Air conditioner user identification method based on big data analysis

Abstract

The invention is suitable for the technical field of air conditioner user identification, and provides an air conditioner user identification method and device based on big data analysis, and the method comprises the steps: obtaining historical load data and historical environment data of a user; based on historical load data and historical environment data of the user, obtaining associated data among time, load size and environment temperature; determining a daily maximum load and a daily temperature index based on the associated data; determining a correlation index of the plurality of time periods based on the daily maximum load and the daily temperature index; and based on the correlation indexes of the multiple time periods, a judgment result about whether the user is the air conditioner user is obtained. The air conditioner user can be accurately recognized, and air conditioner load management can be efficiently carried out.

Description

Technical Field

[0001] The present application belongs to the technical field of air conditioner user identification, and in particular, relates to an air conditioner user identification method and device based on big data analysis. Background Art

[0002] Good air conditioning load management has become an important measure to ensure stable power operation. In order to carry out air conditioning load management more accurately, it is necessary to identify air conditioning users. On the basis of identifying air conditioning users and their loads, solid air conditioning load optimization management should be carried out.

[0003] At present, the method of obtaining the specific situation of whether the user uses the air conditioner and the air conditioner load is to conduct a household survey of the users, and install corresponding data collection or metering equipment on all the air conditioners of each household, and collect data in a similar way as by installing hardware on the user's air conditioner side, and then upload the air conditioner load data collected or measured by the equipment. Among them, the method of household survey of whether the user uses the air conditioner includes on-site survey and online survey, both of which require a lot of manpower and material resources, and there are situations such as users not cooperating with the survey and providing misleading information. The method of monitoring the user's air conditioner load through hardware requires the installation of hardware equipment at the user's air conditioner and the establishment of an upload channel for the data collected by the hardware equipment. This method requires a lot of manpower and material resources to install relevant hardware equipment, install hardware household by household, and in some cases, the air conditioner equipment needs to be modified, and a data collection and upload channel needs to be established. After the construction, the hardware needs to be maintained for a long time, such as the patent document with the publication number CN117366783A, which needs to obtain the user's air conditioner power when the air conditioner is operating normally and abnormally. For users who are unwilling to cooperate with the installation of hardware or modify the existing equipment environment, the method of installing hardware is not feasible.

[0004] In this case, it is impossible to carry out air conditioning load management accurately and efficiently. Therefore, a method for accurately identifying whether a user is an air conditioning user is urgently needed. Summary of the invention

[0005] The embodiments of the present application provide an air-conditioning user identification method and device based on big data analysis, which can accurately identify air-conditioning users and facilitate efficient air-conditioning load management.

[0006] This application is implemented through the following technical solutions:

[0007] In a first aspect, an embodiment of the present application provides an air conditioner user identification method based on big data analysis, comprising:

[0008] Obtain the user's historical load data and historical environmental data;

[0009] Based on the user's historical load data and historical environmental data, the correlation data between time, load size and ambient temperature is obtained;

[0010] Based on the correlation data, determine the daily maximum load and daily temperature index;

[0011] Based on the daily maximum load and daily temperature index, determine the correlation indicators for multiple time periods;

[0012] Based on the correlation indicators of multiple time periods, a determination result of whether the user is an air-conditioning user is obtained.

[0013] In a possible implementation of the first aspect, determining the daily temperature index based on the correlation data includes:

[0014] Extract daily ambient temperature from linked data;

[0015] Based on the daily ambient temperature, determine the daily minimum and maximum temperatures;

[0016] Based on the daily minimum temperature and the daily maximum temperature, the daily temperature index is determined.

[0017] In a possible implementation of the first aspect, determining a daily temperature index based on a daily minimum temperature and a daily maximum temperature includes:

[0018] If the daily minimum temperature is greater than the preset temperature, the daily temperature index = daily maximum temperature - preset temperature;

[0019] If the daily maximum temperature is lower than the preset temperature, the daily temperature index = preset temperature - daily minimum temperature;

[0020] If the daily minimum temperature is less than or equal to the preset temperature, and the daily maximum temperature is greater than or equal to the preset temperature, then the daily temperature index = max{(daily maximum temperature - preset temperature), (preset temperature - daily minimum temperature)}.

[0021] In a possible implementation of the first aspect, determining correlation indicators of multiple time periods based on the daily maximum load and the daily temperature index includes:

[0022] Based on the daily maximum load and the daily temperature index, calculating a first correlation between the daily temperature index and the daily maximum load in a preset full-temperature time period;

[0023] Based on the daily maximum load and the daily temperature index, calculating a second correlation between the daily temperature index and the daily maximum load in a preset high temperature time period;

[0024] Based on the daily maximum load and the daily temperature index, a third correlation between the daily temperature index and the daily maximum load in a preset low temperature time period is calculated; the correlation indicators of multiple time periods include a first correlation, a second correlation and a third correlation.

[0025] In a possible implementation of the first aspect, obtaining a determination result of whether the user is an air-conditioning user based on correlation indicators of multiple time periods includes:

[0026] Based on the first correlation, the second correlation, and the third correlation, it is determined whether the user is an air-conditioning user.

[0027] In a possible implementation of the first aspect, determining whether the user is an air conditioner user based on the first correlation, the second correlation, and the third correlation includes:

[0028] If at least one of the first correlation is greater than the first preset judgment threshold, the second correlation is greater than the second preset judgment threshold, and the third correlation is greater than the third preset judgment threshold is satisfied, the user is an air-conditioning user; otherwise, the user is a non-air-conditioning user.

[0029] In a possible implementation of the first aspect, obtaining correlation data among time, load size, and ambient temperature based on historical load data and historical environmental data of a user includes:

[0030] Extracting the characteristics and first timestamp of the load size of the historical load data;

[0031] Extracting the characteristics and second timestamp of the ambient temperature of the historical ambient data;

[0032] The first timestamp corresponding to the feature of the load size and the second timestamp corresponding to the feature of the ambient temperature are aligned to obtain correlation data among time, load size and ambient temperature.

[0033] In a possible implementation of the first aspect, determining the daily maximum load based on the correlation data includes:

[0034] Extract daily load magnitude from the correlation data;

[0035] Based on the daily load size curve, determine the maximum daily load.

[0036] In a possible implementation of the first aspect, the air conditioner user identification method based on big data analysis further includes:

[0037] Save the determination results and correlation indicators to the big data platform;

[0038] The air conditioning load management system is used to obtain the correlation index of air conditioning users in the judgment results from the big data platform;

[0039] Based on the correlation index of air-conditioning users, the air-conditioning load ratio of users is determined.

[0040] In a second aspect, an embodiment of the present application provides an air conditioner user identification device based on big data analysis, which executes the air conditioner user identification method based on big data analysis as in the first aspect, including:

[0041] A data acquisition module is used to obtain the user's historical load data and historical environment data;

[0042] A data association module is used to obtain the association data among time, load size and ambient temperature based on the user's historical load data and historical environmental data;

[0043] A data determination module, for determining a daily maximum load and a daily temperature index based on the associated data;

[0044] A correlation calculation module, used to determine correlation indicators for multiple time periods based on the daily maximum load and the daily temperature index;

[0045] The result determination module is used to obtain a determination result of whether the user is an air-conditioning user based on the correlation indicators of multiple time periods.

[0046] Compared with the related art, the embodiments of the present application have the following beneficial effects:

[0047] The air-conditioning user identification method and device based on big data analysis in the embodiment of the present application do not directly use the ambient temperature to analyze the correlation with the load, but first calculate the daily temperature index that can measure the comfort of the ambient temperature. The larger the value of the daily temperature index, the more uncomfortable the ambient temperature on that day. The correlation between the daily temperature index and the load is then analyzed. This can make the correlation index more significantly reflect the correlation between the ambient temperature and the load, thereby accurately identifying air-conditioning users, which is conducive to efficient air-conditioning load management, optimizing power system scheduling, reducing system load peaks, and improving power supply reliability and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0049] Figure 1 It is a flowchart of an air-conditioning user identification method based on big data analysis provided by an embodiment of the present application;

[0050] Figure 2 It is a structural diagram of an air-conditioning user identification device based on big data analysis provided in one embodiment of the present application. DETAILED DESCRIPTION

[0051] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0052] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0053] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0055] References to "an embodiment", "one embodiment" or "some embodiments" described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0056] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0057] Figure 1 is a flow chart of an air conditioner user identification method based on big data analysis provided by an embodiment of the present application, with reference to Figure 1The air-conditioning user identification method based on big data analysis includes:

[0058] Step 101: Obtain the user's historical load data and historical environment data.

[0059] For example, the user's historical load data and historical environmental data can be obtained from the user's power consumption information collection system or power load management system, as well as the weather data system. The historical load data is the user's total load curve for more than one year, and the collection frequency is once every 15 minutes. The historical environmental data can be meteorological data including temperature, etc.

[0060] The user's archival data may also be obtained, which may include the user's geographic location or a related code identifying the user's geographic location, etc. The historical environmental data is the historical meteorological data corresponding to the user's geographic location or the related address code.

[0061] The load data collection system will store historical load data for a period of time to ensure the system's operating speed and timely update. The older historical load data and weather data obtained from the weather data system will be stored in the archive database in an orderly manner to ensure the order and security of the data.

[0062] Step 102, based on the user's historical load data and historical environmental data, obtain the correlation data between time, load size and environmental temperature.

[0063] Exemplarily, the user's historical load data and historical meteorological data are associated to generate data with the content of [time, load size, ambient temperature] and connected to the big data platform.

[0064] Then step 102 includes:

[0065] Extract the characteristics of the load size and the first timestamp of the historical load data; extract the characteristics of the ambient temperature and the second timestamp of the historical ambient data; align the first timestamp corresponding to the characteristics of the load size and the second timestamp corresponding to the characteristics of the ambient temperature to obtain the correlation data among time, load size and ambient temperature.

[0066] Step 103, determining the daily maximum load and the daily temperature index based on the associated data.

[0067] Exemplarily, determining the daily maximum load based on the associated data includes: extracting the daily load size from the associated data; and determining the daily maximum load based on a curve of the daily load size.

[0068] Exemplarily, determining a daily temperature index based on associated data includes: extracting daily ambient temperature from the associated data; determining a daily minimum temperature and a daily maximum temperature based on the daily ambient temperature; and determining a daily temperature index based on the daily minimum temperature and the daily maximum temperature.

[0069] Exemplarily, based on the daily minimum temperature and the daily maximum temperature, determining the daily temperature index includes:

[0070] If the daily minimum temperature is greater than the preset temperature, the daily temperature index Tc = daily maximum temperature - preset temperature.

[0071] If the daily maximum temperature is lower than the preset temperature, the daily temperature index Tc = preset temperature - daily minimum temperature.

[0072] If the daily minimum temperature is less than or equal to the preset temperature, and the daily maximum temperature is greater than or equal to the preset temperature, the daily temperature index Tc=max{(daily maximum temperature-preset temperature), (preset temperature-daily minimum temperature)}.

[0073] In order to make the temperature index more relevant to a region, the preset temperature can be set to 20°C, which can be the average temperature of a region. Then the temperature index is different when the daily minimum temperature is greater than 20°C and when the daily maximum temperature is less than 20°C, that is, the temperature index is different when the temperature is higher and when the temperature is lower.

[0074] This embodiment does not directly use the ambient temperature to analyze the correlation with the load, but first calculates the temperature index Tc that can measure the comfort of the ambient temperature. The larger the Tc, the more uncomfortable the ambient temperature on that day. Then, the correlation between Tc and the load is analyzed, so that the correlation index can more significantly reflect the correlation between the ambient temperature and the load.

[0075] Step 104 : determining correlation indicators of multiple time periods based on the daily maximum load and the daily temperature index.

[0076] Exemplarily, there are two types of air-conditioning users among electricity users. One type uses air-conditioning only when the temperature is not suitable in winter and summer, such as ordinary residents, and the other type uses air-conditioning in spring and autumn, such as users with computer rooms, whose cooling air-conditioning is turned on all year round. Since the cooling and heating efficiency of air-conditioning is related to the temperature difference between indoor and outdoor, the greater the temperature difference between indoor and outdoor, the lower the cooling efficiency of the air-conditioning, and the greater the air-conditioning power to achieve the same cooling effect. Therefore, the air-conditioning load shows a correlation with temperature. When cooling, the higher the outside temperature is than the indoor temperature, the greater the air-conditioning power; when heating, the lower the outside temperature is than the indoor temperature, the greater the air-conditioning power. Based on the above analysis, for users whose non-air-conditioning load remains stable, their total load shows a correlation with the ambient temperature, and then the correlation between the load performance and the temperature index is determined.

[0077] In the process of determining the correlation between load performance and temperature index, it is taken into account that some users turn on the air conditioner in both winter and summer, which is manifested as a high correlation between the load and temperature index throughout the year. Some users only turn on the air conditioner in summer, which is manifested as a high correlation in summer and no correlation in winter. If the overall correlation is calculated directly, the correlation is low. Some users have a low air conditioning usage rate in summer, but use air conditioning for heating in winter, such as electric heating users. This is manifested as a high correlation in winter, but a low overall correlation. Therefore, this embodiment adopts the above combination logic, and the total correlation reaches a certain threshold, or the summer and winter correlations reach a certain threshold, which can determine that the user has a significant air conditioning load.

[0078] Therefore, step 104 includes:

[0079] Based on the daily maximum load and the daily temperature index, a first correlation between the daily temperature index and the daily maximum load in a preset full-temperature time period is calculated, wherein the preset full-temperature time period may be a whole year.

[0080] For example, the correlation index of the above multiple time periods can be expressed by the Pearson correlation coefficient. The Pearson correlation coefficient of the sample can be obtained by estimating the covariance and standard deviation of the sample. The expression of the first correlation r1 is as follows:

[0081]

[0082] in, Represents the mean value of the samples of the daily temperature index in the preset full temperature time period; represents the mean of the samples of the daily maximum load in the preset full-temperature period; n represents the number of samples of the daily temperature index and the daily maximum load in the preset full-temperature period; x i represents the i-th sample of the daily temperature index in the preset full temperature time period; y i Represents the i-th sample of the daily maximum load in the preset full-temperature time period.

[0083] Based on the daily maximum load and the daily temperature index, a second correlation between the daily temperature index and the daily maximum load in a preset high temperature time period is calculated, wherein the preset high temperature time period may be from April to September.

[0084] The expression of the second correlation r2 is as follows:

[0085]

[0086] in, Represents the mean value of the samples of the daily temperature index in the preset high temperature period; represents the mean of the samples of the daily maximum load in the preset high temperature time period; m represents the number of samples of the daily temperature index and the daily maximum load in the preset high temperature time period; x jrepresents the jth sample of the daily temperature index in the preset high temperature period; j Represents the jth sample of the daily maximum load in the preset high temperature time period.

[0087] Based on the daily maximum load and the daily temperature index, a third correlation between the daily temperature index and the daily maximum load in a preset low temperature time period is calculated; wherein the preset low temperature time period may be October-December and January-March. The correlation indicators of the multiple time periods include a first correlation, a second correlation and a third correlation.

[0088] The expression of the third correlation r3 is as follows:

[0089]

[0090] in, The mean value of the samples of the daily temperature index in the preset low temperature period; represents the mean of the samples of the daily maximum load in the preset low temperature time period; p represents the number of samples of the daily temperature index and the daily maximum load in the preset low temperature time period; x k represents the kth sample of the daily temperature index in the preset low temperature period; k Indicates the kth sample of the daily maximum load in the preset low temperature time period.

[0091] In this embodiment, the Pearson correlation coefficient is used to calculate the correlation index of multiple time periods, which can measure the strength and direction of the linear relationship between the daily maximum load and the daily temperature index in different time periods. It is fast and easy to calculate.

[0092] Step 105: based on the correlation indicators of the multiple time periods, a determination result of whether the user is an air-conditioning user is obtained.

[0093] Exemplarily, step 105 includes: determining whether the user is an air-conditioning user based on the first correlation, the second correlation, and the third correlation.

[0094] Based on the first correlation, the second correlation, and the third correlation, determining whether the user is an air-conditioning user includes:

[0095] If at least one of the first correlation is greater than the first preset judgment threshold, the second correlation is greater than the second preset judgment threshold, and the third correlation is greater than the third preset judgment threshold, the user is an air-conditioning user; otherwise, the user is a non-air-conditioning user, that is, the first correlation, the second correlation and the third correlation do not exceed the corresponding thresholds.

[0096] The first preset decision threshold, the second preset decision threshold and the third preset decision threshold are configurable and modifiable parameters, and the default value may be set to 0.2.

[0097] It should be noted that in some special areas, the selection of multiple time periods may be adjusted, or the corresponding preset determination thresholds may be adjusted.

[0098] Regardless of whether the user uses the air conditioner in summer, winter, or all year round, as long as one correlation exceeds the threshold, it means that the user uses the air conditioner in both winter and summer, or only in summer, or only in winter. This allows non-air conditioner users and air conditioner users to be accurately screened out.

[0099] In addition, different types of correlation indicators can be used to mark users who use air conditioners in summer, users who use air conditioners in winter, and users who use air conditioners all year round, so as to accurately distinguish various types of air conditioner users and improve analysis efficiency.

[0100] Exemplarily, the air conditioning user identification method based on big data analysis also includes: saving the determination results and correlation indicators to a big data platform; obtaining the correlation indicators of the air conditioning users in the determination results from the big data platform using the air conditioning load management system; and determining the user's air conditioning load ratio based on the correlation indicators of the air conditioning users.

[0101] In summary, the air-conditioning user identification method based on big data analysis provided in the embodiment of the present application does not directly use the ambient temperature to analyze the correlation with the load, but first calculates the daily temperature index that can measure the comfort of the ambient temperature. The larger the value of the daily temperature index, the more uncomfortable the ambient temperature on that day. Then, the correlation between the daily temperature index and the load is analyzed. The correlation index can more significantly reflect the correlation between the ambient temperature and the load, thereby accurately identifying air-conditioning users, which is conducive to efficient air-conditioning load management, optimizing power system scheduling, reducing system load peak, and improving power supply reliability and efficiency.

[0102] For energy management, this method can achieve refined energy monitoring and management. By accurately identifying air-conditioning users, it can better monitor and predict load demand, thereby optimizing power system scheduling, reducing system load peaks, and improving power supply reliability and efficiency.

[0103] This method is helpful to promote the construction of smart grid. Through air conditioner user identification, it can provide more comprehensive user behavior data support for smart grid, realize dynamic load management, intelligent power market transactions and other functions, and promote the safe and stable operation and intelligent development of power grid.

[0104] This method can assist in analyzing user electricity consumption behavior. Combined with other user data analysis, it can deeply study the user's electricity consumption behavior pattern and provide support for personalized energy services and customized solutions.

[0105] Air conditioner user identification methods also have a positive impact on marketing and service improvement. Based on user behavior analysis, companies can develop targeted marketing strategies, provide customized products and services, enhance customer satisfaction, and promote market competitiveness and brand influence.

[0106] See also Figure 2 An embodiment of the present application provides an air-conditioning user identification device based on big data analysis, which executes the air-conditioning user identification method based on big data analysis as described above. The device includes a data acquisition module 201, a data association module 202, a data determination module 203, a correlation calculation module 204 and a result judgment module 205.

[0107] The data acquisition module 201 is used to acquire the user's historical load data and historical environment data.

[0108] The data association module 202 is used to obtain association data among time, load size and ambient temperature based on the user's historical load data and historical environmental data.

[0109] The data determination module 203 is used to determine the daily maximum load and the daily temperature index based on the associated data.

[0110] The correlation calculation module 204 is used to determine correlation indicators of multiple time periods based on the daily maximum load and the daily temperature index.

[0111] The result determination module 205 is used to obtain a determination result of whether the user is an air-conditioning user based on the correlation indicators of multiple time periods.

[0112] Exemplarily, the data determination module 203 is specifically configured to determine a daily temperature index based on the associated data.

[0113] Based on the correlation data, determine the daily temperature index, including:

[0114] Extract daily ambient temperature from linked data;

[0115] Based on the daily ambient temperature, determine the daily minimum and maximum temperatures;

[0116] Based on the daily minimum temperature and the daily maximum temperature, the daily temperature index is determined.

[0117] Exemplarily, based on the daily minimum temperature and the daily maximum temperature, determining the daily temperature index includes:

[0118] If the daily minimum temperature is greater than the preset temperature, the daily temperature index = daily maximum temperature - preset temperature;

[0119] If the daily maximum temperature is lower than the preset temperature, the daily temperature index = preset temperature - daily minimum temperature;

[0120] If the daily minimum temperature is less than or equal to the preset temperature, and the daily maximum temperature is greater than or equal to the preset temperature, then the daily temperature index = max{(daily maximum temperature - preset temperature), (preset temperature - daily minimum temperature)}.

[0121] Exemplarily, the correlation calculation module 204 is specifically used for:

[0122] Based on the daily maximum load and the daily temperature index, calculating a first correlation between the daily temperature index and the daily maximum load in a preset full-temperature time period;

[0123] Based on the daily maximum load and the daily temperature index, calculating a second correlation between the daily temperature index and the daily maximum load in a preset high temperature time period;

[0124] Based on the daily maximum load and the daily temperature index, a third correlation between the daily temperature index and the daily maximum load in a preset low temperature time period is calculated; the correlation indicators of multiple time periods include a first correlation, a second correlation and a third correlation.

[0125] Exemplarily, the result determination module 205 is specifically used for:

[0126] Based on the first correlation, the second correlation, and the third correlation, it is determined whether the user is an air-conditioning user.

[0127] Exemplarily, determining whether the user is an air-conditioning user based on the first correlation, the second correlation, and the third correlation includes:

[0128] If at least one of the first correlation is greater than the first preset judgment threshold, the second correlation is greater than the second preset judgment threshold, and the third correlation is greater than the third preset judgment threshold is satisfied, the user is an air-conditioning user; otherwise, the user is a non-air-conditioning user.

[0129] Exemplarily, the data association module 202 is specifically configured to:

[0130] Extracting the characteristics and first timestamp of the load size of the historical load data;

[0131] Extracting the characteristics and second timestamp of the ambient temperature of the historical ambient data;

[0132] The first timestamp corresponding to the load size feature and the second timestamp corresponding to the ambient temperature feature are aligned to obtain correlation data among time, load size and ambient temperature.

[0133] Exemplarily, the data determination module 203 is further configured to determine the daily maximum load based on the associated data. Determining the daily maximum load based on the associated data includes:

[0134] Extract daily load magnitude from the correlation data;

[0135] Based on the daily load size curve, determine the maximum daily load.

[0136] Exemplarily, the air-conditioning user identification device based on big data analysis also includes an air-conditioning load ratio determination module.

[0137] The air conditioning load ratio determination module is specifically used for:

[0138] Save the determination results and correlation indicators to the big data platform;

[0139] The air conditioning load management system is used to obtain the correlation index of air conditioning users in the judgment results from the big data platform;

[0140] Based on the correlation index of air-conditioning users, the air-conditioning load ratio of users is determined.

[0141] It should be noted that although several units / modules or sub-units / modules of the air conditioner user identification device based on big data analysis are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiment of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0142] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0143] The present application also provides a computer-readable storage medium, in which computer execution instructions are stored. When a processor executes the computer execution instructions, the air-conditioning user identification method based on big data analysis provided in the above-mentioned embodiment of the present application is implemented.

[0144] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0145] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An air conditioning user identification method based on big data analysis, characterized in that: include: Obtain the user's historical load data and historical environmental data; Based on the historical load data and historical environmental data of the user, obtaining correlation data among time, load size and environmental temperature; Based on the associated data, determining a daily maximum load and a daily temperature index; Determining correlation indicators for multiple time periods based on the daily maximum load and the daily temperature index; Based on the correlation indicators of the multiple time periods, a determination result of whether the user is an air-conditioning user is obtained.

2. The air conditioning user identification method based on big data analysis according to claim 1, characterized in that: Based on the correlation data, a daily temperature index is determined, including: extracting daily ambient temperature from the associated data; Based on the daily ambient temperature, determining a daily minimum temperature and a daily maximum temperature; The daily temperature index is determined based on the daily minimum temperature and the daily maximum temperature.

3. The air conditioning user identification method based on big data analysis according to claim 2, characterized in that: The determining the daily temperature index based on the daily minimum temperature and the daily maximum temperature comprises: If the daily minimum temperature is greater than the preset temperature, the daily temperature index = daily maximum temperature - preset temperature; If the daily maximum temperature is lower than the preset temperature, the daily temperature index = preset temperature - daily minimum temperature; If the daily minimum temperature is less than or equal to the preset temperature, and the daily maximum temperature is greater than or equal to the preset temperature, the daily temperature index = max{(daily maximum temperature - preset temperature), (preset temperature - daily minimum temperature)}.

4. The air conditioning user identification method based on big data analysis according to claim 1, characterized in that: The determining of correlation indicators of multiple time periods based on the daily maximum load and the daily temperature index includes: Based on the daily maximum load and the daily temperature index, calculating a first correlation between the daily temperature index and the daily maximum load in a preset full-temperature time period; Based on the daily maximum load and the daily temperature index, calculating a second correlation between the daily temperature index and the daily maximum load in a preset high temperature time period; Based on the daily maximum load and the daily temperature index, a third correlation between the daily temperature index and the daily maximum load in a preset low temperature time period is calculated; the correlation indicators of the multiple time periods include the first correlation, the second correlation and the third correlation.

5. The air-conditioning user identification method based on big data analysis according to claim 4, characterized in that: The determining result of whether the user is an air-conditioning user based on the correlation indicators of the multiple time periods includes: Based on the first correlation, the second correlation, and the third correlation, it is determined whether the user is an air-conditioning user.

6. The air-conditioning user identification method based on big data analysis according to claim 5, characterized in that: The determining whether the user is an air-conditioning user based on the first correlation, the second correlation, and the third correlation includes: If at least one of the first correlation is greater than a first preset judgment threshold, the second correlation is greater than a second preset judgment threshold, and the third correlation is greater than a third preset judgment threshold, the user is an air-conditioning user; otherwise, the user is a non-air-conditioning user.

7. The air conditioning user identification method based on big data analysis according to claim 1, characterized in that: The obtaining of correlation data among time, load size and ambient temperature based on the historical load data and historical environmental data of the user includes: Extracting a load size characteristic and a first timestamp of the historical load data; Extracting the characteristics of the ambient temperature of the historical ambient data and a second timestamp; The first timestamp corresponding to the characteristic of the load size and the second timestamp corresponding to the characteristic of the ambient temperature are aligned to obtain correlation data among time, load size and ambient temperature.

8. The air conditioning user identification method based on big data analysis according to claim 1, characterized in that: The determining of the daily maximum load based on the associated data comprises: extracting daily load magnitude from the associated data; Based on the daily load magnitude curve, the daily maximum load is determined.

9. The air conditioning user identification method based on big data analysis according to claim 1, characterized in that: The air conditioner user identification method based on big data analysis also includes: Saving the determination results and correlation indicators to a big data platform; Using the air conditioning load management system to obtain the correlation index of the air conditioning users in the determination result from the big data platform; Based on the correlation index of the air-conditioning user, the air-conditioning load ratio of the user is determined.

10. An air conditioner user identification device based on big data analysis, executing the air conditioner user identification method based on big data analysis according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to obtain the user's historical load data and historical environment data; A data association module, used to obtain association data between time, load size and ambient temperature based on the historical load data and historical environmental data of the user; A data determination module, used for determining a daily maximum load and a daily temperature index based on the associated data; A correlation calculation module, used to determine correlation indicators of multiple time periods based on the daily maximum load and the daily temperature index; The result determination module is used to obtain a determination result of whether the user is an air-conditioning user based on the correlation indicators of the multiple time periods.

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