Intelligent door and window control system

Abstract

The invention particularly relates to an intelligent door and window control system which comprises the components of a data collection module which acquires rainfall data and wind power data at a window and indoor air pollution data; the data analysis module is used for respectively analyzing the rainfall data and the wind power data at the window to obtain a rainfall coefficient and a wind power coefficient; analyzing the indoor air pollution data to obtain a pollution coefficient, and determining a to-be-controlled window; a comprehensive processing module; and a judgment execution module. According to the invention, the rainwater sensor and the anemograph which are arranged at multiple heights of the window guardrail and the indoor gridding air detection sensor realize refined collection of outdoor rainfall distribution, wind power gradient and indoor pollution space characteristics; when the window is closed preferentially in storm weather, micro ventilation of the window near an indoor smoking area is opened, excessive closing or ventilation blind area caused by traditional single parameter control is avoided, and dynamic balance of severe weather protection and indoor air quality is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent doors and windows, and particularly to an intelligent door and window control system. Background Art

[0002] Traditional intelligent door and window control systems mostly rely on a single environmental parameter (such as rainfall, wind force or a single pollutant concentration) to achieve automatic control, and it is difficult to meet the multi-dimensional environmental requirements in complex scenarios.

[0003] For example, when monitoring windy and rainy weather, the system cannot simultaneously take into account the ventilation requirements for indoor air pollution (such as excessive formaldehyde, cooking fumes) or smoking; while when dealing with indoor pollution, it is difficult to avoid the risks of outdoor storms, high PM2.5 and other bad weather.

[0004] In addition, traditional systems lack the ability to adaptively adjust to dynamic changes in the outdoor environment. The window opening and closing strategies are fixed (such as fully opening or closing when reaching a single threshold), and it is impossible to achieve fine control according to spatial characteristics such as wind force gradient and rainfall distribution, resulting in a mismatch between the opening and closing actions and the actual environmental requirements, and there are energy consumption waste or safety hazards.

[0005] Therefore, there is an urgent need for an intelligent system that can integrate multi-source data such as rainfall, wind force, and air pollution, and accurately control the opening and closing amplitude of doors and windows based on spatial distribution characteristics (such as wind and rain differences at different heights of windows, indoor area pollution concentrations) to solve the limitations of traditional control strategies. Summary of the Invention

[0006] The purpose of the present invention is to propose an intelligent door and window control system to solve the above problems.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions:

[0008] An intelligent door and window control system, comprising:

[0009] A data collection module: obtaining rainfall data, wind force data at the window, and air pollution data indoors;

[0010] A data analysis module: analyzing the rainfall data and wind force data at the window respectively to obtain a rainfall coefficient and a wind force coefficient; analyzing the air pollution data indoors to obtain a pollution coefficient, and determining the window to be controlled;

[0011] A comprehensive processing module: comprehensively processing the rainfall coefficient, wind force coefficient and pollution coefficient to obtain an opening and closing evaluation coefficient;

[0012] A judgment and execution module: regulating the opening and closing amplitude of the window based on the opening and closing evaluation coefficient.

[0013] Preferably, the data collection module specifically includes:

[0014] Install a rain sensor and an anemometer at a preset position on the safety guardrail behind the window to obtain rainfall data and wind force data at the window;

[0015] The installed rain sensor and anemometer are distributed at various heights of the safety guardrail;

[0016] Install air detection sensors at various preset positions indoors, including particulate matter sensors, gas sensors, and nicotine sensors, to obtain indoor air pollution data;

[0017] Preprocess the data including cleaning, standardization and normalization, and time synchronization and alignment.

[0018] Preferably, the process of obtaining the rainfall coefficient includes:

[0019] Obtain the rainfall data detected at the safety guardrail behind the window at a preset time interval, and classify it according to the height of the rain sensor corresponding to the rainfall data;

[0020] Preset a rainfall threshold, compare the obtained rainfall data with the rainfall threshold, and record the rainfall data greater than the rainfall threshold as abnormal rainfall values; and calculate the difference between the abnormal rainfall values and the rainfall threshold to obtain an abnormal rainfall deviation value;

[0021] Mark the positions of the rain sensors corresponding to each abnormal rainfall deviation value, arrange the abnormal rainfall deviation values corresponding to the rain sensors at the same position in descending order according to the numerical value, and extract the maximum abnormal rainfall deviation value as the required abnormal rainfall deviation value for the rain sensor at this position, and mark it as the marked rainfall deviation value;

[0022] After obtaining the number of marked rainfall deviation values, divide it by the total number of rain sensors to obtain an abnormal ratio;

[0023] Take the positions of the rain sensors corresponding to all the marked rainfall deviation values as endpoints, and connect each pair of endpoints with a straight line in turn to form a closed figure; calculate the area of the closed figure, denoted as the rainfall area;

[0024] Obtain all the positions of the rain sensors at the safety guardrail behind the window, and take the positions of all the rain sensors as endpoints respectively, and connect each pair of endpoints with a straight line in turn to form a closed figure; calculate the area of the closed figure, denoted as the total rainfall area;

[0025] Divide the rainfall area by the total rainfall area to obtain an area ratio;

[0026] After comprehensively processing the abnormal ratio and the area ratio, obtain the rainfall coefficient.

[0027] Preferably, the process of obtaining the wind force coefficient includes:

[0028] Obtain the wind force data at each height of the safety fence at a preset time interval, sort the wind force data at the same height obtained at the same time in descending order according to the numerical value, and extract the maximum wind force value and the minimum wind force value among them;

[0029] Calculate the difference between the maximum wind force value and the minimum wind force value to obtain the wind difference at the same height; and obtain the distance between the anemometers corresponding to the maximum wind force value and the minimum wind force value, and calculate the product of this distance and the wind difference at the same height to obtain the wind model value;

[0030] Analyze the wind force data at each height of the safety fence in turn to obtain the wind model value corresponding to each height;

[0031] Calculate the difference between the wind model values at adjacent heights in the vertical direction, and take the absolute value to obtain the model difference value; divide the model difference value by the height difference between the anemometers corresponding to the wind model values at adjacent heights to obtain the wind ladder value;

[0032] Obtain all the wind ladder values in turn, and record the maximum wind ladder value as the wind force coefficient.

[0033] Preferably, the process of obtaining the pollution coefficient includes:

[0034] Divide the interior into areas according to the preset space size, denoted as detection sub-areas;

[0035] In the detection sub-areas, obtain the parameter data detected by the air detection sensor at a preset time interval, including particulate matter data, carbon monoxide data and nicotine data;

[0036] After detecting and analyzing the parameter data of each detection sub-area, obtain the area pollution value of the detection sub-area;

[0037] After analyzing the area pollution value of the detection sub-area, determine the windows to be controlled and the pollution coefficient.

[0038] Preferably, after detecting and analyzing the parameter data of each detection sub-area to obtain the area pollution value of the detection sub-area, specifically includes:

[0039] Set the standard value of the detection parameter corresponding to the sensor, calculate the difference between the parameter value detected by the air detection sensor and the parameter standard value corresponding to the parameter, and obtain the standard difference value corresponding to the parameter;

[0040] Set the allowable floating range of the standard deviation of the set parameters. If the standard deviation is not within its corresponding allowable floating range, mark the standard deviation as a deviation from the standard deviation;

[0041] Arrange all the standard deviation deviations in descending order according to their numerical values, and extract the maximum standard deviation deviation among them;

[0042] Accumulate the quantities corresponding to the standard deviation deviations of the parameters to obtain the total number of all standard deviation deviations corresponding to the parameters, and divide it by the total number of all standard deviations corresponding to the parameters to obtain the deviation ratio;

[0043] After performing weighted calculation on the maximum standard deviation deviation and the deviation ratio, obtain the single-parameter value corresponding to the parameter;

[0044] Obtain the single-parameter values of each parameter within the detection sub-region;

[0045] According to the type of the air detection sensor, assign corresponding weight factors. After multiplying the single-parameter values of each parameter within the detection sub-region by their corresponding weight factors respectively, sum them up to obtain the regional pollution value of the detection sub-region.

[0046] Preferably, after analyzing the regional pollution value of the detection sub-region to determine the window to be controlled and the pollution coefficient, it specifically includes:

[0047] Obtain the regional pollution values of each detection sub-region in sequence; and arrange the regional pollution values in descending order according to their numerical sizes, and extract the two largest regional pollution values, denoted as the marked regional pollution values;

[0048] Respectively obtain the centers of the detection sub-regions corresponding to the two marked regional pollution values, connect the centers of the two detection sub-regions with a straight line, and obtain the center point of this straight line. Starting from the center point, connect it with the preset positions of each window with a straight line in sequence, and project the straight line onto the ground, and calculate the length of the projected straight line, denoted as the length value;

[0049] Obtain the length values of each straight line, and mark the window corresponding to the smallest length value as the window to be controlled;

[0050] Perform mean calculation on the two marked regional pollution values and then perform product calculation with the length value to obtain the pollution coefficient.

[0051] Preferably, the acquisition logic of the opening and closing evaluation coefficient is:

[0052] After normalizing the rainfall coefficient, wind force coefficient and pollution coefficient, use the rainfall coefficient and wind force coefficient as the major semi-axis and minor semi-axis of the ellipse respectively to establish an ellipse model, use the pollution coefficient as the height of the ellipse model to establish an ellipsoid model, and calculate the volume of the ellipsoid model, denoted as the opening and closing evaluation coefficient.

[0053] Preferably, the opening and closing amplitude of the window is regulated based on the opening and closing evaluation coefficient, which specifically includes:

[0054] Preset a set number of threshold value ranges. Each group of threshold value ranges corresponds to an opening and closing amplitude for regulating the window. Match the opening and closing evaluation coefficient with the set number of threshold value ranges to obtain the opening and closing amplitude of the window corresponding to the threshold value range corresponding to the opening and closing evaluation coefficient.

[0055] In summary, due to the adoption of the above technical solutions, the beneficial effects of the present invention are:

[0056] 1. By deploying rain sensors and anemometers at multiple heights of the window guardrail, as well as indoor grid air detection sensors, the present invention realizes the refined collection of outdoor rainfall distribution, wind gradient, and indoor pollution space characteristics; it can preferentially close the window during stormy weather while opening the window near the indoor smoking area for micro-ventilation, avoiding "over-closing" or "ventilation blind spots" caused by traditional single-parameter control, and achieving a dynamic balance between bad weather protection and indoor air quality.

[0057] 2. By parameters such as the ratio of the rainfall area to the total rainfall area and the projection distance between the pollution center and the window, the environmental data is upgraded from "single-point numerical values" to "spatial characteristic quantities". For example, the spatial diffusion range of abnormal rainfall is characterized by the largest area closed figure, and the pollution core area is located at the midpoint of the connection line of the sub-region centers; the positioning of the windows to be controlled based on the distance from the pollution center avoids the energy consumption waste caused by the simultaneous opening and closing of all the windows in the house. Description of the Drawings

[0058] In the following description of exemplary embodiments in conjunction with the drawings, more details, features, and advantages of the present application are disclosed. In the drawings:

[0059] Figure 1 is a flowchart of the present invention; Detailed Embodiments

[0060] The following will describe several embodiments of the present application in more detail with reference to the drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0061] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0062] Please refer to Figure 1 As shown, the present invention provides a technical solution:

[0063] An intelligent door and window control system, comprising:

[0064] A data collection module: obtaining rainfall data, wind force data at the window, and indoor air pollution data;

[0065] Specifically, it includes:

[0066] Install rain sensors and anemometers at preset positions on the safety guardrail behind the window to obtain rainfall data and wind force data at the window;

[0067] Among them, the installed rain sensors and anemometers are distributed at various heights of the safety guardrail;

[0068] Install air detection sensors at various preset positions indoors, including particulate matter sensors, gas sensors, and nicotine sensors, to obtain indoor air pollution data;

[0069] Perform preprocessing on the data, including cleaning, standardization and normalization, and time synchronization and alignment;

[0070] Data cleaning includes: removing or correcting abnormal data caused by sensor failures, installation errors, environmental interference (such as birds hitting the anemometer), etc.;

[0071] Standardization and normalization include: converting the original units of different sensors into a unified standard to ensure data consistency;

[0072] Time synchronization and alignment include: taking the system master clock as a reference, regularly calibrating the clocks of each sensor (such as automatically synchronizing at midnight every day); for data with different acquisition frequencies, aligning them through time interpolation or resampling (such as unifying to a 5-minute interval) to ensure that the data points correspond one by one on the time axis;

[0073] A data analysis module: analyzing the rainfall data and wind force data at the window respectively to obtain a rainfall coefficient and a wind force coefficient; analyzing the indoor air pollution data to obtain a pollution coefficient, and determining the window to be controlled;

[0074] The process of obtaining the rainfall coefficient includes:

[0075] Obtaining rainfall data detected at the safety guardrail behind the window at a preset time interval, and classifying the rainfall data according to the height of the rain sensor corresponding to the rainfall data;

[0076] By height classification, the rainfall distribution patterns at different locations can be analyzed (for example, the data at the bottom of the guardrail may be higher due to splashing water); rainfall that exceeds the normal range can be identified, short-term rain showers can be excluded, and abnormal scenes that need attention can be focused on;

[0077] A rainfall threshold is preset, the obtained rainfall data is compared with the rainfall threshold, and rainfall data greater than the rainfall threshold is recorded as an abnormal rainfall value; and the difference between the abnormal rainfall value and the rainfall threshold is calculated to obtain an abnormal rainfall deviation value;

[0078] The rain sensor positions corresponding to the abnormal rainfall deviation values ​​are marked, and the abnormal rainfall deviation values ​​corresponding to the rain sensors at the same position are arranged in descending order according to the numerical values, and the maximum abnormal rainfall deviation value is extracted as the required abnormal rainfall deviation value of the rain sensor at the position, and marked as the marked rainfall deviation value;

[0079] For multiple rain sensors at the same location, their corresponding abnormal rainfall deviation values ​​are extracted and arranged in descending order according to the numerical values;

[0080] For example, there are three sensors at a certain location, and their abnormal rainfall deviation values ​​are 15mm, 10mm, and 8mm respectively. After sorting, they are 15mm, 10mm, and 8mm. Then, the largest abnormal rainfall deviation value is extracted as the abnormal rainfall deviation value required by the rain sensor at this location, and marked as the marked rainfall deviation value.

[0081] This data aggregation and sorting method can avoid repeated interference of multiple sensor data at the same location, use the most representative maximum deviation value to reflect the abnormal situation at the location, simplify the data processing process, and improve analysis efficiency;

[0082] Obtain the number of marked rainfall deviation values ​​and divide it by the total number of rain sensors to obtain the abnormal proportion;

[0083] The rain sensor positions corresponding to all the marked rainfall deviation values ​​are taken as endpoints, and the endpoints are connected with straight lines in sequence to form a closed figure, and the area of ​​the figure is the maximum area figure that can be formed; the area of ​​the closed figure is calculated and recorded as the rainfall area;

[0084] Obtain the positions of all rain sensors at the safety guardrail behind the window, and respectively use the positions of all rain sensors as endpoints. Connect each pair of endpoints with a straight line in sequence to form a closed figure, and the area of this figure is the figure with the largest area that can be formed; calculate the area of the closed figure and denote it as the total rain area;

[0085] Divide the rain area by the total rain area to obtain the area ratio;

[0086] After comprehensively processing the anomaly ratio and the area ratio, obtain the rain coefficient;

[0087] Preset the weight factors for the anomaly ratio and the area ratio. After respectively multiplying the anomaly ratio and the area ratio by their corresponding weight factors and then summing them up, obtain the rain coefficient;

[0088] The process of obtaining the wind force coefficient includes:

[0089] Obtain the wind force data at each height of the safety guardrail at a preset time interval, sort the wind force data at the same height obtained at the same time in descending order according to the numerical value, and extract the maximum wind force value and the minimum wind force value among them;

[0090] Calculate the difference between the maximum wind force value and the minimum wind force value to obtain the wind difference at the same height; and obtain the distance between the anemometers corresponding to the maximum wind force value and the minimum wind force value, and multiply this distance by the wind difference at the same height to obtain the wind model value;

[0091] Analyze the wind force data at each height of the safety guardrail in sequence to obtain the wind model value corresponding to each height;

[0092] Calculate the difference between the wind model values at adjacent heights in the vertical direction and take the absolute value to obtain the model difference; divide the model difference by the height difference between the anemometers corresponding to the wind model values at adjacent heights to obtain the wind ladder value;

[0093] Obtain all the wind ladder values in sequence, and denote the maximum wind ladder value as the wind force coefficient;

[0094] Quantify the spatial inhomogeneity of the wind speed with the "wind model value";

[0095] Quantify the vertical change rate of the inhomogeneity with the "wind ladder value";

[0096] Capture the most dangerous air flow mutation position with the "maximum wind ladder value";

[0097] Based on this, judge the wind force situation at the window to provide a basis for the subsequent opening and closing of the window;

[0098] The process of obtaining the pollution coefficient includes:

[0099] Divide the interior into regions according to preset spatial dimensions, denoted as detection sub-regions;

[0100] Within the detection sub-regions, obtain the parameter data detected by the air detection sensors at preset time intervals, including particulate matter data, carbon monoxide data, and nicotine data;

[0101] Set the standard values of the detection parameters corresponding to the sensors, calculate the difference between the parameter values detected by the air detection sensors and their corresponding standard values of the parameters, and obtain the standard difference corresponding to the parameters;

[0102] Set the allowable floating range of the standard difference of the parameters. If the standard difference is not within its corresponding allowable floating range, mark the standard difference as a deviation from the standard difference;

[0103] Arrange all the deviations from the standard differences in descending order according to the numerical values, and extract the maximum deviation from the standard difference among them;

[0104] Accumulate the quantities corresponding to the deviations from the standard differences of the parameters to obtain the total number of all deviations from the standard differences corresponding to the parameters, and divide it by the total number of all standard differences corresponding to the parameters to obtain the deviation ratio;

[0105] After performing a weighted calculation on the maximum deviation from the standard difference and the deviation ratio, obtain the single-parameter value corresponding to the parameter;

[0106] Including: preset the weight factors of the maximum deviation from the standard difference and the deviation ratio. Respectively multiply the maximum deviation from the standard difference and the deviation ratio by their corresponding weight factors, and then sum to obtain the single-parameter value;

[0107] Obtain the single-parameter values of each parameter within the detection sub-regions;

[0108] According to the type of the air detection sensor, assign corresponding weight factors. Respectively multiply the single-parameter values of each parameter within the detection sub-regions by their corresponding weight factors, and then sum to obtain the regional pollution value of the detection sub-region;

[0109] For example:

[0110] Weight factor setting logic: Pollutant hazard level: nicotine (carcinogenic) weight > CO (toxicity) > PM (particulate matter pollution), reflecting the priority of health risks;

[0111] Scene adaptation: In the kitchen scene, the PM weight can be dynamically adjusted to 0.5, the CO weight to 0.4, and the nicotine weight to 0.1 (because cooking fumes mainly consist of particulate matter and CO);

[0112] Obtain the regional pollution values of each detection sub-region in sequence; and sort the regional pollution values in descending order according to the numerical size, and extract the two largest regional pollution values, which are denoted as the marked regional pollution values;

[0113] Obtain the centers of the detection sub-regions corresponding to the two marked regional pollution values respectively, connect the two centers of the detection sub-regions with a straight line, and obtain the center point of this straight line. Starting from the center point, connect it with the preset positions of each window with a straight line in sequence, and project the straight line onto the ground, and calculate the length of the straight line projection, which is denoted as the length value;

[0114] Among them: Projecting onto the ground: Ignoring the vertical height difference (such as windows at different heights on the wall), it is simplified to two-dimensional plane distance calculation to reduce the algorithm complexity;

[0115] Minimum distance first: Assume that the ventilation effect of the window is inversely proportional to the distance, and the pollutants can be diluted faster after the window close to the pollution center is opened;

[0116] Obtain the length values of each straight line, and mark the window corresponding to the smallest length value as the window to be controlled;

[0117] Perform a mean calculation on the two marked regional pollution values and then perform a product calculation with the length value to obtain the pollution coefficient;

[0118] For example:

[0119] Identify high-pollution areas:

[0120] Extract the two largest regional pollution values (marked regional pollution values) in all sub-regions, corresponding to the two sub-regions with the most serious pollution (such as smoking point A and kitchen point B);

[0121] Determine the window control priority:

[0122] Calculate the center point of the connection line between the centers of the two high-pollution sub-regions as the virtual "pollution center".

[0123] Calculate the projection distance from this center to each window (the length of the straight line projected onto the ground), and the window with the smallest distance is the window to be controlled (prioritize being close to the pollution center to improve ventilation efficiency);

[0124] Quantify the pollution coefficient:

[0125] The pollution coefficient combines the mean pollution intensity (the average pollution level of the two high-pollution regions) and the distance between the window and the pollution center (the closer the distance, the more urgent the control requirement). The larger the value, the more serious the pollution and the closer the window is to the pollution source, and it needs to be prioritized;

[0126] Avoid the energy consumption waste of uniform ventilation throughout the house and take precise actions for local pollution;

[0127] Comprehensive processing module: After comprehensively processing the rainfall coefficient, wind force coefficient, and pollution coefficient, an opening and closing evaluation coefficient is obtained;

[0128] After normalizing the rainfall coefficient, wind force coefficient, and pollution coefficient, the rainfall coefficient and wind force coefficient are respectively used as the major semi-axis and minor semi-axis of an ellipse to establish an ellipse model, and the pollution coefficient is used as the height of the ellipse model to establish an ellipsoid model. Calculate the volume of the ellipsoid model, which is denoted as the opening and closing evaluation coefficient

[0129] Judgment and execution module: Based on the opening and closing evaluation coefficient, control the opening and closing amplitude of the window;

[0130] Specifically include:

[0131] Preset a set number of threshold value ranges. Each group of threshold value ranges corresponds to an opening and closing amplitude for controlling the window. Match the opening and closing evaluation coefficient with the set number of threshold value ranges to obtain the opening and closing amplitude of the window corresponding to the threshold value range corresponding to the opening and closing evaluation coefficient.

[0132] The above formulas are all obtained by collecting a large amount of data for software simulation and selecting a formula close to the true value. The influence weight factors and specific coefficient values in the formula are set by those skilled in the art according to the actual situation and can be adjusted and modified later.

[0133] The above description of the embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent door and window control system, characterized in that, Including: Data collection module: Obtain rainfall data, wind force data at the window, and indoor air pollution data; Data analysis module: Analyze the rainfall data and wind force data at the window respectively to obtain a rainfall coefficient and a wind force coefficient; Analyze the indoor air pollution data to obtain a pollution coefficient, and determine the window to be controlled; Comprehensive processing module: Comprehensively process the rainfall coefficient, wind force coefficient and pollution coefficient to obtain an opening and closing evaluation coefficient; Judgment and execution module: Regulate the opening and closing amplitude of the window based on the opening and closing evaluation coefficient.

2. The intelligent door and window control system according to claim 1, characterized in that, The data collection module specifically includes: Install a rain sensor and an anemometer at a preset position on the safety guardrail behind the window to obtain rainfall data and wind force data at the window; Among them, the installed rain sensor and anemometer are distributed at various heights of the safety guardrail; Install air detection sensors at various preset positions indoors, including a particulate matter sensor, a gas sensor and a nicotine sensor, to obtain indoor air pollution data; Perform preprocessing on the data including cleaning, standardization and normalization, and time synchronization and alignment.

3. An intelligent door and window control system according to claim 2, characterized in that, The process of obtaining the rainfall coefficient includes: Obtain the rainfall data detected at the safety guardrail behind the window at a preset time interval, and classify it according to the height of the rain sensor corresponding to the rainfall data; Preset a rainfall threshold, compare the obtained rainfall data with the rainfall threshold, and record the rainfall data greater than the rainfall threshold as an abnormal rainfall value; And calculate the difference between the abnormal rainfall value and the rainfall threshold to obtain an abnormal rainfall deviation value; Mark the positions of the rain sensors corresponding to each abnormal rainfall deviation value, and arrange the abnormal rainfall deviation values corresponding to the rain sensors at the same position in descending order according to the numerical value, and extract the maximum abnormal rainfall deviation value as the required abnormal rainfall deviation value of the rain sensor at this position, and mark it as the marked rainfall deviation value; After obtaining the number of marked rainfall deviation values, divide it by the total number of rain sensors to obtain an abnormal ratio; Take the positions of the rain sensors corresponding to all the marked rainfall deviation values as endpoints, and connect the endpoints between each other in turn with straight lines to form a closed figure; Calculate the area of the closed figure, denoted as the rainfall area; Obtain all the rain sensor positions at the safety guardrail behind the window, and take each of the rain sensor positions as an endpoint, and connect the endpoints between each other in turn with straight lines to form a closed figure; Calculate the area of the closed figure, denoted as the total rainfall area; Divide the rainfall area by the total rainfall area to obtain an area ratio; Comprehensively process the abnormal ratio and the area ratio to obtain the rainfall coefficient.

4. An intelligent door and window control system according to claim 3, characterized in that, The process of obtaining the wind force coefficient includes: Obtain the wind force data at various heights of the safety guardrail at a preset time interval, and arrange the wind force data at the same height obtained at the same time in descending order according to the numerical value, and extract the maximum wind force value and the minimum wind force value among them; Calculate the difference between the maximum wind force value and the minimum wind force value to obtain the wind difference at the same height; And obtain the distance between the anemometers corresponding to the maximum wind force value and the minimum wind force value, and calculate the product of this distance and the wind difference at the same height to obtain the wind model value; Analyze the wind force data at each height of the safety guardrail in sequence to obtain the wind force model values corresponding to each height; Calculate the difference between the wind force model values at adjacent heights in the vertical direction, and take the absolute value to obtain the modulus difference; divide the modulus difference by the height difference of the anemometer corresponding to the wind force model values at adjacent heights to obtain the wind ladder value; Obtain all the wind ladder values in sequence, and record the maximum wind ladder value as the wind force coefficient.

5. An intelligent door and window control system according to claim 4, characterized in that, The process of obtaining the pollution coefficient includes: Divide the indoor area into sub-regions according to the preset spatial dimensions, denoted as detection sub-regions; In the detection sub-regions, obtain the parameter data detected by the air detection sensor at a preset time interval, including particulate matter data, carbon monoxide data, and nicotine data; After detecting and analyzing each parameter data of each detection sub-region, obtain the regional pollution value of the detection sub-region; After analyzing the regional pollution value of the detection sub-region, determine the windows to be controlled and the pollution coefficient.

6. The intelligent door and window control system according to claim 5, characterized in that, The step of obtaining the regional pollution value of the detection sub-region after detecting and analyzing each parameter data of each detection sub-region specifically includes: Set the standard value of the detection parameter corresponding to the sensor, calculate the difference between the parameter value detected by the air detection sensor and the parameter standard value corresponding to the parameter to obtain the standard difference corresponding to the parameter; Set the allowable floating range of the standard difference of the parameter. If the standard difference is not within its corresponding allowable floating range, mark the standard difference as a deviation from the standard difference; Arrange all the deviations from the standard difference in descending order according to the numerical value, and extract the maximum deviation from the standard difference; Accumulate the quantities corresponding to the deviations from the standard difference of the parameter to obtain the total number of all deviations from the standard difference corresponding to the parameter, and divide it by the total number of all standard differences corresponding to the parameter to obtain the deviation ratio; After performing weighted calculation on the maximum deviation from the standard difference and the deviation ratio, obtain the single-parameter value corresponding to the parameter; Obtain the single-parameter values of each parameter in the detection sub-region; According to the type of the air detection sensor, assign corresponding weight factors, multiply the single-parameter values of each parameter in the detection sub-region by their corresponding weight factors respectively, and sum them to obtain the regional pollution value of the detection sub-region.

7. An intelligent door and window control system according to claim 6, characterized in that, The step of determining the windows to be controlled and the pollution coefficient after analyzing the regional pollution value of the detection sub-region specifically includes: Obtain the regional pollution values of each detection sub-region in sequence; arrange the regional pollution values in descending order according to the numerical value, and extract the two largest regional pollution values, denoted as the marked regional pollution values; Respectively obtain the centers of the detection sub-regions corresponding to the two marked regional pollution values, connect the centers of the two detection sub-regions with a straight line, and obtain the center point of this straight line. Starting from the center point, connect it with the preset positions of each window with a straight line in sequence, and project the straight line onto the ground, calculate the length of the projected straight line, denoted as the length value; Obtain the length values of each straight line, and mark the window corresponding to the minimum length value as the window to be controlled; Perform a mean calculation on the two marked regional pollution values and then multiply it by the length value to obtain the pollution coefficient.

8. An intelligent door and window control system according to claim 7, characterized in that, The acquisition logic of the opening and closing evaluation coefficient is: After normalizing the rainfall coefficient, wind force coefficient, and pollution coefficient, respectively use the rainfall coefficient and wind force coefficient as the major semi-axis and minor semi-axis of the ellipse to establish an ellipse model, and use the pollution coefficient as the height of the ellipse model to establish an ellipsoid model. Calculate the volume of the ellipsoid model, which is denoted as the opening and closing evaluation coefficient.

9. An intelligent door and window control system according to claim 8, characterized in that, Regulate the opening and closing amplitude of the window based on the opening and closing evaluation coefficient, specifically including: Preset a set number of threshold value ranges. Each group of threshold value ranges corresponds to an opening and closing amplitude for regulating the window. Match the opening and closing evaluation coefficient with the set number of threshold value ranges to obtain the opening and closing amplitude of the window corresponding to the threshold value range corresponding to the opening and closing evaluation coefficient.

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