An intelligent air conditioner status judgment and analysis system and method based on the Internet of Things

Abstract

The present invention discloses an intelligent air conditioner state judgment and analysis system and method based on the Internet of Things, which relates to the technical field of intelligent air conditioner analysis. The present invention includes: S10: searching for temperature boundary layers existing in a target area; S20: determining the target temperature boundary layers and preliminarily analyzing the operating states of each diffuser based on the determination results; S30: predicting the temperature influence coefficients of outdoor temperature on the temperatures of each target object within the target temperature boundary layers; S40: judging and analyzing the operating states of the diffusers matching each target temperature boundary layer according to the predicted temperature influence coefficients. Through the operating index of the diffuser, the present invention can accurately analyze various operating states of the diffuser. At the same time, when the air conditioner is turned on or off for a short time, accurate analysis of the operating state can also be achieved, and the influence of the installation position of the window, the window size, the house orientation, etc. within the target area on the target object is eliminated during the analysis process.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent analysis of air conditioners, and specifically to an intelligent air conditioner state judgment and analysis system and method based on the Internet of Things. Background Art

[0002] With the continuous improvement of living standards, air conditioners have become one of the essential household electrical appliances in daily life. The on / off state of the air conditioner directly affects human comfort in the hot summer. Currently, the on / off state of the air conditioner is mainly determined by observing the information on the air conditioner control panel or observing the state of the air conditioner fan blades. However, in some environments with limited space, the air conditioner control panel cannot be installed normally. For example, in some factory areas, it is not allowed to set the air conditioner through the control panel, and there are only fans, so the air conditioner state cannot be determined by checking the air conditioner control panel or the fan blade state.

[0003] In the prior art, the on / off state of the air conditioner in a certain time period is judged by the temperature values in that time period. When the air conditioner starts for a short time or the set temperature is relatively high, misjudgment is likely to occur. In addition, in the prior art, the judgment of the air conditioner state is realized through a training model. This process requires collecting a large amount of data, and different models need to be trained for different situations, which increases the difficulty of collecting data and requires a large amount of time to train new models, reducing the intelligent control effect of the air conditioner and the control accuracy of the air conditioner. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent air conditioner state judgment and analysis system and method based on the Internet of Things to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An intelligent air conditioner state judgment and analysis method based on the Internet of Things, the method comprising:

[0006] S10: Obtain the position information of each diffuser installed in the target area, and search for the temperature boundary layer existing in the target area according to the inclination angle of the guide vanes of each diffuser;

[0007] S20: Use the Internet of Things technology to sense the temperature change of each target object in each searched temperature boundary layer. Based on the sensing result and the positional relationship between each target object, determine the target temperature boundary layer, and preliminarily analyze the operating state of each diffuser based on the determination result;

[0008] S30: Predict the temperature influence coefficient of the outdoor temperature on each target object in the target temperature boundary layer;

[0009] S40: Judge and analyze the operating state of the diffuser matching each target temperature boundary layer according to the predicted temperature influence coefficient.

[0010] Further, the S10 includes:

[0011] S101: Randomly select a point within the target area as the coordinate origin to construct a three-dimensional space coordinate system, and obtain the position coordinates of the centers of each diffuser installed within the target area in the three-dimensional space coordinate system;

[0012] S102: Use an image acquisition device to collect the side images of each diffuser, and based on the collected side images, determine the inclination angles of the guiding vanes of each diffuser;

[0013] S103: When the inclination angle of the diffuser guiding vane is on the right side of the vertical line segment from the diffuser center to the ground, search for the temperature boundary layer matching the diffuser numbered i according to the air supply angle range of the diffuser determined by [γ i , f];

[0014] When the inclination angle of the diffuser guiding vane is on the left side of the vertical line segment from the diffuser center to the ground, search for the temperature boundary layer matching the diffuser numbered i according to the air supply angle range of the diffuser determined by [-f, -γ i ;

[0015] Wherein, γ i represents the inclination angle of the guiding vane of the diffuser numbered i, and f represents the angle between the upper air supply line of the diffuser and the vertical line segment from the diffuser center to the ground. The method for judging and analyzing the operating state of the diffuser according to the temperature change of each target object within the temperature boundary layer has higher accuracy compared with the method of analyzing the operating state of the diffuser according to the average temperature within the target area, is not easily affected by external environmental factors and the aging degree of the diffuser, and reduces the amount of data processing.

[0016] Further, the S20 includes:

[0017] S201: According to the three-dimensional distance calculation formula, calculate the distance value d ij between the center of the j-th target object within the temperature boundary layer matching the diffuser numbered i and the center of the diffuser numbered i. The target objects include mobile terminals and smart appliances, and the temperature values of each target object are collected through the Internet of Things technology;

[0018] S202: At time interval t, use the Internet of Things technology to collect the temperature value of the j-th target object within the temperature boundary layer matching the diffuser numbered i, and randomly select a target object within the temperature boundary layer matching the diffuser numbered i;

[0019] When :

[0020] According to predict the running index of the diffuser numbered i at the moment of T + p * t;

[0021] When :

[0022] According to predict the running index of the diffuser numbered i at the moment of T + p * t;

[0023] where j = 1, 2, …, m, representing the numbers corresponding to each target object in the temperature boundary layer, m represents the total number of target objects existing in the temperature boundary layer, u represents the number corresponding to the selected target object, u = 1, 2, ……, m, p = 0, 1, 2, …, q, representing the numbering process of the temperature acquisition time of the control terminal in chronological order, q represents the total number of numbers, T represents the time when the control terminal first acquires the temperature value of the target object, W ij(T+p*t) represents the temperature value corresponding to the j-th target object in the temperature boundary layer matched with the diffuser numbered i at the moment of T + p * t, U i(T+p*t) represents the running index of the diffuser numbered i at the moment of T + p * t;

[0024] S203: If U i(T+p*t) > 0.1, then it is considered that the temperature boundary layer matched with the diffuser numbered i is the target temperature boundary layer;

[0025] If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] < W ij(T+p*t) , then it is considered that the temperature boundary layer matched with the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the on state at the moment of T + p * t;

[0026] If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] = W ij(T+p*t) < E T+(p+1)*t , then it is considered that the temperature boundary layer matched with the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the on state at the moment of T + (p + 1) * t, E T+(p+1)*t represents the average temperature value corresponding to the outdoors at the moment of T + (p + 1) * t;

[0027] If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] > W ij(T+p*t) , then it is considered that the temperature boundary layer matched with the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the off state at the moment of T + (p + 1) * t;

[0028] If 0 ≤ Ui(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] = W ij(T+p*t) ≥ E T+(p+1)*t , it is considered that the temperature boundary layer matching the diffuser numbered i is a non - target temperature boundary layer, and the diffuser numbered i is in the closed state at the moment of T+(p + 1)*t. Through the predicted operation index of the diffuser, it is possible to judge and analyze the initial state of the diffuser in the open state, the initial state in the off state, and the stable states in the open state and the off state.

[0029] Further, the S30 includes:

[0030] Within the target temperature boundary layer, according to h xj(T+p*t) =(E T+(p+1)*t - E T+p*t ) / (W xj[T+(p+1)*t] - W xj(T+p*t) ), predict the influence coefficient of the outdoor temperature on the temperature of the j - th target object in the target temperature boundary layer numbered x;

[0031] Among them, x = 1, 2, …, q, representing the numbers corresponding to each target temperature boundary layer, q represents the total number of target temperature boundary layers, and W xj(T+p*t) represents the temperature value corresponding to the j - th target object in the target temperature boundary layer numbered x at the moment of T + p*t.

[0032] Further, the S40 includes:

[0033] S401: If h xj(T+p*t) ≥ 0.6 all hold, it is considered that the diffuser matching the target temperature boundary layer numbered x is in the closed state at the moment of T + p*t;

[0034] If h xj(T+p*t) ≥ 0.6 partially holds, then when 0 ≤ h xj(T+p*t) < 0.6, put the number of the corresponding target object into the set M;

[0035] At this time, according to , predict the operation index of the diffuser matching the target temperature boundary layer numbered x at the moment of T + p*t, where d xj represents the distance value between the center of the j - th target object in the target temperature boundary layer numbered x and the center of the diffuser matching the target temperature boundary layer numbered x. When the number j is stored in the set M, , when the number j is not stored in the set M, This process can eliminate the influence of the installation position of the window, the window size, the house orientation, etc. within the target area on the indoor temperature, ensuring that the operating status of the diffuser can be analyzed more accurately according to the calculated operating index;

[0036] S402: If 0 ≤ U x(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W xj[T+(p+1)*t] <W xj(T+p*t) , it is considered that the diffuser matching the target temperature boundary layer with the number x is in the open state at the moment of T + p * t;

[0037] If 0 ≤ U x(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W xj[T+(p+1)*t] >W xj(T+p*t) , it is considered that the diffuser matching the target temperature boundary layer with the number x is in the closed state at the moment of T + p * t;

[0038] If 0 ≤ U i(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W ij[T+(p+1)*t] =W ij(T+p*t) <E T+(p+1)*t , it is considered that the diffuser matching the target temperature boundary layer with the number x is in the open state at the moment of T + p * t;

[0039] If 0 ≤ U i(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W ij[T+(p+1)*t] =W ij(T+p*t) ≥E T+(p+1)*t , it is considered that the diffuser matching the target temperature boundary layer with the number x is in the closed state at the moment of T + p * t.

[0040] An intelligent air conditioner status judgment and analysis system based on the Internet of Things, the system includes a temperature boundary layer search module, an operating status preliminary analysis module, a temperature influence coefficient prediction module, and an operating status judgment and analysis module;

[0041] The temperature boundary layer search module is used to search for the temperature boundary layer existing in the target area;

[0042] The operating status preliminary analysis module is used to determine the target temperature boundary layer and conduct a preliminary analysis of the driving operating status of each diffuser based on the determination result;

[0043] The temperature influence coefficient prediction module is used to predict the temperature influence coefficient of the outdoor temperature on each target object within the target temperature boundary layer;

[0044] The operating status judgment and analysis module is used to judge and analyze the operating status of the diffusers matching each target temperature boundary layer.

[0045] Further, the temperature boundary layer searching module includes a position coordinate acquisition unit, a guide vane inclination determination unit, and a temperature boundary layer searching unit;

[0046] The position coordinate acquisition unit randomly selects a point in the target area as the coordinate origin to construct a three-dimensional space coordinate system, and acquires the position coordinates of the centers of the diffusers installed in the target area in the three-dimensional space coordinate system;

[0047] The guide vane inclination determination unit uses an image acquisition device to collect the side images of the diffusers, and determines the inclination angles of the guide vanes of the diffusers based on the collected side images;

[0048] The temperature boundary layer searching unit searches for the temperature boundary layer matching each diffuser according to the positional relationship between the inclination angle of the diffuser guide vane and the vertical line segment of the diffuser center relative to the ground, as well as the air supply angle range of the diffuser.

[0049] Further, the preliminary operation state analysis module includes a temperature value acquisition unit, an operation index prediction unit, and a preliminary operation state analysis unit;

[0050] The temperature value acquisition unit acquires the temperature values of the target objects through the Internet of Things technology;

[0051] The operation index prediction unit predicts the operation indices of the diffusers according to the real-time temperature change differences of the target objects and the distance values of the target objects from the corresponding diffusers, and determines the target temperature boundary layer based on the prediction results;

[0052] The preliminary operation state analysis unit preliminarily analyzes the operation states of the diffusers according to the prediction results of the operation index prediction unit, the real-time temperature change conditions of the target objects, and the real-time outdoor temperature values.

[0053] Further, the temperature influence coefficient prediction module predicts the temperature influence coefficients of the outdoor temperature on the target objects in the target temperature boundary layer by using the constructed mathematical model.

[0054] ' Further, the operation state judgment and analysis module includes a temperature influence coefficient analysis unit and an operation state judgment and analysis unit;

[0055] The temperature influence coefficient analysis unit compares the prediction results transmitted by the temperature influence coefficient prediction module with the set threshold values, and based on the comparison results, selects whether to put the numbers of the corresponding target objects into the set, and analyzes the operation states of the diffusers matching the target temperature boundary layer;

[0056] The operating status judgment and analysis unit constructs a mathematical formula to predict the operation index of the diffuser matching each target temperature boundary layer based on the numbers of the target objects stored in the set and the temperature influence coefficients of each target object by the outdoor temperature, and judges and analyzes the operating status of the diffuser matching each target temperature boundary layer based on the prediction results.

[0057] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0058] 1. By searching for the temperature boundary layers of each diffuser, and based on the temperature value changes of each target object within the temperature boundary layer and the real-time outdoor temperature value, the present invention realizes the judgment and analysis of the operating status of the diffuser. Compared with the traditional method, this process reduces the amount of temperature data acquisition and processing. At the same time, it can accurately analyze the operating status of the air conditioner when the air conditioner is turned on or off for a short time, which is beneficial to improving the intelligent control effect of the air conditioner.

[0059] 2. The present invention judges and analyzes the initial state when the diffuser is in the on state, the initial state when the diffuser is in the off state, and the stable states when the diffuser is in the on state and the off state through the operation index of the diffuser, improving the use effect of the system.

[0060] 3. The present invention predicts the operation index of the diffuser matching each target temperature boundary layer through the temperature influence coefficients of each target object within the target temperature boundary layer by the predicted outdoor temperature. During the prediction process, the influences of the installation position of the window, the window size, the outdoor temperature, the house orientation, etc. within the target area on the target object are eliminated, thereby improving the prediction accuracy of the operation index and the analysis accuracy of the operating status of the diffuser. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a schematic diagram of the working process of an intelligent air conditioner status judgment and analysis system and method based on the Internet of Things according to the present invention;

[0062] Figure 2 is a schematic diagram of the working principle structure of an intelligent air conditioner status judgment and analysis system and method based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] Embodiment: As Figure 1 - Figure 2As shown, the present invention provides a technical solution for an intelligent air-conditioning status judgment and analysis system and method based on the Internet of Things. An intelligent air-conditioning status judgment and analysis method based on the Internet of Things, the method comprising:

[0065] S10: Obtain the position information of each diffuser installed in the target area, and search for the temperature boundary layer existing in the target area according to the inclination angle of the guide vanes of each diffuser;

[0066] S10 includes:

[0067] S101: Randomly select a point in the target area as the coordinate origin to construct a three-dimensional space coordinate system, and obtain the position coordinates of the centers of each diffuser installed in the target area in the three-dimensional space coordinate system. A diffuser refers to the air outlet in the central air-conditioning pipeline system;

[0068] S102: Use an image acquisition device (the image acquisition device includes a camera) to collect the side images of each diffuser, and determine the inclination angle of the guide vanes of each diffuser based on the collected side images. The inclination angle of the guide vane refers to the angle between the guide vane and the vertical line segment from the diffuser center to the ground;

[0069] S103: When the inclination angle of the diffuser guide vane is on the right side of the vertical line segment from the diffuser center to the ground, search for the temperature boundary layer matching the diffuser numbered i according to the air supply angle range of the diffuser determined by [γ i , f];

[0070] When the inclination angle of the diffuser guide vane is on the left side of the vertical line segment from the diffuser center to the ground, search for the temperature boundary layer matching the diffuser numbered i according to the air supply angle range of the diffuser determined by [-f, -γ i ;

[0071] Wherein, γ i represents the inclination angle of the guide vane of the diffuser numbered i, f represents the angle between the upper air supply line of the diffuser and the vertical line segment from the diffuser center to the ground. The upper air supply line refers to the upper air supply boundary line among the two air supply boundary lines corresponding to the air supply area of the diffuser air outlet. The position of the upper air supply boundary line is not affected by the inclination angle of the diffuser guide vane, and the position of the lower air supply boundary line is affected by the inclination angle of the diffuser guide vane; the temperature difference between two randomly selected positions inside the two boundaries of the temperature boundary layer is within the error range, and the temperature outside the two boundaries of the temperature boundary layer shows a gradient change. The two boundaries of the temperature boundary layer respectively refer to the plane where the diffuser guide vane is located and the plane where the diffuser upper air supply line is located. The angle between the plane where the diffuser upper air supply line is located and the plane where the diffuser guide vane is located = |γ i -f|;

[0072] S20: Sense the temperature changes of each target object within each found temperature boundary layer using Internet of Things technology. Based on the sensing results and the positional relationships between the target objects, determine the target temperature boundary layer, and conduct a preliminary analysis of the operating states of each diffuser based on the determination result;

[0073] S20 includes:

[0074] S201: According to the three-dimensional distance calculation formula, calculate the distance value d between the center of the j-th target object within the temperature boundary layer matching the diffuser numbered i and the center of the diffuser numbered i. The target objects include mobile terminals and smart appliances. Collect the temperature values of each target object through Internet of Things technology (specific method: Use the temperature sensors installed in the mobile terminals and smart appliances to sense the temperature values at the locations where the mobile terminals and smart appliances are located, and the control terminal wirelessly connected to the smart appliances and mobile terminals collects the temperature values sensed by the mobile terminals and smart appliances); ij

[0075] S202: At time interval t, use Internet of Things technology to collect the temperature value of the j-th target object within the temperature boundary layer matching the diffuser numbered i. Randomly select a target object in the temperature boundary layer matching the diffuser numbered i. There are multiple target objects within the temperature boundary layer;

[0076] When :

[0077] According to Predict the operating index of the diffuser numbered i at time T + p * t;

[0078] When :

[0079] According to Predict the operating index of the diffuser numbered i at time T + p * t;

[0080] Among them, j = 1, 2, …, m, representing the numbers corresponding to each target object within the temperature boundary layer, m represents the total number of target objects existing in the temperature boundary layer, u represents the number corresponding to the selected target object, u = 1, 2, ……, m, p = 0, 1, 2, …, q, representing the numbering process of the temperature collection times of the control terminal in chronological order, q represents the total number of numbers, T represents the time when the control terminal first collects the temperature value of the target object, W ij(T+p*t) represents the temperature value corresponding to the j-th target object within the temperature boundary layer matching the diffuser numbered i at time T + p * t, U i(T+p*t) represents the operating index of the diffuser numbered i at time T + p * t;

[0081] S203: If U i(T+p*t) > 0.1, it is considered that the temperature boundary layer matching the diffuser numbered i is the target temperature boundary layer;

[0082] If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] < W ij(T+p*t) , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the open state at time T + p * t;

[0083] If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] = W ij(T+p*t) < E T+(p+1)*t , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the open state at time T + (p + 1) * t, where E T+(p+1)*t represents the corresponding average outdoor temperature value at time T + (p + 1) * t;

[0084] If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] > W ij(T+p*t) , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the closed state at time T + (p + 1) * t;

[0085] If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] = W ij(T+p*t) ≥ E T+(p+1)*t , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the closed state at time T + (p + 1) * t;

[0086] S30: Predict the temperature influence coefficient of the outdoor temperature on each target object in the target temperature boundary layer;

[0087] S30 includes:

[0088] In the target temperature boundary layer, according to h xj(T+p*t) =(E T+(p+1)*t - E T+p*t ) / (W xj[T+(p+1)*t] - W xj(T+p*t) ), predict the temperature influence coefficient of the outdoor temperature on the j-th target object in the target temperature boundary layer numbered x;

[0089] Among them, x = 1, 2,..., q, representing the numbers corresponding to each target temperature boundary layer, q represents the total number of target temperature boundary layers, W xj(T+p*t)Indicates the temperature value of the jth target object in the target temperature boundary layer numbered x at time T+p*t;

[0090] S40: judging and analyzing the operating status of the diffuser matching each target temperature boundary layer based on the predicted temperature influence coefficient;

[0091] S40 includes:

[0092] S401: If h xj(T+p*t) ≥0.6, the diffuser matching the target temperature boundary layer numbered x is considered to be in the closed state at time T+p*t;

[0093] If h xj(T+p*t) ≥0.6 part is true, then at 0≤h xj(T+p*t) When <0.6, the number of the corresponding target object is placed in the set M;

[0094] according to , predict the operating index of the diffuser matching the target temperature boundary layer numbered x at time T+p*t, where d xj Indicates the distance between the center of the j-th target object in the target temperature boundary layer numbered x and the center of the diffuser matching the target temperature boundary layer numbered x. When number j is stored in the set M, , when number j is not stored in the set M, , when number u is stored in set M, , when number u is not stored in the set M, ;

[0095] S402: If 0≤U x(T+p*t) ≤0.1 and when number j is stored in set M, W xj[T+(p+1)*t] <W xj(T+p*t) , then the diffuser matching the target temperature boundary layer numbered x at time T+p*t is considered to be in the open state;

[0096] If 0≤U x(T+p*t) ≤0.1 and when number j is stored in set M, W xj[T+(p+1)*t] >W xj(T+p*t) , it is considered that the diffuser matching the target temperature boundary layer numbered x is in the closed state at the time T+p*t;

[0097] If 0≤U i(T+p*t) ≤0.1 and when number j is stored in set M, W ij[T+(p+1)*t] =W ij(T+p*t) <E T+(p+1)*t , then the diffuser matching the target temperature boundary layer numbered x at time T+p*t is considered to be in the open state;

[0098] If 0 ≤ U i(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W ij[T+(p+1)*t] = W ij(T+p*t) ≥ E T+(p+1)*t , it is considered that the diffuser matching the target temperature boundary layer with the number x is in the closed state at the moment of T + p * t.

[0099] An intelligent air conditioner state judgment and analysis system based on the Internet of Things, the system includes a temperature boundary layer search module, a preliminary operation state analysis module, a temperature influence coefficient prediction module, and an operation state judgment and analysis module;

[0100] The temperature boundary layer search module is used to search for the temperature boundary layers existing in the target area;

[0101] The temperature boundary layer search module includes a position coordinate acquisition unit, a guide vane inclination determination unit, and a temperature boundary layer search unit;

[0102] The position coordinate acquisition unit randomly selects a point in the target area as the coordinate origin to construct a three-dimensional space coordinate system, and acquires the position coordinates of the centers of each diffuser installed in the target area in the three-dimensional space coordinate system;

[0103] The guide vane inclination determination unit uses an image acquisition device to collect the side images of each diffuser, and determines the inclination angles of the guide vanes of each diffuser based on the collected side images;

[0104] The temperature boundary layer search unit searches for the temperature boundary layers matching each diffuser according to the positional relationship between the inclination angle of the diffuser guide vane and the vertical line segment of the diffuser center with respect to the ground, as well as the air supply angle range of the diffuser;

[0105] The preliminary operation state analysis module is used to determine the target temperature boundary layer, and based on the determination result, preliminarily analyze the driving operation states of each diffuser;

[0106] The preliminary operation state analysis module includes a temperature value acquisition unit, an operation index prediction unit, and a preliminary operation state analysis unit;

[0107] The temperature value acquisition unit acquires the temperature values of each target object through the Internet of Things technology;

[0108] The operation index prediction unit predicts the operation indices of each diffuser according to the real-time temperature change differences of each target object and the distance values of each target object from the corresponding diffuser, and based on the prediction results, determines the target temperature boundary layer;

[0109] The preliminary operation status analysis unit preliminarily analyzes the operation status of each diffuser according to the prediction results of the operation index prediction unit, as well as the real-time temperature changes of each target object and the real-time outdoor temperature value.

[0110] The temperature influence coefficient prediction module is used to predict the temperature influence coefficient of the outdoor temperature on each target object within the target temperature boundary layer.

[0111] The temperature influence coefficient prediction module predicts the temperature influence coefficient of the outdoor temperature on each target object within the target temperature boundary layer by using the constructed mathematical model.

[0112] The operation status judgment and analysis module is used to judge and analyze the operation status of the diffusers matching each target temperature boundary layer.

[0113] The operation status judgment and analysis module includes a temperature influence coefficient analysis unit and an operation status judgment and analysis unit.

[0114] The temperature influence coefficient analysis unit compares the prediction results transmitted by the temperature influence coefficient prediction module with the set threshold value. Based on the comparison results, it selects whether to put the number of the corresponding target object into the set, and analyzes the operation status of the diffusers matching the target temperature boundary layer.

[0115] The operation status judgment and analysis unit constructs a mathematical formula to predict the operation index of the diffusers matching each target temperature boundary layer according to the numbers of the target objects stored in the set and the temperature influence coefficient of the outdoor temperature on each target object. Based on the prediction results, it judges and analyzes the operation status of the diffusers matching each target temperature boundary layer.

[0116] Embodiment 1: Suppose there are two target objects within the temperature boundary layer matching the diffuser numbered 1. At the moment of T + t, the temperature values of the 1st and 2nd target objects collected by the control terminal are 30°C and 32°C respectively. At the moment of T + 2*t, the temperature values of the 1st and 2nd target objects collected by the control terminal are 28°C and 31°C respectively. The distance value d between the center of the 1st target object within the temperature boundary layer matching the diffuser numbered 1 and the center of the diffuser numbered 1 11 = 2 meters, and the distance value d between the center of the 2nd target object within the temperature boundary layer matching the diffuser numbered 1 and the center of the diffuser numbered 1 11 = 3.8 meters. Suppose u = 1, then the operation index of the diffuser numbered 1 at the moment of T * t is:

[0117] ;

[0118] Since 0 ≤ U 1(T+t) = 0.01 ≤ 0.1 and W 1j[T+2*t] <W1j(T+t) , it is considered that the temperature boundary layer matching the diffuser numbered 1 is a non-target temperature boundary layer, and the diffuser numbered 1 is in the open state at time T+t.

[0119] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. An intelligent air conditioner status judgment and analysis method based on the Internet of Things, characterized in that: The method includes: S10: Obtain the position information of each diffuser installed in the target area, and search for the temperature boundary layer existing in the target area according to the inclination angle of the guide vanes of each diffuser; S20: Use the Internet of Things technology to sense the temperature change of each target object in each found temperature boundary layer, determine the target temperature boundary layer based on the sensing result and the positional relationship between the target objects, and preliminarily analyze the operating state of each diffuser based on the determination result; S20 includes: S201: Calculate the distance value d between the center of the j-th target object in the temperature boundary layer matching the diffuser numbered i and the center of the diffuser numbered i according to the three-dimensional distance calculation formula. The target objects include mobile terminals and smart appliances, and the temperature values of each target object are collected through Internet of Things technology; ij The target objects include mobile terminals and smart appliances, and the temperature values of each target object are collected through Internet of Things technology; S202: At time interval t, use the Internet of Things technology to collect the temperature value of the j-th target object in the temperature boundary layer matching the diffuser numbered i, and randomly select a target object in the temperature boundary layer matching the diffuser numbered i; When : According to Predict the running index of the diffuser numbered i at the moment of T + p*t; When : According to predict the running index of the diffuser numbered i at the moment of T + p*t; Among them, j = 1, 2, …, m, representing the numbers corresponding to each target object within the temperature boundary layer, m representing the total number of target objects existing in the temperature boundary layer, u representing the number corresponding to the selected target object, u = 1, 2, ……, m, p = 0, 1, 2, …, q, representing the numbering process of the temperature acquisition times of the control terminal in chronological order, q representing the total number of numbers, T representing the time when the control terminal first acquires the temperature value of the target object, W ij(T+p*t) represents the temperature value corresponding to the j-th target object within the temperature boundary layer that matches the diffuser numbered i at the moment of T + p * t, U i(T+p*t) represents the operation index of the diffuser numbered i at the moment of T + p * t; S203: If U i(T+p*t) > 0.1, it is considered that the temperature boundary layer matching the diffuser numbered i is the target temperature boundary layer; If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] <W ij(T+p*t) , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the open state at time T + p*t; If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] = W ij(T+p*t) <E T+(p+1)*t , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the open state at T + (p + 1)*t, and E T+(p+1)*t represents the corresponding average outdoor temperature value at T + (p + 1)*t; If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] > W ij(T+p*t) , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the closed state at the moment of T+(p + 1)*t; If 0 ≤ U i(T+p*t) ≤ 0.1 and W ij[T+(p+1)*t] = W ij(T+p*t) ≥ E T+(p+1)*t , it is considered that the temperature boundary layer matching the diffuser numbered i is not the target temperature boundary layer, and the diffuser numbered i is in the closed state at the moment of T + (p + 1)*t; S30: Predict the temperature influence coefficient of the outdoor temperature on each target object in the target temperature boundary layer; S30 includes: Within the target temperature boundary layer, according to h xj(T+p*t) =(E T+(p+1)*t -E T+p*t ) / (W xj[T+(p+1)*t] -W xj(T+p*t) ) predict the temperature influence coefficient of the outdoor temperature on the j-th target object within the target temperature boundary layer numbered x; where \(x = 1, 2, \ldots, q\) represents the numbers corresponding to the respective target temperature boundary layers, \(q\) represents the total number of target temperature boundary layers, \(W\) xj(T+p*t) represents the temperature value corresponding to the \(j\)th target object in the target temperature boundary layer numbered \(x\) at the moment \(T + p\times t\); S40: Judge and analyze the operating state of the diffusers matching each target temperature boundary layer according to the predicted temperature influence coefficient; S40 includes: S401: If h xj(T+p*t) ≥ 0.6 all hold, it is considered that the diffuser matching the target temperature boundary layer numbered x is in the closed state at the moment of T + p * t; If h xj(T+p*t) ≥ 0.6 part holds, then when 0 ≤ h xj(T+p*t) < 0.6, put the number of the corresponding target object into the set M; At this time, according to , predict the running index of the diffuser matching the target temperature boundary layer numbered x at time T + p * t, where d xj represents the distance value between the center of the j-th target object in the target temperature boundary layer numbered x and the center of the diffuser matching the target temperature boundary layer numbered x. When the number j is stored in the set M, , when the number j is not stored in the set M, ; S402: If 0 ≤ U x(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W xj[T+(p+1)*t] < W xj(T+p*t) , then it is considered that the diffuser matching the target temperature boundary layer with the number x is in the open state at the moment T + p * t; If 0 ≤ U x(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W xj[T+(p+1)*t] > W xj(T+p*t) , it is considered that the diffuser matching the target temperature boundary layer numbered x is in the closed state at the moment of T + p * t; If 0 ≤ U i(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W ij[T+(p+1)*t] = W ij(T+p*t) <E T+(p+1)*t , then it is considered that the diffuser matching the target temperature boundary layer numbered x is in the open state at the moment T + p * t; If 0 ≤ U i(T+p*t) ≤ 0.1 and when the number j is stored in the set M, W ij[T+(p+1)*t] = W ij(T+p*t) ≥ E T+(p+1)*t , it is considered that the diffuser matching the target temperature boundary layer numbered x is in the closed state at the moment T + p*t.

2. The intelligent air conditioner state judgment and analysis method based on the Internet of Things according to claim 1, wherein: S10 includes: S101: Randomly select a point in the target area as the coordinate origin to construct a three-dimensional space coordinate system, and obtain the position coordinates of the centers of each diffuser installed in the target area in the three-dimensional space coordinate system; S102: Use an image acquisition device to collect the side images of each diffuser, and determine the inclination angle of the guide vanes of each diffuser based on the collected side images; S103: When the inclination angle of the diffuser guide vane is on the right side of the vertical line segment from the diffuser center to the ground, search for the temperature boundary layer matching the diffuser numbered i within the air supply angle range of the diffuser determined according to [γ i , f]; When the inclination angle of the diffuser guide vane is on the left side of the vertical line segment of the diffuser center relative to the ground, according to [-f, -γ i , find the temperature boundary layer matching the diffuser numbered i within the determined air supply angle range of the diffuser; Among them, γ i represents the inclination angle of the deflector blade of the diffuser numbered i, and f represents the angle between the upper air supply line of the diffuser and the vertical line segment from the center of the diffuser to the ground.

3. An Internet of Things-based intelligent air conditioner status judgment and analysis system applied to the Internet of Things-based intelligent air conditioner status judgment and analysis method according to any one of claims 1-2, characterized in that: The system includes a temperature boundary layer search module, a preliminary operating state analysis module, a temperature influence coefficient prediction module, and an operating state judgment and analysis module; The temperature boundary layer search module is used to search for the temperature boundary layer existing in the target area; The preliminary operating state analysis module is used to determine the target temperature boundary layer and preliminarily analyze the driving operating state of each diffuser based on the determination result; The preliminary operating state analysis module includes a temperature value acquisition unit, an operating index prediction unit, and a preliminary operating state analysis unit; The temperature value acquisition unit collects the temperature values of each target object through the Internet of Things technology; The operating index prediction unit predicts the operating index of each diffuser according to the real-time temperature change difference of each target object and the distance value of each target object from the corresponding diffuser, and determines the target temperature boundary layer based on the prediction result; The preliminary operating state analysis unit preliminarily analyzes the operating state of each diffuser according to the prediction result of the operating index prediction unit, the real-time temperature change of each target object, and the outdoor real-time temperature value; The temperature influence coefficient prediction module predicts the temperature influence coefficient of the outdoor temperature on each target object in the target temperature boundary layer by using the constructed mathematical model; The operating state judgment and analysis module is used to judge and analyze the operating state of the diffusers matching each target temperature boundary layer; The operating state judgment and analysis module includes a temperature influence coefficient analysis unit and an operating state judgment and analysis unit; The temperature influence coefficient analysis unit compares the prediction result transmitted by the temperature influence coefficient prediction module with a set threshold. Based on the comparison result, it selects whether to put the number of the corresponding target object into the set and analyzes the operating state of the diffuser that matches the target temperature boundary layer; The operating state judgment and analysis unit constructs a mathematical formula to predict the operating index of the diffuser that matches each target temperature boundary layer according to the number of the target object stored in the set and the temperature influence coefficient of each target object by the outdoor temperature. Based on the prediction result, it judges and analyzes the operating state of the diffuser that matches each target temperature boundary layer.

4. The intelligent air conditioner state judgment and analysis system based on the Internet of Things according to claim 3, characterized in that: The temperature boundary layer searching module includes a position coordinate acquisition unit, a guide vane inclination angle determination unit, and a temperature boundary layer searching unit; The position coordinate acquisition unit randomly selects a point in the target area as the coordinate origin to construct a three-dimensional space coordinate system, and acquires the position coordinates of the centers of each diffuser installed in the target area in the three-dimensional space coordinate system; The guide vane inclination angle determination unit uses an image acquisition device to collect the side images of each diffuser, and determines the inclination angles of the guide vanes of each diffuser based on the collected side images; The temperature boundary layer searching unit searches for the temperature boundary layer that matches each diffuser according to the positional relationship between the inclination angle of the diffuser guide vane and the perpendicular line segment from the diffuser center to the ground, as well as the air supply angle range of the diffuser.

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