual window opening, and the ratio of the opening angle of the window at the time when the marked manual window opening is completed to the maximum opening angle of the window is determined as the historical window opening degree corresponding to the marked manual window opening.

[0040] In combination with the first aspect, in a possible implementation, the current window opening increment is determined according to the historical window opening degree and the historical window opening degree corresponding to the historical manual window opening corresponding to all historical main dimension groups in the target cluster to which the group composed of all current main dimensions belongs, including:

[0041] The target cluster to which the group composed of all current main dimensions belongs is determined as a matching cluster.

[0042] If the historical window opening degree corresponding to the historical manual window opening is greater than the historical window opening degree corresponding to the historical manual window opening, the historical manual window opening is determined as a manual small window opening.

[0043] If the historical window opening degree corresponding to the historical manual window opening is less than the historical window opening degree corresponding to the historical manual window opening, the historical manual window opening is determined as a manual large window opening.

[0044] determine each manual small opening corresponding historical main dimension group as a historical small dimension group, and determine each manual large opening corresponding historical main dimension group as a historical large dimension group;

[0045] determine the current opening increment according to the number of the historical small dimension groups and the number of the historical large dimension groups in the matching cluster, and the difference between the historical opening degree and the historical opening degree corresponding to the historical main dimension group in the matching cluster.

[0046] In combination with the first aspect, in a possible implementation manner, the determining the current opening increment according to the number of the historical small dimension groups and the number of the historical large dimension groups in the matching cluster, and the difference between the historical opening degree and the historical opening degree corresponding to the historical main dimension group in the matching cluster includes:

[0047] if the number of the historical small dimension groups is less than the number of the historical large dimension groups, determining the current opening increment according to the number of the historical large dimension groups in the matching cluster, and the difference between the historical opening degree and the historical opening degree corresponding to the historical large dimension group in the matching cluster;

[0048] if the number of the historical small dimension groups is equal to the number of the historical large dimension groups, setting the current opening increment as a constant 0;

[0049] if the number of the historical small dimension groups is greater than the number of the historical large dimension groups, determining the current opening increment according to the number of the historical small dimension groups in the matching cluster, and the difference between the historical opening degree and the historical opening degree corresponding to the historical small dimension group in the matching cluster.

[0050] In a second aspect, the present application provides an intelligent door and window adaptive control system based on Internet of Things, which comprises:

[0051] a data acquisition module, configured to acquire indoor dimension data and outdoor dimension data of each preset dimension in a current time period, and acquire a dimension comfort range under each preset dimension;

[0052] a comfort coefficient and influence index determination module, configured to determine an indoor comfort coefficient, an outdoor comfort coefficient and an indoor non-comfort influence index corresponding to each preset dimension according to distribution changes of indoor dimension data and outdoor dimension data of each preset dimension in a current time period, and comparison between the indoor dimension data and the outdoor dimension data and the dimension comfort range respectively;

[0053] The screening and determining module is configured to screen current main dimensions from all preset dimensions according to indoor non-comfort influence indexes corresponding to all the preset dimensions, and determine current door and window opening degrees according to indoor comfort coefficients, outdoor comfort coefficients and indoor non-comfort influence indexes corresponding to all the current main dimensions.

[0054] The obtaining and clustering module is configured to obtain a historical main dimension group, a historical door and window opening degree and a historical window opening degree corresponding to each historical manual window opening, and cluster all the historical main dimension groups corresponding to all the historical manual window openings and a group formed by all the current main dimensions to obtain a target cluster.

[0055] The current window opening increment determining module is configured to determine a current window opening increment based on historical door and window opening degrees and historical window opening degrees corresponding to historical manual window openings corresponding to all the historical main dimension groups in a target cluster to which a group formed by all the current main dimensions belongs.

[0056] The intelligent door and window control module is configured to perform intelligent door and window control according to the current door and window opening degrees and the current window opening increment.

[0057] In a third aspect, a server is provided, including a memory and a processor. The memory is configured to store executable program code, and the processor is configured to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0058] In a fourth aspect, a computer program product is provided, including computer program code. When the computer program code runs on a computer, the computer program code causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0059] In a fifth aspect, a computer readable storage medium is provided, which stores computer program code. When the computer program code runs on a computer, the computer program code causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0060] The present application has the following advantages:

[0061] The intelligent door and window adaptive control method based on the Internet of Things realizes intelligent door and window adaptive control by analyzing data under different preset dimensions, solves the technical problem that the rationality of door and window control is poor, and improves the rationality of door and window control. Specifically, the indoor dimension data and outdoor dimension data under multiple preset dimensions are comprehensively considered, multiple indicators related to environmental comfort conditions are quantified, such as indoor comfort coefficient, outdoor comfort coefficient and indoor discomfort influence index, so as to quantify the current door and window opening degree and the current window opening increment, and intelligent door and window control is carried out based on the current door and window opening degree and the current window opening increment, thereby improving the rationality of door and window control. BRIEF DESCRIPTION OF DRAWINGS

[0062] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0063] Figure 1 A flow chart of a smart door and window adaptive control method based on the Internet of Things according to the present application;

[0064] Figure 2 A composition structure diagram of a smart door and window adaptive control system based on the Internet of Things according to the present application;

[0065] Figure 3 A structure diagram of a computer device according to the present application. DETAILED DESCRIPTION

[0066] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object, the specific embodiments, structures, features and effects of the technical solutions according to the present application are described in detail below in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0068] Reference Figure 1 Fig. 1 shows a flow of some embodiments of a smart door and window adaptive control method based on the Internet of Things according to the present application. The smart door and window adaptive control method based on the Internet of Things includes the following steps:

[0069] In step S1, indoor dimension data and outdoor dimension data of each preset dimension in a current time period are obtained, and a dimension comfort range under each preset dimension is obtained.

[0070] The preset dimension can be a dimension related to an environment and representing different environmental factors. For example, the preset dimension can be, but is not limited to, a temperature dimension, a humidity dimension, a carbon dioxide concentration dimension, and a formaldehyde concentration dimension. The current time period can be a period of time ending at a current time. The length of the current time period can be preset, and can be 30 minutes. The indoor dimension data in the embodiment of the present application can be environmental data in a room to which a target window belongs. The target window can be a window to be controlled in opening and closing. The outdoor dimension data can be environmental data outside the room to which the target window belongs. The dimension comfort range under the preset dimension can be a range suitable for human life preset under the preset dimension.

[0071] It should be noted that the indoor environment is not constant due to the influence of various factors. For example, due to weather changes, the temperature, humidity, air quality, and other conditions indoors and outdoors will fluctuate. Therefore, when meeting the needs of users for living conditions, it is often necessary to determine the environmental factor that is currently more influential in real time according to environmental conditions, so as to further analyze the environmental data corresponding to the environmental factor that is currently more influential in the subsequent process, to achieve adaptive control of doors and windows.

[0072] As an example, taking the carbon dioxide concentration dimension as an example, obtaining indoor dimension data and outdoor dimension data of the carbon dioxide concentration dimension in a current time period, and obtaining a dimension comfort range under the carbon dioxide concentration dimension can include the following steps:

[0073] In a first step, a carbon dioxide concentration sensor installed in a room to which a target window belongs is used to collect the carbon dioxide concentration in the room every 1 second in a current time period, and all indoor carbon dioxide concentrations collected in the current time period are used to constitute all indoor dimension data of the carbon dioxide concentration dimension in the current time period.

[0074] In a second step, a carbon dioxide concentration sensor installed outside the room to which the target window belongs is used to collect the carbon dioxide concentration outside the room every 1 second in the current time period, and all outdoor carbon dioxide concentrations collected in the current time period are used to constitute all outdoor dimension data of the carbon dioxide concentration dimension in the current time period.

[0075] In a third step, the dimension comfort range under the carbon dioxide concentration dimension is set to 400-800 ppm (parts per million).

[0076] The carbon dioxide concentration in the range of 400-800 ppm, which is close to the fresh outdoor air, is an ideal carbon dioxide concentration range, which can be set as the dimension comfort range in the carbon dioxide concentration dimension.

[0077] It should be noted that, since the environmental condition is usually a slow change process, when the comfort of the current living environment of the user is analyzed, the control parameters of the door and window are not adjusted at each moment through data analysis, so as to cause a large amount of calculation resource consumption and frequent adjustment of the door and window. Therefore, in the embodiment of the present application, the environmental data in the set time length range is analyzed, so as to avoid the waste of resources caused by too high frequency of adaptive control of the door and window, and improve the reliability of the evaluation of the indoor environment. The time length range corresponding to the current time period is the set time length range.

[0078] In step S2, the indoor comfort coefficient, the outdoor comfort coefficient and the indoor non-comfort influence index corresponding to each preset dimension are determined according to the distribution change of the indoor dimension data and the outdoor dimension data of each preset dimension in the current time period, and the comparison between the indoor dimension data and the outdoor dimension data and the dimension comfort range thereof, respectively.

[0079] As an example, the present step can include the following steps:

[0080] Firstly, the current indoor dimension change distribution index corresponding to each preset dimension is determined according to the standard deviation of all indoor dimension data of each preset dimension in the current time period, and the absolute value of the slope of the fitting straight line formed by all indoor dimension data of each preset dimension in the current time period.

[0081] The method for obtaining the fitting straight line formed by all indoor dimension data of the preset dimension in the current time period can be that the data acquisition time in the current time period is taken as the horizontal coordinate, and the indoor dimension data of the preset dimension in the current time period is taken as the vertical coordinate for straight line fitting, and the fitting straight line obtained at this time is the fitting straight line formed by all indoor dimension data of the preset dimension in the current time period.

[0082] For example, the formula for determining the current indoor dimension change distribution index corresponding to the preset dimension can be:

[0083] ;

[0084] Wherein, is the current indoor dimension change distribution index corresponding to the i th preset dimension. i is the serial number of the preset dimension. is the standard deviation of all indoor dimension data of the i th preset dimension in the current time period. is the absolute value function. is the slope of the fitted straight line of all indoor dimension data of the ith preset dimension in the current time period. a is a preset factor greater than 0, mainly used to prevent the denominator from being 0, which can be 0.0001.

[0085] It should be noted that, embodies the stability of the change of the indoor environment data under the ith preset dimension in the current time period. The greater the value, the more stable the change of the indoor environment data under the ith preset dimension in the current time period, the more concentrated the indoor environment data under the ith preset dimension in the current time period, and the more stable the indoor environment data under the ith preset dimension in the current time period.

[0086] The second step, according to the comparison between the indoor dimension data of each preset dimension in the current time period and its dimension comfort range, determining the current indoor relative comfort degree corresponding to each preset dimension can include the following sub-steps:

[0087] The first sub-step, the half of the sum of the maximum and minimum values of the dimension comfort range of each preset dimension is determined as the comfort median of each preset dimension.

[0088] The second sub-step, the half of the difference between the maximum and minimum values of the dimension comfort range of each preset dimension is determined as the comfort radius of each preset dimension.

[0089] The third sub-step, the absolute value of the difference between the mean value of all indoor dimension data of each preset dimension in the current time period and the comfort median is determined as the comfort deviation of each preset dimension.

[0090] The fourth sub-step, according to the difference between the comfort deviation of each preset dimension and the comfort radius, and the number of indoor dimension data of each preset dimension in the current time period belonging to its dimension comfort range, determining the current indoor relative comfort degree corresponding to each preset dimension.

[0091] For example, the formula for determining the current indoor relative comfort degree corresponding to the preset dimension can be:

[0092] ;

[0093] ;

[0094] ;

[0095] Among them, is the current indoor relative comfort degree corresponding to the ith preset dimension. i is the serial number of the preset dimension. is the number of indoor dimension data of the i-th preset dimension within the current time period, which belongs to the comfort range of the i-th preset dimension. is the number of indoor dimension data of the i-th preset dimension within the current time period. is an exponential function with a natural constant as the base. is an absolute value function. is the mean value of all indoor dimension data of the i-th preset dimension within the current time period. is the comfort median value of the i-th preset dimension, which can represent the appropriate average level value. is the comfort radius of the i-th preset dimension. is the minimum value of the dimension comfort range of the i-th preset dimension. is the maximum value of the dimension comfort range of the i-th preset dimension. is the comfort deviation of the i-th preset dimension.

[0096] It should be noted that when is greater, it often means that there are more appropriate indoor dimension data of the i-th preset dimension within the current time period, and it often means that the proportion of the environment factor represented by the i-th preset dimension is more appropriate within the current time period, and it often means that the environment factor represented by the i-th preset dimension is more appropriate within the current time period. represents the deviation between the indoor dimension data of the i-th preset dimension within the current time period and the appropriate average level value, and the smaller the value, the closer the indoor dimension data of the i-th preset dimension within the current time period to the appropriate average level value. When is smaller, it often means that the environmental comfort deviation of the i-th preset dimension is smaller than the comfort radius, it often means that the fluctuation range of the corresponding dimension data under the corresponding environment factor of the i-th preset dimension is more likely to be within the allowed fluctuation range, and it often means that the indoor comfort is higher. Therefore, when is greater, it often means that the indoor environment factor corresponding to the i-th preset dimension is relatively more appropriate within the current time period.

[0097] Thirdly, the product of the current indoor dimension change distribution index corresponding to each preset dimension and the current indoor relative comfort degree is determined as the indoor comfort coefficient corresponding to each preset dimension.

[0098] For example, the formula for determining the indoor comfort coefficient corresponding to the preset dimension can be:

[0099] ;

[0100] wherein, is the indoor comfort coefficient corresponding to the i-th preset dimension. i is the serial number of the preset dimension. is the current indoor relative comfort degree corresponding to the ith preset dimension. is the current indoor dimension change distribution index corresponding to the ith preset dimension.

[0101] It should be noted that when is greater, it often indicates that the indoor environment data under the ith preset dimension in the current time period is more stable, and often indicates that the indoor comfort degree is possibly higher. When is greater, it often indicates that the indoor environment factor corresponding to the ith preset dimension in the current time period is relatively more suitable. Therefore, when is greater, it often indicates that the indoor environment factor corresponding to the ith preset dimension in the current time period is relatively more suitable, and often indicates that the influence degree of the indoor environment factor corresponding to the ith preset dimension on the indoor comfort in the current time period is relatively greater.

[0102] Fourthly, according to the distribution change of the outdoor dimension data of each preset dimension in the current time period, and the comparison between the outdoor dimension data of each preset dimension in the current time period and the dimension comfort range thereof, the outdoor comfort coefficient corresponding to each preset dimension is determined.

[0103] Wherein, when the outdoor comfort coefficient corresponding to the preset dimension is greater, it often indicates that the outdoor environment factor corresponding to the ith preset dimension in the current time period is relatively more suitable.

[0104] It should be noted that the method for obtaining the outdoor comfort coefficient corresponding to the preset dimension is the same as the method for obtaining the indoor comfort coefficient corresponding to the preset dimension, which will not be repeated here.

[0105] Fifthly, according to the indoor comfort coefficient corresponding to each preset dimension, the indoor non-comfort influence index corresponding to each preset dimension can include the following sub-steps:

[0106] First sub-step, according to the indoor comfort coefficient corresponding to each preset dimension, the initial non-comfort factor corresponding to each preset dimension is determined.

[0107] Wherein, the indoor comfort coefficient can be in a negative correlation relationship with the initial non-comfort factor.

[0108] For example, the formula for determining the initial non-comfort factor corresponding to the preset dimension can be:

[0109] ;

[0110] Wherein, is the initial non-comfort factor corresponding to the ith preset dimension. i is the serial number of the preset dimension. is an exponential function with a natural constant as the base. is the indoor comfort coefficient corresponding to the i-th preset dimension.

[0111] It should be noted that, when is larger, it often indicates that the indoor environmental factor corresponding to the i-th preset dimension is relatively more suitable in the current time period, and often indicates that the indoor environmental factor corresponding to the i-th preset dimension has a relatively greater degree of influence on indoor comfort in the current time period. Therefore, when is larger, it often indicates that the indoor environmental factor corresponding to the i-th preset dimension is relatively less suitable in the current time period, and often indicates that the indoor environmental factor corresponding to the i-th preset dimension has a relatively greater degree of influence on indoor discomfort in the current time period.

[0112] In a second sub-step, the proportion of the initial non-comfort factor corresponding to each preset dimension in the total sum of the initial non-comfort factors corresponding to all preset dimensions is determined as the indoor non-comfort influence index corresponding to each preset dimension.

[0113] For example, the formula corresponding to the indoor non-comfort influence index corresponding to the preset dimension can be:

[0114] ;

[0115] wherein, is the indoor non-comfort influence index corresponding to the i-th preset dimension. i is the serial number of the preset dimension. is the initial non-comfort factor corresponding to the i-th preset dimension. A is the cumulative value of the initial non-comfort factors corresponding to all preset dimensions.

[0116] It should be noted that, since there are multiple environmental factors in the living environment, the influence of these multiple environments on the user's living discomfort experience depends on which environmental factor has a higher degree of influence relative to other environmental factors. When the degree of influence relative to other environments is higher, the user's demand for improving this environmental factor will be higher, that is, this environmental factor dominates the living discomfort experience among all environmental factors. When is larger, it often indicates that the i-th preset dimension has a relatively higher influence on the living discomfort experience, and often indicates that the i-th preset dimension is more likely to dominate the living discomfort experience.

[0117] Step S3, according to the indoor non-comfort influence index corresponding to all preset dimensions, the current main dimension is screened from all preset dimensions, and the current door and window opening degree is determined according to the indoor comfort coefficient, the outdoor comfort coefficient and the indoor non-comfort influence index corresponding to all current main dimensions.

[0118] It should be noted that since there can be multiple main environmental data, under the influence of multiple environmental factors, different factors can have different requirements for the opening and closing state of the door and window. For example, when the indoor carbon dioxide concentration is high and needs to be ventilated, but the outdoor temperature is too low and the window needs to be closed to maintain the room temperature. In this case, it is necessary to weigh the influence of multiple environmental factors to determine the opening degree of the door and window to meet the user's comfort experience as much as possible under the influence of multiple main environmental factors at the current time period. Therefore, it is necessary to screen out the current main dimension representing the current main environmental factor and the current door and window opening degree representing the current door and window opening degree.

[0119] As an example, the step can include the following steps:

[0120] First, according to the indoor non-comfort influence indicators corresponding to all preset dimensions, all preset dimensions are sorted in descending order to obtain a preset dimension sequence.

[0121] Second, the cumulative value of the indoor non-comfort influence indicators corresponding to each preset dimension and the previous preset dimension in the above preset dimension sequence is determined as the non-comfort representative indicator corresponding to each preset dimension in the above preset dimension sequence.

[0122] For example, the formula for determining the non-comfort representative indicator corresponding to the preset dimension in the preset dimension sequence can be:

[0123] ;

[0124] Wherein, is the non-comfort representative indicator corresponding to the jth preset dimension in the preset dimension sequence. j is the serial number of the preset dimension in the preset dimension sequence. b is the serial number of the jth preset dimension or the previous preset dimension in the preset dimension sequence. is the indoor non-comfort influence indicator corresponding to the bth preset dimension in the preset dimension sequence.

[0125] It should be noted that when is larger, it often means that the bth preset dimension has a relatively higher impact on the living discomfort experience, and it often means that the bth preset dimension is more likely to play a major role in the living discomfort experience. Therefore, when is larger, and j is smaller, it often means that the jth preset dimension and the previous preset dimension in the preset dimension sequence are more likely to play a major role in the living discomfort experience.

[0126] Third, from the above preset dimension sequence, the preset dimension corresponding to the non-comfort representative indicator greater than the preset non-comfort influence threshold is screened out as a candidate dimension.

[0127] The preset non-comfort influence threshold can be a preset threshold, which can be 0.7.

[0128] In the fourth step, the candidate dimension corresponding to the minimum non-comfort representative index is selected from all candidate dimensions as a calibration dimension.

[0129] In the fifth step, each preset dimension before the calibration dimension in the preset dimension sequence is recorded as a current main dimension.

[0130] In the sixth step, if the indoor comfort coefficient corresponding to the current main dimension is greater than the outdoor comfort coefficient corresponding to the current main dimension, the expected state value corresponding to the current main dimension is set to a constant -1.

[0131] It should be noted that if the indoor comfort coefficient corresponding to the current main dimension is greater than the outdoor comfort coefficient corresponding to the current main dimension, it is often indicated that the indoor condition is more suitable than the outdoor condition for the current main dimension, and it is often indicated that window ventilation is not needed at this time.

[0132] In the seventh step, if the indoor comfort coefficient corresponding to the current main dimension is equal to the outdoor comfort coefficient corresponding to the current main dimension, the expected state value corresponding to the current main dimension is set to a constant 0.

[0133] It should be noted that if the indoor comfort coefficient corresponding to the current main dimension is equal to the outdoor comfort coefficient corresponding to the current main dimension, it is often indicated that the indoor condition is the same as the outdoor condition for the current main dimension, and it is often indicated that window ventilation or no window ventilation is acceptable at this time.

[0134] In the eighth step, if the indoor comfort coefficient corresponding to the current main dimension is less than the outdoor comfort coefficient corresponding to the current main dimension, the expected state value corresponding to the current main dimension is set to a constant 1.

[0135] It should be noted that if the indoor comfort coefficient corresponding to the current main dimension is less than the outdoor comfort coefficient corresponding to the current main dimension, it is often indicated that the outdoor condition is more suitable than the indoor condition for the current main dimension, and it is often indicated that window ventilation is needed at this time.

[0136] In the ninth step, the current door and window opening degree is determined according to the expected state value corresponding to all current main dimensions and the indoor non-comfort influence index.

[0137] The expected state value and the indoor non-comfort influence index can be in a positive correlation with the current door and window opening degree.

[0138] For example, the formula for determining the current door and window opening degree can be:

[0139] ;

[0140] Q is the current door and window opening degree. is a normalization function. n is the number of current main dimensions. d is the serial number of the current main dimension. is the expected state value corresponding to the dth current main dimension. is the indoor non-comfort impact indicator corresponding to the dth current main dimension.

[0141] It should be noted that, when is larger, it often means that the bth current main dimension has a relatively higher impact on the residential discomfort experience, and it often means that the bth current main dimension is more likely to play a major role in the residential discomfort experience. When is larger, it often means that for the bth current main dimension, the outdoor is more likely to be more suitable than the indoor, and it often means that at this time, window ventilation is more needed. Therefore, when Q is larger, it often means that at the current time, window ventilation is more needed, and in order to exchange air more quickly, the window opening degree should be larger.

[0142] Step S4, obtaining the historical main dimension group, the historical door and window opening degree and the historical window opening degree corresponding to each historical manual window opening, and clustering the group formed by all historical main dimension groups corresponding to all historical manual window openings and all current main dimensions to obtain a target cluster.

[0143] Among them, the historical manual window opening can be a manual window opening according to the user's will, rather than a smart window opening calculated by an algorithm. In actual situations, when manual window opening is performed, the smart window opening function is often closed or automatically timed, that is, the smart window opening function is opened again after several hours.

[0144] It should be noted that, since different users often have different environmental acceptance, different users often have different window opening preferences, therefore, analyzing the historical manual window opening situation can facilitate understanding of the user's preferred window opening situation, so as to facilitate subsequent correction of the door and window opening degree.

[0145] As an example, this step can include the following steps:

[0146] First, obtain the historical main dimension group and the historical door and window opening degree corresponding to each historical manual window opening.

[0147] For example, any historical manual window opening is determined as a marked manual window opening, a historical time period corresponding to the marked manual window opening is constructed, and a historical main dimension and a historical window opening degree in a historical main dimension group corresponding to the marked manual window opening are obtained by using the same method of obtaining the current main dimension and the historical window opening degree according to indoor dimension data and outdoor dimension data in the historical time period according to different preset dimensions. At this time, the plurality of historical main dimensions obtained constitute the historical main dimension group. The end moment of the historical time period corresponding to the marked manual window opening can be the start moment of the marked manual window opening. The time length corresponding to the historical time period can be the same as the time length corresponding to the current time period, and the historical time period is before the current time period.

[0148] The second step of obtaining the historical window opening degree can include the following sub-steps:

[0149] The first sub-step is to determine any historical manual window opening as a marked manual window opening.

[0150] The second sub-step is to determine the ratio of the opening angle of the window at the time when the marked manual window opening is completed to the maximum opening angle that the window can reach as the historical window opening degree corresponding to the marked manual window opening.

[0151] The opening angle refers to the rotation angle of the sash relative to the window frame.

[0152] For example, the formula corresponding to the historical window opening degree corresponding to the historical manual window opening can be:

[0153] ;

[0154] wherein, is the historical window opening degree corresponding to the yth historical manual window opening. y is the order of the historical manual window opening. is the opening angle of the target window at the time when the yth historical manual window opening is completed. is the maximum opening angle that the target window can reach.

[0155] It should be noted that when is larger, it often indicates that the user opens the target window to a greater extent.

[0156] The third step of clustering the group formed by all historical main dimension groups corresponding to all historical manual window openings and all current main dimensions to obtain the target cluster can include the following sub-steps:

[0157] The first sub-step is to collectively refer to the group formed by the historical main dimension group and all current main dimensions as a main dimension group.

[0158] The second sub-step of obtaining a target similarity between any two main dimension groups can include the following steps:

[0159] Firstly, any two main dimension groups are determined as a first main dimension group and a second main dimension group respectively.

[0160] Then, the number of elements in the intersection of the first main dimension group and the second main dimension group is determined as a target intersection number.

[0161] Then, the number of elements in the union of the first main dimension group and the second main dimension group is determined as a target union number.

[0162] Finally, the ratio between the target intersection number and the target union number is determined as a target similarity between the first main dimension group and the second main dimension group.

[0163] The third sub-step is to cluster the main dimension groups with a target similarity between them greater than a preset similarity threshold into the same cluster, and record each cluster obtained at this time as a target cluster.

[0164] The preset similarity threshold can be a preset threshold, which can be 0.6.

[0165] It should be noted that the more similar the main dimensions included in different main dimension groups in the target cluster are to the main environmental impact factors.

[0166] Step S5, based on the historical door and window opening degree and the historical window opening degree corresponding to all historical manual window openings corresponding to all historical main dimension groups in the target cluster to which the group composed of all current main dimensions belongs, determine the current window opening increment.

[0167] As an example, this step can include the following steps:

[0168] First, the target cluster to which the group composed of all current main dimensions belongs is determined as a matching cluster.

[0169] Second, if the historical door and window opening degree corresponding to the historical manual window opening is greater than the historical window opening degree corresponding to it, the historical manual window opening is determined as a manual small opening.

[0170] It should be noted that if the historical door and window opening degree corresponding to the historical manual window opening is greater than the historical window opening degree corresponding to it, it often indicates that the actual window opening degree of the user in this historical manual window opening is more likely to be greater than the calculated door and window opening degree, and it often indicates that the window opening degree of the user in this historical manual window opening is relatively small.

[0171] Third, if the historical door and window opening degree corresponding to the historical manual window opening is less than the historical window opening degree corresponding to it, the historical manual window opening is determined as a manual large opening.

[0172] It should be noted that if the historical manual window opening corresponding to the historical window opening degree is less than the corresponding historical window opening degree, it is often that the actual window opening degree of the user in the historical manual window opening is more likely to be less than the calculated window opening degree, and it is often that the window opening degree of the user in the historical manual window opening is relatively large.

[0173] Fourthly, the historical main dimension group corresponding to each manual small window opening is determined as a historical small dimension group, and the historical main dimension group corresponding to each manual large window opening is determined as a historical large dimension group.

[0174] Fifthly, according to the number of historical small dimension groups and the number of historical large dimension groups in the matching cluster, and the difference between the historical window opening degree and the historical window opening degree corresponding to the historical manual window opening corresponding to the historical main dimension group in the matching cluster, the current window opening increment can include the following sub-steps:

[0175] The first sub-step, if the number of historical small dimension groups in the matching cluster is less than the number of historical large dimension groups, then according to the number of historical large dimension groups in the matching cluster, and the difference between the historical window opening degree and the historical window opening degree corresponding to the historical manual window opening corresponding to the historical main dimension group in the matching cluster, the current window opening increment is determined.

[0176] For example, if the number of historical small dimension groups in the matching cluster is less than the number of historical large dimension groups, the formula corresponding to the current window opening increment can be:

[0177] ;

[0178] ;

[0179] Where G is the current window opening increment, and its value range is [-0.2, 0.2]. is a normalization function. Indicates the proportion of historical large dimension groups in the matching cluster. is the number of historical large dimension groups in the matching cluster. M is the number of historical main dimension groups in the matching cluster. q is the serial number of historical large dimension groups in the matching cluster. is the historical window opening degree corresponding to the historical manual window opening corresponding to the historical large dimension group in the matching cluster. is the historical window opening degree corresponding to the historical manual window opening corresponding to the historical large dimension group in the matching cluster.

[0180] It should be noted that if the number of historical small dimension groups in the matching cluster is less than the number of historical large dimension groups, it is often that the user is relatively more inclined to set the window opening degree to be relatively large under the current environment. When The greater G is, the more times the user manually opens the window in a historical environment similar to the current environment, and the greater the need to appropriately increase the opening degree at the current time. The weight of the average opening deviation G can be The weight of the average opening deviation G can be The greater G is, the more times the user manually opens the window in a historical environment similar to the current environment, and the greater the need to appropriately increase the opening degree at the current time.

[0181] In the second sub-step, if the number of historical small-dimension groups in the matching cluster is equal to the number of historical large-dimension groups, the current opening increment is set to a constant 0.

[0182] It should be noted that if the number of historical small-dimension groups in the matching cluster is equal to the number of historical large-dimension groups, it means that the user's preference for the opening degree in the current environment is relatively not obvious, and the door and window opening degree can not be modified.

[0183] In the third sub-step, if the number of historical small-dimension groups in the matching cluster is greater than the number of historical large-dimension groups, the current opening increment is determined according to the number of historical small-dimension groups in the matching cluster and the difference between the historical opening degree corresponding to the historical manual opening corresponding to the historical small-dimension group in the matching cluster and the historical door and window opening.

[0184] For example, if the number of historical small-dimension groups in the matching cluster is greater than the number of historical large-dimension groups, the formula corresponding to the current opening increment can be:

[0185] ;

[0186] ;

[0187] G is the current opening increment, and its value range is [-0.2, 0.2]. is a normalization function. represents the proportion of historical small-dimension groups in the matching cluster. is the number of historical small-dimension groups in the matching cluster. M is the number of historical main-dimension groups in the matching cluster. p is the serial number of the historical small-dimension group in the matching cluster. is the historical opening degree corresponding to the historical manual opening corresponding to the pth historical small-dimension group in the matching cluster. is the historical door and window opening corresponding to the historical manual opening corresponding to the pth historical small-dimension group in the matching cluster.

[0188] ​It should be noted that if the number of historical small-dimension groups in the matching cluster is greater than the number of historical large-dimension groups, it often indicates that the user is relatively more inclined to set a relatively small window opening degree in the current environment. When is greater, it often indicates that the user manually sets a smaller window opening degree in a historical environment similar to the current environment more often, and it often indicates that the window opening degree at the current time needs to be appropriately reduced. At this time can be used as weight, is negative. can represent the average window opening deviation, and the smaller the value, the more the window opening degree at the current time needs to be appropriately reduced. Therefore, when G is smaller, the window opening degree at the current time needs to be appropriately reduced.

[0189] Step S6, intelligent door and window control according to the current door and window opening degree and the current window opening increment.

[0190] As an example, the present step can include the following steps:

[0191] First, determine the target door and window opening degree according to the current door and window opening degree and the current window opening increment.

[0192] For example, the formula for determining the target door and window opening degree can be:

[0193] ;

[0194] Wherein, E is the target door and window opening degree. Q is the current door and window opening degree. G is the current window opening increment.

[0195] It should be noted that when Q is greater, it often indicates that window ventilation is needed at the current time, and in order to exchange air faster, the window opening degree should be larger. When G is greater, it often indicates that the window opening degree at the current time needs to be appropriately increased. Therefore, when E is greater, it often indicates that window ventilation is needed at the current time, and the window opening degree should be larger.

[0196] Second, intelligent door and window control according to the target door and window opening degree can include the following sub-steps:

[0197] First sub-step, if the target door and window opening degree is less than the first preset opening threshold, set the state of the target window to the closed state.

[0198] Wherein, the first preset opening threshold can be a pre-set threshold, which can be 0.6.

[0199] In a second sub-step, if the target door / window opening degree is greater than or equal to the first preset opening degree threshold and less than the second preset opening degree threshold, the state of the target window is set to an open state, and the opening angle of the target window is set to .

[0200] wherein, is the maximum opening angle that the target window can reach. The second preset opening degree threshold can be a preset threshold, and it can be greater than the first preset opening degree threshold. For example, the second preset opening degree threshold can be 0.7.

[0201] In a third sub-step, if the target door / window opening degree is greater than or equal to the second preset opening degree threshold and less than the third preset opening degree threshold, the state of the target window is set to an open state, and the opening angle of the target window is set to .

[0202] wherein, is the maximum opening angle that the target window can reach. The third preset opening degree threshold can be a preset threshold, and it can be greater than the second preset opening degree threshold. For example, the third preset opening degree threshold can be 0.8.

[0203] In a fourth sub-step, if the target door / window opening degree is greater than or equal to the third preset opening degree threshold and less than the fourth preset opening degree threshold, the state of the target window is set to an open state, and the opening angle of the target window is set to .

[0204] wherein, is the maximum opening angle that the target window can reach. The fourth preset opening degree threshold can be a preset threshold, and it can be greater than the third preset opening degree threshold. For example, the fourth preset opening degree threshold can be 0.9.

[0205] In a fifth sub-step, if the target door / window opening degree is greater than or equal to the fourth preset opening degree threshold, the state of the target window is set to an open state, and the opening angle of the target window is set to .

[0206] wherein, is the maximum opening angle that the target window can reach.

[0207] With reference to Figure 2 , based on the same inventive concept as the above method embodiments, the present application provides an intelligent door / window self-adaptive control system based on the Internet of Things, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. The above computer program is executed by the processor to realize the steps of an intelligent door / window self-adaptive control method based on the Internet of Things, which can specifically include:

[0208] The data acquisition module 201 is configured to acquire indoor dimension data and outdoor dimension data of each preset dimension in a current time period, and acquire a dimension comfort range of each preset dimension.

[0209] The comfort coefficient and influence index determination module 202 is configured to determine an indoor comfort coefficient, an outdoor comfort coefficient and an indoor discomfort influence index corresponding to each preset dimension according to a distribution change of the indoor dimension data and the outdoor dimension data of each preset dimension in the current time period, and a comparison between the indoor dimension data and the outdoor dimension data and the dimension comfort range of each preset dimension.

[0210] The screening and determination module 203 is configured to screen out a current main dimension from all preset dimensions according to the indoor discomfort influence index corresponding to all preset dimensions, and determine a current door and window opening degree according to the indoor comfort coefficient, the outdoor comfort coefficient and the indoor discomfort influence index corresponding to all current main dimensions.

[0211] The acquisition and clustering module 204 is configured to acquire a historical main dimension group, a historical door and window opening degree and a historical window opening degree corresponding to each historical manual window opening, and cluster a group formed by all historical main dimension groups corresponding to all historical manual window openings and all current main dimensions to obtain a target cluster.

[0212] The current window opening increment determination module 205 is configured to determine a current window opening increment based on the historical door and window opening degree and the historical window opening degree corresponding to the historical manual window opening corresponding to all historical main dimension groups in a target cluster to which the group formed by all current main dimensions belongs.

[0213] The intelligent door and window control module 206 is configured to perform intelligent door and window control according to the current door and window opening degree and the current window opening increment.

[0214] Figure 3 is a structural schematic diagram of a computer device provided by an embodiment of the present application. As shown in the example, Figure 3 the computer device 300 includes a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein the processor 302 executes the computer program 303, so that the computer device can execute any one of the above-mentioned intelligent door and window adaptive control methods based on the Internet of Things.

[0215] Based on the same inventive concept as the above method embodiment, the present application provides a server including a memory and a processor. The memory is configured to store executable program code, and the processor is configured to call and run the executable program code from the memory, so that the device executes any one of the above-mentioned intelligent door and window adaptive control methods based on the Internet of Things.

[0216] Based on the same inventive concept as the above method embodiments, the present application provides a computer program product comprising computer program code which, when executed on a computer, causes the computer to perform any of the above-described methods for adaptive control of smart doors and windows based on the Internet of Things.

[0217] Based on the same inventive concept as the above method embodiments, the present application provides a computer-readable storage medium storing computer program code which, when executed on a computer, causes the computer to perform any of the above-described methods for adaptive control of smart doors and windows based on the Internet of Things.

[0218] In summary, the present application comprehensively considers indoor dimensional data and outdoor dimensional data under multiple preset dimensions, quantifies multiple indicators related to environmental comfort conditions, such as indoor comfort coefficients, outdoor comfort coefficients, and indoor non-comfort impact indicators, thereby quantifying the current door and window opening degree and the current window opening increment, and performing intelligent door and window control based on the current door and window opening degree and the current window opening increment, thereby improving the rationality of door and window control.

[0219] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. An intelligent door and window adaptive control method based on the Internet of Things, characterized in that, The method comprises the following steps: obtaining indoor dimension data and outdoor dimension data of each preset dimension in a current time period, and obtaining a dimension comfort range of each preset dimension; determining an indoor comfort coefficient, an outdoor comfort coefficient and an indoor discomfort influence index corresponding to each preset dimension according to a distribution change of the indoor dimension data and the outdoor dimension data of each preset dimension in the current time period, and a comparison between the indoor dimension data and the outdoor dimension data of each preset dimension and the dimension comfort range thereof respectively; screening a current main dimension from all preset dimensions according to the indoor discomfort influence index corresponding to all preset dimensions, and determining a current door and window opening degree according to the indoor comfort coefficient, the outdoor comfort coefficient and the indoor discomfort influence index corresponding to all current main dimensions; obtaining a historical main dimension group, a historical door and window opening degree and a historical window opening degree corresponding to each historical manual window opening, and clustering all historical main dimension groups corresponding to the historical manual window opening of all historical main dimension groups belonging to a target cluster formed by all current main dimensions to obtain a target cluster; determining a current window opening increment based on the historical door and window opening degree and the historical window opening degree corresponding to all historical main dimension groups belonging to the target cluster formed by all current main dimensions; performing intelligent door and window control according to the current door and window opening degree and the current window opening increment; The method comprises the following steps: determining a current indoor dimension change distribution index corresponding to each preset dimension according to a standard deviation of all indoor dimension data of each preset dimension in the current time period and an absolute value of a slope of a fitting straight line formed by all indoor dimension data of each preset dimension in the current time period; a formula corresponding to the current indoor dimension change distribution index of each preset dimension is determined as follows: ； in, It is the first i The current indoor dimensional change distribution index corresponding to each preset dimension; i It is the sequence number of the preset dimension; It is the first i The standard deviation of all indoor dimension data within the current time period for each preset dimension; || is the absolute value function; It is the first i The slope of the fitted straight line formed by all indoor dimension data within the current time period for each preset dimension; a It is a pre-set factor greater than 0; determining a current indoor relative comfort degree corresponding to each preset dimension according to a comparison between the indoor dimension data of each preset dimension in the current time period and the dimension comfort range thereof; determining an indoor comfort coefficient corresponding to each preset dimension by multiplying the current indoor dimension change distribution index and the current indoor relative comfort degree corresponding to each preset dimension; Similarly, an outdoor comfort coefficient corresponding to each preset dimension is determined according to a distribution change of outdoor dimension data of each preset dimension in the current time period and a comparison between the outdoor dimension data of each preset dimension in the current time period and the dimension comfort range thereof; determining an indoor discomfort influence index corresponding to each preset dimension according to the indoor comfort coefficient corresponding to each preset dimension. 2.The smart door and window adaptive control method based on the Internet of Things according to claim 1, characterized in that, The method comprises the following steps: determining a comfort median value of each preset dimension by taking half of a sum of a maximum value and a minimum value of the dimension comfort range of each preset dimension as the comfort median value of each preset dimension; Half of the difference between the maximum and minimum of the dimension comfort range of each preset dimension is determined as the comfort radius of each preset dimension; The absolute value of the difference between the mean value of all indoor dimension data of each preset dimension in the current time period and the comfort median value thereof is determined as the comfort deviation of each preset dimension; According to the difference between the comfort deviation and the comfort radius of each preset dimension, and the number of indoor dimension data of each preset dimension in the current time period belonging to the dimension comfort range thereof, the current indoor relative comfort degree corresponding to each preset dimension is determined. 3.The smart door and window adaptive control method based on the Internet of Things according to claim 1, characterized in that, The indoor discomfort influence index corresponding to each preset dimension is determined according to the indoor comfort coefficient corresponding to each preset dimension, including: The initial discomfort factor corresponding to each preset dimension is determined according to the indoor comfort coefficient corresponding to each preset dimension, wherein the indoor comfort coefficient and the initial discomfort factor are in a negative correlation relationship; The proportion of the initial discomfort factor corresponding to each preset dimension in the total value of the initial discomfort factors corresponding to all preset dimensions is determined as the indoor discomfort influence index corresponding to each preset dimension. 4.The smart door and window adaptive control method based on the Internet of Things according to claim 1, characterized in that, The current main dimension is screened out from all preset dimensions according to the indoor discomfort influence index corresponding to all preset dimensions, including: All preset dimensions are sorted in descending order according to the indoor discomfort influence index corresponding to all preset dimensions, to obtain a preset dimension sequence; The non-comfort representative index corresponding to each preset dimension in the preset dimension sequence is determined as the cumulative value of the indoor discomfort influence index corresponding to each preset dimension and the preset dimension before it in the preset dimension sequence; The preset dimension corresponding to the non-comfort representative index greater than the preset discomfort influence threshold is screened out from the preset dimension sequence as a candidate dimension; The candidate dimension corresponding to the minimum non-comfort representative index is screened out from all candidate dimensions as a calibration dimension; The calibration dimension and each preset dimension before it in the preset dimension sequence are recorded as the current main dimension. 5.The smart door and window adaptive control method based on the Internet of Things according to claim 1, characterized in that, The current door and window opening degree is determined according to the indoor comfort coefficient, the outdoor comfort coefficient and the indoor discomfort influence index corresponding to all current main dimensions, including: If the indoor comfort coefficient corresponding to the current main dimension is greater than the outdoor comfort coefficient corresponding thereto, the expected state value corresponding to the current main dimension is set as a constant -1; If the indoor comfort coefficient corresponding to the current main dimension is equal to the outdoor comfort coefficient corresponding thereto, the expected state value corresponding to the current main dimension is set as a constant 0; If the indoor comfort coefficient corresponding to the current main dimension is less than the outdoor comfort coefficient corresponding thereto, the expected state value corresponding to the current main dimension is set as a constant 1; The current door and window opening degree is determined according to the expected state value and the indoor discomfort influence index corresponding to all current main dimensions, wherein the expected state value and the indoor discomfort influence index are both in a positive correlation relationship with the current door and window opening degree. 6.The smart door and window adaptive control method based on the Internet of Things according to claim 1, characterized in that, The method for obtaining the historical window opening degree corresponding to each historical manual window opening, including: The ratio of the opening angle of the marked manual window to the maximum opening angle of the window when the marked manual window is completed is determined as the historical opening degree corresponding to the marked manual window. 7.The smart door and window adaptive control method based on the Internet of Things according to claim 1, characterized in that, The current opening increment is determined based on the historical door and window opening degree and the historical opening degree corresponding to the historical manual window corresponding to the historical main dimension group in the target cluster to which the group of all current main dimensions belongs. The target cluster to which the group of all current main dimensions belongs is determined as the matching cluster. If the historical door and window opening degree corresponding to the historical manual window is greater than the historical opening degree corresponding to the historical manual window, the historical manual window is determined as a manual small window. If the historical door and window opening degree corresponding to the historical manual window is less than the historical opening degree corresponding to the historical manual window, the historical manual window is determined as a manual large window. The historical main dimension group corresponding to each manual small window is determined as a historical small dimension group, and the historical main dimension group corresponding to each manual large window is determined as a historical large dimension group. The current opening increment is determined based on the number of historical small dimension groups and the number of historical large dimension groups in the matching cluster, and the difference between the historical opening degree and the historical door and window opening degree corresponding to the historical manual window corresponding to the historical main dimension group in the matching cluster.

8. The self-adaptive control method of the smart door and window based on the Internet of Things according to claim 7, characterized in that, The current opening increment is determined based on the number of historical small dimension groups and the number of historical large dimension groups in the matching cluster, and the difference between the historical opening degree and the historical door and window opening degree corresponding to the historical manual window corresponding to the historical main dimension group in the matching cluster. If the number of historical small dimension groups in the matching cluster is less than the number of historical large dimension groups, the current opening increment is determined based on the number of historical large dimension groups in the matching cluster and the difference between the historical opening degree and the historical door and window opening degree corresponding to the historical manual window corresponding to the historical large dimension group in the matching cluster. If the number of historical small dimension groups in the matching cluster is equal to the number of historical large dimension groups, the current opening increment is set to a constant 0. If the number of historical small dimension groups in the matching cluster is greater than the number of historical large dimension groups, the current opening increment is determined based on the number of historical small dimension groups in the matching cluster and the difference between the historical opening degree and the historical door and window opening degree corresponding to the historical manual window corresponding to the historical small dimension group in the matching cluster.

9. An intelligent door and window adaptive control system based on the Internet of Things, characterized in that, The processor is configured to process instructions stored in the memory to implement the method of claim 1-8.

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