Air conditioner control method and device based on user habits, equipment and medium

Abstract

The application discloses an air conditioner control method and device based on user habits, equipment and medium. The method comprises the following steps: extracting features of collected environmental data and user habit data to obtain target feature data; calculating the weight of the target feature data according to a preset weight calculation method to obtain the adjustment weight of a preset key feature corresponding to the target feature data; weighting and accumulating the quantitative score of the preset key feature according to the corresponding adjustment weight to obtain a weighted comprehensive score; determining whether to enter an autonomous adjustment mode according to the weighted comprehensive score, and if the autonomous adjustment mode is entered, adjusting the corresponding air conditioner operating parameters according to the adjustment weight of the preset key feature to control the operation of the air conditioner equipment. The method of the application can solve the problem that the air conditioner system of the prior art cannot be autonomously adjusted according to user habits.

Description

Technical Field

[0001] This invention relates to the technical field of air conditioning control, and in particular to an air conditioning control method, device, equipment and medium based on user habits. Background Technology

[0002] With the rapid advancement of technology, smart home systems are gradually permeating people's daily lives, bringing unprecedented convenience and comfort to the living experience. Among these, intelligent air conditioning control, as a crucial component of smart homes, has become a key element in improving living comfort and energy efficiency. However, many air conditioning products on the market still rely on traditional control modes, largely depending on manual settings to adjust their operating status. This method is not only cumbersome, requiring frequent manual intervention from users based on environmental changes, but also unable to intelligently adjust dynamically based on user habits, indoor-outdoor temperature differences, humidity variations, and other factors. This leads to energy waste and a decrease in living comfort, and prevents existing air conditioning systems from autonomously adjusting to user habits. Summary of the Invention

[0003] This invention provides an air conditioning control method, device, equipment, and medium based on user habits, aiming to solve the problem that existing air conditioning systems cannot perform targeted autonomous adjustments according to user habits.

[0004] In a first aspect, embodiments of the present invention provide an air conditioning control method based on user habits, comprising: extracting features from collected environmental data and user habit data to obtain target feature data; performing weight calculation on the target feature data according to a preset weight calculation method to obtain adjustment weights for preset key features corresponding to the target feature data; performing weighted summation calculation on the quantified scores of the obtained preset key features according to the corresponding adjustment weights to obtain a weighted comprehensive score; determining whether to enter an autonomous adjustment mode based on the weighted comprehensive score; if entering the autonomous adjustment mode, adjusting the corresponding air conditioning operating parameters according to the adjustment weights of the preset key features to control the operation of the air conditioning equipment.

[0005] Secondly, embodiments of the present invention also provide an air conditioning control device based on user habits, comprising: a feature extraction unit, used to extract features from collected environmental data and user habit data to obtain target feature data; a weight calculation unit, used to perform weight calculation on the target feature data according to a preset weight calculation method to obtain adjustment weights for preset key features corresponding to the target feature data; a score acquisition unit, used to perform weighted summation calculation on the quantified scores of the acquired preset key features according to the corresponding adjustment weights to obtain a weighted comprehensive score; and a parameter adjustment unit, used to determine whether to enter an autonomous adjustment mode based on the weighted comprehensive score, and if the autonomous adjustment mode is entered, to adjust the corresponding air conditioning operating parameters according to the adjustment weights of the preset key features to control the operation of the air conditioning equipment.

[0006] Thirdly, embodiments of the present invention also provide a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0007] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, can implement the above-described method.

[0008] This invention provides an air conditioning control method, apparatus, device, and medium based on user habits. The method includes: extracting features from collected environmental data and user habit data to obtain target feature data; calculating the weights of the target feature data according to a preset weighting method to obtain adjustment weights for preset key features corresponding to the target feature data; weighting and accumulating the quantified scores of the obtained preset key features according to the corresponding adjustment weights to obtain a weighted comprehensive score; determining whether to enter an autonomous adjustment mode based on the weighted comprehensive score; if entering the autonomous adjustment mode, adjusting the corresponding air conditioning operating parameters according to the adjustment weights of the preset key features to control the operation of the air conditioning equipment. This invention extracts target feature data from environmental and user habit data to reduce data volume, while focusing on key factors that directly affect air conditioning control. It calculates adjustment weights for different preset key features based on the target feature data to rationally allocate the influence of each key feature in control decisions. Through weighted summation, the influence of multiple key features is integrated to obtain a comprehensive score that fully reflects the current environment and user needs. This weighted comprehensive score serves as an important basis for determining whether to enter autonomous adjustment mode and how to adjust air conditioning operating parameters, making air conditioning control decisions more comprehensive and scientific. Finally, based on the adjustment weights of each preset key feature, the corresponding air conditioning operating parameters, such as temperature, fan speed, and mode, are adjusted. This allows the air conditioner to make targeted adjustments based on the importance of different features, achieving intelligent automatic air conditioning control based on user habits. This eliminates the need for manual user intervention, improving living comfort and convenience while enabling timely adjustment of air conditioning operating parameters according to dynamic changes in the environment and user needs, thus improving energy efficiency and reducing energy consumption. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating the air conditioning control method based on user habits provided in an embodiment of the present invention;

[0011] Figure 2 This is a schematic diagram of a sub-process of an air conditioning control method based on user habits provided in an embodiment of the present invention;

[0012] Figure 3 This is a schematic diagram of a sub-process of an air conditioning control method based on user habits provided in an embodiment of the present invention;

[0013] Figure 4 This is a schematic diagram of a sub-process of an air conditioning control method based on user habits provided in an embodiment of the present invention;

[0014] Figure 5 This is a schematic diagram of a sub-process of an air conditioning control method based on user habits provided in an embodiment of the present invention;

[0015] Figure 6 This is a schematic diagram of a sub-process of an air conditioning control method based on user habits provided in an embodiment of the present invention;

[0016] Figure 7 This is a schematic diagram of a sub-process of an air conditioning control method based on user habits provided in an embodiment of the present invention;

[0017] Figure 8 A schematic block diagram of an air conditioning control device based on user habits provided in an embodiment of the present invention;

[0018] Figure 9 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0023] Please see Figure 1 , Figure 1This is a flowchart illustrating the user-habit-based air conditioning control method provided in this embodiment of the invention. The user-habit-based air conditioning control method in this embodiment can be applied to various smart home scenarios, such as smart apartments and smart villas. Various environmental sensors can collect environmental data such as temperature, humidity, and air quality in real time. Simultaneously, the system records user operating habits data regarding air conditioning settings such as temperature, fan speed, and operating mode over a long period. Related smart home control systems using this method can automatically and accurately adjust air conditioning operating parameters based on real-time environmental changes and user-personalized habits, improving user comfort while effectively increasing energy efficiency and reducing energy consumption.

[0024] Figure 1 This is a flowchart illustrating an air conditioning control method based on user habits provided in an embodiment of the present invention. As shown in the figure, the method includes the following steps S110-S140.

[0025] S110. Extract features from the collected environmental data and user habit data to obtain target feature data.

[0026] In this embodiment, the environmental data refers to various physical parameters of the space where the air conditioner is located and its surroundings, including indoor temperature, indoor humidity, outdoor temperature, and outdoor humidity. The user habit data refers to the behavioral patterns and preferences exhibited by the user during air conditioner use, reflecting the user's personalized needs regarding the air conditioner's operating status. Examples include the user-set temperature (e.g., often set to 24℃ in summer), the user-set fan speed (e.g., preferring "automatic fan" or "low fan"), the on / off time (e.g., turning it on at 22:00 every night and turning it off at 7:00 in the morning), and the room activity time (e.g., spending time in the living room from 20:00 to 23:00 at night, requiring the air conditioner to primarily adjust the living room temperature). The target feature data is data that meets preset key feature requirements after feature extraction. Feature extraction is performed on collected environmental data and user habit data to obtain target feature data. Specifically, environmental data is collected through various sensors installed inside or around the air conditioning equipment, and user habit data is collected based on data from the user's use of the air conditioner. The collected data is analyzed and filtered to select representative feature data. For example, after preprocessing and standardizing the collected data, data related to features such as temperature, humidity, and behavioral habits (e.g., ambient temperature, air density, wind speed, on / off time, etc.) are filtered out and used as target feature data. By extracting features from the collected environmental data and user habit data to obtain target feature data, and applying it to the intelligent control and energy-saving optimization of the air conditioning equipment, the operating efficiency of the air conditioner and the user experience are improved.

[0027] In one embodiment, such as Figure 2As shown, step S110 further includes steps S111-S112.

[0028] S111. Preprocess and standardize the environmental data and the user habit data;

[0029] S112. Based on the preset key features, filter and extract from the preprocessed and standardized data to obtain the corresponding target feature data.

[0030] In this embodiment, the preset key features are feature categories that play a key role in comfort and energy saving, determined in advance based on the needs of air conditioning adaptive adjustment. In this embodiment, the preset key features are time features, environmental features, and user habit features. The preprocessing is a preliminary processing operation performed on the raw collected environmental data and user habit data. The standardization process is the process of converting environmental data and user habit data of different dimensions and orders of magnitude into data with the same scale, which can eliminate the influence of differences in dimensions or orders of magnitude between data. The environmental data and user habit data are preprocessed and standardized. Specifically, the collected data is cleaned to remove outliers and missing values, and then the data format is converted according to a unified format. For example, the time information in the environmental data and user habit data is unified to the format "YYYY-MM-DD HH:MM:SS", the unit of temperature data is unified to degrees Celsius (°C), and the unit of humidity data is unified to percentage (%). For categorized data, such as the air conditioning operation mode (cooling, heating, dehumidifying, ventilation, etc.), a unified coding rule can be established to represent it using numbers or letters. For example, "1" represents cooling mode, "2" represents heating mode, "3" represents dehumidification mode, and "4" represents air supply mode. After data preprocessing and standardization, the processed data is filtered and extracted based on preset key features to obtain corresponding target feature data. Specifically, the correlation between the processed data and each preset key feature can be calculated using the Pearson correlation coefficient. For example, the correlation between data such as indoor temperature, humidity, user temperature setting, indoor-outdoor temperature difference, and air conditioner running time and time characteristics, environmental characteristics, and user habit characteristics is calculated. Data with high correlation are selected as the target feature data corresponding to the preset key feature. It can be understood that each preset key feature can have several target feature data that match it. Through preprocessing and standardization, the quality problem of the original data is solved, making different types of data comparable. Then, based on the preset key features, target feature data that can be directly used for subsequent adaptive adjustment decisions of the air conditioner is obtained, providing an accurate basis for the air conditioner to adjust autonomously according to the environment and user habits.

[0031] S120. The target feature data is weighted according to a preset weighting calculation method to obtain the adjustment weight of the preset key feature corresponding to the target feature data.

[0032] In this embodiment, the adjustment weight is a numerical representation of the relative importance of different preset key features in the adaptive adjustment process of the air conditioner. The larger the weight value, the greater the influence of the feature on the air conditioner's adjustment decision. The preset weight calculation method is a pre-determined method for calculating the weights of different preset key features. In this embodiment, the preset weight calculation method is the Analytic Hierarchy Process (AHP). The target feature data is weighted according to the preset weight calculation method to obtain the adjustment weight of the preset key feature corresponding to the target feature data. Specifically, the weight can be determined by constructing a judgment matrix through the Analytic Hierarchy Process and calculating row products, nth roots, normalization, etc. By calculating the preset weights of different key features, more accurate autonomous adjustment decisions can be made. For example, when the adjustment weight of the temperature feature is high, the air conditioner will prioritize adjusting its operating state based on the temperature-related target feature data.

[0033] In one embodiment, such as Figure 3 As shown, step S120 further includes steps S121-S122.

[0034] S121. Calculate the weights of the target feature data according to a preset judgment matrix to obtain initial weights;

[0035] S122. Normalize the initial weights to obtain the adjustment weights of each preset key feature to which the target feature data belongs.

[0036] In this embodiment, the preset judgment matrix is ​​a matrix pre-constructed based on air conditioning adjustment needs, used to quantify the relative importance between preset key features. The matrix element aij in the preset judgment matrix represents the importance of the i-th feature relative to the j-th feature (using a 1-9 scale: 1 for equal importance, 3 for slightly important, 5 for significantly important, 7 for strongly important, 9 for extremely important, and 2, 4, 6, and 8 for intermediate values ​​between adjacent judgments). The initial weights are unnormalized weight values ​​calculated using the preset judgment matrix, reflecting the initial importance ratio of each feature. The normalization process, which transforms the initial weights into weight values ​​summing to 1, aims to make the weights of each feature comparable and additive, ensuring that the total weights satisfy the constraint of "1" (i.e., the sum of all feature weights is 1). The target feature data is weighted according to the preset judgment matrix to obtain the initial weights. Specifically, the preset key features are set as time features (F1), environmental features (F2), and user habit features (F3), where the weight calculation ensures that the row product of the preset judgment matrix satisfies:

[0037]

[0038] Among them, M i For the product of the elements in the i-th row, a ij Let j be the element in the i-th row. Specifically, after determining the relative importance ratio aij between each element, the product M of the elements in each row can be obtained. i Then M i The initial weights are obtained by taking the nth root. Right now The initial weights are normalized to obtain the adjusted weights for each preset key feature to which the target feature data belongs. Specifically, the nth root of each row and column vector of the matrix is ​​normalized to obtain w. i ,Right now:

[0039]

[0040] Among them, w i Let be the final weight of the i-th element. These are the initial weights. Combining these equations, we can obtain the data weight calculation method:

[0041]

[0042] The parameter functions have the same meaning as the parameters mentioned above, and will not be repeated here. By normalizing the obtained initial weights, adjustment weights are obtained, facilitating subsequent reasonable control of the air conditioner's operating parameters to achieve a balance between energy saving and comfort.

[0043] In one embodiment, such as Figure 4 As shown, step S120 is followed by steps S1201-S1204.

[0044] S1201. Perform feature calculation on the preset judgment matrix and the adjustment weight according to the preset feature calculation formula to obtain the maximum feature value of the preset key feature;

[0045] S1202. Calculate the consistency index based on the largest eigenvalue to obtain the random consistency ratio based on the consistency index;

[0046] S1203. Verify whether the preset judgment matrix has consistency based on the random consistency ratio;

[0047] S1204. If the consistency is not achieved, adjust the preset judgment matrix to recalculate the adjustment weight.

[0048] In this embodiment, the maximum eigenvalue is the core eigenvalue of the judgment matrix, used to measure the degree to which the matrix deviates from "complete consistency." A completely consistent matrix has a maximum eigenvalue equal to its order n. The consistency index is calculated based on the maximum eigenvalue; the smaller the value, the better the matrix consistency. The random consistency ratio is data that quantifies whether the consistency of the judgment matrix is ​​acceptable. The maximum eigenvalue of the preset key feature is obtained by performing feature calculation on the preset judgment matrix and the adjustment weights according to a preset feature calculation formula. Specifically, the preset feature calculation formula is:

[0049]

[0050] Where, λ max Let A be the largest eigenvalue, A be the judgment matrix, and wi be the corresponding adjustment weight. (Aw) i Let be the i-th vector resulting from the product of A and w. A consistency index is calculated based on the largest eigenvalue, and the random consistency ratio is obtained based on this consistency index. The formula for calculating the consistency index is:

[0051]

[0052] Wherein, CI is the consistency index, and the other parameters have the same meaning as the parameters mentioned above, and will not be repeated here.

[0053]

[0054] Wherein, CR is the random consistency ratio, and RI is the average random consistency index (obtainable from a table). The consistency of the preset judgment matrix is ​​verified based on the random consistency ratio. Specifically, a judgment threshold (0.1) is set, and the CR is checked against the threshold. If CR < 0.1, the judgment matrix is ​​considered consistent; otherwise, it is not. If consistency is lacking, the elements aij of the judgment matrix are modified to make the relative importance between features more reasonable. For example, if the original a13 = 7 (F1 is significantly more important than F3) leads to a contradiction, it can be adjusted to a13 = 5 (significantly more important) to reduce extremism. Based on the adjusted judgment matrix, the adjustment weights are repeatedly obtained until CR < 0.1. By verifying the consistency of the preset judgment matrix, the rationality of the weight calculation is ensured, ultimately achieving precise and logically consistent personalized adjustment decisions.

[0055] S130. The quantitative scores of the obtained preset key features are weighted and accumulated according to the corresponding adjustment weights to obtain a weighted comprehensive score.

[0056] In this embodiment, the quantitative score is a value obtained for each preset key feature based on certain evaluation criteria or measurement methods. For example, for user habit features, scores can be given based on the user's commonly used temperature setting range. For instance, if the suitable temperature range is set to 24-26℃, a higher score, such as 8-10 points, is given if the user frequently sets the temperature within this range; if the temperature setting deviates significantly from this range, such as frequently setting it below 20℃ or above 28℃, the score is lower, such as 2-4 points. Specific scores can also be given based on the degree of temperature deviation from the suitable range. If the user sets the temperature to 22℃, deviating from the lower limit of the suitable range by 2℃, 1 point can be deducted for every 1℃ deviation, resulting in a temperature setting score of 10-(22-24)=8 points (assuming the lower limit of the suitable range is 24℃, this example uses a simple linear deduction method). The specific scoring criteria are not limited; the goal is to obtain an accurate quantitative score. The quantified scores of the acquired preset key features are weighted and accumulated according to the corresponding adjustment weights to obtain a weighted comprehensive score. Specifically, the formula for obtaining the weighted comprehensive score is as follows:

[0057]

[0058] Where X is the weighted composite score, w i S is the adjustment weight for the i-th preset key feature. i This involves assigning quantitative scores to corresponding preset key features. For example, there are four key features, each with a quantitative score of 85, 90, 78, and 88 (higher scores indicate greater importance), and corresponding weights of 0.3, 0.25, 0.2, and 0.25. The weighted composite score X = 0.3 × 85 + 0.25 × 90 + 0.2 × 78 + 0.25 × 88. By converting the multi-dimensional performance of the air conditioner into a comparable composite score, the problem of biased evaluation by a single indicator is avoided, thus comprehensively determining whether to enter autonomous adjustment mode.

[0059] S140. Determine whether to enter the autonomous adjustment mode based on the weighted comprehensive score. If the autonomous adjustment mode is entered, adjust the corresponding air conditioning operating parameters according to the adjustment weight of the preset key features to control the operation of the air conditioning equipment.

[0060] In this embodiment, the autonomous adjustment mode is an intelligent operating mode of the air conditioner. In this mode, the air conditioner can automatically adjust its operating parameters according to preset rules and information such as the current environment and user habits to provide a more comfortable and energy-saving operating effect without manual intervention from the user. The air conditioner operating parameters are parameters that control the operation of the air conditioner, and may include temperature setting, fan speed, operating mode (cooling, heating, dehumidification, ventilation, etc.). Whether to enter the autonomous adjustment mode can be determined based on the weighted comprehensive score, that is, whether the weighted comprehensive score is greater than the preset score threshold. If it is greater than the preset score threshold, the adjustment mechanism is triggered, and the air conditioner enters the autonomous adjustment mode. The direction of adjustment is determined according to the weight priority. For example, if the ambient temperature weight is greater than the humidity weight, the system will prioritize adjusting the ambient temperature, and the ambient temperature value will be comprehensively referenced based on the user's usual temperature preference and the temperature values ​​at different times. Otherwise, the humidity value will be adjusted. The objectivity of the adjustment trigger is achieved by judging through the comprehensive score, and the targeted adjustment is ensured by the weight priority, so that the air conditioner can dynamically adapt to environmental changes without human intervention.

[0061] In one embodiment, such as Figure 5 As shown, step S140 includes steps S141-S142.

[0062] S141. Determine whether the weighted composite score is greater than a preset score threshold;

[0063] S142. If the score is greater than the preset score threshold, the air conditioning device is determined to have entered the autonomous adjustment mode.

[0064] In this embodiment, the preset scoring threshold is a pre-set numerical standard used to determine whether the air conditioning equipment meets the conditions for entering the autonomous adjustment mode. The preset scoring threshold can be set comprehensively based on factors such as the performance requirements of the air conditioning system, user needs and expectations, and actual application scenarios, and is not limited in this regard. After determining the preset scoring threshold, the weighted comprehensive score is compared with the preset scoring threshold to determine the magnitude of the weighted comprehensive score. If the weighted comprehensive score is greater than the preset scoring threshold, the air conditioning equipment is determined to have entered the autonomous adjustment mode, and the air conditioner will automatically adjust its operating parameters according to preset autonomous adjustment rules, based on environmental data and user habits. Assuming that in a certain air conditioning system, preset key features include cooling speed, temperature uniformity, and user temperature setting matching degree, after quantitative evaluation and weighted calculation, the weighted comprehensive score is calculated as follows: Weighted Comprehensive Score = Cooling Speed ​​Quantitative Score × Cooling Speed ​​Adjustment Weight + Temperature Uniformity Quantitative Score × Temperature Uniformity Adjustment Weight + User Temperature Setting Matching Quantitative Score × User Temperature Setting Matching Degree Adjustment Weight. Let the weighting of cooling speed adjustment be 0.4, temperature uniformity adjustment be 0.3, and user temperature setting matching be 0.3. After actual evaluation, the quantitative score for cooling speed is 8 points, the quantitative score for temperature uniformity is 7 points, and the quantitative score for user temperature setting matching is 9 points. Therefore, the weighted comprehensive score is: 8 × 0.4 + 7 × 0.3 + 9 × 0.3 = 3.2 + 2.1 + 2.7 = 8 points. The preset score threshold is set to 7.5 points. Since the calculated weighted comprehensive score of 8 points is greater than the preset score threshold of 7.5 points, the air conditioner is determined to enter the automatic adjustment mode. After entering the automatic adjustment mode, the air conditioner will automatically adjust its operating parameters according to indoor and outdoor temperatures and the user's daily usage habits (such as commonly used temperature settings and usage periods). The comprehensive score determines whether to enter the automatic adjustment mode to dynamically adapt to environmental changes.

[0065] In one embodiment, such as Figure 6 As shown, step S140 further includes steps S143-S144.

[0066] S143. Sort the preset key features according to the adjustment weights to determine the adjustment priority;

[0067] S144. Adjust the air conditioning operating parameters corresponding to the preset key feature with the highest adjustment priority.

[0068] In this embodiment, the adjustment priority is a sequence determined by ranking preset key features according to their adjustment weights. This sequence guides the order of adjusting air conditioning operating parameters, with higher-priority features adjusting their parameters first. The preset key features are ranked according to their adjustment weights to determine the adjustment priority. Specifically, the preset key features are ranked from largest to smallest adjustment weight. Preset key features with larger adjustment weights have higher adjustment priorities. For example, if there are three preset key features A, B, and C with adjustment weights of 0.5, 0.3, and 0.2 respectively, their adjustment priorities from highest to lowest are A, B, and C. The air conditioning operating parameters corresponding to the preset key feature with the highest adjustment priority are adjusted. Specifically, after determining the preset key feature with the highest adjustment priority, the relationship between this feature and the air conditioning operating parameters is clarified. For example, if the preset key feature with the highest adjustment priority is the user's temperature setting preference, then it is directly related to the air conditioning temperature setting parameters. For example, if the ambient temperature weight is greater than the humidity weight, the system prioritizes adjusting the ambient temperature, and the ambient temperature value is determined by comprehensively considering the user's usual temperature settings and temperature values ​​at different times. Conversely, the humidity value is adjusted. By prioritizing adjustments, optimizations can be performed around the performance dimensions that users care about most, thus avoiding resource waste.

[0069] In one embodiment, such as Figure 7 As shown, step S140 is followed by steps S1401-S1403.

[0070] S1401. Determine whether the user has changed based on the user habit data;

[0071] S1402. If it is determined that a user has changed, then perform weight calculation to obtain the latest user's behavioral habit weight;

[0072] S1403. Update the weights based on the latest user behavior habit weights and the preset historical smoothing coefficients to obtain the corresponding new adjustment weights.

[0073] In this embodiment, the user habit data records various behavioral information of users during air conditioner use, such as temperature setting preferences, operating mode selection (cooling, heating, dehumidification, etc.), usage time periods, and fan speed adjustment habits. This data reflects users' personalized needs and routine operating patterns when using the air conditioner. The preset historical smoothing coefficient is a pre-set value used to comprehensively consider historical weight information and newly calculated behavioral habit weights when updating adjustment weights, making weight updates more stable and avoiding large fluctuations in adjustment weights due to sudden changes in new user data. The user habit data is used to determine whether a user has changed. Specifically, user habit data is continuously collected and stored to form a historical record of user habit data, and certain judgment rules are set. For example, when the differences between newly collected user habit data and historical data in multiple key features (such as temperature setting, operating mode selection, etc.) exceed a certain threshold, a user change is determined. If a user change is determined, weight calculation is performed to obtain the latest user's behavioral habit weight. Specifically, once a user change is determined, the weight is calculated using the analytic hierarchy process (AHP) for the new user's usage habit data, such as constructing a judgment matrix, and then calculating the weight of each behavioral habit feature to obtain the latest user's behavioral habit weight. The weights are updated based on the latest user behavior habit weights and a preset historical smoothing coefficient. Specifically, let the preset historical smoothing coefficient be α (0 < α < 1), the historical adjustment weight be Wold, and the latest user behavior habit weight be Wnew. Then, the formula for calculating the new adjustment weight Wnew-adjusted is: Wnew-adjusted = α × Wold + (1 - α) × Wnew. For example, if α = 0.7, the historical adjustment weight Wold = 0.5, and the latest user behavior habit weight Wnew = 0.3, then the new adjustment weight Wnew-adjusted = 0.7 × 0.5 + (1 - 0.7) × 0.3 = 0.35 + 0.09 = 0.44. By updating the weights using the preset historical smoothing coefficient, the historical weight information and the new user behavior habit weights are integrated to obtain a more stable and reasonable new adjustment weight.

[0074] Figure 8 This is a schematic block diagram of an air conditioning control device 200 based on user habits provided in an embodiment of the present invention. Figure 8 As shown, corresponding to the above-described user-habit-based air conditioning control method, the present invention also provides a user-habit-based air conditioning control device. This user-habit-based air conditioning control device includes a unit for executing the above-described user-habit-based air conditioning control method, and the device can be configured in a terminal such as a desktop computer, tablet computer, or laptop computer. For details, please refer to... Figure 8The user-habit-based air conditioning control device includes a feature extraction unit 210, a weight calculation unit 220, a score acquisition unit 230, and a parameter adjustment unit 240.

[0075] The feature extraction unit 210 is used to extract features from the collected environmental data and user habit data to obtain target feature data.

[0076] In one embodiment, the feature extraction unit 210 includes a processing unit and a filtering unit.

[0077] The processing unit is used to preprocess and standardize the environmental data and the user habit data;

[0078] The filtering unit is used to filter and extract data from the preprocessed and standardized data based on the preset key features to obtain the corresponding target feature data.

[0079] The weight calculation unit 220 is used to perform weight calculation on the target feature data according to a preset weight calculation method to obtain the adjustment weight of the preset key feature corresponding to the target feature data.

[0080] In one embodiment, the weight calculation unit 220 includes a first acquisition unit and a second acquisition unit.

[0081] The first acquisition unit is used to perform weight calculation on the target feature data according to a preset judgment matrix to obtain initial weights;

[0082] The second acquisition unit is used to normalize the initial weights to obtain the adjustment weights of each preset key feature to which the target feature data belongs.

[0083] In one embodiment, the weight calculation unit 220 includes a feature calculation unit, an index calculation unit, a matrix judgment unit, and a matrix adjustment unit.

[0084] The feature calculation unit is used to perform feature calculation on the preset judgment matrix and the adjustment weight according to the preset feature calculation formula, and obtain the maximum feature value of the preset key feature.

[0085] The indicator calculation unit is used to calculate the consistency indicator based on the maximum eigenvalue, and to obtain the random consistency ratio based on the consistency indicator.

[0086] A matrix judgment unit is used to verify whether the preset judgment matrix has consistency based on the random consistency ratio.

[0087] A matrix adjustment unit is used to adjust the preset judgment matrix to recalculate the adjustment weights if the consistency is not achieved.

[0088] The scoring acquisition unit 230 is used to perform weighted summation calculation on the acquired quantitative scores of the preset key features according to the corresponding adjustment weights to obtain a weighted comprehensive score.

[0089] The parameter adjustment unit 240 is used to determine whether to enter the autonomous adjustment mode based on the weighted comprehensive score. If the autonomous adjustment mode is entered, the corresponding air conditioning operating parameters are adjusted according to the adjustment weight of the preset key features to control the operation of the air conditioning equipment.

[0090] In one embodiment, the parameter adjustment unit 240 includes a threshold determination unit and a mode determination unit.

[0091] A threshold determination unit is used to determine whether the weighted comprehensive score is greater than a preset score threshold.

[0092] The mode determination unit is used to determine that the air conditioning device has entered the autonomous adjustment mode if the score is greater than the preset score threshold.

[0093] In one embodiment, the parameter adjustment unit 240 includes a sorting unit and a parameter adjustment subunit.

[0094] A sorting unit is used to sort the preset key features according to the adjustment weights to determine the adjustment priority;

[0095] The parameter adjustment subunit is used to adjust the air conditioning operating parameters corresponding to the preset key features with the highest adjustment priority.

[0096] In one embodiment, the parameter adjustment unit 240 includes a user judgment unit, a weight acquisition unit, and a weight update unit.

[0097] The user judgment unit is used to determine whether the user has changed based on the user habit data.

[0098] The weight acquisition unit is used to calculate the latest user's behavioral habit weight if it is determined that a user has changed.

[0099] The weight update unit is used to update the weights based on the latest user behavior habit weights and the preset historical smoothing coefficients to obtain the corresponding new adjustment weights.

[0100] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned air conditioning control device 200 based on user habits and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0101] The aforementioned air conditioning control device based on user habits can be implemented as a computer program, which can, for example... Figure 9 It runs on the computer device shown.

[0102] Please see Figure 9 , Figure 9 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a terminal or a server. The terminal can be an electronic device with communication functions, such as a smartphone, tablet, laptop, desktop computer, personal digital assistant, or wearable device. The server can be a standalone server or a server cluster composed of multiple servers.

[0103] See Figure 9 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0104] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a composite laser printing method.

[0105] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0106] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute an air conditioning control method based on user habits.

[0107] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0108] The processor 502 is used to run a computer program 5032 stored in a memory to implement the steps of the above method.

[0109] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0110] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0111] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When executed by a processor, the program instructions cause the processor to perform the steps of the method described above.

[0112] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0114] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0115] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0116] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0117] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An air conditioning control method based on user habits, characterized in that, include: The collected environmental data and user habit data are used to extract features to obtain target feature data; The target feature data is weighted according to a preset judgment matrix to obtain initial weights; The initial weights are normalized to obtain the adjustment weights for each preset key feature to which the target feature data belongs; The quantitative scores of the acquired preset key features are weighted and accumulated according to the corresponding adjustment weights to obtain a weighted comprehensive score. The system determines whether to enter the autonomous adjustment mode based on the weighted comprehensive score. If it does, the corresponding air conditioning operating parameters are adjusted according to the adjustment weights of the preset key features to control the operation of the air conditioning equipment.

2. The method according to claim 1, characterized in that, The step of extracting features from the collected environmental data and user habit data to obtain target feature data includes: The environmental data and the user habit data are preprocessed and standardized; Based on the preset key features, the preprocessed and standardized data are filtered and extracted to obtain the corresponding target feature data.

3. The method according to claim 1, characterized in that, After the step of obtaining the adjusted weight of each preset key feature to which the target feature data belongs, the following is included: The preset judgment matrix and the adjustment weight are used to perform feature calculation according to the preset feature calculation formula to obtain the maximum feature value of the preset key feature; A consistency index is obtained by calculating the index based on the largest eigenvalue, and a random consistency ratio is obtained based on the consistency index. Verify whether the preset judgment matrix has consistency based on the random consistency ratio; If the consistency is not achieved, the preset judgment matrix is ​​adjusted to recalculate the adjustment weights.

4. The method according to claim 1, characterized in that, The step of determining whether to enter the self-regulation mode based on the weighted composite score includes: Determine whether the weighted composite score is greater than a preset score threshold; If the score exceeds the preset threshold, the air conditioning device is determined to have entered the autonomous adjustment mode.

5. The method according to claim 4, characterized in that, The step of adjusting the corresponding air conditioning operating parameters according to the adjustment weight of the preset key features includes: The preset key features are sorted according to the adjustment weights to determine the adjustment priority; Adjust the air conditioning operating parameters corresponding to the preset key features with the highest adjustment priority.

6. The method according to claim 1, characterized in that, After the step of adjusting the corresponding air conditioning operating parameters according to the adjustment weight of the preset key features, the method further includes: Determine whether the user has changed based on the user habit data; If a user change is detected, the latest user behavior habit weight is obtained by weight calculation; the weight is updated according to the latest user behavior habit weight and the preset historical smoothing coefficient to obtain the corresponding new adjustment weight.

7. An air conditioning control device based on user habits, characterized in that, include: The feature extraction unit is used to extract features from the collected environmental data and user habit data to obtain target feature data. The weight calculation unit is used to calculate the weight of the target feature data according to a preset judgment matrix to obtain the initial weight; The initial weights are normalized to obtain the adjustment weights for each preset key feature to which the target feature data belongs; The scoring unit is used to perform weighted summation calculation on the quantitative scores of the preset key features obtained according to the corresponding adjustment weights to obtain a weighted comprehensive score. The parameter adjustment unit is used to determine whether to enter the autonomous adjustment mode based on the weighted comprehensive score. If the autonomous adjustment mode is entered, the corresponding air conditioning operating parameters are adjusted according to the adjustment weight of the preset key features to control the operation of the air conditioning equipment.

8. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-6.

9. A storage medium, characterized in that, The storage medium stores a computer program, which includes program instructions that, when executed by a processor, can implement the method as described in any one of claims 1-6.

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