A method of expanding a display screen and related apparatus

Abstract

The application discloses an expansion method of a display screen and a related device, which can be applied to display scenes such as cloud technology, artificial intelligence, intelligent transportation, auxiliary driving, vehicle-mounted scenes and the like, for example, content (video) display. In the process of displaying target content on the display screen, first color information data and first light intensity information data displayed on the display screen, and second color information data and second light intensity information data of the environment where the display screen is located are obtained. Through a neural network model, a prediction result of the neural network model is obtained according to the first color information data, the first light intensity information data, the second color information data and the second light intensity information data, so as to determine the light-emitting parameters including the to-be-simulated color value and the to-be-simulated light intensity value according to the prediction result. Then, the expansion area of the display screen is controlled to display color light with the to-be-simulated color value and the to-be-simulated light intensity value. By increasing the color light in the expansion area, the visual perception range of the object is expanded, and the immersive viewing experience is increased.

Description

Technical Field

[0001] This application relates to the field of display technology, and in particular to a method and related apparatus for expanding a display screen. Background Technology

[0002] With the rapid development of display technology, display screens are widely used in various display scenarios. For example, everyday mobile phones, home televisions, cinemas, billboards, virtual reality (VR) devices, or augmented reality (AR) devices.

[0003] Currently, when displaying content on a screen, the fixed size of the screen makes it difficult to cover the entire field of vision. In normal scenarios, viewers' viewing experience is affected by the environment, resulting in a relatively poor viewing experience. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method and related apparatus for extending a display screen, simulating the viewing effect of an object on an extended area within its field of vision, thereby expanding the object's visual perception range. This allows the extended display screen to cover the entire field of vision of the object as much as possible, greatly reducing the possibility that the object's viewing will be affected by the environment and increasing the immersive viewing experience.

[0005] The embodiments of this application disclose the following technical solutions:

[0006] On one hand, embodiments of this application provide a method for extending a display screen, the method comprising:

[0007] The target content is displayed on the display screen, and the target content is the content shown to the object through the display screen.

[0008] During the process of displaying the target content on the display screen, the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment in which the display screen is located, are acquired.

[0009] Using a neural network model, features are extracted from the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data to obtain corresponding feature vectors.

[0010] Based on the obtained feature vectors, predictions are made to obtain the prediction results of the neural network model;

[0011] The result type of the prediction result is determined, and the prediction result is processed in a way that matches the result type to obtain the luminescence parameters, which include the color value to be simulated and the light intensity value to be simulated.

[0012] The extended area of ​​the display screen is controlled to display colored light with the color value to be simulated and the light intensity value to be simulated. The extended area is the area outside the display screen in the color recognition area corresponding to the object.

[0013] On one hand, embodiments of this application provide an extension system for a display screen. The system includes a display screen and a central processing platform. The display screen is used to display target content, and the central processing platform is used to display colored light with simulated color values ​​and simulated light intensity values ​​in the extended area of ​​the display screen in accordance with the method described in the foregoing aspect during the display of the target content on the display screen.

[0014] On one hand, embodiments of this application provide an extension device for a display screen, the device including a display unit, an acquisition unit, an extraction unit, a prediction unit, a determination unit, and a control unit:

[0015] The display unit is used to display target content through a display screen, wherein the target content is content displayed to an object through the display screen.

[0016] The acquisition unit is used to acquire, during the process of displaying the target content on the display screen, the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment in which the display screen is located;

[0017] The extraction unit is used to extract features based on the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data using a neural network model, and obtain corresponding feature vectors.

[0018] The prediction unit is used to make predictions based on the obtained feature vectors to obtain the prediction results of the neural network model.

[0019] The determining unit is used to determine the result type of the prediction result, and to perform a processing method on the prediction result that matches the result type to obtain luminescence parameters, wherein the luminescence parameters include the color value to be simulated and the light intensity value to be simulated.

[0020] The control unit is used to control the extended area of ​​the display screen to display colored light with the color value to be simulated and the light intensity value to be simulated, wherein the extended area is the area outside the display screen in the color recognition area corresponding to the object.

[0021] On one hand, embodiments of this application provide an electronic device for extending a display screen, the electronic device including a processor and a memory:

[0022] The memory is used to store program code and transmit the program code to the processor;

[0023] The processor is configured to execute the display screen expansion method described above according to instructions in the program code.

[0024] In one aspect, embodiments of this application provide a computer-readable storage medium for storing program code for executing the display screen extension method described above.

[0025] On one hand, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method for expanding a display screen.

[0026] As can be seen from the above technical solution, a display screen can display content, such as target content, to an object. During the display of the target content, since the screen size is fixed, to cover the entire field of view of the object, the display screen can be blurred and extended based on the object's color recognition area. Specifically, the first color information data and the first light intensity information data displayed on the screen, as well as the second color information data and the second light intensity information data of the environment where the screen is located, can be acquired. Then, through a neural network model, feature vectors are extracted based on the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data, respectively. Based on the obtained feature vectors, prediction results from the neural network model are obtained. The prediction results are then processed according to the result type to obtain the luminous emission parameters, including the color value to be simulated and the light intensity value to be simulated. Afterward, the extended area of ​​the display screen can be controlled to display the color light with the simulated color value and the simulated light intensity value. Since the extended area is the area outside the display screen within the color recognition area corresponding to the object, the object can see the corresponding color light in the extended area, thus simulating the viewing effect of the object in the extended area within the object's field of vision. This expands the object's visual perception range, allowing the extended display screen to cover the entire field of vision of the object as much as possible, greatly reducing the possibility that the object's viewing will be affected by the environment, and increasing the immersive viewing experience. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 An example diagram of the field of view in a vertical plane is provided for an embodiment of this application;

[0029] Figure 2 An example diagram of the field of view in a horizontal plane is provided for an embodiment of this application;

[0030] Figure 3 A schematic diagram of the architecture of an expansion system for a display screen provided in an embodiment of this application;

[0031] Figure 4 A flowchart illustrating a method for expanding a display screen as provided in an embodiment of this application;

[0032] Figure 5 An example diagram of the area division of a display screen provided in an embodiment of this application;

[0033] Figure 6 This is a training example diagram for training a first model, provided as an embodiment of this application.

[0034] Figure 7 This is a training example diagram for training a second model, provided as an embodiment of this application.

[0035] Figure 8 An example diagram illustrating the application of a neural network model provided in this application embodiment;

[0036] Figure 9 Another training example diagram for training a second model provided in an embodiment of this application;

[0037] Figure 10 The visual effect comparison diagrams provided in the embodiments of this application for display scenarios in enclosed spaces are shown below.

[0038] Figure 11 These are visual effect comparison diagrams provided in the embodiments of this application for display scenarios in open spaces;

[0039] Figure 12 Visual effect comparison diagrams in VR / AR spatial display scenarios provided in the embodiments of this application;

[0040] Figure 13 A flowchart illustrating a method for expanding a display screen, as provided in an embodiment of this application.

[0041] Figure 14 A flowchart illustrating another method for expanding a display screen provided in an embodiment of this application;

[0042] Figure 15 Example diagrams illustrating the overall effect of screen expansion achieved through the method provided in this application embodiment;

[0043] Figure 16 A structural diagram of a display screen extension device provided in an embodiment of this application;

[0044] Figure 17 A structural diagram of a terminal provided in an embodiment of this application;

[0045] Figure 18 This is a structural diagram of a server provided in an embodiment of this application. Detailed Implementation

[0046] The embodiments of this application will now be described with reference to the accompanying drawings.

[0047] Under normal circumstances, the human field of vision is relatively wide, see Figure 1 and Figure 2 As shown, Figure 1 and Figure 2 These refer to the field of view in the vertical plane and the field of view in the horizontal plane, respectively. Figure 1 and Figure 2 As can be seen from this, the range of human vision can be seen in... Figure 1 The range between the two visual boundaries, or Figure 2 The visual field boundaries of the right and left eyes can be divided into clear and blurry areas. Colors can be recognized in the blurry area (i.e.,...). Figure 1 and Figure 2 (Color recognition area in the middle).

[0048] However, when displaying content on a screen, the fixed size of the screen makes it difficult to cover the entire field of vision. In normal scenarios, viewers' viewing experience is affected by the environment, resulting in a relatively poor viewing experience.

[0049] To address the aforementioned technical problems, this application provides a method for extending a display screen. This method utilizes intelligent fuzzy extension technology, taking advantage of the human field of vision, especially the color recognition area, to indirectly expand the visible range of the display screen. Specifically, it combines second color information data and second light intensity information data of the environment where the display screen is located, with first color information data and first light intensity information data displayed on the screen, to display corresponding colored light. This intelligently extends the display screen through the display of colored light, expanding the range of visual perception and enhancing the immersive experience.

[0050] like Figure 3 As shown, Figure 3 A schematic diagram of the architecture of an expansion system for a display screen is shown. This scenario may include a display screen 301 and a central processing platform 302. The display screen 301 can be a standalone display screen, such as a display screen in a cinema, a billboard, or a roadshow backdrop; the display screen 301 can also be a display screen located on a terminal, including but not limited to mobile phones, computers, smart voice interaction devices, smart home appliances, vehicle terminals, and aircraft. Embodiments of this application can be applied to various scenarios, including but not limited to cloud technology, artificial intelligence, smart transportation, and assisted driving.

[0051] The central processing platform 302 can be a server or a terminal. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. When the display screen 301 is an independent display screen, the terminal serving as the central processing platform 302 can be a terminal independent of the display screen; when the display screen 301 is a display screen located on a terminal, the terminal serving as the central processing platform 302 can be the terminal where the display screen 301 is located. The terminal used to execute the extended methods of the display screen can be a mobile phone, computer, intelligent voice interaction device, smart home appliance, vehicle terminal, aircraft, etc., but is not limited to these. Figure 3 The main example used is the central processing platform 302, which is a server.

[0052] Display screen 301 can display content, such as target content, to an object, which may be a user. During the display of target content on display screen 301, since the size of display screen 301 is fixed, in order to cover the entire field of view of the object, the display screen can be blurred and expanded based on the object's color recognition area.

[0053] Specifically, the central processing platform 302 can acquire the first color information data and the first light intensity information data displayed on the display screen 301, as well as the second color information data and the second light intensity information data of the environment in which the display screen 301 is located. Then, the central processing platform 302 uses a neural network model to extract features based on the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data to obtain corresponding feature vectors. Based on the obtained feature vectors, prediction is made to obtain the prediction result of the neural network model. This allows the central processing platform 302 to determine the result type of the prediction result and perform processing on the prediction result in a manner that matches the result type to obtain the luminous parameters. The luminous parameters include the color value to be simulated and the light intensity value to be simulated.

[0054] Subsequently, the central processing platform 302 can control the extended area of ​​the display screen to display the colored light with the color value and light intensity value to be simulated. The extended area can be found in [reference needed]. Figure 3 The extended area is the region between the solid and dashed edges of the display screen 301. Since the extended area is the region outside the display screen within the color recognition area corresponding to the object, the object can see the corresponding color light in the extended area. This simulates the viewing effect of the object in the extended area within its field of vision, thereby expanding the object's visual perception range. This allows the extended display screen to cover as much of the object's field of vision as possible, greatly reducing the possibility that the object's viewing will be affected by the environment and increasing the immersive viewing experience.

[0055] It is understood that the methods provided in this application may involve artificial intelligence (AI). AI is the theory, methods, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results. AI technology is a comprehensive discipline involving a wide range of fields, encompassing both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operating / interactive systems, and mechatronics. AI software technologies mainly include computer vision technology, speech processing technology, natural language processing technology, as well as machine learning / deep learning, autonomous driving, and intelligent transportation.

[0056] The methods provided in this application specifically relate to Machine Learning (ML), a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and instructional learning. For example, a neural network model is obtained by training based on machine learning.

[0057] Next, the method for expanding the display screen provided in the embodiments of this application will be described in detail with reference to the accompanying drawings.

[0058] See Figure 4 , Figure 4 A flowchart of a method for extending a display screen is shown, the method comprising:

[0059] S401. Display target content through a display screen, wherein the target content is content displayed to the object through the display screen.

[0060] Display screens are widely used in various display scenarios, such as everyday mobile phones, home televisions, cinemas, billboards, roadshow backdrops, virtual reality (VR) devices, and augmented reality (AR) devices. The target content is what needs to be displayed to an object, such as a user, through the display screen. For example, in a home television display scenario, the target content could be the displayed homepage or specific video content; in a cinema display scenario, the target content could be the movie being played; in a billboard display scenario, the target content could be the advertisements being played, and so on. Since home television and cinema display scenarios are typically located indoors, even in opaque rooms, they can be considered enclosed space display scenarios. Conversely, billboards and roadshow backdrops are open space display scenarios. VR devices belong to VR spatial display scenarios, and AR devices belong to AR spatial display scenarios.

[0061] S402. During the process of displaying the target content on the display screen, acquire the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment where the display screen is located.

[0062] During the display of target content on the screen, since the screen size is fixed, to ensure coverage of the entire field of view of the object, the screen can be blurred and expanded based on the object's color recognition area. Specifically, this involves first acquiring the first color information data and the first light intensity information data displayed on the screen, as well as the second color information data and the second light intensity information data of the environment in which the screen is located.

[0063] The first color information data and the first light intensity information data can be collectively referred to as screen light data, and the second color information data and the second light intensity information data can be collectively referred to as ambient light data. Ambient light data can be collected by an ambient light sensor installed in the environment, while screen light data can be collected in different ways depending on the integration method. This application mainly provides two integration methods: non-immersion integration and immersion integration. The main difference lies in whether the display screen integrates functions such as display color and light intensity. If these functions are integrated, it is an immersion integration method, where screen light data can be directly obtained through the display screen without the need for additional light intensity or color sensors. If these functions are not integrated into the display screen, it is a non-immersion integration method, where the first light intensity information data can be obtained through a light intensity sensor installed on or around the display screen, and the first color information data can be obtained through a color sensor installed on or around the display screen.

[0064] In one possible implementation, the display screen can be simply divided into three different types of areas, see [link to relevant documentation]. Figure 5 As shown, the regions are: ① corner area, ② edge area, and ③ center area. In this case, the first color information data can include the main screen color information data, edge color information data, and corner color information data of the display screen, and the first light intensity information data can include the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen. Specifically, the edge color information data is the average color value of the edge area of ​​the display screen, the edge color information data is the average color value of the corner area of ​​the display screen, and the main screen color information data is the average color value of the entire display screen; the edge light intensity information data is the light intensity of the edge area of ​​the display screen, the corner light intensity information data is the light intensity of the corner area of ​​the display screen, and the main screen light intensity information data is the light intensity of the entire display screen. The color units corresponding to the color information data, such as the first color information data and the second color information data, can be black (0,0,0) – white (255,255,255); the light intensity units for the light intensity information data, such as the first light intensity information data and the second light intensity information data, can be dark 0 – bright 100.

[0065] S403. Using a neural network model, feature extraction is performed based on the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data to obtain corresponding feature vectors.

[0066] S404. Based on the obtained feature vectors, make predictions to obtain the prediction results of the neural network model.

[0067] Then, the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data are input into a trained neural network model. The neural network model extracts features from these data to obtain corresponding feature vectors, and then uses these feature vectors to make predictions. The first color information data, the first light intensity information data, the second color information data, and the second light intensity information data can be referred to as influencing factors.

[0068] The neural network model is pre-trained. The training method for the neural network model is as follows: initial training samples are constructed based on historical influence factors, historical luminance parameters, and evaluation results of the historical luminance parameters. The evaluation results can serve as labels for the historical luminance parameters. Historical influence factors include historical screen parameters, historical color information data, and historical luminance information data. Historical screen parameters may include the screen manufacturer, the display screen's technical parameters, and the display screen size; historical color information data includes the display screen's color information data and the color information data of the environment in which the display screen is located, and the display screen's color information data may include main screen color information data and edge color information data, and in some cases, corner color information data; historical luminance information data includes the display screen's luminance information data and the luminance information data of the environment in which the display screen is located, and the display screen's luminance information data may include main screen luminance information data and edge luminance information data, and in some cases, corner luminance information data. Historical luminance parameters include historical color values ​​and historical luminance values. The evaluation result of the historical luminance parameters can refer to the expected result regarding the visual comfort of displaying colored light on the display screen according to those historical luminance parameters.

[0069] Then, the initial training samples are screened using the first model to obtain target training samples. The evaluation results determined by the historical influence factors and historical luminescence parameters in the target training samples meet the preset conditions. The fact that the evaluation results determined by the historical influence factors and historical luminescence parameters in the target training samples meet the preset conditions indicates that the historical influence factors and historical luminescence parameters in the target training samples are effective, and therefore, the target training samples can be used for secondary training. See also... Figure 6 As shown, Figure 6 In the diagram, 601 represents the first model, 602 represents the initial training sample, and 603 represents the evaluation result output based on the historical influence factors and historical luminescence parameters in the initial training sample. The evaluation result can be categorized as comfortable, uncomfortable, or unknown. This result is then compared with the evaluation result used as a label to complete the training of the first model. The first model can be used to select target training samples that meet preset conditions from the initial training sample.

[0070] Then, the second model is trained using the target training samples to obtain a neural network model. It should be noted that the output of the second model can vary depending on its structure. In one possible implementation, the second model is a network model representing the relationship between historical influence factors and historical luminescence parameters, equivalent to the influence coefficient of the historical influence factors. In this case, the second model can be found in [reference needed]. Figure 7 As shown, the input to the second model is the historical impact factor, and the historical luminescence parameter can be used as a label for the historical impact factor. The second model determines the corresponding output when the input is the historical impact factor, and then compares it with the historical luminescence parameter to optimize and train the second model, thereby obtaining a neural network model with the impact factor as input and the luminescence parameter as output (see...). Figure 8 (As shown).

[0071] At this time, if the first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen, and the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen, the prediction result can be color value and light intensity value.

[0072] In another possible implementation, the main screen color information data, edge / corner color information data, and second color information data (i.e., ambient color information data) can influence the simulated color value at the edges / corners, and the main screen light intensity information data, edge / corner light intensity information data, and second light intensity information data (i.e., ambient light intensity information data) can influence the simulated light intensity value at the edges / corners. The degree of influence of these information data can be represented by influence coefficients, which can be the first influence coefficient of the main screen color information data, the second influence coefficient of the edge color information data, the third influence coefficient of the second color information data, the fourth influence coefficient of the main screen light intensity information data, the fifth influence coefficient of the edge light intensity information data, and the sixth influence coefficient of the second light intensity information data.

[0073] The first, second, fourth, and fifth influence coefficients are calculated and obtained based on an intelligent model (such as a neural network model) (including evaluation results, display screen type, screen size, and other influencing factors). The model is updated regularly and preset according to different display screen types.

[0074] The third and sixth influence coefficients can be calculated based on intelligent models (such as neural network models), with the models updated periodically and preset.

[0075] In this context, the second model is a network model used to determine the influence coefficients based on historical influence factors and historical luminescence parameters. The second model can be found here. Figure 9As shown, a neural network model is obtained by training the second model, which takes the influence factor as input and the influence coefficient corresponding to the influence factor as output.

[0076] At this point, if the first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen, and the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen, the prediction result can be a color value and a light intensity value. The prediction result is a color influence coefficient and a light intensity influence coefficient. The color influence coefficient includes the first influence coefficient of the main screen color information data, the second influence coefficient of the edge color information data, and the third influence coefficient of the second color information data. The light intensity influence coefficient includes the fourth influence coefficient of the main screen light intensity information data, the fifth influence coefficient of the edge light intensity information data, and the sixth influence coefficient of the second light intensity information data.

[0077] S405. Determine the result type of the prediction result, and perform a processing method on the prediction result that matches the result type to obtain the luminescence parameters, wherein the luminescence parameters include the color value to be simulated and the light intensity value to be simulated.

[0078] Understandably, depending on the neural network model, the type of prediction result may differ, which in turn leads to different processing methods for determining the luminescence parameters based on the prediction result.

[0079] For example, see neural network models. Figure 8 As shown, the prediction result type is color light information, which includes color value and light intensity value. The processing method matching the result type is direct assignment. Therefore, S405 can be implemented by directly using the color value in the prediction result as the color value to be simulated and the light intensity value in the prediction result as the light intensity value to be simulated. This method can improve computational efficiency and enhance the real-time performance of the display screen expansion.

[0080] For example, see neural network models. Figure 9 As shown, the prediction result type is an influence coefficient, which includes a color influence coefficient and a light intensity influence coefficient. The processing method matching the result type is a weighted summation method. The color values ​​to be simulated include edge color values ​​and corner color values, and the light intensity values ​​to be simulated include edge light intensity values ​​and corner light intensity values. Therefore, the implementation of 405 can be achieved by performing a weighted summation process based on the main screen color information data, the first influence coefficient, the edge color information data, the second influence coefficient, the second color information data, and the third influence coefficient to obtain the edge color value to be simulated. See the following formula for details:

[0081] The simulated edge color value = edge color information data * (second influence coefficient β1) + main screen color information data * (first influence coefficient β2) + second color information data * (third influence coefficient β3)

[0082] Among them, β1>β2>β3;

[0083] The corner color value to be simulated is obtained by weighted summation of the main screen color information data, the first influence coefficient, the corner color information data, the second influence coefficient, the third influence coefficient, and the fourth color information data. See the formula below for details:

[0084] The simulated corner color value = corner color information data * (second influence coefficient β1) + main screen color information data * (first influence coefficient β2) + second color information data * (third influence coefficient β3)

[0085] Among them, β1>β2>β3;

[0086] The edge light intensity value to be simulated is obtained by weighted summation of the main screen light intensity data, the fourth influence coefficient, the edge light intensity data, the fifth influence coefficient, the second light intensity data, and the sixth influence coefficient. See the formula below for details:

[0087] The simulated edge light intensity value = edge light intensity information data * (fifth influence coefficient γ1) + main screen light intensity information data * (fourth influence coefficient γ2) + second light intensity information data * (sixth influence coefficient γ3)

[0088] Where γ1 < γ2 < γ3;

[0089] The corner light intensity value to be simulated is obtained by weighted summation of the main screen light intensity data, the fourth influence coefficient, the corner light intensity data, the fifth influence coefficient, the second light intensity data, and the sixth influence coefficient. See the formula below for details:

[0090] The simulated corner light intensity value = corner light intensity information data * (fifth influence coefficient γ1) + main screen light intensity information data * (fourth influence coefficient γ2) + second light intensity information data * (sixth influence coefficient γ3)

[0091] Where γ1 < γ2 < γ3.

[0092] S406. Control the extended area of ​​the display screen to display colored light with the color value to be simulated and the light intensity value to be simulated, wherein the extended area is the area outside the display screen in the color recognition area corresponding to the object.

[0093] After obtaining the color value and light intensity value to be simulated, the color light with the color value and light intensity value to be simulated can be displayed in the extended area of ​​the display screen, thereby simulating the viewing effect of the object in the extended area within the object's field of vision, thus expanding the object's visual perception range, so that the extended display screen can cover the entire field of vision of the object as much as possible.

[0094] It should be noted that, in this embodiment, the configured hardware devices differ depending on the display scenario in which the display screen is applied, thus leading to different implementations of S406. When the display scenario is a closed space display scenario under non-immersive integration, an open space display scenario, or various display scenarios under immersive integration, the light source can be controlled to emit colored light with simulated color values ​​and simulated light intensity values, thereby displaying the simulated color light with simulated color values ​​and simulated light intensity values ​​in the extended area of ​​the display screen. The light source is installed in the extended area, such as the edge of the display screen, and / or a preset position in the environment where the display screen is located.

[0095] Specifically, in one possible implementation, light-emitting sources (i.e., the configured hardware devices are light-emitting sources) are arranged at preset intervals along the edge of the display screen. The method for obtaining the luminous parameters by processing the prediction results to match the result type can be to determine the luminous parameters of the light-emitting sources corresponding to different positions. In this case, S406 can be implemented by controlling the light-emitting sources at different positions to emit colored light according to the corresponding simulated color value and simulated light intensity value, thereby displaying colored light with the simulated color value and simulated light intensity value in the extended area. For example, each light-emitting source can be numbered, with different numbers corresponding to light-emitting sources at different positions. Then, based on the simulated color value and simulated light intensity value corresponding to each number, the light-emitting source corresponding to that number can be controlled to emit colored light.

[0096] In another possible implementation, since the display screen may be located in a confined space, such as inside a room, in order to better extend the display screen, in addition to setting a first light source at a preset interval along the edge of the display screen, a second light source can also be set at a preset position in the environment where the display screen is located. In this case, the method of obtaining the light emission parameters by processing the prediction results in a way that matches the result type can be to determine the light emission parameters corresponding to the first and second light sources at different positions. In this case, S406 can be implemented by controlling the first and second light sources at different positions to emit colored light according to the corresponding simulated color value and simulated light intensity value, so as to display colored light with simulated color value and simulated light intensity value in the extended area.

[0097] When the display scene is a VR space display scene or an AR space display scene in a non-immersive integrated manner, no additional hardware devices are required. The extended area rendering function can be configured for the VR or AR device, and then the extended area rendering function can be used to render the effect map of the color light with the color value and light intensity value to be simulated in the extended area of ​​the display screen.

[0098] Next, we will introduce the hardware and software configured for different display scenarios.

[0099] I. Display scenario in a confined space:

[0100] (1) Home television:

[0101] Hardware equipment: ① A light source is installed at the edge of the display screen, which emits light outwards; ② Light sources are installed in the eight corners of the room where the display screen is located; ③ An ambient light sensor is installed in the room; ④ Color sensors and light intensity sensors are installed around the display screen; ⑤ A control center is installed. In a home television display scenario, the light sources installed in the room can also be installed in other locations, and this application embodiment does not limit this.

[0102] The configured software includes: ① functions for acquiring and calculating first color information data, first light intensity information data, second color information data, and second light intensity information data; ② a central processing platform for processing data and calculating and simulating the color value and light intensity value to be simulated in real time; and ③ a control center.

[0103] (2) Cinema:

[0104] Hardware equipment: ① A light source is installed at the edge of the display screen, which emits light outwards; ② Light sources are installed in the eight corners of the room where the display screen is located, on the ceiling, on the floor, and along the walls; ③ An ambient light sensor is installed in the room; ④ Color sensors and light intensity sensors are installed around the display screen; ⑤ A control center is installed. In a cinema setting, the light sources installed in the room can be located in the eight corners of the room where the display screen is located, or in other locations; this embodiment does not limit this.

[0105] The configured software includes: ① functions for acquiring and calculating first color information data, first light intensity information data, second color information data, and second light intensity information data; ② a central processing platform for processing data and calculating and simulating the color value and light intensity value to be simulated in real time; and ③ a control center.

[0106] II. Open Space Display Scenarios:

[0107] (1) Display scenarios such as billboards and roadshow backdrops:

[0108] Hardware equipment: ① A light source is set at the edge of the display screen, which can emit light outward; ② An ambient light sensor is installed in the environment; ③ A color sensor and a light intensity sensor are installed around the display screen; ⑤ A control center is installed. In display scenarios such as billboards and roadshow backdrops, light sources can also be installed in other locations such as the ground or nearby walls; this application embodiment does not limit this.

[0109] The configured software includes: ① functions for acquiring and calculating first color information data, first light intensity information data, second color information data, and second light intensity information data; ② a central processing platform for processing data and calculating and simulating the color value and light intensity value to be simulated in real time; and ③ a control center.

[0110] III. VR / AR Spatial Display Scenarios:

[0111] (1) VR / AR: Software simulates different colors of light, thereby expanding the display screen;

[0112] Hardware equipment: None.

[0113] The configured software includes: ① functions for acquiring and calculating first color information data, first light intensity information data, second color information data, and second light intensity information data; ② a central processing platform for processing data and calculating and simulating the color value and light intensity value to be simulated in real time; and ③ extended area rendering functions.

[0114] IV. Immersion Integration Method:

[0115] The display screen needs to integrate functions such as color and light intensity. Here, there is no need for additional sensors to obtain screen light data. The data can be directly collected through the display screen and output to the central processing platform. Other components such as light source, ambient light sensor, and control center are still required.

[0116] Based on the above description of different display scenarios and the hardware and software configured in different display scenarios, the display screen can be expanded using the methods provided in the embodiments of this application. Figures 10-12 As shown, illustrations of the effects in three different scenarios are presented. Figures 10-12 The visual effects of normal scenes are compared with the visual effects of screen expansion based on the method provided in the embodiments of this application in closed space display scenarios, open space display scenarios, and VR / AR space display scenarios, respectively, so as to bring different visual experiences to the audience.

[0117] exist Figures 10-12In the diagram, (a) shows the visual effect of a normal scene, and (b) shows the visual effect of screen expansion based on the method provided in this application embodiment. It can be seen that by expanding the screen using the method provided in this application embodiment, viewers can see a similar visual effect to the screen outside of it, thereby expanding the visual perception range of the object. This allows the expanded screen to cover as much of the object's field of vision as possible, greatly reducing the possibility of environmental interference and increasing the immersive viewing experience. When the screen expansion method provided in this application embodiment is applied to billboards, its superior visual experience and user acceptance will further improve the efficiency and value of advertising display.

[0118] As can be seen from the above technical solution, a display screen can display content, such as target content, to an object. During the display of the target content, since the screen size is fixed, to cover the entire field of view of the object, the display screen can be blurred and extended based on the object's color recognition area. Specifically, the first color information data and the first light intensity information data displayed on the screen, as well as the second color information data and the second light intensity information data of the environment where the screen is located, can be acquired. Then, through a neural network model, feature vectors are extracted based on the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data, respectively. Based on the obtained feature vectors, prediction results from the neural network model are obtained. The prediction results are then processed according to the result type to obtain the luminous emission parameters, including the color value to be simulated and the light intensity value to be simulated. Afterward, the extended area of ​​the display screen can be controlled to display the color light with the simulated color value and the simulated light intensity value. Since the extended area is the area outside the display screen within the color recognition area corresponding to the object, the object can see the corresponding color light in the extended area, thus simulating the viewing effect of the object in the extended area within the object's field of vision. This expands the object's visual perception range, allowing the extended display screen to cover the entire field of vision of the object as much as possible, greatly reducing the possibility that the object's viewing will be affected by the environment, and increasing the immersive viewing experience.

[0119] Next, we will mainly use an open space display scenario as an example to introduce the expansion of the display screen. In this display scenario, a light source needs to be installed. The installation position of the light source can be adjusted according to actual needs. For example, the light source can be installed at a preset interval along the edge of the display screen. This application embodiment does not limit the installation position of the light source.

[0120] In this case, the method of expanding the display screen can be achieved through... Figure 13The flowchart shown is implemented in four parts: real-time data acquisition module 1301, real-time calculation module 1302, control module 1303, and light source 1304.

[0121] The real-time data acquisition module 1301 mainly includes a light intensity sensor and a color sensor, capable of acquiring and outputting real-time light intensity and color information data to the real-time computing module 1302. Specifically, the color sensor can be used to acquire first color information data of the display screen and second color information data of the environment surrounding the display screen, while the light intensity sensor can be used to acquire first light intensity information data displayed on the screen and second light intensity information data of the environment surrounding the display screen. Based on this, the display screen expansion method can be achieved through... Figure 14 The flowchart shown is equivalent to implementing the process architecture diagram. Figure 13 The 1301 in the model is divided into two parts: real-time acquisition of first color information data and first light intensity information data, and real-time acquisition of second color information data and second light intensity information data.

[0122] The real-time calculation module 1302 is equivalent to the central processing platform described above. It is responsible for processing and calculating the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data in real time. It uses a neural network model to quickly calculate the simulated color value and simulated light intensity value of the light source at each location. Then, it outputs the final simulated color value and simulated light intensity value to the control module 1303.

[0123] The control module 1303 can be electrically connected to the light source 1304. The control module 1303 is mainly responsible for controlling each light source 1304 to emit colored light according to the corresponding simulated color value and simulated light intensity value. In this sense, the control module 1303 is equivalent to the control center described above. Figure 13 The light source 1304 shown may include light sources A, B, C, D, etc., and the light source may be a colored spotlight, a light-emitting diode (LED), etc.

[0124] The emission of colored light from the light source is the final manifestation of the embodiments of this application, which includes a rich variety of colors and can render various different display scenes.

[0125] The overall effect of expanding the display screen using the method provided in the embodiments of this application can be seen in [reference]. Figure 15As shown, ① represents the light intensity sensor and the color sensor, with the color sensor used to collect first color information data and second color information data, and the light intensity sensor used to collect first light intensity information data and second light intensity information data; ② represents the colored light emitted by the light source; ③ represents the central processing platform and control center. Figure 15 As can be seen, because the light source emits light of a color that matches the display screen, viewers can see a visual effect similar to the display screen from outside the screen, thereby expanding the visual perception range of the object. This allows the expanded display screen to cover the entire field of vision of the object as much as possible, greatly reducing the possibility that the object's viewing will be affected by the environment and increasing the immersive viewing experience.

[0126] It should be noted that, based on the implementation methods provided in the above aspects, this application can be further combined to provide more implementation methods.

[0127] based on Figure 4 Corresponding to the display screen expansion method provided in the embodiments, this application also provides a display screen expansion device 1600. See also Figure 16 The expansion device 1600 for the display screen includes a display unit 1601, an acquisition unit 1602, an extraction unit 1603, a prediction unit 1604, a determination unit 1605, and a control unit 1606.

[0128] The display unit 1601 is used to display target content through a display screen, wherein the target content is content displayed to an object through the display screen.

[0129] The acquisition unit 1602 is used to acquire, during the process of displaying the target content on the display screen, the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment in which the display screen is located.

[0130] The extraction unit 1603 is used to extract features based on the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data respectively through a neural network model to obtain corresponding feature vectors.

[0131] The prediction unit 1604 is used to make predictions based on the obtained feature vectors to obtain the prediction results of the neural network model.

[0132] The determining unit 1605 is used to determine the result type of the prediction result, and to perform a processing method on the prediction result that matches the result type to obtain luminescence parameters, wherein the luminescence parameters include the color value to be simulated and the light intensity value to be simulated.

[0133] The control unit 1606 is used to control the extended area of ​​the display screen to display colored light with the color value to be simulated and the light intensity value to be simulated, wherein the extended area is the area outside the display screen in the color recognition area corresponding to the object.

[0134] In one possible implementation, the first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen, and the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen.

[0135] The prediction result is a result type of influence coefficient, which includes color influence coefficient and light intensity influence coefficient. The color influence coefficient includes a first influence coefficient of the main screen color information data, a second influence coefficient of the edge color information data, and a third influence coefficient of the second color information data. The light intensity influence coefficient includes a fourth influence coefficient of the main screen light intensity information data, a fifth influence coefficient of the edge light intensity information data, and a sixth influence coefficient of the second light intensity information data.

[0136] The color values ​​to be simulated include edge color values ​​and corner color values ​​to be simulated; the light intensity values ​​to be simulated include edge light intensity values ​​and corner light intensity values ​​to be simulated; the determining unit 1605 is specifically used for:

[0137] The edge color value to be simulated is obtained by weighted summation of the main screen color information data, the first influence coefficient, the edge color information data, the second influence coefficient, the second color information data, and the third influence coefficient.

[0138] The corner color value to be simulated is obtained by weighted summation of the main screen color information data, the first influence coefficient, the corner color information data, the second influence coefficient, the second color information data, and the third influence coefficient.

[0139] The edge light intensity value to be simulated is obtained by weighted summation of the main screen light intensity information data, the fourth influence coefficient, the edge light intensity information data, the fifth influence coefficient, the second light intensity information data, and the sixth influence coefficient.

[0140] The corner light intensity value to be simulated is obtained by weighted summation of the main screen light intensity information data, the fourth influence coefficient, the corner light intensity information data, the fifth influence coefficient, the second light intensity information data, and the sixth influence coefficient.

[0141] In one possible implementation, the first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen, and the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen.

[0142] The prediction result is of type color light information, which includes color value and light intensity value.

[0143] The determining unit 1605 is specifically used for:

[0144] The color value in the prediction result is used as the color value to be simulated, and the light intensity value in the prediction result is used as the light intensity value to be simulated.

[0145] In one possible implementation, the apparatus further includes a training unit, the training unit being configured to:

[0146] Based on historical influence factors, historical luminescence parameters, and the evaluation results of the historical luminescence parameters, an initial training sample is constructed. The historical influence factors include historical screen parameters, historical color information data, and historical light intensity information data. The historical color information data includes the color information data of the display screen and the color information data of the environment in which the display screen is located. The historical light intensity information data includes the light intensity information data of the display screen and the light intensity information data of the environment in which the display screen is located.

[0147] The initial training samples are screened using the first model to obtain target training samples. The evaluation results determined based on the historical influence factors and historical luminescence parameters in the target training samples meet the preset conditions.

[0148] The neural network model is obtained by training the second model based on the target training samples.

[0149] In one possible implementation, the edges of the display screen are provided with light-emitting sources at preset intervals, and the determining unit 1605 is specifically used for:

[0150] The prediction results are processed in a way that matches the result type to determine the luminescence parameters of the light source corresponding to different locations;

[0151] The control unit 1606 is specifically used for:

[0152] Light sources at different locations are controlled to emit colored light according to the corresponding simulated color value and simulated light intensity value, so as to display colored light with the simulated color value and simulated light intensity value in the extended area.

[0153] In one possible implementation, a first light source is provided at a preset interval along the edge of the display screen, and a second light source is provided at a preset position in the environment where the display screen is located. The determining unit 1605 is specifically used for:

[0154] The prediction results are processed in a way that matches the result type to determine the luminous parameters corresponding to the first and second light sources at different locations.

[0155] The control unit 1606 is specifically used for:

[0156] The first and second light sources at different positions are controlled to emit colored light according to the corresponding simulated color value and simulated light intensity value, so as to display colored light with the simulated color value and simulated light intensity value in the extended area.

[0157] In one possible implementation, if the display screen is a display screen in a virtual reality space display scene or an augmented reality space display scene, the control unit 1606 is specifically used for:

[0158] The effect diagram of colored light with the color value to be simulated and the light intensity value to be simulated is rendered in the extended area of ​​the display screen.

[0159] This application embodiment also provides a display screen extension system, the system including a display screen and a central processing platform, the display screen being used to display target content, and the central processing platform being used to control the extended area of ​​the display screen to display colored light with simulated color value and simulated light intensity value according to any one of the methods provided in the foregoing embodiments during the display of the target content on the display screen.

[0160] In one possible implementation, light-emitting light sources are arranged at preset intervals along the edge of the display screen. The system also includes a control center, and the light-emitting light sources are electrically connected to the control center. The control center is used to control the light-emitting light sources at different positions to emit colored light according to the corresponding simulated color value and simulated light intensity value.

[0161] In one possible implementation, the edge of the display screen is provided with a first light source at a preset interval. The system also includes a second light source and a control center. The second light source is set at a preset position in the environment where the display screen is located. The control center is electrically connected to the first light source and the second light source respectively. The control center is used to control the first light source and the second light source at different positions to emit colored light according to the corresponding simulated color value and simulated light intensity value.

[0162] This application also provides an electronic device for extending a display screen. This electronic device can be a terminal, with a smartphone as an example:

[0163] Figure 17 The diagram shown is a block diagram of a portion of the structure of a smartphone provided in an embodiment of this application. (Reference) Figure 17 A smartphone includes components such as: a radio frequency (RF) circuit 1710, a memory 1720, an input unit 1730, a display unit 1740, a sensor 1750, an audio circuit 1760, a Wi-Fi module 1770, a processor 1780, and a power supply 1790. The input unit 1730 may include a touch panel 1731 and other input devices 1732, the display unit 1740 may include a display panel 1741, and the audio circuit 1760 may include a speaker 1761 and a microphone 1762. It is understood that... Figure 17 The smartphone structure shown does not constitute a limitation on smartphones and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0164] The memory 1720 can be used to store software programs and modules. The processor 1780 executes various functions and data processing of the smartphone by running the software programs and modules stored in the memory 1720. The memory 1720 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the smartphone (such as audio data, phonebook, etc.). In addition, the memory 1720 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0165] The processor 1780 is the control center of the smartphone, connecting various parts of the smartphone via various interfaces and lines. It performs various functions and processes data by running or executing software programs and / or modules stored in the memory 1720 and by accessing data stored in the memory 1720. Optionally, the processor 1780 may include one or more processing units; preferably, the processor 1780 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 1780.

[0166] In this embodiment, the processor 1780 in the smartphone can perform the following steps:

[0167] The target content is displayed on the display screen, and the target content is the content shown to the object through the display screen.

[0168] During the process of displaying the target content on the display screen, the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment in which the display screen is located, are acquired.

[0169] Using a neural network model, features are extracted from the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data to obtain corresponding feature vectors.

[0170] Based on the obtained feature vectors, a prediction is made to obtain the prediction result of the neural network model; the result type of the prediction result is determined, and the prediction result is processed in a way that matches the result type to obtain the luminescence parameters, which include the color value to be simulated and the light intensity value to be simulated;

[0171] The extended area of ​​the display screen is controlled to display colored light with the color value to be simulated and the light intensity value to be simulated. The extended area is the area outside the display screen in the color recognition area corresponding to the object.

[0172] This application also provides a server; please refer to [link / reference]. Figure 18 As shown, Figure 18 This is a structural diagram of a server 1800 provided in an embodiment of this application. The server 1800 can vary significantly due to different configurations or performance. It may include one or more central processing units (CPUs) 1822 (e.g., one or more processors) and a memory 1832, and one or more storage media 1830 (e.g., one or more mass storage devices) for storing application programs 1842 or data 1844. The memory 1832 and storage media 1830 can be temporary or persistent storage. The program stored in the storage media 1830 may include one or more modules (not shown in the diagram), each module including a series of instruction operations on the server. Furthermore, the CPU 1822 may be configured to communicate with the storage media 1830 and execute the series of instruction operations in the storage media 1830 on the server 1800.

[0173] Server 1800 may also include one or more power supplies 1826, one or more wired or wireless network interfaces 1850, one or more input / output interfaces 1858, and / or one or more operating systems 1841, such as Windows Server. TM Mac OS X TM Unix TM Linux TM FreeBSD TM etc.

[0174] In this embodiment, the central processing unit 1822 in server 1800 can perform the following steps:

[0175] The target content is displayed on the display screen, and the target content is the content shown to the object through the display screen.

[0176] During the process of displaying the target content on the display screen, the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment in which the display screen is located, are acquired.

[0177] Using a neural network model, features are extracted from the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data to obtain corresponding feature vectors.

[0178] Based on the obtained feature vectors, a prediction is made to obtain the prediction result of the neural network model; the result type of the prediction result is determined, and the prediction result is processed in a way that matches the result type to obtain the luminescence parameters, which include the color value to be simulated and the light intensity value to be simulated;

[0179] The extended area of ​​the display screen is controlled to display colored light with the color value to be simulated and the light intensity value to be simulated. The extended area is the area outside the display screen in the color recognition area corresponding to the object.

[0180] According to one aspect of this application, a computer-readable storage medium is provided for storing program code for executing the display screen extension methods described in the foregoing embodiments.

[0181] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various optional implementations of the above embodiments.

[0182] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0183] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0184] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0185] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0186] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0187] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0188] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for expanding a display screen, characterized in that, The method includes: The target content is displayed on the display screen, and the target content is the content shown to the object through the display screen. During the process of displaying the target content on the display screen, the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment in which the display screen is located, are acquired. Using a neural network model, features are extracted from the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data to obtain corresponding feature vectors. Based on the obtained feature vectors, predictions are made to obtain the prediction results of the neural network model; The result type of the prediction result is determined, and the prediction result is processed in a way that matches the result type to obtain the luminescence parameters, which include the color value to be simulated and the light intensity value to be simulated. The extended area of ​​the display screen is controlled to display colored light with the color value to be simulated and the light intensity value to be simulated. The extended area is the area outside the display screen in the color recognition area corresponding to the object.

2. The method according to claim 1, characterized in that, The first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen; the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen. The prediction result is a result type of influence coefficient, which includes color influence coefficient and light intensity influence coefficient. The color influence coefficient includes a first influence coefficient of the main screen color information data, a second influence coefficient of the edge color information data, and a third influence coefficient of the second color information data. The light intensity influence coefficient includes a fourth influence coefficient of the main screen light intensity information data, a fifth influence coefficient of the edge light intensity information data, and a sixth influence coefficient of the second light intensity information data. The color values ​​to be simulated include edge color values ​​and corner color values ​​to be simulated; the light intensity values ​​to be simulated include edge light intensity values ​​and corner light intensity values ​​to be simulated; the process of matching the prediction results with the result type to obtain the luminescence parameters includes: The edge color value to be simulated is obtained by weighted summation of the main screen color information data, the first influence coefficient, the edge color information data, the second influence coefficient, the second color information data, and the third influence coefficient. The corner color value to be simulated is obtained by weighted summation of the main screen color information data, the first influence coefficient, the corner color information data, the second influence coefficient, the second color information data, and the third influence coefficient. The edge light intensity value to be simulated is obtained by weighted summation of the main screen light intensity information data, the fourth influence coefficient, the edge light intensity information data, the fifth influence coefficient, the second light intensity information data, and the sixth influence coefficient. The corner light intensity value to be simulated is obtained by weighted summation of the main screen light intensity information data, the fourth influence coefficient, the corner light intensity information data, the fifth influence coefficient, the second light intensity information data, and the sixth influence coefficient.

3. The method according to claim 1, characterized in that, The first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen; the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen. The prediction result is of type color light information, which includes color value and light intensity value. The process of obtaining luminescence parameters by matching the prediction result with the result type includes: The color value in the prediction result is used as the color value to be simulated, and the light intensity value in the prediction result is used as the light intensity value to be simulated.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Based on historical influence factors, historical luminescence parameters, and the evaluation results of the historical luminescence parameters, an initial training sample is constructed. The historical influence factors include historical screen parameters, historical color information data, and historical light intensity information data. The historical color information data includes the color information data of the display screen and the color information data of the environment in which the display screen is located. The historical light intensity information data includes the light intensity information data of the display screen and the light intensity information data of the environment in which the display screen is located. The initial training samples are screened using the first model to obtain target training samples. The evaluation results determined based on the historical influence factors and historical luminescence parameters in the target training samples meet the preset conditions. The neural network model is obtained by training the second model based on the target training samples.

5. The method according to any one of claims 1-3, characterized in that, The edges of the display screen are provided with light-emitting light sources at preset intervals. The process of obtaining the light emission parameters by matching the prediction results with the result type includes: The prediction results are processed in a way that matches the result type to determine the luminescence parameters of the light source corresponding to different locations; The control of the extended area of ​​the display screen to display colored light having the simulated color value and the simulated light intensity value includes: Light sources at different locations are controlled to emit colored light according to the corresponding simulated color value and simulated light intensity value, so as to display colored light with the simulated color value and simulated light intensity value in the extended area.

6. The method according to any one of claims 1-3, characterized in that, The edge of the display screen is provided with a first light source at a preset interval, and a second light source is provided at a preset position in the environment where the display screen is located. The step of processing the prediction result to obtain the light emission parameters by matching the result type includes: The prediction results are processed in a way that matches the result type to determine the luminous parameters corresponding to the first and second light sources at different locations. The control of the extended area of ​​the display screen to display colored light having the simulated color value and the simulated light intensity value includes: The first and second light sources at different positions are controlled to emit colored light according to the corresponding simulated color value and simulated light intensity value, so as to display colored light with the simulated color value and simulated light intensity value in the extended area.

7. The method according to any one of claims 1-3, characterized in that, If the display screen is a display screen in a virtual reality space display scene or an augmented reality space display scene, controlling the extended area of ​​the display screen to display colored light having the color value to be simulated and the light intensity value to be simulated includes: The effect diagram of colored light with the color value to be simulated and the light intensity value to be simulated is rendered in the extended area of ​​the display screen.

8. A display screen expansion system, characterized in that, The system includes a display screen and a central processing platform. The display screen is used to display target content, and the central processing platform is used, during the process of displaying the target content on the display screen, to control the extended area of ​​the display screen to display colored light with simulated color value and simulated light intensity value according to any one of claims 1-7.

9. The system according to claim 8, characterized in that, The edge of the display screen is provided with light-emitting light sources at preset intervals. The system also includes a control center. The light-emitting light sources are electrically connected to the control center. The control center is used to control the light-emitting light sources at different positions to emit colored light according to the corresponding simulated color value and simulated light intensity value.

10. The system according to claim 8, characterized in that, The edge of the display screen is provided with a first light source at a preset interval. The system also includes a second light source and a control center. The second light source is set at a preset position in the environment where the display screen is located. The control center is electrically connected to the first light source and the second light source respectively. The control center is used to control the first light source and the second light source at different positions to emit colored light according to the corresponding simulated color value and simulated light intensity value.

11. An extension device for a display screen, characterized in that, The device includes a display unit, an acquisition unit, an extraction unit, a prediction unit, a determination unit, and a control unit. The display unit is used to display target content through a display screen, wherein the target content is content displayed to an object through the display screen. The acquisition unit is used to acquire, during the process of displaying the target content on the display screen, the first color information data and the first light intensity information data displayed on the display screen, as well as the second color information data and the second light intensity information data of the environment in which the display screen is located; The extraction unit is used to extract features based on the first color information data, the first light intensity information data, the second color information data, and the second light intensity information data using a neural network model, and obtain corresponding feature vectors. The prediction unit is used to make predictions based on the obtained feature vectors to obtain the prediction results of the neural network model. The determining unit is used to determine the result type of the prediction result, and to perform a processing method on the prediction result that matches the result type to obtain luminescence parameters, wherein the luminescence parameters include the color value to be simulated and the light intensity value to be simulated. The control unit is used to control the extended area of ​​the display screen to display colored light with the color value to be simulated and the light intensity value to be simulated, wherein the extended area is the area outside the display screen in the color recognition area corresponding to the object.

12. The apparatus according to claim 11, characterized in that, The first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen; the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen. The prediction result is a result type of influence coefficient, which includes color influence coefficient and light intensity influence coefficient. The color influence coefficient includes a first influence coefficient of the main screen color information data, a second influence coefficient of the edge color information data, and a third influence coefficient of the second color information data. The light intensity influence coefficient includes a fourth influence coefficient of the main screen light intensity information data, a fifth influence coefficient of the edge light intensity information data, and a sixth influence coefficient of the second light intensity information data. The color values ​​to be simulated include edge color values ​​and corner color values ​​to be simulated; the light intensity values ​​to be simulated include edge light intensity values ​​and corner light intensity values ​​to be simulated; the determining unit is specifically used for: The edge color value to be simulated is obtained by weighted summation of the main screen color information data, the first influence coefficient, the edge color information data, the second influence coefficient, the second color information data, and the third influence coefficient. The corner color value to be simulated is obtained by weighted summation of the main screen color information data, the first influence coefficient, the corner color information data, the second influence coefficient, the second color information data, and the third influence coefficient. The edge light intensity value to be simulated is obtained by weighted summation of the main screen light intensity information data, the fourth influence coefficient, the edge light intensity information data, the fifth influence coefficient, the second light intensity information data, and the sixth influence coefficient. The corner light intensity value to be simulated is obtained by weighted summation of the main screen light intensity information data, the fourth influence coefficient, the corner light intensity information data, the fifth influence coefficient, the second light intensity information data, and the sixth influence coefficient.

13. The apparatus according to claim 11, characterized in that, The first color information data includes the main screen color information data, edge color information data, and corner color information data of the display screen; the first light intensity information data includes the main screen light intensity information data, edge light intensity information data, and corner light intensity information data of the display screen. The prediction result is of type color light information, which includes color value and light intensity value. The determining unit is specifically used for: The color value in the prediction result is used as the color value to be simulated, and the light intensity value in the prediction result is used as the light intensity value to be simulated.

14. The apparatus according to any one of claims 11-13, characterized in that, The expansion device further includes a training unit, the training unit being used for: Based on historical influence factors, historical luminescence parameters, and the evaluation results of the historical luminescence parameters, an initial training sample is constructed. The historical influence factors include historical screen parameters, historical color information data, and historical light intensity information data. The historical color information data includes the color information data of the display screen and the color information data of the environment in which the display screen is located. The historical light intensity information data includes the light intensity information data of the display screen and the light intensity information data of the environment in which the display screen is located. The initial training samples are screened using the first model to obtain target training samples. The evaluation results determined based on the historical influence factors and historical luminescence parameters in the target training samples meet the preset conditions. The neural network model is obtained by training the second model based on the target training samples.

15. The apparatus according to any one of claims 11-13, characterized in that, The edge of the display screen is provided with light-emitting sources at preset intervals, and the determining unit is specifically used for: The prediction results are processed in a way that matches the result type to determine the luminescence parameters of the light source corresponding to different locations; The control unit is specifically used for: Light sources at different locations are controlled to emit colored light according to the corresponding simulated color value and simulated light intensity value, so as to display colored light with the simulated color value and simulated light intensity value in the extended area.

16. The apparatus according to any one of claims 11-13, characterized in that, The edge of the display screen is provided with a first light source at a preset interval, and a second light source is provided at a preset position in the environment where the display screen is located. The determining unit is specifically used for: The prediction results are processed in a way that matches the result type to determine the luminous parameters corresponding to the first and second light sources at different locations. The control unit is specifically used for: The first and second light sources at different positions are controlled to emit colored light according to the corresponding simulated color value and simulated light intensity value, so as to display colored light with the simulated color value and simulated light intensity value in the extended area.

17. The apparatus according to any one of claims 11-13, characterized in that, If the display screen is a display screen in a virtual reality space display scene or an augmented reality space display scene, the control unit is specifically used for: The effect diagram of colored light with the color value to be simulated and the light intensity value to be simulated is rendered in the extended area of ​​the display screen.

18. An electronic device for extending a display screen, characterized in that, The electronic device includes a processor and a memory: The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the method according to any one of claims 1-7 according to the instructions in the program code.

19. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code that, when executed by a processor, causes the processor to perform the method according to any one of claims 1-7.

20. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1-7.

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