Matrix intelligent split screen adjustment method, system and device and storage medium

Abstract

The invention provides a matrix intelligent split-screen adjustment method, system and device and a storage medium, and the method comprises the steps: obtaining video display parameters of different cameras in real time, carrying out the preprocessing of the obtained video display parameters to obtain video display data, carrying out the feature extraction of the video display data in a single device form, and carrying out the feature extraction of the video display data. According to the method, the acquired single-device related video feature vector is used as input, and the optimal matrix split-screen configuration parameter is output through the preset matrix split-screen prediction model, so that the display effect of each device and the video source is maximized, the problem of blurring or distortion caused by improper picture zooming possibly occurring under the traditional fixed layout is effectively avoided, and the user experience is improved. And efficient utilization of display resources is realized. The matrix screen splitting parameters are dynamically adjusted based on the dynamic adjustment technology, the complexity of manual adjustment is avoided, manual configuration errors are reduced, and the stability and reliability of matrix intelligent screen splitting are improved.

Description

Technical Field

[0001] The present invention relates to the field of matrix split-screen technology, and in particular to a matrix intelligent split-screen adjustment method, system, device and storage medium. Background Art

[0002] Nowadays, with the popularization of modern monitoring, video playback and multimedia management systems, how to efficiently and intelligently display multiple videos on one or more display devices has become an important technical issue, especially in security monitoring, smart cities, video conferencing and other fields. Existing matrix split-screen technology usually uses a fixed layout (such as 1x1, 2x2, etc.) to divide the video signal into multiple screens and display them simultaneously. These matrix layouts are usually based on the manual settings of the system administrator, which can meet the needs in occasions where the number of devices is relatively fixed. However, it is difficult to adapt to the dynamically changing number of devices and video source resolution, resulting in poor display effects and easy waste of resources. At the same time, since the administrator needs to manually adjust the split-screen configuration, it greatly increases the difficulty of system management and reduces the user experience. Summary of the invention

[0003] The present invention provides a matrix intelligent split screen adjustment method, system, device and storage medium to solve the problem in the prior art that adaptive adjustment cannot be performed according to the dynamically changing number of devices and video source resolution.

[0004] In a first aspect, an embodiment of the present invention provides a matrix intelligent split screen adjustment method, comprising:

[0005] Real-time monitoring of video display parameters, preprocessing of the acquired video display parameters to obtain video display data, and feature processing to form a video source feature matrix;

[0006] Based on the integrated video source feature vector, the optimal parameter strategy for matrix split screen configuration is output through the preset matrix split screen prediction model;

[0007] Based on the matrix split-screen optimal parameter strategy, the current parameters are adjusted to the parameters corresponding to the matrix split-screen optimal parameter strategy.

[0008] As a preferred embodiment of the first aspect, the video display parameters specifically include: the number of online devices, video resolution and video display ratio; wherein the number of online devices specifically refers to the number of current online cameras.

[0009] The present invention ensures the accuracy and timeliness of video display parameters by real-time detection, provides a reliable data basis for subsequent processing, and avoids the influence of abnormal parameters on video display quality; comprehensively considers video resolution and ratio, and displays the most suitable split-screen matrix according to different resolution videos, avoiding the problem of excessive display of low resolution or blurred display of high resolution; through real-time acquisition and dynamic adjustment of data, when the number of devices and video sources change, the most suitable split-screen matrix is ​​recalculated and adjusted in time to ensure the efficiency and real-time response capability of the system. Through the preset matrix split-screen prediction model, the optimal parameters of the matrix split-screen configuration are obtained and dynamically adjusted, reducing the manual intervention of the administrator and improving the convenience of management.

[0010] As a preferred embodiment of the first aspect, the preprocessing of the acquired video display parameters to obtain video display data; and feature processing and extraction of the video display data to integrate and form a video source feature vector specifically include the following steps:

[0011] Obtain video display parameters through real-time interaction with the camera;

[0012] Cleaning, deduplication and standardization of video display parameters acquired in real time to obtain video display data;

[0013] A corresponding feature vector is obtained for the video display data in a single device form.

[0014] The present invention obtains video display parameters by real-time interaction with the camera, cleans and deduplicates the video display parameters obtained in real time, and improves the accuracy of subsequent analysis; the feature vector is updated in the form of a single device, and each device is independently analyzed through the model, which is conducive to comparison and monitoring between devices and improves the prediction accuracy of the model.

[0015] As a preferred embodiment of the first aspect, the real-time interaction with the camera to obtain the video display parameters is specifically:

[0016] Use heartbeat detection to determine the online status of the camera;

[0017] If the camera is online, the corresponding video display parameters are obtained in real time;

[0018] If the camera is offline, the camera will be marked as grayed out and the corresponding video display parameters cannot be provided.

[0019] The present invention adopts a heartbeat detection mechanism to perceive the online status of the camera in real time, ensures real-time acquisition of data, determines the online status of the camera through heartbeat response to avoid unnecessary waste of resources, and ensures that relevant video display parameters can be obtained in real time when the camera is confirmed to be online and in online loading; and avoids invalid data requests and processing for offline cameras by graying out the status mark of offline devices.

[0020] As a preferred embodiment of the first aspect, the preset matrix split-screen prediction model includes the following steps:

[0021] Collect historical data related to video display parameters, corresponding split-screen configuration parameters, and corresponding matrix split-screen effect quantitative values ​​to generate a historical data set;

[0022] Dividing the historical data set into a test set, a validation set, and a test set to train the matrix split-screen prediction model;

[0023] Set the relevant optimization function, where the calculation formula of the optimization function is as follows:

[0024]

[0025] Among them, ω represents the weight vector, which is used to describe the normal vector of the hyperplane; b represents the bias term, which is used to determine the relative position of the hyperplane; ||ω|| represents the complexity of the control model, which indirectly reflects the distance between the hyperplane and the data point;

[0026] The constraint conditions of the optimization function are set, and the calculation formula is specifically as follows:

[0027]

[0028] Among them, y i represents the classification label of the i-th sample, x i represents the input feature vector of the i-th sample, ω T x i Represents the dot product of the weight vector and the feature vector, reflecting the relative position of the sample and the classification hyperplane. Indicates that the constraint applies to all data points.

[0029] The matrix split-screen prediction model provided by the method integrates relevant historical data such as video display parameters, split-screen configuration parameters and split-screen effect quantization values, divides the integrated historical data set into a training set, a validation set and a test set, trains the model to ensure the generalization ability of the model, controls the complexity of the model through optimization functions to avoid overfitting of the model, and predicts the data obtained in real time with the trained matrix split-screen prediction model to automatically recommend the optimal split-screen configuration parameters, thereby improving the video display effect and user satisfaction.

[0030] As a preferred embodiment of the first aspect, adjusting the current parameters to the parameters corresponding to the matrix split-screen optimal parameter strategy based on the matrix split-screen optimal parameter strategy includes:

[0031] Sending the matrix split screen optimal parameter strategy output by the matrix split screen prediction model to the video matrix, and determining the number of split screens and the split screen layout in the optimal parameter strategy;

[0032] Based on a dynamic adjustment algorithm, the current parameters of the video matrix to be adjusted are adjusted to the parameters corresponding to the matrix screen splitting optimal parameter strategy.

[0033] In a second aspect, an embodiment of the present invention provides a matrix intelligent screen splitting system, the matrix intelligent screen splitting system comprising: a real-time acquisition module, a screen splitting prediction module and a configuration adjustment module;

[0034] The real-time acquisition module is used to monitor the video display parameters in real time, pre-process the acquired video display parameters to obtain video display data, and perform feature processing to form a video source feature matrix;

[0035] The split screen prediction module outputs the optimal parameter strategy for matrix split screen configuration based on the integrated video source feature vector and a preset matrix split screen prediction model;

[0036] The configuration adjustment module is used to adjust the current parameters to the parameters corresponding to the optimal parameter strategy of the matrix split-screen according to the optimal parameter strategy of the matrix split-screen.

[0037] The video display parameters specifically include: the number of online devices, video resolution, and video display ratio; wherein the number of online devices specifically refers to the number of current online cameras.

[0038] The preprocessing of the acquired video display parameters to obtain video display data; and feature processing and extraction of the video display data to integrate and form a video source feature vector specifically include the following steps:

[0039] Obtain video display parameters through real-time interaction with the camera;

[0040] Cleaning, deduplication and standardization of video display parameters acquired in real time to obtain video display data;

[0041] A corresponding feature vector is obtained for the video display data in a single device form.

[0042] This system ensures the accuracy and timeliness of video display parameters by real-time detection, provides a reliable data basis for subsequent processing, and avoids the impact of abnormal parameters on video display quality; comprehensively considers video resolution and ratio, and displays the most appropriate split-screen matrix according to different resolution videos to avoid problems such as low-resolution display being too large or high-resolution display being blurred; through real-time acquisition and dynamic adjustment of data, when the number of devices and video sources change, the most appropriate split-screen matrix is ​​recalculated and adjusted in time to ensure the system's efficiency and real-time responsiveness. Through the preset matrix split-screen prediction model, the optimal parameters of the matrix split-screen configuration are obtained and dynamically adjusted, reducing the administrator's manual intervention and improving the convenience of management.

[0043] As a preferred embodiment of the second aspect, the real-time interaction with the camera to obtain the video display parameters is specifically:

[0044] Use heartbeat detection to determine the online status of the camera;

[0045] If the camera is online, the corresponding video display parameters are obtained in real time;

[0046] If the camera is offline, the camera will be marked as grayed out and the corresponding video display parameters cannot be provided.

[0047] This system uses a heartbeat detection mechanism to sense the online status of the camera in real time, ensuring real-time data acquisition, and determines the online status of the camera through heartbeat response to avoid unnecessary waste of resources. It ensures that when the camera is online, relevant video display parameters can be obtained in real time; by graying out the status mark of offline devices, invalid data requests and processing for offline cameras can be avoided.

[0048] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0049] one or more processors;

[0050] A storage medium for storing one or more programs;

[0051] When the one or more programs are executed by the one or more processors, the one or more processors implement the matrix intelligent split-screen adjustment method described in any embodiment of the present invention.

[0052] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the matrix intelligent screen splitting method described in any embodiment of the present invention.

[0053] In a fifth aspect, an embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the matrix intelligent split-screen adjustment method as described in any embodiment of the present invention.

[0054] The embodiment of the present invention provides a matrix intelligent split screen adjustment method, system, device and storage medium, which obtains video display parameters of different cameras in real time, pre-processes the obtained video display parameters to obtain video display data, extracts features from the obtained video display data, extracts features in the form of a single device, takes the single device related video feature vector as input, and outputs the optimal parameters for matrix split screen configuration through a preset matrix split screen prediction model, ensuring that the display effect of each device and video source is maximized, effectively avoiding the problem of blur or distortion caused by improper picture scaling under the traditional fixed layout, and realizing efficient use of display resources. The matrix split screen parameters are dynamically adjusted based on the dynamic adjustment technology, avoiding the tediousness of manual adjustment, reducing the occurrence of manual configuration errors, and improving the stability and reliability of the matrix intelligent split screen. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 A flowchart of the steps of a matrix intelligent split screen adjustment method provided by this application;

[0057] Figure 2 A structural schematic diagram of a matrix intelligent split-screen adjustment method provided by the present invention;

[0058] Figure 3 The present invention provides a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] Embodiment 1

[0061] Please refer to Figure 1, is a flow chart of a matrix intelligent split screen adjustment method provided by an embodiment of the present invention, including steps S01-S03, each step is specifically as follows:

[0062] Step S01: real-time monitoring of video display parameters, preprocessing the acquired video display parameters to obtain video display data, and performing feature processing to form a video source feature matrix;

[0063] Furthermore, the video display parameters specifically include: the number of online devices, video resolution, and video display ratio; wherein the number of online devices specifically refers to the number of current online cameras.

[0064] Furthermore, the method of preprocessing the acquired video display parameters to obtain video display data; performing feature processing and extraction on the video display data, and integrating to form a video source feature vector specifically comprises the following steps:

[0065] Obtain video display parameters through real-time interaction with the camera;

[0066] Cleaning, deduplication and standardization of video display parameters acquired in real time to obtain video display data;

[0067] A corresponding feature vector is obtained for the video display data in a single device form.

[0068] It should be noted that the number of online devices indicates the number of devices that need to display the screen at that time, which directly affects the scale of the split-screen matrix. In this embodiment, it is characterized by the statistical results of the online and offline states of the camera. Video resolution and video ratio represent that each camera supports independent setting of acquisition resolution and adjustment of video ratio, wherein video resolution includes 4K (3840×2160), 2K (2560×1440), ultra-high definition (1600×900), full high definition (1920×1080), high definition (1280×720), etc. Video resolution determines the size of a single screen to affect the selection of the split-screen matrix. High-resolution videos require more display areas, while low-resolution videos can be displayed in a smaller display area; common video ratios include 16:9, 4:3, etc., video ratio, different video ratios will affect the selection of the split-screen matrix, and the specific settings can be adjusted according to actual needs.

[0069] The purpose of data cleaning is to remove invalid, erroneous or abnormal data in video display parameters, and check whether there are missing values ​​in video display parameters, such as incomplete key information such as resolution and video ratio. For missing values, interpolation, mean filling and other methods can be used to fill them; for data that is out of a reasonable range or illogical, set relevant thresholds and mark or manually correct the data that exceeds the threshold, and standardize the format of all video display data of a single device. Through effective cleaning, deduplication and standardization of video display parameters, more accurate and reliable video display data can be obtained to provide data support for subsequent model predictions.

[0070] The present invention ensures the accuracy and timeliness of video display parameters by real-time detection, provides a reliable data basis for subsequent processing, and avoids the influence of abnormal parameters on video display quality; comprehensively considers video resolution and ratio, and displays the most suitable split-screen matrix according to different resolution videos, avoiding the problem of excessive display of low resolution or blurred display of high resolution; through real-time acquisition and dynamic adjustment of data, when the number of devices and video sources change, the most suitable split-screen matrix is ​​recalculated and adjusted in time to ensure the efficiency and real-time response capability of the system. Through the preset matrix split-screen prediction model, the optimal parameters of the matrix split-screen configuration are obtained and dynamically adjusted, reducing the manual intervention of the administrator and improving the convenience of management.

[0071] Furthermore, the real-time interaction with the camera to obtain video display parameters is specifically as follows:

[0072] Use heartbeat detection to determine the online status of the camera;

[0073] If the camera is online, the corresponding video display parameters are obtained in real time;

[0074] If the camera is offline, it will be marked as grayed out and the corresponding video display parameters cannot be provided;

[0075] In a specific embodiment, a heartbeat packet request is sent to the camera through a preset time threshold. If the camera does not reply to the heartbeat response within the specified time, it is determined that the camera is in an offline state and is marked as a gray state. The offline device will not be able to provide a real-time acquired video stream; for the camera in the online state, the video display parameters are obtained and subsequent processing is performed. When the online state of the camera changes, the relevant feature vector is updated in the form of a single device. For example, when camera A is converted from an offline state to an online state, camera A actively sends a heartbeat response packet. After the system receives the device heartbeat response packet, it updates the device state and obtains the video display parameters of camera A at the same time. By processing the video display parameters of camera A and feature processing, the feature vector of a single device is obtained and input into the preset matrix split screen prediction model; camera B is converted from an online state to an offline state and no longer sends a heartbeat response packet. After the system does not receive a heartbeat response packet within the specified time, it stops obtaining its video display parameters and marks camera B as a gray state.

[0076] The present invention adopts a heartbeat detection mechanism to perceive the online status of the camera in real time, ensures real-time acquisition of data, determines the online status of the camera through heartbeat response to avoid unnecessary waste of resources, and ensures that relevant video display parameters can be obtained in real time after confirming that the camera is online; by graying out the status mark of the offline device, invalid data request and processing for the offline camera is avoided, effectively saving system resources.

[0077] Step S02: Based on the integrated video source feature vector, the optimal matrix split-screen parameter strategy is output through a preset matrix split-screen prediction model.

[0078] Specifically, the preset matrix split-screen prediction model includes the following steps:

[0079] Collect historical data related to video display parameters, corresponding split-screen configuration parameters, and corresponding matrix split-screen effect quantitative values ​​to generate a historical data set;

[0080] Dividing the historical data set into a test set, a validation set, and a test set to train the matrix split-screen prediction model;

[0081] It should be noted that historical data related to video display are collected, including multiple single-device video display parameters (such as resolution, frame rate, video ratio, etc.), corresponding split-screen configuration parameters (such as the number of split-screens, split-screen layout, etc.) and matrix split-screen effect quantification values ​​(such as user satisfaction quantification values, visual comfort quantification values, etc.), and the data are integrated to generate a historical data set to provide data support for model training. The integrated historical data is divided into a test set, a validation set, and a test set, wherein the training set is used to train the matrix split-screen prediction model, the validation set is used to adjust the model parameters, and the test set is used to evaluate the model performance. In a specific embodiment, the data set is divided into a test set, a validation set, and a test set in a ratio of 6:2:2, and the model is rationally divided to avoid overfitting or underfitting.

[0082] In a specific embodiment, the matrix split screen prediction model adopts a support vector machine model; a related optimization function is set, the goal is to obtain a set of weight vectors ω and bias terms b, so that the complexity of the model (i.e., the norm of the weight vector) is as small as possible while satisfying the constraints. In this embodiment, the optimization function calculation formula is as follows:

[0083]

[0084] Among them, ω represents the weight vector, which is used to describe the normal vector of the hyperplane; b represents the bias term, which is used to determine the relative position of the hyperplane; ||ω|| represents the complexity of the control model, which indirectly reflects the distance between the hyperplane and the data point; at the same time, the constraints of the optimization function are set to ensure the accuracy of the model prediction. The specific calculation formula is:

[0085]

[0086] Among them, y i represents the classification label of the i-th sample, x i represents the input feature vector of the i-th sample, ω T x i Represents the dot product of the weight vector and the feature vector, reflecting the relative position of the sample and the classification hyperplane. Indicates that the constraint applies to all data points.

[0087] The trained matrix split-screen prediction model is verified using validation set data to evaluate performance, and the model is further optimized and adjusted based on the validation results to improve generalization and accuracy. The most appropriate matrix distribution is predicted through training with historical data, and the relationship between the model's learning video display parameters and matrix distribution is used. The single device feature vector extracted from the video display parameters is used as input, and the matrix split-screen prediction model outputs the optimal matrix split-screen parameter strategy. When the device status changes, the split-screen matrix configuration is updated in real time, and the new display requirements are automatically adapted to ensure that the display effect is always optimized.

[0088] The matrix split-screen prediction model provided by the method integrates relevant historical data such as video display parameters, split-screen configuration parameters and split-screen effect quantization values, divides the integrated historical data set into a training set, a validation set and a test set, trains the model to ensure the generalization ability of the model, controls the complexity of the model through optimization functions to avoid overfitting of the model, and predicts the data obtained in real time with the trained matrix split-screen prediction model to automatically recommend the optimal split-screen configuration parameters, thereby improving the video display effect and user satisfaction.

[0089] Step S03: Based on the matrix split-screen optimal parameter strategy, the current parameters are adjusted to the parameters corresponding to the matrix split-screen optimal parameter strategy.

[0090] Furthermore, the adjusting the current parameters to the parameters corresponding to the matrix split-screen optimal parameter strategy based on the matrix split-screen optimal parameter strategy includes:

[0091] Sending the matrix split screen optimal parameter strategy output by the matrix split screen prediction model to the video matrix, and determining the number of split screens and the split screen layout in the optimal parameter strategy;

[0092] Based on a dynamic adjustment algorithm, the current parameters of the video matrix to be adjusted are adjusted to the parameters corresponding to the matrix screen splitting optimal parameter strategy.

[0093] In a specific embodiment, the matrix split-screen optimal parameter strategy output by the matrix split-screen prediction model is sent to the video matrix control system, and the video matrix control system determines the number and layout of split screens based on the received parameter strategy. For example, if the output parameter strategy indicates a 3-split screen and the layout is a left-right-upper layout, the control system divides the video matrix into three areas, two of which are located on the left (arranged up and down), and one area is located on the right.

[0094] As a preferred solution of this embodiment, a dynamic adjustment algorithm is set to gradually adjust the current parameters of the video matrix to the parameters corresponding to the matrix split screen optimal parameter strategy. The dynamic adjustment algorithm dynamically adjusts the system parameters according to the optimal parameter strategy output in real time to ensure the smoothness of video display and system stability.

[0095] Specifically, according to the optimal parameter strategy of matrix split screen, the parameters of the video matrix are initialized, including the number of split screens, layout, etc., and these parameters serve as the starting point for dynamic adjustment. A transition mechanism is set to achieve a smooth transition of the video when it is displayed in a transposed position; in this embodiment, the mechanism gradually adjusts the split screen parameters when the video is transposed to avoid discomfort to the user caused by sudden display changes. For example, when it is necessary to switch a split screen video to another split screen, the resolution and frame rate of the target split screen are gradually adjusted to gradually approach the source split screen parameters, and a smooth transition effect is achieved by controlling the speed and method of switching.

[0096] The present invention ensures smooth transition of the video matrix during the split-screen process when the video is transposed, parameters change, etc., through dynamic adjustment algorithms, so as to achieve user viewing comfort and satisfaction; through adaptive adjustment, the activity and scalability of the system are improved, and when the number of devices and video source resolution dynamically change in security monitoring, video conferencing, video live broadcast, etc., it can respond quickly and make corresponding adjustments to maximize the utilization efficiency of screen display resources. The algorithm is combined with existing hardware facilities, and the split-screen layout is automatically optimized through software to reduce dependence on complex hardware.

[0097] The embodiment of the present invention provides a matrix intelligent split screen adjustment method, system, device and storage medium, which obtains video display parameters of different cameras in real time, pre-processes the obtained video display parameters to obtain video display data, extracts features from the obtained video display data, extracts features in the form of a single device, takes the single device related video feature vector as input, and outputs the optimal parameters for matrix split screen configuration through a preset matrix split screen prediction model, ensuring that the display effect of each device and video source is maximized, effectively avoiding the problem of blur or distortion caused by improper picture scaling under the traditional fixed layout, and realizing efficient use of display resources. The matrix split screen parameters are dynamically adjusted based on the dynamic adjustment technology, avoiding the tediousness of manual adjustment, reducing the occurrence of manual configuration errors, and improving the stability and reliability of the matrix intelligent split screen.

[0098] Embodiment 2

[0099] Please refer to Figure 2, is a schematic diagram of the structure of a matrix intelligent split screen adjustment system provided in an embodiment of the present application. The system can be implemented by software and / or hardware, and is generally integrated on any electronic device with network communication function, including but not limited to: servers, computers, personal digital assistants and other devices. Figure 2 The embodiment of the present application provides a matrix intelligent split screen adjustment system, including: a real-time acquisition module 201, a split screen prediction module 202 and a configuration adjustment module 203;

[0100] The real-time acquisition module 201 is used to monitor video display parameters in real time, pre-process the acquired video display parameters to obtain video display data, and perform feature processing to form a video source feature matrix;

[0101] The split screen prediction module 202 outputs the optimal parameter strategy for matrix split screen configuration based on the integrated video source feature vector and through a preset matrix split screen prediction model;

[0102] The configuration adjustment module 203 is used to adjust the current parameters to the parameters corresponding to the optimal parameter strategy of the matrix split-screen according to the optimal parameter strategy of the matrix split-screen.

[0103] The video display parameters specifically include: the number of online devices, video resolution, and video display ratio; wherein the number of online devices specifically refers to the number of current online cameras.

[0104] The preprocessing of the acquired video display parameters to obtain video display data; and feature processing and extraction of the video display data to integrate and form a video source feature vector specifically include the following steps:

[0105] Obtain video display parameters through real-time interaction with the camera;

[0106] Cleaning, deduplication and standardization of video display parameters acquired in real time to obtain video display data;

[0107] A corresponding feature vector is obtained for the video display data in a single device form.

[0108] Furthermore, the real-time interaction with the camera to obtain video display parameters is specifically as follows:

[0109] Use heartbeat detection to determine the online status of the camera;

[0110] If the camera is online, the corresponding video display parameters are obtained in real time;

[0111] If the camera is offline, the camera will be marked as grayed out and the corresponding video display parameters cannot be provided.

[0112] This system ensures the accuracy and timeliness of video display parameters by real-time detection, provides a reliable data basis for subsequent processing, and avoids the impact of abnormal parameters on video display quality; comprehensively considers video resolution and ratio, and displays the most appropriate split-screen matrix according to different resolution videos to avoid problems such as low-resolution display being too large or high-resolution display being blurred; through real-time acquisition and dynamic adjustment of data, when the number of devices and video sources change, the most appropriate split-screen matrix is ​​recalculated and adjusted in time to ensure the system's efficiency and real-time responsiveness. Through the preset matrix split-screen prediction model, the optimal parameters of the matrix split-screen configuration are obtained and dynamically adjusted, reducing the administrator's manual intervention and improving the convenience of management.

[0113] Embodiment 3

[0114] Figure 3 A schematic diagram of the structure of an electronic device provided for an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0115] like Figure 3 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0116] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0117] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a matrix intelligent split screen adjustment method.

[0118] In some embodiments, the matrix intelligent split screen adjustment method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the broadcast volume automatic adjustment method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the matrix intelligent split screen adjustment method in any other appropriate manner (for example, by means of firmware).

[0119] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0121] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0123] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0124] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0125] Embodiment 4

[0126] The present application embodiment also provides a computer program product, including a computer program, which implements the matrix intelligent split screen adjustment method provided by any embodiment of the present application when executed by a processor. In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform computer program codes for performing the operation of the present invention, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer, partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0127] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0128] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A matrix intelligent split screen adjustment method, characterized in that: include: Real-time monitoring of video display parameters, preprocessing of the acquired video display parameters to obtain video display data, and feature processing to form a video source feature matrix; Based on the integrated video source feature vector, the optimal parameter strategy for matrix split screen configuration is output through the preset matrix split screen prediction model; Based on the matrix split-screen optimal parameter strategy, the current parameters are adjusted to the parameters corresponding to the matrix split-screen optimal parameter strategy.

2. A matrix intelligent split screen adjustment method according to claim 1, characterized in that: The video display parameters specifically include: the number of online devices, video resolution, and video display ratio; wherein the number of online devices specifically refers to the number of current online cameras.

3. A matrix intelligent split screen adjustment method according to claim 2, characterized in that: The preprocessing of the acquired video display parameters to obtain video display data; and feature processing and extraction of the video display data to integrate and form a video source feature vector specifically include the following steps: Obtain video display parameters through real-time interaction with the camera; Cleaning, deduplication and standardization of video display parameters acquired in real time to obtain video display data; A corresponding feature vector is obtained for the video display data in a single device form.

4. A matrix intelligent split screen adjustment method according to claim 3, characterized in that: The real-time interaction with the camera to obtain video display parameters is specifically as follows: Use heartbeat detection to determine the online status of the camera; If the camera is online, the corresponding video display parameters are obtained in real time; If the camera is offline, the camera will be marked as grayed out and the corresponding video display parameters cannot be provided.

5. A matrix intelligent split screen adjustment method according to claim 1, characterized in that: The preset matrix split-screen prediction model comprises the following steps: Collect historical data related to video display parameters, corresponding split-screen configuration parameters, and corresponding matrix split-screen effect quantitative values ​​to generate a historical data set; Dividing the historical data set into a test set, a validation set, and a test set to train the matrix split-screen prediction model; Set the relevant optimization function, where the calculation formula of the optimization function is as follows: Among them, ω represents the weight vector, which is used to describe the normal vector of the hyperplane; b represents the bias term, which is used to determine the relative position of the hyperplane; ||ω|| represents the complexity of the control model, which indirectly reflects the distance between the hyperplane and the data point; The constraint conditions of the optimization function are set, and the calculation formula is specifically as follows: Among them, y i represents the classification label of the i-th sample, x i represents the input feature vector of the i-th sample, ω T x i Represents the dot product of the weight vector and the feature vector, reflecting the relative position of the sample and the classification hyperplane. Indicates that the constraint applies to all data points.

6. A matrix intelligent split screen adjustment method according to claim 1, characterized in that: The adjusting the current parameters to the parameters corresponding to the matrix split-screen optimal parameter strategy based on the matrix split-screen optimal parameter strategy includes: Sending the matrix split screen optimal parameter strategy output by the matrix split screen prediction model to the video matrix, and determining the number of split screens and the split screen layout in the optimal parameter strategy; Based on a dynamic adjustment algorithm, the current parameters of the video matrix to be adjusted are adjusted to the parameters corresponding to the matrix screen splitting optimal parameter strategy.

7. A matrix intelligent split screen adjustment system, characterized in that: include: Real-time acquisition module, split-screen prediction module, and configuration adjustment module; The real-time acquisition module is used to monitor the video display parameters in real time, pre-process the acquired video display parameters to obtain video display data, and perform feature processing to form a video source feature matrix; The split screen prediction module outputs the optimal parameter strategy for matrix split screen configuration based on the integrated video source feature vector and a preset matrix split screen prediction model; The configuration adjustment module is used to adjust the current parameters to the parameters corresponding to the optimal parameter strategy of the matrix split-screen according to the optimal parameter strategy of the matrix split-screen. The video display parameters specifically include: the number of online devices, video resolution, and video display ratio; wherein the number of online devices specifically refers to the number of online cameras currently; The preprocessing of the acquired video display parameters to obtain video display data; and feature processing and extraction of the video display data to integrate and form a video source feature vector specifically include the following steps: Obtain video display parameters through real-time interaction with the camera; Cleaning, deduplication and standardization of video display parameters acquired in real time to obtain video display data; A corresponding feature vector is obtained for the video display data in a single device form.

8. A matrix intelligent split screen adjustment system, characterized in that: The real-time interaction with the camera to obtain video display parameters is specifically as follows: Use heartbeat detection to determine the online status of the camera; If the camera is online, the corresponding video display parameters are obtained in real time; If the camera is offline, the camera will be marked as grayed out and the corresponding video display parameters cannot be provided.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement a matrix intelligent split-screen adjustment method as described in any one of claims 1-6.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to execute a matrix intelligent split-screen adjustment method as described in any one of claims 1-6 when executed by a computer processor.

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