CN115268282A - Control method and device of household appliance, storage medium and electronic device - Google Patents
Control method and device of household appliance, storage medium and electronic device Download PDFInfo
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Abstract
The application discloses a control method and device of household electrical appliance, a storage medium and an electronic device, relating to the technical field of smart families, wherein the control method of the household electrical appliance comprises the following steps: acquiring N device operation logs; generating a heterogeneous graph based on the N equipment operation logs, wherein the heterogeneous graph comprises a plurality of operation jumping nodes among the M household electrical appliances and a connection relation among the operation jumping nodes; determining jump probability distribution between a target operation jump node and other operation jump nodes in a heterogeneous graph so as to determine jump nodes associated with the target operation jump node; and determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
Description
Technical Field
The present application relates to the field of devices, and in particular, to a method and an apparatus for controlling a home appliance, a storage medium, and an electronic apparatus.
Background
When the development of "smart home" is more and more appropriate, but the traditional "single-point intelligence" or "single-machine intelligence" has been gradually difficult to bring better experience to users, and the demand of "network device linkage" is increasingly strong. For example, when a user wants to take a bath, a bath scene (the scene is the linkage operation of a plurality of net devices, and the bath scene can be the scene of turning on a water heater, setting the temperature to 39 ℃, turning on a bath heater and turning on a ventilation fan) is intelligently recommended to the user, so that the user experience can be greatly improved.
But scene generation is a difficulty, and involves permutation and combination of multiple functions of multiple networkable, which may be in the order of hundreds of millions, but not all permutation and combination are meaningful, and it is basically impossible to realize manual screening. The scenes predefined manually in advance are monotonous, and the personalized requirements of the users are difficult to consider.
At present, the control of household appliances is also on single-point intelligence, namely, the intellectualization of a network appliance single machine is concerned. And generating a control instruction based on the isomorphic graph, and ignoring the attributes of the nodes and edges in the graph. And the control command for interconnection between the home appliances cannot be accurately generated.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a storage medium and an electronic device for controlling household appliances, which are used for at least solving the problem that interconnected control instructions among the household appliances cannot be accurately generated in the related technology.
According to an embodiment of the present invention, there is provided a method for controlling a home appliance, including: acquiring N device operation logs, wherein each device operation log comprises a plurality of operation jump instructions executed on M household appliances, the operation jump instructions are used for indicating that the household appliances are jumped from a first operation control to other operation controls, and M and N are natural numbers which are greater than or equal to 1; generating an abnormal graph based on the N equipment operation logs, wherein the abnormal graph comprises a plurality of operation jumping nodes among the M household electrical appliances and a connection relation among the operation jumping nodes; determining jump probability distribution between a target operation jump node and other operation jump nodes in the abnormal graph so as to determine jump nodes associated with the target operation jump node; and determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
According to an embodiment of the present invention, there is provided a control apparatus for a home appliance, including: a first obtaining module, configured to obtain N device operation logs, where each device operation log includes multiple operation jump instructions executed on M home appliances, where the operation jump instructions are used to indicate that a first operation control on the home appliance jumps to another operation control, and M and N are both natural numbers greater than or equal to 1; a first generating module, configured to generate an exception graph based on the N device operation logs, where the exception graph includes connection relationships between multiple operation skip nodes between the M home appliances and the multiple operation skip nodes; a first determining module, configured to determine, in the heterogeneous graph, a jump probability distribution between a target operation jump node and other operation jump nodes, so as to determine a jump node associated with the target operation jump node; and the second determining module is used for determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
In an exemplary embodiment, the first obtaining module includes: a first obtaining unit, configured to obtain each device operation log to obtain N device operation logs, where obtaining each device operation log performs the following operations: acquiring operation instructions for controlling the household electrical appliance in a preset area to obtain a plurality of operation instructions; determining at least one of the following information as the device operation log: the operation device comprises a plurality of operation commands, time information corresponding to the operation commands, position information corresponding to the operation commands and environment information corresponding to the operation commands.
In an exemplary embodiment, the first generating module includes: a first processing unit, configured to perform deduplication processing on N pieces of the device operation logs to obtain K pieces of the device operation logs, where K is a natural number that is less than or equal to M; a first determining unit, configured to determine an operation type of each of K pieces of the device operation logs; a second determining unit, configured to determine an operation jump node corresponding to each operation jump instruction in K device operation logs, to obtain multiple operation jump nodes; a third determining unit, configured to determine a connection relationship between multiple operation skip nodes to obtain multiple connection edges; a first generating unit configured to generate the abnormal pattern based on the operation type, the plurality of operation skip nodes, and the plurality of connection edges.
In an exemplary embodiment, the apparatus further includes: a third determining module, configured to determine an operation category corresponding to each operation jump node after generating a heterogeneous graph based on the N device operation logs; and a first response module, configured to respond to an operation instruction applied to the home appliance by using the heteromorphic graph based on an operation type corresponding to each of the operation jump nodes.
In an exemplary embodiment, the first determining module includes: a first receiving unit, configured to receive an operation instruction acting on the target home appliance device, where the operation instruction includes the target operation skip node; a fourth determining unit, configured to determine environment information in a preset area corresponding to the operation instruction and trigger feature information of the operation instruction, where the target home appliance device is set in the preset area; a fifth determining unit configured to determine a jump probability distribution between the target operation jump node and an adjacent operation jump node included in the other operation jump nodes, using the anomaly map; a sixth determining unit, configured to determine a jumping node associated with the target operation jumping node based on the jumping probability distribution, the environment information, and the feature information.
In an exemplary embodiment, the fifth determining unit includes: a first determining subunit, configured to determine a plurality of operation hop nodes associated with the target operation hop node; a second determining subunit, configured to determine, in the abnormal graph, a connection relationship between the plurality of operation skip nodes and the target operation skip node, and determine a plurality of connection relationships; a third determining subunit configured to determine a sum of the weights of the plurality of connection relationships; a first processing subunit, configured to normalize the sum of the weights to determine the hop probability distribution.
In an exemplary embodiment, the fifth determining unit includes: a fourth determining subunit, configured to determine multiple operation skip nodes associated with the target operation skip node; a fifth determining subunit, configured to determine, as sample data, a plurality of connection edges between the target operation skip node and the associated plurality of operation skip nodes; a first establishing subunit, configured to establish a network model by using the sample data, so as to output the jump probability distribution by using the network model.
In an exemplary embodiment, the apparatus further includes: and the first correcting module is used for correcting the target operation instruction according to the equipment information of the target household appliance after determining the target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
According to a further embodiment of the present invention, there is also provided a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to, when executed, perform the steps of any of the method embodiments described above.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the method and the device, N equipment operation logs are obtained, wherein each equipment operation log comprises a plurality of operation jump instructions executed on M household appliances, the operation jump instructions are used for indicating that the household appliances are jumped from a first operation control to other operation controls, and M and N are natural numbers which are larger than or equal to 1; generating a heterogeneous graph based on the N equipment operation logs, wherein the heterogeneous graph comprises a plurality of operation jumping nodes among the M household electrical appliances and a connection relation among the operation jumping nodes; determining jump probability distribution between the target operation jump node and other operation jump nodes in the heterogeneous graph so as to determine jump nodes associated with the target operation jump node; and determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node. In the method, the heterogeneous graph of the operation log is constructed, so that the attributes of the edges among the operation skip nodes and the attributes of the operation skip nodes can be obtained. The attributes of the edges and the nodes and the jump probability distribution between the target operation jump node and other operation jump nodes are integrated, so that the control instruction of the household appliance can be generated more practically. The accuracy and diversity of the search control instruction are improved while the search speed is ensured. Therefore, the problem that the control command of interconnection between the household appliances cannot be accurately generated in the related art can be solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a hardware environment diagram of a method for controlling a home appliance according to an embodiment of the present application;
fig. 2 is a control method of a home appliance according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of nodes and edges according to an embodiment of the invention;
FIG. 4 is a schematic illustration of probability distributions according to an embodiment of the invention;
fig. 5 is a block diagram of a control apparatus of a home appliance according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the accompanying drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an aspect of the embodiments of the present application, a method for controlling a home appliance is provided. The control method of the household appliance is widely applied to full-House intelligent digital control application scenes such as intelligent homes (Smart Home), intelligent homes, intelligent household appliance ecology, intelligent residence ecology and the like. Alternatively, in the present embodiment, the control method of the home device may be applied to a hardware environment formed by the terminal device 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal device 102 through a network, and may be configured to provide a service (e.g., an application service) for the terminal or a client installed on the terminal, set a database on the server or independent of the server, and provide a data storage service for the server 104, and configure a cloud computing and/or edge computing service on the server or independent of the server, and provide a data operation service for the server 104.
The network may include, but is not limited to, at least one of: wired network, wireless network. The wired network may include, but is not limited to, at least one of: wide area networks, metropolitan area networks, local area networks, which may include, but are not limited to, at least one of the following: WIFI (Wireless Fidelity ), bluetooth. Terminal equipment 102 can be but not limited to be PC, the cell-phone, the panel computer, intelligent air conditioner, intelligent cigarette machine, intelligent refrigerator, intelligent oven, intelligent kitchen range, intelligent washing machine, intelligent water heater, intelligent washing equipment, intelligent dish washer, intelligent projection equipment, intelligent TV, intelligent clothes hanger, intelligent (window) curtain, intelligence audio-visual, smart jack, intelligent stereo set, intelligent audio amplifier, intelligent new trend equipment, intelligent kitchen guarding equipment, intelligent bathroom equipment, intelligence robot of sweeping the floor, intelligence robot of wiping the window, intelligence robot of mopping the ground, intelligent air purification equipment, intelligent steam ager, intelligent microwave oven, intelligent kitchen is precious, intelligent clarifier, intelligent water dispenser, intelligent lock etc..
In the embodiment, a method for controlling a home appliance is provided, and fig. 2 is a method for controlling a home appliance according to an embodiment of the present invention, and as shown in fig. 2, the process includes the following steps:
step S202, N equipment operation logs are obtained, wherein each equipment operation log comprises a plurality of operation jump instructions executed on M household appliances, the operation jump instructions are used for indicating that the household appliances are jumped from a first operation control to other operation controls, and M and N are natural numbers which are larger than or equal to 1;
step S204, generating a heterogeneous graph based on the N equipment operation logs, wherein the heterogeneous graph comprises a plurality of operation jumping nodes among the M household appliances and a connection relation among the plurality of operation jumping nodes;
step S206, determining jump probability distribution between the target operation jump node and other operation jump nodes in the heterogeneous graph so as to determine jump nodes related to the target operation jump node;
and step S208, determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
Optionally, the values of N and M may be flexibly set based on an actual reference scene or a composition requirement, for example, more than 500 ten thousand device operation logs are obtained, and each device operation log is one operation transfer to the home appliance in one home. For example, control operations from turning on the air conditioner to turning off the air conditioner are recorded on a home basis. In addition, one device operation log comprises the contextual characteristics such as the time from the operation to the operation, the place, the weather and the like and a part of the family portrait characteristics.
The main body of the above steps may be a terminal, a server, a specific processor provided in the terminal or the server, or a processor or a processing device provided independently from the terminal or the server, but is not limited thereto.
Through the steps, N equipment operation logs are obtained, wherein each equipment operation log comprises a plurality of operation jump instructions executed on M household appliances, the operation jump instructions are used for indicating that the household appliances are jumped from a first operation control to other operation controls, and M and N are natural numbers which are larger than or equal to 1; generating a heterogeneous graph based on the N equipment operation logs, wherein the heterogeneous graph comprises a plurality of operation jumping nodes among the M household electrical appliances and a connection relation among the operation jumping nodes; determining jump probability distribution between a target operation jump node and other operation jump nodes in a heterogeneous graph so as to determine jump nodes associated with the target operation jump node; and determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node. Because the heterogeneous graph of the operation log is constructed in the method, the attributes of the edges among the operation skip nodes and the attributes of the operation skip nodes can be obtained. The attributes of the edges and the nodes and the jump probability distribution between the target operation jump node and other operation jump nodes are integrated, so that the control instruction of the household appliance can be generated more practically. The accuracy and diversity of the search control instruction are improved while the search speed is ensured. Therefore, the problem that the control command of interconnection between the household appliances cannot be accurately generated in the related art can be solved.
In one exemplary embodiment, obtaining N device operation logs comprises:
s1, obtaining each equipment operation log to obtain N equipment operation logs, wherein the obtained equipment operation logs execute the following operations:
acquiring operation instructions for controlling household appliances in a preset area to obtain a plurality of operation instructions;
determining at least one of the following information as a device operation log: the method comprises the steps of obtaining a plurality of operation commands, time information corresponding to the operation commands, position information corresponding to the operation commands and environment information corresponding to the operation commands.
Alternatively, the preset area may be a local area network, for example, in a unit of a home, operations such as "turn on air conditioner-turn off air conditioner" and the like occurring in a home are acquired, and the time of issuing each operation instruction in "turn on air conditioner-turn off air conditioner", the position of the air conditioner, weather information (for example, sunny days or rainy days), and the like are recorded. By acquiring each device operation log in units of a home, an abnormal picture can be accurately generated in combination with the context characteristics of the device operation log.
In one exemplary embodiment, generating the anomaly map based on the N device operation logs includes:
s1, carrying out duplicate removal processing on N equipment operation logs to obtain K equipment operation logs, wherein K is a natural number less than or equal to M;
s2, determining the operation type of each device operation log in the K device operation logs;
s3, determining operation jump nodes corresponding to each operation jump instruction in K equipment operation logs to obtain a plurality of operation jump nodes;
s4, determining the connection relation among a plurality of operation skip nodes to obtain a plurality of connection edges;
and S5, generating an abnormal graph based on the operation type, the operation skip nodes and the connecting edges.
Optionally, the value of K may be flexibly set based on an actual reference scene or a composition requirement, for example, removing duplicate logs in 500 ten thousand device operation logs to obtain 300 ten thousand device operation logs.
Alternatively, the deduplicated device operation logs may be arranged in one set. To record log information (e.g., log category, time of acquisition, location, etc.) corresponding to the device operation log in the collection.
In one exemplary embodiment, after generating the abnormal pattern based on the N device operation logs, the method further includes:
s1, determining an operation type corresponding to each operation jump node;
and S2, responding to an operation instruction acting on the household appliance by using the heterogeneous graph based on the operation type corresponding to each operation jump node.
Optionally, the operation types corresponding to different operation jump nodes may be the same or different. For example, the operation category of the instruction to turn on the air conditioner is "on". The temperature of the refrigerator is adjusted to 4 degrees, and the refrigerator belongs to the category of temperature adjustment. And selecting an operation jump node corresponding to the operation instruction of the user from the heterogeneous graph, wherein the operation jump node corresponding to the air conditioner can be 'turn on the air conditioner, turn down the temperature of the air conditioner and turn up the wind speed of the air conditioner', for example.
In one exemplary embodiment, determining a hop probability distribution between a target operation hop node and other operation hop nodes in a heterogeneous graph to determine a hop node associated with the target operation hop node comprises:
the method comprises the following steps of S1, receiving an operation instruction acting on target household electrical appliance equipment, wherein the operation instruction comprises a target operation jumping node;
s2, determining environmental information in a preset area corresponding to the operation instruction and characteristic information for triggering the operation instruction, wherein the target household appliance is arranged in the preset area;
s3, determining jump probability distribution between the target operation jump node and the adjacent operation jump node by using the heteromorphic graph, wherein the adjacent operation jump node is included in other operation jump nodes;
and S4, determining the jump node associated with the target operation jump node based on the jump probability distribution, the environment information and the characteristic information.
Alternatively, the operation instruction may be in a voice manner or in a touch screen manner. For example, the user issues an instruction to turn on the water heater, searches for an operation jump node adjacent to the turn on water heater in the abnormality map, the air conditioner temperature modulation 28 degrees, the turn on ventilation device, and the like. And calculating the jump probability between the air conditioner temperature modulation temperature of 28 degrees and the air exchange device opening. And when the jump probability between the air conditioner temperature modulation node and the air conditioner temperature modulation node is more than 70%, determining that the air conditioner temperature modulation node is 28 degrees and the air exchange device is opened as a jump node associated with the water heater, and controlling the air conditioner and the air exchange device to be opened according to a control instruction corresponding to the associated jump node.
In one exemplary embodiment, determining a hop probability distribution between a target operational hop node and an adjacent operational hop node using an anomaly map comprises:
s1, determining a plurality of operation skip nodes associated with a target operation skip node;
s2, determining the connection relation between the associated multiple operation jumping nodes and the target operation jumping node in the heterogeneous graph, and determining multiple connection relations;
s3, determining the sum of the weights of a plurality of connection relations;
and S4, normalizing the sum of the weights to determine the jump probability distribution.
Alternatively, the plurality of connection relationships may be connecting lines of the operation jumping nodes in the abnormal graph. Forming the edges of the heterogeneous graph. For example, the transition from operation i to operation j is an edge, and the edge is a directed graph. If multiple transfer operations exist between the node i and the node j, multiple edges exist, homogeneous edges exist in the multiple edges, and the edges with the same attributes can be aggregated according to the edge attributes.
Optionally, in this embodiment, under the condition that the user or the family feature information and the context feature are not considered, all the edge weights belonging to the nodes i to j may be added and then normalized to determine the probability distribution.
In one exemplary embodiment, determining a hop probability distribution between a target operational hop node and an adjacent operational hop node using an anomaly map comprises:
s1, determining a plurality of operation jumping nodes associated with a target operation jumping node;
s2, determining a plurality of connecting edges between the target operation jump node and the associated operation jump nodes as sample data;
and S3, establishing a network model by using the sample data so as to output the jump probability distribution by using the network model.
Alternatively, each connecting edge in the heteromorphic graph may be taken as one sample. For example, in a heterogeneous graph, nodes i through j may yield T samples. After the training sample is obtained, the probability from the learning node i to any adjacent node can be modeled by adopting the xgboost or LSTM time sequence.
In an exemplary embodiment, after determining the target operation instruction of the target home device according to the jump node associated with the target operation jump node, the method further includes:
s1, correcting a target operation instruction according to the equipment information of the target household electrical equipment.
Optionally, the revised rules include, but are not limited to: the starting of the sub-functions of the household electrical appliance is carried out after the local machine of the household electrical appliance is started; the time difference between the opening and closing of the same household appliance needs to be larger than a set threshold; the output operation instruction does not include the operation instruction of the pause class.
The invention is illustrated below with reference to specific examples:
in this embodiment, the operation (action) of the home appliance is a specific operation of the internet appliance function, for example, "turn on air conditioner", "adjust air speed of air conditioner high", "pause of television", and the internet appliance function is used to indicate a control command for the home appliance. In this embodiment, the net device function may generate a usage scenario of the home appliance, for example, during bathing, a bathing scenario is recommended to the user (the scenario is a linkage operation of a plurality of net devices, where the bathing scenario may be < turn on the water heater, set temperature to 39 ℃, turn on the bath heater, turn on the ventilation fan >).
Scene (scene) is used to represent an operation sequence (i.e. associated operation instructions), for example, < turn on air conditioner, air conditioner temperature turn down, air conditioner wind speed turn up >.
The present embodiment generates a heterogeneous graph using a plurality of operation logs, where the heterogeneous graph G = (V, E), V is used to represent a set of nodes, and V = { V =1，v2，...，viIs 1 ≦ i ≦ V |, E is used to represent the set of edges E = { E1，e2，...，ejThe momentum is 1 is more than or equal to j and less than or equal to | E |. If there is a mapping Φ: v → O and mapping Ψ: e → R, O and R are used to represent the set of types of nodes and edges, respectively, if | O | + | R! R>FIG. 2 is a schematic view of the hetero-structure. In this embodiment, actions are used to indicate nodes in the abnormal composition, and the transition relationship between the actions is an edge of the abnormal composition.
The main implementation process of this embodiment includes:
step1, collecting and cleaning historical operation logs of all users;
step2, constructing a heteromorphic graph based on the operation log;
step3, searching by using a beacon search strategy based on the heteromorphic graph, and generating a plurality of scenes (operation instructions related to the household electrical appliance);
step4, the generated scene is corrected according to a predetermined rule.
In this embodiment, the collecting and cleaning the historical operation logs of all users specifically includes: more than 500 ten thousand records are acquired, each record being one operation shift of one home, for example, from turning on the air conditioner to turning off the air conditioner, here in units of homes. In addition, one record will contain the contextual characteristics of the time from the operation to occur, the location, the weather, etc., as well as a portion of the family portrait characteristics.
In this embodiment, the constructing the heterogeneous graph specifically includes:
1) Definition node (corresponding to the operation jumping node in the above): based on the total data acquired by Step1, the action set can be obtained by removing the duplicates of all the actions, and the action set forms all the nodes v of the heterogeneous graphiE.g. V, and defining each node ViClass c to whichi，ciE.g. "turn on air conditioner" belongs to the category "cool" and "turn on oven" belongs to the category "cook".
2) Defining edges (corresponding to the connecting edges in the above): one side is formed by transferring operation i to operation j once, and e is usedijTo represent (here, a directed graph). If multiple transfer operations exist between the node i and the node j, multiple edges exist, homogeneous edges exist in the multiple edges, and the edges with the same attributes can be aggregated according to the edge attributes.
A schematic of the nodes and edges is shown in fig. 3. Optionally using
Representing a set of edges i through j, where,
for the t-th edge of nodes i to j, using vectors
To represent EijBy scalar quantity
To represent EijThe weight of the t-th edge of (a), the weight actually means the number of times operation i is transferred to operation j within the statistical range.
3) Defining an abnormal picture: constructing an abnormal graph G = (V, E) after all nodes and edges are obtained;
4) Defining metapath: the metapath is a wandering rule designed by people and used for restricting the type of a selected node during wandering, and M = [ M = [ M ]1，m2，...，mm]M is derived from a set of node types C, e.g. if M = [ cooling, light ray = [ ]]Then the first two positions in the sequence of the resulting scene operation must be nodes of the "cool" class and the third must be nodes of the "light" class. Here we resemble the idea of a metapath2vec, but it is not required that the metapath must be symmetrical.
In this embodiment, based on the heterogeneous graph, a beacon strategy (used for selecting a better result) is used for searching, and a plurality of scenes are generated; searching based on beamsearch is a trade-off between search complexity and search speed. If beam constraint of search is not carried out, complexity is exponentially increased every time the beam is expanded by one step backwards, namely full arrangement is carried out, and if depth is set as depth, n is the number of nodes, k is search bandwidth, and complexity is O (n)depth) The complexity can be reduced to O (depth k n) by using the beamseach constraint to search the bandwidth. If a greedy strategy is adopted for searching, beam =1 is the equivalent, namely, the search only produces one sequence.
When the beacon search is carried out, the probability distribution from the current node to the receiving node is required to be referred to each time the propagation is carried out for one step,
representing the domain of node i. User or family characteristics and context characteristics that trigger scene generation, here denoted as q, q and a, are also consideredijAre all vectors of order n, so the probability distribution to be learned is P (node)j|nodei，q)。
In this embodiment, the probability distribution can be determined in two ways:
1) Adopting a generating model generating-model:
based on the Step2 heterogeneous graph, if the user or family characteristics and the context characteristics are not considered, the probability distribution is obtained by adding all the edge weights belonging to the nodes i to j and then carrying out normalization, namely the probability distribution is obtained
If user/family characteristics and context characteristics are considered,
similarity is a similarity calculation function.
2) Adopting a discriminant model discrete-model:
each edge of the heterogeneous graph in Step2 can be used as a sample, the action corresponding to the node i and the attributes of the edges from i to j are used as independent variables x, and the action corresponding to the node j is used as a dependent variable y. More formally: in a heterogeneous graph, nodes i to j may yield T samples, each sample s(t)＝{x(t)，y(t)}，
y(t)＝actionjWherein t is [1, T ]]. After the training sample is obtained, xgboost or LS can be adoptedTM timing modeling learns the probability P (node) of a node i to any adjacent node thereofj|nodei，p)＝model(nidei，p)。
In practical application, the special pattern is found to be highly sparse, and the discriminant method is more practical.
In addition, when selecting the next node, not only the probability distribution of the current node to its neighboring nodes but also the node of the previous step is considered, for example, as shown in fig. 4:
t- > v- > z, weight 0 because vz has no path;
t- > v- > t, the weight is 0 because no look-back is allowed;
t->v->x3, weight is w (t, v) + w (v, x)3)；
t->v->x1, weight is w (t, v) + w (v, x)1)+α·w(t，x1) Here because of t->x1 also has paths, and the weight of this part is also taken into account by a coefficient α, α > 0, the larger the value, the more likely it is to wander locally.
In the embodiment, when performing the migration, the migration may be performed based on the defined metapath, and the node that does not meet the metapath requirement is not selected as the next node.
In the present embodiment, the produced scene is corrected according to a predetermined rule. The specific revision rule includes: the opening of the sub-function of the equipment is needed after the local machine of the equipment is opened; the opening and closing time difference of the same equipment is required to be larger than a set threshold; the output scene does not contain pause operation.
In summary, in the field of network device linkage intellectualization, the embodiment proposes that an actual network device linkage problem is abstracted into a heterogeneous graph for modeling, attributes of each network device and each transfer operation are defined, and user characteristics and context characteristics are combined during reasoning, so that the method is more practical; the method can realize the requirement of the beacon search on scene generation, and improve the accuracy and diversity of the search result while ensuring the search speed; the machine learning based approach predicts the user's next actions taking into account user features and contextual features. The method is more practical, so that the output result is more personalized, and the user experience can be improved; better algorithm performance, even if depth-first search is carried out, the complexity of the result is ensured to be controlled at the same time, so that the engineering performance is better, and the usability is higher.
In this embodiment, an image processing apparatus is further provided, and the apparatus is used to implement the foregoing embodiments and preferred embodiments, and the description of the apparatus is omitted for brevity. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware or a combination of software and hardware is also possible and contemplated.
Fig. 5 is a block diagram of a control apparatus of a home appliance according to an embodiment of the present invention, and as shown in fig. 5, the apparatus includes:
a first obtaining module 52, configured to obtain N device operation logs, where each device operation log includes multiple operation jump instructions executed on M household appliances, an operation jump instruction is used to indicate that a jump is made from a first operation control on a household appliance to another operation control, and M and N are both natural numbers greater than or equal to 1;
a first generating module 54, configured to generate an isomerous graph based on the N device operation logs, where the isomerous graph includes a plurality of operation jump nodes between the M home devices and a connection relationship between the plurality of operation jump nodes;
a first determining module 56, configured to determine a jump probability distribution between the target operation jump node and other operation jump nodes in the heterogeneous graph, so as to determine a jump node associated with the target operation jump node;
and a second determining module 58, configured to determine a target operation instruction of the target home device according to the jump node associated with the target operation jump node.
In an exemplary embodiment, the first obtaining module includes:
a first obtaining unit, configured to obtain each device operation log to obtain N device operation logs, where obtaining each device operation log performs the following operations: acquiring operation instructions for controlling the household electrical appliance in a preset area to obtain a plurality of operation instructions; determining at least one of the following information as the device operation log: the operation device comprises a plurality of operation commands, time information corresponding to the operation commands, position information corresponding to the operation commands and environment information corresponding to the operation commands.
In an exemplary embodiment, the first generating module includes:
a first processing unit, configured to perform deduplication processing on N pieces of the device operation logs to obtain K pieces of the device operation logs, where K is a natural number that is less than or equal to M;
a first determining unit, configured to determine an operation type of each of K pieces of the device operation logs;
a second determining unit, configured to determine an operation skip node corresponding to each operation skip instruction in K device operation logs, to obtain multiple operation skip nodes;
a third determining unit, configured to determine a connection relationship between multiple operation skip nodes to obtain multiple connection edges;
a first generating unit configured to generate the abnormal pattern based on the operation type, the plurality of operation skip nodes, and the plurality of connection edges.
In an exemplary embodiment, the apparatus further includes:
a third determining module, configured to determine an operation type corresponding to each operation jump node after generating a heterogeneous graph based on the N device operation logs;
and a first response module, configured to respond to an operation instruction applied to the home appliance by using the heteromorphic graph based on an operation type corresponding to each of the operation jump nodes.
In an exemplary embodiment, the first determining module includes:
a first receiving unit, configured to receive an operation instruction acting on the target household appliance, where the operation instruction includes the target operation skip node;
a fourth determining unit, configured to determine environment information in a preset area corresponding to the operation instruction and trigger feature information of the operation instruction, where the target home appliance device is set in the preset area;
a fifth determining unit configured to determine a jump probability distribution between the target operation jump node and an adjacent operation jump node included in the other operation jump nodes, using the anomaly map;
a sixth determining unit, configured to determine a jumping node associated with the target operation jumping node based on the jumping probability distribution, the environment information, and the feature information.
In an exemplary embodiment, the fifth determining unit includes:
a first determining subunit, configured to determine a plurality of operation skip nodes associated with the target operation skip node;
a second determining subunit, configured to determine, in the abnormal graph, a connection relationship between the plurality of operation skip nodes and the target operation skip node, and determine a plurality of connection relationships;
a third determining subunit configured to determine a sum of the weights of the plurality of connection relationships;
a first processing subunit, configured to normalize the sum of the weights to determine the hop probability distribution.
In an exemplary embodiment, the fifth determining unit includes:
a fourth determining subunit, configured to determine multiple operation skip nodes associated with the target operation skip node;
a fifth determining subunit, configured to determine, as sample data, a plurality of connection edges between the target operation skip node and the associated plurality of operation skip nodes;
a first establishing subunit, configured to establish a network model by using the sample data, so as to output the jump probability distribution by using the network model.
In an exemplary embodiment, the apparatus further includes:
and the first correcting module is used for correcting the target operation instruction according to the equipment information of the target household appliance after determining the target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (11)
1. A method for controlling a home appliance, comprising:
acquiring N device operation logs, wherein each device operation log comprises a plurality of operation jump instructions executed on M household appliances, the operation jump instructions are used for jumping from a first operation control on the household appliances to other operation controls, and M and N are natural numbers larger than or equal to 1;
generating an abnormal graph based on the N equipment operation logs, wherein the abnormal graph comprises a plurality of operation jumping nodes among the M household electrical appliances and the connection relation among the operation jumping nodes;
determining jump probability distribution between a target operation jump node and other operation jump nodes in the abnormal graph so as to determine jump nodes associated with the target operation jump node;
and determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
2. The method of claim 1, wherein obtaining N device oplogs comprises:
obtaining each device operation log to obtain N device operation logs, wherein the obtaining of each device operation log executes the following operations:
acquiring operation instructions for controlling the household electrical appliance in a preset area to obtain a plurality of operation instructions;
determining at least one of the following information as the device operation log: the operation instructions, the time information corresponding to the operation instructions, the position information corresponding to the operation instructions and the environment information corresponding to the operation instructions.
3. The method of claim 1, wherein generating a metamorphic graph based on the N device oplogs comprises:
carrying out duplicate removal processing on the N equipment operation logs to obtain K equipment operation logs, wherein K is a natural number less than or equal to M;
determining the operation category of each of the K equipment operation logs;
determining an operation jump node corresponding to each operation jump instruction in K device operation logs to obtain a plurality of operation jump nodes;
determining the connection relation among a plurality of operation jumping nodes to obtain a plurality of connection edges;
and generating the abnormal graph based on the operation category, the operation skip nodes and the connecting edges.
4. The method of claim 3, wherein after generating the anomaly map based on the N device operation logs, the method further comprises:
determining an operation type corresponding to each operation jump node;
and responding to an operation instruction acting on the household appliance by using the abnormal graph based on the operation type corresponding to each operation jump node.
5. The method according to claim 1, wherein determining a hop probability distribution between a target operation hop node and other operation hop nodes in the anomaly map to determine a hop node associated with the target operation hop node comprises:
receiving an operation instruction acting on the target household appliance, wherein the operation instruction comprises the target operation jumping node;
determining environment information in a preset area corresponding to the operation instruction and triggering characteristic information of the operation instruction, wherein the target household appliance is arranged in the preset area;
determining a jump probability distribution between the target operation jump node and the adjacent operation jump node by using the abnormal graph, wherein the adjacent operation jump node is included in other operation jump nodes;
and determining the skip nodes associated with the target operation skip nodes based on the skip probability distribution, the environmental information and the characteristic information.
6. The method according to claim 5, wherein determining a hop probability distribution between the target operational hop node and the adjacent operational hop node using the anomaly map comprises:
determining a plurality of operation skip nodes associated with the target operation skip node;
determining a plurality of connection relations between a plurality of associated operation jumping nodes and the target operation jumping node in the abnormal graph;
determining a sum of weights of a plurality of the connection relations;
normalizing the sum of the weights to determine the hop probability distribution.
7. The method according to claim 5, wherein determining a hop probability distribution between the target operational hop node and the adjacent operational hop node using the anomaly map comprises:
determining a plurality of operation skip nodes associated with the target operation skip node;
determining a plurality of connecting edges between the target operation skip node and a plurality of associated operation skip nodes as sample data;
and establishing a network model by using the sample data so as to output the jump probability distribution by using the network model.
8. The method of claim 1, wherein after determining the target operation instruction of the target home device according to the hop node associated with the target operation hop node, the method further comprises:
and correcting the target operation instruction according to the equipment information of the target household electrical equipment.
9. A control device for a home appliance, comprising:
the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring N device operation logs, each device operation log comprises a plurality of operation jump instructions executed on M household appliances, the operation jump instructions are used for indicating that the household appliances are jumped from a first operation control to other operation controls, and M and N are natural numbers which are more than or equal to 1;
a first generation module, configured to generate an abnormal graph based on the N device operation logs, where the abnormal graph includes a plurality of operation skip nodes between the M home devices and a connection relationship between the plurality of operation skip nodes;
the first determining module is used for determining jump probability distribution between a target operation jump node and other operation jump nodes in the abnormal graph so as to determine jump nodes related to the target operation jump node;
and the second determining module is used for determining a target operation instruction of the target household appliance according to the jump node associated with the target operation jump node.
10. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 8.
11. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program and the processor is arranged to execute the method of any of claims 1 to 8 by means of the computer program.
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| CN202210751813.9A CN115268282A (en) | 2022-06-29 | 2022-06-29 | Control method and device of household appliance, storage medium and electronic device |
| PCT/CN2023/075053 WO2024001196A1 (en) | 2022-06-29 | 2023-02-08 | Household appliance control method and apparatus, storage medium, and electronic apparatus |
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