Air conditioner operation intelligent control system based on data analysis

Abstract

The application discloses an air conditioner operation intelligent control system based on data analysis and belongs to the technical field of air conditioner control, which comprises an environment analysis module, a target analysis module and a control module; the environment analysis module is used for analyzing the working environment of the air conditioner and establishing an indoor model; the corresponding air conditioner model is supplemented in the indoor model according to each air conditioner information; the target analysis module is used for performing target analysis and showing the indoor model to a user, and the user determines a target area in the indoor model; the target area is segmented to obtain each unit area; the position of each unit area and the position of the air conditioner are integrated into control analysis data; the preset temperature of the user to the target area is acquired; the control analysis data and the preset temperature are marked in the indoor model; the air conditioner adjustment parameters of each unit area are determined according to the indoor model, and the air conditioner control scheme is set; and the control module is used for controlling the air conditioner according to the air conditioner control scheme.

Description

Technical Field

[0001] This invention belongs to the field of air conditioning control technology, specifically an intelligent control system for air conditioning operation based on data analysis. Background Technology

[0002] With the rapid development of smart home technology, users' demands for intelligent and personalized indoor environment control are increasing. Traditional air conditioning control systems often use a uniform temperature setting, adjusted via mechanical thermostat switches or remote controls. This control method has several shortcomings. First, it lacks specificity and cannot accurately control the temperature of specific areas within the room, resulting in a mismatch between the user's actual usage area and the set temperature, affecting comfort and energy efficiency. Second, users need to frequently and manually adjust the air conditioner temperature, which is cumbersome and inefficient. Furthermore, adjusting the entire indoor temperature often leads to unnecessary resource waste, especially in large spaces or complex floor plans. Although many air conditioners now have mobile applications for control, the software's functions are essentially the same as the physical remote control, resulting in low utilization and failing to solve the aforementioned problems.

[0003] Therefore, in order to solve the above problems and improve the application effect of air conditioning software, this invention provides an intelligent control system for air conditioning operation based on data analysis. Summary of the Invention

[0004] To address the problems of the above solutions, this invention provides an intelligent control system for air conditioning operation based on data analysis.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] The intelligent control system for air conditioning operation based on data analysis includes an environmental analysis module, a target analysis module, and a control module.

[0007] The environmental analysis module is used to analyze the working environment of the air conditioner, identify and analyze the indoor image displayed by the user, and establish a corresponding indoor model; obtain the air conditioning information of each air conditioner, supplement the corresponding air conditioning model in the indoor model according to the air conditioning information; and connect with each air conditioner according to the air conditioning information.

[0008] The target analysis module is used to perform target analysis, display the indoor model to the user, and allow the user to determine the target area based on the indoor model; the target area is then segmented to obtain individual unit areas.

[0009] The locations of each unit area and the air conditioner are integrated into control analysis data; the user's preset temperature for the target area is obtained; and the control analysis data and preset temperature are marked in the indoor model.

[0010] Based on the indoor model, determine the air conditioning adjustment parameters for each unit area, and set the air conditioning control scheme according to the corresponding air conditioning adjustment parameters for each unit area.

[0011] Furthermore, the methods by which users determine the target area based on the indoor model include:

[0012] Identify initial marker points marked by the user on the indoor model; identify the indoor attributes of the initial marker points; generate an initial region based on the indoor attributes;

[0013] The initial area is marked on the indoor model accordingly. The user adjusts the initial area based on the displayed initial area and marks the adjusted initial area as the target area.

[0014] Furthermore, methods for segmenting the target region include:

[0015] Obtain the adjustment data of the air conditioner corresponding to the target area, identify the location of the target area and the location of the air conditioner, and integrate them into location data;

[0016] Application simulations are performed based on the adjusted data and location data to determine the target segmentation scheme. The target area is then segmented according to the target segmentation scheme to obtain each unit area.

[0017] Furthermore, adjustments to the data acquisition methods include:

[0018] Set up each reserve air conditioner, and set the area of ​​effect of each reserve air conditioner for cooling or heating at different angles and distances, and integrate them into the corresponding reserve air conditioner adjustment data;

[0019] Establish a reserve warehouse based on each reserve air conditioner and its corresponding adjustment data;

[0020] Identify air conditioning information and match corresponding adjustment data from the reserve database based on that information.

[0021] Furthermore, methods for application simulation based on adjustment data and location data include:

[0022] Step SA1: Identify the area of ​​effect on the target area when the air conditioner is turned on based on the adjustment data, and mark it as the starting area;

[0023] When the starting region is not smaller than the target region, the target segmentation scheme is no segmentation required, and the analysis ends.

[0024] When the starting area is smaller than the target area, proceed to step SA2;

[0025] Step SA2: Divide the region into separate regions based on the initial region; establish an equivalence judgment model based on the adjusted data;

[0026] Step SA3: Analyze the starting region and adjacent separating regions using the equivalence judgment model to obtain the corresponding equivalence judgment values;

[0027] Step SA4: When there is no equivalent judgment value of 1, mark the starting region as a unit region, mark the adjacent dividing regions as new starting regions, and return to step SA3. When there is no starting region, form the target segmentation scheme according to each unit region and end the analysis.

[0028] When the equivalence judgment value is 1, the separating region with the equivalence judgment value of 1 is merged with the starting region to obtain the merged region; the merged region and the adjacent separating regions are analyzed by the equivalence judgment model to obtain the corresponding equivalence judgment value.

[0029] Step SA5: When there is no equivalence judgment value of 1, mark the merged region as a unit region and mark the adjacent separated regions as new starting regions, and return to step SA3. When there is no starting region, form the target segmentation scheme according to each unit region and end the analysis.

[0030] When the equivalence judgment value is 1, the separating region with the equivalence judgment value of 1 is merged with the merging region to obtain a new merging region; the new merging region and the adjacent separating region are analyzed by the equivalence judgment model to obtain the corresponding equivalence judgment value.

[0031] Step SA6: Return to step SA5.

[0032] Furthermore, the expression for the equivalence judgment model is:

[0033] ;

[0034] In the formula: s is the input data, and the output data is the equivalence judgment value DP(s), which is 1 or 0.

[0035] The control module is used to control the air conditioner, obtain the air conditioner control scheme, and control the air conditioner according to the air conditioner control scheme.

[0036] Furthermore, it also includes an effect evaluation module, which is used to evaluate the air conditioning effect, perform real-time temperature detection on each unit area, obtain the detection temperature corresponding to each unit area, and obtain the preset temperature corresponding to each detection temperature.

[0037] According to the formula Calculate the corresponding deviation value;

[0038] In the formula: PQ is the deviation value; YB is the preset temperature; CB is the detection temperature;

[0039] Identify the deviation value corresponding to each verification area; identify the detection time corresponding to each deviation value; generate a corresponding deviation curve based on each detection time, with the horizontal axis of the deviation curve representing the detection time and the vertical axis representing the deviation value;

[0040] The effect is evaluated based on the deviation curve to obtain the corresponding effect evaluation results, which include abnormal effect and normal effect.

[0041] Furthermore, methods for evaluating effectiveness based on deviation curves include:

[0042] Fit the deviation curve to obtain the corresponding deviation function, and label the deviation function as HY(t);

[0043] According to the formula Calculate the corresponding outliers;

[0044] In the formula: PK represents outliers; t represents the detection time;

[0045] When an outlier exceeds the threshold X1, the evaluation result is abnormal.

[0046] When the outlier is no greater than the threshold X1, the evaluation effect is normal.

[0047] Compared with the prior art, the beneficial effects of the present invention are:

[0048] By coordinating the environmental analysis module, target analysis module, and control module, intelligent control of the air conditioner is achieved based on user needs. This improves resource utilization and reduces waste while meeting user requirements. Simultaneously, it increases the utilization rate of the control software, facilitating information transmission between the air conditioner and the user. Furthermore, by setting up an effect evaluation module, the system fully utilizes air conditioner data, promptly identifying anomalies during operation and enabling timely handling by the user. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a block diagram illustrating the principle of the present invention;

[0051] Figure 2 This is an example of an indoor layout for this invention. Detailed Implementation

[0052] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0053] like Figures 1 to 2 As shown, the intelligent control system for air conditioning operation based on data analysis includes an environmental analysis module, a target analysis module, a control module, and an effect evaluation module.

[0054] The environmental analysis module is used to analyze the working environment of the air conditioner, obtain the floor plan of the user's home, and mark it as an indoor diagram, such as... Figure 2 As shown; using an indoor map with corresponding size markings, a corresponding two-dimensional model is generated using existing model generation technology and marked as the indoor model. A three-dimensional model would be ideal, but for ease of identification and creation, a two-dimensional indoor model is generally generated based on a plan view; according to the location, model, and other air conditioning information of each air conditioner, the corresponding air conditioner model is added to the indoor model to represent its location and other information; according to the information of each air conditioner, it is paired and connected with each air conditioner, and the air conditioning can be controlled by software afterwards.

[0055] The target analysis module is used to perform target analysis. The target is the area where air conditioning needs to be applied. When a user needs to use air conditioning, the software is opened on the user's mobile phone to display an indoor model. The user determines the target area based on the indoor model. The target area is the area where the user expects the air conditioning to be applied.

[0056] The target region is divided into individual unit regions;

[0057] Integrate the locations of each unit area and the air conditioner locations into control analysis data; obtain the user's preset temperature for the target area, i.e., the temperature input by the user, such as 25℃; and mark the control analysis data and preset temperature in the indoor model.

[0058] The air conditioning adjustment parameters for each unit area are determined based on the indoor model, including parameters such as temperature, angle, and wind force, so that the perceived temperature in that unit area is the preset temperature. Specifically, this is determined by analyzing the air conditioning performance. For example, the platform provider can pre-analyze each reserve air conditioner to determine its adjustment performance, and then determine the impact of the air conditioner on the corresponding unit area under the current spatial conditions. Specifically, the air conditioning adjustment parameters can be determined based on existing artificial intelligence technologies, such as establishing an intelligent model based on neural networks, and then conducting analysis through the intelligent model.

[0059] The air conditioning control scheme is set according to the air conditioning adjustment parameters corresponding to each unit area.

[0060] In one embodiment, the method by which the user determines the target area based on the indoor model is: the user manually marks the area directly in the indoor model.

[0061] In another embodiment, because determining the target area according to the above embodiment will result in a large error and requires a lot of effort from the user, this embodiment adopts the following method:

[0062] Users click on the interior model, meaning they don't need to mark the entire target area; they can click on things like sofas, beds, or dining tables. The clicked location is then marked as an initial marker point. The interior attributes of the initial marker point are identified, such as those of sofas, beds, dining tables, and living room floor tiles. Initial areas are generated based on these attributes. For example, for a sofa, the initial area is the area corresponding to the sofa and its surrounding area; for a dining table, it's the area corresponding to the dining table and its surrounding area; and for living room floor tiles, it's a preset area, such as the entire living room or a preset area of ​​living room activity space centered on that point. The specific settings can be pre-set by the platform according to user habits, and users can also adjust them during use to form initial areas corresponding to each interior attribute.

[0063] The initial area is marked on the indoor model. The user can make corresponding adjustments based on the displayed initial area. It also has the functions of the previous embodiment, that is, the user can adjust the area based on the initial area, such as increasing or decreasing it, to form the target area.

[0064] Methods for segmenting the target region include:

[0065] Obtain the adjustment data of the air conditioner corresponding to the target area, identify the location of the target area and the location of the air conditioner, and integrate them into location data; perform application simulation based on the adjustment data and location data, determine the target segmentation scheme, and segment the target area according to the target segmentation scheme to obtain each unit area.

[0066] Adjustment methods for obtaining data include:

[0067] The platform will compile a list of air conditioners that meet the application requirements and mark them as reserve air conditioners, such as various types of air conditioners from major brands, or various models of air conditioners from the same brand.

[0068] Obtain the effective area of ​​each reserve air conditioner for cooling or heating at different angles and distances. The effective area is set according to certain cooling or heating effect standards, such as optimal effect. The adjustment data for each reserve air conditioner is integrated and set through testing, public data, and other methods.

[0069] Establish a reserve warehouse based on each reserve air conditioner and its corresponding adjustment data;

[0070] Identify air conditioning information and match corresponding adjustment data from the reserve database based on that information.

[0071] Methods for application simulation based on adjustment data and location data include:

[0072] Step SA1: Identify the area of ​​action of the air conditioner in the target area when it is turned on based on the adjustment data, and mark it as the starting area. That is, match it according to the angle when the air conditioner is turned on to determine its corresponding area of ​​action in the target area.

[0073] If the starting region is not smaller than the target region, that is, the starting region can include the target region, then no segmentation is needed, and the target segmentation scheme does not require segmentation; it can be applied directly.

[0074] When the starting area is smaller than the target area, in order to achieve a good experience in the target area, subsequent control measures such as air oscillation are required; proceed to step SA2.

[0075] Step SA2: According to the swing principle of the air conditioner, the starting area is used as a reference to move and divide the area to obtain each segmented area. That is, the corresponding effective area can be calculated because of the distance, swing angle, etc. Compare it with the previous area to determine the added area corresponding to this swing. When the swing ends (the target area on this side is completely divided), return to swing. There is no need to redivide the already divided areas until a new segmented area is formed on the other side of the starting area. This process is repeated to form each segmented area.

[0076] An equivalence judgment model is established based on the adjusted data. This model is used to determine whether the air conditioning effect is the same in different locations. If the air conditioning effect is the same, it is considered to meet the equivalence judgment requirements. Various location areas can be simulated and determined based on the adjusted data. The number of location areas is limited by actual indoor area and size. Each location area is manually labeled to form a training set. The model is then trained to determine the effect based on the equivalence judgment model. Alternatively, other existing technologies can be used to determine whether the air conditioning effect is the same in different locations. The expression for the equivalence judgment model is: In the formula: s is the input data, which is the location area to be compared, such as each dividing area and the starting area; the output data is the equivalence judgment value DP(s);

[0077] Step SA3: Analyze the starting region and the adjacent dividing region using the equivalence judgment model. If there are two regions, analyze both separately; if there is only one region, analyze one separately to obtain the corresponding equivalence judgment value.

[0078] Step SA4: When there is no equivalence judgment value of 1, mark the starting region as a unit region, mark the adjacent dividing region as the new starting region, and return to step SA3;

[0079] When the equivalence judgment value is 1, the separating region with the equivalence judgment value of 1 is merged with the starting region to obtain the merged region; the merged region and the adjacent separating regions are analyzed by the equivalence judgment model to obtain the corresponding equivalence judgment value.

[0080] Step SA5: When there is no equivalence judgment value of 1, mark the merged region as a unit region and mark the adjacent separated regions as new starting regions, and return to step SA3. When there is no starting region, end the analysis and form the target segmentation scheme based on each unit region.

[0081] When the equivalence judgment value is 1, the separating region with the equivalence judgment value of 1 is merged with the merging region to obtain a new merging region; the new merging region and the adjacent separating region are analyzed by the equivalence judgment model to obtain the corresponding equivalence judgment value.

[0082] Step SA6: Return to step SA5.

[0083] The control module is used to control the air conditioner, obtain the air conditioner control scheme, and control the air conditioner according to the air conditioner control scheme.

[0084] By coordinating the environmental analysis module, target analysis module, and control module, intelligent control of the air conditioner can be achieved based on user needs. This improves resource utilization and reduces resource waste while meeting user requirements. At the same time, it increases the utilization rate of the configuration control software, facilitating information transmission between the air conditioner and the user based on the control software.

[0085] The effect evaluation module is used to evaluate the air conditioning effect. Based on the above embodiments, the preset temperature for each unit area can be determined. Using this data as a basis, when the actual temperature is detected via a mobile device, the actual effect of the air conditioner can be analyzed. This eliminates the need for monitoring equipment, various sensors, and other devices at home to perform the effect analysis. The analysis process is as follows:

[0086] Real-time temperature detection is performed on each unit area to obtain the corresponding detection temperature for each unit area; that is, one detection temperature is retained, and unit areas that cannot be detected are not detected. Generally, the temperature of the corresponding unit area is detected based on the location of the mobile phone; the unit areas with the detection temperature are marked as the verification area; as the user moves, other unit areas may become verification areas. As long as the air conditioner is turned on, it can participate in the evaluation, that is, data at the same moment is not required.

[0087] Obtain the preset temperature corresponding to each detection temperature, according to the formula. Calculate the corresponding deviation value; where: PQ is the deviation value; YB is the preset temperature; CB is the detection temperature;

[0088] Identify the deviation values ​​corresponding to each verification area; identify the detection time corresponding to each deviation value; generate a corresponding deviation curve based on each detection time, with the horizontal axis representing the detection time and the vertical axis representing the deviation value. In other words, all deviation values ​​are integrated into the same deviation curve according to the detection order, and the same detection time can be in any order.

[0089] The effect is evaluated based on the deviation curve to obtain the corresponding effect evaluation results, which include abnormal and normal effects. Abnormal effects can be caused by doors and windows not being closed, or by abnormal air conditioning cooling or heating, etc.

[0090] Methods for evaluating effectiveness based on deviation curves include:

[0091] Fit the deviation curve to obtain the corresponding deviation function, and label the deviation function as HY(t);

[0092] According to the formula Calculate the corresponding outlier; where: PK is the outlier; t is the detection time;

[0093] When an outlier exceeds the threshold X1, the evaluation result is abnormal.

[0094] When the outlier is no greater than the threshold X1, the evaluation effect is normal.

[0095] By setting up an effect evaluation module, the system can make full use of air conditioning data, promptly identify abnormal situations during air conditioning use, and facilitate timely handling by users.

[0096] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0097] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An intelligent control system for air conditioning operation based on data analysis, characterized in that, It includes an environmental analysis module, a target analysis module, and a control module; The environmental analysis module is used to analyze the working environment of the air conditioner, identify and analyze the indoor image displayed by the user, and establish a corresponding indoor model; obtain the air conditioning information of each air conditioner, supplement the corresponding air conditioning model in the indoor model according to the air conditioning information; and connect with each air conditioner according to the air conditioning information. The target analysis module is used to perform target analysis, display the indoor model to the user, and allow the user to determine the target area based on the indoor model; the target area is then segmented to obtain individual unit areas. The locations of each unit area and the air conditioner are integrated into control analysis data; the user's preset temperature for the target area is obtained; and the control analysis data and preset temperature are marked in the indoor model. Based on the indoor model, determine the air conditioning adjustment parameters for each unit area, and set the air conditioning control scheme according to the corresponding air conditioning adjustment parameters for each unit area. The control module is used to control the air conditioner, obtain the air conditioner control scheme, and control the air conditioner according to the air conditioner control scheme; Methods for segmenting the target region include: Obtain the adjustment data of the air conditioner corresponding to the target area, identify the location of the target area and the location of the air conditioner, and integrate them into location data; Application simulations are performed based on the adjusted data and location data to determine the target segmentation scheme. The target area is then segmented according to the target segmentation scheme to obtain each unit area. Methods for application simulation based on adjustment data and location data include: Step SA1: Identify the area of ​​effect on the target area when the air conditioner is turned on based on the adjustment data, and mark it as the starting area; When the starting region is not smaller than the target region, the target segmentation scheme is no segmentation required, and the analysis ends. When the starting area is smaller than the target area, proceed to step SA2; Step SA2: Divide the region into separate regions based on the initial region; establish an equivalence judgment model based on the adjusted data; Step SA3: Analyze the starting region and adjacent separating regions using the equivalence judgment model to obtain the corresponding equivalence judgment values; Step SA4: When there is no equivalent judgment value of 1, mark the starting region as a unit region, mark the adjacent dividing regions as new starting regions, and return to step SA3. When there is no starting region, form the target segmentation scheme according to each unit region and end the analysis. When the equivalence judgment value is 1, the separating region with the equivalence judgment value of 1 is merged with the starting region to obtain the merged region; the merged region and the adjacent separating regions are analyzed by the equivalence judgment model to obtain the corresponding equivalence judgment value. Step SA5: When there is no equivalence judgment value of 1, mark the merged region as a unit region and mark the adjacent separated regions as new starting regions, and return to step SA3. When there is no starting region, form the target segmentation scheme according to each unit region and end the analysis. When the equivalence judgment value is 1, the separating region with the equivalence judgment value of 1 is merged with the merging region to obtain a new merging region; the new merging region and the adjacent separating region are analyzed by the equivalence judgment model to obtain the corresponding equivalence judgment value. Step SA6: Return to step SA5.

2. The intelligent air conditioning operation control system based on data analysis according to claim 1, characterized in that, Methods for users to determine target areas based on indoor models include: Identify initial marker points marked by the user on the indoor model; identify the indoor attributes of the initial marker points; generate an initial region based on the indoor attributes; The initial area is marked on the indoor model accordingly. The user adjusts the initial area based on the displayed initial area and marks the adjusted initial area as the target area.

3. The intelligent air conditioning operation control system based on data analysis according to claim 1, characterized in that, Adjustment methods for obtaining data include: Set up each reserve air conditioner, and set the area of ​​effect of each reserve air conditioner for cooling or heating at different angles and distances, and integrate them into the corresponding reserve air conditioner adjustment data; Establish a reserve warehouse based on each reserve air conditioner and its corresponding adjustment data; Identify air conditioning information and match corresponding adjustment data from the reserve database based on that information.

4. The intelligent air conditioning operation control system based on data analysis according to claim 1, characterized in that, The expression for the equivalence judgment model is: ； In the formula: s is the input data, and the output data is the equivalence judgment value DP(s), which is 1 or 0.

5. The intelligent air conditioning operation control system based on data analysis according to claim 1, characterized in that, It also includes an effect evaluation module, which is used to evaluate the air conditioning effect, perform real-time temperature detection on each unit area, obtain the detection temperature corresponding to each unit area, and obtain the preset temperature corresponding to each detection temperature. According to the formula Calculate the corresponding deviation value; In the formula: PQ is the deviation value; YB is the preset temperature; CB is the detection temperature; Identify the deviation value corresponding to each verification area; identify the detection time corresponding to each deviation value; generate a corresponding deviation curve based on each detection time, with the horizontal axis of the deviation curve representing the detection time and the vertical axis representing the deviation value; The effect is evaluated based on the deviation curve to obtain the corresponding effect evaluation results, which include abnormal effect and normal effect.

6. The intelligent air conditioning operation control system based on data analysis according to claim 5, characterized in that, Methods for evaluating effectiveness based on deviation curves include: Fit the deviation curve to obtain the corresponding deviation function, and label the deviation function as HY(t); According to the formula Calculate the corresponding outliers; In the formula: PK represents outliers; t represents the detection time; When an outlier exceeds the threshold X1, the evaluation result is abnormal. When the outlier is no greater than the threshold X1, the evaluation effect is normal.

Images