CN117194947A - Smart home equipment characteristic determining method and system - Google Patents
Smart home equipment characteristic determining method and system Download PDFInfo
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Abstract
The invention provides a method and a system for determining equipment characteristics of smart home, and relates to the technical field of data processing. According to the invention, a plurality of reference intelligent home devices are classified according to the corresponding reference operation behavior description vectors of the reference intelligent home devices so as to form at least one reference device set; analyzing the behavior description vector of the type equipment corresponding to each reference equipment set; analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined according to the type equipment behavior description vector and the operation behavior description vector corresponding to the intelligent home equipment to be determined, and marking the reference equipment set as a corresponding matched reference equipment set; and marking the device characteristic type information corresponding to the matched reference device set to be marked as the device characteristic type information corresponding to the smart home device to be determined. Based on the method, the reliability of equipment characteristic type determination can be improved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a system for determining equipment characteristics of smart home.
Background
The smart home is realized through the Internet of things under the influence of the Internet of things. The smart home is connected with various devices (such as audio and video equipment, a lighting system, curtain control, air conditioner control, a security system, a digital cinema system, network home appliances, three-meter copy and the like) in the home through the internet of things technology, and various functions and means such as home appliance control, lighting control, curtain control, telephone remote control, indoor and outdoor remote control, anti-theft alarm, environment monitoring, heating ventilation control, infrared forwarding and programmable timing control are provided. Compared with the common home, the intelligent home has the traditional living function, integrates the functions of building, network communication, information home appliances and equipment automation, integrates the system, structure, service and management into a whole, is efficient, comfortable, safe, convenient and environment-friendly, provides an omnibearing information interaction function, helps the home to keep information communication smooth with the outside, optimizes the life style of people, helps people to effectively arrange time, enhances the safety of home life, and even saves funds for various energy costs.
In the application of smart home, based on some requirements, the smart home equipment needs to be analyzed to determine equipment characteristic type information corresponding to the smart home equipment, namely equipment classification identifiers, however, in the prior art, the problem of low reliability of equipment characteristic type determination exists.
Disclosure of Invention
Therefore, the present invention is directed to a method and a system for determining the device characteristics of smart home, so as to improve the reliability of determining the device characteristics.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical scheme:
a device characteristic determining method of smart home comprises the following steps:
digging out an operation behavior description vector corresponding to the intelligent home equipment to be determined, and digging out a reference operation behavior description vector corresponding to the reference intelligent home equipment;
classifying a plurality of reference smart home devices according to the reference operation behavior description vectors corresponding to the reference smart home devices to form at least one corresponding reference device set, wherein any one of the at least one reference device set comprises at least one reference smart home device with the same device characteristic type information;
analyzing the device behavior description vector of each type corresponding to the reference device set based on the reference operation behavior description vector corresponding to the reference smart home device;
according to the device behavior description vector and the operation behavior description vector corresponding to the smart home device to be determined, analyzing a reference device set which is most matched with the smart home device to be determined from the at least one reference device set, and marking the reference device set as a corresponding matched reference device set;
And marking the device characteristic type information corresponding to the matched reference device set to be marked as the device characteristic type information corresponding to the smart home device to be determined.
In some preferred embodiments, in the method for determining device characteristics of smart home, before the step of mining the operation behavior description vector corresponding to the smart home device to be determined and mining the reference operation behavior description vector corresponding to the reference smart home device, the method for determining device characteristics of smart home further includes:
acquiring multi-angle equipment operation behavior data corresponding to reference intelligent home equipment, and determining equipment characteristic type information corresponding to the reference intelligent home equipment;
performing key information mining on the multi-angle equipment operation behavior data corresponding to the reference intelligent home equipment, and outputting an operation behavior description vector corresponding to the reference intelligent home equipment;
updating the initial neural network through typical equipment operation behavior data, and outputting a corresponding candidate neural network, wherein the typical equipment operation behavior data comprises operation behavior description vectors corresponding to the reference intelligent home equipment and corresponding equipment characteristic type information;
Analyzing the network reliability of the candidate neural network to output a network reliability characterization parameter corresponding to the candidate neural network;
and under the condition that the network reliability characterization parameter is greater than or equal to a pre-configured reference reliability characterization parameter, marking the candidate neural network so as to be marked as a corresponding operation behavior analysis neural network.
In some preferred embodiments, in the above method for determining device characteristics of smart home, the step of analyzing, from the at least one reference device set, a reference device set that is the best match between the smart home device to be determined and the reference device set as a corresponding matching reference device set according to the device behavior description vector of the category and the operation behavior description vector corresponding to the smart home device to be determined includes:
analyzing a neural network through the operation behavior according to the corresponding device behavior description vector and the operation behavior description vector of the smart home device to be determined, and analyzing a reference device set which is most matched with the smart home device to be determined from the at least one reference device set;
The best matching reference device set is marked as the corresponding matching reference device set.
In some preferred embodiments, in the method for determining device characteristics of smart home, the step of analyzing, by the operation behavior analysis neural network, a reference device set that is most matched with the smart home device to be determined from the at least one reference device set according to the corresponding device behavior description vector of the type and the operation behavior description vector of the smart home device to be determined includes:
analyzing a self focusing characteristic analysis result corresponding to the reference equipment set and a correlation focusing characteristic analysis result corresponding to the reference equipment set according to the equipment behavior description vector of the type through the operation behavior analysis neural network;
based on the associated focusing characteristic analysis result and the self focusing characteristic analysis result corresponding to the equipment behavior description vector, analyzing vector matching parameters between the equipment behavior description vector and the operation behavior description vector corresponding to the smart home equipment to be determined;
and analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined from the at least one reference equipment set based on the vector matching parameters.
In some preferred embodiments, in the method for determining device characteristics of smart home, the step of analyzing, by the operation behavior analysis neural network, a self-focusing characteristic analysis result and a corresponding correlation focusing characteristic analysis result corresponding to the reference device set according to the device behavior description vector of the type includes:
analyzing a neural network through the operation behaviors, and analyzing a self-focusing characteristic analysis result according to the device behavior description vector of the type corresponding to the reference device set;
and analyzing an associated focusing characteristic analysis result of each reference equipment set according to the operation behavior description vector corresponding to the intelligent home equipment to be determined and each equipment behavior description vector of each type corresponding to the reference equipment set through the operation behavior analysis neural network.
In some preferred embodiments, in the method for determining device characteristics of smart home, the step of analyzing, by the operation behavior analysis neural network, an associated focus characteristic analysis result of each reference device set according to an operation behavior description vector corresponding to the smart home device to be determined and each device behavior description vector of the type corresponding to the reference device set includes:
Based on first parameter distribution included in the operation behavior analysis neural network, carrying out weighting processing on operation behavior description vectors corresponding to the smart home equipment to be determined so as to output corresponding weighted operation behavior description vectors, and carrying out row and column exchange operation of vector parameters on the type of equipment behavior description vectors corresponding to each reference equipment set so as to output corresponding exchange equipment behavior description vectors;
and for each reference equipment set, multiplying the change equipment behavior description vector corresponding to the reference equipment set and the weighted operation behavior description vector corresponding to the smart home equipment to be determined, and performing excitation mapping processing on the multiplied result to output an excitation mapping description vector corresponding to the reference equipment set, and multiplying the equipment behavior description vector of the type corresponding to the reference equipment set and the excitation mapping description vector to output an associated focusing characteristic analysis result corresponding to the reference equipment set.
In some preferred embodiments, in the method for determining device characteristics of smart home, the step of analyzing, by the operation behavior analysis neural network, a self-focusing characteristic analysis result according to the device behavior description vector of the type corresponding to the reference device set includes:
Taking any one of the reference device sets as a first reference device set;
based on a second parameter distribution matrix included in the operation behavior analysis neural network, weighting the class device behavior description vectors corresponding to the first reference device set to output corresponding weighted class device behavior description vectors;
performing row-column exchange operation of vector parameters on the class device behavior description vectors corresponding to the first reference device set so as to output corresponding exchange class behavior description vectors;
performing a first excitation mapping process on the weighted category behavior description vector to output a first excitation mapping description vector corresponding to the first reference device set, performing a multiplication process on the transposed category behavior description vector and the first excitation mapping description vector, and performing a second excitation mapping process on the result of the multiplication process to output a corresponding second excitation mapping description vector, the first excitation mapping process being different from the second excitation mapping process;
and multiplying the device behavior description vector of the type corresponding to the first reference device set and the corresponding second excitation mapping description vector to output a self-focusing characteristic analysis result corresponding to the first reference device set.
In some preferred embodiments, in the method for determining device characteristics of smart home, the step of mining the operation behavior description vector corresponding to the smart home device to be determined includes:
collecting multi-angle equipment operation behavior data of the smart home equipment to be determined;
performing key information mining operation on the multi-angle equipment operation behavior data corresponding to the smart home equipment to be determined so as to output operation behavior description vectors of a plurality of angles of the smart home equipment to be determined;
and carrying out vector cascading combination processing on the operation behavior description vectors of the angles corresponding to the smart home equipment to be determined so as to output the operation behavior description vectors corresponding to the smart home equipment to be determined.
The embodiment of the invention also provides a system for determining the equipment characteristics of the smart home, which comprises the following steps:
the description vector mining module is used for mining operation behavior description vectors corresponding to the intelligent home equipment to be determined and mining reference operation behavior description vectors corresponding to the reference intelligent home equipment;
the device classification module is used for performing classification operation on a plurality of the reference smart home devices according to the reference operation behavior description vectors corresponding to the reference smart home devices so as to form at least one corresponding reference device set, wherein any one of the at least one reference device set comprises at least one reference smart home device with the same device characteristic type information;
The category vector determining module is used for analyzing category equipment behavior description vectors corresponding to each reference equipment set based on the included reference operation behavior description vectors corresponding to the reference intelligent home equipment;
the matching set determining module is used for analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined in the at least one reference equipment set according to the equipment behavior description vector of the type and the operation behavior description vector corresponding to the intelligent home equipment to be determined, and marking the reference equipment set as a corresponding matching reference equipment set;
and the equipment characteristic type determining module is used for carrying out marking processing on the equipment characteristic type information corresponding to the matched reference equipment set so as to mark the equipment characteristic type information corresponding to the intelligent home equipment to be determined.
In some preferred embodiments, in the smart home device feature determination system, the device feature determination system further includes at least one other functional module for:
acquiring multi-angle equipment operation behavior data corresponding to reference intelligent home equipment, and determining equipment characteristic type information corresponding to the reference intelligent home equipment;
Performing key information mining on the multi-angle equipment operation behavior data corresponding to the reference intelligent home equipment, and outputting an operation behavior description vector corresponding to the reference intelligent home equipment;
updating the initial neural network through typical equipment operation behavior data, and outputting a corresponding candidate neural network, wherein the typical equipment operation behavior data comprises operation behavior description vectors corresponding to the reference intelligent home equipment and corresponding equipment characteristic type information;
analyzing the network reliability of the candidate neural network to output a network reliability characterization parameter corresponding to the candidate neural network;
and under the condition that the network reliability characterization parameter is greater than or equal to a pre-configured reference reliability characterization parameter, marking the candidate neural network so as to be marked as a corresponding operation behavior analysis neural network.
According to the method and the system for determining the device characteristics of the smart home, provided by the embodiment of the invention, a plurality of reference smart home devices can be classified according to the reference operation behavior description vectors corresponding to the reference smart home devices so as to form at least one reference device set; analyzing the behavior description vector of the type equipment corresponding to each reference equipment set; analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined according to the type equipment behavior description vector and the operation behavior description vector corresponding to the intelligent home equipment to be determined, and marking the reference equipment set as a corresponding matched reference equipment set; and marking the device characteristic type information corresponding to the matched reference device set to be marked as the device characteristic type information corresponding to the smart home device to be determined. Based on the foregoing, on the one hand, the matching analysis is performed on the device behavior description vectors of the types corresponding to the reference device set, so that the basis is more sufficient, the reliability of determining the device feature types is improved, and in addition, the number of matching analysis can be reduced, so that the efficiency is higher.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
Fig. 1 is a block diagram of a device feature determining platform of an intelligent home according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating steps included in the method for determining the device characteristics of the smart home according to the embodiment of the present invention.
Fig. 3 is a schematic diagram of each module included in the smart home device feature determining system according to the embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the embodiment of the invention provides a device feature determining platform for smart home. The smart home device feature determination platform may include a memory and a processor.
In detail, the memory and the processor are electrically connected directly or indirectly to realize transmission or interaction of data. For example, electrical connection may be made to each other via one or more communication buses or signal lines. The memory may store at least one software functional module (computer program) that may exist in the form of software or firmware. The processor may be configured to execute the executable computer program stored in the memory, so as to implement the device feature determining method for smart home provided by the embodiment of the present invention (as described below).
Alternatively, in some embodiments, the Memory may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.
Alternatively, in some embodiments, the processor may be a general purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a System on Chip (SoC), etc.; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Alternatively, in some embodiments, the smart home device feature determination platform may be a server with data processing capabilities.
With reference to fig. 2, the embodiment of the invention further provides a method for determining the device characteristics of the smart home, which can be applied to the device characteristics determining platform of the smart home. The method steps defined by the flow related to the smart home device feature determination method can be realized by the smart home device feature determination platform. The specific flow shown in fig. 2 will be described in detail.
Step S110, the operation behavior description vector corresponding to the intelligent home equipment to be determined is mined, and the reference operation behavior description vector corresponding to the reference intelligent home equipment is mined.
In the embodiment of the invention, the device characteristic determining platform of the smart home can mine the operation behavior description vector corresponding to the smart home device to be determined and mine the reference operation behavior description vector corresponding to the reference smart home device.
Step S120, performing a classification operation on the plurality of reference smart home devices according to the reference operation behavior description vector corresponding to the reference smart home device, so as to form at least one corresponding reference device set.
In the embodiment of the invention, the device feature determining platform of the smart home can classify the plurality of reference smart home devices according to the reference operation behavior description vector corresponding to the reference smart home devices, and can be realized based on any clustering algorithm to form at least one corresponding reference device set. Any one of the at least one reference device set includes at least one reference smart home device having the same device characteristic category information.
Step S130, analyzing the device behavior description vector of each type corresponding to the reference device set based on the reference operation behavior description vector corresponding to the included reference smart home device.
In the embodiment of the present invention, the device feature determining platform of the smart home may analyze, based on the reference operation behavior description vector corresponding to the included reference smart home device, a class device behavior description vector corresponding to each reference device set, and for each reference device set, by way of example, average calculation may be performed on the reference operation behavior description vector corresponding to each reference smart home device included in the reference device set to obtain a class device behavior description vector corresponding to the reference device set, or a cluster center of the reference operation behavior description vector corresponding to each reference smart home device included in the reference device set, that is, an analysis center that performs a classification operation on a plurality of reference smart home devices based on the corresponding reference operation behavior description vector may be used as the class device behavior description vector corresponding to the reference device set.
Step S140, according to the device behavior description vector of the type and the operation behavior description vector corresponding to the smart home device to be determined, analyzing a reference device set that is the best match with the smart home device to be determined from the at least one reference device set, and marking the reference device set as a corresponding matching reference device set.
In the embodiment of the invention, the device feature determining platform of the smart home may analyze a reference device set that is most matched with the smart home device to be determined from the at least one reference device set according to the device behavior description vector and the operation behavior description vector corresponding to the smart home device to be determined, and mark the reference device set as a corresponding matched reference device set.
And step S150, marking the equipment characteristic type information corresponding to the matched reference equipment set so as to be marked as the equipment characteristic type information corresponding to the smart home equipment to be determined.
In the embodiment of the invention, the device feature determining platform of the smart home can perform marking processing on the device feature type information corresponding to the matching reference device set so as to mark the device feature type information corresponding to the smart home device to be determined. The device feature type information may have content with different dimensions in different applications, or may not have any practical meaning, and is only used for distinguishing different smart home devices, such as codes 001, 002, 003, and the like.
Based on the foregoing, on the one hand, the matching analysis is performed on the device behavior description vectors of the types corresponding to the reference device set, so that the basis is more sufficient, the reliability of determining the device feature types is improved, and in addition, the number of matching analysis can be reduced, so that the efficiency is higher.
Optionally, in some embodiments, the step S110 of mining the operation behavior description vector corresponding to the smart home device to be determined (where the step may be the same as or different from the step of mining the reference operation behavior description vector corresponding to the reference smart home device) may further include the following steps:
collecting multi-angle equipment operation behavior data (such as data reflecting internal operation, data reflecting interaction with other equipment and the like) of the smart home equipment to be determined;
performing key information mining operation, such as feature space mapping, convolution operation and the like, on the multi-angle equipment operation behavior data corresponding to the smart home equipment to be determined so as to output operation behavior description vectors of a plurality of angles of the smart home equipment to be determined;
And carrying out vector cascading combination processing on the operation behavior description vectors of the angles corresponding to the smart home equipment to be determined, and if the operation behavior description vectors are combined together, forming a large description vector so as to output the operation behavior description vector corresponding to the smart home equipment to be determined.
Optionally, in some embodiments, before the step of mining the operation behavior description vector corresponding to the smart home device to be determined and mining the reference operation behavior description vector corresponding to the reference smart home device, that is, before step S110, the device feature determining method of the smart home may further include the following contents:
acquiring multi-angle equipment operation behavior data corresponding to reference intelligent home equipment, and determining equipment characteristic type information corresponding to the reference intelligent home equipment;
performing key information mining on the multi-angle equipment operation behavior data corresponding to the reference intelligent home equipment, and outputting an operation behavior description vector corresponding to the reference intelligent home equipment;
updating an initial neural network through typical equipment operation behavior data, and outputting a corresponding candidate neural network, wherein the typical equipment operation behavior data comprises an operation behavior description vector corresponding to the reference smart home equipment and corresponding equipment characteristic type information, and the operation behavior description vector is used for predicting corresponding predicted equipment characteristic type information, performing difference analysis based on the predicted equipment characteristic type information and the equipment characteristic type information, and updating parameters of the initial neural network based on the analyzed difference;
The candidate neural network is subjected to analysis operation of network reliability so as to output network reliability characterization parameters corresponding to the candidate neural network, and the candidate neural network can be subjected to analysis operation of network reliability by configuring some detection equipment operation behavior data, wherein the detection equipment operation behavior data can comprise operation behavior description vectors and equipment characteristic type information, so that the corresponding network reliability characterization parameters can be determined based on the accuracy between the predicted equipment characteristic type information and the actual equipment characteristic type information;
and under the condition that the network reliability characterization parameter is greater than or equal to a pre-configured reference reliability characterization parameter, marking the candidate neural network to be a corresponding operation behavior analysis neural network, wherein the reference reliability characterization parameter can be configured according to actual requirements.
Optionally, in some embodiments, the step of analyzing, from the at least one reference device set, a reference device set that is most matched with the smart home device to be determined, and marking the reference device set as a corresponding matched reference device set according to the device behavior description vector of the category and the operation behavior description vector corresponding to the smart home device to be determined may further include the following:
Analyzing a reference equipment set which is the best match with the intelligent household equipment to be determined in the at least one reference equipment set through the operation behavior analysis neural network according to the corresponding equipment behavior description vector and the operation behavior description vector corresponding to the intelligent household equipment to be determined, namely the corresponding equipment behavior description vector and the operation behavior description vector of the intelligent household equipment to be determined are the best match;
the best matching reference device set is marked as the corresponding matching reference device set.
Optionally, in some embodiments, the step of analyzing, by the operation behavior analysis neural network, a reference device set that is most matched with the smart home device to be determined from the at least one reference device set according to the corresponding device behavior description vector and the corresponding operation behavior description vector of the smart home device to be determined may further include the following:
analyzing a self focusing characteristic analysis result corresponding to the reference equipment set and a correlation focusing characteristic analysis result corresponding to the reference equipment set according to the equipment behavior description vector of the type through the operation behavior analysis neural network;
Based on the correlation focusing characteristic analysis result and the self focusing characteristic analysis result corresponding to the class device behavior description vector, analyzing vector matching parameters between the class device behavior description vector and the operation behavior description vector corresponding to the smart home device to be determined, wherein the vector matching parameters between the correlation focusing characteristic analysis result and the self focusing characteristic analysis result and the operation behavior description vector corresponding to the smart home device to be determined can be calculated respectively, and then the two obtained vector matching parameters can be fused to obtain vector matching parameters between the class device behavior description vector and the operation behavior description vector corresponding to the smart home device to be determined, and mean value calculation or weighted summation calculation can be performed during fusion;
based on the vector matching parameters, a reference equipment set which is most matched with the smart home equipment to be determined is analyzed in the at least one reference equipment set, and the corresponding reference equipment set with the largest vector matching parameters can be taken as the reference equipment set which is most matched with the smart home equipment to be determined.
Optionally, in some embodiments, the step of analyzing, by the operation behavior analysis neural network, the self-focusing feature analysis result and the corresponding correlation focusing feature analysis result corresponding to the reference device set according to the device behavior description vector may further include the following contents:
analyzing self-focusing characteristic analysis results according to the device behavior description vectors of the types corresponding to the reference device sets through the operation behavior analysis neural network, wherein each reference device set is provided with a self-focusing characteristic analysis result;
and analyzing an associated focusing characteristic analysis result of each reference equipment set according to the operation behavior description vector corresponding to the intelligent home equipment to be determined and each equipment behavior description vector of each type corresponding to the reference equipment set through the operation behavior analysis neural network.
Optionally, in some embodiments, the step of analyzing, by the operation behavior analysis neural network, the self-focusing feature analysis result according to the device behavior description vector of the type corresponding to the reference device set may further include the following:
Taking any one of the reference device sets as a first reference device set;
based on a second parameter distribution matrix (the second parameter distribution matrix may be used as a network parameter of the operation behavior analysis neural network and is formed by updating) included in the operation behavior analysis neural network, performing weighting processing on a class device behavior description vector corresponding to the first reference device set to output a corresponding weighted class device behavior description vector, that is, multiplying the second parameter distribution matrix and the class device behavior description vector;
performing column-row switching operation of vector parameters on the class device behavior description vector corresponding to the first reference device set to output a corresponding switching class behavior description vector, that is, a row vector parameter in the class device behavior description vector may be used as a column vector parameter in the switching class behavior description vector, and a column vector parameter in the class device behavior description vector may be used as a row vector parameter in the switching class behavior description vector;
performing a first excitation mapping process on the weighted category behavior description vector to output a first excitation mapping description vector corresponding to the first reference device set, performing a multiplication process on the transposed category behavior description vector and the first excitation mapping description vector, performing a second excitation mapping process on a result of the multiplication process to output a corresponding second excitation mapping description vector, wherein the first excitation mapping process is different from the second excitation mapping process, that is, an excitation function corresponding to the first excitation mapping process is different from an excitation function corresponding to the second excitation mapping process;
And multiplying the device behavior description vector of the type corresponding to the first reference device set and the corresponding second excitation mapping description vector to output a self-focusing characteristic analysis result corresponding to the first reference device set, and based on the self-focusing characteristic analysis result, self-key information can be mined.
Optionally, in some embodiments, the step of analyzing, by the operation behavior analysis neural network, the associated focusing feature analysis result of each reference device set according to the operation behavior description vector corresponding to the smart home device to be determined and each of the device behavior description vectors corresponding to the reference device set may further include the following contents:
based on a first parameter distribution (the first parameter distribution matrix may be used as a network parameter of the operation behavior analysis neural network, and is formed by updating), the operation behavior description vector corresponding to the smart home device to be determined is weighted to output a corresponding weighted operation behavior description vector, that is, the first parameter distribution matrix and the operation behavior description vector may be multiplied, and for each reference device set, a column-row switching operation of vector parameters is performed on a device behavior description vector of a type corresponding to the reference device set to output a corresponding switching device behavior description vector, that is, a row vector parameter in the device behavior description vector of a type may be used as a column vector parameter in the switching device behavior description vector, and a column vector parameter in the device behavior description vector of a type may be used as a row vector parameter in the switching device behavior description vector;
And for each reference equipment set, multiplying the change equipment behavior description vector corresponding to the reference equipment set and the weighted operation behavior description vector corresponding to the smart home equipment to be determined, and performing excitation mapping processing on the multiplied result, namely through a corresponding excitation function, so as to output an excitation mapping description vector corresponding to the reference equipment set, and multiplying the equipment behavior description vector of the type corresponding to the reference equipment set and the excitation mapping description vector so as to output an associated focusing characteristic analysis result corresponding to the reference equipment set.
With reference to fig. 3, the embodiment of the invention further provides a system for determining the device characteristics of the smart home, which can be applied to the device characteristics determining platform of the smart home. The smart home device feature determination system may include the following software function modules:
the description vector mining module is used for mining operation behavior description vectors corresponding to the intelligent home equipment to be determined and mining reference operation behavior description vectors corresponding to the reference intelligent home equipment;
the device classification module is used for performing classification operation on a plurality of the reference smart home devices according to the reference operation behavior description vectors corresponding to the reference smart home devices so as to form at least one corresponding reference device set, wherein any one of the at least one reference device set comprises at least one reference smart home device with the same device characteristic type information;
The category vector determining module is used for analyzing category equipment behavior description vectors corresponding to each reference equipment set based on the included reference operation behavior description vectors corresponding to the reference intelligent home equipment;
the matching set determining module is used for analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined in the at least one reference equipment set according to the equipment behavior description vector of the type and the operation behavior description vector corresponding to the intelligent home equipment to be determined, and marking the reference equipment set as a corresponding matching reference equipment set;
and the equipment characteristic type determining module is used for carrying out marking processing on the equipment characteristic type information corresponding to the matched reference equipment set so as to mark the equipment characteristic type information corresponding to the intelligent home equipment to be determined.
Alternatively, in some embodiments, the description vector mining module is specifically configured to:
collecting multi-angle equipment operation behavior data of the smart home equipment to be determined;
performing key information mining operation on the multi-angle equipment operation behavior data corresponding to the smart home equipment to be determined so as to output operation behavior description vectors of a plurality of angles of the smart home equipment to be determined;
And carrying out vector cascading combination processing on the operation behavior description vectors of the angles corresponding to the smart home equipment to be determined so as to output the operation behavior description vectors corresponding to the smart home equipment to be determined.
Optionally, in some embodiments, the device feature determination system further comprises at least one other functional module that may be configured to:
acquiring multi-angle equipment operation behavior data corresponding to reference intelligent home equipment, and determining equipment characteristic type information corresponding to the reference intelligent home equipment;
performing key information mining on the multi-angle equipment operation behavior data corresponding to the reference intelligent home equipment, and outputting an operation behavior description vector corresponding to the reference intelligent home equipment;
updating the initial neural network through typical equipment operation behavior data, and outputting a corresponding candidate neural network, wherein the typical equipment operation behavior data comprises operation behavior description vectors corresponding to the reference intelligent home equipment and corresponding equipment characteristic type information;
analyzing the network reliability of the candidate neural network to output a network reliability characterization parameter corresponding to the candidate neural network;
And under the condition that the network reliability characterization parameter is greater than or equal to a pre-configured reference reliability characterization parameter, marking the candidate neural network so as to be marked as a corresponding operation behavior analysis neural network.
Optionally, in some embodiments, the matching set determining module is specifically configured to:
analyzing a neural network through the operation behaviors according to the corresponding device behavior description vector of the type and the corresponding operation behavior description vector of the smart home device to be determined, and analyzing a reference device set which is most matched with the smart home device to be determined from the at least one reference device set;
the best matching reference device set is marked as the corresponding matching reference device set.
In summary, according to the method and the system for determining the device characteristics of the smart home provided by the invention, the plurality of reference smart home devices can be classified according to the reference operation behavior description vector corresponding to the reference smart home devices so as to form at least one reference device set; analyzing the behavior description vector of the type equipment corresponding to each reference equipment set; analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined according to the type equipment behavior description vector and the operation behavior description vector corresponding to the intelligent home equipment to be determined, and marking the reference equipment set as a corresponding matched reference equipment set; and marking the device characteristic type information corresponding to the matched reference device set to be marked as the device characteristic type information corresponding to the smart home device to be determined. Based on the foregoing, on the one hand, the matching analysis is performed on the device behavior description vectors of the types corresponding to the reference device set, so that the basis is more sufficient, the reliability of determining the device feature types is improved, and in addition, the number of matching analysis can be reduced, so that the efficiency is higher.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The method for determining the equipment characteristics of the smart home is characterized by comprising the following steps of:
digging out an operation behavior description vector corresponding to the intelligent home equipment to be determined, and digging out a reference operation behavior description vector corresponding to the reference intelligent home equipment;
classifying a plurality of reference smart home devices according to the reference operation behavior description vectors corresponding to the reference smart home devices to form at least one corresponding reference device set, wherein any one of the at least one reference device set comprises at least one reference smart home device with the same device characteristic type information;
analyzing the device behavior description vector of each type corresponding to the reference device set based on the reference operation behavior description vector corresponding to the reference smart home device;
According to the device behavior description vector and the operation behavior description vector corresponding to the smart home device to be determined, analyzing a reference device set which is most matched with the smart home device to be determined from the at least one reference device set, and marking the reference device set as a corresponding matched reference device set;
and marking the device characteristic type information corresponding to the matched reference device set to be marked as the device characteristic type information corresponding to the smart home device to be determined.
2. The smart home device feature determining method according to claim 1, wherein before the step of mining out the operation behavior description vector corresponding to the smart home device to be determined and mining out the reference operation behavior description vector corresponding to the reference smart home device, the smart home device feature determining method further comprises:
acquiring multi-angle equipment operation behavior data corresponding to reference intelligent home equipment, and determining equipment characteristic type information corresponding to the reference intelligent home equipment;
performing key information mining on the multi-angle equipment operation behavior data corresponding to the reference intelligent home equipment, and outputting an operation behavior description vector corresponding to the reference intelligent home equipment;
Updating the initial neural network through typical equipment operation behavior data, and outputting a corresponding candidate neural network, wherein the typical equipment operation behavior data comprises operation behavior description vectors corresponding to the reference intelligent home equipment and corresponding equipment characteristic type information;
analyzing the network reliability of the candidate neural network to output a network reliability characterization parameter corresponding to the candidate neural network;
and under the condition that the network reliability characterization parameter is greater than or equal to a pre-configured reference reliability characterization parameter, marking the candidate neural network so as to be marked as a corresponding operation behavior analysis neural network.
3. The smart home device feature determining method according to claim 2, wherein the step of analyzing, from the at least one reference device set, a reference device set that is the best match between the smart home device to be determined and the class device behavior description vector and the operation behavior description vector corresponding to the smart home device to be determined, and marking the reference device set as a corresponding matching reference device set includes:
Analyzing a neural network through the operation behavior according to the corresponding device behavior description vector and the operation behavior description vector of the smart home device to be determined, and analyzing a reference device set which is most matched with the smart home device to be determined from the at least one reference device set;
the best matching reference device set is marked as the corresponding matching reference device set.
4. A smart home device characteristic determining method as claimed in claim 3, wherein the step of analyzing, from the at least one reference device set, a reference device set that is the best match with the smart home device to be determined by the operation behavior analysis neural network according to the corresponding device behavior description vector of the category and the operation behavior description vector of the smart home device to be determined, comprises:
analyzing a self focusing characteristic analysis result corresponding to the reference equipment set and a correlation focusing characteristic analysis result corresponding to the reference equipment set according to the equipment behavior description vector of the type through the operation behavior analysis neural network;
based on the associated focusing characteristic analysis result and the self focusing characteristic analysis result corresponding to the equipment behavior description vector, analyzing vector matching parameters between the equipment behavior description vector and the operation behavior description vector corresponding to the smart home equipment to be determined;
And analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined from the at least one reference equipment set based on the vector matching parameters.
5. The smart home device feature determination method according to claim 4, wherein the step of analyzing, by the operation behavior analysis neural network, a self-focusing feature analysis result and a corresponding correlation focusing feature analysis result corresponding to the reference device set according to the device behavior description vector of the type, includes:
analyzing a neural network through the operation behaviors, and analyzing a self-focusing characteristic analysis result according to the device behavior description vector of the type corresponding to the reference device set;
and analyzing an associated focusing characteristic analysis result of each reference equipment set according to the operation behavior description vector corresponding to the intelligent home equipment to be determined and each equipment behavior description vector of each type corresponding to the reference equipment set through the operation behavior analysis neural network.
6. The smart home device feature determining method as claimed in claim 5, wherein the step of analyzing, by the operation behavior analysis neural network, the associated focus feature analysis result of each of the reference device sets according to the operation behavior description vector corresponding to the smart home device to be determined and each of the device behavior description vectors of the category corresponding to the reference device sets, includes:
Based on first parameter distribution included in the operation behavior analysis neural network, carrying out weighting processing on operation behavior description vectors corresponding to the smart home equipment to be determined so as to output corresponding weighted operation behavior description vectors, and carrying out row and column exchange operation of vector parameters on the type of equipment behavior description vectors corresponding to each reference equipment set so as to output corresponding exchange equipment behavior description vectors;
and for each reference equipment set, multiplying the change equipment behavior description vector corresponding to the reference equipment set and the weighted operation behavior description vector corresponding to the smart home equipment to be determined, and performing excitation mapping processing on the multiplied result to output an excitation mapping description vector corresponding to the reference equipment set, and multiplying the equipment behavior description vector of the type corresponding to the reference equipment set and the excitation mapping description vector to output an associated focusing characteristic analysis result corresponding to the reference equipment set.
7. The smart home device feature determination method of claim 5, wherein the step of analyzing, by the operation behavior analysis neural network, a self-focusing feature analysis result according to the device behavior description vector of the type corresponding to the reference device set comprises:
Taking any one of the reference device sets as a first reference device set;
based on a second parameter distribution matrix included in the operation behavior analysis neural network, weighting the class device behavior description vectors corresponding to the first reference device set to output corresponding weighted class device behavior description vectors;
performing row-column exchange operation of vector parameters on the class device behavior description vectors corresponding to the first reference device set so as to output corresponding exchange class behavior description vectors;
performing a first excitation mapping process on the weighted category behavior description vector to output a first excitation mapping description vector corresponding to the first reference device set, performing a multiplication process on the transposed category behavior description vector and the first excitation mapping description vector, and performing a second excitation mapping process on the result of the multiplication process to output a corresponding second excitation mapping description vector, the first excitation mapping process being different from the second excitation mapping process;
and multiplying the device behavior description vector of the type corresponding to the first reference device set and the corresponding second excitation mapping description vector to output a self-focusing characteristic analysis result corresponding to the first reference device set.
8. The smart home device feature determination method as claimed in any one of claims 1 to 7, wherein the step of mining out the operation behavior description vector corresponding to the smart home device to be determined comprises:
collecting multi-angle equipment operation behavior data of the smart home equipment to be determined;
performing key information mining operation on the multi-angle equipment operation behavior data corresponding to the smart home equipment to be determined so as to output operation behavior description vectors of a plurality of angles of the smart home equipment to be determined;
and carrying out vector cascading combination processing on the operation behavior description vectors of the angles corresponding to the smart home equipment to be determined so as to output the operation behavior description vectors corresponding to the smart home equipment to be determined.
9. An intelligent home device feature determination system, comprising:
the description vector mining module is used for mining operation behavior description vectors corresponding to the intelligent home equipment to be determined and mining reference operation behavior description vectors corresponding to the reference intelligent home equipment;
the device classification module is used for performing classification operation on a plurality of the reference smart home devices according to the reference operation behavior description vectors corresponding to the reference smart home devices so as to form at least one corresponding reference device set, wherein any one of the at least one reference device set comprises at least one reference smart home device with the same device characteristic type information;
The category vector determining module is used for analyzing category equipment behavior description vectors corresponding to each reference equipment set based on the included reference operation behavior description vectors corresponding to the reference intelligent home equipment;
the matching set determining module is used for analyzing a reference equipment set which is most matched with the intelligent home equipment to be determined in the at least one reference equipment set according to the equipment behavior description vector of the type and the operation behavior description vector corresponding to the intelligent home equipment to be determined, and marking the reference equipment set as a corresponding matching reference equipment set;
and the equipment characteristic type determining module is used for carrying out marking processing on the equipment characteristic type information corresponding to the matched reference equipment set so as to mark the equipment characteristic type information corresponding to the intelligent home equipment to be determined.
10. The smart home device feature determination system of claim 9, further comprising at least one other functional module for:
acquiring multi-angle equipment operation behavior data corresponding to reference intelligent home equipment, and determining equipment characteristic type information corresponding to the reference intelligent home equipment;
Performing key information mining on the multi-angle equipment operation behavior data corresponding to the reference intelligent home equipment, and outputting an operation behavior description vector corresponding to the reference intelligent home equipment;
updating the initial neural network through typical equipment operation behavior data, and outputting a corresponding candidate neural network, wherein the typical equipment operation behavior data comprises operation behavior description vectors corresponding to the reference intelligent home equipment and corresponding equipment characteristic type information;
analyzing the network reliability of the candidate neural network to output a network reliability characterization parameter corresponding to the candidate neural network;
and under the condition that the network reliability characterization parameter is greater than or equal to a pre-configured reference reliability characterization parameter, marking the candidate neural network so as to be marked as a corresponding operation behavior analysis neural network.
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