A method, device and computer equipment for identifying opening behavior of a household

Abstract

The application provides a kind of identification method, device and computer equipment of household window opening behavior, which comprises: obtaining the historical acquisition data of target household and other households in the unit where the target household is located;According to the historical acquisition data, the first room temperature drop data of the target household in the data acquisition time period, the second room temperature drop data of the target household relative to the average room temperature of other households in the unit where the target household is located and the flow change data of the target household relative to the average heat supply flow of other households in the unit where the target household is located are obtained;When the first room temperature drop data and the second room temperature drop data are both greater than the corresponding temperature setting threshold, and the flow change data is less than the corresponding flow setting threshold, it is determined that there is window opening behavior.The application can accurately identify the window opening behavior of the household, thereby ensuring the accuracy of the room temperature acquisition, and enabling the heat company to achieve low energy consumption and high quality heating.

Description

Technical Field

[0001] This invention relates to the field of heating control technology, and in particular to a method, device, and computer equipment for identifying residents' window-opening behavior. Background Technology

[0002] User room temperature is an important basis for heating companies to assess heating quality, adjust heating plans, and verify energy-saving effects. Timely collection of room temperature data allows heating companies to promptly grasp the heating status of different areas, and also provides a reliable basis for implementing load optimization and adjustment of the heating network.

[0003] Although room temperature data acquisition devices can collect the room temperature of heat users in real time, in actual application scenarios, room temperature anomalies often occur due to various reasons, making it impossible for heating companies to achieve a high-efficiency and high-quality heating mode based on room temperature feedback.

[0004] In existing technologies, when correcting abnormal room temperature, only the situation of unreasonable installation position of temperature acquisition panel is considered. The abnormal room temperature caused by residents opening windows is not considered. It is impossible to accurately realize the identification of residents opening windows and the room temperature abnormality caused by residents opening windows, thus failing to guarantee the accuracy of room temperature acquisition and affecting the heating regulation of heat exchange station. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method, apparatus and computer device for recognizing residents' window opening behavior to overcome the above problems.

[0006] One aspect of the present invention provides a method for identifying residents' window-opening behavior, the method comprising:

[0007] Acquire historical data of the target household and other households in the same unit as the target household. The historical data includes the household identification and the room temperature and flow data of the household collected at each data collection time.

[0008] Based on the historical data collected, the following data are obtained: the first temperature drop data of the target household during the data collection period; the second temperature drop data of the target household relative to the average temperature of other households in the same unit; and the flow rate change data of the target household relative to the average heating flow rate of other households in the same unit.

[0009] When both the first room temperature drop data and the second room temperature drop data are greater than their respective temperature setting thresholds, and the flow rate change data is less than the corresponding flow rate setting threshold, it is determined that there is window opening behavior within the corresponding data collection time period.

[0010] Optionally, the step of acquiring, based on the historical data, the first temperature drop data of the target household within the data collection period, the second temperature drop data of the target household relative to the average temperature of other households in the same unit, and the flow rate change data of the target household relative to the average heating flow rate of other households in the same unit, includes:

[0011] Based on the room temperature data of the target household and other households in the target household's unit, calculate the first room temperature change ratio of the target household and the second room temperature change ratio of the average room temperature of other households in the target household's unit at each preset statistical time. Then, calculate the relative room temperature change ratio of the target household relative to other households in the target household's unit at each statistical time based on the difference between the first room temperature change ratio and the second room temperature change ratio.

[0012] Based on the heating flow data of the target household and other households in the target household's unit, calculate the first flow change ratio of the target household and the second flow change ratio of the average heating flow of other households in the target household's unit at each preset statistical time. Based on the first flow change ratio and the second flow change ratio at each statistical time, calculate the relative flow change ratio of the target household relative to other households in the target household's unit.

[0013] The absolute value of the data less than 0 in the first room temperature change ratio at each statistical time is taken as the first room temperature decrease ratio at the corresponding statistical time. The maximum value of each first room temperature decrease ratio is obtained and the cumulative value of each first room temperature decrease ratio is calculated. The maximum value and / or cumulative value of the first room temperature decrease ratio are taken as the first room temperature decrease data.

[0014] The absolute value of the relative room temperature change ratio less than 0 at each statistical time point is taken as the relative room temperature decrease ratio at the corresponding statistical time point, and the maximum value of the relative room temperature decrease ratio is taken as the second room temperature decrease data.

[0015] Obtain the maximum absolute value of the relative flow change ratio at each statistical time point, and use the maximum absolute value of the relative flow change ratio as the flow change data.

[0016] Optionally, the first room temperature change ratio and the second room temperature change ratio are obtained in the following ways:

[0017] The first and second room temperature change ratios at each statistical time point are calculated using the following formulas:

[0018]

[0019] Where r is the first room temperature change ratio or the second room temperature change ratio, and when r is the first room temperature change ratio, y t Let y be the room temperature data of the target household at time t. t+1 The data represents the room temperature of the target household at time t+1; when r is the second room temperature change ratio, y t Let y be the average room temperature of other households in the same unit as the target household at time t. t+1 The average room temperature of other residents in the same unit as the target resident at time t+1.

[0020] Optionally, the first and second flow rate change ratios are obtained as follows:

[0021] The first and second flow rate changes at each statistical time point are calculated using the following formulas:

[0022]

[0023] Where r' is the first flow rate change ratio or the second flow rate change ratio, and when r' is the first flow rate change ratio, y' T Let y' be the heating flow rate for the target household at time t. T+1 Let r' be the heating flow rate for the target household at time t+1; when r' is the second flow rate change ratio, y' T Let y' be the average room temperature of other residents in the same unit as the target resident at time t. t+1 This represents the average heating flow rate of other households in the same unit as the target household at time t+1.

[0024] Optionally, the relative room temperature change ratio and the relative flow rate change ratio are obtained as follows:

[0025] The formula for calculating the percentage change in relative room temperature or relative flow rate is:

[0026]

[0027] Where, r relative This represents the percentage change in relative room temperature or relative flow rate, when r relative When expressing the proportion of change relative to room temperature, Indicates the first percentage change in room temperature. This indicates the second percentage change in room temperature; when r relative When expressing the proportion of relative flow change, Indicates the percentage change in the first flow rate. This indicates the percentage change in the second flow rate.

[0028] Optionally, after determining that windowing behavior exists within the corresponding data collection time period, the method further includes:

[0029] Determine whether the room temperature of the target resident is abnormal at the current moment;

[0030] If the room temperature of the target resident is abnormal at the current moment, the room temperature of the target resident will be corrected based on the average room temperature of other residents in the same unit at the current moment.

[0031] Optionally, determining whether the room temperature of the target resident is abnormal at the current moment includes:

[0032] Calculate the ratio of the room temperature of the target household to the average room temperature of other households in the same unit at each statistical time point during the data collection period prior to the current time.

[0033] Obtain the acquisition time corresponding to the maximum value of the room temperature ratio;

[0034] The mean of all room temperature ratios prior to the sampling time was used as the estimated normal ratio.

[0035] When the difference between the estimated normal ratio and the current room temperature ratio is greater than the preset ratio threshold, the room temperature of the target resident at the current time is determined to be abnormal.

[0036] Optionally, the step of correcting the room temperature of the target resident based on the average room temperature of other residents in the same unit at the current time includes:

[0037] The following room temperature correction algorithm is used to correct the room temperature of the target household at the current time:

[0038] Corrected room temperature = estimated normal ratio × average room temperature of other households in the same unit as the target household at the current time.

[0039] In another aspect, the present invention provides a device for recognizing residents' window-opening behavior, the device comprising a functional module for implementing the method for recognizing residents' window-opening behavior as described in any of the preceding claims.

[0040] In another aspect, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor; when executed by the processor, the computer program implements the steps of the method for recognizing resident window opening behavior as described in any of the preceding claims.

[0041] This invention provides a method, apparatus, and computer device for identifying residents' window-opening behavior. By acquiring historical data from the target resident and other residents in the same unit, the method obtains first room temperature decrease data, second room temperature decrease data relative to the average room temperature of other residents in the same unit, and flow rate change data relative to the average heating flow rate of other residents in the same unit. Based on the relationship between the first room temperature decrease data, second room temperature decrease data, and flow rate change data and corresponding set thresholds, the method determines the window-opening behavior that exists within the corresponding data collection period. This enables accurate identification of residents' window-opening behavior, ensuring the accuracy of room temperature data collection. It provides strong data support for heating companies to regulate heat exchange stations and balance secondary heating networks, allowing heating companies to achieve a low-energy, high-quality heating mode based on more accurate room temperature feedback, thus achieving energy-saving heating.

[0042] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings:

[0044] Figure 1 A flowchart illustrating a method for recognizing a resident's window-opening behavior according to an embodiment of the present invention;

[0045] Figure 2 This is a flowchart illustrating a detailed implementation of step S2 in a method for recognizing residents' window-opening behavior according to an embodiment of the present invention.

[0046] Figure 3 A flowchart illustrating a method for recognizing a resident's window-opening behavior according to another embodiment of the present invention;

[0047] Figure 4 This is a schematic diagram of the structure of a device for recognizing residents' window-opening behavior according to an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of the structure of a resident window opening behavior recognition device according to another embodiment of the present invention. Detailed Implementation

[0049] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0050] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined.

[0051] Example 1

[0052] This invention provides a method for recognizing residents' window-opening behavior, such as... Figure 1 As shown, the method for identifying residents' window-opening behavior proposed in this invention includes the following steps:

[0053] S1. Obtain historical data of the target resident and other residents in the same unit. The historical data includes resident identification and room temperature and flow data of the resident collected at each data collection time.

[0054] In this embodiment, the target resident and other residents in the unit where the target resident resides are all unit-paying residents.

[0055] In this embodiment, the historical data collected from residents paying bills includes basic information data, room temperature data, and flow rate data. The basic information data includes the resident ID, payment status, unit ID, panel ID, and meter ID. Room temperature data includes the collection time, panel ID, and room temperature. Flow rate data includes the collection time, meter ID, and flow rate. The room temperature and flow rate data in the acquired historical data can be data collected within a preset historical data collection period, for example, data collected over the past day.

[0056] Furthermore, after acquiring historical data of the target resident and other residents in the same unit, the method further includes a preprocessing step for the historical data. The specific implementation steps of the preprocessing include one or more of the following processing methods:

[0057] First, remove unreasonable data from historical data collection. Unreasonable data includes data where the room temperature is not within the preset temperature range, such as data where the room temperature is less than 0-5 degrees Celsius or greater than 30-33 degrees Celsius, or data where the flow rate is 0.

[0058] Second, remove room temperature data from historical data collection where the dispersion is 0, i.e., data showing a constant room temperature. Specifically, calculate the coefficient of variation (COP) of the room temperature data; if the COP is 0, remove the room temperature data for that household. The formula for calculating the COP is:

[0059]

[0060] Where c v Let represent the coefficient of variation, σ represent the standard deviation of the room temperature data, and μ represent the mean of the room temperature data.

[0061] Third, when rounding up the collection time of the room temperature and flow rate data in the historical data, duplicate data for each household's room temperature and flow rate are removed according to the collection time, retaining the last data entry. In this embodiment, since the data collection time of the room temperature collection panel is not transmitted on the hour, and the specific collection time of each panel is not uniform, this invention aligns the collected data to the hour to facilitate the subsequent calculation of room temperature drop data and flow rate change data. It is understood that the method of aligning the collected data to the hour in this invention is only an exemplary implementation method. In addition, the time length for rounding up the data can be arbitrarily set, and this invention does not specifically limit it in this way.

[0062] S2. Based on the historical data, obtain the first room temperature decrease data of the target household during the data collection period, the second room temperature decrease data of the target household relative to the average room temperature of other households in the target household's unit, and the flow rate change data of the target household's heating flow rate relative to the average heating flow rate of other households in the target household's unit.

[0063] S3. When both the first room temperature drop data and the second room temperature drop data are greater than their respective temperature setting thresholds, and the flow rate change data is less than the corresponding flow rate setting threshold, it is determined that there is a window opening behavior within the corresponding data collection time period.

[0064] This invention provides a method, apparatus, and computer device for identifying residents' window-opening behavior. By acquiring historical data from the target resident and other residents in the same unit, the method obtains first room temperature decrease data, second room temperature decrease data relative to the average room temperature of other residents in the same unit, and flow rate change data relative to the average heating flow rate of other residents in the same unit. Based on the relationship between the first room temperature decrease data, second room temperature decrease data, and flow rate change data and corresponding set thresholds, the method determines the window-opening behavior that exists within the corresponding data collection period. This enables accurate identification of residents' window-opening behavior, ensuring the accuracy of room temperature data collection. It provides strong data support for heating companies to regulate heat exchange stations and balance secondary heating networks, allowing heating companies to achieve a low-energy, high-quality heating mode based on more accurate room temperature feedback, thus achieving energy-saving heating.

[0065] In one embodiment of the present invention, such as Figure 2 As shown, step S2, which involves acquiring the first room temperature decrease data of the target household within the data collection period based on the historical data, the second room temperature decrease data of the target household relative to the average room temperature of other households in the same unit, and the flow rate change data of the target household relative to the average heating flow rate of other households in the same unit, includes the following steps:

[0066] S21. Calculate the first room temperature change ratio of the target household and the second room temperature change ratio of the average room temperature of other households in the target household at each preset statistical time based on the room temperature data of the target household and other households in the target household. Calculate the relative room temperature change ratio of the target household relative to other households in the target household at each statistical time based on the difference between the first room temperature change ratio and the second room temperature change ratio.

[0067] S22. Calculate the first flow change ratio of the target household and the second flow change ratio of the average heating flow of other households in the target household at each preset statistical time based on the heating flow data of the target household and other households in the target household. Calculate the relative flow change ratio of the target household relative to other households in the target household at each statistical time based on the first flow change ratio and the second flow change ratio.

[0068] S23. Take the absolute value of the data less than 0 in the first room temperature change ratio at each statistical time as the first room temperature decrease ratio at the corresponding statistical time, obtain the maximum value of each first room temperature decrease ratio and calculate the cumulative value of each first room temperature decrease ratio, and take the maximum value and / or cumulative value of the first room temperature decrease ratio as the first room temperature decrease data.

[0069] S24. Take the absolute value of the data that is less than 0 in the relative room temperature change ratio at each statistical time as the relative room temperature decrease ratio at the corresponding statistical time, and take the maximum value of the relative room temperature decrease ratio as the second room temperature decrease data.

[0070] S25. Obtain the maximum value of the absolute value of the relative flow change ratio at each statistical time point, and use the maximum value of the absolute value of the relative flow change ratio as the flow change data.

[0071] In this embodiment of the invention, the room temperature data of the target resident is first resampled, that is, the room temperature data of the missing hours of the most recent day is supplemented from the room temperature data collection panel. Then, based on the resident identifier, the room temperature data and airflow data of the target resident and other residents in the same unit collected at each data collection time are retrieved. Finally, the hourly room temperature change rate of the target resident and the hourly room temperature change rate of the average room temperature of other residents in the same unit are calculated. In this embodiment, the preset statistical times can be the hourly times.

[0072] Furthermore, in this embodiment of the invention, the first room temperature change ratio and the second room temperature change ratio are obtained as follows:

[0073] The first and second room temperature change ratios at each statistical time point are calculated using the following formulas:

[0074]

[0075] Where r is the first room temperature change ratio or the second room temperature change ratio, and when r is the first room temperature change ratio, y t Let y be the room temperature data of the target household at time t. t+1 The data represents the room temperature of the target household at time t+1; when r is the second room temperature change ratio, y t Let y be the average room temperature of other households in the same unit as the target household at time t. t+1 The average room temperature of other residents in the same unit as the target resident at time t+1.

[0076] Furthermore, in this embodiment of the invention, the methods for obtaining the first flow rate change ratio and the second flow rate change ratio are as follows:

[0077] The first and second flow rate changes at each statistical time point are calculated using the following formulas:

[0078]

[0079] Where r' is the first flow rate change ratio or the second flow rate change ratio, and when r' is the first flow rate change ratio, y't Let y' be the heating flow rate for the target household at time t. t+1 Let r' be the heating flow rate for the target household at time t+1; when r' is the second flow rate change ratio, y' T Let y' be the average heating flow rate of other households in the unit where the target household is located at time t. t+1 This represents the average heating flow rate of other households in the same unit as the target household at time t+1.

[0080] Furthermore, the methods for obtaining the relative room temperature change ratio and the relative flow rate change ratio in this embodiment of the invention are as follows:

[0081] The formula for calculating the percentage change in relative room temperature or relative flow rate is:

[0082]

[0083] Where, r relative This represents the percentage change in relative room temperature or relative flow rate, when r relative When expressing the proportion of change relative to room temperature, Indicates the first percentage change in room temperature. This indicates the second percentage change in room temperature; when r relative When expressing the proportion of relative flow change, Indicates the percentage change in the first flow rate. This indicates the percentage change in the second flow rate.

[0084] In this embodiment, the first room temperature decrease ratio of the target resident, the relative room temperature decrease ratio of the target resident relative to other residents in the same unit, and the relative flow rate change ratio of the target resident relative to other residents in the same unit are used as data to determine window opening behavior. If the maximum value of the first room temperature decrease ratio of the target resident is greater than a first set threshold and / or the cumulative value is greater than a second set threshold, at least one of the two conditions must be met, and the maximum value of the relative room temperature decrease ratio of the target resident relative to other residents in the same unit is greater than a third set threshold, and at the same time the maximum absolute value of the relative flow rate change ratio of the target resident relative to other residents in the same unit is less than a fourth set threshold, then it can be determined that the window is open. This can accurately identify the window opening behavior of residents, so that when the room temperature of residents with window opening behavior becomes abnormal, the room temperature can be corrected. This improves the accuracy of room temperature control and greatly helps the heating company to control heat exchange stations and balance the secondary network. It enables the heating company to achieve a low-energy consumption and high-quality heating mode based on more accurate room temperature feedback.

[0085] Furthermore, since a drop in room temperature is a prerequisite for detecting window opening behavior, the rules for setting the first, second, and third thresholds are as follows: the smaller the threshold value, the higher the recall rate of window opening detection (e.g., some users may open the window only slightly, but it can still be detected); if the threshold value is large, the precision rate of window opening detection is improved, for example, by eliminating interference factors such as weather or other factors. The setting method and function of the fourth threshold are the opposite of the first, second, and third thresholds, and will not be elaborated here. Under the premise of the above setting rules, the values ​​of the first, second, third, and fourth thresholds can be specifically set according to the application scenario requirements.

[0086] In another embodiment of the invention, such as Figure 3 As shown, after determining that windowing behavior exists within the corresponding data collection time period, the method further includes the following steps:

[0087] S4. Determine whether the room temperature of the target resident is abnormal at the current moment. Specifically, step S4, determining whether the room temperature of the target resident is abnormal at the current moment, includes the following steps (not shown in the attached diagram): S41. Calculate the ratio of the room temperature of the target resident at each statistical moment during the data collection period prior to the current moment to the average room temperature of other residents in the same unit; S42. Obtain the collection time corresponding to the maximum value of the room temperature ratio; S43. Use the average of all room temperature ratios before the collection time as the estimated normal ratio; S44. When the difference between the estimated normal ratio and the room temperature ratio at the current moment is greater than a preset ratio threshold, the room temperature of the target resident at the current moment is determined to be abnormal.

[0088] S5. If the room temperature of the target resident is abnormal at the current moment, the room temperature of the target resident is corrected based on the average room temperature of other residents in the same unit at the current moment. Specifically, step S5, which corrects the room temperature of the target resident based on the average room temperature of other residents in the same unit at the current moment, includes the following implementation: The following room temperature correction algorithm is used to correct the room temperature of the target resident at the current moment:

[0089] Corrected room temperature = estimated normal ratio × average room temperature of other households in the same unit as the target household at the current time.

[0090] The present invention provides a method for identifying residents' window-opening behavior, which can not only accurately identify residents' window-opening behavior and thus ensure the accuracy of room temperature collection, but also, based on the target resident's window-opening behavior in the past period, continue to identify whether the target resident's room temperature is abnormal at the current moment, and accurately correct abnormal room temperature, thereby feeding back the real and accurate room temperature to the user.

[0091] For the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0092] Another embodiment of the present invention provides a device for recognizing residents' window-opening behavior, the device including a functional module for implementing the method for recognizing residents' window-opening behavior as described in any of the preceding claims. Figure 4 This schematic diagram illustrates the structure of a device for recognizing residents' window-opening behavior according to an embodiment of the present invention. Figure 4 An embodiment of the present invention provides a device for recognizing residents' window-opening behavior, specifically comprising a first data acquisition module 401, a second data acquisition module 402, and a window-opening behavior judgment module 403, wherein:

[0093] The first data acquisition module 401 is used to acquire historical data of the target household and other households in the unit where the target household is located. The historical data includes the household identification and the room temperature data and flow data of the household collected at each data acquisition time.

[0094] The second data acquisition module 402 is used to acquire, based on the historical data, the first room temperature decrease data of the target household during the data acquisition period, the second room temperature decrease data of the target household relative to the average room temperature of other households in the unit where the target household is located, and the flow rate change data of the target household relative to the average heating flow rate of other households in the unit where the target household is located.

[0095] The window opening behavior judgment module 403 is used to determine that window opening behavior exists within the corresponding data collection time period when both the first room temperature drop data and the second room temperature drop data are greater than their respective corresponding temperature setting thresholds, and the flow rate change data is less than the corresponding flow rate setting threshold.

[0096] This invention provides a device for identifying residents' window-opening behavior. A first data acquisition module 401 acquires historical data of the target resident and other residents in the same unit. A second data acquisition module 402, based on the historical data acquired by the first module 401, acquires data on the target resident's first room temperature decrease, the target resident's second room temperature decrease relative to the average room temperature of other residents in the same unit, and the target resident's heating flow rate relative to the average heating flow rate of other residents in the same unit. A window-opening behavior judgment module 403 determines the window-opening behavior present during the corresponding data acquisition period based on the relationship between the first room temperature decrease data, the second room temperature decrease data, the flow rate change data, and corresponding set thresholds. This device accurately identifies residents' window-opening behavior, ensuring accurate room temperature data acquisition. It provides strong data support for heating companies to regulate heat exchange stations and balance secondary heating networks, enabling heating companies to achieve low-energy, high-quality heating based on more accurate room temperature feedback, thus achieving energy-saving heating.

[0097] Furthermore, the device also includes a data preprocessing module (not shown in the accompanying drawings). This module is used to preprocess the historical data collected after acquiring the target resident and other residents within the target resident's unit. The specific preprocessing steps include one or more of the following processing methods:

[0098] First, remove unreasonable data from historical data collection; unreasonable data includes data where the room temperature is less than 0 or greater than 32 degrees Celsius, and data where the flow rate is 0.

[0099] Second, remove room temperature data from historical data collection where the dispersion is 0, i.e., data showing a constant room temperature. Specifically, calculate the coefficient of variation (COP) of the room temperature data; if the COP is 0, remove the room temperature data for that household. The formula for calculating the COP is:

[0100]

[0101] Where c v Let represent the coefficient of variation, σ represent the standard deviation of the room temperature data, and μ represent the mean of the room temperature data.

[0102] Third, when rounding up the collection time of the room temperature and flow rate data in the historical data, duplicate data for each household's room temperature and flow rate are removed according to the collection time, retaining the last data entry. In this embodiment, since the data collection time of the room temperature collection panel is not transmitted on the hour, and the specific collection time of each panel is not uniform, this invention aligns the collected data to the hour to facilitate the subsequent calculation of room temperature drop data and flow rate change data. It is understood that the method of aligning the collected data to the hour in this invention is only an exemplary implementation method. In addition, the time length for rounding up the data can be arbitrarily set, and this invention does not specifically limit it in this way.

[0103] In embodiments of the present invention, such as Figure 5 As shown, the resident window opening behavior recognition device provided in this embodiment of the invention further includes a room temperature anomaly judgment module 404 and a room temperature correction module 405, wherein:

[0104] The room temperature anomaly detection module 404 is used to determine whether the room temperature of the target resident is abnormal at the current moment.

[0105] The room temperature correction module 405 is used to correct the room temperature of the target household based on the average room temperature of other households in the unit where the target household is located when the room temperature anomaly judgment module 404 determines that the room temperature of the target household is abnormal at the current time.

[0106] The present invention provides a device for identifying residents' window-opening behavior, which can not only accurately identify residents' window-opening behavior and thus ensure the accuracy of room temperature collection, but also, based on the target resident's window-opening behavior in the past period, continue to identify whether the target resident's room temperature is abnormal at the current moment, and accurately correct abnormal room temperature, thereby feeding back the real and accurate room temperature to the user.

[0107] As the device embodiment is basically similar to the method embodiment, the description is relatively simple. For relevant parts, please refer to the description of the method embodiment, and it has the corresponding technical effects.

[0108] Example 3

[0109] This invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps described in the embodiments of the methods for recognizing residents' window-opening behaviors. Figure 1 Steps S1-S3 are shown. Alternatively, when the processor executes the computer program, it implements the functions of each module in the above embodiments of the device for recognizing residents' window-opening behaviors, for example... Figure 4The first data acquisition module 401, the second data acquisition module 402, and the window opening behavior judgment module 403 are shown.

[0110] This invention provides a method, apparatus, and computer device for identifying residents' window-opening behavior. By acquiring historical data from the target resident and other residents in the same unit, the method obtains first room temperature decrease data, second room temperature decrease data relative to the average room temperature of other residents in the same unit, and flow rate change data relative to the average heating flow rate of other residents in the same unit. Based on the relationship between the first room temperature decrease data, second room temperature decrease data, and flow rate change data and corresponding set thresholds, the method determines the window-opening behavior that exists within the corresponding data collection period. This enables accurate identification of residents' window-opening behavior, ensuring the accuracy of room temperature data collection. It provides strong data support for heating companies to regulate heat exchange stations and balance secondary heating networks, allowing heating companies to achieve a low-energy, high-quality heating mode based on more accurate room temperature feedback, thus achieving energy-saving heating.

[0111] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, any of the claimed embodiments can be used in any combination.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying residents' window-opening behavior, characterized in that, The method includes: acquiring historical data of the target household and other households in the same unit, the historical data including household identification and room temperature and flow rate data collected at each data acquisition time; acquiring, based on the historical data, a first room temperature decrease data for the target household, a second room temperature decrease data relative to the average room temperature of other households in the same unit, and a flow rate change data relative to the average heating flow rate of other households in the same unit; determining that window opening occurred during the corresponding data acquisition period when both the first and second room temperature decrease data are greater than their respective temperature setting thresholds, and the flow rate change data is less than the corresponding flow rate setting threshold; the acquisition of the first room temperature decrease data, the second room temperature decrease data relative to the average room temperature of other households in the same unit, and the flow rate change data relative to the average heating flow rate of other households in the same unit, based on the historical data, includes: Based on the room temperature data of the target household and other households in the same unit, calculate the first room temperature change ratio of the target household and the second room temperature change ratio of the average room temperature of other households in the same unit at each preset statistical time. Then, calculate the relative room temperature change ratio of the target household relative to other households in the same unit at each statistical time based on the difference between the first and second room temperature change ratios. Based on the heating flow rate data of the target household and other households in the same unit, calculate the first flow rate change ratio of the target household and the second flow rate change ratio of the average heating flow rate of other households in the same unit at each preset statistical time. Then, calculate the relative flow rate change ratio of the target household relative to other households in the same unit based on the first and second flow rate change ratios at each statistical time. Take the absolute value of the data less than 0 in the first room temperature change ratios at each statistical time as the first room temperature decrease ratio for the corresponding statistical time. Obtain the maximum value of each first room temperature decrease ratio and calculate the cumulative value of each first room temperature decrease ratio. Take the maximum value and / or cumulative value of the first room temperature decrease ratios as the first room temperature decrease data. The absolute value of the data with a relative room temperature change ratio less than 0 at each statistical time point is taken as the relative room temperature decrease ratio at the corresponding statistical time point, and the maximum value of the relative room temperature decrease ratio is taken as the second room temperature decrease data; the maximum value of the absolute value of the relative flow rate change ratio at each statistical time point is obtained, and the maximum value of the absolute value of the relative flow rate change ratio is taken as the flow rate change data.

2. The method according to claim 1, characterized in that, The first and second room temperature change ratios are obtained as follows: The first and second room temperature change ratios at each statistical time point are calculated using the following formulas: Where r is the first room temperature change ratio or the second room temperature change ratio, and when r is the first room temperature change ratio, y t Let y be the room temperature data of the target household at time t. t+1 The data represents the room temperature of the target household at time t+1; when r is the second room temperature change ratio, y t Let y be the average room temperature of other households in the same unit as the target household at time t. t+1 The average room temperature of other residents in the same unit as the target resident at time t+1.

3. The method according to claim 1, characterized in that, The first and second flow rate change ratios are obtained as follows: The first and second flow rate change ratios at each statistical time point are calculated using the following formulas: Where r' is the first flow rate change ratio or the second flow rate change ratio, and when r' is the first flow rate change ratio, y' t Let y' be the heating flow rate for the target household at time t. t+1 Let r' be the heating flow rate for the target household at time t+1; when r' is the second flow rate change ratio, y' t Let y' be the average heating flow rate of other households in the unit where the target household is located at time t. t+1 This represents the average heating flow rate of other households in the same unit as the target household at time t+1.

4. The method according to claim 1, characterized in that, The relative room temperature change ratio and the relative flow rate change ratio are obtained as follows: The calculation formula for the relative room temperature change ratio or the relative flow rate change ratio is: Where, r relative This represents the percentage change in relative room temperature or relative flow rate, when r relative When expressing the proportion of change relative to room temperature, Indicates the first percentage change in room temperature. This indicates the second percentage change in room temperature; when r relative When expressing the proportion of relative flow change, Indicates the percentage change in the first flow rate. This indicates the percentage change in the second flow rate.

5. The method according to any one of claims 1-4, characterized in that, After determining that there was window opening behavior during the corresponding data collection time period, the method further includes: determining whether the room temperature of the target resident is abnormal at the current time; if the room temperature of the target resident is abnormal at the current time, then correcting the room temperature of the target resident based on the average room temperature of other residents in the unit where the target resident is located at the current time.

6. The method according to claim 5, characterized in that, The determination of whether the room temperature of the target resident is abnormal at the current moment includes: calculating the room temperature ratio of the target resident's room temperature to the average room temperature of other residents in the same unit at each statistical time point within the data collection period before the current moment; obtaining the collection time corresponding to the maximum value of the room temperature ratio; using the average of all room temperature ratios before the collection time as the normal ratio estimate; and determining that the room temperature of the target resident is abnormal at the current moment when the difference between the normal ratio estimate and the room temperature ratio at the current moment is greater than a preset ratio threshold.

7. The method according to claim 6, characterized in that, The step of correcting the room temperature of the target resident based on the average room temperature of other residents in the unit where the target resident is located at the current time includes: using the following room temperature correction algorithm to correct the room temperature of the target resident at the current time: Corrected room temperature = estimated normal ratio × average room temperature of other residents in the unit where the target resident is located at the current time.

8. A device for recognizing residents' window-opening behavior, characterized in that, The apparatus includes a functional module for implementing the method as described in any one of claims 1-7.

9. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the computer program is executed by the processor, it implements the steps of the method as described in any one of claims 1-7.

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