An intelligent light effect generation method and device, electronic equipment and storage medium

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for generating intelligent lighting effects. The method includes: receiving a user's lighting control request input in natural language; inputting the lighting control request into an intent recognition model, where the model performs semantic understanding and intent recognition on the request; based on the intent recognition result, scheduling an intelligent agent matching the intent type; and having the intelligent agent determine corresponding lighting control parameters according to a preset parameter template to control the lighting equipment to achieve a lighting effect corresponding to the natural language input. This disclosure constructs a complete, closed-loop intelligent control framework from natural language input to lighting equipment execution, realizing end-to-end natural language lighting effect generation without human intervention. Users only need to state their request to automatically complete the entire process of intent understanding, decision scheduling, parameter generation, and equipment control, greatly improving the naturalness, convenience, and automation of the interaction.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, and more specifically, to a method, apparatus, electronic device, and storage medium for generating intelligent light effects. Background Technology

[0002] With the development of smart lighting technology, users' demands for personalized and intelligent light effect control are increasing. Existing smart lighting control systems typically use a preset light effect library, where users manually combine different light effect parameters to adjust the lighting effect. For example, if a user wants to achieve a "cozy reading mode," they manually adjust the relevant parameters one by one based on experience.

[0003] However, the aforementioned existing technologies rely on manual selection or combination of lighting effects from an existing lighting effect library to meet the user's needs. When users submit complex or personalized lighting effect requests via natural language, the system still requires manual intervention for lighting effect matching and parameter setting, resulting in slow response speed, high operational threshold, and limited user experience. Summary of the Invention

[0004] This disclosure provides at least one intelligent light effect generation method, apparatus, electronic device, and storage medium to solve the above-mentioned technical problems.

[0005] In a first aspect, embodiments of this disclosure provide a method for generating intelligent light effects, including:

[0006] Receive user requests for lighting control in natural language input; The lighting control request is input into the intent recognition model, and the intent recognition model performs semantic understanding and intent recognition on the lighting control request. Based on the results of intent recognition, schedule instruction agents that match the intent type; The intelligent agent determines the corresponding lighting control parameters based on the preset parameter template to control the lighting equipment to achieve the lighting effect corresponding to the natural language input.

[0007] In one possible implementation, the semantic understanding and intent recognition of the lighting control request through the intent recognition big data model includes: Extract the luminous efficacy requirement features from the lighting control requirements; The intent type is determined based on the characteristics of the light effect requirements; the intent type includes at least a general light effect intent type and a special light effect intent type.

[0008] In one possible implementation, determining the intent type based on the light effect demand characteristics includes: Analyze at least one of the following characteristics in the vocabulary distribution, verb proportion, general vocabulary proportion, emotional vocabulary proportion, or scene vocabulary proportion in the lighting control requirements: Based on the analysis results, the lighting control requests are routed to the corresponding instruction agents for processing.

[0009] In one possible implementation, the instruction agent includes at least a general instruction agent and a special instruction agent; The general instruction intelligent agent is used to process at least one basic control function among switch control, timing control, brightness adjustment or color temperature adjustment. The dedicated instruction agent is used to handle complex lighting effect requirements involving scene, emotion, or dynamic changes.

[0010] In one possible implementation, determining the corresponding lighting control parameters based on a preset parameter template includes: Identify the lighting control function that matches the stated intent type; Invoke the control tool corresponding to the light control function, the control tool including at least one of the following: switch control tool, brightness adjustment tool, color adjustment tool, and color temperature adjustment tool; Based on the parameter template associated with the control tool, specific lighting control parameter values ​​are generated.

[0011] In one possible implementation, the parameter template is a set of instruction sequences composed of multiple basic control instructions, used to support the automatic invocation and parameter generation of templated light effects.

[0012] In one possible implementation, the method further includes: The lighting control parameters are encapsulated into structured lighting control parameter data; The structured lighting control parameter data is sent to the execution terminal, which then drives the lighting equipment to perform the operation.

[0013] In one possible implementation, the structured lighting control parameter data is in JSON format.

[0014] In one possible implementation, it also includes: The validity of the lighting control parameters is verified. When the color temperature value in the lighting control parameters exceeds the preset range, an error message is generated and returned, and no control operation is performed.

[0015] Secondly, this disclosure also provides an intelligent light effect generating device, comprising: The receiving module is used to receive the user's lighting control requests input in natural language; The recognition module is used to input the lighting control request into the intent recognition model, and to perform semantic understanding and intent recognition on the lighting control request through the intent recognition model. The scheduling module is used to schedule instruction agents that match the intent type based on the intent recognition results. The control module is used by the instruction agent to determine the corresponding lighting control parameters according to the preset parameter template, so as to control the lighting equipment to achieve the lighting effect corresponding to the natural language input.

[0016] Thirdly, this disclosure also provides an electronic device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the intelligent light effect generation method as described in any one of the first aspects and its various embodiments is executed.

[0017] Fourthly, this disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the intelligent light effect generation method as described in any one of the first aspects and its various embodiments.

[0018] The aforementioned intelligent light effect generation method, device, electronic device, and storage medium receive user-inputted lighting control requests in natural language and input these requests into a large-scale intent recognition model. The model performs semantic understanding and intent recognition on the requests. Based on the intent recognition results, it schedules an intelligent agent matching the intent type. Finally, the intelligent agent determines the corresponding lighting control parameters according to a preset parameter template to control the lighting equipment to achieve the lighting effect corresponding to the natural language input. This disclosure constructs a complete, closed-loop intelligent control framework from natural language input to lighting equipment execution, realizing end-to-end natural language lighting effect generation without human intervention. Users only need to state their requests to automatically complete the entire process of intent understanding, decision scheduling, parameter generation, and equipment control. This fundamentally changes the traditional operation mode that relies on manual selection or combination of lighting effects, greatly improving the naturalness, convenience, and automation level of the interaction.

[0019] Other advantages of this disclosure will be explained in more detail in conjunction with the following description and accompanying drawings.

[0020] It should be understood that the above description is merely an overview of the technical solution of this disclosure, so as to enable a general understanding of the technical means of this disclosure and to implement it in accordance with the contents of the specification. In order to make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described in detail below with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the embodiments will be briefly described below. The accompanying drawings are incorporated in and constitute a part of this specification. These drawings illustrate embodiments conforming to this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure. It should be understood that the drawings only illustrate certain embodiments of this disclosure and should not be considered as a limitation on the scope of protection. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. Furthermore, the same reference numerals denote the same components throughout the drawings. In the drawings: Figure 1 A flowchart of an intelligent light effect generation method provided by an embodiment of this disclosure is shown; Figure 2 This diagram illustrates an application flowchart of the intelligent light effect generation method provided in an embodiment of the present disclosure; Figure 3 A schematic diagram of an intelligent light effect generation device provided in an embodiment of this disclosure is shown; Figure 4 A schematic diagram of an electronic device provided in an embodiment of this disclosure is shown. Detailed Implementation

[0022] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0023] In the description of embodiments disclosed herein, it should be understood that terms such as “comprising” or “having” are intended to indicate the presence of the disclosed features, figures, steps, behaviors, components, portions or combinations thereof in this specification, and do not exclude the possibility of the presence of one or more other features, figures, steps, behaviors, components, portions or combinations thereof.

[0024] Unless otherwise stated, " / " means "or". For example, A / B can mean A or B. In this article, "and / or" is merely a way of describing the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A alone, A and B at the same time, and B alone.

[0025] The terms "first," "second," etc., are used only for ease of description to distinguish identical or similar technical features and should not be construed as indicating or implying the relative importance or number of these technical features. Therefore, a feature defined by "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of embodiments of this disclosure, unless otherwise stated, the term "multiple" means two or more.

[0026] Research has revealed that current technologies rely on manual selection or combination of lighting effects from existing libraries to meet user needs. When users submit complex or personalized lighting effect requests via natural language, the system still requires manual intervention for effect matching and parameter setting, resulting in slow response times, high operational barriers, and limited user experience.

[0027] In order to at least partially solve one or more of the above-mentioned problems and other potential problems, this disclosure provides a method, apparatus, electronic device and storage medium for generating intelligent light effects, in order to solve the problems of existing technologies that rely on manual selection or combination of light effects and cannot intelligently understand and respond to natural language needs.

[0028] To facilitate understanding of this embodiment, a detailed description of the intelligent light effect generation method disclosed in this disclosure is provided first. The executing entity of the intelligent light effect generation method provided in this disclosure is generally an electronic device with a certain computing power. This electronic device may include, for example, a terminal device, a server, or other processing devices. The terminal device may be a user equipment (UE), a mobile device, a user terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), an in-vehicle device, a wearable device, etc. In some possible implementations, this intelligent light effect generation method can be implemented by a processor calling computer-readable instructions stored in memory.

[0029] See Figure 1 The diagram illustrates a flowchart of the intelligent light effect generation method provided in this embodiment, the method comprising the following steps S101-S104: S101: Receives user's lighting control requests input in natural language; S102: Input the lighting control requirements into the intent recognition model, and use the intent recognition model to perform semantic understanding and intent recognition on the lighting control requirements; S103: Based on the results of intent recognition, schedule instruction agents that match the intent type; S104: The instruction agent determines the corresponding lighting control parameters based on the preset parameter template to control the lighting equipment to achieve the lighting effect corresponding to the natural language input.

[0030] To facilitate understanding of the intelligent light effect generation method provided in this disclosure, the application scenarios of this method will be briefly introduced first. This method can be applied to various scenarios requiring light effect control, such as smart home central control, mobile applications (APP), and cloud service platforms. The following examples will primarily focus on smart homes.

[0031] Here, users can input their lighting control needs through voice assistants, the chat window of mobile apps, or other text / voice input interfaces. For example, "Make the lights softer when reading at night," "I want a dynamic color lighting mode for weekend parties," or "Turn on the lights automatically at eight o'clock in the morning."

[0032] The intent recognition model in this embodiment performs deep semantic understanding, which can extract lighting effect requirement features from the input request to obtain the user's intent type. Specifically, the intent type can be determined through the following steps: Step 1: Extract the lighting effect requirements from the lighting control needs; Step 2: Determine the intent type based on the characteristics of the lighting effect requirements; the intent type should include at least general lighting effect intent type and special lighting effect intent type.

[0033] In this embodiment, by actively extracting light effect demand features from the original input and distinguishing between general and specific intent types based on these features, the intent recognition process is determined to be an interpretable and directional feature-driven process. This improves the accuracy and reliability of intent classification, lays a solid foundation for subsequent precise scheduling, and avoids the confusion or errors that may result from a single model processing all requests.

[0034] In this embodiment of the disclosure, different intent types can be scheduled to be processed by different instruction agents, which can be determined according to the following steps: Step 1: Analyze at least one of the following characteristics in the vocabulary distribution of lighting control requirements: verb proportion, general vocabulary proportion, emotional vocabulary proportion, or scene vocabulary proportion. Step 2: Based on the analysis results, the lighting control requirements are distributed to the corresponding command agents for processing.

[0035] This section refines the feature analysis from multiple dimensions. By comprehensively considering various features such as vocabulary distribution, verbs, general words, emotional words, and scene words, a more detailed and comprehensive profile of user intent is achieved. Compared to methods relying on single keywords, this multi-feature fusion analysis method can more accurately distinguish between different granularities of needs, such as "turn up the lights" (a general command) and "create a romantic atmosphere" (a specific lighting effect), thereby achieving more precise agent-based traffic allocation and improving robustness in handling complex and ambiguous statements.

[0036] like Figure 2 As shown, the intent recognition model in this embodiment, such as the Large Language Model (LLM), can extract the lighting effect requirement features from the user's input lighting control request. Specifically, the model analyzes the lexical distribution of the statement (e.g., whether it contains verbs such as "turn up" or "automatic"), the proportion of general command-related words (e.g., on, off, brightness, color temperature), the proportion of emotional words (e.g., warm, romantic, excited), and the proportion of scene-related words (e.g., reading, party, getting up). Based on the comprehensive analysis of these features, the model determines the user's intent into at least one of two types: a general lighting effect intent type (corresponding to basic, single lighting control functions) or a special lighting effect intent type (corresponding to complex, composite scene-based lighting effects).

[0037] Here, based on the intent recognition results, a command agent matching the intent type is dispatched. If the intent type is identified as a general lighting effect, a general command agent can be dispatched. This agent specifically handles basic functions such as on / off control, timing control, brightness adjustment by percentage (e.g., default increase / decrease of 10%), and color temperature adjustment within the 2700K-6500K range. If the intent type is identified as a specific lighting effect, a specific command agent can be dispatched. This agent has a richer scene knowledge base to handle complex needs involving specific atmospheres, emotions, or dynamic changes, such as "cozy reading mode" or "festive light mode."

[0038] This disclosure achieves modularization of control logic by setting up general-purpose intelligent agents and dedicated intelligent agents with focused functions. The general-purpose intelligent agent efficiently processes standardized basic instructions, while the dedicated intelligent agent focuses on solving complex scenario-based requirements. This architecture not only improves the processing efficiency and accuracy of various tasks but also makes the system easy to expand and maintain; adding new functions only requires supplementing or updating the corresponding intelligent agent module, such as... Figure 2 As shown, control functions can be added or removed by adding or removing agents without affecting the overall architecture, significantly enhancing the system's scalability and flexibility.

[0039] In this embodiment of the disclosure, the scheduled instruction agent determines the corresponding lighting control parameters according to a preset parameter template, which can be achieved through the following steps: Step 1: Identify the lighting control function that matches the intent type; Step 2: Call the control tool corresponding to the light control function. The control tool includes at least one of the following: switch control tool, brightness adjustment tool, color adjustment tool, and color temperature adjustment tool. Step 3: Generate specific lighting control parameter values ​​based on the parameter template associated with the control tool.

[0040] Here, the scheduled agent first determines which specific control functions need to be invoked, then matches the corresponding control tools according to the functions, including but not limited to switch control tools, brightness adjustment tools, color adjustment tools, and color temperature adjustment tools, and finally obtains or calculates the specific parameter values ​​from the parameter template associated with the tool.

[0041] like Figure 2 As shown, if it's a switch control → select the ON / OFF tool; if it's brightness adjustment → select the brightness tool; if it's color adjustment → select the color tool; if it's color temperature adjustment → select the temperature tool.

[0042] Each control tool is associated with a preset parameter template. A parameter template is "a set of instruction sequences composed of on / off control commands and lighting parameter commands such as brightness adjustment." Based on the current needs, the agent instantiates the corresponding template and generates specific parameter values.

[0043] In practical applications, parameter templates are essentially predefined sets of instruction sequences composed of multiple basic control commands, used to support the automatic invocation and parameter generation of templated lighting effects. For example, if a user says, "Turn the brightness up a bit," the general instruction agent invokes the brightness adjustment tool, whose parameter template includes the rule "default increase / decrease of 10% at a time." Therefore, the agent generates the instruction: increase brightness by 10%. As another example, if a user says, "Turn on the lights automatically at 8 AM," the general instruction agent recognizes this as a combination of "timer" and "on / off" instructions. It invokes the corresponding tool and template to generate an instruction sequence containing "time parameter 08:00:00" and "on / off state ON."

[0044] This allows complex lighting effects such as "Cozy Reading" and "Holiday Party" to be predefined as an ordered combination of a series of basic instructions (such as specific color temperature, brightness, and gradient speed). This enables the template-based and standardized management of complex lighting effects. Thus, when a user submits a request, there's no need to calculate all parameters from scratch in real time; simply calling and instantiating the corresponding template sequence greatly improves response speed and ensures the stability and reusability of the lighting effects, achieving a balance between personalization and execution efficiency.

[0045] As can be seen, this embodiment of the disclosure transforms abstract intents into a concrete, executable sequence of operations through a three-step method: "function identification - tool and template invocation - parameter value generation." The advantage of this method lies in its high degree of structure and configurability. It encapsulates complex control logic within "tools" and "templates," making the parameter generation process clear and controllable, reducing reliance on the randomness of large model outputs, and ensuring the consistency and reliability of generated parameters. This is a key step in achieving stable and accurate control.

[0046] In this embodiment, after generating specific lighting control parameters, they can be encapsulated into structured data, such as standardized, unannotated JSON data. This JSON data is then sent to the execution end (such as a smart lighting controller app or directly to the hardware). The execution end parses the data and drives the lighting device to execute, thereby accurately achieving the lighting effect described by the user.

[0047] In addition, after the execution is completed, the APP can provide feedback to the user through AI chat window and other means, such as replying "Okay, the light brightness has been increased" or "Okay, the automatic light on at 8 am has been set for you".

[0048] The reason for this structured encapsulation is twofold: firstly, it facilitates format parsing, improving the maintainability and debugging convenience of the equipment; secondly, the standardized data format enhances compatibility with hardware devices of different brands and models, providing a technical foundation for building an open smart lighting ecosystem.

[0049] To further enhance the user experience, parameter validity can be verified before or after sending. For example, if the color temperature parameter value exceeds the allowable range of 2700K-6500K, an error message will be automatically sent and execution will be suspended to ensure device safety.

[0050] Here, by validating key parameters (such as color temperature) before execution, erroneous commands that may damage the device or affect the user experience can be proactively intercepted, improving security and reliability. Simultaneously, the automatic generation and return of error messages creates a closed-loop user feedback system, guiding users to provide valid input and enhancing the intelligence and user-friendliness of the interaction.

[0051] In the description of this specification, references to terms such as "some possible implementations," "some implementations," "example," "specific example," or "some examples" indicate that a specific feature, structure, material, or characteristic described in connection with that implementation or example is included in at least one implementation or example of this disclosure, and the aforementioned terms do not necessarily refer to the same implementation or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in a suitable manner in any one or more implementations or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different implementations or examples described in this specification, as well as the features of different implementations or examples.

[0052] Regarding the method flowcharts of embodiments of this disclosure, certain operations are described as different steps performed in a certain order. Such flowcharts are illustrative and not restrictive. Some steps described herein may be grouped together and performed in a single operation, or some steps may be divided into multiple sub-steps, and some steps may be performed in an order different from that shown herein. The various steps shown in the flowcharts may be implemented in any way by any circuit structure and / or tangible mechanism (e.g., software running on a computer device, hardware (e.g., logic functions implemented by a processor or chip), and / or any combination thereof).

[0053] Those skilled in the art will understand that in the methods described in the above specific embodiments, the order in which the steps are written does not imply a strict execution order, and the specific execution order of each step should be determined by its function and possible internal logic.

[0054] Based on the same inventive concept, this disclosure also provides an intelligent light effect generation device corresponding to the intelligent light effect generation method. Since the principle of the device in this disclosure for solving the problem is similar to the intelligent light effect generation method described above in this disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be described again.

[0055] Reference Figure 3 The diagram shown is a schematic of an intelligent light effect generation device provided in an embodiment of this disclosure. The device includes: a receiving module 201, an identification module 202, a scheduling module 203, and a control module 204; wherein, The receiving module 201 is used to receive the user's lighting control request input in natural language; The recognition module 202 is used to input the lighting control requirements into the intent recognition big model, and to perform semantic understanding and intent recognition on the lighting control requirements through the intent recognition big model; The scheduling module 203 is used to schedule instruction agents that match the intent type based on the result of intent recognition; The control module 204 is used by the instruction intelligent agent to determine the corresponding lighting control parameters according to the preset parameter template, so as to control the lighting equipment to achieve the lighting effect corresponding to the natural language input.

[0056] The intelligent light effect generation device provided in this disclosure receives light control requests input by users in natural language and inputs these requests into an intent recognition model. The model performs semantic understanding and intent recognition on the light control requests. Based on the intent recognition results, it schedules an intelligent agent matching the intent type. Finally, the intelligent agent determines the corresponding light control parameters according to a preset parameter template to control the lighting equipment to achieve the light effect corresponding to the natural language input. This disclosure constructs a complete, closed-loop intelligent control framework from natural language input to lighting equipment execution, realizing end-to-end natural language light effect generation without human intervention. Users only need to state their requests to automatically complete the entire process of intent understanding, decision scheduling, parameter generation, and equipment control. This fundamentally changes the traditional operation mode that relies on manual selection or combination of light effects, greatly improving the naturalness, convenience, and automation level of the interaction.

[0057] In one possible implementation, the recognition module 202 is specifically used to perform semantic understanding and intent recognition of lighting control requirements through an intent recognition big model via the following steps: Extract the characteristics of light effect requirements from lighting control needs; The intent type is determined based on the characteristics of the lighting effect requirements; the intent type includes at least general lighting effect intent type and special lighting effect intent type.

[0058] In one possible implementation, the identification module 202 is specifically used to determine the intent type based on the light effect demand characteristics through the following steps: Analyze at least one of the following characteristics in the vocabulary distribution of lighting control requirements: verb proportion, general vocabulary proportion, emotional vocabulary proportion, or scene vocabulary proportion. Based on the analysis results, the lighting control requests are routed to the corresponding command agents for processing.

[0059] In one possible implementation, the instruction agent includes at least a general instruction agent and a special instruction agent; Among them, the general instruction intelligent agent is used to handle at least one basic control function among switch control, timing control, brightness adjustment or color temperature adjustment; Specialized instruction agents are used to handle complex lighting effect requirements involving scene, emotion, or dynamic changes.

[0060] In one possible implementation, the control module 204 is specifically configured to determine the corresponding lighting control parameters based on a preset parameter template through the following steps: Identify lighting control functions that match the intent type; Call the control tool corresponding to the light control function. The control tool includes at least one of the following: switch control tool, brightness adjustment tool, color adjustment tool, and color temperature adjustment tool. Based on the parameter template associated with the control tool, specific lighting control parameter values ​​are generated.

[0061] In one possible implementation, the parameter template is a set of instruction sequences composed of multiple basic control instructions, used to support the automatic invocation and parameter generation of templated lighting effects.

[0062] In one possible implementation, the device further includes: The encapsulation module 205 is used to encapsulate the lighting control parameters into structured lighting control parameter data; and send the structured lighting control parameter data to the execution end, which drives the lighting equipment to execute.

[0063] In one possible implementation, the structured lighting control parameter data is in JSON format.

[0064] In one possible implementation, it also includes: The verification module 206 is used to verify the validity of the lighting control parameters; when the color temperature value in the lighting control parameters exceeds the preset range, an error message is generated and returned, and no control operation is performed.

[0065] It should be noted that the apparatus in this embodiment can implement the various processes of the aforementioned method and achieve the same effects and functions, which will not be elaborated here.

[0066] This disclosure also provides an electronic device, such as... Figure 4 The diagram shown is a schematic representation of an electronic device structure provided in this embodiment of the present disclosure, including: a processor 301, a memory 302, and a bus 303. The memory 302 stores machine-readable instructions executable by the processor 301 (e.g., ...). Figure 3 The device includes the receiving module 201, identification module 202, scheduling module 203, and control module 204 (and their corresponding execution instructions). When the electronic device is running, the processor 301 communicates with the memory 302 via the bus 303. When a machine-readable instruction is executed by the processor 301, the following processing is performed: Receive user requests for lighting control in natural language input; The lighting control request is input into the intent recognition model, which then performs semantic understanding and intent recognition on the lighting control request. Based on the results of intent recognition, schedule instruction agents that match the intent type; The intelligent agent determines the corresponding lighting control parameters based on the preset parameter template, so as to control the lighting equipment to achieve the lighting effect corresponding to the natural language input.

[0067] This disclosure also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the intelligent light effect generation method described in the above-described method embodiments. The storage medium can be a volatile or non-volatile computer-readable storage medium.

[0068] This disclosure also provides a computer program product carrying program code. The program code includes instructions that can be used to execute the steps of the intelligent light effect generation method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0069] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0070] The various embodiments in this disclosure are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments. In particular, the description of the apparatus, device, and computer-readable storage medium embodiments is simplified because they are basically similar to the method embodiments, and the relevant parts can be referred to the description of the method embodiments.

[0071] The apparatus, device, and computer-readable storage medium provided in this disclosure correspond one-to-one with the method. Therefore, the apparatus, device, and computer-readable storage medium also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the apparatus, device, and computer-readable storage medium will not be repeated here.

[0072] Those skilled in the art will understand that embodiments of this disclosure can be implemented as methods and apparatus (devices or systems), or as computer-readable storage media. Therefore, this disclosure can be implemented entirely in hardware, entirely in software, or in a combination of software and hardware. Furthermore, this disclosure can be implemented as a computer-readable storage medium on one or more computer-readable storage media containing computer-usable program code (including, but not limited to, disk storage, read-only optical disc storage (CD-ROM), optical storage, etc.).

[0073] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices or systems), and computer-readable storage media according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to create a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or block diagrams.

[0074] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article including instruction means, wherein the instruction means implement the functions specified in one or more flowcharts and / or one or more blocks in a block diagram.

[0075] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more blocks in the block diagram.

[0076] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] Memory can include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0078] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory, read-only memory, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. Furthermore, although the operations of the methods of this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the operations shown must be performed to achieve the desired result. Additionally, certain steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple sub-steps.

[0079] While the spirit and principles of this disclosure have been described above with reference to several specific embodiments, it should be understood that this disclosure is not limited to the disclosed specific embodiments, and the division of aspects does not imply that features in these aspects cannot be combined. This disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A method for generating intelligent light effects, characterized in that, include: Receive user requests for lighting control in natural language input; The lighting control request is input into the intent recognition model, and the intent recognition model performs semantic understanding and intent recognition on the lighting control request. Based on the results of intent recognition, schedule instruction agents that match the intent type; The intelligent agent determines the corresponding lighting control parameters based on the preset parameter template to control the lighting equipment to achieve the lighting effect corresponding to the natural language input.

2. The method according to claim 1, characterized in that, The process of semantic understanding and intent recognition of the lighting control request using the intent recognition big data model includes: Extract the luminous efficacy requirement features from the lighting control requirements; The intent type is determined based on the characteristics of the light effect requirements; the intent type includes at least a general light effect intent type and a special light effect intent type.

3. The method according to claim 2, characterized in that, The determination of intent type based on the light effect demand characteristics includes: Analyze at least one of the following characteristics in the vocabulary distribution, verb proportion, general vocabulary proportion, emotional vocabulary proportion, or scene vocabulary proportion in the lighting control requirements: Based on the analysis results, the lighting control requests are routed to the corresponding instruction agents for processing.

4. The method according to claim 3, characterized in that, The instruction agent includes at least a general instruction agent and a special instruction agent; The general instruction intelligent agent is used to process at least one basic control function among switch control, timing control, brightness adjustment or color temperature adjustment. The dedicated instruction agent is used to handle complex lighting effect requirements involving scene, emotion, or dynamic changes.

5. The method according to claim 1, characterized in that, The step of determining the corresponding lighting control parameters based on the preset parameter template includes: Identify the lighting control function that matches the stated intent type; Invoke the control tool corresponding to the light control function, the control tool including at least one of the following: switch control tool, brightness adjustment tool, color adjustment tool, and color temperature adjustment tool; Based on the parameter template associated with the control tool, specific lighting control parameter values ​​are generated.

6. The method according to claim 5, characterized in that, The parameter template is a set of instruction sequences composed of multiple basic control instructions, used to support the automatic invocation and parameter generation of templated light effects.

7. The method according to claim 1, characterized in that, The method further includes: The lighting control parameters are encapsulated into structured lighting control parameter data; The structured lighting control parameter data is sent to the execution terminal, which then drives the lighting equipment to perform the operation.

8. The method according to claim 7, characterized in that, The structured lighting control parameter data is in JSON format.

9. The method according to any one of claims 1 to 8, characterized in that, Also includes: The validity of the lighting control parameters is verified. When the color temperature value in the lighting control parameters exceeds the preset range, an error message is generated and returned, and no control operation is performed.

10. A smart light effect generating device, characterized in that, include: The receiving module is used to receive the user's lighting control requests input in natural language; The recognition module is used to input the lighting control request into the intent recognition model, and to perform semantic understanding and intent recognition on the lighting control request through the intent recognition model. The scheduling module is used to schedule instruction agents that match the intent type based on the intent recognition results. The control module is used by the instruction agent to determine the corresponding lighting control parameters according to the preset parameter template, so as to control the lighting equipment to achieve the lighting effect corresponding to the natural language input.

11. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the intelligent light effect generation method as described in any one of claims 1 to 9 is performed.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the intelligent light effect generation method as described in any one of claims 1 to 9.

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