An Internet smart home data processing method and system

Abstract

The present invention provides an Internet smart home data processing method and system, including: based on the Internet, a data acquisition center acquires individual home data of each sensor of the smart home, and performs target data format conversion on the individual home data to obtain target individual data; based on the smart home distribution of each sensor, using the fuzzy control theory, data fusion is performed on the target individual data to obtain multi-fusion data; according to the multi-fusion data, the working state of the home equipment of the smart home is set; firstly, by performing format conversion on the individual home data, the accuracy of the acquired home data is ensured. Secondly, through fuzzy control, the individual home data is fused and processed, ensuring the accuracy of the smart home state detection. Finally, it is ensured that the set working state of the home equipment of the smart home brings a comfortable home environment to the user.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing based on the Internet, and particularly relates to an Internet smart home data processing method and system. Background Art

[0002] Smart home is the embodiment of the Internet of Things under the influence of the Internet and the Internet of Things. Compared with ordinary homes, smart homes not only have traditional living functions, but also have functions such as network communication, data analysis, information appliances, and device automation; for example, household devices can be controlled through home touch screens, wireless remote controls, telephones, the Internet, or voice recognition, and more can perform scene operations to form linkages among multiple devices; on the other hand, various devices in the smart home can communicate with each other and can operate interactively according to different states without the need for user commands, which can help the home maintain smooth data information communication with the outside, optimize people's lifestyles, help people effectively arrange time, enhance the safety of home life, and reduce energy consumption.

[0003] Currently, there are a wide variety of smart home types and the home environment is complex. A single sensor can only reflect the home environment from one aspect, and there are limitations in detecting the operating state of smart homes. Moreover, the data formats of different smart homes are different. Directly using the unprocessed smart home data collected as the data for determining the operating state of the smart home will result in inaccurate detection of the home environment state of the smart home, bringing an uncomfortable home environment to users. Summary of the Invention

[0004] The present invention provides an Internet smart home data processing method and system, which ensure the accuracy of detecting the smart home state through fuzzy control, and ultimately ensure that the working state of the home devices of the set smart home brings a comfortable home environment to users.

[0005] An Internet smart home data processing method includes:

[0006] Step 1: Based on the Internet, a data collection center obtains the single home data of each sensor of the smart home, and performs target data format conversion on the single home data to obtain target single data;

[0007] Step 2: Based on the smart home distribution of each sensor, using the fuzzy control theory, perform data fusion on the target single data to obtain multi-fusion data;

[0008] Step 3: Set the working state of the home devices of the smart home according to the multi-fusion data.

[0009] Preferably, in step 1, based on the Internet, obtaining the single home data of each sensor of the smart home includes:

[0010] Based on the Internet, receive a data collection request from the data collection center;

[0011] According to the user information, identify the data collection request, and after successful identification, based on the data collection request, collect the single home data of each sensor in the smart home device during operation at the same time;

[0012] Based on the Internet, send the single home data to the data collection center.

[0013] Preferably, in step 1, perform target data format conversion on the single home data to obtain target single data, including:

[0014] Based on the device characteristics of each sensor, determine the initial data format of the single home data;

[0015] Based on the initial data format, divide the single home data into multiple groups of format data;

[0016] Based on the format types of the multiple groups of format data, set corresponding conversion rules for each group of format data, and based on the conversion rules, obtain target single data.

[0017] Preferably, before step 2, it further includes: obtaining the smart home distribution of each sensor, including:

[0018] Obtain the spatial distribution map of the smart home;

[0019] According to the position characteristics of each sensor, determine the position points of each sensor in the spatial distribution map;

[0020] According to the sensing characteristics of each sensor, perform feature marking on the corresponding position points to obtain the smart home distribution of each sensor.

[0021] Preferably, according to the sensing characteristics of each sensor, performing feature marking on the corresponding position points includes:

[0022] Based on the sensing characteristics, obtain the detection type and detection range of the corresponding sensor;

[0023] Based on the feature sequence composed of the detection type and detection range, use the feature sequence to perform feature marking on the corresponding position points.

[0024] Preferably, based on the format types of the multiple groups of format data, setting corresponding conversion rules for each group of format data, and based on the conversion rules, obtaining target single data, including:

[0025] Obtain the initial data format of each group of format data;

[0026] Obtain the conversion relationship from the initial data format to the target data format from a preset data format conversion table;

[0027] Take the format data with a direct conversion relationship as the first format data, and take the format data with an indirect conversion relationship as the second format data;

[0028] Obtain the first conversion rule corresponding to the first format data from the preset data format conversion table, and determine the first character mapping relationship based on the first conversion rule;

[0029] Obtain the initial character of the first format data, determine the data logic of the initial character, match the corresponding target character for the initial character according to the first character mapping relationship, and form the target single data corresponding to the first format data with the target characters according to the data logic;

[0030] Obtain the second conversion rule corresponding to the second format data from the preset data format conversion table;

[0031] Parse the second conversion rule to determine the conversion nodes of the second format data and the intermediate data type corresponding to each conversion node;

[0032] Establish a character mapping table based on the conversion order and intermediate data type of the conversion nodes, and determine the direct mapping relationship between the initial character of the second format data and the target character of the target single data based on the character mapping table;

[0033] Simplify the second conversion rule based on the direct mapping relationship to obtain a third conversion rule;

[0034] Convert the second format data into the corresponding target single data based on the third conversion rule.

[0035] Preferably, in step 2, based on the smart home distribution of each sensor, use the fuzzy control theory to perform data fusion on the target single data to obtain multi-fusion data, including:

[0036] Divide the target single data according to the data type to obtain measurement data, behavior data, and device operation data;

[0037] Divide the measurement data according to attributes to obtain multiple groups of single measurement data;

[0038] Perform recursive fusion calculation on the measurement values of each group of single measurement data according to the measurement time to obtain the optimal measurement value corresponding to each group of single measurement data;

[0039] Based on the smart home distribution of each sensor, determine the position influence value of each sensor on the measurement data;

[0040] Based on the behavior data, set the behavior influence value of the user behavior on the measurement data, and based on the device operation data, determine the error influence value of the device state on the measurement data;

[0041] Establish a membership function table according to the position influence value, behavior influence value and error influence value respectively, determine the fuzzy control rules based on the membership function table, and establish a fuzzy control model based on the fuzzy control rules;

[0042] Obtain the optimal measurement value set with the same measurement unit, obtain the position distribution of the sensors corresponding to the optimal measurement value set, and its related behavior data and related device operation data;

[0043] Based on the position distribution, related behavior data and related device operation data, set the target membership function for the fuzzy control model to obtain the target fuzzy control model;

[0044] Input the optimal measurement value set into the target fuzzy control model, and output the final data value;

[0045] Obtain the final data value of each same measurement unit as the multi-fusion data.

[0046] Preferably, according to the measurement time, perform recursive fusion calculation on the measurement values of each group of single measurement data,

[0047] Obtain the optimal measurement value corresponding to each group of single measurement data, including:

[0048] Eliminate abnormal data from each group of single measurement data to obtain normal single measurement data;

[0049] According to the measurement time, obtain the initial measurement value from the normal single measurement data, and obtain the adjacent next measurement value, perform recursive calculation on the initial measurement value and the next measurement value to obtain the estimated measurement value;

[0050] Judge whether the estimated measurement value is within the preset range. If so, use the estimated measurement value as the latest measurement value; otherwise, use the initial measurement value as the latest measurement value;

[0051] Based on the above method, perform recursive calculation on each measurement value in the normal single measurement data in sequence according to the measurement time, and finally obtain the optimal measurement value.

[0052] Preferably, in step 3, setting the working state of the home appliances of the smart home according to the multi-fusion data includes:

[0053] Obtaining the final data value of the same type of sensors from the multi-fusion data;

[0054] Obtaining the monitoring indicators for the smart home and determining the types of sensors required for the monitoring indicators;

[0055] Obtaining behavior data from the target single data and setting a set of sensor thresholds for the monitoring indicators based on the behavior data;

[0056] Obtaining device operation data from the target single data and determining the error relationship between each device state and the sensor measurement based on the device operation data;

[0057] Based on the error relationship, correcting the set of sensor thresholds to obtain a target set of sensor thresholds;

[0058] Under the monitoring indicators, determining whether the final data value under the monitoring indicators is within the set of sensor thresholds;

[0059] If so, determining that the monitoring indicator is normal;

[0060] Otherwise, determining that the monitoring indicator is an abnormal monitoring indicator and obtaining the data difference between the final data value and the set of sensor thresholds;

[0061] Obtaining the abnormal monitoring indicators with anomalies and determining the overlapping final data values under the abnormal monitoring indicators as the target final data values;

[0062] Obtaining a set of data differences of the target final data value under the different abnormal monitoring indicators and setting a weight value for each abnormal monitoring indicator with respect to the target final data value according to the degree of correlation between the sensor type of the target final data value and the abnormal monitoring indicators;

[0063] Based on the weight value, performing weighted analysis on each data difference in the set of data differences to obtain a weighted data difference;

[0064] Based on the weighted data difference, setting the working state of the home appliances of the smart home.

[0065] An Internet of Things smart home data processing system includes:

[0066] A format conversion module for obtaining, based on the Internet, the single home data of each sensor of the smart home from a data collection center and performing target data format conversion on the single home data to obtain target single data;

[0067] A data fusion module, configured to perform data fusion on the target single data based on the distribution of smart homes of each sensor, by using the fuzzy control theory, to obtain multi-fused data;

[0068] A status setting module, configured to set the working status of the home appliances of the smart home according to the multi-fused data.

[0069] Other features and advantages of the present invention will be set forth in the following description, and in part will be obvious from the description, or may be learned by practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the structure particularly pointed out in the written description, claims, as well as the drawings.

[0070] The technical solutions of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0071] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0072] Figure 1 is a flowchart of a method for processing Internet smart home data in an embodiment of the present invention;

[0073] Figure 2 is another flowchart of a method for processing Internet smart home data in an embodiment of the present invention;

[0074] Figure 3 is a structural diagram of a system for processing Internet smart home data in an embodiment of the present invention. Detailed Embodiments

[0075] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustration and explanation of the present invention, and are not used to limit the present invention.

[0076] Embodiment 1

[0077] An embodiment of the present invention provides a method for processing Internet smart home data, as Figure 1 shown, including:

[0078] Step 1: Based on the Internet, a data collection center acquires the single home data of each sensor of the smart home, and performs target data format conversion on the single home data to obtain target single data;

[0079] Step 2: Based on the distribution of smart homes for each sensor, utilize the fuzzy control theory to perform data fusion on the target single data to obtain multi-fused data;

[0080] Step 3: According to the multi-fused data, set the working states of the home appliances in the smart home.

[0081] In this embodiment, the initial data format of the single home data is determined by the characteristics of the sensor, and the target data format is related to the characteristics of the device for analyzing the smart home data obtained from the Internet.

[0082] In this embodiment, the multi-fused data is used to represent the home data of the overall home.

[0083] In this embodiment, the home data includes environmental data, behavior data, user status data, device operation data, etc.

[0084] The beneficial effects of the above design are as follows: First, by performing format conversion on the single home data, the accuracy of the collected home data is guaranteed, and at the same time, it facilitates the further analysis of the home data. Second, through fuzzy control, the single home data is fused to avoid the limitations of the single home data, making the obtained multi-fused data serve as the data for determining the smart home, ensuring the accuracy of the smart home status detection. Finally, the working states of the home appliances in the set smart home are guaranteed to bring a comfortable home environment for users.

[0085] Embodiment 2

[0086] Based on Embodiment 1, an embodiment of the present invention provides a method for processing smart home data from the Internet. In Step 1, based on the Internet, obtain the single home data of each sensor in the smart home, including:

[0087] Based on the Internet, receive a data collection request from the data collection center;

[0088] According to the user information, identify the data collection request, and after successful identification, based on the data collection request, collect the single home data of each sensor in the smart home devices during operation at the same time;

[0089] Based on the Internet, send the single home data to the data collection center.

[0090] In this embodiment, regarding how to obtain "the space distribution map of the smart home", for example, it can be obtained through the drawings of the smart home or by taking pictures of the smart home and performing image processing, etc. Conventional methods for obtaining the space distribution map can be used in this solution to obtain the space distribution map of the smart home;

[0091] In this embodiment, regarding how to obtain the "position feature", for example, it can be to mark the coordinates of the spatial distribution map to obtain the position where the sensor is located, and mark the position to obtain the position feature;

[0092] In this embodiment, regarding how to determine the position points of each sensor in the spatial distribution map, for example, due to the appearance of the sensor, the corresponding position feature may include multiple coordinate points, and the center point of the sensor can be found according to the appearance of the sensor, and the coordinate point corresponding to the center point is used as the position point of the sensor.

[0093] The beneficial effect of the above design solution is that by collecting and sending smart home data based on the Internet according to user information, it provides a data basis for subsequent data analysis and processing.

[0094] Embodiment 3

[0095] Based on Embodiment 1, an embodiment of the present invention provides an Internet smart home data processing method. As Figure 2 shown, in step 1, converting the single home data into a target data format to obtain target single data, including:

[0096] Step 101: Determine the initial data format of the single home data based on the device characteristics of each sensor;

[0097] Step 102: Divide the single home data into multiple groups of format data based on the initial data format;

[0098] Step 103: Set corresponding conversion rules for each group of format data based on the format types of the multiple groups of format data, and obtain target single data based on the conversion rules.

[0099] In this embodiment, the format of each group of format data is the same.

[0100] The beneficial effect of the above design solution is that by converting the format of the single home data, the accuracy of the collected home data is guaranteed, and at the same time, it is convenient for further analysis of the home data.

[0101] Embodiment 4

[0102] Based on Embodiment 1, an embodiment of the present invention provides an Internet smart home data processing method. Before step 2, it further includes: obtaining the smart home distribution of each sensor, including:

[0103] Obtaining the spatial distribution map of the smart home;

[0104] Determine the position points of each sensor in the spatial distribution map according to the position characteristics of each sensor;

[0105] According to the sensing characteristics of each sensor, perform feature marking on the corresponding position points to obtain the smart home distribution of each sensor.

[0106] In this embodiment, the sensing characteristics include the detection type and the detection range.

[0107] The beneficial effect of the above design solution is that by determining the marked positions according to the position characteristics of each sensor and determining the marked content according to the sensing characteristics of each sensor, it provides a basis for data fusion.

[0108] Embodiment 5

[0109] Based on Embodiment 4, the embodiment of the present invention provides an Internet smart home data processing method, which performs feature marking on the corresponding position points according to the sensing characteristics of each sensor, including:

[0110] Based on the sensing characteristics, obtain the detection type and detection range of the corresponding sensor;

[0111] Based on the detection type and detection range, form a feature sequence, and use the feature sequence to perform feature marking on the corresponding position points.

[0112] The beneficial effect of the above design solution is that by forming a marking sequence using the detection type and detection range of the sensor and performing feature marking on the position points, it ensures that the feature marking can comprehensively and accurately reflect the characteristics of the sensor and provides a basis for data fusion.

[0113] Embodiment 6

[0114] Based on Embodiment 3, the embodiment of the present invention provides an Internet smart home data processing method, which sets corresponding conversion rules for each group of format data based on the format type of the multiple groups of format data, and obtains the target single data based on the conversion rules, including:

[0115] Obtain the initial data format of each group of format data;

[0116] Obtain the conversion relationship from the initial data format to the target data format from the preset data format conversion table;

[0117] Take the format data with a direct conversion relationship as the first format data, and take the format data with an indirect conversion relationship as the second format data;

[0118] Obtain the first conversion rule corresponding to the first format data from the preset data format conversion table, and based on the first conversion rule, determine the first character mapping relationship;

[0119] Obtain the initial characters of the first format data, determine the data logic of the initial characters, and according to the first character mapping relationship, match the corresponding target characters for the initial characters, and according to the data logic, form the target single data corresponding to the first format data with the target characters;

[0120] Obtain the second conversion rule corresponding to the second format data from the preset data format conversion table;

[0121] Parse the second conversion rule to determine the conversion nodes of the second format data and the intermediate data types corresponding to each conversion node;

[0122] Based on the conversion order and intermediate data types of the conversion nodes, establish a character mapping table, and based on the character mapping table, determine the direct mapping relationship between the initial characters of the second format data and the target characters of the target single data;

[0123] Based on the direct mapping relationship, simplify the second conversion rule to obtain a third conversion rule;

[0124] Based on the third conversion rule, convert the second format data into the corresponding target single data.

[0125] In this embodiment, due to the different format types of multiple groups of format data, when converting to the target data format, some can be directly converted according to the preset data format conversion table, and some need to be converted multiple times to obtain the final target data format. Therefore, it is first necessary to classify multiple groups of format data.

[0126] In this embodiment, the third conversion rule establishes a direct conversion rule from the second format data to the target single data according to the second conversion rule and the direct mapping relationship.

[0127] In this embodiment, based on the third conversion rule, the method of converting the second format data into the corresponding target single data is the same as the conversion method of converting the first format data into the corresponding target single data according to the first conversion rule.

[0128] In this embodiment, the present invention analyzes the indirectly convertible third conversion rule from the second conversion rule, improving the efficiency of data conversion.

[0129] The beneficial effects of the above design solution are as follows: By matching the optimal data conversion rules for the multiple sets of format data according to the different conversion relationships, while ensuring the data conversion accuracy, the conversion efficiency is improved, and the target single data can be obtained quickly and accurately, facilitating the further analysis of home data.

[0130] Embodiment 7

[0131] Based on Embodiment 1, an embodiment of the present invention provides an Internet smart home data processing method. In step 2, based on the smart home distribution of each sensor, using the fuzzy control theory, data fusion is performed on the target single data to obtain multi-fusion data, including:

[0132] The target single data is divided according to the data type to obtain measurement data, behavior data, and device operation data;

[0133] The measurement data is divided according to the attributes to obtain multiple sets of single measurement data;

[0134] According to the measurement time, recursive fusion calculation is performed on the measurement values of each set of single measurement data to obtain the optimal measurement value corresponding to each set of single measurement data;

[0135] Based on the smart home distribution of each sensor, determine the position influence value of each sensor's position on the measurement data;

[0136] Based on the behavior data, set the behavior influence value of the user behavior on the measurement data, and based on the device operation data, determine the error influence value of the device state on the measurement data;

[0137] According to the position influence value, behavior influence value, and error influence value, establish membership function tables respectively, and based on the membership function tables, determine fuzzy control rules, and based on the fuzzy control rules, establish a fuzzy control model;

[0138] Obtain a set of optimal measurement values with the same measurement unit, obtain the position distribution of the sensors corresponding to the set of optimal measurement values, and their related behavior data and related device operation data;

[0139] Based on the position distribution, related behavior data, and related device operation data, set a target membership function for the fuzzy control model to obtain a target fuzzy control model;

[0140] Input the set of optimal measurement values into the target fuzzy control model, and output to obtain the final data value;

[0141] Obtain the final data value of each with the same measurement unit as the multi-fusion data.

[0142] In this embodiment, the attribute is the sensor identifier, and the measurement data of one sensor is used as a set of single measurement data.

[0143] In this embodiment, different positions have different position influence values on the measurement data, different user behaviors have different behavior influence values on the measurement data, and different device operation data have different error influence values on the measurement data.

[0144] In this embodiment, the membership function table is used to represent the relationships between the position influence value, the behavior influence value, the error influence value, and the measurement data.

[0145] In this embodiment, the target membership function is related to the position influence value, the behavior influence value, and the error influence value corresponding to the position distribution, the relevant behavior data, and the relevant device operation data.

[0146] In this embodiment, the set of optimal measurement values is the set of optimal measurement values obtained by the same type of sensors with the same measurement unit at different position distributions, and the final data value obtained is the final value determined by this type of sensor in the smart home.

[0147] The beneficial effects of the above design solution are as follows: First, data fusion is performed on a set of single measurement data corresponding to one sensor through recursive fusion calculation to obtain the fusion data of one sensor. Second, a fuzzy control model is set up based on the behavior data, the device operation data, and the smart home distribution of the sensors. Finally, the fusion data corresponding to the same type of sensors is obtained. This solution performs data fusion on a single target data through fuzzy control theory, avoiding the limitation of relying solely on a single target data for smart home analysis, and providing a basis for accurately determining the working state of the home devices in the smart home.

[0148] Embodiment 8

[0149] Based on Embodiment 7, the embodiment of the present invention provides an Internet smart home data processing method. According to the measurement time, recursive fusion calculation is performed on the measurement values of each set of single measurement data to obtain the optimal measurement value corresponding to each set of single measurement data, including:

[0150] Eliminate the abnormal data from each set of single measurement data to obtain normal single measurement data;

[0151] According to the measurement time, obtain the initial measurement value from the normal single measurement data, and obtain the adjacent next measurement value, and perform recursive calculation on the initial measurement value and the next measurement value to obtain an estimated measurement value;

[0152] Judge whether the estimated measurement value is within a preset range. If so, use the estimated measurement value as the latest measurement value; otherwise, use the initial measurement value as the latest measurement value;

[0153] Based on the above method, the measured values in each normal single measurement data are recursively calculated in sequence according to the measurement time, and finally the optimal measured value is obtained.

[0154] The beneficial effect of the above design scheme is that: by performing data fusion on each group of single measurement data through recursive fusion calculation according to the measurement time, the obtained optimal measured value can more accurately reflect the situation of the smart home.

[0155] Embodiment 9

[0156] Based on Embodiment 1, an embodiment of the present invention provides an Internet smart home data processing method. Step 3: Set the working states of the home appliances of the smart home according to the multi-fusion data, including:

[0157] Obtain the final data value of the same type of sensors from the multi-fusion data;

[0158] Obtain the monitoring indicators for the smart home and determine the types of sensors required for the monitoring indicators;

[0159] Obtain the behavior data from the target single data, and based on the behavior data, set the sensor threshold set for the monitoring indicators;

[0160] Obtain the device operation data from the target single data, and based on the device operation data, determine the error relationship between the state of each device and the sensor measurement;

[0161] Based on the error relationship, correct the sensor threshold set to obtain the target sensor threshold set;

[0162] Under the monitoring indicators, determine whether the final data value under the monitoring indicators is within the sensor threshold set;

[0163] If so, determine that the monitoring indicator is normal;

[0164] Otherwise, determine that the monitoring indicator is an abnormal monitoring indicator, and obtain the data difference between the final data value and the sensor threshold set;

[0165] Obtain the abnormal monitoring indicators with abnormalities, and determine the overlapping final data values under the abnormal monitoring indicators as the target final data values;

[0166] Obtain the data difference set of the target final data value under the different abnormal monitoring indicators, and set the weight value of each abnormal monitoring indicator for the target final data value according to the degree of correlation between the sensor type of the target final data value and the abnormal monitoring indicators;

[0167] Based on the weight values, perform weighted analysis on each data difference in the data difference set to obtain weighted data differences;

[0168] Based on the weighted data differences, set the working states of the home appliances of the smart home.

[0169] In this embodiment, the monitoring indicators are, for example, air pollution degree, temperature, noise, etc.

[0170] In this embodiment, for example, under the monitoring indicators of temperature and noise, there are measurements of the corresponding sensors of the air conditioner. At this time, the corresponding sensors of the air conditioner are the target final data values.

[0171] In this embodiment, for example, the relevance of the temperature as the target final data value of the corresponding sensor of the air conditioner is greater than the relevance of the noise as the target final data value of the corresponding sensor of the air conditioner. The corresponding weight value is that the greater the relevance, the greater the weight value.

[0172] In this embodiment, the weighted data differences are, for example, the final adjustment values for the corresponding sensors of the air conditioner. Use the weighted data differences to change the working states of the home appliances of the smart home.

[0173] The beneficial effects of the above design scheme are as follows: By designing the monitoring indicators of the smart home and performing threshold analysis on the multi-fusion data under different monitoring indicators, finally determine the appropriate working states of the home appliances of the smart home, so that it can most likely meet the requirements of all monitoring indicators and ensure that the set working states of the home appliances of the smart home bring a comfortable home environment for users.

[0174] Embodiment 10

[0175] An Internet of Things smart home data processing system, as Figure 3 shown, includes:

[0176] A format conversion module, which is used to obtain the individual home data of each sensor of the smart home based on the Internet, and perform target data format conversion on the individual home data to obtain target individual data;

[0177] A data fusion module, which is used to perform data fusion on the target individual data based on the distribution of the smart home of each sensor and using the fuzzy control theory to obtain multi-fusion data;

[0178] A state setting module, which is used to set the working states of the home appliances of the smart home according to the multi-fusion data.

[0179] The beneficial effects of the above design are as follows: Firstly, by converting the format of individual home data, the accuracy of the collected home data is ensured, and at the same time, it facilitates the further analysis of the home data. Secondly, through fuzzy control, the individual home data is fused to avoid the limitations of individual home data, and the obtained multi-fused data is used as the data for determining the operating state of the smart home, ensuring the accuracy of the detection of the operating state of the smart home.

[0180] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An Internet smart home data processing method, characterized in that Including: Step 1: Based on the Internet, the data acquisition center obtains the individual home data of each sensor in the smart home, and performs target data format conversion on the individual home data to obtain target individual data; Step 2: Based on the smart home distribution of each sensor, using the fuzzy control theory, perform data fusion on the target individual data to obtain multi-fused data; Step 3: According to the multi-fused data, set the working state of the home devices in the smart home; In Step 2, based on the smart home distribution of each sensor, using the fuzzy control theory, perform data fusion on the target individual data to obtain multi-fused data, including: Divide the target individual data according to the data type to obtain measurement data, behavior data, and device operation data; Divide the measurement data according to the attributes to obtain multiple groups of single measurement data; According to the measurement time, perform recursive fusion calculation on the measurement values of each group of single measurement data to obtain the optimal measurement value corresponding to each group of single measurement data; Based on the smart home distribution of each sensor, determine the position influence value of each sensor's position on the measurement data; Based on the behavior data, set the behavior influence value of the user behavior on the measurement data, and based on the device operation data, determine the error influence value of the device state on the measurement data; Establish membership function tables according to the position influence value, behavior influence value, and error influence value respectively, determine the fuzzy control rules based on the membership function tables, and establish a fuzzy control model based on the fuzzy control rules; Obtain a set of optimal measurement values in the same measurement unit, obtain the position distribution of the sensors corresponding to the set of optimal measurement values, and their related behavior data and related device operation data; Based on the position distribution, related behavior data, and related device operation data, set the target membership function for the fuzzy control model to obtain the target fuzzy control model; Input the set of optimal measurement values into the target fuzzy control model, and output to obtain the final data value; Obtain the final data value of each in the same measurement unit as the multi-fused data.

2. The method for processing Internet smart home data according to claim 1, wherein In Step 1, based on the Internet, obtaining the individual home data of each sensor in the smart home includes: Based on the Internet, receive a data acquisition request from the data acquisition center; According to the user information, identify the data acquisition request, and after successful identification, based on the data acquisition request, collect the individual home data of each sensor in the smart home device during operation at the same time; Based on the Internet, send the individual home data to the data acquisition center.

3. A method for processing Internet smart home data according to claim 1, characterized in that, In Step 1, performing target data format conversion on the individual home data to obtain target individual data includes: Based on the device characteristics of each sensor, determine the initial data format of the individual home data; Based on the initial data format, perform data format division on the individual home data to obtain multiple groups of format data; Based on the format types of the multiple groups of format data, set corresponding conversion rules for each group of format data, and based on the conversion rules, obtain the target individual data.

4. A method for processing Internet smart home data according to claim 1, characterized in that, Before step 2, it further includes: obtaining the smart home distribution of each sensor, including: Obtaining the spatial distribution map of the smart home; Determining the position points of each sensor in the spatial distribution map according to the position characteristics of each sensor; Performing feature marking on the corresponding position points according to the sensing characteristics of each sensor to obtain the smart home distribution of each sensor.

5. A method for processing Internet smart home data according to claim 4, characterized in that, Performing feature marking on the corresponding position points according to the sensing characteristics of each sensor includes: Based on the sensing characteristics, obtaining the detection type and detection range of the corresponding sensor; Composing a feature sequence based on the detection type and detection range, and using the feature sequence to perform feature marking on the corresponding position points.

6. A method for processing Internet smart home data according to claim 3, characterized in that, Based on the format types of the multiple sets of format data, setting corresponding conversion rules for each set of format data, and based on the conversion rules, obtaining target single data, including: Obtaining the initial data format of each set of format data; Obtaining the conversion relationship from the initial data format to the target data format from a preset data format conversion table; Regarding the format data with a direct conversion relationship in the conversion relationship as the first format data, and regarding the format data with an indirect conversion relationship in the conversion relationship as the second format data; Obtaining the first conversion rule corresponding to the first format data from the preset data format conversion table, and based on the first conversion rule, determining the first character mapping relationship; Obtaining the initial character of the first format data, determining the data logic of the initial character, and according to the first character mapping relationship, matching the corresponding target character for the initial character, and forming the target single data corresponding to the first format data according to the data logic with the target character; Obtaining the second conversion rule corresponding to the second format data from the preset data format conversion table; Analyzing the second conversion rule to determine the conversion nodes of the second format data and the intermediate data type corresponding to each conversion node; Based on the conversion order and intermediate data type of the conversion nodes, establishing a character mapping table, and based on the character mapping table, determining the direct mapping relationship between the initial character and the target character of the target single data of the second format data; Simplifying the second conversion rule based on the direct mapping relationship to obtain a third conversion rule; Based on the third conversion rule, converting the second format data into the corresponding target single data.

7. A method for processing Internet smart home data according to claim 1, characterized in that, Performing recursive fusion calculation on the measurement values of each set of single measurement data according to the measurement time to obtain the optimal measurement value corresponding to each set of single measurement data, including: Eliminating abnormal data from each set of single measurement data to obtain normal single measurement data; According to the measurement time, obtaining the initial measurement value from the normal single measurement data, and obtaining the next adjacent measurement value, and performing recursive calculation on the initial measurement value and the next measurement value to obtain an estimated measurement value; Judging whether the estimated measurement value is within a preset range. If so, using the estimated measurement value as the latest measurement value, otherwise, using the initial measurement value as the latest measurement value; Based on the above method, calculate recursively each measured value in the normal single measurement data in sequence according to the measurement time, and finally obtain the optimal measured value.

8. A method for processing Internet smart home data according to claim 1, characterized in that, In step 3, set the working states of the home appliances of the smart home according to the multi-fusion data, including: Obtain the final data values of the sensors of the same type from the multi-fusion data; Obtain the monitoring indicators of the smart home and determine the types of sensors required for the monitoring indicators; Obtain the behavior data from the target single data, and based on the behavior data, set the sensor threshold set of the monitoring indicators; Obtain the device operation data from the target single data, and based on the device operation data, determine the error relationship between each device state and the sensor measurement; Based on the error relationship, correct the sensor threshold set to obtain the target sensor threshold set; Under the monitoring indicators, determine whether the final data value under the monitoring indicators is in the sensor threshold set; If so, determine that the monitoring indicator is normal; Otherwise, determine that the monitoring indicator is an abnormal monitoring indicator, and obtain the data difference between the final data value and the sensor threshold set; Obtain the abnormal monitoring indicators with abnormalities, and determine the overlapping final data values under the abnormal monitoring indicators as the target final data values; Obtain the data difference set of the target final data value under different abnormal monitoring indicators, and set the weight value of each abnormal monitoring indicator for the target final data value according to the degree of correlation between the sensor type of the target final data value and the abnormal monitoring indicators; Based on the weight value, perform weighted analysis on each data difference in the data difference set to obtain the weighted data difference; Based on the weighted data difference, set the working states of the home appliances of the smart home.

9. An Internet smart home data processing system for implementing the steps of the smart home data processing method according to any one of claims 1-8, characterized in that, Including: A format conversion module, which is used to obtain the single home data of each sensor of the smart home based on the Internet by the data acquisition center, and perform target data format conversion on the single home data to obtain target single data; A data fusion module, which is used to perform data fusion on the target single data by using the fuzzy control theory based on the smart home distribution of each sensor to obtain multi-fusion data; A state setting module, which is used to set the working states of the home appliances of the smart home according to the multi-fusion data.

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