Scene detection method, device, electronic device and storage medium

Abstract

Embodiments of the present invention relate to a scene detection method, apparatus, electronic device, and storage medium. The method comprises: obtaining a first object feature of a target object and determining a target smart scene; determining a degree of match between the first object feature and the target smart scene; and outputting the degree of match. This allows for objective and accurate determination of the compatibility between the target object and the target smart scene, and timely output of an accurate degree of match. This prevents impacts on the target object due to incompatibility between the target scene and the target object's object features, thereby improving the user experience.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of smart home technology, and in particular to a scene detection method, device, electronic device, and storage medium. Background Art

[0002] Smart scenes are designed to fully meet various needs in life. They are a combination of a series of home functions, creating various adjustable, flexible, and multi-scene home modes with the help of a series of smart home devices.

[0003] With the development of the times, the usage rate of smart home appliances is gradually increasing, and the control of the entire home appliances based on smart scenes is deeply loved by many users due to its convenience and speed.

[0004] In the existing technology, users typically access smart scenes in two ways: system recommendation and user creation. However, due to user subjectivity, it's impossible to objectively determine whether the smart scenes obtained through these two methods meet their actual needs. Furthermore, the selected smart scene may meet the user's preferences but not be physically capable of handling it, resulting in health problems and a poor user experience. Summary of the Invention

[0005] In view of this, in order to solve the technical problem that due to the subjectivity of the user, it is impossible to objectively know whether the smart scene meets the user's actual needs, and there may also be a situation where the selected smart scene meets the user's preferences but the user's body cannot bear the load, causing damage to the user's health and poor user experience, the embodiments of the present invention provide a scene detection method, device, electronic device and storage medium.

[0006] In a first aspect, an embodiment of the present invention provides a scene detection method, the method comprising:

[0007] Acquire a first object feature of a target object and determine a target smart scene;

[0008] Determining a degree of matching between the first object feature and the target smart scene;

[0009] The matching degree is output.

[0010] In an optional implementation, determining the degree of matching between the first object feature and the target smart scene includes:

[0011] Determining scene characteristics of the target smart scene;

[0012] performing matching processing on the first object feature and the scene feature to obtain a first matching degree between the first object feature and the scene feature;

[0013] The first matching degree is determined to be a matching degree between the first object feature and the target smart scene.

[0014] In an optional implementation, determining the scene feature of the target smart scene includes:

[0015] Determine the number of smart devices in the target smart scene;

[0016] For each smart device in the target smart scene, obtain device characteristics of the smart device;

[0017] The number of device features is determined as scene features of the target scene.

[0018] In an optional embodiment, the performing matching processing on the first object feature and the scene feature to obtain a first matching degree between the first object feature and the scene feature includes:

[0019] determining a target feature from the number of device features, the target feature representing a device feature among the number of device features that affects the first object feature;

[0020] Dividing a plurality of feature groups to be matched according to the target feature and the first object feature, wherein each feature group includes the first object feature and each feature group includes a different target feature;

[0021] For each of the feature groups, matching the first object feature in the feature group with the target feature to obtain a second matching degree;

[0022] The second matching degree of the plurality of feature groups is determined as a first matching degree between the first object feature and the scene feature.

[0023] In an optional embodiment, the method further includes:

[0024] Comparing the matching degree with a preset matching degree threshold;

[0025] When the matching degree is less than a preset matching degree threshold, triggering a reminder that the target object and the target smart scene are not adapted; and

[0026] For each feature group in the plurality of feature groups, determining a device optimization strategy according to the second matching degree corresponding to the feature group, wherein the device optimization strategy is used to optimize the target feature in the feature group;

[0027] Determining the device optimization strategies corresponding to the plurality of feature groups as a first optimization strategy for the target smart scene;

[0028] The first optimization strategy is output to a preset object to remind the object to optimize the target smart scene according to the first optimization strategy.

[0029] In an optional embodiment, the method further includes:

[0030] Acquiring a second object feature of the target object detected by the detection device;

[0031] determining an object feature change value between the second object feature and the first object feature;

[0032] Determining whether the object feature change value exceeds a preset first change threshold range;

[0033] If the object feature change value exceeds the first change threshold range, determining a third matching degree between the second object feature and the target smart scene;

[0034] comparing the third matching degree with a preset matching degree threshold;

[0035] If the third matching degree is less than the matching degree threshold, a second optimization strategy corresponding to the target smart scene is output.

[0036] In an optional embodiment, the method further includes:

[0037] Acquiring current environmental characteristics of the environment in which the target object is located detected by a detection device;

[0038] Determining an environmental characteristic change value between the current environmental characteristic and the pre-collected initial environmental characteristic;

[0039] Determining whether the environmental characteristic change value exceeds a preset second change threshold range;

[0040] If the environmental feature change value exceeds the second change threshold range, determining a fourth matching degree between the current environmental feature and the target smart scene;

[0041] comparing the fourth matching degree with a preset matching degree threshold;

[0042] If the fourth matching degree is less than the matching degree threshold, a third optimization strategy corresponding to the target smart scene is output.

[0043] In a second aspect, an embodiment of the present invention provides a scene detection device, the device comprising:

[0044] an acquisition module, configured to acquire a first object feature of a target object and determine a target smart scene;

[0045] a matching degree determination module, configured to determine a matching degree between the first object feature and the target smart scene;

[0046] An output module is used to output the matching degree.

[0047] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor and a memory, wherein the processor is configured to execute a scene detection program stored in the memory to implement the scene detection method described in any one of the first aspects.

[0048] In a fourth aspect, an embodiment of the present invention provides a storage medium, which stores one or more programs, and the one or more programs can be executed by one or more processors to implement the scene detection method described in any one of the first aspects.

[0049] The technical solution provided by the embodiments of the present invention obtains a first object feature of a target object and determines a target smart scene; then determines the degree of match between the first object feature and the target smart scene, and outputs the degree of match. This allows for objective and accurate understanding of the compatibility between the target object and the target smart scene, preventing the target object from being affected by an incompatible target smart scene due to the subjectivity of the target object. Furthermore, the operation is simple and enhances the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram illustrating an application scenario of an embodiment of the present invention;

[0051] Figure 2 A flow chart of an embodiment of a scene detection method provided by an embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a visualization interface provided by an embodiment of the present invention;

[0053] Figure 4 A flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention;

[0054] Figure 5 A flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention;

[0055] Figure 6 A flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention;

[0056] Figure 7 A flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention;

[0057] Figure 8A flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention;

[0058] Figure 9 A block diagram of an embodiment of a scene detection device provided by an embodiment of the present invention;

[0059] Figure 10 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0061] To facilitate understanding of the embodiments of the present invention, the following first describes an application scenario of the present invention with reference to the accompanying drawings:

[0062] See also Figure 1 , is a schematic diagram of an application scenario shown in an embodiment of the present invention. Figure 1 The application scenario shown may include: a smart scene 10 , an object 11 , devices 12 - 16 and a detection device 17 .

[0063] The aforementioned devices 12-16 may be hardware devices or software that provide services, and are smart devices in the smart scene 10. Hardware devices include, but are not limited to, smart home appliances such as smart air conditioners, smart TVs, smart refrigerators, and robot vacuums. Software devices may include smart home programs, applets, and systems installed on hardware devices such as smartphones and tablets.

[0064] In practice, devices 12 to 15 can install corresponding server applications to provide corresponding services. Devices 12 to 15 can be called user devices, and device 16 can be called a server. Among them, device 12 can be a terminal device of object 11, such as a smart phone, smart tablet, etc. It can be understood that in order to determine the adaptation between object 11 and smart scene 10, device 12 can be installed with a scene detection APP (Application) to provide scene detection services to object 11. In addition, other services can be provided to the object, such as scene optimization services, scene editing services, etc., which are not limited in this embodiment of the present invention. Figure 1 In the example, only device 12 is a smart phone, and devices 13 to 15 are smart home devices (smart air conditioner, smart TV, smart refrigerator) in smart scene 10.

[0065] The detection device 17 can be a hardware device or software that provides data detection services. For example, when the detection device 17 is a hardware device, it can be a sleep detector, temperature and humidity detector, etc. When the detection device 17 is a software device, it can be a detection program, applet, system, etc. installed on a hardware device such as a smart watch or smart bracelet.

[0066] Based on the above description, when detection device 17 is a sleep monitor, it can be used to detect sleep data of subject 11, such as heart rate, respiratory rate, sleep quality, sleep duration, sleep stages, etc. When detection device 17 is a temperature and humidity detector, it can be used to detect temperature and humidity data of subject 11 and the environment in which subject 11 resides. The above is merely an exemplary description of detection device 17 and detection data; embodiments of the present invention do not limit the type, quantity, or detection data of detection device 17.

[0067] It is understandable that Figure 1 The number and types of devices in the smart scene are for illustration only. Any number and type of devices may be included as needed. Furthermore, an object may configure different smart scenes in different time periods or based on different needs. The embodiments of the present invention do not limit the number of smart scenes in an object's environment.

[0068] The scene detection method provided by the present invention is further explained below with reference to specific embodiments in conjunction with the accompanying drawings. The embodiments do not limit the embodiments of the present invention.

[0069] See also Figure 2 , is a flow chart of an embodiment of a scene detection method provided by an embodiment of the present invention. Figure 2 As shown, the process may include the following steps:

[0070] Step 201: Acquire a first object feature of a target object and determine a target smart scene.

[0071] The above-mentioned target objects include the terminal devices currently using the scene detection APP (such as Figure 1 In practice, since some special subjects (such as the elderly and children) are not proficient in using smart devices, family members or other subjects may perform operations such as selecting and detecting target scenes. In this case, the target objects may also include observation objects pre-set in the scene detection app.

[0072] The above-mentioned first object feature is the characteristic information of the target object, which can be the information used by the target object in the scene detection APP, including but not limited to: personal information, historical operation behavior information; it can also be the physical information of the target object detected in real time by the detection device, including but not limited to: physical status information, physical health information, etc.

[0073] The above personal information represents the basic information of the target object in life and / or work, such as name, age, occupation, marital status, work status, preferences, etc. Among them, the preferences can represent the preferences of the target object in various aspects. For example, in life, it can be sports preferences, film and television viewing preferences, music preferences, etc.; in environmental aspects, it can be temperature preferences, humidity preferences, etc., which are not limited in this embodiment of the present invention. The above historical operation behavior information represents the operation behavior records generated by the target object in the process of using the scene detection APP, such as browsing records, editing records, etc.

[0074] The above-mentioned physical state information represents the state of the target object, such as sleeping state, working state, eating state, fitness state, etc. The above-mentioned physical health information represents the physical data of the target object, such as body water content information, body organ information, etc., which is not limited in the embodiment of the present invention.

[0075] In one embodiment, the preset object can input the first object feature of the target object on the scene detection APP through the terminal device. Specifically, a visual interface can be output to the preset object so that the preset object can input information based on the target visual interface and determine the information as the first object feature of the target object. In this way, the first object feature of the target object can be obtained. For example Figure 3 The figure shows a schematic diagram of a visualization interface according to an embodiment of the present invention. Figure 3 In the scene detection APP, the target object can input information based on the visual interface shown in the first module, and determine the input information as the first object feature of the target object.

[0076] The preset object may be the same object as the target object, or may be another object, without limitation. For example, when the target object is a pre-set observation object (e.g., a child), the preset object may be the guardian of the target object.

[0077] In another embodiment, a detection device may be used to perform feature detection on the target object to obtain detected information, and the information may be determined as the first object feature of the target object.

[0078] The target smart scene is used to control the smart devices in the smart scene, provide services for the target object, and meet the needs of the target object. It should be noted that the target smart scene is associated with the smart devices in the target smart scene. The embodiments of the present invention do not limit the type or number of smart devices in the target scene.

[0079] In one embodiment, the target object can access the Figure 1 The device 12 shown in FIG selects any smart scene from a preset smart scene library and determines the smart scene selected by the target object as the target smart scene. In this way, the target smart scene can be determined simply and quickly.

[0080] Optionally, a visual interface for smart scene selection can be output to the target object, in which the smart scenes are all the smart scenes in the preset smart scene library. The target object can make a selection based on the smart scenes output in the visual interface, and the smart scene selected by the target object is determined as the target smart scene.

[0081] It should be noted that the target object can be selected based on actual needs or preferences, or can be selected based on the sorting results of the smart scenes in the visual interface according to preset sorting rules (for example, descending or ascending order), and the embodiment of the present invention does not limit this. The preset sorting rules can be the type, number, and editability of the smart devices in the smart scene, etc., which can be set or selected by the preset object.

[0082] Optionally, the smart scenes in the preset smart scene library can be pre-screened based on the first object feature of the target object to obtain a preset number of screened smart scenes. The preset number of smart scenes are output to the target object, and the smart scene selected by the target object from the preset number of smart scenes is determined as the target smart scene. It should be noted that the information of the smart device represented by the first object feature (such as the type of device, the number of devices, etc.) and the information of the smart device in each smart scene in the preset smart scene library can be analyzed, and then the smart scenes in the preset smart scene library can be pre-screened based on the information of the smart device.

[0083] For example, suppose the first object characteristic is the target subject's temperature preference of 25 degrees Celsius. The pre-set smart scene library includes a first smart scene and a second smart scene. In the first smart scene, the smart device is a smart air conditioner; in the second smart scene, the smart device is a smart TV. Based on the above description, the first object characteristic is analyzed to determine that the smart device is a temperature-adjustable device, namely a smart air conditioner. Based on this, the smart scenes in the smart scene library are filtered to obtain the first filtered smart scene. The target subject can then select the first smart scene as the target smart scene.

[0084] In another embodiment, the target object can select a smart device from a preset smart device library and establish a target smart scene based on the selected smart device. The target smart scene thus obtained better meets the needs of the target object and improves the target object's satisfaction and experience.

[0085] For example, the preset smart device library includes smart air conditioners, smart TVs, smart desk lamps, and smart refrigerators. Let's assume the target smart scene is a waking scene: turn on the lights when the temperature is 28 degrees Celsius. Based on the above description, you can select the smart air conditioner and smart desk lamp as the smart devices from the preset smart device library. Then, you can set the smart air conditioner and smart desk lamp in the smart scene to achieve the target smart scene.

[0086] Step 202: Determine the matching degree between the first object feature and the target smart scene.

[0087] Step 203: Output the matching degree.

[0088] The following is a unified description of step 202 and step 203:

[0089] In one embodiment, to determine the compatibility between the target object and the target smart scene, after obtaining the first object feature of the target object, the degree of match between the first object feature and the target smart scene can be determined. This degree of match is used to characterize the compatibility between the target object and the target smart scene. It can be the target object's satisfaction with the target smart scene, or the suitability of the target smart scene for the target object, without specific limitation.

[0090] In one embodiment, a matching degree determination visualization interface may be output to the target object, and the target object may trigger the matching degree determination visualization interface to perform matching degree calculation processing on the first object feature and the scene feature of the target smart scene to determine the matching degree between the first object feature and the target smart scene. The trigger operation may be a click operation (including a single-click operation or a double-click operation) on a function icon in the matching degree determination visualization interface, and the embodiment of the present invention does not limit this. For example Figure 3 In the second module shown, the user can click the "Information Matching" function icon to perform a matching operation on the first object feature and the scene feature of the target smart scene (for example, Figure 3 The third module in the example is used to determine the matching degree between the first object feature and the target intelligent scene. Figure 3 The matching degree shown in the fourth module is 73 points.

[0091] Optionally, a change value between the first object feature and the scene feature of the target smart scene may be determined, and a matching degree between the first object feature and the target smart scene may be determined based on the change value.

[0092] Optionally, the corresponding matching degree may be determined from a table of pre-set correspondences between change values ​​and matching degrees according to the change value between the current first object feature and the scene feature.

[0093] Optionally, a difference or ratio between the above change value and a preset change threshold may be determined, and the difference or ratio may be determined as the degree of matching between the first object feature and the target smart scene.

[0094] Optionally, a preset matching algorithm may be used to determine the degree of match between the first object feature and the target smart scene. The matching algorithm is used to calculate the first object feature of the target object and the scene features of the target smart scene. The matching algorithm may be a SIFT (Scale-invariant feature transform) algorithm, a SURF (Speeded Up Robust Features) algorithm, or the like.

[0095] Optionally, a pre-trained matching model may be used to determine the matching degree between the first object feature and the target smart scene.

[0096] It should be noted that the above is merely an exemplary description of determining the degree of matching between the first object feature and the target smart scene without considering other influencing factors (such as the environment in which the target object is located) or in a standard environment. In practice, other methods can also be used for determination, and the embodiments of the present invention do not limit this.

[0097] In an embodiment of the present invention, when determining the matching degree between the first object feature and the target smart scene, the adaptation between the target object and the target smart scene can be determined based on the matching degree, and the matching degree can be output to the preset object for the preset object to view.

[0098] In addition, after determining the compatibility between the target object and the target smart scene and outputting the matching degree to the preset object, a reminder can be triggered for the above-mentioned adaptation to remind the preset object of the adaptation status and that the preset object can perform operations (such as modification, deletion, addition) on the target smart scene or redefine the target smart scene. The embodiments of the present invention do not limit this.

[0099] Through the above processing method, the user only needs to click the corresponding function icon to obtain the matching degree between the first object feature and the target smart scene, which is simple to operate.

[0100] So far, completed Figure 2 A description of the process shown.

[0101] pass Figure 2As can be seen from the illustrated process, the technical solution of the present invention obtains the first object feature of the target object and determines the target smart scene; then determines the degree of match between the first object feature and the target smart scene, and outputs the match degree. This allows for objective and accurate understanding of the compatibility between the target object and the target smart scene, preventing the target object from being affected by an incompatible target smart scene due to the subjectivity of the target object. Furthermore, the operation is simple and enhances the user experience.

[0102] See also Figure 4 , is a flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention. Figure 4 The process shown in the above Figure 2 Based on the process shown in FIG, how to determine the matching degree between the first object feature and the target intelligent scene is described. Figure 4 As shown, the process may include the following steps:

[0103] Step 401: Acquire a first object feature of a target object and determine a target smart scene.

[0104] For a detailed description of step 401, please refer to Figure 2 The relevant description of step 201 is not repeated here.

[0105] Step 402: Determine the scene characteristics of the target smart scene.

[0106] Step 403: Perform matching processing on the first object feature and the scene feature to obtain a first matching degree between the first object feature and the scene feature.

[0107] Step 404: Determine a first matching degree as a matching degree between the first object feature and the target smart scene.

[0108] The following is a unified description of steps 402 to 404:

[0109] In practice, in order to determine the degree of match between the target object and the target smart scene, it is necessary to determine the first object feature of the target object and the scene feature of the target smart scene respectively, and perform matching processing on the first object feature and the scene feature to obtain the degree of match between the first object feature and the scene feature (hereinafter referred to as the first matching degree). The scene feature may include a combination of output information of smart devices present in the target smart scene. For example, if the smart device is a smart air conditioner, its output information may be temperature information; if the smart device is a humidifier, its output information may be humidity information; in this case, when the target smart scene includes a smart air conditioner and a humidifier, its scene features may be temperature information and humidity information.

[0110] In one embodiment, there may be at least one smart device in the target smart scene; the implementation method of determining the scene characteristics of the target smart scene may specifically include: determining the number of smart devices in the target smart scene, and obtaining the device characteristics of the smart device for each smart device in the target smart scene; the device characteristics of the determined number are the scene characteristics of the target scene.

[0111] The device characteristics may be the output information of the smart devices in the target smart scene. In practice, the device characteristics of a smart device may be a single characteristic or a combination of multiple characteristics. For example, if a smart air conditioner primarily outputs temperature, its device characteristics may be temperature; if a smart air conditioner outputs temperature and ventilation, its device characteristics may be temperature and wind speed. The embodiments of the present invention do not limit the type or number of device characteristics of a smart device.

[0112] In one embodiment, the aforementioned matching process of the first object feature with the scene feature to obtain a matching degree between the first object feature and the scene feature (hereinafter referred to as the first matching degree) may include: determining a target feature from a number of device features; dividing a plurality of feature groups to be matched based on the target feature and the first object feature, wherein each feature group includes the first object feature and each feature group includes a different target feature; for each feature group, matching the first object feature with the target feature in the feature group to obtain a matching degree (hereinafter referred to as the second matching degree); and determining the second matching degree of the plurality of feature groups as the first matching degree between the first object feature and the scene feature.

[0113] The target feature represents the device feature that affects the first object feature among the device features. For example, if the first object feature is humidity preference: 40%, the device feature is humidifier output humidity: 45%, and the smart desk lamp is turned on, then the target feature can be determined to be the humidifier output humidity.

[0114] The feature groups include first object features and target features. The first object features correspond to the target features one-to-one, and each feature group includes different first object features and different target features.

[0115] For example, assume that a unique target feature among the device characteristics can affect the first object feature. Also, assume that the device characteristics include temperature, humidity, and movies, and the first object characteristics include temperature preference, humidity preference, and movie preference. Based on the above description, multiple feature groups to be matched can be created based on the target feature and the first object feature. The first feature group includes temperature and temperature preference; the second feature group includes humidity and humidity preference; and the third feature group includes movies and movie preference.

[0116] In addition, it should be noted that, in practice, there may be multiple smart devices that affect the same type of object features in the first object features (such as the heart rate value of the target object, etc.). Therefore, in this case, the same type of object features and different target features in the first object features can be divided into multiple feature groups respectively, and then for each feature group, the second matching degree between the first object feature and the target feature is determined.

[0117] For example, assume the target smart scene contains the following smart devices: a smart air conditioner, a smart desk lamp, and a smart speaker, and assume the first object feature is heart rate. It is known that, in practice, temperature, strong light, and the target subject's mood all affect their heart rate. Therefore, the target features can be determined to be temperature, light, and music. The same object features and different target features in the first object feature can be divided into multiple feature groups. The fourth feature group includes temperature and heart rate; the fifth feature group includes strong light and heart rate; and the sixth feature group includes music and heart rate.

[0118] Optionally, a weight ratio can be set according to the smart devices present in the target smart scene. After determining the second matching degree between the target feature of the smart device and the first object feature, the second matching degrees corresponding to multiple feature groups are calculated according to the weight ratio of each smart device to obtain the first matching degree between the first object feature and the scene feature.

[0119] Optionally, the weight percentages of the smart devices can be determined by analyzing the first object feature of the target object according to a preset standard and by the correlation between the first object feature and the device features of the smart devices. For example, the first object feature of the target object includes temperature and humidity. The smart devices in the target smart scene include a smart air conditioner, a smart refrigerator, a smartphone, and a humidifier. Therefore, it can be seen that the correlation between the first object feature and the smart air conditioner and humidifier is high, so the weight percentages of the smart devices can be determined according to the preset standard.

[0120] The correlation refers to the degree of association between the first object feature and the device feature. This can be determined by the number of smart devices corresponding to the target feature that influences the first object feature, or by querying a pre-set correlation table, although this is not a limitation in the present embodiment.

[0121] Optionally, after the correlation between the first object feature and the device feature is known, the weight ratio may be searched from a table recording the corresponding relationship between the correlation and the weight ratio by looking up the table.

[0122] Through the above processing method, the number of smart devices existing in the target smart scene can be determined, the target features can be screened out from the device features of the above number of smart devices, and the target features can be matched with the corresponding first object features, thereby more accurately determining the matching degree between the target smart scene and the target object and improving accuracy.

[0123] Step 405: Output the matching degree.

[0124] For a detailed description of step 405, see Figure 2 The relevant description of step 203 is not repeated here.

[0125] So far, completed Figure 4 A description of the process shown.

[0126] Through this processing method, the scene characteristics of the target smart scene can be matched with the first object characteristics, and the matching degree between the target object and the target smart scene can be obtained, and then the adaptation of the target object and the target smart scene can be determined. It is more accurate and objective, and the operation process is relatively simple.

[0127] See also Figure 5 , is a flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention. Figure 5 The process shown in Figure 4 Based on the process shown in the figure, this paper describes how to achieve scenario optimization based on the comparison results. Figure 5 As shown, the process may include the following steps:

[0128] Step 501: Acquire a first object feature of a target object and determine a target smart scene.

[0129] For a detailed description of step 501, please refer to Figure 2 The relevant description of step 201 is not repeated here.

[0130] Step 502: Determine the number of smart devices in the target smart scene.

[0131] Step 503: For each smart device in the target smart scene, obtain the device characteristics of the smart device.

[0132] Step 504: Determine a number of device features as scene features of the target scene.

[0133] Step 505: Determine a target feature from the quantity of device features, where the target feature represents a device feature in the quantity of device features that affects the first object feature.

[0134] Step 506 : Divide the target feature and the first object feature into a plurality of feature groups to be matched, wherein each feature group includes the first object feature and each feature group includes a different target feature.

[0135] Step 507: For each feature group, match the first object feature and the target feature in the feature group to obtain a second matching degree.

[0136] Step 508: Determine the second matching degree of the plurality of feature groups as the first matching degree between the first object feature and the scene feature.

[0137] Step 509: Determine the first matching degree as the matching degree between the first object feature and the target smart scene.

[0138] For detailed description of steps 502 to 509, please refer to Figure 4 The relevant descriptions of steps 402 to 404 are not repeated here.

[0139] Step 510: Output the matching degree.

[0140] For a detailed description of step 510, please refer to Figure 2 The relevant description of step 203 is not repeated here.

[0141] Step 511: Compare the matching degree with a preset matching degree threshold.

[0142] Step 512: When the matching degree is less than a preset matching degree threshold, a reminder of mismatch between the target object and the target smart scene is triggered.

[0143] The following is a unified description of step 511 and step 512:

[0144] In an embodiment of the present invention, in order to accurately know the adaptation between the target object and the target smart scene, after determining the matching degree between the first object feature and the target smart scene, the matching degree can be compared with a preset matching degree threshold (for example, 75 or 75%, 80 or 80%, etc.) to accurately determine the adaptation between the target object and the target smart scene based on the comparison result.

[0145] In one embodiment, when the comparison result indicates that the degree of match between the first object feature and the target smart scene is greater than or equal to a preset matching degree threshold, it can be determined that the degree of adaptability between the target object and the target smart scene is high, and the specific adaptation situation is the adaptation between the target object and the target smart scene. The degree of match between the above-mentioned first object feature and the target smart scene can be output to the preset object for the preset object to view. In addition, if there is a situation where the preset object is not satisfied with the target smart scene or is dissatisfied with the setting of a smart device in the target smart scene, the preset object can redefine the target smart scene according to actual needs, or operate on the target smart scene (such as modifying, deleting, adding), and the embodiment of the present invention does not limit this.

[0146] In one embodiment, if the comparison result indicates that the degree of match between the first object feature and the target smart scene is less than a preset match threshold, the compatibility between the target object and the target smart scene may be determined to be low, specifically, the target object and the target smart scene are not compatible. Therefore, a reminder that the target object and the target smart scene are not compatible may be triggered to alert the preset object of the poor compatibility between the target object and the target smart scene. Furthermore, the degree of match between the first object feature and the target smart scene may be output to the preset object for review.

[0147] In addition, after triggering the reminder corresponding to the adaptation between the target object and the target smart scene and the matching degree to the preset object, the preset object can operate on the target smart scene (such as modifying, deleting, adding), or redefine the target smart scene. That is, the target smart scene is optimized. The specific method of optimizing the target smart scene can be found in the detailed description of the following steps, which will not be detailed here.

[0148] Step 513 : For each feature group in the plurality of feature groups, determine a device optimization strategy according to the second matching degree corresponding to the feature group, where the device optimization strategy is used to optimize the target feature in the feature group.

[0149] Step 514: Determine the device optimization strategies corresponding to the multiple feature groups as the first optimization strategy for the target smart scene.

[0150] Step 515: Output the first optimization strategy to the preset object to remind the object to optimize the target smart scene according to the first optimization strategy.

[0151] The following is a unified description of steps 513 to 515:

[0152] In an embodiment of the present invention, the adaptation between the target object and the target smart scene is determined by comparing the matching degree between the first object feature and the target smart scene with a preset matching degree threshold. Specifically, when it is determined that the matching degree is less than the preset matching degree threshold, a reminder that the target object is not adapted between the target smart scene can be triggered. At the same time, in order to improve the target object's satisfaction with the target smart scene and improve the adaptability between the target object and the target smart scene, while outputting the matching degree between the first object feature of the target object and the target smart scene, an optimization strategy for the target smart scene (hereinafter referred to as the first optimization strategy) can also be output. Among them, the above-mentioned optimization strategy is an adjustment strategy for smart devices in the target smart scene, such as an adjustment strategy for the temperature output by a smart air conditioner. It can be used to optimize the target features in the feature group.

[0153] Specifically, in the embodiments of the present invention, since it may be necessary to control multiple smart devices in the target smart scene, the optimization operations performed on different smart devices are also different. For example, the optimization strategy of the smart air conditioner can be temperature adjustment, the optimization strategy of the humidifier can be humidity adjustment, and the optimization strategy of the smart TV can be playing movies and TV, etc. The embodiments of the present invention do not limit this.

[0154] It should be noted that the above is only the optimization strategy determined when the device characteristics of the smart device and the first object characteristics are one-to-one. This optimization strategy can adjust a single characteristic. In practice, there may also be situations where the device characteristics of the smart device affect multiple first object characteristics. For example, a smart air conditioner can affect the temperature characteristic, wind speed characteristic, or humidity characteristic of the first object characteristic. In this case, the optimization strategy of the smart air conditioner can adjust multiple characteristics, or select a primary characteristic (such as temperature) for adjustment.

[0155] Based on the above description, in embodiments of the present invention, a device optimization strategy can be determined for each feature group among the determined multiple features based on the second matching degree corresponding to the feature group. Furthermore, the device optimization strategy for the multiple feature groups can be determined as the first optimization strategy for the target smart scene, and the first optimization strategy can be output to the preset object so that the preset object can optimize the target smart scene according to the first optimization strategy.

[0156] Optionally, the specific implementation of determining the device optimization strategy based on the second matching degree corresponding to the feature group can be determined by searching a table set based on the second matching degree and the device optimization strategy, and combining the first object feature and target feature in the feature group.

[0157] For example, assuming the first object feature is a temperature preference of 25 degrees Celsius, the target feature in the feature group corresponding to the first object feature is a smart air conditioner output temperature of 28 degrees Celsius, and the second matching degree corresponding to this feature group is 70%, a preset table corresponding to matching degrees and optimization strategies can be searched. Combining the first object feature with the target feature, the device optimization strategy is determined to be controlling the smart air conditioner's output temperature to lower by 3 degrees Celsius. The device optimization strategy for this feature group is determined as the first optimization strategy for the target smart scene, and this first optimization strategy is output to the preset object so that the preset object can optimize the target smart scene according to the first optimization strategy, namely, controlling the smart air conditioner's output temperature to lower by 3 degrees Celsius.

[0158] In addition, it should be noted that after determining the first optimization strategy for the target smart scene, whether to optimize the target smart scene and which smart device among the smart devices in the target smart scene to optimize can be independently selected by the target object or the preset object.

[0159] In the embodiment of the present invention, after the target smart scene is optimized, the optimized scene features of the optimized target smart scene may be reacquired, and the matching degree between the optimized scene features and the first object features may be determined.

[0160] In one embodiment, the target object or the preset object can select which smart device to optimize among the smart devices in the target smart scene through the visual interface output by the terminal device, for example Figure 3 The fourth module outputs the optimization function icons of each smart device in the target smart scene. The preset object or target object can be selected by itself, and the optimized scene features of the optimized target smart scene are re-matched with the first object features to determine the matching degree between the optimized target smart scene and the target object. For example Figure 3 The matching degree of the fifth module output is 93 points.

[0161] In addition, in one embodiment, there may be some special reasons that cause certain object features of the target object in the target smart scene to change; or, among the object features of the target object in the target smart scene, the following object features may change in real time. Therefore, after the target object uses the target smart scene, the object features of the target object (hereinafter referred to as the second object features) can be obtained again through the detection device, and the matching degree between the target object and the target smart scene can be re-determined based on the second object features. For details, please refer to Figure 6 , is a flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention. Figure 6 As shown, the process may include the following steps:

[0162] Step 601: Acquire a second object feature of a target object detected by a detection device.

[0163] Step 602: Determine an object feature change value between the second object feature and the first object feature.

[0164] Step 603: Determine whether the object feature change value exceeds a preset first change threshold range. If so, execute step 604; if not, end the operation.

[0165] Step 604: Determine a third matching degree between the second object feature and the target smart scene.

[0166] Step 605 : Compare the third matching degree with a preset matching degree threshold. If the third matching degree is less than the matching degree threshold, execute step 606 .

[0167] Step 606: Output a second optimization strategy corresponding to the target smart scenario.

[0168] The following is a unified description of steps 601 to 606:

[0169] In practice, the target object's object characteristics may change in real time (e.g., heart rate, respiratory rate, etc.), but such changes are normal and do not affect the target object. Therefore, after obtaining the second object characteristic of the target object, the object characteristic change value between the second object characteristic and the first object characteristic of the target object can be determined. Furthermore, it is determined whether the object characteristic change value exceeds a preset first change threshold range to determine whether to re-determine the match between the target object and the target smart scene and optimize the target smart scene.

[0170] The object feature change value is used to represent a specific change between the first object feature and the second object feature of the target object. It can be a difference or ratio between the first object feature and the second object feature, without limitation. The first change threshold range represents a standard change range of the object feature of the target object.

[0171] Furthermore, when the object feature change value exceeds the preset first change threshold range, a third matching degree between the second object feature and the target smart scene can be determined, and the third matching degree can be compared with the preset matching degree threshold to determine whether to optimize the target smart scene. As for how to calculate the third matching degree, please refer to Figure 2 and Figure 4 The description of the matching degree between the first object feature and the target intelligent scene will not be repeated here.

[0172] When the third degree of match is less than the preset degree of match threshold, the optimization strategy corresponding to the third degree of match (hereinafter referred to as the second optimization strategy) can be determined, and the third degree of match and the second optimization strategy can be output to the preset object to remind the object to optimize the target smart scene according to the second optimization strategy. When the third degree of match is greater than or equal to the preset degree of match threshold, the third degree of match can be output to the preset object. As for how to determine the second optimization strategy corresponding to the third degree of match, and how to optimize the target smart scene according to the second optimization strategy, please refer to Figure 2 and Figure 4 The first optimization strategy and the related description of optimizing the target intelligent scene according to the first optimization strategy will not be repeated here.

[0173] For example, assuming that the target smart scene is a sleep smart scene, the first object feature is the heart rate value of the target object of 72 beats / minute, and assuming that the target object has a nightmare during sleep, the second object feature of the current target object is detected to be 120 beats / minute, and the preset first change threshold range is [-12, 28). Then, according to the above description, the object feature change value between the second object feature and the first object feature is determined to be 48. By comparing the object feature change value with the preset first change threshold range, it can be determined that the object feature change value exceeds the first change threshold range. Therefore, the third matching degree between the second object feature and the target smart scene is determined, wherein it is assumed that the temperature preference of the target object has a low matching degree with the temperature set by the smart air conditioner in the target smart environment. The third matching degree is compared with the preset matching degree threshold. If the third matching degree is less than the matching degree threshold, then the second optimization strategy can be output as setting the smart air conditioner to reduce 5 degrees Celsius, so that the target object or the preset object optimizes the target smart scene according to the second optimization strategy.

[0174] So far, completed Figure 6 A description of the process shown.

[0175] Through the above processing, after the target object uses the target smart scene, the changes in the target object itself can be detected in real time, and intelligent analysis can be performed based on this change to re-determine the matching degree between the target object and the target smart scene. The corresponding optimization strategy can be triggered according to the environmental changes, and the target smart scene can be optimized to improve the user experience.

[0176] In practice, the target object's environment may affect the target object's first object characteristic. For example, if the ambient temperature is 34 degrees Celsius, the target object's temperature preference will change significantly compared to the temperature preference at an ambient temperature of 10 degrees Celsius. Therefore, in the above process of determining the matching degree between the target object and the target smart scene, the matching degree between the environmental characteristics of the target object's environment and the target smart scene must also be considered to determine the final matching degree.

[0177] In one embodiment, since the environmental characteristics of the target object's environment may also change, after the target object uses the target smart scene, the environmental characteristics of the target object's environment may be acquired again through the detection device, and the matching degree between the target object and the target smart scene may be determined based on the environmental characteristics. Figure 7 , is a flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention. Figure 7 As shown, the process may include the following steps:

[0178] Step 701: Acquire the current environmental characteristics of the environment in which the target object detected by the detection device is located.

[0179] Step 702: Determine the environmental feature change value between the current environmental feature and the pre-collected initial environmental feature.

[0180] Step 703: Determine whether the environmental characteristic change value exceeds a preset second change threshold range. If so, execute step 704; if not, end the operation.

[0181] Step 704: Determine a fourth matching degree between the current environment characteristics and the target smart scene.

[0182] Step 705 : Compare the fourth matching degree with a preset matching degree threshold. If the fourth matching degree is less than the matching degree threshold, execute step 706 .

[0183] Step 706: Output a third optimization strategy corresponding to the target smart scenario.

[0184] The following is a unified description of steps 701 to 706:

[0185] From the above Figure 6 As described in the relevant section, a detection device can detect the current environmental characteristics of the target object's environment and the initial environmental characteristics detected by the target object before using the target smart scene. A change in the environmental characteristics between the current and initial environmental characteristics can then be determined. Furthermore, a determination is made as to whether the change in environmental characteristics exceeds a preset second change threshold to determine whether to re-determine the match between the target object's environment and the target smart scene and optimize the target smart scene.

[0186] The above-mentioned environmental characteristics may be ambient temperature, ambient humidity, etc., without limitation. The above-mentioned environmental characteristic change value is used to characterize the specific change between the current environmental characteristics and the initial environmental characteristics of the environment in which the target object is located in the target intelligent scenario. It may be the difference or ratio between the current environmental characteristics and the initial environmental characteristics, without limitation. The above-mentioned second change threshold range characterizes the standard change range of the environmental characteristics of the environment in which the target object is located.

[0187] Furthermore, when the change value of the environmental feature exceeds the preset second change threshold range, the fourth matching degree between the current environmental feature and the target smart scene can be determined, and the fourth matching degree can be compared with the preset matching degree threshold to determine whether to optimize the target smart scene. As for how to calculate the fourth matching degree, please refer to Figure 2 and Figure 4 The description of the matching degree between the first object feature and the target intelligent scene will not be repeated here.

[0188] When the fourth degree of match is less than the preset degree of match threshold, the optimization strategy corresponding to the fourth degree of match (hereinafter referred to as the third optimization strategy) can be determined, and the fourth degree of match and the third optimization strategy can be output to the preset object to remind the object to optimize the target smart scene according to the third optimization strategy. When the fourth degree of match is greater than or equal to the preset degree of match threshold, the fourth degree of match can be output to the preset object. As for how to determine the third optimization strategy corresponding to the fourth degree of match, and how to optimize the target smart scene according to the third optimization strategy, please refer to Figure 2 and Figure 4 The first optimization strategy and the related description of optimizing the target intelligent scene according to the first optimization strategy will not be repeated here.

[0189] So far, completed Figure 7 A description of the process shown.

[0190] Through the above processing, after the target object uses the target smart scene, the changes in the environment of the target object can be detected in real time, and intelligent analysis can be performed based on this change to re-determine the matching degree between the environment and the target smart scene. The corresponding optimization strategy can be triggered according to the environmental changes, and the target smart scene can be optimized to improve the user experience.

[0191] In addition, after the preset object optimizes the target smart scene, the object characteristics of the target object can be determined again, the matching degree between the object characteristics and the target smart scene can be determined, and the optimization strategy can be output until the preset object or the target object is satisfied.

[0192] So far, completed Figure 5 A description of the process shown.

[0193] Through this processing method, it is possible to determine the second matching degree between each smart device in the target smart scene and the target object, and then determine the matching degree between the target object and the target smart scene based on multiple second matching degrees. The accuracy is higher, and the adaptation of the target object and the target smart scene can be known more objectively. The operation process is relatively simple. It is also possible to trigger corresponding reminders based on the adaptation between the target object and the target smart scene, and determine the optimization strategy of the target smart scene, and then optimize the target smart scene, improve the adaptation of the target object and the target smart scene, and enhance the object experience.

[0194] See also Figure 8 , is a flow chart of another embodiment of a scene detection method provided by an embodiment of the present invention. Figure 8 As shown, the process may include the following steps:

[0195] exist Figure 8 In the process shown, taking the scene detection APP as a smart home APP as an example, the target object can open the smart home APP and enter the "My Section" so that the user (ie, the target object) can use the visual interface shown in the "My Section" (for example, Figure 3 In the first module (in the user interface), the user inputs personal information such as physical fitness and creates their own user profile. Thus, the first object feature of the target object (i.e., the above-mentioned personal information such as physical fitness) can be obtained.

[0196] After determining the target smart scene, you can click the "Information Matching" button on the scene specific information interface output by the smart home APP (for example Figure 3 The smart home app can output a matching visualization interface to determine the degree of match between the first object feature and the target smart scene. The degree of match can be in the form of a score, which is not specifically limited.

[0197] After determining the above matching degree, the user can be informed of the matching degree score between the solution (i.e., the target smart scene) and the user, as well as the optimization suggestions (i.e., the first optimization strategy). For details, please refer to Figure 3 In the fourth module. In this way, the adaptation between the target object and the target smart scene can be determined according to the matching score. And the target object can optimize the determined target smart scene according to the optimization suggestion. After the scene optimization, the optimized target scene can be matched with the first object feature again (for example Figure 3 The fifth module in the optimization process continues until the user is satisfied with the current scenario solution (that is, the optimized target smart scenario).

[0198] Through the above processing method, taking the smart home APP scene as an example, the situation of the user performing the scene detection operation can be clearly and intuitively presented. For the user, the operation of implementing scene detection is simple, and the adaptation between the user and the target smart scene can be objectively and accurately determined.

[0199] Corresponding to the above-mentioned embodiment of the scene detection method, the present invention also provides a block diagram of an embodiment of a device.

[0200] See also Figure 9 , is a block diagram of an embodiment of a scene detection device provided by an embodiment of the present invention. Figure 9 As shown, the device includes:

[0201] An acquisition module 901 is configured to acquire a first object feature of a target object and determine a target smart scene;

[0202] A matching degree determination module 902 is configured to determine a matching degree between the first object feature and the target smart scene;

[0203] The output module 903 is configured to output the matching degree.

[0204] In an optional implementation, the matching degree determination module 902 includes (not shown in the figure):

[0205] a feature determination unit, configured to determine scene features of the target smart scene;

[0206] a matching processing unit, configured to perform matching processing on the first object feature and the scene feature to obtain a first matching degree between the first object feature and the scene feature;

[0207] A matching degree determining unit is configured to determine that the first matching degree is a matching degree between the first object feature and the target smart scene.

[0208] In an optional implementation manner, the feature determination unit is specifically configured to:

[0209] Determine the number of smart devices in the target smart scene;

[0210] For each smart device in the target smart scene, obtain device characteristics of the smart device;

[0211] The number of device features is determined as scene features of the target scene.

[0212] In an optional implementation manner, the matching processing unit is specifically configured to:

[0213] determining a target feature from the number of device features, the target feature representing a device feature among the number of device features that affects the first object feature;

[0214] Dividing a plurality of feature groups to be matched according to the target feature and the first object feature, wherein each feature group includes the first object feature and each feature group includes a different target feature;

[0215] For each of the feature groups, matching the first object feature in the feature group with the target feature to obtain a second matching degree;

[0216] The second matching degree of the plurality of feature groups is determined as a first matching degree between the first object feature and the scene feature.

[0217] In an optional embodiment, the device further includes (not shown in the figure):

[0218] A comparison module, configured to compare the matching degree with a preset matching degree threshold;

[0219] a reminder triggering module, configured to trigger a reminder of mismatch between the target object and the target smart scene when the matching degree is less than a preset matching degree threshold; and

[0220] a first determining module, configured to determine, for each feature group among a plurality of feature groups, a device optimization strategy according to the second matching degree corresponding to the feature group, wherein the device optimization strategy is used to optimize the target feature in the feature group;

[0221] a second determination module, configured to determine that the device optimization strategies corresponding to the plurality of feature groups are the first optimization strategies for the target smart scene;

[0222] The first strategy output module is used to output the first optimization strategy to a preset object to remind the object to optimize the target smart scene according to the first optimization strategy.

[0223] In an optional embodiment, the device further includes (not shown in the figure):

[0224] an object feature acquisition module, configured to acquire a second object feature of the target object detected by the detection device;

[0225] a third determining module, configured to determine an object feature change value between the second object feature and the first object feature;

[0226] A first judging module is configured to judge whether the object feature change value exceeds a preset first change threshold range;

[0227] a fourth determining module, configured to determine a third matching degree between the second object feature and the target smart scene if the object feature change value exceeds the first change threshold range;

[0228] A first comparison module, configured to compare the third matching degree with a preset matching degree threshold;

[0229] The second strategy output module is configured to output a second optimization strategy corresponding to the target smart scenario if the third matching degree is less than the matching degree threshold.

[0230] In an optional embodiment, the device further includes (not shown in the figure):

[0231] An environmental feature acquisition module, configured to acquire the current environmental features of the environment in which the target object is located as detected by the detection device;

[0232] A fifth determining module, configured to determine an environmental characteristic change value between the current environmental characteristic and the pre-collected environmental initial characteristic;

[0233] A second judgment module is used to judge whether the change value of the environmental characteristic exceeds a preset second change threshold range;

[0234] a sixth determining module, configured to determine a fourth matching degree between the current environmental feature and the target smart scene if the environmental feature change value exceeds the second change threshold range;

[0235] a second comparison module, configured to compare the fourth matching degree with a preset matching degree threshold;

[0236] The third strategy output module is configured to output a third optimization strategy corresponding to the target smart scenario if the fourth matching degree is less than the matching degree threshold.

[0237] Figure 10 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is provided. Figure 10 The electronic device 1000 shown includes: at least one processor 1001, a memory 1002, at least one network interface 1004, and a user interface 1003. The various components in the electronic device 1000 are coupled together via a bus system 1005. It is understood that the bus system 1005 is used to achieve connection and communication between these components. In addition to including a data bus, the bus system 1005 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, the bus system 1005 is not shown in FIG. Figure 10 Various buses are labeled as bus system 1005.

[0238] The user interface 1003 may include a display, a keyboard, a pointing device (eg, a mouse, a trackball), a touch pad, or a touch screen.

[0239] It is understood that the memory 1002 in the embodiment of the present invention can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 1002 described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0240] In some embodiments, the memory 1002 stores the following elements, executable units, or data structures, or a subset thereof, or an extended set thereof: an operating system 10021 and application programs 10022 .

[0241] The operating system 10021 includes various system programs, such as a framework layer, a core library layer, and a driver layer, for implementing various basic services and handling hardware-based tasks. Application programs 10022 include various application programs, such as a media player and a browser, for implementing various application services. Programs implementing the methods of the embodiments of the present invention may be included in application programs 10022.

[0242] In an embodiment of the present invention, by calling a program or instruction stored in the memory 1002, specifically, a program or instruction stored in the application 10022, the processor 1001 is configured to execute the method steps provided in each method embodiment, for example, including:

[0243] Acquire a first object feature of a target object and determine a target smart scene;

[0244] Determining a degree of matching between the first object feature and the target smart scene;

[0245] The matching degree is output.

[0246] The methods disclosed in the above embodiments of the present invention can be applied to or implemented by processor 1001. Processor 1001 may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in processor 1001 or by software instructions. The above processor 1001 may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The methods, steps, and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in conjunction with the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software units in the decoding processor. The software units can be located in storage media well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory 1002 , and the processor 1001 reads the information in the memory 1002 and completes the steps of the above method in combination with its hardware.

[0247] It is understood that the embodiments described herein may be implemented using hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or a combination thereof.

[0248] For software implementation, the technology described herein can be implemented by a unit that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0249] The electronic device provided in this embodiment may be Figure 10 The electronic device shown in FIG. 1 can perform the following operations: Figure 1 、 Figure 3-8 All steps of the scene detection method in Figure 1 、 Figure 3-8 For details on the technical effects of the scene detection method, please refer to Figure 1 、 Figure 3-8 For the sake of brevity, the relevant description will not be repeated here.

[0250] An embodiment of the present invention further provides a storage medium (computer-readable storage medium). The storage medium stores one or more programs. The storage medium may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive; and the memory may also include a combination of the aforementioned types of memory.

[0251] When one or more programs in the storage medium can be executed by one or more processors, the scene detection method executed on the electronic device side can be implemented.

[0252] The processor is configured to execute a scene detection program stored in the memory to implement the following steps of a scene detection method executed on the electronic device side:

[0253] Acquire a first object feature of a target object and determine a target smart scene;

[0254] Determining a degree of matching between the first object feature and the target smart scene;

[0255] The matching degree is output.

[0256] Professionals should also be further aware that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0257] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0258] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A scene detection method, characterized in that: The method comprises: Acquire a first object feature of a target object and determine a target smart scene; Determining a degree of matching between the first object feature and the target smart scene; outputting the matching degree; Acquiring a second object feature of the target object detected by the detection device; determining an object feature change value between the second object feature and the first object feature; Determining whether the object feature change value exceeds a preset first change threshold range; If the object feature change value exceeds the first change threshold range, determining a third matching degree between the second object feature and the target smart scene; comparing the third matching degree with a preset matching degree threshold; If the third matching degree is less than the matching degree threshold, a second optimization strategy corresponding to the target smart scene is output.

2. The method according to claim 1, characterized in that Determining the matching degree between the first object feature and the target smart scene includes: Determining scene characteristics of the target smart scene; performing matching processing on the first object feature and the scene feature to obtain a first matching degree between the first object feature and the scene feature; The first matching degree is determined to be a matching degree between the first object feature and the target smart scene.

3. The method according to claim 2, characterized in that The determining of the scene feature of the target smart scene includes: Determine the number of smart devices in the target smart scene; For each smart device in the target smart scene, obtain device characteristics of the smart device; Determine the number of device features as scene features of the target smart scene.

4. The method according to claim 3, characterized in that The matching processing of the first object feature with the scene feature to obtain a first matching degree between the first object feature and the scene feature includes: determining a target feature from the number of device features, the target feature representing a device feature among the number of device features that affects the first object feature; Dividing a plurality of feature groups to be matched according to the target feature and the first object feature, wherein each feature group includes the first object feature and each feature group includes a different target feature; For each of the feature groups, matching the first object feature in the feature group with the target feature to obtain a second matching degree; The second matching degree of the plurality of feature groups is determined as a first matching degree between the first object feature and the scene feature.

5. The method according to claim 4, characterized in that The method further comprises: Comparing the matching degree with a preset matching degree threshold; When the matching degree is less than a preset matching degree threshold, triggering a reminder that the target object and the target smart scene are not adapted; and For each feature group in the plurality of feature groups, determining a device optimization strategy according to the second matching degree corresponding to the feature group, wherein the device optimization strategy is used to optimize the target feature in the feature group; Determining the device optimization strategies corresponding to the plurality of feature groups as a first optimization strategy for the target smart scene; The first optimization strategy is output to a preset object to remind the object to optimize the target smart scene according to the first optimization strategy.

6. The method according to claim 1, characterized in that The method further comprises: Acquiring current environmental characteristics of the environment in which the target object is located detected by the detection device; Determining an environmental characteristic change value between the current environmental characteristic and the pre-collected initial environmental characteristic; Determining whether the environmental characteristic change value exceeds a preset second change threshold range; If the environmental feature change value exceeds the second change threshold range, determining a fourth matching degree between the current environmental feature and the target smart scene; comparing the fourth matching degree with a preset matching degree threshold; If the fourth matching degree is less than the matching degree threshold, a third optimization strategy corresponding to the target smart scene is output.

7. A scene detection device, characterized in that: The device comprises: an acquisition module, configured to acquire a first object feature of a target object and determine a target smart scene; a matching degree determination module, configured to determine a matching degree between the first object feature and the target smart scene; An output module, configured to output the matching degree; an object feature acquisition module, configured to acquire a second object feature of the target object detected by the detection device; a third determining module, configured to determine an object feature change value between the second object feature and the first object feature; A first judging module is configured to judge whether the object feature change value exceeds a preset first change threshold range; a fourth determining module, configured to determine a third matching degree between the second object feature and the target smart scene if the object feature change value exceeds the first change threshold range; A first comparison module, configured to compare the third matching degree with a preset matching degree threshold; The second strategy output module is configured to output a second optimization strategy corresponding to the target smart scenario if the third matching degree is less than the matching degree threshold.

8. An electronic device, characterized in that: include: A processor and a memory, wherein the processor is configured to execute a scene detection program stored in the memory to implement the scene detection method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the scene detection method according to any one of claims 1 to 6.

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