An intelligent light equipment recommendation method, system and device

Abstract

The application discloses a kind of intelligent light equipment recommendation method and system, device, by implementing the present application, the light use demand of user can be automatically detected, and according to these needs determine the light equipment that has been enabled in current application scene, again through the operation data of these equipment, scene-device use data for specific application scene is generated, the intelligent data acquisition, analysis and record function of enabled light equipment is realized, and the generated scene-device use data can be used as the habit / history data of user using light equipment, provide data support for subsequent light equipment recommendation;In addition, when meeting the preset recommendation condition, device recommendation information can be intelligently generated based on scene-device use data and scene association information and feedback user, that is, light equipment can be accurately recommended according to actual application scene and the specific needs of user, which is beneficial to improve the convenience and efficiency of user light adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent light control, and in particular to an intelligent light equipment recommendation method, system and device. BACKGROUND

[0002] In the existing light equipment use scene, the user's control of the light equipment generally adopts a manual mode to realize manual control of the light equipment, which means that the user needs to set the device parameters one by one according to the specific needs of the current scene. This manual configuration of device parameters has the problems of low efficiency of device parameter adjustment and inadaptation to real-time shooting requirements of the scene when facing multiple light equipment and complex application scenes.

[0003] In recent years, with the development of data analysis and machine learning technology, intelligent light control technology has gradually been applied in practice to solve the above technical problems. Among them, the existing light control technology is mainly based on basic physical and electronic principles, and realizes the required light effect by adjusting parameters such as brightness, color temperature and light angle. However, the existing light control technology often lacks the support of more in-depth intelligent technology, for example, it cannot be personalized / adaptively adjusted according to the specific needs and preferences of the user. It can be seen that it is particularly important to provide a corresponding solution to the technical problems of low intelligent degree of control and low intelligent control flexibility in the intelligent control of the existing light equipment. SUMMARY

[0004] The present application provides an intelligent light equipment recommendation method, system and device, which can improve the intelligent degree of control of the light equipment and improve the adjustment flexibility and convenience of the light equipment.

[0005] In order to solve the above technical problems, the present application discloses an intelligent light equipment recommendation method in the first aspect, which comprises:

[0006] When the user's light use demand is detected, the light equipment enabling information is determined according to the light use demand; the light equipment enabling information includes all first light equipment enabled in the current application scene;

[0007] Collecting device running data of all the first light equipment in the application scene, and generating scene-device use data for the application scene according to the device running data; the device running data at least includes light output parameters of each first light equipment and at least one light equipment combination;

[0008] determining whether the preset light device recommendation condition is met, and generating device recommendation information according to the scene-device usage data and the pre-determined scene association information when it is determined that the light device recommendation condition is met, and feeding back the device recommendation information to the user;

[0009] The scene association information includes first association information and second association information, the first association information is used to indicate a scene type of the application scene, and the second association information includes device usage templates of a plurality of historical record scenes, and a scene type of each of the historical record scenes matches the scene type of the application scene.

[0010] As an optional implementation, in the first aspect of the application, the generation of the scene-device usage data for the application scene according to the device running data includes:

[0011] For each of the first light devices, the device running data corresponding to the first light device is analyzed based on a plurality of preset first parameters to obtain first parameter data corresponding to each of the first parameters of the first light device, and all of the first parameters at least include light brightness, light color, light irradiation range, light irradiation angle, and light flicker frequency.

[0012] The device running data corresponding to the first light device is analyzed based on a plurality of preset second parameters to obtain second parameter data corresponding to each of the second parameters of the first light device, and all of the second parameters at least include device placement position, light usage function, device start-stop condition, device rotation angle, and device rotation frequency.

[0013] The scene-device usage data for the application scene is generated according to all of the first parameter data, all of the second parameter data corresponding to each of the first light devices, and the scene identification type of the application scene.

[0014] As an optional implementation, in the first aspect of the application, before the scene-device usage data for the application scene is generated according to all of the first parameter data, all of the second parameter data corresponding to each of the first light devices, and the scene identification type of the application scene, the method further includes:

[0015] The scene identification type of the application scene is determined according to device models of all of the first light devices.

[0016] A plurality of time segments in which the user uses all of the first light devices in the application scene are determined, and each of the time segments matches a piece of shooting content in the application scene.

[0017] For each of the first light devices, all the first parameter data and all the second parameter data corresponding to each of the first light devices are divided into segment record data corresponding to each of the time segments;

[0018] All the first light devices used in each of the time segments are determined to obtain light device combinations corresponding to each of the time segments;

[0019] In addition, the scene-device usage data for the application scene is generated according to all the first parameter data and all the second parameter data corresponding to each of the first light devices and the scene recognition type of the application scene, and includes:

[0020] The scene-device usage data for the application scene is generated according to all the time segments, the light device combinations corresponding to each of the time segments, the segment record data corresponding to each of the time segments, and the scene recognition type.

[0021] As an optional implementation, in the first aspect of the present application, the determination of whether the preset light device recommendation condition is met currently includes:

[0022] It is determined whether the light device recommendation request triggered by the user is received, and when it is determined that the light device recommendation request triggered by the user is received, it is determined that the preset light device recommendation condition is met currently; or,

[0023] It is determined whether the device running data indicates that the current time enters a preset scene switching node, and when it is determined that the device running data indicates that the current time enters the scene switching node, it is determined that the preset light device recommendation condition is met currently; or,

[0024] It is determined whether the device running data indicates that the combined light effects of all the first light devices reach a preset alarm point, and when it is determined that the device running data indicates that the combined light effects of all the first light devices reach the alarm point, it is determined that the preset light device recommendation condition is met currently; or,

[0025] It is determined whether the device running data indicates that a certain first light device has a running fault, and when it is determined that the device running data indicates that a certain first light device has a running fault, it is determined that the preset light device recommendation condition is met currently;

[0026] The node switching object corresponding to the scene switching node is the shooting content of the application scene; and the combination light effect of all the first light devices reaching the preset alarm point includes at least one of the following situations: regional overexposure, regional underexposure, and regional color difference.

[0027] As an optional implementation, in the first aspect of the present application, the generating of the device recommendation information according to the scene-device use data and the predetermined scene association information comprises:

[0028] determining the scene type of the application scene according to first association information in the predetermined scene association information, and determining a plurality of device use templates matching the scene type according to second association information in the predetermined scene association information;

[0029] determining the target device use template most matching the application scene from the plurality of device use templates according to the light use requirement and all the first light devices;

[0030] determining the non-enabled light device in the application scene from all the historical light devices recorded in the target device use template, taking all the first light devices as the reference;

[0031] judging whether the non-enabled light device has a matching registered device model in a database; the database has a plurality of registered light devices and device models thereof;

[0032] when it is judged that the non-enabled light device has a matching registered device model in the database, determining the non-enabled light device as a second light device, and generating the device recommendation information according to the second light device and the device model thereof and the device configuration parameters recorded in the target device use template.

[0033] As an optional implementation, in the first aspect of the present application, the determining of the target device use template most matching the application scene from the plurality of device use templates according to the light use requirement and all the first light devices comprises:

[0034] determining the light effect requirement of the user in the application scene according to the light use requirement;

[0035] for each device use template, determining a plurality of sets of historical light device combinations in the historical use of the device use template, and each set of the historical light device combination comprises at least one light output sequence for controlling the light output parameters of all the historical light devices in the set of the historical light device combination;

[0036] For each set of historical lighting equipment combinations in the template, a lighting simulation is performed on the historical lighting equipment combination according to each set of lighting output sequences corresponding to the historical lighting equipment combination, so as to obtain a lighting simulation effect corresponding to each set of lighting output sequences.

[0037] Using the lighting effect requirement as the first screening criterion, the lighting simulation effect corresponding to each set of lighting output sequences for each device using the template is compared and screened with the lighting effect requirement to obtain at least one set of first lighting output sequences whose matching value with the lighting effect requirement is higher than the first matching threshold.

[0038] Using the number and model of all the first lighting devices as the second screening criterion, at least one set of second lighting output sequences with a matching value higher than the second matching threshold is selected from all the second lighting output sequences;

[0039] Randomly select any set of the second light output sequence as the target light output sequence, and determine the device usage template corresponding to the target light output sequence as the target device usage template that best matches the application scenario.

[0040] As an optional implementation, in a first aspect of the invention, the target light output sequence includes a first output sequence for all the first light devices and a second output sequence for the second light devices;

[0041] The step of generating device recommendation information based on the second lighting device and its model number, and the device configuration parameters recorded in the target device usage template, includes:

[0042] Based on the second lighting device and its model number, the second output sequence of the second lighting device, and the first output sequences of all the first lighting devices, device recommendation information is generated.

[0043] A second aspect of this invention discloses an intelligent lighting equipment recommendation system, the system comprising:

[0044] The determination module is used to determine lighting device activation information based on a user's lighting usage needs when such needs are detected; the lighting device activation information includes all first lighting devices that are enabled in the current application scenario.

[0045] The data acquisition module is used to collect the device operation data of all the first lighting devices in the application scenario; the device operation data includes at least the lighting output parameters of each of the first lighting devices and at least one combination of lighting devices;

[0046] a first generating module configured to generate scene-device usage data for the application scenario according to the device operation data;

[0047] a judging module configured to judge whether a preset light device recommendation condition is met at present;

[0048] a second generating module configured to generate device recommendation information according to the scene-device usage data and pre-determined scene association information when the judging module judges that the light device recommendation condition is met at present, and feed back the device recommendation information to the user;

[0049] The scene association information comprises first association information and second association information, the first association information is used to indicate the scene type of the application scenario, and the second association information comprises device usage templates of a plurality of historical record scenes, and the scene type of each historical record scene matches the scene type of the application scenario; and the device recommendation information at least comprises a second light device to be recommended to start.

[0050] As an optional implementation, in the second aspect of the application, the first generating module generates scene-device usage data for the application scenario according to the device operation data in the following manner:

[0051] For each first light device, the device operation data corresponding to the first light device is analyzed based on a plurality of preset first parameters to obtain first parameter data corresponding to each first parameter of the first light device; all the first parameters at least comprise light brightness, light color, light irradiation range, light irradiation angle and light flicker frequency;

[0052] The device operation data corresponding to the first light device is analyzed based on a plurality of preset second parameters to obtain second parameter data corresponding to each second parameter of the first light device; all the second parameters at least comprise device placement position, light usage function, device start-stop condition, device rotation angle and rotation frequency;

[0053] The scene-device usage data for the application scenario is generated according to all the first parameter data, all the second parameter data corresponding to each first light device and the scene recognition type of the application scenario.

[0054] As an optional implementation, in the second aspect of the present application, the determining module is further configured to determine the scene recognition type of the application scene according to the device models of all the first light devices before the first generating module generates the scene-device usage data for the application scene according to all the first parameter data and all the second parameter data corresponding to each of the first light devices and the scene recognition type of the application scene.

[0055] The determining module is further configured to determine a plurality of time segments in which the user uses all the first light devices in the application scene, and each of the time segments matches a segment of the shooting content in the application scene.

[0056] The system further comprises:

[0057] A data dividing module configured to divide, for each of the first light devices, all the first parameter data and all the second parameter data corresponding to the first light device into segment record data corresponding to each of the time segments.

[0058] The determining module is further configured to determine all the first light devices used in each of the time segments to obtain a light device combination corresponding to each of the time segments.

[0059] The first generating module generates the scene-device usage data for the application scene according to all the first parameter data and all the second parameter data corresponding to each of the first light devices and the scene recognition type of the application scene in the following manner:

[0060] The first generating module generates the scene-device usage data for the application scene according to all the first parameter data and all the second parameter data corresponding to each of the first light devices and the scene recognition type of the application scene in the following manner:

[0061] As an optional implementation, in the second aspect of the present application, the determining module determines whether the preset light device recommendation condition is met in the following manner:

[0062] The determining module determines whether a light device recommendation request triggered by the user is received, and determines that the preset light device recommendation condition is met when it is determined that the light device recommendation request triggered by the user is received; or

[0063] The determining module determines whether the device running data indicates that a preset scene switching node is entered at the current time, and determines that the preset light device recommendation condition is met when it is determined that the device running data indicates that the scene switching node is entered at the current time; or

[0064] determining whether the device operation data represents that the combined light effects of all the first light devices reach a preset alarm point, and determining that the preset light device recommendation condition is met when it is determined that the device operation data represents that the combined light effects of all the first light devices reach the alarm point; or

[0065] determining whether the device operation data represents that a certain first light device has a running fault, and determining that the preset light device recommendation condition is met when it is determined that the device operation data represents that a certain first light device has a running fault;

[0066] The node switching object corresponding to the scene switching node is the shooting content of the application scene, and the combined light effects of all the first light devices reaching the preset alarm point include at least one of the following situations: the combined light effects of all the first light devices have regional overexposure, regional underexposure, and regional color difference.

[0067] As an optional implementation, in the second aspect of the present application, the second generation module generates the device recommendation information according to the scene-device use data and the pre-determined scene association information, and the generation manner specifically includes:

[0068] determining the scene type of the application scene according to first association information in the pre-determined scene association information, and determining a plurality of device use templates matching the scene type according to second association information in the scene association information;

[0069] determining a target device use template most matching the application scene from the plurality of device use templates according to the light use requirement and all the first light devices;

[0070] determining, from all the historical light devices recorded in the target device use template, a non-enabled light device in the application scene based on all the first light devices;

[0071] determining whether the non-enabled light device has a matching registered device model in a database, wherein the database has a plurality of registered light devices and device models thereof;

[0072] when it is determined that the non-enabled light device has a matching registered device model in the database, determining the non-enabled light device as a second light device, and generating the device recommendation information according to the second light device and the device model thereof and the device configuration parameters recorded in the target device use template.

[0073] As an optional implementation, in the second aspect of the present application, the second generation module determines the target device usage template that is most matched with the application scenario from the plurality of device usage templates according to the light usage requirement and all the first light devices in the following manner:

[0074] According to the light usage requirement, the light effect requirement of the user in the application scenario is determined;

[0075] For each device usage template, a plurality of sets of historical light device combinations in the historical usage of the device usage template are determined, and each set of the historical light device combinations includes at least one set of light output sequences for controlling the light output parameters of all the historical light devices in the set of historical light device combinations;

[0076] For each set of the historical light device combinations in the device usage template, according to each set of the light output sequences corresponding to the historical light device combination, light simulation is performed on the historical light device combination to obtain the light simulation effect corresponding to each set of the light output sequences;

[0077] Taking the light effect requirement as a first screening criterion, the light simulation effect corresponding to each set of the light output sequences corresponding to each device usage template is compared and screened with the light effect requirement to obtain at least one first light output sequence whose matching value with the light effect requirement is higher than a first matching threshold;

[0078] Taking the number and type of all the first light devices as a second screening reference, at least one second light output sequence whose matching value with the second screening reference is higher than a second matching threshold is screened from all the second light output sequences;

[0079] Randomly selecting any one of the second light output sequences as a target light output sequence, and determining the device usage template corresponding to the target light output sequence as the target device usage template that is most matched with the application scenario.

[0080] As an optional implementation, in the second aspect of the present application, the target light output sequence includes a first output sequence for all the first light devices and a second output sequence for the second light device;

[0081] The second generation module generates the device recommendation information according to the second light device, the device type thereof, and the device configuration parameters recorded in the target device usage template in the following manner:

[0082] According to the second light device and its device model, the second output sequence of the second light device, and the first output sequence of all the first light devices, device recommendation information is generated.

[0083] The third aspect of the present application discloses another intelligent light device recommendation device, which comprises:

[0084] A memory storing executable program codes;

[0085] A processor coupled with the memory;

[0086] The processor invokes the executable program codes stored in the memory to execute the intelligent light device recommendation method disclosed in the first aspect of the present application.

[0087] The fourth aspect of the present application discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the intelligent light device recommendation method disclosed in the first aspect of the present application.

[0088] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0089] In the embodiment of the present application, a kind of intelligent lighting equipment recommendation method is provided, which comprises: when detecting the lighting use demand of user, lighting equipment activation information is determined according to lighting use demand;Lighting equipment activation information includes all first lighting equipment activated in current application scene;Device running data of all first lighting equipment in application scene is collected, and scene-device use data for application scene is generated according to device running data;Device running data at least includes the light output parameter of each first lighting equipment and at least one lighting equipment combination;It is judged whether the preset lighting equipment recommendation condition is met at present, when it is judged that the lighting equipment recommendation condition is met at present, device recommendation information is generated according to scene-device use data and pre-determined scene association information, and device recommendation information is fed back to user;Wherein, scene association information includes first association information and second association information, first association information is used to indicate the scene type of application scene, and second association information includes the device use template of multiple historical record scenes, and the scene type of each historical record scene is matched with the scene type of application scene;Device recommendation information at least includes recommended second lighting equipment.The application can automatically detect the lighting use demand of user, and determine the lighting equipment activated in current application scene according to these demands, then generate scene-device use data for specific application scene by collecting the running data of these devices, realize the intelligent data collection, analysis and record function of user's current activated lighting equipment, and the generated scene-device use data can be used as the habit data and historical data of user's lighting equipment, provide data support for subsequent lighting equipment recommendation;In addition, when the preset recommendation condition is met, device recommendation information can be intelligently generated based on scene-device use data and pre-determined scene association information, and fed back to user, that is, personalized lighting equipment recommendation can be provided according to actual application scene and specific demand of user, improve the intelligent level of lighting equipment use, and improve the convenience and efficiency of user's lighting adjustment. BRIEF DESCRIPTION OF DRAWINGS

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0091] Figure 1 It is a flowchart of the intelligent lighting equipment recommendation method disclosed in the embodiment of the present application.

[0092] Figure 2 It is a flowchart of another intelligent lighting equipment recommendation method disclosed in the embodiment of the present application.

[0093] Figure 3 is a structural schematic view of an intelligent lighting equipment recommendation system disclosed by an embodiment of the present application;

[0094] Figure 4 is a structural schematic view of an intelligent lighting equipment recommendation device disclosed by an embodiment of the present application;

[0095] Figure 5 is a structural schematic view of another intelligent lighting equipment recommendation device disclosed by an embodiment of the present application. DETAILED DESCRIPTION

[0096] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor fall within the scope of protection of the present application.

[0097] The terms "first", "second", and the like in the specification of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product, or end including a series of steps or units is not limited to the listed steps or units, but can optionally include steps or units not listed, or can optionally include other steps or units inherent to the process, method, product, or end.

[0098] In this document, the term "embodiment" means that the specific features, structures, or characteristics described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily mean the same embodiment, nor is it independent or alternative to other embodiments. The person skilled in the art explicitly and implicitly understands that the embodiments described herein can be combined with other embodiments.

[0099] The application discloses an intelligent light equipment recommendation method and system and device, which can automatically detect the light use demand of a user, determine the light equipment enabled in a current application scene according to the demand, collect the operation data of the equipment, generate scene-equipment use data for a specific application scene, realize intelligent data collection, analysis and recording of the light equipment enabled by the user, and use the generated scene-equipment use data as habit data and historical data of the user using the light equipment to provide data support for subsequent light equipment recommendation. In addition, when a preset recommendation condition is met, the device recommendation information can be intelligently generated based on the scene-equipment use data and pre-determined scene association information and fed back to the user, that is, personalized light equipment recommendation can be provided according to the actual application scene and the specific demand of the user, thereby improving the intelligent level of the light equipment use and the convenience and efficiency of the user in light adjustment. The following will be described in detail.

[0100] In order to better understand the intelligent light equipment recommendation method and system and device described in the application, first, the scene architecture applicable to the intelligent light equipment recommendation method is described. Specifically, the scene architecture can include:

[0101] The light equipment 101, the light equipment 102, the light equipment 103 and the light equipment 104 are used as main lights, and the light equipment 101-104 are arranged around the object to be photographed, for example, the light equipment 101 is arranged on the left side of the oblique front, the light equipment 102 is arranged on the right side of the oblique front, the light equipment 103 is arranged on the left side of the rear, and the light equipment 104 is arranged on the right side of the rear, thereby providing uniform light coverage for the current application scene.

[0102] The four light equipments are large black umbrella-shaped lights, commonly known as soft umbrella lights or umbrella lights, which can provide soft and extensive illumination light; and in the actual application scene, the visual effect of the light equipment can be adjusted by adjusting the device arrangement position, the inclination angle of the device light surface and the light output brightness and the number of the light equipment 101-104.

[0103] The light equipment 105 and the light equipment 106 are used as special effect lights to create special light effects, and the light equipment 105 can be arranged at the lower position of the oblique front of the object to be photographed, and the light equipment 106 can be arranged at the upper position of the oblique front of the object to be photographed.

[0104] The light equipment 105 and the light equipment 106 can adopt small round spotlights.

[0105] Optionally, according to the shooting requirements of the actual application scene, the number and / or type of light equipment can be further increased, for example, in addition to the above-mentioned basic main light and special effects, etc., fill light, background light, side light and environment light, etc. can be added, wherein:

[0106] The fill light can use a small soft light or LED panel light to reduce the shadow caused by the main light, balance the picture brightness, and avoid the influence of the dark area on the picture details;

[0107] The background light can use a strip light, a ring light or a spotlight to provide a light source from behind the character or object, separate the subject from the background, enhance the stereoscopic effect, and create a contour light effect;

[0108] The side light can use a hard light source or a light with a honeycomb grid to project light from the side, enhance the texture and depth of the object, and form a sharp light and shadow contrast;

[0109] The environment light can use a small LED light, a fluorescent lamp, etc. to simulate daily environmental light sources; used to provide basic lighting for the overall environment, making the scene look more real and natural.

[0110] Further, it should be noted that in the current application scene, an intelligent light equipment recommendation system, referred to as a light control system, is provided, which can interact with all light equipment. After starting any one of the light equipment in the application scene, a record of the device start will be generated in the light control system, and then the light control system will collect, analyze and record the data of the running of the light equipment in real time. At the same time, the user can issue different light control instructions through the light control system to control the start and stop of one or more light equipment, light output parameters, etc. through the light control instructions.

[0111] It should be noted that the above scene architecture is only to represent the scene to which the intelligent light equipment recommendation method is applied, and the light equipment 101-light equipment 106 involved is only selectively described and exhibited. The specific structure / size / shape / location / installation method, etc. can be adaptively adjusted according to the actual scene, and the scene architecture description corresponding to the embodiments of the present application does not limit this.

[0112] The application scene to which the intelligent light equipment recommendation method is applied is described above, and the intelligent light equipment recommendation method and system are described in detail below.

[0113] Embodiment one

[0114] Please refer to Figure 1 , Figure 1 is a flowchart of an intelligent light equipment recommendation method disclosed by the embodiments of the present application. Among them,Figure 1 The intelligent light equipment recommendation method described can be applied to an intelligent light equipment recommendation system or device, and embodiments of the present application are not limited. As shown in the figure, the intelligent light equipment recommendation method can include the following operations: Figure 1

[0115] 201. When the light use demand of the user is detected, the light equipment enabling information is determined according to the light use demand.

[0116] In an embodiment of the present application, the light equipment enabling information includes all first light equipment enabled in the current application scenario.

[0117] In an embodiment of the present application, the light use demand includes an active trigger type or a passive trigger type, the active trigger type refers to a control mode in which the user starts the light equipment in the application scenario through a remote (background system or application program, etc.) or short-range (manually triggers the switch of the light equipment) control; the passive trigger type refers to that the user sets a timing start program for the light equipment, or starts the light equipment through a linkage trigger; the linkage trigger can refer to that the light equipment is passively started after meeting any non-user active trigger running condition, for example, when the light equipment A is started, the light equipment B is also started.

[0118] 202. Collect device running data of all first light equipment in the application scenario.

[0119] In an embodiment of the present application, the device running data at least includes light output parameters of each first light equipment and at least one light equipment combination.

[0120] In an embodiment of the present application, the background recording system of the device running data sets a data update mechanism, which can realize real-time update and recording of the device running data, and the data update mechanism can be realized through a timing task or a trigger.

[0121] 203. Generate scene-equipment use data for the application scenario according to the device running data.

[0122] 204. Determine whether the preset light equipment recommendation condition is met.

[0123] 205. When it is determined that the light equipment recommendation condition is met, generate the equipment recommendation information according to the scene-equipment use data and the pre-determined scene association information, and feed back the equipment recommendation information to the user.

[0124] ​In the embodiment of the present application, the scene association information includes first association information and second association information, the first association information is used to indicate the scene type of the application scene, and the second association information includes a plurality of device use templates of historical record scenes, and the scene type of each historical record scene matches the scene type of the application scene; and the device recommendation information at least includes recommendation information of starting a second light device.

[0125] In the embodiment of the present application, the device recommendation information can be one or more of the following: recommending the user to newly start other light devices, recommending the user to adjust the light output parameters of one or more first light devices, and recommending the user to turn off one or more first light devices.

[0126] In the embodiment of the present application, the storage database corresponding to the light device activation information, the device running data, the scene-device use data and the device recommendation information is designed to support the storage of a plurality of data types, including structured and unstructured data, to ensure that the above data can be effectively managed.

[0127] In the embodiment of the present application, the device recommendation information can be displayed to the user through a mobile application or a web application.

[0128] It can be seen that, by implementing the above-mentioned intelligent light device recommendation method, Figure 1 The described intelligent light device recommendation method can automatically detect the light use demand of the user, determine the light devices activated in the current application scene according to the demand, collect the running data of the devices, generate scene-device use data for the specific application scene, realize the intelligent data collection, analysis and recording function of the currently activated light devices of the user, and use the generated scene-device use data as the habit data and historical data of the user using the light devices, to provide data support for subsequent light device recommendation. In addition, when the preset recommendation condition is met, the device recommendation information can be intelligently generated based on the scene-device use data and the pre-determined scene association information, and fed back to the user, that is, personalized light device recommendation can be provided according to the actual application scene and the specific demand of the user, to improve the intelligent level of the light device use and the convenience and efficiency of the user in adjusting the light.

[0129] In an optional embodiment, the way of generating the scene-device use data for the application scene according to the device running data in step 203 specifically includes:

[0130] For each first light device, the device running data corresponding to the first light device is analyzed based on a plurality of preset first parameters, to obtain first parameter data corresponding to each first parameter of the first light device; the first parameters at least include light brightness, light color, light irradiation range, light irradiation angle and light flicker frequency.

[0131] According to the preset second parameters, the device operation data corresponding to the first light device is analyzed to obtain second parameter data corresponding to each second parameter of the first light device; all the second parameters at least include device placement position, light use function, device start-stop condition, device rotation angle and rotation frequency;

[0132] According to all the first parameter data, all the second parameter data corresponding to each first light device, and the scene recognition type of the application scene, scene-device use data for the application scene is generated.

[0133] In the optional embodiment, by presetting a plurality of first parameters (such as light brightness, light color, light irradiation range, etc.) and second parameters (such as device placement position, light use function, etc.), the device operation data is comprehensively analyzed, which can ensure that the extracted data is comprehensive and accurate.

[0134] In the optional embodiment, by combining the extracted device use data with the scene recognition type of the application scene, customized scene-device use data for a specific application scene can be generated.

[0135] As can be seen, in the optional embodiment, by taking a plurality of preset first parameters (such as light brightness, color, etc.) and second parameters (such as device placement position, use function, etc.) as a basis, the device operation data is analyzed to obtain detailed parameter data. These data, in combination with the scene recognition type of the application scene, generate comprehensive scene-device use data, which is beneficial to improve the generation detail, reliability and accuracy of the scene-device use data.

[0136] In another optional embodiment, before the above-mentioned generation of scene-device use data for the application scene according to all the first parameter data, all the second parameter data corresponding to each first light device, and the scene recognition type of the application scene, the method further comprises:

[0137] According to the device model of all the first light devices, the scene recognition type of the application scene is determined;

[0138] A plurality of time segments in which the user uses all the first light devices in the application scene are determined; each time segment matches a piece of shooting content in the application scene;

[0139] For each first light device, all the first parameter data and all the second parameter data corresponding to the first light device are divided into segment record data corresponding to each time segment;

[0140] All the first light devices used in each time segment are determined to obtain a light device combination corresponding to each time segment;

[0141] And, according to all first parameter data, all second parameter data corresponding to each first light device and the scene recognition type of the application scene, the way of generating the scene-device usage data for the application scene specifically includes:

[0142] According to all time segments, the light device combination corresponding to each time segment, the segment record data corresponding to each time segment and the scene recognition type, the scene-device usage data for the application scene is generated.

[0143] In this optional embodiment, by dividing the time segments and matching each time segment with the shooting content, the use of the light device is associated with the specific scene content, which is conducive to improving the accuracy and relevance of the data; and the segment record data can be generated based on each time segment, including all first parameter data and second parameter data, ensuring the completeness and detail of the data.

[0144] As can be seen, in this optional embodiment, a series of data processing steps are added before the scene-device usage data is generated, including determining the scene recognition type of the application scene, determining the time segment of the user using the light device, dividing the segment record data and determining the light device combination, etc. These data processing steps realize accurate slicing of data based on shooting content (time segment), improve the fineness of the division and recording of first and second parameter data; and are conducive to improving the consistency of the subsequently generated scene-device usage data with the actual application scene, while improving the accuracy and practicality of the data.

[0145] In yet another optional embodiment, the way of determining whether the preset light device recommendation condition is met in the above step 204 specifically includes:

[0146] It is determined whether a light device recommendation request triggered by the user is received, and when it is determined that the light device recommendation request triggered by the user is received, it is determined that the preset light device recommendation condition is met; or,

[0147] It is determined whether the device running data indicates that the current time enters a preset scene switching node, and when it is determined that the device running data indicates that the current time enters the scene switching node, it is determined that the preset light device recommendation condition is met; or,

[0148] It is determined whether the device running data indicates that the combined light effect of all first light devices reaches a preset alarm point, and when it is determined that the device running data indicates that the combined light effect of all first light devices reaches the alarm point, it is determined that the preset light device recommendation condition is met; or,

[0149] determining whether the device operation data indicates that a first light device has an operation fault, and determining that the preset light device recommendation condition is met when it is determined that the device operation data indicates that a first light device has an operation fault;

[0150] The node switching object corresponding to the scene switching node is the shooting content in the application scene, and the combination light effect of all the first light devices reaching the preset alarm point includes at least one of the following situations: the combination light effect of all the first light devices has regional overexposure, regional underexposure, and regional color difference.

[0151] In this optional embodiment, the light device recommendation request triggered by the user actively can be generated after the user inputs light recommendation information in an instruction box corresponding to the light recommendation in the background system, for example, the user inputs "indoor" and "product close-up" in the instruction box; or inputs "indoor", "movie", "warm", "winter evening", and the like, and a corresponding light device recommendation request is generated.

[0152] It can be seen that in this optional embodiment, multiple ways of determining whether the preset light device recommendation condition is met are proposed, including the user triggering the recommendation request actively, entering the preset scene switching node, the light effect reaching the alarm point, and the light device operation fault, and the like; the setting of these judgment conditions can provide device recommendation in time according to the actual application scene and the specific needs of the user, which is conducive to improving the flexibility and intelligent degree of device recommendation, and is also conducive to improving the pertinence and accuracy of device recommendation.

[0153] In another optional embodiment, after the execution of step 205, the method further includes:

[0154] detecting an authorization level of the user for the device recommendation information, the authorization level including a first authorization level or a second authorization level; the first authorization level indicating that the light device adjustment operation matched with the device recommendation information is allowed to be executed directly; and the second authorization level indicating that the light device adjustment operation matched with the device recommendation information is not allowed to be executed directly;

[0155] when the authorization level is the first authorization level, performing the light device adjustment operation on all the light devices to be adjusted according to the device recommendation information; and obtaining a light adjustment evaluation fed back by the user;

[0156] When the light adjustment evaluation indicates that the light device adjustment operation meets the user's light adjustment needs, a recommendation report is generated based on the scene-device usage data, the currently met light device recommendation conditions, and the device recommendation information. The recommendation report is used to optimize the recommendation algorithm for generating the device recommendation information. The recommendation algorithm can be a combination of content-based recommendation and collaborative filtering to comprehensively consider the user's personal preferences and group behavior. This hybrid recommendation method can improve the accuracy and relevance of the recommendations.

[0157] In the optional embodiment, the light adjustment evaluation is the information fed back by the user after performing the light device adjustment operation based on the device recommendation information, and the relevance with the authorization level of the device recommendation information is not high.

[0158] In the optional embodiment, if the light adjustment evaluation does not match the user's light adjustment needs, the above-mentioned related information is excluded or used as auxiliary data for algorithm optimization.

[0159] In the optional embodiment, the recommendation engine used in the above-mentioned method can analyze user behavior data in real time and adjust the recommendation strategy, so that it can flexibly adapt to the needs of different shooting scenes. In multi-scene tests, the probability that the user feedbacks that the recommended scheme provided by the system meets the actual needs is as high as 85%, showing good adaptability. In addition, through the method, the user can more effectively use the existing light devices and avoid resource waste. For example, data analysis shows that after using the intelligent recommendation system corresponding to the method, the use frequency of the light devices has increased by 20%, and the idle time of the devices has been significantly reduced, thereby improving the overall use efficiency of the devices.

[0160] In the optional embodiment, the intelligent recommendation system can continuously optimize the recommendation algorithm according to user feedback, so that the intelligence of the system is continuously improved as the use time increases. After three months of use, a user satisfaction survey on the recommendations shows that the satisfaction has increased by 15%, reflecting the learning ability and adaptability of the system.

[0161] As can be seen, in the optional embodiment, the verification and execution mechanism of the permission level is set. For the case where the authorization level of the device recommendation information is the first authorization level, the light device adjustment operation can be directly executed, reducing the process of user confirmation and re-operation, which is beneficial to improve the use efficiency of the light recommendation information after generation and improve the use convenience of the user.

[0162] Embodiment Two

[0163] Please refer to Figure 2 , Figure 2 is a flowchart of another intelligent light device recommendation method disclosed in the embodiments of the present application. Among them, Figure 2The intelligent light equipment recommendation method described can be applied to an intelligent light equipment recommendation device, and embodiments of the present application are not limited. As shown in Figure 2 The intelligent light equipment recommendation method can include the following operations:

[0164] 301. When the light use demand of the user is detected, the light equipment enabling information is determined according to the light use demand.

[0165] 302. The device running data of all first light equipment in the application scene is collected.

[0166] 303. The scene-equipment use data for the application scene is generated according to the device running data.

[0167] 304. It is judged whether the preset light equipment recommendation condition is met at present.

[0168] 305. When it is judged that the light equipment recommendation condition is met at present, the scene type of the application scene is determined according to the first association information in the pre-determined scene association information, and a plurality of device use templates matching the scene type are determined according to the second association information in the scene association information.

[0169] 306. The target device use template most matching the application scene is determined from the plurality of device use templates according to the light use demand, all first light equipment.

[0170] 307. The un-enabled light equipment in the application scene is determined from all historical light equipment recorded in the target device use template, with all first light equipment as the reference.

[0171] 308. It is judged whether the un-enabled light equipment exists in the database with a matching registered device model; the database exists a plurality of registered light equipment and device models thereof.

[0172] 309. When it is judged that the un-enabled light equipment exists in the database with a matching registered device model, the un-enabled light equipment is determined as the second light equipment.

[0173] 310. The device recommendation information is generated according to the second light equipment and the device model thereof, and the device configuration parameters recorded in the target device use template, and the device recommendation information is fed back to the user.

[0174] In the embodiments of the present application, other descriptions of steps 301-304 can refer to other specific descriptions of steps 201-204 in Embodiment I, and the embodiments of the present application will not be repeated.

[0175] It can be seen that the embodiments Figure 2The described intelligent light equipment recommendation method determines the scene type of the application scene, matches the device use template and the unenabled light equipment, and finally generates the device recommendation information containing the recommended second light equipment. Before generating the device recommendation information, the verification process of the device model is set to ensure that the recommended device exists in the registered device model before generating and pushing the device recommendation information, further improving the accuracy and reliability of the device recommendation.

[0176] In an optional embodiment, the step 306 determines the target device use template that is most matched with the application scene from the plurality of device use templates according to the light use demand and all first light equipment, and the determination manner specifically includes:

[0177] According to the light use demand, the light effect demand of the user in the application scene is determined.

[0178] For each device use template, a plurality of sets of historical light equipment combinations in the historical use of the device use template are determined, and each set of historical light equipment combination includes at least one set of light output sequence for controlling the light output parameters of all historical light equipment in the set of historical light equipment combination.

[0179] For each set of historical light equipment combination in the device use template, light simulation is performed on the historical light equipment combination according to each set of light output sequence corresponding to the historical light equipment combination, to obtain the light simulation effect corresponding to each set of light output sequence.

[0180] Taking the light effect demand as the first screening standard, the light simulation effect corresponding to each set of light output sequence corresponding to each device use template is compared and screened with the light effect demand, to obtain at least one set of first light output sequence with a matching value higher than a first matching threshold with the light effect demand.

[0181] Taking the number and device model of all first light equipment as the second screening reference, at least one set of second light output sequence with a matching value higher than a second matching threshold with the second screening reference is screened from all second light output sequences.

[0182] Randomly selecting any one set of second light output sequence as a target light output sequence, and determining the device use template corresponding to the target light output sequence as the target device use template that is most matched with the application scene.

[0183] In the optional embodiment, the light effect requirement further includes an information requirement urgency for subsequent device recommendation information, the information requirement urgency represents a first urgency level lower than a minimum urgency threshold, a second urgency level higher than the minimum urgency threshold and greater than a maximum urgency threshold, or a third urgency level greater than the maximum urgency threshold; wherein the higher the information requirement urgency, the higher the algorithm complexity used to generate the device recommendation information, and the more data involved / required for analysis. For example, for the information requirement urgency of the third urgency level, a conventional static algorithm can be used, which does not involve deep analysis of machine learning / neural networks, only performs simple scene type comparison, and after the scene type comparison is consistent, directly calls the historical device usage template matched with the scene type.

[0184] In the optional embodiment, the information requirement urgency of the user can be reflected through the light effect requirement, so as to adjust the complexity of the recommendation algorithm, and then adjust the generation speed, detail and accuracy of the device recommendation information, that is, the flexible and dynamic adjustment of the recommendation algorithm is realized, and the practicability of the recommendation algorithm is further improved.

[0185] In the optional embodiment, the light effect requirement of the user in the application scene is first determined according to the light usage requirement, ensuring that the subsequent light configuration is highly matched with the user requirement; and then the historical light device combinations in the multiple device usage templates are simulated for light, and the high-matching-degree light output sequence is selected through comparison and screening, improving the screening accuracy of the light output sequence and the adaptation degree to the user light effect requirement.

[0186] In the optional embodiment, through the simulation and analysis of the historical light device combinations, the process of repeatedly testing different device combinations and light parameters in actual application is avoided, and the efficiency of device configuration is significantly improved; at the same time, taking the number of devices and the type of devices as the screening criterion, the selection range is further narrowed, so that the device configuration is more accurate and efficient. In addition, through in-depth analysis of the user historical usage data, the user preference can be accurately identified, so as to generate personalized light configuration suggestions. According to internal tests, the user using the system improves the accuracy of light configuration by about 30%, and greatly reduces unnecessary trial and error time. In addition, the traditional manual configuration of light devices often requires the user to invest a lot of time for debugging. Through the method, the user can obtain the optimal configuration suggestion in a short time, and the actual operation time is reduced by about 40%. This means that the user can spend more time on creation rather than device adjustment, greatly improving the user device adjustment efficiency and creation speed.

[0187] It can be seen that in the optional embodiment, when the target device use template is determined, the final determined target device use template is more in line with the requirements of the actual application scene by determining the light effect requirement, performing light simulation, comparing and screening, and determining the target device use template, and the determination accuracy of the target device use template is improved, and the accuracy of subsequent device recommendation is further improved.

[0188] In another optional embodiment, the target light output sequence includes a first output sequence for all first light devices, a second output sequence for a second light device;

[0189] The manner of generating the device recommendation information according to the second light device and the device model thereof and the device configuration parameters recorded in the target device use template in the step 310 includes:

[0190] Generating the device recommendation information according to the second light device and the device model thereof, the second output sequence of the second light device, and the first output sequence of all first light devices.

[0191] It can be seen that in the optional embodiment, the output parameters of all light devices can be considered comprehensively to generate device recommendation information containing detailed device configuration information, and the method makes the device recommendation more comprehensive and specific, provides a more detailed light device configuration scheme for the user, and thus improves the optimization degree of the light effect.

[0192] Embodiment three

[0193] Please refer to Figure 3 , Figure 3 is a structural schematic diagram of an intelligent light device recommendation system disclosed by the embodiment of the application. As shown in Figure 3 , the intelligent light device recommendation system can include a determination module 401, a data acquisition module 402, a first generation module 403, a judgment module 404, and a second generation module 405, wherein:

[0194] The determination module 401 is configured to determine light device enabling information according to a light use requirement when detecting the light use requirement of the user; the light device enabling information includes all first light devices enabled in the current application scene.

[0195] The data acquisition module 402 is configured to acquire device running data of all first light devices in the application scene; the device running data at least includes light output parameters of each first light device and at least one light device combination.

[0196] The first generation module 403 is configured to generate scene-device use data for the application scene according to the device running data.

[0197] The judgment module 404 is used to determine whether the current conditions for the preset lighting equipment are met.

[0198] The second generation module 405 is used to generate device recommendation information based on scene-device usage data and pre-determined scene association information when the judgment module 404 determines that the current lighting device recommendation conditions are met, and at the same time, to feed back the device recommendation information to the user.

[0199] The scene association information includes first association information and second association information. The first association information is used to indicate the scene type of the application scene, and the second association information includes device usage templates for multiple historical scene scenes. The scene type of each historical scene matches the scene type of the application scene. The device recommendation information includes at least the recommended second lighting device to be activated.

[0200] It is evident that implementation Figure 3 The described intelligent lighting equipment recommendation device can automatically detect users' lighting needs and determine the lighting equipment already in use in the current application scenario based on these needs. It then collects operational data from these devices to generate scenario-equipment usage data specific to the application scenario. This enables intelligent data collection, analysis, and recording of currently used lighting equipment. Furthermore, this generated scenario-equipment usage data can serve as user habit data and historical data for subsequent lighting equipment recommendations. In addition, when preset recommendation conditions are met, it can intelligently generate equipment recommendation information based on the scenario-equipment usage data and pre-determined scenario association information, and provide this information back to the user. In other words, it can provide personalized lighting equipment recommendations based on the actual application scenario and the user's specific needs, improving the intelligence level of lighting equipment use while enhancing the convenience and efficiency of users adjusting lighting.

[0201] In an optional embodiment, the first generation module 403 generates scenario-device usage data for the application scenario based on device operation data in the following specific ways:

[0202] For each first lighting device, based on a number of preset first parameters, the device operation data corresponding to the first lighting device is analyzed to obtain the first parameter data corresponding to each first parameter for the first lighting device; all first parameters include at least light brightness, light color, light illumination range, light illumination angle, and light flicker frequency;

[0203] Based on a number of preset second parameters, analyze the equipment operation data corresponding to the first lighting device to obtain the second parameter data corresponding to each second parameter of the first lighting device; all second parameters include at least the device placement position, lighting function, device start / stop status, device rotation angle and rotation frequency;

[0204] Based on all first parameter data, all second parameter data, and the scene recognition type of the application scenario corresponding to each first lighting device, scene-device usage data for the application scenario is generated.

[0205] As can be seen, in this optional embodiment, by using multiple preset first parameters (such as light brightness, color, etc.) and second parameters (such as device placement position, usage function, etc.) as a benchmark, the device operation data is analyzed to obtain detailed parameter data. This data is combined with the scene recognition type of the application scenario to generate comprehensive scene-device usage data, which helps to improve the detail, reliability and accuracy of the generated scene-device usage data.

[0206] In another optional embodiment, the determining module 401 is further configured to determine the scene recognition type of the application scenario based on the device model of all the first lighting devices before the first generating module 403 generates scene-device usage data for the application scenario based on all the first parameter data, all the second parameter data and the scene recognition type of the application scenario corresponding to each first lighting device.

[0207] The determination module 401 is also used to determine multiple time segments in which the user uses all the first lighting devices in the application scenario; each time segment is matched with a piece of shooting content in the application scenario;

[0208] like Figure 4 As shown, the system also includes a data partitioning module 406, wherein:

[0209] The data partitioning module 406 is used to divide all first parameter data and all second parameter data corresponding to each first lighting device into segment record data corresponding to each time segment;

[0210] The determining module 401 is also used to determine all the first lighting devices used in each time segment, so as to obtain the lighting device combination corresponding to each time segment;

[0211] Furthermore, the first generation module 403 generates scene-device usage data for each application scenario based on all first parameter data, all second parameter data, and the scene recognition type of the application scenario corresponding to each first lighting device. Specifically, this includes:

[0212] Based on all time segments, the lighting equipment combination corresponding to each time segment, the segment recording data corresponding to each time segment, and the scene recognition type, scene-device usage data for the application scenario is generated.

[0213] It can be seen that in the optional embodiment, before the scene-device usage data is generated, a series of data processing steps are also added, including determining the scene recognition type of the application scene, determining the time segment of the user using the light device, dividing the segment record data, and determining the light device combination, etc. These data processing steps realize accurate slicing of data based on the shooting content (time segment), improve the fineness of the division and recording of the first and second parameter data, and are also beneficial to improve the consistency of the subsequently generated scene-device usage data with the actual application scene, while improving the accuracy and practicality of the data.

[0214] In yet another optional embodiment, the manner in which the judging module 404 judges whether the preset light device recommendation condition is currently met specifically includes:

[0215] judging whether a light device recommendation request triggered by the user is received, and when it is judged that the light device recommendation request triggered by the user is received, determining that the preset light device recommendation condition is currently met; or,

[0216] judging whether the device running data indicates that a preset scene switching node is entered at the current time, and when it is judged that the device running data indicates that the scene switching node is entered at the current time, determining that the preset light device recommendation condition is currently met; or,

[0217] judging whether the device running data indicates that the combined light effects of all the first light devices reach a preset alarm point, and when it is judged that the device running data indicates that the combined light effects of all the first light devices reach the alarm point, determining that the preset light device recommendation condition is currently met; or,

[0218] judging whether the device running data indicates that a certain first light device has a running fault, and when it is judged that the device running data indicates that the certain first light device has the running fault, determining that the preset light device recommendation condition is currently met.

[0219] Among them, the node switching object corresponding to the scene switching node is the shooting content of the application scene; and the combined light effects of all the first light devices reaching the preset alarm point includes at least one of the following situations: the combined light effects of all the first light devices exist regional overexposure, regional underexposure, and regional color difference.

[0220] It can be seen that in the optional embodiment, multiple manners of judging whether the preset light device recommendation condition is currently met are proposed, including the user actively triggering the recommendation request, entering the preset scene switching node, the light effects reaching the alarm point, and the light device running fault, etc. The setting of these judgment conditions can provide device recommendations in a timely manner according to the actual application scene and the specific needs of the user, which is beneficial to improve the flexibility and intelligent degree of device recommendation, and is also beneficial to improve the pertinence and accuracy of device recommendation.

[0221] In another optional embodiment, the second generation module 405 generates the device recommendation information according to the scene-device use data and the predetermined scene association information, and the manner specifically includes:

[0222] determining the scene type of the application scene according to the first association information in the predetermined scene association information, and determining a plurality of device use templates matching the scene type according to the second association information in the scene association information;

[0223] determining a target device use template most matching the application scene from the plurality of device use templates according to the light use demand and all the first light devices;

[0224] determining, from all the historical light devices recorded in the target device use template, a non-enabled light device in the application scene based on all the first light devices;

[0225] judging whether the non-enabled light device has a matching registered device model in the database; the database has a plurality of registered light devices and their device models;

[0226] when it is judged that the non-enabled light device has a matching registered device model in the database, determining the non-enabled light device as a second light device, and generating the device recommendation information according to the second light device and its device model and the device configuration parameters recorded in the target device use template.

[0227] It can be seen that in this optional embodiment, by determining the scene type of the application scene, the matching device use template and the non-enabled light device, and finding the matching registered device model in the database, the device recommendation information containing the recommended second light device is finally generated. Before generating the device recommendation information, the device model verification process is set to ensure that the device to be recommended exists in the registered device model before the generation and pushing of the device recommendation information, further improving the accuracy and reliability of the device recommendation.

[0228] In yet another optional embodiment, the second generation module 405 determines the target device use template most matching the application scene from the plurality of device use templates according to the light use demand and all the first light devices, and the manner specifically includes:

[0229] determining the light effect demand of the user in the application scene according to the light use demand;

[0230] for each device use template, determining a plurality of historical light device combinations used in the historical use of the device use template, each historical light device combination including at least one light output sequence, and the light output sequence is used to control the light output parameters of all the historical light devices in the historical light device combination;

[0231] For each set of historical lighting device combination in the device use template, according to each set of lighting output sequence corresponding to the historical lighting device combination, performing lighting simulation on the historical lighting device combination to obtain a lighting simulation effect corresponding to each set of lighting output sequence;

[0232] Taking the lighting effect requirement as the first screening standard, performing comparison and screening between the lighting simulation effect corresponding to each set of lighting output sequence of each device use template and the lighting effect requirement, to obtain at least one set of first lighting output sequence with a matching value higher than a first matching threshold value with the lighting effect requirement;

[0233] Taking the number and model of all first lighting devices as the second screening reference, screening at least one set of second lighting output sequence from all second lighting output sequences with a matching value higher than a second matching threshold value with the second screening reference;

[0234] Randomly selecting any one set of second lighting output sequence as a target lighting output sequence, and determining the device use template corresponding to the target lighting output sequence as a target device use template most matched with the application scenario.

[0235] It can be seen that in this optional embodiment, when determining the target device use template, through steps such as determining the lighting effect requirement, performing lighting simulation, comparison and screening, and determining the target device use template, the finally determined target device use template is more in line with the requirements of the actual application scenario, the determination accuracy of the target device use template is improved, and the accuracy of subsequent device recommendation is further improved.

[0236] In this optional embodiment, the target lighting output sequence includes a first output sequence for all first lighting devices and a second output sequence for second lighting devices;

[0237] The second generation module 405 generates the device recommendation information according to the second lighting devices and their device models, and the device configuration parameters recorded in the target device use template, and the generation manner of the device recommendation information includes:

[0238] Generating the device recommendation information according to the second lighting devices and their device models, the second output sequence of the second lighting devices, and the first output sequence of all first lighting devices.

[0239] It can be seen that in this optional embodiment, the output parameters of all lighting devices can be considered comprehensively to generate device recommendation information containing detailed device configuration information. This method makes the device recommendation more comprehensive and specific, provides a more detailed lighting device configuration scheme for the user, and thus improves the optimization degree of the lighting effect.

[0240] Embodiment Four

[0241] Please refer to Figure 5 , Figure 5 is a structural schematic diagram of an intelligent lighting equipment recommendation device disclosed by the embodiment of the present application. As shown in Figure 5 , the intelligent lighting equipment recommendation device can include:

[0242] a memory 401 storing executable program codes;

[0243] a processor 402 coupled with the memory 401;

[0244] The processor 402 invokes the executable program codes stored in the memory 401 to execute the steps in the intelligent lighting equipment recommendation method described in the embodiment one or the embodiment two of the present application.

[0245] Embodiment five

[0246] The embodiment of the present application discloses a computer storage medium, which stores computer instructions. When the computer instructions are invoked, the computer instructions are used to execute the steps in the intelligent lighting equipment recommendation method described in the embodiment one or the embodiment two of the present application.

[0247] Embodiment six

[0248] The embodiment of the present application discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to make a computer execute the steps in the intelligent lighting equipment recommendation method described in the embodiment one or the embodiment two.

[0249] The device embodiments described above are only schematic, wherein the modules illustrated as separate components can or can not be physically separate, and the components illustrated as modules can or can not be physical modules, that is, can be located in one place, or can be distributed on multiple network modules. According to actual needs, some or all of the modules can be selected to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0250] Through the above specific description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software plus necessary universal hardware platforms, and of course can also be realized by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, and the computer software product can be stored in a computer storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, a magnetic disk storage, a magnetic tape storage, or any other computer readable medium that can be used to carry or store data.

[0251] Finally, it should be noted that: the intelligent lighting equipment recommendation method and system, device disclosed by the embodiments of the present application are only the preferred embodiments of the present application, and are used to illustrate the technical solutions of the present application, but not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that; it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent light device recommendation method, characterized by, The method comprises: When detecting the light use demand of the user, determining light device enabling information according to the light use demand; the light device enabling information comprises all first light devices enabled in the current application scenario; Collecting device running data of all the first light devices in the application scenario, and generating scene-device use data for the application scenario according to the device running data; the device running data at least comprises light output parameters of each first light device and at least one light device combination; Determining whether the preset light device recommendation condition is met at present, and when it is determined that the light device recommendation condition is met at present, generating device recommendation information according to the scene-device use data and pre-determined scene association information, and feeding back the device recommendation information to the user; The scene association information comprises first association information and second association information, the first association information is used to indicate the scene type of the application scenario, and the second association information comprises device use templates of a plurality of historical record scenes, and the scene type of each historical record scene matches the scene type of the application scenario; the device recommendation information at least comprises a second light device to be recommended to start. 2.The intelligent light device recommendation method of claim 1, wherein, The device running data for the application scenario is generated according to the device running data, which comprises: For each first light device, a plurality of first parameters are preset as a benchmark to analyze the device running data corresponding to the first light device, so as to obtain first parameter data corresponding to each first parameter of the first light device; all the first parameters at least comprise light brightness, light color, light irradiation range, light irradiation angle and light flicker frequency; A plurality of second parameters are preset as a benchmark to analyze the device running data corresponding to the first light device, so as to obtain second parameter data corresponding to each second parameter of the first light device; all the second parameters at least comprise device placement position, light use function, device start-stop condition, device rotation angle and rotation frequency; According to all the first parameter data, all the second parameter data corresponding to each first light device and the scene recognition type of the application scenario, the scene-device use data for the application scenario is generated. 3.The intelligent light device recommendation method of claim 2, wherein, Before the scene-device use data for the application scenario is generated according to all the first parameter data, all the second parameter data corresponding to each first light device and the scene recognition type of the application scenario, the method further comprises: According to the device models of all the first light devices, the scene recognition type of the application scenario is determined; A plurality of time segments in which the user uses all the first light devices in the application scenario are determined; each time segment matches a piece of shooting content in the application scenario; For each first light device, all the first parameter data and all the second parameter data corresponding to the first light device are divided into segment record data corresponding to each time segment. determining all the first light devices used in each of the time segments to obtain a light device combination corresponding to each of the time segments; and generating scene-device usage data for the application scene according to all the first parameter data corresponding to each of the first light devices, all the second parameter data, and the scene recognition type of the application scene, including: generating scene-device usage data for the application scene according to all the time segments, the light device combination corresponding to each of the time segments, the segment record data corresponding to each of the time segments, and the scene recognition type.

4. The intelligent light device recommendation method according to any one of claims 1-3, wherein, judging whether a preset light device recommendation condition is met, including: judging whether a light device recommendation request triggered by the user is received, and determining that the preset light device recommendation condition is met when it is judged that the light device recommendation request triggered by the user is received; or judging whether the device running data indicates that a preset scene switching node is entered at the current time, and determining that the preset light device recommendation condition is met when it is judged that the device running data indicates that the scene switching node is entered at the current time; or judging whether the device running data indicates that a preset alarm point of a combined light effect of all the first light devices is reached, and determining that the preset light device recommendation condition is met when it is judged that the device running data indicates that the alarm point of the combined light effect of all the first light devices is reached; or judging whether the device running data indicates that an operating failure exists in a certain first light device, and determining that the preset light device recommendation condition is met when it is judged that the device running data indicates that the operating failure exists in the certain first light device; wherein a node switching object corresponding to the scene switching node is a shooting content in the application scene, and the combined light effect of all the first light devices reaching the preset alarm point includes at least one of the following situations: regional overexposure, regional underexposure, and regional color difference of the combined light effect of all the first light devices.

5. The intelligent light device recommendation method according to any one of claims 1-3, wherein, generating device recommendation information according to the scene-device usage data and pre-determined scene association information, including: determining a scene type of the application scene according to first association information in the pre-determined scene association information, and determining a plurality of device usage templates matching the scene type according to second association information in the pre-determined scene association information; determining a target device usage template most matching the application scene from the plurality of device usage templates according to the light usage requirement and all the first light devices; determining a non-enabled light device in the application scene from all historical light devices recorded in the target device usage template, with all the first light devices as a reference; judging whether the non-enabled light device has a matching registered device model in a database, wherein the database has a plurality of registered light devices and device models thereof; When it is judged that the un-enabled lighting device has a matching registered device model in the database, the un-enabled lighting device is determined as a second lighting device, and device recommendation information is generated according to the second lighting device and its device model and device configuration parameters recorded in the target device use template. 6.The intelligent light device recommendation method of claim 5, wherein, The determining, according to the lighting use demand and all the first lighting devices, of a target device use template that is most matched with the application scenario from the plurality of device use templates comprises: determining, according to the lighting use demand, a lighting effect demand of the user in the application scenario; for each device use template, determining a plurality of sets of historical lighting device combinations in historical use of the device use template, each set of the historical lighting device combinations comprising at least one set of lighting output sequences for controlling lighting output parameters of all historical lighting devices in the set of historical lighting device combinations; for each set of the historical lighting device combinations in the device use template, performing lighting simulation on the historical lighting device combination according to each set of the lighting output sequences corresponding to the historical lighting device combination to obtain a lighting simulation effect corresponding to each set of the lighting output sequences; taking the lighting effect demand as a first screening criterion, performing comparison and screening between the lighting simulation effect corresponding to each set of the lighting output sequences corresponding to each device use template and the lighting effect demand to obtain at least one set of first lighting output sequences with a matching value higher than a first matching threshold with respect to the lighting effect demand; taking a device quantity and device models of all the first lighting devices as a second screening criterion, screening at least one set of second lighting output sequences with a matching value higher than a second matching threshold with respect to the second screening criterion from all the second lighting output sequences; randomly selecting any set of the second lighting output sequences as a target lighting output sequence, and determining the device use template corresponding to the target lighting output sequence as a target device use template that is most matched with the application scenario.

7. The intelligent light fixture recommendation method of claim 6, wherein, The target lighting output sequence comprises a first output sequence for all the first lighting devices and a second output sequence for the second lighting device. The generating, according to the second lighting device and its device model and device configuration parameters recorded in the target device use template, of device recommendation information comprises: generating device recommendation information according to the second lighting device and its device model, the second output sequence of the second lighting device and the first output sequence of all the first lighting devices.

8. An intelligent lighting device recommendation system, characterized by, The system comprises: a determination module configured to, when detecting a lighting use demand of a user, determine lighting device enabling information according to the lighting use demand, wherein the lighting device enabling information comprises all first lighting devices enabled in a current application scenario; a data acquisition module configured to acquire device running data of all the first lighting devices in the application scenario, wherein the device running data at least comprises lighting output parameters of each first lighting device and at least one lighting device combination; and a device recommendation information generation module configured to, according to the second lighting device and its device model and device configuration parameters recorded in the target device use template, generate device recommendation information. The first generating module is configured to generate scene-device usage data for the application scenario according to the device operation data; The judging module is configured to judge whether a preset light device recommendation condition is met or not at present; The second generating module is configured to generate device recommendation information according to the scene-device usage data and pre-determined scene association information when the judging module judges that the light device recommendation condition is met at present, and feed back the device recommendation information to the user. The scene association information includes first association information and second association information, the first association information is used to indicate a scene type of the application scenario, the second association information includes device usage templates of a plurality of historical record scenes, and a scene type of each historical record scene matches the scene type of the application scenario; and the device recommendation information at least includes a second light device to be recommended to be started.

9. An intelligent light device recommendation apparatus, characterized by comprising: The device includes: a memory storing executable program codes; a processor coupled with the memory; The processor invokes the executable program codes stored in the memory to execute the intelligent light device recommendation method according to any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, and the computer instructions are used to execute the intelligent light device recommendation method according to any one of claims 1-7 when invoked.

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