Smart home decision-making system, working method thereof and computer storage medium

Abstract

The invention discloses a smart home decision-making system, a working method thereof and a computer storage medium. The intelligent household decision-making system comprises at least one decision-making engine module which is used for performing active decision-making based on a preset decision-making rule and generating corresponding interaction information; and the interaction module is used for interacting with an interaction object corresponding to the interaction information based on the generated interaction information. The invention aims to enable the intelligent home system to be more intelligent from the system software level.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a smart home decision-making system and a working method thereof, and a computer storage medium. Background Art

[0002] The concept of whole-house smart home was proposed several years ago. In the past two years, major manufacturers have proposed their own whole-house smart solutions, but all of them are scene control solutions with smart host as the core and other smart home elements. With the development of artificial intelligence, the whole-house smart home system needs to be improved in a more intelligent direction. Summary of the invention

[0003] The main purpose of the present invention is to provide a smart home decision-making system and its working method, and a computer storage medium, aiming to make the smart home system more intelligent from the system software level.

[0004] To achieve the above object, the present invention provides a smart home decision system, the smart home decision system comprising:

[0005] At least one decision engine module, used to make active decisions based on preset decision rules and generate corresponding interaction information;

[0006] The interaction module is used to interact with the interaction object corresponding to the interaction information based on the generated interaction information.

[0007] Optionally, the decision engine module includes a control decision engine module, and the control decision engine module is used to make active decisions based on preset control decision rules and generate control instructions.

[0008] Optionally, the control decision engine module is also used to: obtain the perception information required for decision-making based on preset control decision rules, make active decisions on the device according to the perception information, and generate control instructions for the device.

[0009] Optionally, the control instructions of the device include: a power-on instruction, a power-off instruction and / or a parameter adjustment instruction.

[0010] Optionally, the control decision engine module is also used to: obtain the perception information required for decision-making based on preset control decision rules, automatically identify the scene according to the perception information, and generate control instructions for the scene.

[0011] Optionally, the scene control instruction includes: a scene entry instruction, a scene exit instruction and / or a scene switching instruction.

[0012] Optionally, the decision engine module includes a recommendation decision engine module, and the recommendation decision engine module is used to make active decisions based on preset recommendation decision rules to generate recommendation information.

[0013] Optionally, the recommendation decision engine module is further used to: obtain user information required for recommendation based on preset recommendation decision rules, and generate recommendation information corresponding to the user information according to the user information.

[0014] Optionally, the recommendation information includes: product recommendation, service recommendation, content recommendation and / or service usage recommendation.

[0015] Optionally, the decision engine module includes an operation decision engine module, and the operation decision engine module is used to make active decisions based on preset operation decision rules to generate operation information.

[0016] Optionally, the decision engine module is further used to identify user tags based on preset operation decision rules and generate operation information corresponding to the user tags.

[0017] Optionally, the operation information includes: operation activity information and / or community platform information.

[0018] Optionally, the interaction object includes at least one of a home appliance, a central control screen, a wired controller, and a mobile terminal;

[0019] The interaction module interacts with the interaction object through at least one of a virtual image, voice broadcast, and text presentation.

[0020] In addition, in order to achieve the above purpose, the present application also proposes a working method of a smart home decision-making system, including:

[0021] Make active decisions based on preset decision rules and generate corresponding interactive information;

[0022] Based on the generated interaction information, interaction is performed with an interaction object corresponding to the interaction information.

[0023] Optionally, the step of making an active decision based on a preset decision rule and generating corresponding interaction information includes:

[0024] Make active decisions based on preset control decision rules and generate control instructions; and / or,

[0025] Based on the preset control decision rules, the sensory information required for decision-making is obtained, and the scene is automatically identified according to the sensory information, and the control instructions of the scene are generated; and / or,

[0026] Based on a preset recommendation decision rule, obtaining user information required for recommendation, and generating recommendation information corresponding to the user information according to the user information; and / or,

[0027] Based on the preset operation decision rules, the user tags are identified and the operation information corresponding to the user tags is generated.

[0028] Optionally, the step of interacting with an interactive object corresponding to the interaction information based on the generated interaction information includes:

[0029] Based on the generated interactive information, interaction is performed with the interactive object through at least one of a virtual image, voice broadcast, and text presentation.

[0030] In addition, in order to achieve the above-mentioned purpose, the present application also proposes a computer storage medium, on which is stored an operating program of a smart home decision-making system that can be run on a processor. When the operating program of the smart home decision-making system is executed by the processor, the steps of the working method of the smart home decision-making system as described in any of the above items are implemented.

[0031] The smart home decision-making system proposed in the present invention is different from the whole-house smart home solution in the prior art. Instead, it is a more complete and comprehensive smart home decision-making system designed from the software system level, which realizes independent decision-making of smart homes and can better play a role in the smart home system, making the whole-house smart home solution more intelligent and improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of the structure framework of an embodiment of the smart home decision-making system of the present invention;

[0033] Figure 2 A functional structure diagram of an embodiment of a smart home decision-making system of the present invention;

[0034] Figure 3 It is a flowchart of an embodiment of a working method of a smart home decision-making system of the present invention;

[0035] Figure 4 It is a detailed flowchart diagram of an embodiment of the working method of the smart home decision-making system of the present invention for interacting based on the generated interaction information.

[0036] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0037] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0038] The embodiment of the present invention proposes a smart home decision system that is more intelligent than the existing smart home solution. Figure 1 , the smart home decision system includes:

[0039] At least one decision engine module 1, used to make active decisions based on preset decision rules and generate corresponding interactive information;

[0040] The interaction module 2 is used to interact with the interaction object corresponding to the interaction information based on the generated interaction information.

[0041] In one embodiment, the space where the smart home decision system is located refers to all relatively closed spaces, such as the whole house space. The whole house space includes all spaces such as the living room, kitchen, bedroom 1, bedroom 2, corridors / hallways, balconies, etc., and also includes all objects existing in the space, such as wardrobes, bookcases, cabinets, furniture, home appliances / home appliances, air, etc. Of course, the whole house space also includes human bodies moving in the space.

[0042] In the above embodiment, at least one decision engine module 1 can make active decisions for objects in the space where the smart home decision system is located based on preset decision rules, generate corresponding interaction information, and interact with the interaction object corresponding to the interaction information based on the generated interaction information through the interaction module 2.

[0043] This embodiment is different from the prior art. Instead, it designs a more complete and comprehensive smart home decision-making system from the software system level, realizes independent decision-making of smart homes, can better play a role in the smart home system, makes the whole house smart home solution more intelligent, and improves user experience.

[0044] In some embodiments, reference Figure 2 The at least one decision engine module 1 includes a control decision engine module 11, and the control decision engine module 11 is used to make active decisions based on preset control decision rules and generate control instructions.

[0045] The control decision engine module 11 is used to obtain the perception information required for decision-making based on preset control decision rules, and to make active decisions on the equipment according to the perception information to generate control instructions for the equipment.

[0046] In one embodiment, the decision engine module 1 includes a control decision engine module 11, which is used to obtain the perception information required for decision-making based on the preset control decision rules, and actively make decisions on the device according to the perception information to generate control instructions for the device. The control instructions are used to control the power on and off of each home appliance, the adjustment of operating parameters, etc.

[0047] In one embodiment, the smart home decision system at the software system level is realized by implementing a system at the hardware level. The hardware level system includes: a connection system for establishing a whole-house communication network of the smart home decision system using at least two connection methods, so as to realize data transmission between the cloud server, the edge device, and the perception module.

[0048] The above connection methods include, for example, WiFi, Bluetooth, infrared, radio frequency, cable and other connection methods.

[0049] The above cloud servers are mainly used for data collection, big data analysis, processing, etc. Edge devices include, for example, air conditioners, refrigerators, air purifiers, range hoods, gas hoods, water dispensers, water heaters, washing machines, central control screens, wired controllers, and other equipment.

[0050] The above-mentioned perception module includes, for example, an environment sensing module, a human body sensing module, and a device status collection module.

[0051] The environmental sensing module includes, for example, a temperature / humidity sensor or an air quality sensor installed on an edge device or installed independently, and is used to collect environmental information in the space where the smart home system is located. The environmental information includes at least one of temperature, humidity, and air quality information.

[0052] In one embodiment, control instructions for air conditioning equipment are generated based on the environmental information, wherein the air conditioning equipment may be equipment such as air conditioners, air purifiers, and range hoods. When the environmental information includes temperature and / or humidity, the air conditioning equipment is equipment such as air conditioners. When the environmental information includes air quality information, the air conditioning equipment is equipment such as air purifiers and / or range hoods.

[0053] Specifically, in one embodiment, if the environmental information includes temperature, when the temperature meets a preset temperature condition, the parameters of the air conditioner in the turned-on state are controlled.

[0054] In another embodiment, if the environmental information includes air quality information, when the air quality information does not meet a preset air quality condition, an air purifier and / or a range hood that is in a closed state is controlled to be turned on.

[0055] In another embodiment, if the environmental information includes air quality information, when the air quality information meets a preset air quality condition, the air purifier and / or range hood that is in an on state is controlled to be turned off.

[0056] The human body sensing module includes a human body sensor for sensing human body parameters. The human body sensor is configured on a home appliance, or independently set in the space where the smart home system is located, and is used to collect human body status information in the space where the smart home system is located. The human body status information includes at least one of the number of people, position, breathing, heartbeat, and body movement information. The body movement information includes human body movement information. The human body movement information includes posture changes of various parts of the human body, and the various parts include at least one of the head, hands, legs, and trunk.

[0057] In one embodiment, if the human body state information includes position, based on the position, the position change information of the human body is determined, and based on the position change information, a control instruction for the home appliance is generated.

[0058] Specifically, in one embodiment, if the location change information includes change information from outside the home to inside the home, an opening instruction for the home appliance in a closed state is generated. At this time, the home appliance may be an air conditioner or other home appliance such as a television.

[0059] In another embodiment, if the location change information includes change information from inside the home to outside the home, a closing instruction is generated for the home appliance in the turned-on state. At this time, the home appliance may be an air conditioner or other home appliance such as a television.

[0060] In another embodiment, if the human body state information includes the number of people and the position of each human body, a parameter adjustment instruction for the home appliance is generated based on the number of people and the position of each human body.

[0061] In another embodiment, if the human body state information includes breathing, heartbeat and / or body movement information, the state of the human body is determined based on the human body state information, and the state includes but is not limited to sleeping state, exercise state and normal operation state. Based on the state of the human body, control instructions for the home appliance are generated. For example, when the state of the human body is sleeping, a volume reduction instruction for sound-emitting devices such as a television is generated.

[0062] In one embodiment, if the perception information includes human state information and environmental information, a control instruction for the home appliance is generated based on the human state information and the environmental information.

[0063] Specifically, in one embodiment, if the human body status information includes the number of people and the environmental information includes temperature, then the control parameters of the air conditioner are generated based on the number of people and the temperature.

[0064] The device status collection module is used to collect the operating status information of each home appliance in the smart home decision-making system. The device status collection module is generally set on each home appliance, or on the control device that controls the home appliance.

[0065] In one embodiment, control instructions for home appliances are generated based on the operating status information of each home appliance and the human body status information.

[0066] In another embodiment, control instructions for home appliances are generated based on the operating status information and environmental information of each home appliance.

[0067] In yet another embodiment, control instructions for home appliances are generated based on the operating status information, environmental information, and human body status information of each home appliance.

[0068] By setting up a perception module in the whole house space, the perception module is connected to the control decision engine module 11, so that the control decision engine module 11 can obtain the perception information required for decision-making. The perception information includes environmental information, human state information and / or device state information.

[0069] In one embodiment, the control decision rules include mapping relationships between various perception information and various control instructions, wherein the mapping relationship can be a one-to-one correspondence between perception information and control instructions, or one type of perception information corresponds to multiple control instructions, or multiple types of perception information corresponds to one control instruction. There is no limitation here, and the acquired perception information is mapped to control instructions through the control decision rules.

[0070] In another embodiment, the control decision rule includes a decision model, which constructs a perception feature based on the acquired perception information, and maps the perception feature into a control instruction through the decision model.

[0071] It is understandable that different types of perception information can be used together as interaction information of the same interaction object. For example, human state information and environmental information can be used together to generate interaction information of devices such as air conditioners and refrigerators, and device state information and human state information can be used together to generate interaction information of the device. Therefore, the above-mentioned perception information can include multiple types of information, and the above-mentioned perception features can include multiple types of features.

[0072] It is understandable that using environmental information, human body status information and / or device status information as a decision basis for generating interactive information makes the generated interactive information more in line with user usage habits and improves user experience.

[0073] The control decision engine module 11 is also used to obtain the perception information required for decision-making based on the preset scene control decision rules, automatically identify the scene mode according to the perception information, and generate an operation strategy for controlling the scene. The operation strategy is used to control the entry, exit and switching of the scene mode, for example.

[0074] In this embodiment, the scene modes include but are not limited to learning mode, exercise mode, sleeping mode, movie watching mode and leisure mode.

[0075] In one embodiment, the scene control decision rule includes a mapping relationship between various preset scenes and various perception information, and the perception information is mapped into a scene mode through the scene control decision rule.

[0076] In another embodiment, the scene control decision rules include a mapping relationship between the control of various preset scenes and various perception information. The perception information is mapped to the control of the scene through the scene control decision rules. The control of the scene includes at least one of controlling the entry of the scene mode, the exit of the scene mode, and the switching of the scene mode.

[0077] In another embodiment, perception features are extracted based on the perception information, and the perception features are mapped into an operation strategy for controlling the scene through a scene control decision model.

[0078] In one embodiment, the state of the human body is determined based on the human body state information, and the scene mode is automatically recognized based on the state of the human body.

[0079] Specifically, in one embodiment, if the state of the human body is a sleeping state, the scene mode is identified as a sleeping mode; and / or, if the state of the human body is a moving state, the scene mode is identified as a moving mode.

[0080] In this embodiment, the smart home decision system is actively decided by controlling the decision engine module 11, thereby realizing active adjustment of the equipment and automatic recognition and control of the scene mode, making the smart home decision system more intelligent.

[0081] In another embodiment, referring to Figure 2 , the decision engine module 1 also includes a recommendation decision engine module 12, which is used to make active decisions based on preset recommendation decision rules, obtain user information required for recommendation, and generate recommendation information corresponding to the user information based on the user information. The recommendation information, for example, recommends products, components and other product recommendations to users; recommends after-sales repair, cleaning and maintenance and other service recommendations to users; recommends music, videos, articles and other content recommendations to users; recommends usage tips and other usage service recommendations to users, wherein the user information includes product information and / or user portraits that the user already has.

[0082] In one embodiment, if the user information includes product information that the user already owns, and the product information that the user already owns includes product categories, then based on the product categories, product recommendation information corresponding to the user information is generated.

[0083] In another embodiment, if the user information includes product information that the user already owns, and the product information that the user already owns includes product usage information and product categories, service recommendation information corresponding to the user information is generated based on the product usage information and the product categories.

[0084] Specifically, in one embodiment, the product usage information includes product wear and tear, and a first product having a corresponding product wear and tear greater than a preset wear and tear threshold is selected from the products already owned by the user, and based on the product category of the first product, after-sales repair service recommendation information corresponding to the user information is generated.

[0085] In another embodiment, the product usage information includes product contamination level, and a second product having a corresponding product contamination level greater than a preset contamination level threshold is selected from the products already owned by the user, and based on the product category of the second product, cleaning and maintenance service recommendation information corresponding to the user information is generated.

[0086] In another embodiment, if the user information includes a user portrait, and the user portrait includes the content used by the user, the frequency of using each content, and the category corresponding to each content, and the content includes but is not limited to music, video, and articles, then based on the user portrait, content recommendation information corresponding to the user information is generated.

[0087] In another embodiment, if the user information includes a user portrait, and the user portrait includes services used by the user, the frequency of using each of the services, and the category corresponding to each of the services, service recommendation information corresponding to the user information is generated based on the user portrait.

[0088] In this embodiment, the recommendation decision engine module 12 is used to make active decisions for the smart home decision system, thereby realizing active recommendations of various recommendation information, making the smart home decision system more intelligent.

[0089] In another embodiment, referring to Figure 2 The decision engine module 1 also includes an operation decision engine module 13, which is used to identify user tags based on preset operation decision rules and generate operation information corresponding to the user tags. The operation information, for example, identifies user tags, divides users into multiple groups, sends operation activity information to groups / individual users, pushes community platform information built for groups / individuals, etc.

[0090] In one embodiment, the user's age is obtained and a user tag is generated based on the age.

[0091] In another embodiment, the user activity habits are obtained, and a user tag is generated based on the user activity habits.

[0092] In this embodiment, the smart home decision system is proactively decided by recommending the decision engine module 13, thereby realizing operation services, meeting different needs of users, and making the smart home decision system more intelligent.

[0093] In some embodiments, the interactive objects include, for example, household appliances, central control screens, wired controllers, and mobile terminals; and / or the interactive information content includes, for example, at least one of control parameters, reminder messages, and recommended information.

[0094] The above-mentioned preset decision rules include the sensed information required for decision-making, the judgment rules of the sensed information, and the generated decision information. For example, a decision rule is preset, when the ambient temperature is ≥30℃, the cooling mode is turned on, that is, the sensed information required for the decision is the ambient temperature; the judgment rule is "when the ambient temperature is ≥30℃, the cooling mode is turned on"; the generated decision information is the control instruction for controlling the air-conditioning equipment to operate in the cooling mode.

[0095] In some embodiments, the above-mentioned preset decision rules can be configured at the factory, can be obtained from a cloud server, or can be obtained based on a training model and machine learning. For example, a decision rule obtained through machine learning determines whether to turn on the exercise mode based on breathing, posture, and body movement, that is, the sensory information required for the decision is breathing and posture parameters; the judgment rule is based on the sensory information to determine whether it is a sports mode rule, such as if the breathing frequency is greater than a certain threshold and the posture is active, it is judged that the human body is in a state of motion; the generated decision information is a control instruction to control the smart home decision system to enter the sports scene.

[0096] In some embodiments, the above-mentioned preset decision rules include, but are not limited to: at least one of: device control decision rules, scene control decision rules, dimension control decision rules, recommendation decision rules, and operation decision rules. The device control decision rules are used to make active decisions based on the perceived information perceived by a certain device, so as to realize decision control such as controlling the device to start up, shut down, or adjust operating parameters. The scene control decision rules are used to make active decisions based on the perceived information corresponding to a certain scene, realize scene recognition and control scene entry, scene exit, scene switching, and other decision controls. The dimension control decision rules are used to make active decisions based on the perceived information corresponding to the dimension, so as to realize control of a single dimension. The recommendation decision rules are used to make active decisions based on the perceived information required for service recommendation, so as to realize recommending corresponding services to users. The operation decision rules are used to make active decisions based on the perceived information required for operation, so as to realize providing operation services to users.

[0097] In some embodiments, the decision system 2 may include multiple sub-decision systems, which are configured as needed in actual use, for example, according to the function of the decision, according to the scope of the decision, according to the dimension of the decision, and so on. By configuring multiple sub-decision systems, when each sub-decision system is working, it retrieves the perception information required for decision-making in the perception system 1, makes decisions based on the perception information, and generates corresponding operation strategies. Therefore, the smart home decision system has independent decision-making capabilities, and each sub-decision system can run in parallel. Among them, one sub-decision system corresponds to an interactive object, and each sub-decision system sends its corresponding operation strategy to its corresponding interactive object.

[0098] It should be noted that, in the process of making decisions, the decision-making system 2 can retrieve the perception information at the current moment, and can also retrieve the perception information over a period of time.

[0099] Reference Figure 3 In some embodiments, the present invention provides a working method of a smart home decision system, comprising the following steps:

[0100] S10, making active decisions based on preset decision rules and generating corresponding interactive information;

[0101] S20: Based on the generated interaction information, interact with the interaction object corresponding to the interaction information.

[0102] In this embodiment, at least one decision engine module 1 makes active decisions based on preset decision rules and generates corresponding interaction information, and the interaction module 2 interacts with the interaction object corresponding to the interaction information based on the generated interaction information.

[0103] This embodiment is different from the prior art. Instead, it designs a more complete and comprehensive smart home decision-making system from the software system level, realizes independent decision-making of smart homes, can better play a role in the smart home system, makes the whole house smart home solution more intelligent, and improves user experience.

[0104] In some embodiments, step S10 specifically includes: making active decisions based on preset control decision rules and generating control instructions; and / or, based on preset control decision rules, obtaining perception information required for decision-making, and automatically identifying scenes based on the perception information, and generating control instructions for the scenes; and / or, based on preset recommendation decision rules, obtaining user information required for recommendation, and generating recommendation information corresponding to the user information based on the user information; and / or, based on preset operation decision rules, identifying user tags, and generating operation information corresponding to the user tags.

[0105] As mentioned above, during the operation of the smart home decision system, control instructions are generated directly based on preset control decision rules; and / or, perception information and control decision rules are used together as the decision basis for generating control instructions for scenes; and / or, user information required for recommendation and recommendation decision rules are used together as the decision basis for generating recommendation information corresponding to user information; and / or, the identified user tags and operation decision rules are used together as the decision basis for generating operation information corresponding to user tags, so that the generated interactive information is more comprehensive, and thus the decision of the smart home decision system can be multidimensional, making the whole house smart home solution more intelligent and improving the user experience. It should be noted that the specific content of the above-mentioned preset decision rules and interactive information can be referred to the previous embodiments, and will not be repeated here.

[0106] Reference Figure 4 In some embodiments, the above step S20 includes:

[0107] S21, based on the generated interaction information, interact with the interaction object through at least one of a virtual image, voice broadcast, and text presentation.

[0108] In this embodiment, the above-mentioned interaction objects include at least one of home appliances, central control screens, wire controllers, and mobile terminals; and / or the interaction methods include at least one of device control, voice broadcast, text display, and virtual human interaction; and / or the interaction information includes at least one of control parameters, reminder messages, and recommended information.

[0109] In this embodiment, by setting up an interactive module 2, after the interactive information is generated, it is sent to the corresponding interactive object to achieve decision-making control of the interactive object. There is no need for the interactive object to control it by itself. The decision structure of the overall system is integrated into the smart home decision system, thereby simplifying the decision-making function structure of the interactive object.

[0110] In addition, an embodiment of the present invention also proposes a computer storage medium, on which is stored an operating program of a smart home decision-making system that can be run on a processor, and the operating program is called by the processor to implement the relevant steps in any embodiment of the working method of the smart home decision-making system as described above.

[0111] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0112] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, a control device of a smart home decision system, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0114] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A smart home decision-making system, characterized in that: The smart home decision-making system comprises: At least one decision engine module, used to make active decisions based on preset decision rules and generate corresponding interaction information; The interaction module is used to interact with the interaction object corresponding to the interaction information based on the generated interaction information.

2. The smart home decision system according to claim 1, characterized in that: The decision engine module includes a control decision engine module, and the control decision engine module is used to make active decisions based on preset control decision rules and generate control instructions.

3. The smart home decision system as claimed in claim 2, characterized in that: The control decision engine module is also used to: obtain the perception information required for decision-making based on preset control decision rules, make active decisions on the equipment according to the perception information, and generate control instructions for the equipment.

4. The smart home decision system as claimed in claim 3, characterized in that: The control instructions of the device include: a power-on instruction, a power-off instruction and / or a parameter adjustment instruction.

5. The smart home decision system as claimed in claim 2, characterized in that: The control decision engine module is also used to: obtain the perception information required for decision-making based on preset control decision rules, automatically identify the scene according to the perception information, and generate control instructions for the scene.

6. The smart home decision system as claimed in claim 5, characterized in that: The scene control instructions include: scene entry instructions, scene exit instructions and / or scene switching instructions.

7. The smart home decision system according to any one of claims 1 to 6, characterized in that: The decision engine module includes a recommendation decision engine module, and the recommendation decision engine module is used to make active decisions based on preset recommendation decision rules and generate recommendation information.

8. The smart home decision system as claimed in claim 7, characterized in that: The recommendation decision engine module is also used to: obtain user information required for recommendation based on preset recommendation decision rules, and generate recommendation information corresponding to the user information according to the user information.

9. The smart home decision system according to claim 8, characterized in that: The recommendation information includes: product recommendations, service recommendations, content recommendations and / or service usage recommendations.

10. The smart home decision system according to any one of claims 1 to 9, characterized in that: The decision engine module includes an operation decision engine module, and the operation decision engine module is used to make active decisions based on preset operation decision rules and generate operation information.

11. The smart home decision system according to claim 10, characterized in that: The decision engine module is also used to identify user tags based on preset operation decision rules and generate operation information corresponding to the user tags.

12. The smart home decision system according to claim 11, characterized in that: The operation information includes: operation activity information and / or community platform information.

13. The smart home decision-making system according to claim 1, characterized in that: The interactive object includes at least one of a home appliance, a central control screen, a wired controller, and a mobile terminal; The interaction module interacts with the interaction object through at least one of a virtual image, voice broadcast, and text presentation.

14. A working method of a smart home decision system, characterized in that: The working method comprises: Make active decisions based on preset decision rules and generate corresponding interactive information; Based on the generated interaction information, interaction is performed with an interaction object corresponding to the interaction information.

15. The working method according to claim 14, characterized in that: The step of making active decisions based on preset decision rules and generating corresponding interactive information includes: Make active decisions based on preset control decision rules and generate control instructions; and / or, Based on the preset control decision rules, the sensory information required for decision-making is obtained, and the scene is automatically identified according to the sensory information, and the control instructions of the scene are generated; and / or, Based on a preset recommendation decision rule, obtaining user information required for recommendation, and generating recommendation information corresponding to the user information according to the user information; and / or, Based on the preset operation decision rules, the user tags are identified and the operation information corresponding to the user tags is generated.

16. The working method according to claim 14, characterized in that: The step of interacting with the interactive object corresponding to the interactive information based on the generated interactive information includes: Based on the generated interactive information, interaction is performed with the interactive object through at least one of a virtual image, voice broadcast, and text presentation.

17. A computer storage medium, characterized in that: An operating program of a smart home decision-making system that can be run on a processor is stored, and the operating program is called by the processor to implement the steps of any one of the methods described in claims 14-16.

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