Rule-based intelligent device behavior arrangement method and system

Abstract

The invention discloses a rule-based intelligent equipment behavior arrangement method and system, and the method comprises the steps: carrying out the scene clustering according to historical intelligent equipment, historical intelligent equipment behavior data and a plurality of historical scenes, and obtaining a clustering result of K scenes; selecting a clustering result corresponding to the to-be-arranged scene from the clustering results of the K scenes, and calculating a first popularity value of each intelligent device and a second popularity value of each intelligent device behavior in the clustering result corresponding to the to-be-arranged scene; determining a target intelligent device behavior corresponding to each target intelligent device according to the first popularity value and the second popularity value; and arranging the target intelligent equipment behavior corresponding to each target intelligent equipment by adopting a rule engine to obtain an intelligent equipment behavior arrangement result. According to the invention, the arrangement efficiency can be improved, and the arrangement can be changed according to public preferences, thereby better meeting the public preferences.

Description

Technical Field

[0001] The present application relates to the field of intelligent orchestration technology, and in particular to a rule-based intelligent device behavior orchestration method and system. Background Art

[0002] At present, smart devices have become more and more mature. Users can install mobile software (Application, APP) related to smart devices on the terminal, and can control smart devices by remotely operating the mobile software APP, or directly operate the smart device to control the smart device. Nowadays, in order to meet the needs of users to control multiple smart devices at the same time, users can create one-click scenes through mobile software APP. The process of creating a one-click scene can be called scene arrangement. Subsequently, the user can control multiple smart devices at the same time by operating the APP on the terminal with one click.

[0003] However, the relevant smart device behavior arrangement method can set the scene according to personal preferences and select the corresponding smart device, but this can only meet personal preferences and is not a suitable method for public occasions. However, the current relevant smart device behavior arrangement method must meet the public preferences. The arrangement method used needs to first determine the scene, then investigate the smart devices of the current scene that everyone likes and the smart device behavior corresponding to each smart device, and then count the smart devices and smart device behaviors, and then select the corresponding smart devices and smart device behaviors based on the statistical results, and then arrange the smart device behaviors according to the selected smart devices. Each scene needs to be counted and arranged once. Therefore, the current relevant smart device behavior arrangement method cannot change according to changes in public preferences, the arrangement effect is not very good, and the arrangement efficiency of the current relevant smart device behavior arrangement method is not high. Summary of the invention

[0004] The present application aims to propose a rule-based smart device behavior orchestration method and system, which can improve the orchestration efficiency and can change according to changes in public preferences, thereby improving the orchestration effect of smart device behavior.

[0005] In a first aspect, an embodiment of the present application provides a rule-based smart device behavior orchestration method, the method comprising:

[0006] Acquire a historical smart device, historical smart device behavior data corresponding to the historical smart device, multiple historical scenes corresponding to the historical smart device, and scenes to be arranged, wherein the historical smart device behavior data includes rules and events of each smart device behavior, and the rules include conditions and actions;

[0007] Performing scene clustering according to the historical smart device, the historical smart device behavior data, and the multiple historical scenes to obtain clustering results of K scenes;

[0008] Selecting a clustering result corresponding to the to-be-programmed scene from the clustering results of the K scenes, and calculating a first heat value of each smart device and a second heat value of each smart device behavior in the clustering result corresponding to the to-be-programmed scene;

[0009] Determine a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value;

[0010] A rule engine is used to arrange the target smart device behavior corresponding to each target smart device to obtain a smart device behavior arrangement result.

[0011] Compared with the prior art, the first aspect of the present application has the following beneficial effects:

[0012] This method clusters scenes according to historical smart devices, historical smart device behavior data, and multiple historical scenes to obtain clustering results of K scenes; selects the clustering results corresponding to the scenes to be arranged from the clustering results of the K scenes, and calculates the first heat value of each smart device and the second heat value of each smart device behavior in the clustering results corresponding to the scenes to be arranged; determines the target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value; and uses a rule engine to arrange the target smart device behavior corresponding to each target smart device to obtain the smart device behavior arrangement result. In this way, by clustering the smart devices and smart device behavior data for each scene, and then obtaining the historical scenes corresponding to the scenes to be arranged, the clustering results corresponding to the scenes to be arranged can be obtained. This clustering result includes all the smart devices and smart device behaviors required for the scenes to be arranged, and can lay a good data foundation for the later smart device behaviors. Then the heat value is calculated, and the target smart device behavior corresponding to each target smart device is determined according to the heat value. The smart devices and smart device behaviors that are popular with the public can be obtained, and the scheduling efficiency can be improved. It can change according to changes in public preferences, thereby improving the scheduling effect of smart device behaviors.

[0013] In some implementations, clustering the scenarios according to the historical smart devices, the historical smart device behavior data, and the multiple historical scenarios to obtain clustering results of K scenarios includes:

[0014] Numbering the multiple historical scenes;

[0015] Tagging the historical smart device and the historical smart device behavior data according to the numbers corresponding to the numbered multiple historical scenes to obtain the labeled historical smart device and the labeled historical smart device behavior data, wherein the labeled historical smart device and the labeled historical smart device behavior data correspond to at least one label;

[0016] The marked historical smart devices and the marked historical smart device behavior data are clustered by scenarios to obtain K scenario clustering results, each of which includes at least one first historical smart device and all historical smart device behavior data corresponding to the first historical smart device.

[0017] In some implementations, the performing scene clustering on the marked historical smart devices and the marked historical smart device behavior data to obtain clustering results of K scenes includes:

[0018] Selecting a target scene from the multiple historical scenes and obtaining a serial number of the target scene;

[0019] Compare the number of the target scene with the label of the marked historical smart device and the label of the marked historical smart device behavior data. If the number of the target scene is the same as the label of the marked historical smart device and the number of the target scene is the same as the label of the marked historical smart device behavior data, then use the marked historical smart device and the marked historical smart device behavior data as the clustering result of the target scene with the corresponding number; until each historical scene is selected as the target scene, and obtain the clustering results of K scenes.

[0020] In some implementations, the calculating the first heat value of each smart device and the second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged includes:

[0021] Obtaining a first total number of times each smart device is used in a historical scenario corresponding to the scenario to be arranged, and obtaining a second total number of times all smart devices are used in a clustering result corresponding to the scenario to be arranged;

[0022] A ratio between the first total number of times and the second total number of times is calculated to obtain a first heat value of each smart device in the clustering result corresponding to the scene to be arranged.

[0023] In some implementations, the calculating the first heat value of each smart device and the second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged includes:

[0024] Obtaining the number of times each smart device behavior is triggered, and obtaining the total number of times all smart device behaviors are triggered in the clustering result corresponding to the scene to be choreographed;

[0025] The ratio of the number of times each smart device behavior is triggered to the total number of times is calculated to obtain a second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged.

[0026] In some implementations, determining a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value includes:

[0027] Sorting the first heat values ​​to obtain sorted first heat values;

[0028] Selecting a plurality of target smart devices whose heat values ​​are greater than a first preset value from the sorted first heat values;

[0029] Selecting a second heat value of the smart device behavior corresponding to each of the target smart devices;

[0030] Sorting the second heat values ​​of the smart device behaviors corresponding to each of the target smart devices to obtain sorted second heat values;

[0031] A maximum heat value is selected from the sorted second heat values, and a smart device behavior corresponding to the maximum heat value is used as a target smart device behavior.

[0032] In some implementations, the rule-based smart device behavior orchestration method further includes:

[0033] Preset update cycle;

[0034] Re-arrange the behavior of the smart device for the scene to be arranged according to the update cycle;

[0035] The target smart device in the smart device behavior arrangement result before the update cycle is updated is used as the historical smart device, and the behavior of the target smart device after arrangement is used as the historical smart device behavior data.

[0036] In a second aspect, the embodiment of the present application further provides a rule-based smart device behavior orchestration system, the system comprising:

[0037] A data acquisition unit, configured to acquire a historical smart device, historical smart device behavior data corresponding to the historical smart device, a plurality of historical scenes corresponding to the historical smart device, and scenes to be arranged, wherein the historical smart device behavior data includes rules and events of each smart device behavior, and the rules include conditions and actions;

[0038] A scene clustering unit, configured to perform scene clustering according to the historical smart device, the historical smart device behavior data and the plurality of historical scenes, to obtain clustering results of K scenes;

[0039] a heat calculation unit, configured to select a clustering result corresponding to the to-be-programmed scene from the clustering results of the K scenes, and calculate a first heat value of each smart device and a second heat value of each smart device behavior in the clustering result corresponding to the to-be-programmed scene;

[0040] a data determination unit, configured to determine a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value;

[0041] The behavior arrangement unit is used to arrange the target smart device behavior corresponding to each target smart device by using a rule engine to obtain a smart device behavior arrangement result.

[0042] In a third aspect, an embodiment of the present application also provides an electronic device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute a rule-based smart device behavior orchestration method as described above.

[0043] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute a rule-based smart device behavior orchestration method as described above.

[0044] It can be understood that the beneficial effects of the second to fourth aspects compared with the related art are the same as the beneficial effects of the first aspect compared with the related art. Please refer to the relevant description in the first aspect, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0046] Figure 1 It is a flowchart of an embodiment of a rule-based smart device behavior orchestration method provided by the present application;

[0047] Figure 2 is a structural diagram of an embodiment of a rule-based intelligent device behavior orchestration system provided by the present application;

[0048] Figure 3It is a structural schematic diagram of an embodiment of an electronic device provided by the present application. DETAILED DESCRIPTION

[0049] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0050] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0051] In the description of the present application, it should be understood that the descriptions involving orientation, such as the orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0052] In the description of this application, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution.

[0053] At present, smart devices have become more and more mature. Users can install mobile software (Application, APP) related to smart devices on the terminal, and can control smart devices by remotely operating the mobile software APP, or directly operate the smart device to control the smart device. Nowadays, in order to meet the needs of users to control multiple smart devices at the same time, users can create one-click scenes through mobile software APP. The process of creating a one-click scene can be called scene arrangement. Subsequently, the user can control multiple smart devices at the same time by operating the APP on the terminal with one click.

[0054] However, the relevant smart device behavior arrangement method can set the scene according to personal preferences and select the corresponding smart device, but this can only meet personal preferences and is not a suitable method for public occasions. However, the current relevant smart device behavior arrangement method must meet the public preferences. The arrangement method used needs to first determine the scene, then investigate the smart devices of the current scene that everyone likes and the smart device behavior corresponding to each smart device, and then count the smart devices and smart device behaviors, and then select the corresponding smart devices and smart device behaviors based on the statistical results, and then arrange the smart device behaviors according to the selected smart devices. Each scene needs to be counted and arranged once. Therefore, the current relevant smart device behavior arrangement method cannot change according to changes in public preferences, the arrangement effect is not very good, and the arrangement efficiency of the current relevant smart device behavior arrangement method is not high.

[0055] In order to solve the above-mentioned problems that the behavior of smart devices cannot be changed according to changes in public preferences, the programming efficiency is low, and it is not very in line with public preferences, this application proposes a rule-based smart device behavior programming method and system.

[0056] Reference Figure 1 The present invention provides a rule-based smart device behavior scheduling method, which includes the following steps:

[0057] Step S100: acquiring historical smart devices, historical smart device behavior data corresponding to the historical smart devices, multiple historical scenes corresponding to the historical smart devices, and scenes to be arranged, wherein the historical smart device behavior data includes rules and events of each smart device behavior, and the rules include conditions and actions;

[0058] Step S200: clustering scenes according to historical smart devices, historical smart device behavior data and multiple historical scenes to obtain clustering results of K scenes;

[0059] Step S300: Select a clustering result corresponding to the scene to be arranged from the clustering results of the K scenes, and calculate a first heat value of each smart device and a second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged;

[0060] Step S400: determining a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value;

[0061] Step S500: Use a rule engine to arrange the target smart device behavior corresponding to each target smart device to obtain a smart device behavior arrangement result.

[0062] In this embodiment, by clustering scenes according to historical smart devices, historical smart device behavior data and multiple historical scenes, K scenes are clustered; the clustering results corresponding to the scene to be arranged are selected from the clustering results of the K scenes, and the first heat value of each smart device and the second heat value of each smart device behavior in the clustering results corresponding to the scene to be arranged are calculated; according to the first heat value and the second heat value, the target smart device behavior corresponding to each target smart device is determined; the rule engine is used to arrange the target smart device behavior corresponding to each target smart device to obtain the smart device behavior arrangement result. In this way, by clustering the smart devices and smart device behavior data for each scene, and then by obtaining the historical scenes corresponding to the scene to be arranged, the clustering results corresponding to the scene to be arranged can be obtained. This clustering result contains all the smart devices and smart device behaviors required for the scene to be arranged, which can lay a good data foundation for the later smart device behavior. Then the heat value is calculated, and the target smart device behavior corresponding to each target smart device is determined according to the heat value. The smart devices and smart device behaviors that are popular with the public can be obtained, and the scheduling efficiency can be improved. It can change according to changes in public preferences, thereby improving the scheduling effect of smart device behaviors.

[0063] The above historical smart device behavior data may include smart device behaviors of multiple smart devices, and the smart device behaviors include rules and events, so that the target smart device behaviors can be arranged using a rule engine according to the rules and events of the target smart device behaviors.

[0064] The above historical smart device behavior data corresponding to the historical smart devices may be a plurality of historical smart device behavior data corresponding to each historical smart device.

[0065] The above-mentioned scene clustering based on historical smart devices, historical smart device behavior data and multiple historical scenes can be performed by using a clustering algorithm in the prior art to cluster scenes based on historical smart devices, historical smart device behavior data and multiple historical scenes to obtain clustering results of K scenes.

[0066] The above-mentioned selecting the clustering result corresponding to the scene to be arranged from the clustering results of the K scenes may be selecting the scene corresponding to the scene to be arranged from the K scenes, and then obtaining the clustering result corresponding to the scene to be arranged according to the selected scene.

[0067] The target smart device behavior corresponding to each target smart device is determined according to the first heat value and the second heat value. The first heat value and the second heat value may be sorted, and then the target smart device behavior corresponding to each target smart device having a heat value greater than a preset value or the highest heat value is selected.

[0068] The rule engine can be developed from the reasoning engine. It is a component embedded in the application, which separates business decisions from application code and uses predefined semantic modules to write business decisions. It accepts data input, interprets business rules, and makes business decisions based on business rules.

[0069] Since the clustering results are obtained based on historical smart device behavior data, and the historical smart device behavior data includes the rules and events of each smart device behavior, and the rules include conditions and actions. First, the first heat value of each smart device and the second heat value of each smart device behavior in the clustering results corresponding to the scene to be arranged are calculated, and then the hot smart devices (i.e., target smart devices) and hot smart device behaviors (i.e., target smart device behaviors) can be selected according to the first heat value and the second heat value. Therefore, the hot smart device behaviors obtained can be the rules and events of each smart device behavior obtained from the historical smart device behavior data. The hot smart devices and the rules and events of the hot smart device behaviors are input into the rule engine, and the target smart device behaviors corresponding to each target smart device are arranged using the rule engine to obtain the smart device behavior arrangement results.

[0070] In some implementations, scene clustering is performed based on historical smart devices, historical smart device behavior data, and multiple historical scenes to obtain clustering results of K scenes, including:

[0071] Numbering of multiple historical scenes;

[0072] Label the historical smart devices and the historical smart device behavior data according to the numbers corresponding to the numbered multiple historical scenes to obtain the labeled historical smart devices and the labeled historical smart device behavior data, wherein the labeled historical smart devices and the labeled historical smart device behavior data correspond to at least one label;

[0073] The marked historical smart devices and the marked historical smart device behavior data are clustered by scenarios to obtain K scenario clustering results, and the clustering result of each scenario includes at least one first historical smart device and all historical smart device behavior data corresponding to the first historical smart device.

[0074] In this embodiment, multiple historical scenes are numbered, and then historical smart devices and historical smart device behavior data are labeled according to the numbers corresponding to the numbered historical scenes, and then the labeled historical smart devices and the labeled historical smart device behavior data are clustered to obtain K scene clustering results. The historical smart devices and historical smart device behavior data can be classified according to the historical scenes, which can lay a good data foundation for the later smart device behavior arrangement.

[0075] The above-mentioned labeling of historical smart devices and historical smart device behavior data according to the numbers corresponding to the numbered multiple historical scenes can be labeling the historical smart devices and historical smart device behavior data according to the numbers corresponding to each historical scene. For example, the numbers corresponding to multiple historical scenes are 1, 2, and 3. Since a historical smart device is used in multiple historical scenes, a historical smart device may correspond to multiple historical smart device behavior data. Therefore, if a historical smart device is used in a historical scene numbered 2 and a historical scene numbered 3, the labels of the historical smart device are marked as 2 and 3. If two of the multiple historical smart device behavior data corresponding to a historical smart device are used in the historical scene numbered 2 and one is used in the historical scene numbered 3, both historical smart device behavior data are numbered 2 and one historical smart device behavior data is numbered 3.

[0076] In some implementations, the marked historical smart devices and the marked historical smart device behavior data are clustered by scenarios to obtain clustering results of K scenarios, including:

[0077] Select a target scene from multiple historical scenes and obtain the number of the target scene;

[0078] Compare the number of the target scene with the label of the marked historical smart device and the label of the marked historical smart device behavior data. If the number of the target scene is the same as the label of the marked historical smart device and the number of the target scene is the same as the label of the marked historical smart device behavior data, then use the marked historical smart device and the marked historical smart device behavior data as the clustering results of the target scene with the corresponding number; until each historical scene is selected as the target scene, the clustering results of K scenes are obtained.

[0079] In this embodiment, the target scene number is compared with the label of the marked historical smart device and the label of the marked historical smart device behavior data. If the target scene number is the same as the label of the marked historical smart device and the target scene number is the same as the label of the marked historical smart device behavior data, the marked historical smart device and the marked historical smart device behavior data are used as the clustering result of the target scene number. Through fast clustering, the efficiency of later target smart device and target smart device behavior arrangement can be improved.

[0080] The above-mentioned comparison of the target scene number with the label of the marked historical smart device and the label of the marked historical smart device behavior data, if the target scene number is the same as the label of the marked historical smart device, and the target scene number is the same as the label of the marked historical smart device behavior data, then the marked historical smart device and the marked historical smart device behavior data are used as the clustering results of the target scene with the corresponding number. This may be because the labels of the historical smart devices and the historical smart device behavior data are marked by the scene number. Then, by selecting the target scene and then putting the historical smart devices and the historical smart device behavior data with the same label and the target scene number into a target scene, the clustering result of the target scene is obtained.

[0081] For example, there are scenes numbered 1 and 2, and then there is a first smart device with tags 1, 2, and 3 and a second smart device with tag 1, and there are multiple first smart device behaviors corresponding to the first smart device with tag 1 and multiple second smart device behaviors corresponding to the second smart device with tag 1. After selecting scene numbered 1, the first smart device, the second smart device, multiple first smart device behaviors, and multiple second smart device behaviors will be classified as scene numbered 1; after selecting scene numbered 2, only the first smart device will be classified as scene numbered 2.

[0082] In some implementations, calculating a first heat value of each smart device and a second heat value of each smart device behavior in a clustering result corresponding to a scene to be arranged includes:

[0083] Obtaining a first total number of times each smart device is used in a historical scenario corresponding to the scenario to be arranged, and obtaining a second total number of times all smart devices are used in a clustering result corresponding to the scenario to be arranged;

[0084] The ratio between the first total number of times and the second total number of times is calculated to obtain a first heat value of each smart device in the clustering result corresponding to the scene to be arranged.

[0085] In this embodiment, by calculating the ratio between the first total number of times and the second total number of times, the first popularity value of each smart device in the clustering result corresponding to the scene to be arranged is obtained, which can lay a good data foundation for the later selection of smart devices that meet the public's preferences.

[0086] Specifically, the ratio between the first total number of times and the second total number of times is calculated in the following manner to obtain the first heat value of each smart device in the clustering result corresponding to the scene to be arranged:

[0087]

[0088] Among them, H1 irepresents the first heat value of the i-th smart device, T1 represents the second total number of times all smart devices are used in the clustering results corresponding to the scenario to be arranged, and p1 i Indicates the first total number of times the i-th smart device is used in the historical scene corresponding to the scene to be programmed.

[0089] In some implementations, calculating a first heat value of each smart device and a second heat value of each smart device behavior in a clustering result corresponding to a scene to be arranged includes:

[0090] Obtain the number of times each smart device behavior is triggered, and obtain the total number of times all smart device behaviors are triggered in the clustering results corresponding to the scenario to be choreographed;

[0091] The ratio between the number of times each smart device behavior is triggered and the total number of times is calculated to obtain the second heat value of each smart device behavior in the clustering result corresponding to the scene to be choreographed.

[0092] In this embodiment, by calculating the ratio between the number of times each smart device behavior is triggered and the total number of times, the second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged is obtained, which can lay a good data foundation for the later selection of smart device behaviors that are popular with the public.

[0093] Specifically, the ratio between the number of times each smart device behavior is triggered and the total number of times is calculated in the following manner to obtain the second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged:

[0094]

[0095] Among them, H2 i represents the second heat value of the i-th smart device behavior, T2 represents the total number of times all smart device behaviors are triggered in the clustering results corresponding to the scenario to be orchestrated, and p2 i Indicates the number of times the i-th smart device behavior is triggered.

[0096] In some implementations, determining a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value includes:

[0097] Sorting the first heat values ​​to obtain sorted first heat values;

[0098] Selecting a plurality of target smart devices whose heat values ​​are greater than a first preset value from the sorted first heat values;

[0099] Selecting a second heat value of the smart device behavior corresponding to each target smart device;

[0100] sorting the second heat values ​​of the smart device behaviors corresponding to each target smart device to obtain sorted second heat values;

[0101] The maximum heat value is selected from the sorted second heat values, and the smart device behavior corresponding to the maximum heat value is used as the target smart device behavior.

[0102] In this embodiment, by sorting the first heat values, multiple target smart devices whose heat is greater than the first preset value are selected from the sorted first heat values, so that the target smart devices that meet the public's preferences can be selected. Then, the second heat value of the smart device behavior corresponding to each target smart device is selected, and the second heat value of the smart device behavior corresponding to each target smart device is sorted to obtain the sorted second heat value. The maximum heat value is selected from the sorted second heat values, and the smart device behavior corresponding to the maximum heat value is used as the target smart device behavior, so that the target smart device behavior that meets the public's preferences can be selected. Therefore, this embodiment can change according to changes in public preferences, so as to better meet public preferences.

[0103] The second heat value of the smart device behavior corresponding to each target smart device is selected because each target smart device may have multiple smart device behaviors, so the second heat value of the smart device behavior corresponding to each target smart device can be selected.

[0104] In some implementations, the rule-based smart device behavior orchestration method further includes:

[0105] Preset update cycle;

[0106] Re-arrange the behavior of smart devices for the orchestration scenarios according to the update cycle;

[0107] The target smart device in the smart device behavior orchestration result before the update cycle is updated is used as the historical smart device, and the orchestrated target smart device behavior is used as the historical smart device behavior data.

[0108] In this embodiment, by presetting an update cycle, and then re-arranging the behavior of smart devices for the arrangement scene according to the update cycle, by continuously updating the behavior of smart devices in real time, it can be changed according to changes in public preferences, so as to be more in line with public preferences. Then, the target smart device in the smart device behavior arrangement result before the update cycle is updated is used as the historical smart device, and the behavior of the target smart device after arrangement is used as the historical smart device behavior data, so that the historical data can be continuously updated, so that the smart device behavior arrangement can be more in line with public preferences, and there is no need for manual statistics and arrangement, and the smart device behavior arrangement can be intelligently and automatically performed directly according to the historical data.

[0109] The above-mentioned preset update period can be an update period that can be changed according to actual conditions, and this embodiment does not impose any specific limitation.

[0110] The above-mentioned re-arranging of the behavior of the smart device for the arranged scene according to the update cycle can be that when the update cycle is reached, the arranged scene (that is, the current scene is re-arranged as the scene to be arranged) will be re-arranged for the behavior of the smart device. This is because it is possible that the previously selected smart devices and smart device behaviors are not optimal, and some smart devices and smart devices may not be used or triggered many times in actual applications, so continuous updates can better meet the preferences of the public.

[0111] Reference Figure 2 The embodiment of the present application also provides a rule-based smart device behavior orchestration system, which includes a data acquisition unit 100, a scene clustering unit 200, a heat calculation unit 300, a data determination unit 400 and a behavior orchestration unit 500, wherein:

[0112] The data acquisition unit 100 is used to acquire historical smart devices, historical smart device behavior data corresponding to the historical smart devices, multiple historical scenes corresponding to the historical smart devices, and scenes to be arranged, wherein the historical smart device behavior data includes rules and events of each smart device behavior, and the rules include conditions and actions;

[0113] A scene clustering unit 200 is used to perform scene clustering according to historical smart devices, historical smart device behavior data and multiple historical scenes to obtain clustering results of K scenes;

[0114] The heat calculation unit 300 is used to select a clustering result corresponding to the scene to be arranged from the clustering results of the K scenes, and calculate a first heat value of each smart device and a second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged;

[0115] A data determination unit 400 is used to determine a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value;

[0116] The behavior arrangement unit 500 is used to arrange the target smart device behavior corresponding to each target smart device by using a rule engine to obtain a smart device behavior arrangement result.

[0117] It should be noted that since the rule-based smart device behavior orchestration system in this embodiment and the above-mentioned rule-based smart device behavior orchestration method are based on the same inventive concept, the corresponding contents in the method embodiment are also applicable to the system embodiment and will not be described in detail here.

[0118] Reference Figure 3 The embodiment of the present application further provides an electronic device, the electronic device comprising:

[0119] at least one memory;

[0120] at least one processor;

[0121] at least one program;

[0122] The program is stored in the memory, and the processor executes at least one program to implement the rule-based smart device behavior orchestration method described above in the present disclosure.

[0123] The electronic device may be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0124] The electronic device according to the embodiment of the present application is described in detail below.

[0125] The processor 1600 may be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present disclosure;

[0126] The memory 1700 may be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1700 may store an operating system and other application programs. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1700, and the processor 1600 calls and executes the rule-based intelligent device behavior arrangement method of the embodiment of the present disclosure.

[0127] Input / output interface 1800, used to implement information input and output;

[0128] The communication interface 1900 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0129] Bus 2000 , which transmits information between various components of the device (e.g., processor 1600 , memory 1700 , input / output interface 1800 , and communication interface 1900 );

[0130] The processor 1600 , the memory 1700 , the input / output interface 1800 , and the communication interface 1900 are connected to each other in communication within the device via the bus 2000 .

[0131] The embodiment of the present disclosure also provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned rule-based smart device behavior orchestration method.

[0132] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0133] The embodiments described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0134] Those skilled in the art will appreciate that the technical solutions shown in the figures do not limit the embodiments of the present disclosure and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0135] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0137] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0138] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0139] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0142] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable an electronic device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store programs. The above is a detailed description of the embodiments of the present application in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments. Within the scope of knowledge possessed by ordinary technicians in the relevant technical field, various changes can be made without departing from the purpose of the present application.

Claims

1. A rule-based intelligent device behavior orchestration method, characterized in that: The method comprises: Acquire a historical smart device, historical smart device behavior data corresponding to the historical smart device, multiple historical scenes corresponding to the historical smart device, and scenes to be arranged, wherein the historical smart device behavior data includes rules and events of each smart device behavior, and the rules include conditions and actions; Performing scene clustering according to the historical smart device, the historical smart device behavior data, and the multiple historical scenes to obtain clustering results of K scenes; Selecting a clustering result corresponding to the to-be-programmed scene from the clustering results of the K scenes, and calculating a first heat value of each smart device and a second heat value of each smart device behavior in the clustering result corresponding to the to-be-programmed scene; Determine a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value; A rule engine is used to arrange the target smart device behavior corresponding to each target smart device to obtain a smart device behavior arrangement result.

2. The rule-based smart device behavior orchestration method according to claim 1, characterized in that: The performing scene clustering according to the historical smart device, the historical smart device behavior data and the multiple historical scenes to obtain clustering results of K scenes includes: Numbering the multiple historical scenes; Tagging the historical smart device and the historical smart device behavior data according to the numbers corresponding to the numbered multiple historical scenes to obtain the labeled historical smart device and the labeled historical smart device behavior data, wherein the labeled historical smart device and the labeled historical smart device behavior data correspond to at least one label; The marked historical smart devices and the marked historical smart device behavior data are clustered by scenarios to obtain K scenario clustering results, each of which includes at least one first historical smart device and all historical smart device behavior data corresponding to the first historical smart device.

3. The rule-based smart device behavior orchestration method according to claim 2, characterized in that: The performing scene clustering on the marked historical smart devices and the marked historical smart device behavior data to obtain clustering results of K scenes includes: Selecting a target scene from the multiple historical scenes and obtaining a serial number of the target scene; Compare the number of the target scene with the label of the marked historical smart device and the label of the marked historical smart device behavior data. If the number of the target scene is the same as the label of the marked historical smart device and the number of the target scene is the same as the label of the marked historical smart device behavior data, then use the marked historical smart device and the marked historical smart device behavior data as the clustering result of the target scene with the corresponding number; until each historical scene is selected as the target scene, and obtain the clustering results of K scenes.

4. The rule-based smart device behavior orchestration method according to claim 1, characterized in that: The calculating the first heat value of each smart device and the second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged includes: Obtaining a first total number of times each smart device is used in a historical scenario corresponding to the scenario to be arranged, and obtaining a second total number of times all smart devices are used in a clustering result corresponding to the scenario to be arranged; A ratio between the first total number of times and the second total number of times is calculated to obtain a first heat value of each smart device in the clustering result corresponding to the scene to be arranged.

5. The rule-based smart device behavior orchestration method according to claim 1, characterized in that: The calculating the first heat value of each smart device and the second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged includes: Obtaining the number of times each smart device behavior is triggered, and obtaining the total number of times all smart device behaviors are triggered in the clustering result corresponding to the scene to be choreographed; The ratio of the number of times each smart device behavior is triggered to the total number of times is calculated to obtain a second heat value of each smart device behavior in the clustering result corresponding to the scene to be arranged.

6. The rule-based smart device behavior orchestration method according to claim 1, characterized in that: The determining, according to the first heat value and the second heat value, a target smart device behavior corresponding to each target smart device includes: Sorting the first heat values ​​to obtain sorted first heat values; Selecting a plurality of target smart devices whose heat values ​​are greater than a first preset value from the sorted first heat values; Selecting a second heat value of the smart device behavior corresponding to each of the target smart devices; Sorting the second heat values ​​of the smart device behaviors corresponding to each of the target smart devices to obtain sorted second heat values; A maximum heat value is selected from the sorted second heat values, and a smart device behavior corresponding to the maximum heat value is used as a target smart device behavior.

7. The rule-based smart device behavior orchestration method according to claim 1, characterized in that: The rule-based smart device behavior orchestration method further includes: Preset update cycle; Re-arrange the behavior of the smart device for the scene to be arranged according to the update cycle; The target smart device in the smart device behavior arrangement result before the update cycle is updated is used as the historical smart device, and the behavior of the target smart device after arrangement is used as the historical smart device behavior data.

8. A rule-based intelligent device behavior orchestration system, characterized in that: The system comprises: A data acquisition unit, configured to acquire a historical smart device, historical smart device behavior data corresponding to the historical smart device, a plurality of historical scenes corresponding to the historical smart device, and scenes to be arranged, wherein the historical smart device behavior data includes rules and events of each smart device behavior, and the rules include conditions and actions; A scene clustering unit, configured to perform scene clustering according to the historical smart device, the historical smart device behavior data and the plurality of historical scenes, to obtain clustering results of K scenes; a heat calculation unit, configured to select a clustering result corresponding to the to-be-programmed scene from the clustering results of the K scenes, and calculate a first heat value of each smart device and a second heat value of each smart device behavior in the clustering result corresponding to the to-be-programmed scene; a data determination unit, configured to determine a target smart device behavior corresponding to each target smart device according to the first heat value and the second heat value; The behavior arrangement unit is used to arrange the target smart device behavior corresponding to each target smart device by using a rule engine to obtain a smart device behavior arrangement result.

9. An electronic device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor to enable the at least one control processor to execute the rule-based smart device behavior orchestration method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the rule-based smart device behavior orchestration method according to any one of claims 1 to 7.

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