Intelligent light equipment recommendation method, system and device

Abstract

The invention discloses an intelligent lamplight equipment recommendation method, system and device, and the method, system and device can automatically detect the lamplight use demands of a user, determine the started lamplight equipment in a current application scene according to the demands, and collect the operation data of the equipment, thereby achieving the intelligent recommendation of the lamplight equipment. The scene-equipment use data aiming at the specific application scene is generated, intelligent data acquisition, analysis and recording functions of the started lighting equipment are realized, and the generated scene-equipment use data can be used as habits / historical data of a user for using the lighting equipment, so that data support is provided for subsequent recommendation of the lighting equipment; besides, when the preset recommendation condition is met, the equipment recommendation information can be intelligently generated and fed back to the user based on the scene-equipment use data and the scene association information, that is, the lamplight equipment can be accurately recommended according to the actual application scene and the specific requirements of the user, and the convenience and efficiency of lamplight adjustment of the user can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent lighting control, and in particular to an intelligent lighting equipment recommendation method, system and device. Background Art

[0002] In existing lighting equipment usage scenarios, users generally use manual methods to control lighting equipment, which means that users need to set device parameters one by one according to the specific needs of the current scene. This manual configuration method of device parameters has the problem of low efficiency in adjusting device parameters and incompatibility with the real-time shooting requirements of the scene when facing a variety of lighting equipment and complex application scenarios.

[0003] In recent years, with the development of data analysis and machine learning technologies, intelligent lighting control technologies have gradually been put into practical applications to solve the above technical problems. Among them, the existing lighting control technologies are mainly based on basic physical and electronic principles, and the desired lighting effects are achieved by adjusting parameters such as brightness, color temperature and lighting angle. However, the existing lighting control technologies often lack the support of deeper intelligent technologies. For example, they cannot be personalized / adaptively adjusted according to the specific needs and preferences of users. It can be seen that it is particularly important to provide a corresponding solution to the technical problems of low control intelligence and low intelligent control flexibility in the intelligent control of existing lighting equipment. Summary of the invention

[0004] The present invention provides an intelligent lighting equipment recommendation method, system and device, which can improve the intelligence level of lighting equipment control and improve the adjustment flexibility and convenience of lighting equipment.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses an intelligent lighting equipment recommendation method, the method comprising:

[0006] When a user's lighting use demand is detected, lighting device activation information is determined according to the lighting use demand; the lighting device activation information includes all first lighting devices activated in the current application scenario;

[0007] Collecting device operation data of all the first lighting devices in the application scenario, and generating scene-device usage data for the application scenario according to the device operation data; the device operation data at least includes a lighting output parameter of each of the first lighting devices and at least one lighting device combination;

[0008] Determine whether a preset lighting equipment recommendation condition is currently met, and when it is determined that the lighting equipment recommendation condition is currently met, generate equipment recommendation information according to the scene-equipment usage data and predetermined scene association information, and feed back the equipment 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 the scene type of the application scenario, the second association information includes device usage templates for multiple 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 includes a second lighting device recommended for startup.

[0010] As an optional implementation manner, in the first aspect of the present invention, generating the scenario-device usage data for the application scenario according to the device operation data includes:

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

[0012] Based on the preset multiple second parameters, the device operation data corresponding to the first lighting device is analyzed to obtain the second parameter data corresponding to the first lighting device and each of the second parameters; all the second parameters at least include the device placement position, the lighting function, the device start and stop status, the device rotation angle and the rotation frequency;

[0013] According to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scene, scene-device usage data for the application scene is generated.

[0014] As an optional implementation manner, in the first aspect of the present invention, before generating the scene-device usage data for the application scenario according to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scenario, the method further includes:

[0015] Determining a scene recognition type of the application scene according to device models of all the first lighting devices;

[0016] Determine a plurality of time segments in which the user uses all the first lighting devices in the application scene; each of the time segments matches a segment of captured content in the application scene;

[0017] For each of the first lighting devices, dividing all of the first parameter data and all of the second parameter data corresponding to the first lighting device into segment record data corresponding to each of the time segments;

[0018] Determine all the first lighting devices used in each of the time segments, and obtain a lighting device combination corresponding to each of the time segments;

[0019] And, generating scene-device usage data for the application scenario according to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scenario, comprises:

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

[0021] As an optional implementation, in the first aspect of the present invention, the determining whether the preset lighting equipment recommendation condition is currently met includes:

[0022] Determine whether a lighting equipment recommendation request actively triggered by the user is received, and when it is determined that a lighting equipment recommendation request actively triggered by the user is received, determine whether a preset lighting equipment recommendation condition is currently satisfied; or,

[0023] Determine whether the device operation data indicates that a preset scene switching node is currently entered, and when it is determined that the device operation data indicates that a preset scene switching node is currently entered, determine that the preset lighting device recommendation condition is currently met; or,

[0024] determining whether the device operation data indicates that the combined lighting effects of all the first lighting devices have reached a preset alarm point, and when it is determined that the device operation data indicates that the combined lighting effects of all the first lighting devices have reached the alarm point, determining that the preset lighting device recommendation condition is currently met; or,

[0025] Determining whether the device operation data indicates that a certain first lighting device has an operation fault, and when it is determined that the device operation data indicates that a certain first lighting device has an operation fault, determining that a preset lighting device recommendation condition is currently met;

[0026] Among them, the node switching object corresponding to the scene switching node is the shooting content in the application scene; and the combined lighting effect of all the first lighting devices reaches a preset alarm point, including that the combined lighting effect of all the first lighting devices has at least one of regional overexposure, regional underexposure, and regional color difference.

[0027] As an optional implementation manner, in the first aspect of the present invention, generating device recommendation information according to the scenario-device usage data and predetermined scenario association information includes:

[0028] Determine a scenario type of the application scenario according to first association information in the predetermined scenario association information, and determine a plurality of device usage templates matching the scenario type according to second association information in the scenario association information;

[0029] According to the lighting usage requirements and all the first lighting devices, determining a target device usage template that best matches the application scenario from the multiple device usage templates;

[0030] Taking all the first lighting devices as a reference, determining the unused lighting devices in the application scenario from all the historical lighting devices recorded by the target device using the template;

[0031] Determine whether the unactivated lighting device has a matching registered device model in a database; the database contains multiple registered lighting devices and their device models;

[0032] When it is determined that the unactivated lighting device has a matching registered device model in the database, the unactivated lighting device is determined to be a second lighting device, and device recommendation information is generated based on the second lighting device and its device model and the device configuration parameters recorded in the target device usage template.

[0033] As an optional implementation manner, in the first aspect of the present invention, determining a target device usage template that best matches the application scenario from the plurality of device usage templates according to the lighting usage requirements and all the first lighting devices includes:

[0034] Determining the user's lighting effect requirements in the application scenario according to the lighting usage requirements;

[0035] For each of the equipment usage templates, determining a plurality of historical lighting equipment combinations when the equipment usage template was used historically, each of the historical lighting equipment combinations comprising at least one set of lighting output sequences, and the set of lighting output sequences is used to control lighting output parameters of all historical lighting equipment in the set of historical lighting equipment combinations;

[0036] For each set of the historical lighting device combination in the device usage template, according to each set of the lighting output sequence corresponding to the historical lighting device combination, a lighting simulation is performed on the historical lighting device combination to obtain a lighting simulation effect corresponding to each set of the lighting output sequence;

[0037] Taking the lighting effect requirement as the first screening criterion, comparing and screening the lighting simulation effect corresponding to each set of the lighting output sequence corresponding to each device usage template with the lighting effect requirement, and obtaining at least one set of first lighting output sequence whose matching value with the lighting effect requirement is higher than a first matching threshold;

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

[0039] Any set of the second light output sequences is randomly selected as a target light output sequence, and the device usage template corresponding to the target light output sequence is determined as a target device usage template that best matches the application scenario.

[0040] As an optional implementation, in the first aspect of the present 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 generating device recommendation information according to the second lighting device and its device model and the device configuration parameters recorded in the target device usage template includes:

[0042] Device recommendation information is generated according to the second lighting device and its device model, the second output sequence of the second lighting device, and the first output sequences of all the first lighting devices.

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

[0044] A determination module, configured to determine lighting device activation information according to the lighting device usage demand when a lighting usage demand of a user is detected; the lighting device activation information includes all first lighting devices activated in the current application scenario;

[0045] A data collection module, used for collecting device operation data of all the first lighting devices in the application scenario; the device operation data at least includes a lighting output parameter of each of the first lighting devices and at least one lighting device combination;

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

[0047] A judgment module, used to judge whether the preset lighting equipment recommendation conditions are met at present;

[0048] A second generating module is used to generate device recommendation information according to the scene-device usage data and predetermined scene association information when the judging module judges that the lighting device recommendation condition is currently met, and feed back the device recommendation information to the user;

[0049] 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 scenario, the second association information includes device usage templates for multiple 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 includes a second lighting device recommended for startup.

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

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

[0052] Based on the preset multiple second parameters, the device operation data corresponding to the first lighting device is analyzed to obtain the second parameter data corresponding to the first lighting device and each of the second parameters; all the second parameters at least include the device placement position, the lighting function, the device start and stop status, the device rotation angle and the rotation frequency;

[0053] According to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scene, scene-device usage data for the application scene is generated.

[0054] As an optional implementation, in the second aspect of the present invention, the determination module is further used to determine the scene recognition type of the application scenario according to device models of all the first lighting devices before the first generation module generates the scene-device usage data for the application scenario according to all the first parameter data, all the second parameter data and the scene recognition type of the application scenario corresponding to each of the first lighting devices;

[0055] The determination module is further used to determine a plurality of time segments in which the user uses all the first lighting devices in the application scene; each of the time segments matches a segment of captured content in the application scene;

[0056] The system further comprises:

[0057] a data division module, configured to divide, for each of the first lighting devices, all of the first parameter data and all of the second parameter data corresponding to the first lighting device into segment record data corresponding to each of the time segments;

[0058] The determining module is further used to determine all the first lighting devices used in each of the time segments, and obtain a lighting device combination corresponding to each of the time segments;

[0059] Furthermore, the first generating module generates the scene-device usage data for the application scenario according to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scenario, specifically including:

[0060] The scene-device usage data for the application scenario is generated according to all the time segments, the lighting device combination corresponding to each time segment, the segment record data corresponding to each time segment, and the scene recognition type.

[0061] As an optional implementation, in the second aspect of the present invention, the manner in which the judgment module judges whether the preset lighting equipment recommendation condition is currently met specifically includes:

[0062] Determine whether a lighting equipment recommendation request actively triggered by the user is received, and when it is determined that a lighting equipment recommendation request actively triggered by the user is received, determine whether a preset lighting equipment recommendation condition is currently satisfied; or,

[0063] Determine whether the device operation data indicates that a preset scene switching node is currently entered, and when it is determined that the device operation data indicates that a preset scene switching node is currently entered, determine that the preset lighting device recommendation condition is currently met; or,

[0064] determining whether the device operation data indicates that the combined lighting effects of all the first lighting devices have reached a preset alarm point, and when it is determined that the device operation data indicates that the combined lighting effects of all the first lighting devices have reached the alarm point, determining that the preset lighting device recommendation condition is currently met; or,

[0065] Determining whether the device operation data indicates that a certain first lighting device has an operation fault, and when it is determined that the device operation data indicates that a certain first lighting device has an operation fault, determining that a preset lighting device recommendation condition is currently met;

[0066] Among them, the node switching object corresponding to the scene switching node is the shooting content in the application scene; and the combined lighting effect of all the first lighting devices reaches a preset alarm point, including that the combined lighting effect of all the first lighting devices has at least one of regional overexposure, regional underexposure, and regional color difference.

[0067] As an optional implementation, in the second aspect of the present invention, the second generating module generates device recommendation information according to the scene-device usage data and the predetermined scene association information, specifically including:

[0068] Determine a scenario type of the application scenario according to first association information in the predetermined scenario association information, and determine a plurality of device usage templates matching the scenario type according to second association information in the scenario association information;

[0069] According to the lighting usage requirements and all the first lighting devices, determining a target device usage template that best matches the application scenario from the multiple device usage templates;

[0070] Taking all the first lighting devices as a reference, determining the unused lighting devices in the application scenario from all the historical lighting devices recorded by the target device using the template;

[0071] Determine whether the unactivated lighting device has a matching registered device model in a database; the database contains multiple registered lighting devices and their device models;

[0072] When it is determined that the unactivated lighting device has a matching registered device model in the database, the unactivated lighting device is determined to be a second lighting device, and device recommendation information is generated based on the second lighting device and its device model and the device configuration parameters recorded in the target device usage template.

[0073] As an optional implementation, in the second aspect of the present invention, the second generating module determines the target device usage template that best matches the application scenario from the multiple device usage templates according to the lighting usage requirements and all the first lighting devices, specifically including:

[0074] Determining the user's lighting effect requirements in the application scenario according to the lighting usage requirements;

[0075] For each of the equipment usage templates, determining a plurality of historical lighting equipment combinations when the equipment usage template was used historically, each of the historical lighting equipment combinations comprising at least one set of lighting output sequences, and the set of lighting output sequences is used to control lighting output parameters of all historical lighting equipment in the set of historical lighting equipment combinations;

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

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

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

[0079] Any set of the second light output sequences is randomly selected as a target light output sequence, and the device usage template corresponding to the target light output sequence is determined as a target device usage template that best matches the application scenario.

[0080] As an optional implementation, in the second aspect of the present 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;

[0081] The second generating module generates the device recommendation information according to the second lighting device and its device model and the device configuration parameters recorded in the target device usage template, specifically including:

[0082] Device recommendation information is generated according to the second lighting device and its device model, the second output sequence of the second lighting device, and the first output sequences of all the first lighting devices.

[0083] The third aspect of the present invention discloses another intelligent lighting equipment recommendation device, the device comprising:

[0084] A memory storing executable program code;

[0085] a processor coupled to the memory;

[0086] The processor calls the executable program code stored in the memory to execute the intelligent lighting equipment recommendation method disclosed in the first aspect of the present invention.

[0087] A fourth aspect of the present invention discloses a computer storage medium, wherein the computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the intelligent lighting equipment recommendation method disclosed in the first aspect of the present invention.

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

[0089] In an embodiment of the present invention, an intelligent lighting device recommendation method is provided, the method comprising: when a user's lighting usage demand is detected, determining lighting device activation information according to the lighting usage demand; the lighting device activation information includes all first lighting devices enabled in the current application scenario; collecting device operation data of all first lighting devices in the application scenario, and generating scene-device usage data for the application scenario according to the device operation data; the device operation data at least includes a lighting output parameter of each first lighting device and at least one lighting device combination; judging whether a preset lighting device recommendation condition is currently met, and when it is judged that the lighting device recommendation condition is currently met, generating device recommendation information according to the scene-device usage data and predetermined scene association information, and feeding back the device recommendation information to the user; wherein 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 scenario, the second association information includes device usage templates of multiple 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 includes the second lighting device recommended for startup. It can be seen that the implementation of the present invention can automatically detect the user's lighting usage needs, and determine the lighting equipment that has been enabled in the current application scenario based on these needs, and then generate scene-device usage data for specific application scenarios by collecting the operating data of these devices, thereby realizing the intelligent data collection, analysis and recording functions of the user's currently enabled lighting equipment, and the generated scene-device usage data can be used as the user's habit data and historical data on the use of lighting equipment, providing data support for subsequent lighting equipment recommendations; in addition, when the preset recommendation conditions are met, the device recommendation information can be intelligently generated based on the scene-device usage data and predetermined scene association information, and fed back to the user, that is, personalized lighting equipment recommendations can be provided based on the actual application scenario and the user's specific needs, while improving the level of intelligence in the use of lighting equipment and the convenience and efficiency of the user in adjusting the light. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

[0093] Figure 3 It is a structural schematic diagram of an intelligent lighting equipment recommendation system disclosed in an embodiment of the present invention;

[0094] Figure 4 It is a structural schematic diagram of an intelligent lighting equipment recommendation device disclosed in an embodiment of the present invention;

[0095] Figure 5 It is a structural schematic diagram of another intelligent lighting equipment recommendation device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0096] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0097] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. 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 may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0098] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0099] The present invention discloses an intelligent lighting equipment recommendation method, system and device, which can automatically detect the lighting use needs of users, and determine the lighting equipment that has been enabled in the current application scene according to these needs, and then generate scene-equipment usage data for specific application scenes by collecting the operation data of these devices, thereby realizing the intelligent data collection, analysis and recording functions of the lighting equipment currently enabled by the user, and the generated scene-equipment usage data can be used as the habit data and historical data of the user using the lighting equipment, providing data support for the subsequent lighting equipment recommendation; in addition, when the preset recommendation conditions are met, the equipment recommendation information can be intelligently generated based on the scene-equipment usage data and the predetermined scene association information, and fed back to the user, that is, personalized lighting equipment recommendations can be provided according to the actual application scene and the specific needs of the user, while improving the intelligent level of the lighting equipment use and the convenience and efficiency of the user in adjusting the lighting. The following are detailed descriptions.

[0100] In order to better understand the intelligent lighting equipment recommendation method, system, and device described in the present invention, the scenario architecture to which the intelligent lighting equipment recommendation method is applicable is first described. Specifically, the scenario architecture may include:

[0101] Lighting devices 101, 102, 103 and 104 are all used as main lights. With the object to be photographed as the center point, the lighting devices 101-104 are arranged around the object to be photographed, such as arranging lighting device 101 on the left side of the front and arranging lighting device 102 on the right side of the front; arranging lighting device 103 on the left side of the back and arranging lighting device 104 on the right side of the back, so as to provide uniform light coverage for the current application scene;

[0102] Among them, the four lighting devices are all large black umbrella-shaped lights, commonly known as soft light umbrella lights or umbrella lights, which can provide soft and extensive lighting; and in actual application scenarios, by adjusting the device layout position of lighting devices 101-lighting devices 104, the inclination angle of the device illumination surface, and the light output brightness of the lighting devices, and the number of devices, the visual effect of the lighting devices can be adjusted.

[0103] Lighting device 105 and lighting device 106 are used to provide focused light beams and are used as special effect lights to create special light effects; and, the lighting device 105 can be arranged at a position below the oblique front of the object to be photographed, and the lighting device 106 can be arranged at a position above the oblique front of the object to be photographed;

[0104] Among them, the two types of lighting equipment, lighting equipment 105 and lighting equipment 106, can be small circular spotlights.

[0105] Optionally, according to the shooting requirements and lighting requirements of the actual application scenario, the number and / or types of lighting equipment can be further increased. For example, in addition to the above-mentioned basic main lights and special effects, fill lights, background lights, side lights, and ambient lights can also be added, among which:

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

[0107] Background lights can be strip lights, ring lights or spotlights, which are used to provide light sources from behind characters or objects, separate the subject from the background, enhance the three-dimensional effect, and create contour light effects;

[0108] Side lights can be hard light sources or lights with honeycomb grids, which are used to project light from the side to enhance the texture and depth of the object and form a sharp contrast between light and shadow;

[0109] Ambient lights can use small LED lights, fluorescent tubes, etc. to simulate daily ambient light sources; they are used to provide basic lighting for the overall environment, making the scene look more real and natural.

[0110] Furthermore, it should be noted that in the current application scenario, an intelligent lighting equipment recommendation system capable of interactive control with all lighting equipment is provided, referred to as a lighting control system. After any lighting equipment in the application scenario is started and operated, a record of equipment startup will be generated in the lighting control system, and then the lighting control system will collect, analyze and record the operation data of the lighting equipment in real time; at the same time, the user can issue different lighting control instructions through the lighting control system to control the start and stop, lighting output parameters, etc. of one or more lighting equipment through the lighting control instructions.

[0111] It should be noted that the above-mentioned scene architecture is only used to represent the scene to which the intelligent lighting equipment recommendation method is applicable. The lighting equipment 101-lighting equipment 106 involved is only selectively illustrated and displayed. The specific structure / size / shape / location / installation method can be adaptively adjusted according to the actual scene. The scene architecture description corresponding to the embodiment of the present invention does not limit this.

[0112] The above describes the application scenarios to which the intelligent lighting equipment recommendation method is applicable. The following describes the intelligent lighting equipment recommendation method and system in detail.

[0113] Embodiment 1

[0114] See also Figure 1 , Figure 1 : is a flow chart of an intelligent lighting equipment recommendation method disclosed in an embodiment of the present invention. Figure 1 The intelligent lighting equipment recommendation method described above can be applied to an intelligent lighting equipment recommendation system or device, and the embodiments of the present invention do not limit this. Figure 1 As shown, the intelligent lighting equipment recommendation method may include the following operations:

[0115] 201. When a user's lighting usage demand is detected, lighting equipment activation information is determined according to the lighting usage demand.

[0116] In the embodiment of the present invention, the lighting device activation information includes all first lighting devices activated in the current application scenario.

[0117] In an embodiment of the present invention, the lighting usage demand includes an active trigger type or a passive trigger type. The active trigger type refers to the user turning on the lighting device in the application scenario by remote (backend system or application, etc.) or short-range (manually triggering the switch of the lighting device) control; the passive trigger type refers to the user setting a timed start-up program for the lighting device, or starting the lighting device by linkage triggering; the linkage triggering can refer to the passive start-up of the lighting device after currently meeting any condition that is not actively triggered by the user. For example, when lighting device A is started, lighting device B will be turned on at the same time.

[0118] 202. Collect device operation data of all first lighting devices in application scenarios.

[0119] In the embodiment of the present invention, the device operation data at least includes lighting output parameters of each first lighting device and at least one lighting device combination.

[0120] In the embodiment of the present invention, the background recording system of the device operation data sets a data update mechanism, which can realize real-time update and recording of the device operation data, and the data update mechanism can be realized by means of a scheduled task or a trigger.

[0121] 203. Generate scenario-device usage data for application scenarios based on the device operation data.

[0122] 204. Determine whether the preset lighting equipment recommendation conditions are currently met.

[0123] 205. When it is determined that the lighting equipment recommendation conditions are currently met, equipment recommendation information is generated according to the scene-equipment usage data and predetermined scene association information, and the equipment recommendation information is fed back to the user.

[0124] In an embodiment of the present invention, 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 scenario, the second association information includes device usage templates for multiple 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 includes a second lighting device recommended for startup.

[0125] In an embodiment of the present invention, the device recommendation information may specifically include: recommending the user to start other lighting devices, recommending the user to adjust the lighting output parameters of one or more first lighting devices, and recommending the user to shut down one or more first lighting devices.

[0126] In an embodiment of the present invention, the storage database corresponding to the lighting equipment activation information, equipment operation data, scene-equipment usage data, and equipment recommendation information is designed to support the storage of multiple data types, including structured and unstructured data, to ensure that the above data can be effectively managed.

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

[0128] It can be seen that implementation Figure 1 The described intelligent lighting equipment recommendation method can automatically detect the user's lighting usage needs, and determine the lighting equipment that has been enabled in the current application scenario based on these needs, and then generate scene-equipment usage data for specific application scenarios by collecting the operating data of these devices, thereby realizing the intelligent data collection, analysis and recording functions of the user's currently enabled lighting equipment, and the generated scene-equipment usage data can be used as the user's habit data and historical data on the use of lighting equipment, providing data support for subsequent lighting equipment recommendations; in addition, when the preset recommendation conditions are met, the device recommendation information can be intelligently generated based on the scene-equipment usage data and predetermined scene association information, and fed back to the user, that is, it can provide personalized lighting equipment recommendations based on the actual application scenario and the user's specific needs, while improving the level of intelligence in the use of lighting equipment and the convenience and efficiency of the user in adjusting the light.

[0129] In an optional embodiment, the method of generating scenario-device usage data for the application scenario according to the device operation data in step 203 specifically includes:

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

[0131] Based on the preset multiple second parameters, the device operation data corresponding to the first lighting device is analyzed to obtain the second parameter data corresponding to the first lighting device and each second parameter; all the second parameters at least include the device placement position, the lighting function, the device start and stop status, the device rotation angle and the rotation frequency;

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

[0133] In this optional embodiment, a comprehensive analysis of the equipment operation data is performed through the preset multiple first parameters (such as light brightness, light color, light illumination range, etc.) and second parameters (such as equipment placement, light usage function, etc.), which can ensure that the extracted data is both comprehensive and accurate.

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

[0135] It can be seen that in this optional embodiment, by analyzing the equipment operation data based on a plurality of preset first parameters (such as light brightness, color, etc.) and second parameters (such as equipment placement, usage function, etc.), detailed parameter data is obtained, and these data are combined with the scene recognition type of the application scenario to generate comprehensive scene-device usage data, which is conducive to improving the generation detail, reliability and accuracy of the scene-device usage data.

[0136] In another optional embodiment, before generating the scene-device usage data for the application scenario according to all the first parameter data, all the second parameter data corresponding to each first lighting device and the scene recognition type of the application scenario, the method further includes:

[0137] Determine the scene recognition type of the application scene according to the device models of all the first lighting devices;

[0138] Determine multiple time segments in which the user uses all first lighting devices in the application scene; each time segment matches a piece of shooting content in the application scene;

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

[0140] Determine all first lighting devices used in each time segment, and obtain a lighting device combination corresponding to each time segment;

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

[0142] Based on all time segments, the lighting equipment combination corresponding to each time segment, the segment record data corresponding to each time segment, and the scene recognition type, scene-device usage data for the application scenario is generated.

[0143] In this optional embodiment, by dividing the time segment and matching each time segment with the shooting content, the usage of the lighting equipment is associated with the specific scene content, which is conducive to improving the accuracy and relevance of the data; it is also possible to generate segment record data based on each time segment, including all first parameter data and second parameter data, thereby ensuring the integrity and detail of the data.

[0144] It can be seen that in this optional embodiment, before generating the scene-device usage data, a series of data processing steps are added, including determining the scene recognition type of the application scenario, determining the time segment when the user uses the lighting equipment, dividing the segment recording data, and determining the lighting equipment combination, etc. These data processing steps realize the precise slicing and division of data based on the shooting content (time segment), and improve the precision of the division and recording of the first and second parameter data; it is also beneficial to improve the consistency of the subsequently generated scene-device usage data with the actual application scenario, while improving the accuracy and practicality of the data.

[0145] In another optional embodiment, the method of determining whether the preset lighting equipment recommendation condition is currently met in step 204 specifically includes:

[0146] Determine whether a lighting equipment recommendation request actively triggered by a user is received, and when it is determined that a lighting equipment recommendation request actively triggered by a user is received, determine that a preset lighting equipment recommendation condition is currently satisfied; or,

[0147] Determine whether the device operation data indicates that the preset scene switching node is currently entered, and when it is determined that the device operation data indicates that the scene switching node is currently entered, determine that the preset lighting device recommendation condition is currently met; or,

[0148] Determine whether the device operation data indicates that the combined lighting effects of all the first lighting devices have reached a preset alarm point, and when it is determined that the device operation data indicates that the combined lighting effects of all the first lighting devices have reached the alarm point, determine that the preset lighting device recommendation condition is currently met; or,

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

[0150] Among them, the node switching object corresponding to the scene switching node is the shooting content in the application scene; and the combined lighting effect of all the first lighting devices reaches a preset alarm point, including that the combined lighting effect of all the first lighting devices has at least one of regional overexposure, regional underexposure, and regional color difference.

[0151] In this optional embodiment, the lighting equipment recommendation request actively triggered by the user can be generated after the lighting recommendation information is entered in the instruction box corresponding to the lighting recommendation in the background system. For example, the user enters "indoor", "product close-up" in the instruction box; or enters "indoor", "movie", "warm", "winter evening" and other key information, and the corresponding lighting equipment recommendation request can be generated.

[0152] It can be seen that in this optional embodiment, multiple ways of judging whether the preset lighting equipment recommendation conditions are currently met are proposed, including the user actively triggering a recommendation request, entering a preset scene switching node, the lighting effect reaching an alarm point, and a lighting equipment operation failure; the setting of these judgment conditions can provide equipment recommendations in a timely manner according to the actual application scenarios and the specific needs of users, which is conducive to improving the flexibility and intelligence of equipment recommendations, and is also conducive to improving the pertinence and accuracy of equipment recommendations.

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

[0154] Detecting the user's authorization level for the device recommendation information, the authorization level includes a first authorization level or a second authorization level; the first authorization level indicates that the light device adjustment operation matching the device recommendation information is allowed to be directly executed; the second authorization level indicates that the light device adjustment operation matching the device recommendation information is not allowed to be directly executed;

[0155] When the authorization level is the first authorization level, the lighting equipment adjustment operation is performed on all lighting equipment to be adjusted according to the equipment recommendation information; and the lighting adjustment evaluation fed back by the user is obtained;

[0156] When the lighting adjustment evaluation indicates that the lighting equipment adjustment operation meets the user's lighting adjustment needs, a recommendation report is generated based on the scene-equipment usage data, the currently met lighting equipment recommendation conditions, and the equipment recommendation information. The recommendation report is used to optimize the recommendation algorithm for generating the equipment 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 recommendations.

[0157] In this optional embodiment, the lighting adjustment evaluation is information fed back by the user after performing the lighting device adjustment operation based on the device recommendation information, and has a low correlation with the authorization level of the device recommendation information.

[0158] In this optional embodiment, if the lighting adjustment evaluation does not match the user's lighting adjustment requirements, the above-mentioned relevant information is discarded or used as auxiliary data for algorithm optimization.

[0159] In this optional embodiment, the recommendation engine used in the above 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 the multi-scene test, users reported that the probability that the recommendation solutions they provided met the actual needs was as high as 85%, showing good adaptability. In addition, through this method, users can use existing lighting equipment more effectively and avoid wasting resources. For example, data analysis shows that after users use the intelligent recommendation system corresponding to this method, the frequency of use of lighting equipment has increased by 20%, and the idle time of the equipment has been significantly reduced, thereby improving the overall efficiency of the equipment.

[0160] In this optional embodiment, the intelligent recommendation system can continuously optimize the recommendation algorithm based on user feedback, so that the intelligence of the system continues to improve with the increase of usage time. After three months of use, the user satisfaction survey on the recommendation showed that the satisfaction increased by 15%, reflecting the learning ability and adaptability of the system.

[0161] It can be seen that in this optional embodiment, a verification and execution mechanism for the authority level is set up. When the authorization level of the device recommendation information is the first authorization level, the lighting equipment adjustment operation can be directly executed, which reduces the process of user confirmation and re-operation. This is beneficial to improving the efficiency of use of the lighting recommendation information after it is generated and improving the user's convenience to a certain extent.

[0162] Embodiment 2

[0163] See also Figure 2 , Figure 2 is a flow chart of another intelligent lighting equipment recommendation method disclosed in an embodiment of the present invention. Figure 2The intelligent lighting equipment recommendation method described above can be applied to an intelligent lighting equipment recommendation device, and the embodiment of the present invention does not limit this. Figure 2 As shown, the intelligent lighting equipment recommendation method may include the following operations:

[0164] 301. When a user's lighting usage demand is detected, lighting equipment activation information is determined according to the lighting usage demand.

[0165] 302. Collect device operation data of all first lighting devices in the application scenario.

[0166] 303. Generate scenario-device usage data for application scenarios based on the device operation data.

[0167] 304. Determine whether the preset lighting equipment recommendation conditions are currently met.

[0168] 305. When it is determined that the lighting equipment recommendation condition is currently met, determine the scene type of the application scene according to the first association information in the predetermined scene association information, and determine multiple device usage templates matching the scene type according to the second association information in the scene association information.

[0169] 306. Determine a target device usage template that best matches the application scenario from the multiple device usage templates according to the lighting usage requirements and all first lighting devices.

[0170] 307. Taking all first lighting devices as a reference, determine the unactivated lighting devices in the application scenario from all historical lighting devices recorded by the target device using the template.

[0171] 308. Determine whether the unactivated lighting device has a matching registered device model in the database; the database contains multiple registered lighting devices and their device models.

[0172] 309. When it is determined that the unactivated lighting device has a matching registered device model in the database, the unactivated lighting device is determined as the second lighting device.

[0173] 310. Generate device recommendation information based on the second lighting device and its device model and the device configuration parameters recorded in the target device usage template, and feed back the device recommendation information to the user.

[0174] In the embodiment of the present invention, for other descriptions of step 301 to step 304, please refer to other specific descriptions of step 201 to step 204 in embodiment 1, and the embodiment of the present invention will not be repeated here.

[0175] It can be seen that implementation Figure 2The described intelligent lighting equipment recommendation method determines the scene type of the application scenario, the matching equipment usage template and the unactivated lighting equipment, and searches for the matching registered equipment model in the database, and finally generates equipment recommendation information containing the second lighting equipment recommended for startup. Before generating the equipment recommendation information, a verification process for the equipment model is set up to ensure that the equipment to be recommended has a registered equipment model before generating and pushing the equipment recommendation information, thereby further improving the accuracy and reliability of the equipment recommendation.

[0176] In an optional embodiment, the above step 306 determines the target device usage template that best matches the application scenario from the multiple device usage templates according to the lighting usage requirements and all the first lighting devices, specifically including:

[0177] Determine the user's lighting effect requirements in the application scenario based on lighting usage requirements;

[0178] For each device usage template, determine multiple sets of historical lighting device combinations when the device usage template was used historically, each set of historical lighting device combinations includes at least one set of lighting output sequences, and the set of lighting output sequences is used to control the lighting output parameters of all historical lighting devices in the set of historical lighting device combinations;

[0179] For each set of historical lighting equipment combinations in the equipment usage template, according to each set of lighting output sequences corresponding to the historical lighting equipment combinations, a lighting simulation is performed on the historical lighting equipment combinations to obtain a lighting simulation effect corresponding to each set of lighting output sequences;

[0180] Taking the lighting effect requirement as the first screening criterion, the lighting simulation effect corresponding to each set of lighting output sequences corresponding to the template used by each device 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 a first matching threshold;

[0181] Taking the device quantity and device model of all first lighting devices as the second screening criterion, screening out at least one set of second lighting output sequences whose matching value with the second screening criterion is higher than a second matching threshold from all second lighting output sequences;

[0182] Any set of the second light output sequence is randomly selected as the target light output sequence, and the device usage template corresponding to the target light output sequence is determined as the target device usage template that best matches the application scenario.

[0183] In this optional embodiment, the lighting effect requirement also includes the urgency of the information requirement for subsequent device recommendation information, and the urgency of the information requirement indicates a first urgency level lower than the minimum urgency threshold, a second urgency level higher than the minimum urgency threshold and greater than the maximum urgency threshold, or a third urgency level greater than the maximum urgency threshold; wherein, the higher the urgency of the information requirement, the higher the complexity of the algorithm used to generate the device recommendation information, and the larger the amount of data involved / needed to be analyzed. For example, for information requirements with an urgency level of the third urgency level, a conventional static algorithm can be used, which does not involve in-depth analysis of machine learning / neural networks, and only performs a simple comparison of scene types. After the scene types are compared and consistent, the historical device usage template that matches the scene type is directly called.

[0184] In this optional embodiment, the lighting effect requirement can be used to reflect the urgency of the user's information needs, thereby adjusting the complexity of the recommendation algorithm used, and then adjusting the generation speed, detail and accuracy of the device recommendation information, that is, achieving flexible and dynamic adjustment of the recommendation algorithm, further improving the practicality of the recommendation algorithm.

[0185] In this optional embodiment, the user's lighting effect requirements in the application scenario are first determined based on the lighting usage requirements, ensuring that the subsequent lighting configuration is highly consistent with the user's needs; then, lighting simulation is performed on the historical lighting device combinations in multiple device usage templates, and comparison and screening are performed to select lighting output sequences that have a high degree of match with the lighting effect requirements, thereby improving the screening accuracy of the lighting output sequence and improving the adaptability to the user's lighting effect requirements.

[0186] In this optional embodiment, by simulating and analyzing historical lighting equipment combinations, the process of repeatedly testing different equipment combinations and lighting parameters in actual applications is avoided, and the efficiency of equipment configuration is significantly improved; at the same time, the number of equipment and equipment model are used as the screening basis to further narrow the selection range, making the equipment configuration more accurate and efficient. In addition, through in-depth analysis of user historical usage data, user preferences can be accurately identified to generate personalized lighting configuration suggestions. According to internal tests, users who use this system have improved the accuracy of lighting configuration by about 30%, greatly reducing unnecessary trial and error time. In addition, traditional manual configuration of lighting equipment often requires users to invest a lot of time in debugging. Through this method, users can obtain the optimal configuration suggestions in a short time, and the actual operation time is reduced by about 40%. This means that users can spend more time on creation rather than equipment adjustment, which greatly improves the efficiency and speed of user equipment adjustment.

[0187] It can be seen that in this optional embodiment, when determining the target device usage template, by determining the lighting effect requirements, performing lighting simulation, comparing and screening, and determining the target device usage template and other steps, the final target device usage template is more in line with the needs of the actual application scenario, thereby improving the accuracy of determining the target device usage template and further improving the accuracy of subsequent device recommendations.

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

[0189] The above step 310 generates the device recommendation information according to the second lighting device and its device model and the device configuration parameters recorded in the target device usage template, specifically including:

[0190] Device recommendation information is generated according to the second lighting device and its device model, the second output sequence of the second lighting device, and the first output sequences of all the first lighting devices.

[0191] It can be seen that in this optional embodiment, the output parameters of all lighting devices can be comprehensively considered to generate device recommendation information containing detailed device configuration information. This method makes the device recommendation more comprehensive and specific, and provides users with more detailed lighting device configuration plans, thereby improving the optimization degree of lighting effects.

[0192] Embodiment 3

[0193] See also Figure 3 , Figure 3 Schematic diagram of the structure of an intelligent lighting equipment recommendation system disclosed in an embodiment of the present invention. Figure 3 As shown, the intelligent lighting equipment recommendation system may include a determination module 401, a data collection module 402, a first generation module 403, a judgment module 404 and a second generation module 405, wherein:

[0194] The determination module 401 is used to determine the lighting device activation information according to the lighting use demand when the lighting use demand of the user is detected; the lighting device activation information includes all the first lighting devices activated in the current application scenario.

[0195] The data collection module 402 is used to collect device operation data of all first lighting devices in the application scenario; the device operation data at least includes the lighting output parameters of each first lighting device and at least one lighting device combination.

[0196] The first generating module 403 is used to generate scenario-device usage data for the application scenario according to the device operation data.

[0197] The judgment module 404 is used to judge whether the preset lighting equipment recommendation conditions are currently met.

[0198] The second generating module 405 is used to generate device recommendation information according to the scene-device usage data and predetermined scene association information when the judging module 404 judges that the lighting device recommendation condition is currently met, and feed back the device recommendation information to the user.

[0199] Among them, 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 scenario, the second association information includes device usage templates for multiple 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 includes a second lighting device recommended for startup.

[0200] It can be seen that implementation Figure 3 The described intelligent lighting equipment recommendation device can automatically detect the user's lighting usage needs, and determine the lighting equipment that has been enabled in the current application scenario based on these needs, and then generate scene-equipment usage data for specific application scenarios by collecting the operating data of these devices, thereby realizing the intelligent data collection, analysis and recording functions of the user's currently enabled lighting equipment, and the generated scene-equipment usage data can be used as the user's habit data and historical data on the use of lighting equipment, providing data support for subsequent lighting equipment recommendations; in addition, when the preset recommendation conditions are met, the device recommendation information can be intelligently generated based on the scene-equipment usage data and predetermined scene association information, and fed back to the user, that is, it can provide personalized lighting equipment recommendations based on the actual application scenario and the user's specific needs, while improving the intelligence level of lighting equipment use and the convenience and efficiency of users in adjusting lights.

[0201] In an optional embodiment, the first generating module 403 generates the scenario-device usage data for the application scenario according to the device operation data, specifically including:

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

[0203] Based on the preset multiple second parameters, the device operation data corresponding to the first lighting device is analyzed to obtain the second parameter data corresponding to the first lighting device and each second parameter; all the second parameters at least include the device placement position, the lighting function, the device start and stop status, the device rotation angle and the rotation frequency;

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

[0205] It can be seen that in this optional embodiment, by analyzing the equipment operation data based on a plurality of preset first parameters (such as light brightness, color, etc.) and second parameters (such as equipment placement, usage function, etc.), detailed parameter data is obtained, and these data are combined with the scene recognition type of the application scenario to generate comprehensive scene-device usage data, which is conducive to improving the generation detail, reliability and accuracy of the scene-device usage data.

[0206] In another optional embodiment, the determination module 401 is further configured to determine the scene recognition type of the application scenario according to the device models of all the first lighting devices before the first generation module 403 generates the scene-device usage data for the application scenario according to 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 further used to determine multiple time segments in which the user uses all the first lighting devices in the application scene; each time segment matches a piece of shooting content in the application scene;

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

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

[0210] The determination module 401 is further used to determine all first lighting devices used in each time segment, and obtain a lighting device combination corresponding to each time segment;

[0211] Furthermore, the first generating module 403 generates scene-device usage data for the application scenario according to all first parameter data, all second parameter data and the scene recognition type of the application scenario corresponding to each first lighting device, specifically including:

[0212] Based on all time segments, the lighting equipment combination corresponding to each time segment, the segment record 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 this optional embodiment, before generating the scene-device usage data, a series of data processing steps are added, including determining the scene recognition type of the application scenario, determining the time segment when the user uses the lighting equipment, dividing the segment recording data, and determining the lighting equipment combination, etc. These data processing steps realize the precise slicing and division of data based on the shooting content (time segment), and improve the precision of the division and recording of the first and second parameter data; it is also beneficial to improve the consistency of the subsequently generated scene-device usage data with the actual application scenario, while improving the accuracy and practicality of the data.

[0214] In another optional embodiment, the determination module 404 determines whether the preset lighting equipment recommendation condition is currently met in the following manner:

[0215] Determine whether a lighting equipment recommendation request actively triggered by a user is received, and when it is determined that a lighting equipment recommendation request actively triggered by a user is received, determine that a preset lighting equipment recommendation condition is currently satisfied; or,

[0216] Determine whether the device operation data indicates that the preset scene switching node is currently entered, and when it is determined that the device operation data indicates that the scene switching node is currently entered, determine that the preset lighting device recommendation condition is currently met; or,

[0217] Determine whether the device operation data indicates that the combined lighting effects of all the first lighting devices have reached a preset alarm point, and when it is determined that the device operation data indicates that the combined lighting effects of all the first lighting devices have reached the alarm point, determine that the preset lighting device recommendation condition is currently met; or,

[0218] Determining whether the device operation data indicates that a certain first lighting device has an operation fault, and when it is determined that the device operation data indicates that a certain first lighting device has an operation fault, determining that a preset lighting device recommendation condition is currently met;

[0219] Among them, the node switching object corresponding to the scene switching node is the shooting content in the application scene; and the combined lighting effect of all the first lighting devices reaches a preset alarm point, including that the combined lighting effect of all the first lighting devices has at least one of regional overexposure, regional underexposure, and regional color difference.

[0220] It can be seen that in this optional embodiment, multiple ways of judging whether the preset lighting equipment recommendation conditions are currently met are proposed, including the user actively triggering a recommendation request, entering a preset scene switching node, the lighting effect reaching an alarm point, and a lighting equipment operation failure; the setting of these judgment conditions can provide equipment recommendations in a timely manner according to the actual application scenarios and the specific needs of users, which is conducive to improving the flexibility and intelligence of equipment recommendations, and is also conducive to improving the pertinence and accuracy of equipment recommendations.

[0221] In another optional embodiment, the second generating module 405 generates the device recommendation information according to the scene-device usage data and the predetermined scene association information, specifically including:

[0222] Determine a scenario type of the application scenario according to first association information in the predetermined scenario association information, and determine a plurality of device usage templates matching the scenario type according to second association information in the scenario association information;

[0223] According to the lighting usage requirements and all the first lighting devices, determining a target device usage template that best matches the application scenario from the multiple device usage templates;

[0224] Taking all first lighting devices as a benchmark, determining the unactivated lighting devices in the application scenario from all historical lighting devices recorded by the target device using the template;

[0225] Determine whether the unactivated lighting device has a matching registered device model in the database; the database contains multiple registered lighting devices and their device models;

[0226] When it is determined that the unactivated lighting device has a matching registered device model in the database, the unactivated lighting device is determined as the second lighting device, and device recommendation information is generated based on the second lighting device and its device model and the device configuration parameters recorded in the target device usage template.

[0227] It can be seen that in this optional embodiment, by determining the scene type of the application scenario, the matching device usage template and the unactivated lighting equipment, and searching the matching registered device model in the database, the device recommendation information containing the second lighting device recommended to be started is finally generated. Before generating the device recommendation information, a device model verification process is set up to ensure that the registered device model of the device to be recommended exists before the device recommendation information is generated and pushed, thereby further improving the accuracy and reliability of the device recommendation.

[0228] In another optional embodiment, the second generating module 405 determines the target device usage template that best matches the application scenario from the multiple device usage templates according to the lighting usage requirements and all the first lighting devices, specifically including:

[0229] Determine the user's lighting effect requirements in the application scenario based on lighting usage requirements;

[0230] For each device usage template, determine multiple sets of historical lighting device combinations when the device usage template was used historically, each set of historical lighting device combinations includes at least one set of lighting output sequences, and the set of lighting output sequences is used to control the lighting output parameters of all historical lighting devices in the set of historical lighting device combinations;

[0231] For each set of historical lighting equipment combinations in the equipment usage template, according to each set of lighting output sequences corresponding to the historical lighting equipment combinations, a lighting simulation is performed on the historical lighting equipment combinations to obtain a lighting simulation effect corresponding to each set of lighting output sequences;

[0232] Taking the lighting effect requirement as the first screening criterion, the lighting simulation effect corresponding to each set of lighting output sequences corresponding to the template used by each device 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 a first matching threshold;

[0233] Taking the device quantity and device model of all first lighting devices as the second screening criterion, screening out at least one set of second lighting output sequences whose matching value with the second screening criterion is higher than a second matching threshold from all second lighting output sequences;

[0234] Any set of the second light output sequence is randomly selected as the target light output sequence, and the device usage template corresponding to the target light output sequence is determined as the target device usage template that best matches the application scenario.

[0235] It can be seen that in this optional embodiment, when determining the target device usage template, by determining the lighting effect requirements, performing lighting simulation, comparing and screening, and determining the target device usage template and other steps, the final target device usage template is more in line with the needs of the actual application scenario, thereby improving the accuracy of determining the target device usage template and further improving the accuracy of subsequent device recommendations.

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

[0237] The second generating module 405 generates the device recommendation information according to the second lighting device and its device model and the device configuration parameters recorded in the target device usage template, specifically including:

[0238] Device recommendation information is generated according to the second lighting device and its device model, the second output sequence of the second lighting device, and the first output sequences of all the first lighting devices.

[0239] It can be seen that in this optional embodiment, the output parameters of all lighting devices can be comprehensively considered to generate device recommendation information containing detailed device configuration information. This method makes the device recommendation more comprehensive and specific, and provides users with more detailed lighting device configuration plans, thereby improving the optimization degree of lighting effects.

[0240] Embodiment 4

[0241] See also Figure 5 , Figure 5 Schematic diagram of the structure of an intelligent lighting equipment recommendation device disclosed in an embodiment of the present invention. Figure 5 As shown, the intelligent lighting equipment recommendation device may include:

[0242] A memory 401 storing executable program codes;

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

[0244] The processor 402 calls the executable program code stored in the memory 401 to execute the steps of the intelligent lighting device recommendation method described in the first embodiment of the present invention or the second embodiment of the present invention.

[0245] Embodiment 5

[0246] The embodiment of the present invention discloses a computer storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps of the intelligent lighting equipment recommendation method described in the first embodiment of the present invention or the second embodiment of the present invention.

[0247] Embodiment 6

[0248] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the intelligent lighting equipment recommendation method described in Embodiment 1 or Embodiment 2.

[0249] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0250] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or partly contributed to the prior art in the form of a software product, and the computer software product can be stored in a computer storage medium, and the storage medium includes 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 electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, 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, system and device disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent lighting equipment recommendation method, characterized in that: The method comprises: When a user's lighting use demand is detected, lighting device activation information is determined according to the lighting use demand; the lighting device activation information includes all first lighting devices activated in the current application scenario; Collecting device operation data of all the first lighting devices in the application scenario, and generating scene-device usage data for the application scenario according to the device operation data; the device operation data at least includes a lighting output parameter of each of the first lighting devices and at least one lighting device combination; Determine whether a preset lighting equipment recommendation condition is currently met, and when it is determined that the lighting equipment recommendation condition is currently met, generate equipment recommendation information according to the scene-equipment usage data and predetermined scene association information, and feed back the equipment 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 the scene type of the application scenario, the second association information includes device usage templates for multiple 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 includes a second lighting device recommended for startup.

2. The intelligent lighting equipment recommendation method according to claim 1, characterized in that: The generating the scenario-device usage data for the application scenario according to the device operation data includes: For each of the first lighting devices, the device operation data corresponding to the first lighting device is analyzed based on a plurality of preset first parameters to obtain first parameter data corresponding to the first lighting device and each of the first parameters; all the first parameters at least include light brightness, light color, light irradiation range, light irradiation angle, and light flickering frequency; Based on the preset multiple second parameters, the device operation data corresponding to the first lighting device is analyzed to obtain the second parameter data corresponding to the first lighting device and each of the second parameters; all the second parameters at least include the device placement position, the lighting function, the device start and stop status, the device rotation angle and the rotation frequency; According to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scene, scene-device usage data for the application scene is generated.

3. The intelligent lighting equipment recommendation method according to claim 2, characterized in that: Before generating the scene-device usage data for the application scenario according to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scenario, the method further includes: Determining a scene recognition type of the application scene according to device models of all the first lighting devices; Determine a plurality of time segments in which the user uses all the first lighting devices in the application scene; each of the time segments matches a segment of captured content in the application scene; For each of the first lighting devices, dividing all of the first parameter data and all of the second parameter data corresponding to the first lighting device into segment record data corresponding to each of the time segments; Determine all the first lighting devices used in each of the time segments, and obtain a lighting device combination corresponding to each of the time segments; And, generating scene-device usage data for the application scenario according to all the first parameter data corresponding to each of the first lighting devices, all the second parameter data and the scene recognition type of the application scenario, comprises: The scene-device usage data for the application scenario is generated according to all the time segments, the lighting device combination corresponding to each time segment, the segment record data corresponding to each time segment, and the scene recognition type.

4. The intelligent lighting equipment recommendation method according to any one of claims 1 to 3, characterized in that: The determining whether the preset lighting equipment recommendation conditions are currently met includes: Determine whether a lighting equipment recommendation request actively triggered by the user is received, and when it is determined that a lighting equipment recommendation request actively triggered by the user is received, determine whether a preset lighting equipment recommendation condition is currently satisfied; or, Determine whether the device operation data indicates that a preset scene switching node is currently entered, and when it is determined that the device operation data indicates that a preset scene switching node is currently entered, determine that the preset lighting device recommendation condition is currently met; or, determining whether the device operation data indicates that the combined lighting effects of all the first lighting devices have reached a preset alarm point, and when it is determined that the device operation data indicates that the combined lighting effects of all the first lighting devices have reached the alarm point, determining that the preset lighting device recommendation condition is currently met; or, Determining whether the device operation data indicates that a certain first lighting device has an operation fault, and when it is determined that the device operation data indicates that a certain first lighting device has an operation fault, determining that a preset lighting device recommendation condition is currently met; Among them, the node switching object corresponding to the scene switching node is the shooting content in the application scene; and the combined lighting effect of all the first lighting devices reaches a preset alarm point, including that the combined lighting effect of all the first lighting devices has at least one of regional overexposure, regional underexposure, and regional color difference.

5. The intelligent lighting equipment recommendation method according to any one of claims 1 to 3, characterized in that: The generating device recommendation information according to the scenario-device usage data and predetermined scenario association information includes: Determine a scenario type of the application scenario according to first association information in the predetermined scenario association information, and determine a plurality of device usage templates matching the scenario type according to second association information in the scenario association information; According to the lighting usage requirements and all the first lighting devices, determining a target device usage template that best matches the application scenario from the multiple device usage templates; Taking all the first lighting devices as a reference, determining the unused lighting devices in the application scenario from all the historical lighting devices recorded by the target device using the template; Determine whether the unactivated lighting device has a matching registered device model in a database; the database contains multiple registered lighting devices and their device models; When it is determined that the unactivated lighting device has a matching registered device model in the database, the unactivated lighting device is determined to be a second lighting device, and device recommendation information is generated based on the second lighting device and its device model and the device configuration parameters recorded in the target device usage template.

6. The intelligent lighting equipment recommendation method according to claim 5, characterized in that: The step of determining a target device usage template that best matches the application scenario from the plurality of device usage templates according to the lighting usage requirement and all the first lighting devices includes: Determining the user's lighting effect requirements in the application scenario according to the lighting usage requirements; For each of the equipment usage templates, determining a plurality of historical lighting equipment combinations when the equipment usage template was used historically, each of the historical lighting equipment combinations comprising at least one set of lighting output sequences, the set of lighting output sequences being used to control lighting output parameters of all historical lighting equipment in the set of historical lighting equipment combinations; For each set of the historical lighting equipment combination in the equipment usage template, according to each set of the lighting output sequence corresponding to the historical lighting equipment combination, a lighting simulation is performed on the historical lighting equipment combination to obtain a lighting simulation effect corresponding to each set of the lighting output sequence; Taking the lighting effect requirement as the first screening criterion, comparing and screening the lighting simulation effect corresponding to each set of the lighting output sequence corresponding to each device usage template with the lighting effect requirement, and obtaining at least one set of first lighting output sequence whose matching value with the lighting effect requirement is higher than a first matching threshold; Taking the device quantity and device model of all the first lighting devices as a second screening criterion, screening out at least one set of second lighting output sequences whose matching value with the second screening criterion is higher than a second matching threshold from all the second lighting output sequences; Any set of the second light output sequences is randomly selected as a target light output sequence, and the device usage template corresponding to the target light output sequence is determined as a target device usage template that best matches the application scenario.

7. The intelligent lighting equipment recommendation method according to claim 6, characterized in that: 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; The generating device recommendation information according to the second lighting device and its device model and the device configuration parameters recorded in the target device usage template includes: Device recommendation information is generated according to the second lighting device and its device model, the second output sequence of the second lighting device, and the first output sequences of all the first lighting devices.

8. An intelligent lighting equipment recommendation system, characterized in that: The system comprises: A determination module, configured to determine lighting device activation information according to the lighting device usage demand when a lighting usage demand of a user is detected; the lighting device activation information includes all first lighting devices activated in the current application scenario; A data collection module, used for collecting device operation data of all the first lighting devices in the application scenario; the device operation data at least includes a lighting output parameter of each of the first lighting devices and at least one lighting device combination; A first generating module, configured to generate scenario-device usage data for the application scenario according to the device operation data; A judgment module, used to judge whether the preset lighting equipment recommendation conditions are met at present; A second generating module is used to generate device recommendation information according to the scene-device usage data and predetermined scene association information when the judging module judges that the lighting device recommendation condition is currently met, 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 the scene type of the application scenario, the second association information includes device usage templates for multiple 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 includes a second lighting device recommended for startup.

9. An intelligent lighting equipment recommendation device, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent lighting equipment recommendation method according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the intelligent lighting equipment recommendation method according to any one of claims 1 to 7.

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