Determination method for analysis data of air conditioner and related equipment
By acquiring the performance and target attributes of air conditioners, and determining the attributes of the presentation layer and core layer based on correlation, the problem of low efficiency in air conditioner data analysis is solved, and efficient and clear data analysis and optimization support are achieved.
Patent Information
- Application Number
- CN202410578491.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-10
- Publication Date
- 2025-11-11
AI Technical Summary
The data analysis process for air conditioners is inefficient, as data analysts need to spend a lot of time manually organizing the data, resulting in low analysis efficiency.
By acquiring the performance and target attributes of the air conditioner, the performance layer attributes are determined based on correlation, merged and combined into the correlation layer attributes, the core layer attributes are extracted, the analysis data is determined from the preset data warehouse, the correlation coefficient is calculated using the Pearson correlation coefficient, and the preset threshold is optimized to improve the analysis efficiency.
It achieves clear and efficient analysis of air conditioning data, enabling better discovery of the inherent relationships and patterns between attributes, and supporting optimized usage and maintenance plans.
Smart Images
Figure CN120929508A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of data processing technology, and more specifically, to a method and related equipment for determining analytical data of an air conditioner. Background Technology
[0002] With the development of intelligent and IoT technologies, air conditioning systems can collect multi-dimensional data, such as indoor and outdoor air quality data and user behavior data. This data provides more possibilities for optimizing air conditioning systems and providing personalized services. However, given the complexity of air conditioning data and the diversity of collection points, it is necessary to mine valuable data from it. Therefore, the process of mining valuable air conditioning data is particularly important.
[0003] Currently, when analyzing data related to air conditioners, data analysts often directly analyze the data based on the performance attributes of the air conditioners and the corresponding business needs.
[0004] However, the corresponding data determined from the air conditioner data warehouse based on the air conditioner's performance attributes and business needs is scattered, requiring data analysts to spend a lot of time and effort to manually organize the air conditioner analysis data, which can easily lead to low efficiency in the air conditioner analysis process. Summary of the Invention
[0005] In view of the above problems, this application proposes a method and related equipment for determining air conditioning analysis data. To solve the problem of low efficiency in the air conditioning analysis process, the specific solution is as follows: A method for determining analytical data of an air conditioner, comprising: The air conditioner's performance attributes and the target attributes for analyzing the air conditioner data are obtained, wherein the target attributes are at least one air conditioner attribute other than the performance attributes. For each acquired attribute, based on the correlation between the acquired attribute and other attributes, at least one related attribute of the acquired attribute is determined from the other attributes, and each attribute group is determined as a presentation layer attribute of the air conditioner. Each attribute group includes an acquired attribute and each related attribute of the acquired attribute. The other attributes are at least one air conditioner attribute other than the acquired attribute. Based on the correlation between the obtained attributes in each group of presentation layer attributes, at least one group of target presentation layer attributes is determined from each group of presentation layer attributes, and the target presentation layer attributes in each group are merged. The merged target presentation layer attributes and the unmerged presentation layer attributes are combined to obtain related layer attributes. Each group of presentation layer attributes consists of any two presentation layer attributes. From the merged target presentation layer attributes, determine the acquired attributes that meet the preset conditions, and use the acquired attributes that meet the preset conditions as core layer attributes. The preset conditions are within the intersection range of the merged target presentation layer attributes. The analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes.
[0006] Optionally, determining at least one related attribute of the acquired attribute from the other attributes based on the correlation between the acquired attribute and other attributes includes: Calculate the correlation coefficient between the acquired attribute and the other attributes according to the Pearson correlation coefficient calculation formula; Determine whether there is a correlation coefficient greater than a first preset threshold among the correlation coefficients of the acquired attribute and the other attributes; If there exists a correlation coefficient greater than the first preset threshold, then each other attribute corresponding to the correlation coefficient greater than the first preset threshold is taken as a related attribute of the acquired attribute.
[0007] Optionally, determining at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between the acquired attributes in each set of presentation layer attributes includes: Calculate the correlation coefficient between the acquired attributes in each group of the presentation layer attributes according to the Pearson correlation coefficient calculation formula; Determine whether there is a correlation coefficient greater than a second preset threshold among the correlation coefficients between the acquired attributes in each group of presentation layer attributes, where the second preset threshold is greater than the first preset threshold; If there exists a correlation coefficient greater than the second preset threshold, then each set of presentation layer attributes corresponding to the correlation coefficient greater than the second preset threshold is taken as a set of target presentation layer attributes.
[0008] Optionally, determining the acquired attributes that satisfy preset conditions from the merged target presentation layer attributes includes: Determine whether there are duplicate obtained attributes in each of the merged target presentation layer attributes; If there are duplicate acquired attributes among the merged target presentation layer attributes, then the duplicate acquired attributes are determined to be the acquired attributes that satisfy the preset conditions.
[0009] Optionally, after determining the analysis data of the air conditioner from a preset data warehouse based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes, the method further includes: The analysis data of the air conditioner is analyzed to obtain the analysis results of the air conditioner; Based on the analysis results, the first preset threshold is optimized to obtain the optimized first preset threshold, and the first preset threshold is updated to the optimized first preset threshold. The second preset threshold is optimized based on the analysis results to obtain an optimized second preset threshold; and the second preset threshold is updated to the optimized second preset threshold.
[0010] Optionally, determining the analysis data of the air conditioner from a preset data warehouse based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes includes: Obtain the analysis method of the air conditioner; If the analysis method is shallow analysis, then based on the presentation layer attributes, the first analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner, and the attributes corresponding to the first analysis data are consistent with the presentation layer attributes; If the analysis method is in-depth analysis, then based on the relevant layer attributes, the second analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner, and the attributes corresponding to the second analysis data are consistent with the relevant layer attributes; If the analysis method is anomaly analysis, then based on the core layer attributes, the third analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner, and the attributes corresponding to the third analysis data are consistent with the core layer attributes.
[0011] An apparatus for determining analytical data of an air conditioner, comprising: An acquisition unit is used to acquire the performance attributes of the air conditioner and the target attributes for analyzing the air conditioner data, wherein the target attributes are at least one air conditioner attribute other than the performance attributes. The first determining unit is configured to, for each acquired attribute, determine at least one related attribute of the acquired attribute from the other attributes based on the correlation between the acquired attribute and other attributes, and determine each attribute group as a presentation layer attribute of the air conditioner. Each attribute group includes an acquired attribute and each related attribute of the acquired attribute. The other attributes are at least one air conditioner attribute other than the acquired attribute. The second determining unit is used to determine at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between the obtained attributes in each set of presentation layer attributes, merge each set of target presentation layer attributes, and combine each merged target presentation layer attribute with the unmerged presentation layer attributes to obtain related layer attributes, wherein each set of presentation layer attributes consists of any two presentation layer attributes. The third determining unit is used to determine the acquired attributes that satisfy the preset conditions from each of the merged target presentation layer attributes, and to take the acquired attributes that satisfy the preset conditions as core layer attributes, wherein the preset conditions are within the intersection range of each of the merged target presentation layer attributes. The fourth determining unit is used to determine the analysis data of the air conditioner from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes.
[0012] An apparatus for determining analytical data of an air conditioner, comprising a memory and a processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the method for determining the analysis data of the air conditioner as described above.
[0013] A readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining analytical data of an air conditioner as described in any of the preceding claims.
[0014] A computer program product includes a computer program that, when executed by a processor, implements the steps of the method for determining analytical data of an air conditioner as described in any of the preceding claims.
[0015] Based on the above technical solution, this invention provides a method and related equipment for determining air conditioner analysis data. It acquires the performance attributes of the air conditioner and the target attributes for analyzing the air conditioner data, wherein the target attribute is at least one air conditioner attribute other than the performance attributes. Using the acquired attributes and their related attributes as presentation layer data, it can display the intuitive and related attributes of the air conditioner. Further processing of the attributes acquired from the presentation layer attributes yields related layer attributes, realizing attribute integration and classification, which helps to further discover the inherent connections and patterns between attributes. The extraction of core layer data highlights key attributes, providing stronger support for analysis. By rationally dividing the data into hierarchical layers, the air conditioner analysis data is determined from a pre-set data warehouse. This analysis data is clearly organized, which is beneficial for improving analysis efficiency. Attached Figure Description
[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a flowchart illustrating a method for determining analytical data of an air conditioner, as disclosed in an embodiment of this application. Figure 2 This is a schematic diagram illustrating a method for determining at least one related attribute of an acquired attribute from other attributes based on the correlation between an acquired attribute and other attributes, as disclosed in an embodiment of this application. Figure 3 This is a schematic diagram illustrating the process of determining at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between attributes obtained from each set of presentation layer attributes, as disclosed in an embodiment of this application. Figure 4 This is a schematic diagram of the structure of an air conditioner analysis data determination device disclosed in an embodiment of this application; Figure 5 This is a hardware structure block diagram of an air conditioner analysis data determination device disclosed in an embodiment of this application. Detailed Implementation
[0019] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.
[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0021] To effectively address the problem of low efficiency in the air conditioning analysis process, this application provides a method for determining air conditioning analysis data. The method for determining air conditioning analysis data provided in this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0022] Please see the appendix Figure 1 , Figure 1 This is a flowchart illustrating a method for determining analytical data of an air conditioner, provided in an embodiment of this application. The method may include the following steps: Step S101: Obtain the performance attributes of the air conditioner and the target attributes for analyzing the air conditioner data.
[0023] It should be noted that the target attribute is at least one air conditioning attribute other than performance attributes. The acquired attribute is usually one that directly monitors the operating status of the air conditioner, and may include indoor temperature / humidity, power consumption, cooling / heating operating time, and operating time, etc.
[0024] In this application, the performance attributes that most intuitively demonstrate the functions of the air conditioner and the target attributes for business requirement analysis can be selected from all the attributes of the air conditioner. These acquired attributes can be defined as... .
[0025] Step S102: For each acquired attribute, based on the correlation between the acquired attribute and other attributes, determine at least one related attribute of the acquired attribute from the other attributes, and determine each attribute group as a representation layer attribute of the air conditioner.
[0026] It should be noted that each attribute group includes one acquired attribute and all related attributes of that acquired attribute. Other attributes are at least one air conditioning attribute other than the acquired attribute, which are usually attributes used to predict potential problems and optimize usage efficiency, such as fault records and maintenance records.
[0027] In this application, the attributes that can be calculated first Other attributes The first correlation coefficient, and then based on this correlation coefficient, other attributes... Determine the attributes to be obtained at least one related attribute If j ≤ n, then each attribute group... This is identified as a presentation layer attribute of the air conditioner. For other presentation layer attributes Perform the above operations to obtain all presentation layer attributes. .
[0028] Step S103: Based on the correlation between the attributes obtained from each group of presentation layer attributes, determine at least one group of target presentation layer attributes from each group of presentation layer attributes, merge each group of target presentation layer attributes, and combine the merged target presentation layer attributes with the unmerged presentation layer attributes to obtain related layer attributes.
[0029] It should be noted that each set of presentation layer attributes consists of any two presentation layer attributes.
[0030] In this application, the correlation coefficients between the attributes obtained from each group of presentation layer attributes can be calculated first. Then, based on these correlation coefficients, at least one set of target presentation layer attributes can be determined from each group of presentation layer attributes. The target presentation layer attributes from each group are then merged. Finally, the merged target presentation layer attributes are combined with the unmerged presentation layer attributes to obtain the relevant layer attributes. It is important to note that the calculation process should avoid repeatedly calculating the data corresponding to the attributes obtained from the presentation layer attributes, thus avoiding duplicate sets.
[0031] For ease of understanding, the following example is provided: Calculate presentation layer attributes Attributes obtained from and presentation layer attributes Attributes obtained from The first correlation coefficient between them, based on the first correlation coefficient and Merge the attributes to obtain the merged target presentation layer attributes. .
[0032] Calculate presentation layer attributes Attributes obtained from and presentation layer attributes Attributes obtained from The second correlation coefficient between them, based on the second correlation coefficient With Merge to obtain the merged target presentation layer attributes. .
[0033] Calculate presentation layer attributes Attributes obtained from Other presentation layer attributes Attributes obtained from Multiple third correlation coefficients between them, based on multiple third correlation coefficients No action will be taken.
[0034] Merged target presentation layer attributes and unmerged presentation layer attributes Combine to obtain the relevant layer attributes .
[0035] Step S104: Determine the attributes that meet the preset conditions from the merged target presentation layer attributes, and use the attributes that meet the preset conditions as core layer attributes.
[0036] It should be noted that the preset condition is within the intersection range of the attributes of each merged target presentation layer.
[0037] In this application, it can first be determined whether there are duplicate attributes among the merged target presentation layer attributes. If there are duplicate attributes among the merged target presentation layer attributes, then the duplicated attributes are determined to be attributes that meet preset conditions, i.e., core layer attributes.
[0038] Furthermore, the higher the number of times an attribute is repeated, the higher the level of the attribute, and the more attention should be paid to the equipment components corresponding to the attribute during daily monitoring and maintenance.
[0039] For ease of understanding, the following example is provided: Merged target presentation layer attributes There are duplicate retrieved attributes. , will repeatedly retrieve attributes The attributes that are determined to meet the preset conditions are the ones to be acquired.
[0040] Step S105: Determine the analysis data of the air conditioner from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes.
[0041] In this application, the analysis method of the air conditioner can be obtained first, and at least one of the presentation layer attribute, related layer attribute and core layer attribute can be selected according to the analysis method of the air conditioner. Data corresponding to the selected attribute can be determined from the preset data warehouse of the air conditioner and used as the analysis data of the air conditioner.
[0042] It should be noted that the analysis methods for air conditioners include shallow analysis, in-depth analysis, and anomaly analysis.
[0043] If the analysis method is shallow analysis, table creation and analysis can be performed using presentation layer attributes. The data categories corresponding to presentation layer attributes are detailed, which can effectively exclude irrelevant attributes. Specifically, if the analysis method is shallow analysis, the first analysis data for air conditioners is determined from the preset data warehouse of air conditioners based on the presentation layer attributes. The attributes corresponding to the first analysis data are consistent with the presentation layer attributes.
[0044] If the analysis method is in-depth analysis, then table creation and analysis can be performed using relevant layer attributes, as the data corresponding to the relevant layer attributes is comprehensive and in-depth. Specifically, if the analysis method is in-depth analysis, then based on the relevant layer attributes, the second analysis data for air conditioners is determined from the preset data warehouse for air conditioners, and the attributes corresponding to the second analysis data are consistent with the relevant layer attributes.
[0045] If the analysis method is anomaly analysis, the core layer attributes can be used first for table creation analysis to determine if the anomaly is caused by the core layer attributes. If not, then the related layer attributes and presentation layer attributes are analyzed. Specifically, if the analysis method is anomaly analysis, the third analysis data for the air conditioner is determined from the preset data warehouse of the air conditioner based on the core layer attributes. The attributes corresponding to the third analysis data are consistent with the core layer attributes.
[0046] In summary, the present invention provides a method for determining air conditioner analysis data, which acquires the performance attributes of the air conditioner and the target attributes for analyzing the air conditioner data. The target attributes are at least one air conditioner attribute other than the performance attributes. Using the acquired attributes and their related attributes as presentation layer data allows for the display of the air conditioner's intuitive and correlated attributes. Further processing of the attributes acquired from the presentation layer attributes yields related layer attributes, achieving attribute integration and classification, which helps to further discover the inherent connections and patterns between attributes. The extraction of core layer data highlights key attributes, providing stronger support for analysis. By rationally dividing the data hierarchically, the air conditioner analysis data is determined from a pre-set data warehouse. This analysis data is clearly organized, which is beneficial for improving analysis efficiency.
[0047] Furthermore, the above method may also include the following steps: Step S106: Analyze the analysis data of the air conditioner to obtain the analysis results of the air conditioner.
[0048] In this application, the analysis results of the air conditioner not only help to understand the current operating status of the air conditioner, but also provide strong data support for optimizing use, developing maintenance plans, and improving energy efficiency.
[0049] Step S107: Optimize the first preset threshold according to the analysis results to obtain the optimized first preset threshold, and update the first preset threshold to the optimized first preset threshold.
[0050] It should be noted that the first preset threshold can be d=0.6.
[0051] In this application, the first preset threshold can be modified based on the analysis results to achieve better analysis results. For ease of understanding, an example is given below: A large amount of air conditioner data has been collected, including data corresponding to each acquired attribute and its related attributes. The correlation coefficient between each acquired attribute and its related attributes is calculated. Analysis results show that the correlation coefficients between some acquired attributes and their related attributes exceed 0.6 but do not exceed 0.65, indicating a relatively small impact on air conditioner performance and demand analysis. This suggests that the initially set first preset threshold d=0.6 may be too lenient, causing a less influential attribute to be included in the consideration. Therefore, the first preset threshold d can be adjusted from 0.6 to 0.65.
[0052] Step S108: Optimize the second preset threshold based on the analysis results to obtain the optimized second preset threshold. Then update the second preset threshold to the optimized second preset threshold.
[0053] It should be noted that the second preset threshold can be f=0.8.
[0054] In this application, the second preset threshold can be modified based on the analysis results to achieve better analysis results. For ease of understanding, an example is given below: A large amount of air conditioner data has been collected, including data corresponding to each acquired attribute. The correlation coefficients between the acquired attributes are calculated. Analysis results show that the correlation coefficients between some acquired attributes are higher than 0.75 and lower than 0.8, indicating a significant impact on air conditioner performance and demand analysis. This suggests that the initially set second preset threshold f=0.8 may be too strict, causing some highly influential attributes to be excluded from consideration. Therefore, the second preset threshold f can be adjusted from 0.8 to 0.75.
[0055] In summary, the method for determining air conditioner analysis data provided in this application optimizes the first and second preset thresholds based on these analysis results, enabling more precise threshold setting. This makes the thresholds more closely reflect actual conditions, thereby more accurately identifying abnormal situations or performance bottlenecks and improving the accuracy and effectiveness of air conditioner data analysis.
[0056] Based on the embodiments disclosed in this application above, in another embodiment of this application, a detailed description is provided of a specific implementation method for determining at least one related attribute of the obtained attribute from other attributes based on the correlation between the obtained attribute and other attributes.
[0057] As one possible implementation method, please refer to the appendix. Figure 2 This is a schematic diagram illustrating a method disclosed in this application for determining at least one related attribute of an acquired attribute from other attributes based on the correlation between the acquired attribute and other attributes. The method may include the following steps: Step S201: Calculate the correlation coefficient between the obtained attribute and other attributes according to the Pearson correlation coefficient calculation formula.
[0058] In this application, the obtained attributes are calculated. Other attributes correlation coefficient The formula for calculating the Pearson correlation coefficient is as follows:
[0059] in, This represents the covariance of two variables. This represents the standard deviation of a. Let b represent the standard deviation, and the denominator is the product of the standard deviations of the two attributes.
[0060] Step S202: Determine whether there is a correlation coefficient greater than the first preset threshold among the obtained correlation coefficients of the attribute and other attributes.
[0061] In this application, each The system determines whether there exists a correlation coefficient greater than the first preset threshold d=0.6.
[0062] If there is a correlation coefficient greater than the first preset threshold, then step S203 is executed.
[0063] Step S203: Take each other attribute corresponding to a correlation coefficient greater than the first preset threshold as a related attribute of the acquired attribute.
[0064] In this application, if a correlation coefficient among the obtained attributes and other attributes is greater than a first preset threshold, then the other attributes corresponding to that correlation coefficient are regarded as related attributes. This means that there is a relatively significant correlation between the obtained attributes and other attributes corresponding to that related attribute, which makes it easier to identify and understand the possible mutual influence or interdependence between attributes, thereby providing a basis for data analysis, prediction or decision-making for air conditioning.
[0065] Based on the embodiments disclosed in this application above, in another embodiment of this application, a detailed description is provided of the specific implementation method for determining at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between attributes obtained from each set of presentation layer attributes.
[0066] As one possible implementation method, please refer to the appendix. Figure 3 This is a schematic diagram illustrating a method disclosed in this application for determining at least one set of target presentation layer attributes based on the correlation between attributes obtained from each set of presentation layer attributes. The method may include the following steps: Step S301: Calculate the correlation coefficient between the attributes obtained from each group of presentation layer attributes according to the Pearson correlation coefficient calculation formula.
[0067] In this application, the presentation layer attributes are calculated. Attributes obtained from Correlation coefficient between The formula for calculating the Pearson correlation coefficient is as follows:
[0068] in, This represents the covariance of two variables. Let represent the standard deviation of 'a', and the denominator is the product of the standard deviations of the two attributes.
[0069] Step S302: Determine whether there is a correlation coefficient greater than the second preset threshold among the correlation coefficients obtained from the attributes in each group of presentation layer attributes.
[0070] It should be noted that the second preset threshold is greater than the first preset threshold. For example, f=0.8 is greater than d=0.6.
[0071] In this application, each The system determines whether there exists a correlation coefficient greater than the second preset threshold f=0.8.
[0072] If there is a correlation coefficient greater than the second preset threshold, then step S303 is executed.
[0073] Step S303: Take each set of presentation layer attributes corresponding to the correlation coefficient greater than the second preset threshold as a set of target presentation layer attributes.
[0074] In this application, if a correlation coefficient among the attributes obtained from each group of presentation layer attributes is greater than a second preset threshold, then the two obtained attributes corresponding to that correlation coefficient are considered as a group of target presentation layer attributes. This effectively realizes the integration and classification of attributes, and further discovers the inherent connections and patterns between attributes, thereby providing a more accurate basis for data analysis, prediction, or decision-making in air conditioning.
[0075] The methods described in the above-disclosed embodiments of this application are detailed in terms of the methods. The methods of this application can be implemented by various forms of devices. Therefore, this application also discloses a device for determining the analysis data of an air conditioner. Specific embodiments are given below for detailed description.
[0076] Please see the appendix Figure 4 , Figure 4 This is a schematic diagram of a device for determining analysis data of an air conditioner, as disclosed in an embodiment of this application. The device includes: The acquisition unit 11 is used to acquire the performance attributes of the air conditioner and the target attributes for analyzing the air conditioner data. The target attributes are at least one air conditioner attribute other than the performance attributes.
[0077] The first determining unit 12 is configured to, for each acquired attribute, determine at least one related attribute of the acquired attribute from the other attributes based on the correlation between the acquired attribute and other attributes, and determine each attribute group as a presentation layer attribute of the air conditioner. Each attribute group includes an acquired attribute and each related attribute of the acquired attribute. The other attributes are at least one air conditioner attribute other than the acquired attribute.
[0078] The second determining unit 13 is used to determine at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between the obtained attributes in each set of presentation layer attributes, merge each set of target presentation layer attributes, and combine each merged target presentation layer attribute with the unmerged presentation layer attributes to obtain related layer attributes, wherein each set of presentation layer attributes consists of any two presentation layer attributes.
[0079] The third determining unit 14 is used to determine the acquired attributes that satisfy preset conditions from each of the merged target presentation layer attributes, and to use the acquired attributes that satisfy the preset conditions as core layer attributes, wherein the preset conditions are within the intersection range of each of the merged target presentation layer attributes.
[0080] The fourth determining unit 15 is used to determine the analysis data of the air conditioner from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes and core layer attributes.
[0081] As one possible implementation, the first determining unit 12, which determines at least one related attribute of the acquired attribute from the other attributes based on the correlation between the acquired attribute and other attributes, includes: The first calculation subunit is used to calculate the correlation coefficient between the acquired attribute and the other attributes according to the Pearson correlation coefficient calculation formula.
[0082] The first judgment subunit is used to determine whether there is a correlation coefficient greater than a first preset threshold among the correlation coefficients of the acquired attribute and the other attributes.
[0083] The first determining subunit is configured to, if there exists a correlation coefficient greater than the first preset threshold, treat each other attribute corresponding to the correlation coefficient greater than the first preset threshold as a related attribute of the acquired attribute.
[0084] As one possible implementation, the second determining unit 13, which determines at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between the acquired attributes in each set of presentation layer attributes, includes: The second calculation subunit is used to calculate the correlation coefficient between the acquired attributes in each group of the presentation layer attributes according to the Pearson correlation coefficient calculation formula.
[0085] The second judgment subunit is used to determine whether there is a correlation coefficient greater than a second preset threshold among the correlation coefficients between the acquired attributes in each group of presentation layer attributes, wherein the second preset threshold is greater than the first preset threshold.
[0086] The second determining subunit is used to, if there is a correlation coefficient greater than the second preset threshold, take each set of presentation layer attributes corresponding to the correlation coefficient greater than the second preset threshold as a set of target presentation layer attributes.
[0087] As one possible implementation, the third determining unit 14, which determines the acquired attributes that satisfy preset conditions from the merged target presentation layer attributes, includes: The third judgment subunit is used to determine whether there are duplicate acquired attributes in each of the merged target presentation layer attributes.
[0088] The third determining subunit is used to determine, if there are duplicate acquired attributes in each of the merged target presentation layer attributes, that the duplicate acquired attributes are the acquired attributes that satisfy the preset conditions.
[0089] As one possible implementation, the device further includes: The analysis unit is used to analyze the analysis data of the air conditioner and obtain the analysis results of the air conditioner.
[0090] The first optimization unit is used to optimize the first preset threshold according to the analysis results, obtain the optimized first preset threshold, and update the first preset threshold to the optimized first preset threshold.
[0091] The second optimization unit is used to optimize the second preset threshold based on the analysis results to obtain an optimized second preset threshold, and then update the second preset threshold to the optimized second preset threshold.
[0092] As one possible implementation, the fourth determining unit 15, which determines the analytical data of the air conditioner from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes, includes: The acquisition subunit is used to acquire the analysis method of the air conditioner.
[0093] The fourth determining subunit is used to determine the first analysis data of the air conditioner from the preset data warehouse of the air conditioner based on the presentation layer attributes if the analysis method is shallow analysis. The attributes corresponding to the first analysis data are consistent with the presentation layer attributes.
[0094] The fifth determining subunit is used to determine the second analysis data of the air conditioner from the preset data warehouse of the air conditioner based on the relevant layer attributes if the analysis method is in-depth analysis. The attributes corresponding to the second analysis data are consistent with the relevant layer attributes.
[0095] The sixth determining subunit is used to determine the third analysis data of the air conditioner from the preset data warehouse of the air conditioner based on the core layer attributes if the analysis method is anomaly analysis. The attributes corresponding to the third analysis data are consistent with the core layer attributes.
[0096] The method for determining the analytical data of this air conditioner is widely used in whole-house intelligent digital control application scenarios such as smart homes, smart home ecosystems, and intelligence house ecosystems. Optionally, in this embodiment, the method for determining the analytical data of the air conditioner can be applied to, for example... Figure 5 The hardware environment shown consists of terminal device 102 and server 104. For example... Figure 5 As shown, server 104 is connected to terminal device 102 via a network and can be used to provide services (such as application services) to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for server 104. Cloud computing and / or edge computing services can be configured on the server or independently of the server to provide data processing services for server 104.
[0097] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal device 102 may not be limited to PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projector, smart TV, smart clothes rack, smart curtains, smart audio-visual equipment, smart socket, smart speaker, smart speaker box, smart fresh air equipment, smart kitchen and bathroom equipment, smart bathroom equipment, smart robot vacuum cleaner, smart window cleaning robot, smart mopping robot, smart air purifier, smart steam oven, smart microwave oven, smart water heater, smart air purifier, smart water dispenser, smart door lock, etc.
[0098] This application embodiment also provides a readable storage medium that can store a program suitable for execution by a processor, the program being used for: The air conditioner's performance attributes and the target attributes for analyzing the air conditioner data are obtained, wherein the target attributes are at least one air conditioner attribute other than the performance attributes. For each acquired attribute, based on the correlation between the acquired attribute and other attributes, at least one related attribute of the acquired attribute is determined from the other attributes, and each attribute group is determined as a presentation layer attribute of the air conditioner. Each attribute group includes an acquired attribute and each related attribute of the acquired attribute. The other attributes are at least one air conditioner attribute other than the acquired attribute. Based on the correlation between the obtained attributes in each group of presentation layer attributes, at least one group of target presentation layer attributes is determined from each group of presentation layer attributes, and the target presentation layer attributes in each group are merged. The merged target presentation layer attributes and the unmerged presentation layer attributes are combined to obtain related layer attributes. Each group of presentation layer attributes consists of any two presentation layer attributes. From the merged target presentation layer attributes, determine the acquired attributes that meet the preset conditions, and use the acquired attributes that meet the preset conditions as core layer attributes. The preset conditions are within the intersection range of the merged target presentation layer attributes. The analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes.
[0099] Optionally, the program's refined and extended functions can be found in the description above.
[0100] This application also provides a computer program product, which may store a program suitable for execution by a processor, the program being used for: The air conditioner's performance attributes and the target attributes for analyzing the air conditioner data are obtained, wherein the target attributes are at least one air conditioner attribute other than the performance attributes. For each acquired attribute, based on the correlation between the acquired attribute and other attributes, at least one related attribute of the acquired attribute is determined from the other attributes, and each attribute group is determined as a presentation layer attribute of the air conditioner. Each attribute group includes an acquired attribute and each related attribute of the acquired attribute. The other attributes are at least one air conditioner attribute other than the acquired attribute. Based on the correlation between the obtained attributes in each group of presentation layer attributes, at least one group of target presentation layer attributes is determined from each group of presentation layer attributes, and the target presentation layer attributes in each group are merged. The merged target presentation layer attributes and the unmerged presentation layer attributes are combined to obtain related layer attributes. Each group of presentation layer attributes consists of any two presentation layer attributes. From the merged target presentation layer attributes, determine the acquired attributes that meet the preset conditions, and use the acquired attributes that meet the preset conditions as core layer attributes. The preset conditions are within the intersection range of the merged target presentation layer attributes. The analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes.
[0101] Optionally, the program's refined and extended functions can be found in the description above.
[0102] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.
[0103] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0104] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of this application.
[0105] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.
Claims
1. A method for determining analytical data of an air conditioner, characterized in that, include: The air conditioner's performance attributes and the target attributes for analyzing the air conditioner data are obtained, wherein the target attributes are at least one air conditioner attribute other than the performance attributes. For each acquired attribute, based on the correlation between the acquired attribute and other attributes, at least one related attribute of the acquired attribute is determined from the other attributes, and each attribute group is determined as a presentation layer attribute of the air conditioner. Each attribute group includes an acquired attribute and each related attribute of the acquired attribute. The other attributes are at least one air conditioner attribute other than the acquired attribute. Based on the correlation between the obtained attributes in each group of presentation layer attributes, at least one group of target presentation layer attributes is determined from each group of presentation layer attributes, and the target presentation layer attributes in each group are merged. The merged target presentation layer attributes and the unmerged presentation layer attributes are combined to obtain related layer attributes. Each group of presentation layer attributes consists of any two presentation layer attributes. From the merged target presentation layer attributes, determine the acquired attributes that meet the preset conditions, and use the acquired attributes that meet the preset conditions as core layer attributes. The preset conditions are within the intersection range of the merged target presentation layer attributes. The analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes.
2. The method for determining the analytical data of an air conditioner according to claim 1, characterized in that, The step of determining at least one related attribute of the acquired attribute from the other attributes based on the correlation between the acquired attribute and other attributes includes: Calculate the correlation coefficient between the acquired attribute and the other attributes according to the Pearson correlation coefficient calculation formula; Determine whether there is a correlation coefficient greater than a first preset threshold among the correlation coefficients of the acquired attribute and the other attributes; If there exists a correlation coefficient greater than the first preset threshold, then each other attribute corresponding to the correlation coefficient greater than the first preset threshold is taken as a related attribute of the acquired attribute.
3. The method for determining the analytical data of an air conditioner according to claim 2, characterized in that, The step of determining at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between the acquired attributes in each set includes: Calculate the correlation coefficient between the acquired attributes in each group of the presentation layer attributes according to the Pearson correlation coefficient calculation formula; Determine whether there is a correlation coefficient greater than a second preset threshold among the correlation coefficients between the acquired attributes in each group of presentation layer attributes, where the second preset threshold is greater than the first preset threshold; If there exists a correlation coefficient greater than the second preset threshold, then each set of presentation layer attributes corresponding to the correlation coefficient greater than the second preset threshold is taken as a set of target presentation layer attributes.
4. The method for determining the analytical data of an air conditioner according to claim 3, characterized in that, The step of determining the acquired attributes that satisfy preset conditions from the merged target presentation layer attributes includes: Determine whether there are duplicate obtained attributes in each of the merged target presentation layer attributes; If there are duplicate acquired attributes among the merged target presentation layer attributes, then the duplicate acquired attributes are determined to be the acquired attributes that satisfy the preset conditions.
5. The method for determining the analysis results of air conditioning data according to claim 3, characterized in that, After determining the analytical data of the air conditioner from a preset data warehouse based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes, the method further includes: The analysis data of the air conditioner is analyzed to obtain the analysis results of the air conditioner; Based on the analysis results, the first preset threshold is optimized to obtain the optimized first preset threshold, and the first preset threshold is updated to the optimized first preset threshold. The second preset threshold is optimized based on the analysis results to obtain an optimized second preset threshold; and the second preset threshold is updated to the optimized second preset threshold.
6. The method for determining the analytical data of an air conditioner according to claim 5, characterized in that, The step of determining the analysis data of the air conditioner from a preset data warehouse based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes includes: Obtain the analysis method of the air conditioner; If the analysis method is shallow analysis, then based on the presentation layer attributes, the first analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner, and the attributes corresponding to the first analysis data are consistent with the presentation layer attributes; If the analysis method is in-depth analysis, then based on the relevant layer attributes, the second analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner, and the attributes corresponding to the second analysis data are consistent with the relevant layer attributes; If the analysis method is anomaly analysis, then based on the core layer attributes, the third analysis data of the air conditioner is determined from the preset data warehouse of the air conditioner, and the attributes corresponding to the third analysis data are consistent with the core layer attributes.
7. A device for determining analytical data of an air conditioner, characterized in that, include: An acquisition unit is used to acquire the performance attributes of the air conditioner and the target attributes for analyzing the air conditioner data, wherein the target attributes are at least one air conditioner attribute other than the performance attributes. The first determining unit is configured to, for each acquired attribute, determine at least one related attribute of the acquired attribute from the other attributes based on the correlation between the acquired attribute and other attributes, and determine each attribute group as a presentation layer attribute of the air conditioner. Each attribute group includes an acquired attribute and each related attribute of the acquired attribute. The other attributes are at least one air conditioner attribute other than the acquired attribute. The second determining unit is used to determine at least one set of target presentation layer attributes from each set of presentation layer attributes based on the correlation between the obtained attributes in each set of presentation layer attributes, merge each set of target presentation layer attributes, and combine each merged target presentation layer attribute with the unmerged presentation layer attributes to obtain related layer attributes, wherein each set of presentation layer attributes consists of any two presentation layer attributes. The third determining unit is used to determine the acquired attributes that satisfy the preset conditions from each of the merged target presentation layer attributes, and to take the acquired attributes that satisfy the preset conditions as core layer attributes, wherein the preset conditions are within the intersection range of each of the merged target presentation layer attributes. The fourth determining unit is used to determine the analysis data of the air conditioner from the preset data warehouse of the air conditioner based on at least one of the presentation layer attributes, related layer attributes, and core layer attributes.
8. A device for determining analytical data of an air conditioner, characterized in that, Including memory and processor; The memory is used to store programs; The processor is configured to execute the program to implement the various steps of the method for determining the analytical data of the air conditioner as described in any one of claims 1 to 6.
9. A readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements each step of the method for determining the analytical data of the air conditioner as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements each step of the method for determining the analytical data of the air conditioner according to any one of claims 1 to 6.