Efficient management method and system for smart home equipment data

Abstract

The invention relates to the technical field of equipment data processing in smart home, in particular to an efficient management method and system for smart home equipment data. The method comprises the steps of obtaining smart home equipment data; obtaining an initial time window and a preset initial threshold value of the reference equipment; classifying the data to obtain effective equipment data and conventional equipment data; obtaining a user behavior matrix, and obtaining a behavior rule strength index according to the similarity between the column vectors and the time distribution; obtaining a final time window; obtaining a linkage coefficient between different devices according to the row vector similarity between the different devices; strong correlation equipment is screened out; obtaining a correction threshold value by using the data change of the strong correlation equipment in each final time window; and filtering all the equipment data according to the correction threshold. According to the invention, daily living habits of the user can be dynamically adapted to obtain an accurate smart home equipment data filtering result, so that the smart home is efficiently managed.

Description

Technical Field

[0001] The present invention relates to the technical field of device data processing in smart homes, and specifically relates to an efficient management method and system for smart home device data. Background Art

[0002] Smart home is an automated home control system that takes a residence as a platform and integrates a variety of advanced technologies to enhance the security, convenience, and comfort of the home. There are a large number of devices in the smart home system, and most of them require real-time data collection, so a huge amount of device data will be generated. These device data are usually stored in the cloud to avoid the storage limitations of local devices. In addition, many functions of smart home, such as remote operation, device linkage, and federated learning, rely on the powerful performance of cloud computing servers. Therefore, how to combine smart home with cloud computing and perform efficient data analysis and processing is an extremely important topic.

[0003] Due to the increasing number and more perfect functions of smart home devices, the amount of data generated by the devices is also becoming more and more huge. If this data is directly transmitted to the cloud for processing without filtering, it will bring a great burden to the cloud, occupying too much network resources and storage resources. In the current smart home system, some simple edge filtering algorithms are basically used to preliminarily process the data at the home device end to reduce the cloud transmission pressure and improve the response efficiency. The most common one is the filtering method based on rule engine and event-driven. This method will filter regular data according to preset rules and only upload abnormal events. However, this method is limited by fixed rules and is difficult to adapt to dynamic scenarios, with problems of rule rigidity and insufficient flexibility. For example, when the behavior habits of family members change, if the rules are not adjusted in time, it is possible to mis-filter valid data, resulting in a decrease in data utilization efficiency and failing to achieve the expected effect. Summary of the Invention

[0004] In order to solve the technical problems that the conventional filtering method is difficult to adapt to the dynamic scenarios of smart homes, the filtering rules are rigid and lack flexibility, resulting in possible mis-filtering of smart home device data, a decrease in data utilization efficiency, failure to achieve the expected filtering effect, and ultimately the inability to efficiently manage smart homes, the purpose of the present invention is to provide an efficient management method and system for smart home device data, and the specific technical solutions adopted are as follows: An efficient management method for smart home device data, the method includes: Obtain the device data of each smart home device at different sampling times; Select the device data of any one smart home device as the reference device data of the reference device; evenly divide all the sampling moments of the reference device to obtain the initial time window during the operation of the reference device; obtain the preset initial threshold for each initial time window of the reference device; classify the reference device data of each time window according to the preset initial threshold to obtain valid device data and regular device data; obtain the user behavior matrix of the reference device according to the distribution characteristics and data differences of all the valid device data and regular device data; obtain the behavior rule intensity index of each column vector according to the similarity characteristics and time distribution characteristics between different column vectors in the user behavior matrix; obtain the final time window during the operation of the reference device according to the behavior rule intensity index; obtain the linkage coefficient between the reference device and each other device according to the similarity characteristics between the corresponding row vectors in the user behavior matrices of the reference device and each other device; Screen out all the strongly correlated devices of the reference device according to the linkage coefficient; obtain the correction threshold for each final time window according to the device data change characteristics of each strongly correlated device in each final time window; filter all the device data according to the correction threshold.

[0005] Furthermore, the method for obtaining the valid device data and the regular device data includes: When the data difference between every two adjacent device data in each initial time window is greater than the preset initial threshold, take the latter device data of every two adjacent device data as the valid device data; when the data difference between every two adjacent device data in each initial time window is less than the preset initial threshold, take the latter device data of every two adjacent device data as the regular device data.

[0006] Furthermore, the method for obtaining the user behavior matrix includes: Take the time period from when the user operates the reference device until the device data of the reference device stops changing as each user behavior period, calculate the average of the continuous regular device data of each user behavior period of the reference device to obtain the regular interval data of each user behavior period in the reference device, and form a sequence of all the regular interval data of each user behavior period as the interval sequence; Form a sequence of all the valid device data of each user behavior period of the reference device as the valid sequence; Statistically analyze the valid sequence length, valid sequence difference, interval sequence length, and interval sequence mean within each user behavior period; Form column vectors with the valid sequence length, valid sequence difference, interval sequence length, and interval sequence mean within each user behavior period, and form a user behavior matrix with all the column vectors.

[0007] Further, the method for obtaining the behavior pattern intensity index includes: Cluster all column vectors in the user behavior matrix to obtain all column vector clustering clusters; Take the column vector clustering clusters with more than one column vector as the clustering clusters to be analyzed; Obtain the behavior pattern intensity index according to the behavior pattern intensity index calculation formula, and the behavior pattern intensity index calculation formula is as follows: ; In the formula, represents the behavior pattern intensity index of each column vector; represents the number of column vectors in the clustering cluster to be analyzed; represents the preset time period included in the user behavior matrix; represents the time interval between every two adjacent column vectors in the clustering cluster to be analyzed; represents the standard deviation of the time interval; represents the normalization function; represents the floor function; represents the absolute value function.

[0008] Further, the method for obtaining the final time window includes: Take the column vectors with the behavior pattern intensity index greater than the preset first threshold as valid column vectors; take the user behavior periods corresponding to all valid column vectors in the user behavior matrix of the reference device as each final time window.

[0009] Further, the method for obtaining the linkage coefficient includes: Each element in the first row vector of the user behavior matrix is the effective sequence length of each user behavior period, each element in the second row vector is the effective sequence difference of each user behavior period, each element in the third row vector is the interval sequence length of each user behavior period, and each element in the fourth row vector is the interval sequence mean of each user behavior period; Obtain the linkage coefficient according to the linkage coefficient calculation formula, and the linkage coefficient calculation formula is as follows: ; In the formula, represents the linkage coefficient between the reference device and each other device; represents the time length corresponding to each effective sequence of the reference device; represents the time length corresponding to each effective sequence of each other device; represents the effective sequence difference of the reference device; represents the effective sequence difference of each other device; Represents the standard deviation function; Represents the first row vector of the user behavior matrix of the reference device; Represents the first row vector of the user behavior matrix of each other device; Represents the second row vector of the user behavior matrix of the reference device; Represents the second row vector of the user behavior matrix of each other device; Represents the third row vector of the user behavior matrix of the reference device; Represents the third row vector of the user behavior matrix of each other device; Represents the fourth row vector of the user behavior matrix of the reference device; Represents the fourth row vector of the user behavior matrix of each other device; Represents the dynamic time warping function.

[0010] Furthermore, the method for obtaining all strongly correlated devices of the reference device includes: Taking each other device with a linkage coefficient greater than a preset second threshold as a device to be analyzed; Sorting all devices to be analyzed in descending order of the linkage coefficient, and selecting a preset number of other devices from front to back as the strongly correlated devices of the reference device.

[0011] Furthermore, the method for obtaining the correction threshold includes: Obtaining the correction threshold according to the correction threshold calculation formula, and the correction threshold calculation formula is as follows: : ; In the formula, Represents the correction threshold of each final time window; Represents the preset initial threshold of each final time window; Is the number of strongly correlated devices of the reference device; Represents the th strongly correlated device's effective sequence length within the time range; Represents the th strongly correlated device's effective sequence difference within the time range; Represents the time range; Represents the start time node of each final time window; Represents the time difference between the start time node of each final time window of the reference device and the start time node of the final time window of the th closest strongly correlated device; Represents the normalization function.

[0012] An efficient management system for smart home device data, the system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned efficient management method for smart home device data are implemented.

[0013] 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, the steps of the above-mentioned efficient management method for smart home device data are implemented.

[0014] The present invention has the following beneficial effects: The present invention obtains the device data of each smart home device at different sampling times to facilitate subsequent screening and filtering operations on the device data. Since in common edge filtering methods, in order to reduce the amount of data and the transmission pressure on the cloud, a threshold-triggered method is usually used to filter data. Therefore, an initial time window is first set for the device data obtained during the operation of the reference device, and a preset initial threshold in each initial time window is set to preliminarily filter the device data of the reference device. The device data of the reference device in each time window is classified according to the initial threshold to obtain valid device data and regular device data. Since the change of the device data of smart home is closely related to the user's behavior, the change of the device data can reflect the user's daily living habits. Therefore, the user behavior matrix of the reference device is obtained. And by using the similar characteristics and time distribution characteristics of the device data under different user behaviors, the regularity of the user behavior is analyzed to obtain a regularity strength index. According to the behavior regularity strength index, the final time window during the operation of the reference device is obtained. Since the preset initial threshold does not adaptively adjust with the change of the user's behavior habits, when the user changes habits, some valid device data may be ignored in some parts where the original preset initial threshold is large, and multi-device fusion analysis can more accurately analyze the data change pattern to adjust the preset initial threshold. Therefore, the linkage coefficient between the reference device and each other device is analyzed, and then the strongly correlated devices of the reference device are screened out. Since in actual situations, when the strongly correlated devices of the reference device show a stable state, the filtering threshold of the final time window of the reference device should be adjusted larger to reduce the upload of regular device data; while when the strongly correlated devices of the reference device show an active state, the filtering threshold of the final time window of the reference device should be adjusted smaller to improve the capture accuracy of valid device data. Therefore, according to the device data change characteristics of each strongly correlated device in each final time window, the correction threshold of each final time window is obtained. All device data is filtered according to the correction threshold. The present invention can dynamically adapt to the user's daily living habits to obtain accurate filtering results of smart home device data, so as to efficiently manage smart home. Description of the Drawings

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of an efficient management method for smart home device data provided by an embodiment of the present invention. Detailed implementation manners

[0017] To further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in conjunction with the drawings and preferred embodiments, details the specific implementation manners, structures, features, and effects of an efficient management method and system for smart home device data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solutions of an efficient management method and system for smart home device data provided by the present invention in conjunction with the drawings.

[0020] Please refer to Figure 1 , which shows an efficient management method for smart home device data provided by an embodiment of the present invention. The method includes: Step S1: Obtain the device data of each smart home device at different sampling times.

[0021] The embodiments of the present invention are mainly applied to the device data filtering scenario when smart home devices upload abnormal events. Since the amount of device data corresponding to smart home abnormal events is small, while the amount of device data generated usually is extremely large, the embodiments of the present invention need to first obtain the device data of each smart home device at different sampling times and perform subsequent screening and filtering operations on the device data.

[0022] In one embodiment of the present invention, the sampling time is set to 1 minute. It should be noted that in other embodiments of the present invention, the sampling time can be set by itself and is not limited herein.

[0023] Step S2: Arbitrarily select the device data of a smart home device as the reference device data of the reference device; evenly divide all the sampling moments of the reference device to obtain the initial time window during the operation of the reference device; obtain the preset initial threshold of the reference device in each initial time window; classify the reference device data in each time window according to the initial threshold to obtain valid device data and regular device data; obtain the user behavior matrix of the reference device according to the distribution characteristics and data differences of all the valid device data and regular device data; obtain the behavior rule strength index of each column vector according to the similarity characteristics and time distribution characteristics between different column vectors in the user behavior matrix; obtain the final time window during the operation of the reference device according to the behavior rule strength index; obtain the linkage coefficient between the reference device and each other device according to the similarity characteristics between the corresponding row vectors in the user behavior matrix of the reference device and each other device.

[0024] In the device layer of the smart home system, the functions and forms of home devices are diverse, and there are many devices that need to be sampled in real time and at a high frequency, such as indoor temperature control devices, air quality monitoring devices, smart refrigerators, etc. The data generated by these devices is large and is the main data source of the smart home system. In the common edge filtering methods, in order to reduce the data volume and the transmission pressure on the cloud, the threshold trigger method is usually used to filter the data. This method will set a fixed threshold range in the rule in advance, and only when the data change exceeds the set range will it be uploaded as abnormal data, otherwise it will be regarded as regular device data for filtering. Therefore, in the embodiment of the present invention, an initial time window is first set for the device data obtained during the operation of the reference device, and a preset initial threshold in each initial time window is set to preliminarily filter the device data of the reference device.

[0025] In an embodiment of the present invention, each initial time window contains the device data of 100 sampling moments, that is, each initial time window is 1 hour and 40 minutes. It should be noted that in other embodiments of the present invention, the initial time window can be set by itself and is not limited here.

[0026] In an embodiment of the present invention, the preset initial threshold is set for each initial time window by using environmental factors or the parameters when the device leaves the factory. In an embodiment of the present invention, a process for setting the preset initial threshold of the device by using environmental factors is provided as follows: Since the temperature changes greatly at sunrise in the morning and sunset in the evening, the normal change range of the indoor temperature per minute is about between 0.1°C and 0.5°C at this time, so the filtering threshold of the initial time window of the indoor temperature measurement device in the two time periods is set to 0.5; while the normal change range of the temperature per minute in other ordinary time periods is about between 0.1°C and 0.3°C, so the filtering threshold of the initial time window in these time periods is set to 0.3.

[0027] The preset initial thresholds for each initial time window of other devices are all set according to the above method. It is required that the setting of the preset initial thresholds conforms to the actual situation, which will not be elaborated here.

[0028] Classify the reference device data for each time window according to the initial threshold to obtain valid device data and regular device data. Preferably, in an embodiment of the present invention, the method for obtaining valid device data and regular device data includes: When the data difference between every two adjacent device data in each initial time window is greater than the preset initial threshold, the latter device data of every two adjacent device data is used as valid device data; when the data difference between every two adjacent device data in each initial time window is less than the preset initial threshold, the latter device data of every two adjacent device data is used as regular device data.

[0029] Since the change of device data in smart home is closely related to the user's behavior, the change of device data can reflect the user's daily living habits. In order to avoid the decrease of filtering accuracy due to the change of user habits, it is necessary to capture the change of user behavior habits according to the data characteristics within the initial time window, and adjust the length of the initial time window and the size of the preset initial threshold. Therefore, in an embodiment of the present invention, according to the distribution characteristics and data differences of all valid device data and regular device data, a user behavior matrix of the reference device is obtained.

[0030] Preferably, in an embodiment of the present invention, the method for obtaining the user behavior matrix includes: Since smart home devices have latency, that is, after the user performs an operation, the reference device will generate a change in device data after a certain period of time. Therefore, the time period from when the user operates on the reference device until the device data of the reference device ends changing is used as each user behavior period. The average of the continuous regular device data of each user behavior period of the reference device is calculated to obtain the regular interval data of each user behavior period of the reference device. The sequence composed of all regular interval data of each user behavior period is used as the interval sequence.

[0031] The sequence composed of all valid device data of each user behavior period of the reference device is used as the valid sequence.

[0032] Statistical the valid sequence length, valid sequence difference, interval sequence length, and interval sequence mean within each user behavior period; The valid sequence length, valid sequence difference, interval sequence length, and interval sequence mean within each user behavior period are formed into a column vector, and all column vectors are formed into a user behavior matrix.

[0033] In one embodiment of the present invention, a user behavior matrix example is provided as follows: Since the data of smart home devices is closely related to people's daily routines, in the embodiments of the present invention, the similarity features and time distribution features of device data under different user behaviors are utilized to analyze the regularity of user behaviors.

[0034] Preferably, in one embodiment of the present invention, the method for obtaining the regularity intensity index includes: Clustering all column vectors in the user behavior matrix to obtain all column vector clustering clusters; taking the column vector clustering clusters with more than one column vector as the clustering clusters to be analyzed.

[0035] Obtain the behavior regularity intensity index according to the behavior regularity intensity index calculation formula, and the behavior regularity intensity index calculation formula is as follows: ; In the formula, represents the behavior regularity intensity index of each column vector; represents the number of column vectors in the clustering cluster to be analyzed; represents the preset time period included in the user behavior matrix; represents the time interval between every two adjacent column vectors in the clustering cluster to be analyzed; represents the standard deviation of the time interval; represents the normalization function; represents the floor function; represents the absolute value function.

[0036] In one embodiment of the present invention, since a user may perform the same behavior multiple times at the same time node on the same day, the preset time period is set to 1 day, and the intensity regularity of each column vector is analyzed by the number of times the user performs the same behavior within each day.

[0037] In the behavior regularity intensity index calculation formula, represents the fractional part of, when the fractional part is closer to 1, that is, is closer to 0, it indicates that the number of user behaviors corresponding to similar column vectors is closer to a multiple of the preset time period, and this multiple is the number of times the user performs the same behavior within the preset time period, and the smaller it is, the higher the intensity of the user behavior regularity, and the larger the behavior regularity intensity index of the column vector; the smaller the standard deviation of the time interval between every two adjacent column vectors in the clustering cluster to be analyzed, the more similar the interval time when the user performs the same behavior, and at this time, the higher the intensity of the user behavior regularity, and the larger the behavior regularity intensity index of the column vector.

[0038] Preferably, in an embodiment of the present invention, obtaining a final time window during the operation of the reference device according to the behavior pattern intensity index includes: Regarding the column vectors with the behavior pattern intensity index greater than a preset first threshold as valid column vectors; regarding the user behavior periods corresponding to all valid column vectors in the user behavior matrix of the reference device as each final time window. In an embodiment of the present invention, the preset first threshold is set to 0.7, and the preset first threshold can be set by itself and is not limited herein.

[0039] After determining the final time window, it is also necessary to adjust the size of the preset initial threshold to further enhance the filtering accuracy of the device data. In conventional algorithms, the preset initial threshold does not adaptively adjust with the change of the user's behavior habits. When the user changes habits, some parts of the original preset initial threshold that are relatively large may ignore some valid device data, resulting in over-filtering; while some parts of the original preset initial threshold that are relatively small may misjudge conventional device data as valid device data, resulting in incomplete filtering. In the analysis of the previous steps, the relationship between the user's behavior and the device data has been roughly identified. However, these rules are the analysis results obtained from the data of a single smart home device. Applying this result to the adjustment of the time window can improve the robustness of the algorithm, but when adjusting the preset initial threshold, it is necessary to analyze the data change pattern more precisely, otherwise the problem of inaccurate preset initial threshold still cannot be solved. Therefore, in the embodiment of the present invention, a fusion analysis is performed in combination with the linkage relationship of multiple devices to obtain a filtering rule that more conforms to the user's behavior characteristics. Since the devices that have a strong correlation with the reference device will have their device data change simultaneously when the data of the reference device changes, but there will be a lag. And the higher the intensity of the user's behavior, the smaller the time delay of the lag. Therefore, according to this relationship, the linkage coefficient between the reference device and other devices is obtained, and then the strongly correlated devices of the reference device are screened out.

[0040] Preferably, in an embodiment of the present invention, the method for obtaining the linkage coefficient includes: Each element in the first row vector of the user behavior matrix is the effective sequence length of each user behavior period, each element in the second row vector is the effective sequence difference of each user behavior period, each element in the third row vector is the interval sequence length of each user behavior period, and each element in the fourth row vector is the interval sequence mean of each user behavior period.

[0041] The linkage coefficient is obtained according to the linkage coefficient calculation formula, and the linkage coefficient calculation formula is as follows: ; In the formula, represents the linkage coefficient between the reference device and each other device; Indicates the time length corresponding to each valid sequence of the reference device; Indicates the time length corresponding to each valid sequence of each other device; Indicates the difference of each valid sequence of the reference device; Indicates the difference of each valid sequence of each other device; Indicates the standard deviation function; Indicates the first row vector of the user behavior matrix of the reference device; Indicates the first row vector of the user behavior matrix of each other device; Indicates the second row vector of the user behavior matrix of the reference device; Indicates the second row vector of the user behavior matrix of each other device; Indicates the third row vector of the user behavior matrix of the reference device; Indicates the third row vector of the user behavior matrix of each other device; Indicates the fourth row vector of the user behavior matrix of the reference device; Indicates the fourth row vector of the user behavior matrix of each other device; Indicates the dynamic time warping function.

[0042] In the calculation formula of the linkage coefficient, analyze each valid sequence of the reference device and each other device. When the difference in the time lengths corresponding to all valid sequences of the reference device is smaller, and the difference in the valid sequence differences is smaller, and at the same time the difference in the time lengths corresponding to all valid sequences of the other device is smaller, and the difference in the valid sequence differences is smaller, it indicates that the lag time between the reference device and the other device is smaller. At this time, the user behavior intensity is greater, that is The smaller it is, the greater the inverse trend of the lag time between the reference device and the other device and the user behavior intensity, and the greater the linkage relationship between the reference device and the other device. At this time, the linkage coefficient between the reference device and the other device is greater; use the dynamic time warping function to compare the similarity between each row vector in the user behavior matrix of the reference device and each other device, and The smaller it is, the higher the similarity between the row vectors of the reference device and each other device, and the greater the linkage relationship between the reference device and the other device. At this time, the linkage coefficient between the reference device and the other device is greater.

[0043] Step S3: Screen out all strongly correlated devices of the reference device according to the linkage coefficient; obtain the correction threshold for each final time window according to the device data change characteristics of each strongly correlated device in each final time window; filter all device data according to the correction threshold.

[0044] According to the above steps, the linkage coefficient between the reference device and each other device can be obtained, and based on this, the strongly correlated devices of the reference device can be screened out.

[0045] Preferably, in an embodiment of the present invention, the method for obtaining all strongly correlated devices of a reference device includes: Regarding each other device with a linkage coefficient greater than a preset second threshold as a device to be analyzed; in an embodiment of the present invention, the preset second threshold is set to 0.5. It should be noted that the preset second threshold can be set by oneself and is not limited herein.

[0046] Sort all the devices to be analyzed in descending order according to the linkage coefficient, and select a preset number of other devices from the front to the back as the strongly correlated devices of the reference device. In an embodiment of the present invention, the preset number is set to 3. It should be noted that the preset number can be set by oneself and is not limited herein.

[0047] In actual situations, when the strongly correlated devices of the reference device show a stable state, the filtering threshold of the final time window of the reference device should be adjusted to be larger to reduce the upload of conventional device data; while when the strongly correlated devices of the reference device show an active state, the filtering threshold of the final time window of the reference device should be adjusted to be smaller to improve the capture accuracy of effective device data. Therefore, in the embodiments of the present invention, according to the device data change characteristics of each strongly correlated device in each final time window, the correction threshold of each final time window is obtained.

[0048] Preferably, in an embodiment of the present invention, the method for obtaining the correction threshold includes: Obtain the correction threshold according to the correction threshold calculation formula, and the correction threshold calculation formula is as follows: : ; In the formula, represents the correction threshold of each final time window; represents the preset initial threshold of each final time window; is the number of strongly correlated devices of the reference device; represents the th strongly correlated device's effective sequence length within the time range; represents the th strongly correlated device's effective sequence difference within the time range; represents the time range; represents the start time node of each final time window; represents the time difference between the start time node of each final time window of the reference device and the start time node of the final time window of the th closest strongly correlated device; represents the normalization function.

[0049] In the corrected threshold calculation formula, since the lag of device data change between strongly correlated devices is weak, a smaller time range is specified to compare the degree of device data change between two devices within the smaller time range, so as to correct the filtering threshold for each final time window. Therefore, in the embodiments of the present invention, is used as the time range for comparing two devices; the greater the difference between the effective sequence length and the effective sequence difference of each strongly correlated device within the time range, the higher the degree of device data change and the longer the change time within the time range. At this time, the state of the strongly correlated device is more active and less suitable as a reference for adjusting the reference device threshold. At this time, the correction value for the preset initial threshold is smaller. Analyze all the strongly correlated devices of the reference device to obtain , so as to correct the preset initial threshold; the 5 in the denominator is used to control the scaling degree of the correction value. In other embodiments of the present invention, other values can be used, which are not limited herein.

[0050] Thus, the corrected threshold for each final time window is obtained.

[0051] Filter the device data of each device through the division of the final time window of each device and the corrected threshold of each final time window, and upload the filtered data to the cloud.

[0052] In summary, obtain the device data of each smart home device at different sampling times; optionally select the device data of a smart home device as the reference device data of the reference device; evenly divide all the sampling times of the reference device to obtain the initial time window during the operation of the reference device; obtain the preset initial threshold of the reference device in each initial time window; classify the reference device data of each time window according to the preset initial threshold to obtain effective device data and regular device data; obtain the user behavior matrix of the reference device according to the distribution characteristics and data differences of all the effective device data and regular device data; obtain the behavior rule strength index of each column vector according to the similarity characteristics and time distribution characteristics between different column vectors in the user behavior matrix; obtain the final time window during the operation of the reference device according to the behavior rule strength index; obtain the linkage coefficient between the reference device and each other device according to the similarity characteristics between the corresponding row vectors in the user behavior matrix of the reference device and each other device; screen out all the strongly correlated devices of the reference device according to the linkage coefficient; obtain the corrected threshold of each final time window according to the device data change characteristics of each strongly correlated device in each final time window; filter all the device data according to the corrected threshold.

[0053] The second object of the embodiments of the present invention is to provide an efficient management system for smart home device data. The system includes a memory, a processor, and a computer program. The memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement the methods described in steps S1 - S3.

[0054] The third object of the embodiments of the present invention is to provide 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 of the methods described in the above steps S1 - S3.

[0055] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0056] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. An efficient management method for smart home device data, characterized in that: The method comprises: Obtain device data of each smart home device at different sampling times; Select the device data of any one smart home device as the reference device data of the reference device; divide all sampling moments of the reference device equally to obtain the initial time window during the working period of the reference device; obtain the preset initial threshold of the reference device in each initial time window; classify the reference device data of each time window according to the preset initial threshold to obtain valid device data and conventional device data; obtain the user behavior matrix of the reference device according to the distribution characteristics and data differences of all valid device data and conventional device data; obtain the behavior law strength index of each column vector according to the similarity characteristics and time distribution characteristics between different column vectors in the user behavior matrix; obtain the final time window during the working period of the reference device according to the behavior law strength index; obtain the linkage coefficient between the reference device and each other device according to the similarity characteristics between the corresponding row vectors in the user behavior matrix of the reference device and each other device; All strongly related devices of the reference device are screened out according to the linkage coefficient; a correction threshold of each final time window is obtained according to the device data change characteristics of each strongly related device in each final time window; and all device data are filtered according to the correction threshold.

2. The efficient management method of smart home device data according to claim 1, characterized in that: Methods for obtaining valid device data and regular device data include: When the data difference between each two adjacent device data in each initial time window is greater than the preset initial threshold, the latter device data of each two adjacent device data is taken as the valid device data; when the data difference between each two adjacent device data in each initial time window is less than the preset initial threshold, the latter device data of each two adjacent device data is taken as the regular device data.

3. The efficient management method of smart home device data according to claim 1, characterized in that: The method for obtaining the user behavior matrix includes: The time period from when the user operates the reference device until the device data of the reference device stops changing is taken as each user behavior period, the continuous regular device data of each user behavior period of the reference device is averaged to obtain the regular interval data of each user behavior period in the reference device, and the sequence composed of all the regular interval data of each user behavior period is taken as the interval sequence; The sequence consisting of all valid device data in each user behavior period of the reference device is taken as a valid sequence; Count the effective sequence length, effective sequence difference, interval sequence length and interval sequence mean in each user behavior period; The effective sequence length, effective sequence difference, interval sequence length and interval sequence mean in each user behavior period are combined into a column vector, and all column vectors are combined into a user behavior matrix.

4. The efficient management method of smart home device data according to claim 1, characterized in that: The method for obtaining the behavior regularity intensity index includes: Clustering all column vectors in the user behavior matrix to obtain clusters of all column vectors; The column vector clusters with more than one column vector are taken as the clusters to be analyzed; The behavior rule strength index is obtained according to the behavior rule strength index calculation formula, and the behavior rule strength index calculation formula is as follows: ； In the formula, Represents the behavioral regularity strength index of each column vector; The column vector number of clusters to be analyzed; Represents the preset time period included in the user behavior matrix; Represents the time interval between each two adjacent column vectors in the cluster to be analyzed; represents the standard deviation of the time interval; represents the normalization function; represents the floor function; represents the absolute value function.

5. The efficient management method of smart home device data according to claim 1, characterized in that: The method for obtaining the final time window includes: The column vector whose behavior regularity strength index is greater than a preset first threshold is taken as a valid column vector; the user behavior period corresponding to all valid column vectors in the user behavior matrix of the reference device is taken as each final time window.

6. The efficient management method of smart home device data according to claim 1, characterized in that: The method for obtaining the linkage coefficient includes: Each element in the first row vector of the user behavior matrix is ​​the effective sequence length of each user behavior period, each element in the second row vector is the effective sequence difference of each user behavior period, each element in the third row vector is the interval sequence length of each user behavior period, and each element in the fourth row vector is the interval sequence mean of each user behavior period; The linkage coefficient is obtained according to the linkage coefficient calculation formula, and the linkage coefficient calculation formula is as follows: ； In the formula, represents the linkage coefficient between the reference device and each other device; Indicates the time length corresponding to each valid sequence of the reference device; Indicates the time length corresponding to each valid sequence of each other device; Represents each valid sequence difference of the reference device; Represents every valid sequence difference for every other device; represents the standard deviation function; The first row vector of the user behavior matrix representing the reference device; The first row vector of the user behavior matrix for each other device; The second row vector of the user behavior matrix representing the reference device; The second row vector of the user behavior matrix for each other device; The third row vector of the user behavior matrix representing the reference device; The third row vector of the user behavior matrix for each other device; The fourth row vector of the user behavior matrix representing the reference device; The fourth row vector of the user behavior matrix for each other device; represents the dynamic time warping function.

7. The efficient management method of smart home device data according to claim 1, characterized in that: The method for obtaining all strongly related devices of the reference device includes: Each other device whose linkage coefficient is greater than a preset second threshold is taken as a device to be analyzed; All devices to be analyzed are sorted from large to small according to the linkage coefficient, and a preset number of other devices are selected from front to back as strongly related devices of the reference devices.

8. The efficient management method of smart home device data according to claim 1, characterized in that: The method for obtaining the correction threshold comprises: The correction threshold is obtained according to the correction threshold calculation formula, and the correction threshold calculation formula is as follows: ： ； In the formula, represents the correction threshold for each final time window; represents the preset initial threshold for each final time window; is the number of strongly correlated devices of the reference device; Indicates The effective sequence length of a strongly correlated device within the time range; Indicates The effective sequence difference of strongly correlated devices within the time range; Indicates the time range; Indicates the starting time node of each final time window; Indicates that the start time node of each final time window of the reference device is the same as the closest The time difference between the start time nodes of the final time windows of the strongly correlated devices; Represents the normalization function.

9. An efficient management system for smart home device data, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for efficiently managing smart home device data as described in any one of claims 1 to 8 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for efficiently managing smart home device data as described in any one of claims 1 to 8 are implemented.

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