Screen brightness adjustment method and apparatus, electronic device, medium and product

Abstract

A screen brightness adjustment method and apparatus, an electronic device (500), a storage medium, and a product. The method comprises: during running of an application program, acquiring brightness adjustment reference information associated with brightness adjustment of a target screen (S110); separately performing information feature processing on information items of each information dimension in the brightness adjustment reference information to obtain corresponding information feature vectors (S120); on the basis of the information items of each information dimension in the brightness adjustment reference information, determining a target multi-modal animated graphic (S130); and inputting the information feature vectors and graph structural features of the target multi-modal animated graphic into a pre-trained screen brightness adjustment prediction model to obtain a corresponding model prediction result, and adjusting the brightness of the target screen on the basis of the model prediction result (S140). The present application adjusts the screen brightness in view of multi-dimensional information, thereby meeting personalized screen use requirements of users.

Description

Screen brightness adjustment method and device, electronic equipment, medium and product

[0001] Cross-reference to Related Applications

[0002] This application claims priority to Chinese Patent Application No. 202411367571.9, filed September 27, 2024, the disclosure of which is incorporated herein in its entirety as part of the present application. TECHNICAL FIELD

[0003] Embodiments of the present disclosure relate to a screen brightness adjustment method, device, electronic equipment, medium and product. BACKGROUND

[0004] In the process of application of intelligent devices, the brightness of the screen affects the user's experience. The brightness of the display screen can be automatically adjusted according to the ambient brightness, or manually adjusted by the user.

[0005] At present, in the process of automatic adjustment of the brightness of the display screen, only the ambient brightness factor or other single factor is considered, the screen brightness adjustment rule is relatively simple, and multiple dimensions of brightness adjustment influencing factors cannot be comprehensively considered, which cannot meet the personalized needs of users. SUMMARY

[0006] The present disclosure provides a screen brightness adjustment method, device, electronic equipment, medium and product, which can analyze based on multiple dimensions of brightness adjustment reference information to determine a screen brightness adjustment strategy, so that the screen brightness adjustment result meets the user's use demand.

[0007] In a first aspect, embodiments of the present disclosure provide a screen brightness adjustment method, which comprises:

[0008] In the process of running an application program, brightness adjustment reference information associated with the brightness adjustment of a target screen is obtained; wherein the target screen is the display screen of a terminal device running the application program;

[0009] The information items in each information dimension of the brightness adjustment reference information are respectively processed for information features to obtain corresponding information feature vectors;

[0010] A target multi-modal dynamic graph is determined based on the information items in each information dimension of the brightness adjustment reference information; wherein the target multi-modal dynamic graph is used to represent the association relationship between the brightness adjustment reference information;

[0011] The information feature vectors and the graph structure features of the target multi-modal dynamic graph are input into a pre-trained screen brightness adjustment prediction model to obtain corresponding model prediction results, and the brightness of the target screen is adjusted according to the model prediction results.

[0012] In a second aspect, the present disclosure also provides a screen brightness adjustment apparatus, which comprises:

[0013] a data acquisition module, configured to acquire brightness adjustment reference information associated with brightness adjustment of a target screen during running of an application program, wherein the target screen is a display screen of a terminal device running the application program;

[0014] a first data processing module, configured to perform information feature processing on each information item in each information dimension of the brightness adjustment reference information, to obtain a corresponding information feature vector;

[0015] a second data processing module, configured to determine a target multi-modal dynamic graph based on each information item in each information dimension of the brightness adjustment reference information, wherein the target multi-modal dynamic graph is used to represent an association relationship between the brightness adjustment reference information;

[0016] a data analysis and brightness adjustment module, configured to input the information feature vector and a graph structure feature of the target multi-modal dynamic graph into a pre-trained screen brightness adjustment prediction model, to obtain a corresponding model prediction result, and to adjust brightness of the target screen according to the model prediction result.

[0017] In a third aspect, the present disclosure also provides an electronic device, which comprises:

[0018] one or more processors;

[0019] a storage device, configured to store one or more programs,

[0020] when the one or more programs are executed by the one or more processors, the one or more processors implement the screen brightness adjustment method according to any of the embodiments of the present disclosure.

[0021] In a fourth aspect, the present disclosure also provides a storage medium containing computer executable instructions, which, when executed by a computer processor, are used to perform the screen brightness adjustment method according to any of the embodiments of the present disclosure.

[0022] In a fifth aspect, the present disclosure also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the screen brightness adjustment method according to any of the embodiments of the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0023] The above and other features, advantages, and aspects of the present disclosure will become more apparent by describing in detail some embodiments thereof with reference to the annexed drawings in which:

[0024] FIG. 1 is a flow diagram of a method for adjusting screen brightness according to an embodiment of the present disclosure;

[0025] FIG. 2 is a flow diagram of a method for adjusting screen brightness according to an embodiment of the present disclosure;

[0026] FIG. 3 is a flow diagram of a method for adjusting screen brightness according to an embodiment of the present disclosure;

[0027] FIG. 4 is a schematic diagram of an application example of a method for adjusting screen brightness according to an embodiment of the present disclosure;

[0028] FIG. 5 is a schematic diagram of a structure of a device for adjusting screen brightness according to an embodiment of the present disclosure;

[0029] FIG. 6 is a schematic diagram of a structure of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] Embodiments of the present disclosure will be described in more detail with reference to the drawings. While certain embodiments of the present disclosure are shown in the drawings, it is understood that the present disclosure can be embodied in various forms and should not be interpreted as being limited to the embodiments set forth herein; rather, these embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present disclosure are only for illustrative purposes and are not intended to limit the scope of protection of the present disclosure.

[0031] It should be understood that the various steps in the method embodiments of the present disclosure can be performed in different orders and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0032] The term "comprising" and variations thereof as used herein are used inclusively, i.e., "comprising, but not limited to." The term "based on" is "based, at least in part, on." The term "one embodiment" means "at least one embodiment." The term "another embodiment" means "at least one additional embodiment." The term "some embodiments" means "at least some embodiments." Related definitions of other terms will be given in the description below.

[0033] It should be noted that the terms "first", "second", and the like in the present disclosure are merely used to distinguish different devices, modules or units, and do not imply the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the terms "one", "multiple" in the present disclosure are illustrative and not limiting, and those skilled in the art should understand that, unless otherwise explicitly stated in the context, it should be understood as "one or more".

[0035] It can be understood that, before using the technical solutions disclosed in the embodiments of the present disclosure, the type of personal information involved in the present disclosure, the use range, the use scenario, etc. should be informed to the user and the authorization of the user should be obtained through appropriate means according to relevant laws and regulations.

[0036] For example, in response to receiving the active request of the user, the prompt information is sent to the user to explicitly prompt the user that the operation requested to be performed will require obtaining and using the personal information of the user. Thus, the user can voluntarily choose whether to provide the personal information to the software or hardware such as the electronic device, the application program, the server or the storage medium, etc. performing the operation of the technical solutions of the present disclosure according to the prompt information.

[0037] As an optional but non-limiting implementation manner, in response to receiving the active request of the user, the prompt information can be sent to the user in the form of a pop-up window, and the prompt information can be presented in the form of text in the pop-up window. In addition, the pop-up window can also carry the selection control for the user to select “agree” or “disagree” to provide the personal information to the electronic device.

[0038] It can be understood that the above notification and obtaining of the authorization of the user are only illustrative, and do not limit the implementation manners of the present disclosure, and other manners meeting the relevant laws and regulations can also be applied to the implementation manners of the present disclosure.

[0039] It can be understood that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solutions should comply with the requirements of the relevant laws and regulations and the relevant provisions.

[0040] FIG. 1 is a flow diagram of a screen brightness adjustment method provided by an embodiment of the present disclosure. The embodiment of the present disclosure is applicable to the scenario of automatically adjusting the brightness of the display screen during the use of an application program. The method can be executed by a screen brightness adjustment apparatus, which can be implemented in the form of software and / or hardware. Optionally, the screen brightness adjustment apparatus can be implemented by an electronic device, which can be a mobile terminal, a PC terminal or a server, etc.

[0041] As shown in FIG. 1, the screen brightness adjustment method comprises:

[0042] S110, obtaining brightness adjustment reference information associated with the brightness adjustment of a target screen during the running of an application program; wherein the target screen is the display screen of a terminal device running the application program.

[0043] When an application program relies on a terminal device to run, the terminal device can display the information content provided by the application program through the screen. For example, the application program with the video playing function plays the video content through the screen, or displays any form of information through the screen.

[0044] The brightness of the target screen of the terminal device running the application program usually affects the experience and / or duration of the user browsing information.

[0045] The target screen is the display screen of the terminal device running the application program, and the brightness adjustment reference information includes multi-dimensional and multi-modal information. In different information dimensions, multiple information items can be included. For example, multiple information items corresponding to the terminal device dimension, information items corresponding to the environment dimension used by the target screen, information items corresponding to the user's brightness setting or adjustment record of the target screen, information items corresponding to the content of the target screen, and information items corresponding to the screen usage state dimension, etc.

[0046] The brightness adjustment reference information associated with the brightness adjustment process of the target screen can be continuously collected in the process of running the application program, and the brightness adjustment reference information for the current brightness adjustment analysis can be obtained through a sliding time window. The speed of the sliding time window can reflect the time granularity of the screen brightness adjustment analysis, which can be a second-level analysis frequency or a minute-level analysis frequency. The preset length of the sliding time window can be set according to the analysis requirements.

[0047] S120, information feature processing is performed on each information item in each information dimension of the brightness adjustment reference information to obtain a corresponding information feature vector.

[0048] The information feature processing is performed on each information item in each information dimension of the brightness adjustment reference information in order to enable the neural network model to recognize, understand and analyze the brightness adjustment reference information.

[0049] For different modal brightness adjustment reference information, an information feature processing method matched with the corresponding information modal can be used. For example, for image information, pixel analysis can be used for feature extraction, or a neural network model can be used for image feature extraction; for a series of time-related sequence data, a model capable of extracting time dimension features (such as a long short-term memory neural network) can be used; or a mathematical method such as mathematical statistics and modeling can be used for information feature calculation.

[0050] S130, a target multi-modal dynamic graph is determined based on each information item in each information dimension of the brightness adjustment reference information, wherein the target multi-modal dynamic graph is used to represent the correlation between the brightness adjustment reference information.

[0051] The target multi-modal dynamic graph is used to represent the correlation between the brightness adjustment reference information, and can reflect the importance of different graph nodes to the screen brightness adjustment through the graph structure features, which serves as an information reference for the neural network model to analyze the brightness adjustment reference information.

[0052] The target multi-modal dynamic graph includes graph nodes and edges, where the edges are connections between the graph nodes. Each graph node corresponds to an information dimension of the brightness adjustment reference information. According to the division of the information dimensions, the target multi-modal dynamic graph contains several information dimensions, and the target multi-modal dynamic graph contains several graph nodes. Further, an edge between the corresponding graph nodes can be established based on the association relationship between the information items of the information dimensions corresponding to the graph nodes.

[0053] For example, the brightness adjustment reference information corresponding to a certain time point, the environmental information indicates that the target screen uses the environment with low brightness, and the user brightness adjustment operation is to reduce the brightness amplitude by 15%, which indicates that the graph node corresponding to the environmental information dimension and the graph node corresponding to the user operation record dimension. The rule for identifying the association relationship between the graph nodes can be a pre-set logical judgment rule. After the graph nodes and the edges between the graph nodes are determined, the corresponding target multi-modal dynamic graph can be obtained to indicate the target multi-modal dynamic graph that meets the user's current screen state when the screen brightness adjustment analysis is performed.

[0054] It should be noted that S120 and S130 do not have strict sequence requirements, and the brightness adjustment reference information can be processed synchronously. The target and method of data processing between the two are different.

[0055] S140, input the information feature vector and the graph structure feature of the target multi-modal dynamic graph into a pre-trained screen brightness adjustment prediction model to obtain a corresponding model prediction result, and adjust the brightness of the target screen according to the model prediction result.

[0056] The graph structure feature of the target multi-modal dynamic graph can be a description feature of the corresponding nodes and edges in the brightness adjustment influence factor relationship graph, including at least one of the node centrality, the path distance corresponding to the edge, the node weight, and the weight of the edge.

[0057] The information feature vector and the graph structure feature of the target multi-modal dynamic graph are input into a pre-trained screen brightness adjustment prediction model to obtain a corresponding model prediction result.

[0058] The screen brightness adjustment prediction model is trained based on a large number of multi-dimensional, multi-modal information samples related to screen brightness adjustment collected in screen use experience or experiments. The learning process of the screen brightness adjustment prediction model is based on the information feature vector of the information samples related to screen brightness adjustment and the corresponding multi-modal dynamic graph. In this process, multi-dimensional features are involved, and the trained model has strong generalization ability and stability.

[0059] The information feature vector and the graph structure feature in the updated brightness adjustment influencing factor relationship graph are input into a pre-trained application program use time length prediction model, and a model prediction result obtained can be a brightness adjustment amplitude prediction result for performing screen brightness adjustment.

[0060] In this embodiment, brightness adjustment reference information associated with brightness adjustment of a target screen is acquired in the process of running an application program, wherein the target screen is a display screen of a terminal device running the application program; information items in each information dimension of the brightness adjustment reference information are respectively subjected to information feature processing to obtain corresponding information feature vectors; a target multi-modal dynamic graph is determined based on the information items in each information dimension of the brightness adjustment reference information, wherein the target multi-modal dynamic graph is used to represent the association relationship between the brightness adjustment reference information; the information feature vectors and the graph structure feature of the target multi-modal dynamic graph are input into a pre-trained screen brightness adjustment prediction model to obtain corresponding model prediction results, and the brightness of the target screen is adjusted according to the model prediction results. The technical scheme of the embodiment of the disclosure solves the problem of single condition parameter for display screen brightness adjustment in some embodiments and lacks individualization, and realizes screen brightness adjustment with more comprehensive condition parameters, that is, analysis based on multi-dimensional brightness adjustment reference information to determine a screen brightness adjustment strategy, so that the screen brightness adjustment result better meets the use demand of a user.

[0061] FIG. 2 is a flowchart of a screen brightness adjustment method provided by an embodiment of the disclosure, which further explains the process of automatic analysis and adjustment of display screen brightness adjustment on the basis of the above-mentioned embodiment. The method can be performed by a screen brightness adjustment device, which can be implemented in the form of software and / or hardware, and can be implemented by an electronic device, which can be a mobile terminal, a PC terminal or a server, etc.

[0062] As shown in FIG. 2, the screen brightness adjustment method comprises the following steps.

[0063] S210, in the process of running an application program, brightness adjustment reference information associated with brightness adjustment of a target screen is acquired, wherein the target screen is a display screen of a terminal device running the application program.

[0064] In an optional implementation, the brightness adjustment reference information can include brightness adjustment record sequence information for brightness adjustment of the target screen, device information of the terminal device, environment feature information of an environment where the terminal device is located, picture feature information of a display picture of the target screen and use state information of the target screen.

[0065] The brightness adjustment record sequence information can be a regular manual brightness adjustment value, a specific numerical value of screen brightness set by a user through manual operation; an automatic brightness adjustment value based on ambient light, a brightness numerical value automatically adjusted by the device according to the intensity of ambient light; a brightness adjustment value in a specific application scenario, for example, a brightness value adjusted by a user or automatically adjusted by the system in different scenarios such as reading applications, game applications, video playback applications, and the like; a brightness adjustment value in different time periods, such as brightness settings in different time periods such as daytime, nighttime, morning, afternoon, and the like; a brightness adjustment value in different geographic locations, such as brightness values set in different places (such as indoors, outdoors, different cities, and the like); and a brightness adjustment value in different use scenarios (such as work, entertainment, learning, and the like).

[0066] The device information of the terminal device can be an energy consumption mode, including different device energy consumption settings such as an energy-saving mode and a high-performance mode; a battery health degree, such as reflecting the remaining life of the battery, the number of charging cycles, the capacity attenuation of the battery, and the like; a screen aging index, measuring the degree of decline in screen display quality over time, such as changes in brightness uniformity, color deviation, and the like; a screen resolution, such as different resolution settings such as high definition, full high definition, and ultra-high definition; a screen refresh rate, commonly including different refresh rates such as 60 Hz, 90 Hz, and 120 Hz; and a screen color rendering capability evaluation, including color accuracy, color gamut coverage (such as sRGB, Adobe RGB, and the like), contrast, and the like.

[0067] The environmental feature information of the environment in which the terminal device is located can be an ambient light level, such as measuring the intensity of ambient light, including different degrees of strong light, weak light, and medium light.

[0068] The picture feature information of the display picture of the target screen can include a video picture average brightness value, a dark area proportion, a color vividness, a video frame rate, a picture definition, and a video encoding format influence coefficient. Among them, the video picture average brightness value represents the average brightness level of the entire video picture; the dark area proportion represents the proportion of the darker part in the picture; the color vividness represents the richness and brightness of the color of the video picture; the video frame rate represents the number of video frames displayed per second, such as 24 frames, 30 frames, 60 frames, and the like; the picture definition includes the sharpness and detail performance of the image; the video encoding format influence coefficient includes the influence degree of different video encoding formats (such as H.264, H.265, and the like) on the picture quality; and the picture dynamic range includes high dynamic range (HDR) and standard dynamic range (SDR).

[0069] The use state information of the target screen includes the frequency and intensity of screen operation in a specific time period, the duration of using the eye protection mode, the duration of continuous use of the same application, the habit preference for screen brightness adjustment, user behavior posture, and the like. Among them, the frequency and intensity of screen operation in a specific time period, for example, can be the number of times of clicking the screen and the intensity of clicking in an hour. The duration of using the eye protection mode is the duration of turning on the eye protection mode. The duration of continuous use of the same application indicates the length of time of continuously using a certain specific application (such as a social media application, a game application, and the like). An exemplary habit preference for screen brightness adjustment can be a tendency to always prefer a higher or lower brightness. The user behavior posture can be the posture of the handheld device (landscape, portrait), the distance of watching the screen, and the like, and whether the device is used in movement (such as walking, taking a vehicle, and the like).

[0070] S220, information feature processing is performed on each information item in each information dimension in the brightness adjustment reference information respectively to obtain a corresponding information feature vector.

[0071] For the information item content of each information dimension, information feature processing can be performed in the following manner.

[0072] For information feature processing of the picture feature information of the display picture of the target screen, image decomposition can be performed on the display picture to obtain a plurality of different sub-band images of preset dimensions and frequencies, and the sub-band images are input into a preset convolutional neural network for image feature extraction to obtain an information feature vector corresponding to the picture feature information of the display picture of the target screen. Based on a bidirectional long short-term memory network, the brightness adjustment record sequence information and the use state information can be encoded respectively to obtain information feature vectors corresponding to the brightness adjustment record sequence information and the use state information. Based on an autoencoder, the device information and the environmental feature information can be encoded respectively to obtain information feature vectors corresponding to the device information and the environmental feature information.

[0073] S230, according to the association relationship between the information items in each information dimension in the brightness adjustment reference information, the node degree of the graph node of each information dimension pair, the clustering coefficient and the path length between the nodes are determined.

[0074] Each information dimension corresponds to a graph node, and edges between the corresponding graph nodes can be established according to the association relationship between each information item in the brightness adjustment reference information, and then the node degree corresponding to the node and the clustering coefficient and the path length between the nodes can be determined.

[0075] The node degree refers to the number of edges connected to a node. It reflects the degree of connection of the node in the network. Nodes with high node degree usually have a more important position in the network because they have direct contact with more nodes.

[0076] The clustering coefficient between nodes measures the degree of aggregation of nodes in the network. It represents the ratio of the actual number of edges between the neighbors of a node to the maximum number of edges that can exist. A high clustering coefficient means that the neighbors of a node have a high density of connections, and the network exhibits a clustered structure.

[0077] The path length refers to the number of edges on the shortest path between two nodes. The average path length is the average of the path lengths between all pairs of nodes in the network. A shorter path length means that information can propagate faster in the network.

[0078] S240, according to the data relationship between the node degree, clustering coefficient and path length and the reference node degree, reference clustering coefficient and reference path length in the preset reference multimodal dynamic graph, determine the node weight of the corresponding graph node and the edge weight corresponding to the connection line between the graph nodes, and obtain the target multimodal dynamic graph.

[0079] Among them, the preset reference multimodal dynamic graph can be a reference information obtained by analyzing sample data based on the training screen brightness adjustment prediction model, reflecting the relationship and importance between various factors in the screen brightness adjustment process. Each graph node in the preset reference multimodal dynamic graph has a corresponding reference node weight calculated and valued, and the edges between nodes have reference edge weights.

[0080] In the application process of the screen brightness adjustment method, personalized brightness adjustment reference information can be obtained according to the current screen use state of the current user, and then the target multimodal dynamic graph corresponding to the current screen use state of the current user is updated, which can adapt to environmental or scene changes in real time, and more accurately analyze the brightness adjustment reference information for screen brightness modulation.

[0081] According to the data relationship between the node degree, clustering coefficient and path length and the reference node degree, reference clustering coefficient and reference path length in the preset reference multimodal dynamic graph, the graph node weight of the corresponding graph node and / or the edge weight corresponding to the connection line between the nodes can be increased or decreased, that is, the target multimodal dynamic graph can be determined. For example, if the node degree of a certain graph node increases compared to the reference node degree, the node weight of the corresponding node can be increased accordingly.

[0082] S250, input the information feature vector and the graph structure feature of the target multimodal dynamic graph into the screen brightness adjustment prediction model pre-trained, and obtain the corresponding model prediction result.

[0083] The information feature vector and the graph structure feature in the updated brightness adjustment influence factor relationship graph are input into the application program use time length prediction model or the screen brightness adjustment prediction model pre-trained, and the corresponding model prediction result is obtained.

[0084] S260, when the model prediction result is the use time length prediction result of the application program, determining a target screen brightness corresponding to the model prediction result according to an application use time length-screen brightness mapping relationship, and adjusting the brightness of the target screen to the target screen brightness; and when the model prediction result is the screen brightness adjustment amplitude prediction result, increasing or decreasing the brightness of the target screen by the brightness adjustment amplitude corresponding to the model prediction result.

[0085] The application use time length-screen brightness mapping relationship can be determined based on statistical values, and the corresponding relationship between the application use time length and the screen brightness value.

[0086] The technical solution of the embodiments of the present disclosure obtains the brightness adjustment reference information associated with the brightness adjustment of the target screen in the process of running the application program, wherein the target screen is the display screen of the terminal device running the application program; respectively processes the information items in each information dimension in the brightness adjustment reference information to obtain the corresponding information feature vectors; determines the node degree of the graph node, the clustering coefficient and the path length between nodes of each information dimension pair according to the association relationship between the information items in each information dimension in the brightness adjustment reference information; determines the node weight of the corresponding graph node and the edge weight corresponding to the connection line between graph nodes according to the data relationship between the node degree, the clustering coefficient and the path length and the reference node degree, the reference clustering coefficient and the reference path length in the preset reference multi-modal dynamic graph, to obtain the target multi-modal dynamic graph; inputs the information feature vectors and the graph structure features of the target multi-modal dynamic graph into the screen brightness adjustment prediction model pre-trained to obtain the corresponding model prediction result; when the model prediction result is the use time length prediction result of the application program, determines the target screen brightness corresponding to the model prediction result according to the application use time length-screen brightness mapping relationship, and adjusts the brightness of the target screen to the target screen brightness; and when the model prediction result is the screen brightness adjustment amplitude prediction result, increases or decreases the brightness of the target screen by the brightness adjustment amplitude corresponding to the model prediction result. The technical solution of the embodiments of the present disclosure solves the problem of single condition parameter of display screen brightness adjustment in some embodiments and lacks individualization, realizes screen brightness adjustment with more comprehensive condition parameters, i.e., analyzes the brightness adjustment reference information in multiple dimensions to determine the screen brightness adjustment strategy, so that the screen brightness adjustment result better meets the use demand of the user.

[0087] FIG. 3 is a flowchart of a screen brightness adjustment method according to an embodiment of the present disclosure. The method can be performed by a screen brightness adjustment device. The device can be implemented in the form of software and / or hardware. Optionally, the device can be implemented by an electronic device, such as a mobile terminal, a PC terminal, or a server.

[0088] As shown in FIG. 3, the screen brightness adjustment method includes the following steps.

[0089] In S310, brightness adjustment reference information sample data is obtained, and a graph node is set for each information dimension in the brightness adjustment reference information sample data. Connection lines between corresponding graph nodes are determined based on the data correlation between each information dimension, and an initial multi-modal dynamic graph is obtained.

[0090] In S310, brightness adjustment reference information sample data is obtained, and a graph node is set for each information dimension in the brightness adjustment reference information sample data. Connection lines between corresponding graph nodes are determined based on the data correlation between each information dimension, and an initial multi-modal dynamic graph is obtained.

[0091] The brightness adjustment reference information sample data includes data of different dimensions that affect screen brightness adjustment, such as terminal device identification information running a specified application, environment information when running the specified application, and user screen brightness operation information. The brightness adjustment reference information sample data can be sample data collected under a preset application usage condition, for example, various brightness adjustment reference information sample data collected under a set running time condition of the application. Alternatively, the information includes the amplitude of the user's screen brightness adjustment under different application running conditions.

[0092] According to the brightness adjustment reference information sample data, an initial multi-modal dynamic graph can also be constructed to represent and process the complex relationship between multi-modal data, so as to represent the relationship between the brightness adjustment influencing factors. The nodes in the initial multi-modal dynamic graph can be determined according to the dimensions of the brightness adjustment reference information sample data. For example, a device feature node represents device-related information, such as screen type, battery status, and the like. An environmental data node includes environmental factors such as light intensity and temperature. A user behavior node reflects user interaction with the device, such as screen brightness adjustment, application usage frequency, and the like. The connection between nodes can be established based on the correlation of the data. For example, there can be a direct correlation between environmental light intensity and user brightness adjustment behavior. According to the nodes and the connection relationship between the nodes, the initial multi-modal dynamic graph can be obtained.

[0093] The initial multi-modal dynamic graph can represent the relationship state between different graph nodes based on sample data statistical analysis. The graph node weights can be dynamically assigned based on the data contribution and influence of each graph node in the initial multi-modal dynamic graph. For example, if a certain environment node frequently affects a user behavior node, the number of edges between the two nodes is relatively large, and the node weight will be increased, and the edge weight will also be increased to emphasize its importance in subsequent model learning.

[0094] S320, denoising the connection relationship between the graph nodes in the initial multi-modal dynamic graph to obtain a reference multi-modal dynamic graph.

[0095] Denoising the connection relationship between the graph nodes in the initial multi-modal dynamic graph to obtain a reference multi-modal dynamic graph.

[0096] In order to improve the accuracy of data processing, so as to improve the robustness of the model obtained by training in the complex model. Specifically, in the process of denoising, two denoising processes can be performed in sequence to optimize and clean the connection relationship between nodes in the dynamic graph.

[0097] First, preliminary denoising can be performed, for example, at least one statistical parameter of the connection relationship between the graph nodes in the initial multi-modal dynamic graph is calculated, and the abnormal connection relationship indicated by the statistical parameter is removed. The initial multi-modal dynamic graph with the abnormal connection relationship removed is taken as the reference multi-modal dynamic graph. Then, deep denoising can be performed, and the initial multi-modal dynamic graph with the abnormal connection removed can be input into a graph neural network based on an attention mechanism to obtain a connection edge weight corresponding to the connection relationship between the graph nodes. Further, the multi-modal dynamic graph with the abnormal connection removed is taken as an updated reference multi-modal dynamic graph, and the reference node weight and the reference edge weight of the connection line between the graph nodes in the reference multi-modal dynamic graph.

[0098] In the preliminary denoising process, the goal is to identify and remove noise connections that may be caused by measurement errors, data transmission errors, or external interference. Statistical parameters such as mean, standard deviation, and proportion of outliers can be calculated for each connection. Threshold methods such as Z-score or IQR method are used to identify and remove statistically significant abnormal connections.

[0099] In the deep denoising process, the goal is to adjust and optimize the weights of the remaining connections. Specifically, an attention mechanism can be introduced in the graph neural network to dynamically adjust the weights of the connections between nodes. The attention weight is determined by the importance and contribution of the connection, and can be normalized by a softmax function:

[0100] Here, `score(i,j)` is a scoring function that can be calculated using a small neural network based on connectivity features. The optimization objective is to minimize the connectivity error after denoising, while maintaining the integrity and representativeness of the data. This can be achieved using a loss function that includes a connectivity error term and a regularization term. Among them, y ij It is the actual connection strength. θ represents the connection strength predicted by the model, θ represents the model parameters, and λ represents the regularization parameter.

[0101] S330. Input the information feature vector corresponding to the brightness adjustment reference information sample data and the graph structure features of the benchmark multimodal animation into the model to be trained to obtain the model output results.

[0102] The information feature vector corresponding to the brightness adjustment reference information sample data and the graph structure feature corresponding to the updated baseline brightness adjustment influencing factor relationship graph are input into the model to be trained to obtain the model output result.

[0103] The graph structure features can be at least one of the following: node weights in the updated baseline brightness adjustment influencing factor relationship graph, weights of connecting edges between nodes, node centrality, node clustering coefficient, and average path length between nodes.

[0104] The model to be trained is a neural network model based on a multi-head attention mechanism, which includes an attention mechanism for the edge weight information of the connection lines between graph nodes in the benchmark multimodal animation graph, such as the Edge-Informed Attention strategy.

[0105] The Edge-Informed Attention strategy incorporates information from the edges between nodes. These edges represent the importance of the nodes themselves, as well as the strength of the relationships and interactions between them. In this way, the model being trained not only learns the importance of the features themselves but also understands the importance of the relationships between different nodes, thus better capturing and representing complex data structures.

[0106] Traditionally, attention scores are calculated using transformations of three vectors: query, key, and value. Edge-Informed Attention, however, multiplies the similarity between the query and key by the weights of the corresponding edges.

[0107] Specifically, if the edge weight between node i and node j is ω ij Then their attention scores Attention(Q) i ,K j) will be adjusted to ω ij *Attention(Q i ,K j ).

[0108] In addition, in some cases, direct connections between nodes may not be sufficient to express the full relationship between them, and the path between nodes may also contain important information. The path-dependent graph attention mechanism considers the indirect relationship between nodes connected by different paths. First, the key paths (such as the shortest path, the strongest path, etc.) between nodes in the graph can be identified for path discovery. Then, path weight calculation is performed, for example, the weight of each path is calculated based on factors such as the weight and length of the edges on the path. Finally, attention score adjustment is performed, and the influence of indirect connection through direct edges and key paths is considered when calculating the attention score between nodes.

[0109] S340, based on the model output result, the sample label data corresponding to the brightness adjustment reference information sample data, and the graph structure feature, the model parameter is updated to obtain a screen brightness adjustment prediction model.

[0110] Based on the model output result, the sample label data corresponding to the brightness adjustment reference information sample data, and the graph structure feature, the model parameter is updated to obtain an application usage time length prediction model and / or a screen brightness adjustment prediction model.

[0111] The sample label data is application usage time length data and / or brightness adjustment amplitude data corresponding to the brightness adjustment reference information sample data.

[0112] The sample label data is application usage time length data and / or actual brightness adjustment amplitude data corresponding to the brightness adjustment reference information sample data. The output result of the model trained based on different sample labels is also different. The application usage time length data is an indirect user satisfaction feedback, which can be used to infer the satisfaction degree of the brightness setting through the application usage time length or the adjustment frequency, etc. as a label. The actual brightness adjustment amplitude data is a direct user feedback during the use of the application, which is recorded as a label during the sample data collection process.

[0113] Specifically, based on the model output result and the sample label data corresponding to the brightness adjustment reference information sample data, and the graph structure feature, the model parameter updating process can calculate the model learning loss according to the model output result and the sample label data corresponding to the brightness adjustment reference information sample data; determine the regularization loss according to at least one feature data in the graph structure feature; and update the gradient of the parameters of the model to be trained according to the model learning loss and the regularization loss.

[0114] The regularization loss utilizes the topological structure information of a dynamic graph to constrain the learning process. This regularization method helps the model optimize the accuracy of feature representation and fusion effect while maintaining the data structure attributes. The core idea of ​​structural regularization is to use the graph's structural information as additional guiding information to guide the model's learning process. This method helps the model better understand and utilize the relationships between nodes, thereby improving the accuracy of feature representation and the effect of data fusion. This regularization term is a function based on structural features; for example, the inverse of the node degree centrality can be used as the regularization strength (using the inverse of the node degree centrality as the regularization strength is actually to reduce the model's over-reliance on highly central nodes. In many cases, highly central nodes may receive too much attention in the model, thus masking the contributions of other nodes). During model training, optimization algorithms such as gradient descent are combined with the structural regularization term to continuously adjust the model parameters to minimize the total loss. Structural regularization helps the model being trained to focus not only on the representation of the data but also on the data's internal structure during the learning process, thereby achieving better generalization results. By introducing graph structural information, the model can consider the inherent connections and dependencies of the data during training, thereby improving generalization ability. Structural regularization can reduce the risk of model overfitting, enabling the model to maintain stable performance across different datasets. It makes the model's decision-making process more transparent because the model's output is closely related not only to data features but also to the structural characteristics of the data.

[0115] S350. During the operation of the application, obtain brightness adjustment reference information associated with the brightness adjustment of the target screen; wherein, the target screen is the display screen of the terminal device running the application.

[0116] During application execution, obtain brightness adjustment reference information associated with the target screen's brightness adjustment process.

[0117] The target screen is the display screen of the terminal device running the application. The brightness adjustment reference information includes brightness adjustment record sequence information for adjusting the brightness of the target screen, device information of the terminal device, environmental characteristic information of the environment in which the terminal device is located, image characteristic information of the display screen of the target screen, and usage status information of the target screen.

[0118] S360. Perform information feature processing on each information item in the brightness adjustment reference information to obtain the corresponding information feature vector.

[0119] S370. Determine the target multimodal animation based on the information items of each information dimension in the brightness adjustment reference information; wherein, the target multimodal animation is used to characterize the correlation between the brightness adjustment reference information.

[0120] The application environment in which the application usage time length prediction model and / or the screen brightness adjustment prediction model obtained through training are used is a dynamic environment. The importance of the node weights and the edges between the nodes in the benchmark multi-modal dynamic graph can change according to different users or change over time and the environment, and a target multi-modal dynamic graph is obtained. In this embodiment, the adaptive edge update can be performed according to the dynamically obtained brightness adjustment reference information.

[0121] The information feature vector and the graph structure feature in the updated brightness adjustment influence factor relationship graph are input into the application usage time length prediction model or the screen brightness adjustment prediction model that is pre-trained, and corresponding model prediction results are obtained.

[0122] S380, the information feature vector and the graph structure feature of the target multi-modal dynamic graph are input into the pre-trained screen brightness adjustment prediction model, and corresponding model prediction results are obtained. The brightness of the target screen is adjusted according to the model prediction results.

[0123] The technical scheme of the embodiment of the present disclosure obtains brightness adjustment reference information sample data, sets a graph node for each information dimension in the brightness adjustment reference information sample data, determines a connection line between the corresponding graph nodes based on the data correlation relationship between each information dimension, and obtains an initial multi-modal dynamic graph. The connection relationship between the graph nodes in the initial multi-modal dynamic graph is denoised to obtain a benchmark multi-modal dynamic graph. The information feature vector corresponding to the brightness adjustment reference information sample data and the graph structure feature of the benchmark multi-modal dynamic graph are input into a model to be trained, and model output results are obtained. The model parameters are updated based on the model output results, sample label data corresponding to the brightness adjustment reference information sample data, and the graph structure feature, and a screen brightness adjustment prediction model is obtained. In the process of running an application, brightness adjustment reference information associated with the brightness adjustment of a target screen is obtained. The target screen is a display screen of a terminal device running the application. The information items in each information dimension in the brightness adjustment reference information are respectively subjected to information feature processing to obtain corresponding information feature vectors. A target multi-modal dynamic graph is determined based on the information items in each information dimension in the brightness adjustment reference information. The target multi-modal dynamic graph is used to represent the correlation relationship between the brightness adjustment reference information. The information feature vector and the graph structure feature of the target multi-modal dynamic graph are input into the pre-trained screen brightness adjustment prediction model to obtain corresponding model prediction results, and the brightness of the target screen is adjusted according to the model prediction results. The embodiment of the present disclosure solves the problem of how to comprehensively analyze multi-dimensional brightness adjustment parameters. A multi-modal input neural network model is trained based on sample data to analyze the collected brightness adjustment reference information, and the brightness of the screen is adjusted according to the model output results. The brightness of the target screen can be automatically adjusted during the use of the application by the user to adapt to the use requirements of the user.

[0124] In one specific example of the screen brightness adjustment method, the implementation process can refer to the flowchart shown in FIG. 4. First, the basic feature data is collected, i.e., the user screen brightness adjustment history data set L (corresponding to the brightness adjustment record sequence information), the device information vector D (corresponding to the device information), the environment data vector N (corresponding to the environmental feature information of the environment where the terminal device is located), the video picture signal feature F (corresponding to the picture feature information of the display picture of the target screen), and the user behavior feature state sequence S (corresponding to the usage state information of the target screen).

[0125] After each basic feature data is processed by an autoencoder, a wavelet transform, and a convolutional neural network, or a bidirectional long short-term memory network to obtain information features, the information features are input into a pre-trained Transformer model based on a multi-head self-attention mechanism. Meanwhile, each basic feature data is also used to construct a dynamic graph (brightness adjustment influencing factor relationship graph), and the graph structure features are also input into the Transformer model based on the multi-head self-attention mechanism. The Transformer model based on the multi-head self-attention mechanism also includes Edge-Informed Attention (edge information attention). Through the Edge-Informed Attention strategy, the edge information in the dynamic graph is combined to assign weights to the interaction between different nodes to reflect their importance and influence in actual application through the Edge-Informed Attention strategy.

[0126] Each attention mechanism is followed by a feedforward network for further processing and nonlinear transformation of the features to enhance the expression ability of the model. Then, the output result of the model can be obtained.

[0127] The Transformer model based on the multi-head self-attention mechanism in this example uses a dynamic graph to integrate multi-modal data from devices, environments, and user behaviors. This structure not only captures the direct relationships between data but also dynamically adjusts these relationships to adapt to environmental changes and the evolution of user habits, providing personalized screen brightness adjustment services for users.

[0128] FIG. 5 is a screen brightness adjustment device provided by an embodiment of the present disclosure, which is suitable for the scenario of adjusting the screen brightness during the use of an application. The screen brightness adjustment device can be implemented in the form of software and / or hardware and can be configured in an electronic device, which can be a mobile terminal, a PC terminal, or a server, etc.

[0129] As shown in FIG. 5, the screen brightness adjustment apparatus comprises a data acquisition module 410, a first data processing module 420, a second data processing module 430, and a data analysis and brightness adjustment module 440.

[0130] The data acquisition module 410 is configured to acquire brightness adjustment reference information associated with brightness adjustment of a target screen during running of an application; the target screen is a display screen of a terminal device running the application; the first data processing module 420 is configured to perform information feature processing on information items in each information dimension of the brightness adjustment reference information respectively to obtain corresponding information feature vectors; the second data processing module 430 is configured to determine a target multi-modal dynamic graph based on the information items in each information dimension of the brightness adjustment reference information; the target multi-modal dynamic graph is used to represent the association relationship between the brightness adjustment reference information; and the data analysis and brightness adjustment module 440 is configured to input the information feature vectors and the graph structure features of the target multi-modal dynamic graph into a pre-trained screen brightness adjustment prediction model to obtain corresponding model prediction results, and adjust the brightness of the target screen according to the model prediction results.

[0131] The technical scheme of the embodiment of the present disclosure solves the problem of single condition parameter for display screen brightness adjustment and lack of personalization in some embodiments, realizes screen brightness adjustment with more comprehensive condition parameters, that is, analysis based on multi-dimensional brightness adjustment reference information to determine a screen brightness adjustment strategy, so that the screen brightness adjustment result meets the use demand of a user more.

[0132] In an optional implementation, the second data processing module 430 is specifically configured to:

[0133] determine the node degree of the graph nodes of each information dimension pair, the clustering coefficient and the path length between the nodes according to the association relationship between the information items in each information dimension of the brightness adjustment reference information;

[0134] According to a data relationship between the node degree, the clustering coefficient and the path length and a reference node degree, a reference clustering coefficient and a reference path length in the preset reference multi-modal dynamic graph, a node weight of a corresponding graph node and an edge weight corresponding to a connection line between the graph nodes are determined, and a target multi-modal dynamic graph is obtained.

[0135] In an optional implementation, the first data processing module 420 is specifically configured to:

[0136] perform image decomposition on the display picture in the brightness adjustment reference information to obtain a sub-band image, and input the sub-band image into a preset convolutional neural network to extract image features and obtain a display picture information feature vector of the target screen; and / or,

[0137] based on a bidirectional long short-term memory network, encode the brightness adjustment record sequence information and the usage state information in the brightness adjustment reference information respectively to obtain information feature vectors corresponding to the brightness adjustment record sequence information and the usage state information; and / or,

[0138] based on an auto-encoder, encode the device information and the environmental feature information in the brightness adjustment reference information respectively to obtain information feature vectors corresponding to the device information and the environmental feature information.

[0139] In an optional implementation, the data analysis and brightness adjustment module 440 is specifically configured to:

[0140] when the model prediction result is the usage duration prediction result of the application program, determine a target screen brightness corresponding to the model prediction result according to a screen brightness and application program usage duration mapping relationship, and adjust the brightness of the target screen to the target screen brightness; and,

[0141] when the model prediction result is the screen brightness adjustment amplitude prediction result, increase or decrease the brightness of the target screen by the brightness adjustment amplitude corresponding to the model prediction result.

[0142] In an optional implementation, the screen brightness adjustment apparatus further includes a model training module configured to train a screen brightness adjustment prediction model, and a training process of the screen brightness adjustment prediction model includes:

[0143] obtain brightness adjustment reference information sample data, set a graph node for each information dimension in the brightness adjustment reference information sample data, and determine a connection line between corresponding graph nodes based on a data association relationship between each information dimension to obtain an initial multi-modal dynamic graph;

[0144] perform denoising processing on a connection relationship between graph nodes in the initial multi-modal dynamic graph to obtain a reference multi-modal dynamic graph;

[0145] The information feature vector corresponding to the brightness adjustment reference information sample data and the graph structure feature of the benchmark multi-modal dynamic graph are input into the to-be-trained model to obtain a model output result.

[0146] Based on the model output result, sample label data corresponding to the brightness adjustment reference information sample data, and the graph structure feature, model parameter updating is performed to obtain a screen brightness adjustment prediction model.

[0147] The sample label data is application program use duration data and / or brightness adjustment amplitude data corresponding to the brightness adjustment reference information sample data.

[0148] In an optional implementation, the model training module can also be configured to:

[0149] At least one statistical parameter of the connection relationship between the graph nodes in the initial multi-modal dynamic graph is calculated, and abnormal connection relationships indicated by the statistical parameter are removed. The initial multi-modal dynamic graph after removing the abnormal connection relationships is taken as the benchmark multi-modal dynamic graph.

[0150] The benchmark multi-modal dynamic graph is input into a graph neural network based on an attention mechanism to obtain benchmark node weights of the graph nodes in the benchmark multi-modal dynamic graph and benchmark edge weights of the connection lines between the graph nodes.

[0151] In an optional implementation, the to-be-trained model is a neural network model based on a multi-head attention mechanism, and the multi-head attention mechanism includes an attention mechanism for edge weight information corresponding to the connection lines between the graph nodes of the benchmark multi-modal dynamic graph.

[0152] In an optional implementation, the model training module is specifically configured to:

[0153] A model learning loss is calculated according to the model output result and the sample label data corresponding to the brightness adjustment reference information sample data.

[0154] A regularization loss is determined according to at least one feature data in the graph structure feature.

[0155] The parameters of the to-be-trained model are updated in gradient according to the model learning loss and the regularization loss.

[0156] The screen brightness adjustment device provided in the embodiments of the present disclosure can perform the screen brightness adjustment method provided in any of the embodiments of the present disclosure, and has the corresponding function modules and beneficial effects of performing the method.

[0157] It should be noted that each unit and module included in the above-described device is only divided according to the function logic, but is not limited to the above-described division, as long as the corresponding function can be implemented. In addition, the specific names of each functional unit are only for convenient distinction, and do not limit the protection scope of the embodiments of the present disclosure.

[0158] FIG. 6 is a structural diagram of an electronic device according to an embodiment of the disclosure. Below, referring to FIG. 6, a structural diagram of an electronic device (e.g., a terminal device or a server in FIG. 6) 500 suitable for implementing an embodiment of the disclosure is illustrated. The terminal device in an embodiment of the disclosure can include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a car terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device illustrated in FIG. 6 is merely an example, and should not impose any limitation on the functions and use range of an embodiment of the disclosure.

[0159] As illustrated in FIG. 6, the electronic device 500 can include a processing device (e.g., a central processing unit, a graphic processing unit, etc.) 501, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the electronic device 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0160] Generally, the following devices can be connected to the I / O interface 505: an input device 506 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, and the like; an output device 507 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, and the like; a storage device 508 including, for example, a magnetic tape, a hard disk, and the like; and a communication device 509. The communication device 509 can allow the electronic device 500 to communicate with other devices wirelessly or via a wire to exchange data. Although FIG. 6 illustrates the electronic device 500 having various devices, it should be understood that all of the illustrated devices are not required to be implemented or possessed. More or fewer devices can be alternatively implemented or possessed.

[0161] In particular, according to an embodiment of the disclosure, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, an embodiment of the disclosure includes a computer program product including a computer program carried on a non-transitory computer readable medium, the computer program containing program code for executing the methods illustrated in the flowcharts. In such an embodiment, the computer program can be downloaded and installed from a network through the communication device 509, or installed from the storage device 508, or installed from the ROM 502. When the computer program is executed by the processing device 501, the above-described functions defined in the methods of an embodiment of the disclosure are performed.

[0162] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present disclosure are only for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0163] The electronic device provided by the embodiments of the present disclosure and the screen brightness adjustment method provided by the above embodiments belong to the same inventive concept, and the technical details not described in detail in the present embodiment can be referred to the above embodiments, and the present embodiment has the same beneficial effects as the above embodiments.

[0164] The embodiments of the present disclosure also provide a computer storage medium, which stores a computer program, and the program is executed by a processor to implement the screen brightness adjustment method provided by the above embodiments.

[0165] It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present disclosure, the computer readable signal medium can include a data signal carried in a baseband or as a part of a carrier wave, which carries computer readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal or any suitable combination of the above. The computer readable signal medium can also be any computer readable medium other than the computer readable storage medium, which can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained in the computer readable medium can be transmitted by any suitable medium, including but not limited to a wire, a cable, an RF (radio frequency) or the like, or any suitable combination of the above.

[0166] In some embodiments, the client, server can communicate using any currently known or future developed network protocols, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include local area networks ("LAN"), wide area networks ("WAN"), internetworks (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

[0167] The computer readable medium described above can be included in the electronic device described above; or can exist separately, without being assembled into the electronic device.

[0168] The computer readable medium described above carries one or more programs, when the one or more programs are executed by the electronic device, the electronic device is caused to:

[0169] During the running of the application program, brightness adjustment reference information associated with the brightness adjustment of the target screen is acquired; wherein the target screen is the display screen of the terminal device running the application program;

[0170] The information items in each information dimension of the brightness adjustment reference information are respectively subjected to information feature processing, to obtain corresponding information feature vectors;

[0171] A target multi-modal dynamic graph is determined based on the information items in each information dimension of the brightness adjustment reference information; wherein the target multi-modal dynamic graph is used to represent the association relationship between the brightness adjustment reference information;

[0172] The information feature vectors and the graph structure features of the target multi-modal dynamic graph are input into a pre-trained screen brightness adjustment prediction model, to obtain corresponding model prediction results, and the brightness of the target screen is adjusted according to the model prediction results.

[0173] Computer program code for carrying out operations of the present disclosure can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0174] The flow diagrams and the block diagrams in the drawings are illustrations of architectures, functionalities, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0175] The units described in the embodiments of the present disclosure can be implemented by software, or by hardware. In some cases, the name of the unit does not constitute a limitation on the unit itself. For example, the first obtaining unit can also be described as a unit for obtaining at least two Internet protocol addresses.

[0176] The functions described in this specification can be implemented in part or in whole by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include Field-programmable Gate Arrays (FPGAs), Program-specific Integrated Circuits (ASICs), Program-specific Standard Products (ASSPs), System-on-a-chip systems (SOCs), Complex Programmable Logic Devices (CPLDs), etc.

[0177] In the context of the present disclosure, a machine-readable medium can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more of: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0178] The embodiments of the present disclosure further provide a computer program product, comprising a computer program which, when executed by a processor, implements the screen brightness adjustment method provided by any one of the embodiments of the present disclosure.

[0179] The computer program product, in implementation, can be written in one or more programming languages or combinations of the same to implement the computer program code for performing the operations of the present disclosure, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" language or similar programming languages. The program code can be executed entirely on a user computer, partially on a user computer, as an independent software package, partially on a user computer and partially on a remote computer, or entirely on a remote computer or server. In the case involving a remote computer, the remote computer can be connected to the user computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0180] The above description is merely preferred embodiments of the present disclosure and a description of the principles of the technology used. Those skilled in the art should understand that the disclosed scope of the present disclosure is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by any combinations of the above technical features or equivalent features without departing from the above disclosed concept. For example, the above technical features can be replaced with technical features disclosed in the present disclosure (but not limited to) having similar functions to form technical solutions.

[0181] Moreover, while operations are depicted in a particular order, this should not be understood as requiring such an order nor infringing on the scope of the disclosure. Certain of the operations described in the discussion are combinable into a single operation, and certain operations can be separated into several operations. In some embodiments, the operations described in the discussion can be performed in an order different than presented in the discussion. In some embodiments, the operations described in the discussion can be performed concurrently. Also, while several specific implementation details are discussed in the discussion, these should not be interpreted as limiting the scope of the disclosure. Rather, certain features described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination.

[0182] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A screen brightness adjustment method, comprising: obtaining brightness adjustment reference information associated with brightness adjustment of a target screen during running of an application; wherein the target screen is a display screen of a terminal device running the application; respectively performing information feature processing on information items of each information dimension in the brightness adjustment reference information to obtain corresponding information feature vectors; determining a target multi-modal dynamic graph based on the information items of each information dimension in the brightness adjustment reference information; wherein the target multi-modal dynamic graph is used to represent the association relationship between the brightness adjustment reference information; inputting the information feature vectors and the graph structure features of the target multi-modal dynamic graph into a pre-trained screen brightness adjustment prediction model to obtain corresponding model prediction results, and adjusting the brightness of the target screen according to the model prediction results.

2. The method of claim 1, wherein, The determination of the target multi-modal dynamic graph based on the information items of each information dimension in the brightness adjustment reference information comprises: determining the node degree of the graph nodes, the clustering coefficient and the path length between the nodes of each information dimension pair according to the association relationship between the information items in each information dimension in the brightness adjustment reference information; determining the node weight of the corresponding graph nodes and the edge weight corresponding to the connection line between the graph nodes according to the data relationship between the node degree, the clustering coefficient and the path length and the reference node degree, the reference clustering coefficient and the reference path length in the preset reference multi-modal dynamic graph, to obtain the target multi-modal dynamic graph.

3. The method of claim 1 or 2, wherein, The information feature processing on the information items of each information dimension in the brightness adjustment reference information to obtain the corresponding information feature vectors comprises: performing image decomposition on the display screen in the brightness adjustment reference information to obtain sub-band images, and inputting the sub-band images into a preset convolutional neural network to extract image features to obtain the display screen information feature vector of the target screen; and / or, respectively encoding the brightness adjustment record sequence information and the use state information in the brightness adjustment reference information based on a bidirectional long short-term memory network to obtain the information feature vectors corresponding to the brightness adjustment record sequence information and the use state information; and / or, respectively encoding the device information and the environmental feature information in the brightness adjustment reference information based on an autoencoder to obtain the information feature vectors corresponding to the device information and the environmental feature information.

4. The method of any one of claims 1-3, wherein, The adjustment of the brightness of the target screen according to the model prediction results comprises: when the model prediction result is a use duration prediction result of the application, determining the target screen brightness corresponding to the model prediction result according to the mapping relationship between the application use duration and the screen brightness, and adjusting the brightness of the target screen to the target screen brightness; and when the model prediction result is a screen brightness adjustment amplitude prediction result, increasing or decreasing the brightness of the target screen by the brightness adjustment amplitude corresponding to the model prediction result.

5. The method of any one of claims 1-4, wherein, The training process of the screen brightness adjustment prediction model comprises: obtaining brightness adjustment reference information sample data, setting a graph node for each information dimension in the brightness adjustment reference information sample data, and determining a connection line between corresponding graph nodes based on a data correlation between each information dimension to obtain an initial multi-modal dynamic graph; performing denoising processing on a connection relationship between graph nodes in the initial multi-modal dynamic graph to obtain a reference multi-modal dynamic graph; inputting an information feature vector corresponding to the brightness adjustment reference information sample data and a graph structure feature of the reference multi-modal dynamic graph into a to-be-trained model to obtain a model output result; updating model parameters based on the model output result, sample label data corresponding to the brightness adjustment reference information sample data, and the graph structure feature to obtain the screen brightness adjustment prediction model; wherein the sample label data is application program use duration data and / or brightness adjustment amplitude data corresponding to the brightness adjustment reference information sample data.

6. The method of claim 5, wherein, The denoising processing on the connection relationship between the graph nodes in the initial multi-modal dynamic graph to obtain the reference multi-modal dynamic graph comprises: calculating at least one statistical parameter of the connection relationship between the graph nodes in the initial multi-modal dynamic graph, removing abnormal connection relationships indicated by the statistical parameters, and taking the initial multi-modal dynamic graph from which the abnormal connection relationships are removed as the reference multi-modal dynamic graph; inputting the reference multi-modal dynamic graph into a graph neural network based on an attention mechanism to obtain reference node weights of the graph nodes in the reference multi-modal dynamic graph and reference edge weights of the connection lines between the graph nodes.

7. The method of claim 5 or 6, wherein, The to-be-trained model is a neural network model based on a multi-head attention mechanism, and the multi-head attention mechanism includes an attention mechanism for edge weight information corresponding to the connection lines between the graph nodes of the reference multi-modal dynamic graph.

8. The method of any one of claims 5-7, wherein, The updating of the model parameters based on the model output result, the sample label data corresponding to the brightness adjustment reference information sample data, and the graph structure feature comprises: calculating a model learning loss according to the model output result and the sample label data corresponding to the brightness adjustment reference information sample data; determining a regularization loss according to at least one feature data in the graph structure feature; performing gradient updating of parameters of the to-be-trained model according to the model learning loss and the regularization loss.

9. A screen brightness adjustment apparatus, comprising: a data acquisition module configured to obtain brightness adjustment reference information associated with brightness adjustment of a target screen during running of an application program; wherein the target screen is a display screen of a terminal device running the application program; a first data processing module configured to perform information feature processing on information items of each information dimension in the brightness adjustment reference information respectively to obtain corresponding information feature vectors; a second data processing module configured to determine a target multi-modal dynamic graph based on the information items of each information dimension in the brightness adjustment reference information; wherein the target multi-modal dynamic graph is used to represent an association relationship between brightness adjustment reference information. The data analysis and brightness adjustment module is configured to input the information feature vector and the graph structure feature of the target multi-modal dynamic graph into a pre-trained screen brightness adjustment prediction model, obtain a corresponding model prediction result, and adjust the brightness of the target screen according to the model prediction result. 10.An electronic device, comprising: one or more processors; a storage configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the screen brightness adjustment method according to any one of claims 1-8.

11. A computer readable storage medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the screen brightness adjustment method according to any one of claims 1-8.

12. A computer program product comprising a computer program, wherein, The computer program is executed by the processor to implement the screen brightness adjustment method according to any one of claims 1-8. The computer program is executed by the processor to implement the screen brightness adjustment method according to any one of claims 1-8.

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