Video data transmission method

Abstract

The invention relates to the technical field of network transmission, in particular to a video data transmission method, which comprises the following steps of: based on a video transmission data packet, analyzing a sending time point of the video data packet, recording a sending moment and a serial number, checking a time stamp at a plurality of transmission nodes, and generating a time stamp data packet. According to the invention, by matching the timestamps and the serial numbers, marking the data packets and checking the transmission nodes, the security and reliability of data transmission are improved, errors and loss of the data packets in the transmission process are reduced, and the video quality is optimized by analyzing the video data, identifying abnormal image frames and adjusting transmission parameters. Compression parameters and server resource allocation are dynamically adjusted according to video contents and in combination with real-time user requirements, so that video quality is maintained under the condition that the bandwidth is limited, the size of a buffer area and the capacity of a data packet are adjusted according to network conditions, and the fluency and continuity of video playing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of network transmission, and in particular to a video transmission method. Background Art

[0002] The field of network transmission technology focuses on the efficient, secure and reliable transmission of data in computer networks, using a variety of protocols and technologies, including the Transmission Control Protocol, the User Datagram Protocol, and the Internet Protocol, to ensure that data is transmitted from the source to the destination. It combines the optimization of data transmission paths, congestion control mechanisms, data flow management, and network security measures to improve transmission capabilities under complex network conditions, and uses network function virtualization and software-defined networks to achieve flexible and efficient network management and configuration methods.

[0003] The video data transmission method utilizes a variety of technologies and protocols to efficiently and stably transmit video content on the network, ensuring that video data can be efficiently transmitted through optimized paths in a variety of network environments, reducing delays and data loss, and ensuring the continuity of video playback. It combines video compression, adaptive bit rate adjustment, and error recovery technologies to optimize the user's viewing experience under poor network conditions, and is applied to online video platforms, video conferencing systems, and real-time broadcast services.

[0004] Traditional video transmission technology is limited by network congestion and insufficient path optimization when processing high-bitrate video data, resulting in delays and data loss during video transmission. In cross-regional transmission or public network environments, it cannot fully optimize the transmission path and congestion control mechanism, resulting in video playback freezes and quality degradation, affecting user experience. It is not responsive enough when dynamic video content and user needs change, and cannot adjust the compression rate and resource allocation in real time, resulting in the inability to effectively guarantee video quality when network conditions deteriorate. In large-scale online video conferencing or real-time broadcasting services, there is a lack of effective error recovery mechanisms and adaptive bitrate adjustment capabilities, resulting in large fluctuations in video transmission quality, affecting service quality and user satisfaction. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a video data transmission method.

[0006] In order to achieve the above object, the present invention adopts the following technical solution, a video data transmission method, comprising the following steps:

[0007] S1: Based on the video transmission data packet, analyze the sending time point of the video data packet, record the sending time and sequence number, and verify the time stamp at multiple transmission nodes to generate a time stamp data packet;

[0008] S2: Based on the time-stamped data packet, the receiving end verifies the timestamp and sequence information of the video transmission data packet, including the timestamp and sequence information of the data packet, and generates a time data verification result;

[0009] S3: Based on the time data verification result, analyze the video data and identify abnormal image frames, adjust data transmission parameters to correct frame anomalies, and generate image frame processing information;

[0010] S4: According to the image frame processing information, by analyzing the content changes and dynamic features of multiple pictures, evaluating the criticality of pictures at multiple time points, adjusting compression parameters to optimize the clarity of key content, and generating a compression parameter adjustment record;

[0011] S5: Based on the compression parameter adjustment record, by real-time monitoring of changes in video content and user needs, adjusting the server-side processor and storage resource allocation parameters to generate a resource scheduling optimization configuration;

[0012] S6: Based on the resource scheduling optimization configuration and in combination with the real-time network status, the video buffer size and data packet capacity parameters are adjusted to optimize the smoothness and continuity of the video playback and generate video data transmission parameters.

[0013] As a further solution of the present invention, the time marking data includes the sending timestamp of the data packet, sequence number information, and time consistency verification record; the time data verification result includes the time sequence verification result, the time window compliance verification record, and the timestamp integrity comparison record; the image frame processing information includes abnormal image frame information, data transmission parameter adjustment record, and abnormal frame correction record; the compression parameter adjustment record includes the compression ratio adjustment parameters of key scenes, compression parameter adjustment data of dynamic scenes, and compression efficiency optimization record of static scenes; the resource scheduling optimization configuration includes the load distribution parameters of the server-side processor, storage resource allocation records, and video data processing records; the video data transmission parameters include buffer capacity adjustment records, data packet capacity configuration, and video fluency evaluation score.

[0014] As a further solution of the present invention, based on the video transmission data packet, the sending time point of the video data packet is analyzed, the sending time and sequence number are recorded, and the time stamp is verified at multiple transmission nodes. The steps of generating the time stamp data packet are specifically as follows:

[0015] S101: Based on the video transmission data packet, by analyzing the sending time of multiple data packets, matching timestamp information for multiple video data packets, and generating a timestamp record;

[0016] S102: Based on the timestamp record, match sequence numbers for multiple data packets according to the sending time, optimize the consistency of the transmission order, and generate a sequence number allocation result;

[0017] S103: Based on the sequence number allocation result, the time stamp of the data packet is verified at multiple transmission nodes to generate a time stamp data packet.

[0018] As a further solution of the present invention, based on the time-stamped data packet, the timestamp and sequence information of the video transmission data packet are verified by the receiving end, including the timestamp and sequence information of the data packet, and the steps of generating the time data verification result are specifically as follows:

[0019] S201: Based on the time-stamped data packet, the receiving end collects the time stamp information of multiple data packets, analyzes the time and sequence information, and generates a sequence information comparison result;

[0020] S202: Based on the sequence information comparison result, compare the timestamp of the data packet with the preset time window, evaluate the consistency between the actual receiving time and the preset time, and generate a time window verification record;

[0021] S203: Based on the time window verification record, by comparing the timestamp information of the target data packet and the previous data packet, the continuity of the time sequence is evaluated to generate a time data verification result.

[0022] As a further solution of the present invention, based on the time data verification result, the video data is analyzed and abnormal image frames are identified, data transmission parameters are adjusted to correct frame anomalies, and the steps of generating image frame processing information are specifically as follows:

[0023] S301: Based on the time data verification result, multiple frames of images in the video data stream are analyzed in real time, abnormal frames with time deviation are identified, and time abnormal frame records are generated;

[0024] S302: Based on the abnormal time frame record, analyze the abnormal frame, identify data errors and information loss, adopt a Kalman filter algorithm, use adjacent frame data for correction, and generate a data error processing result;

[0025] S303: Based on the data error processing result, adjust the video data transmission parameters, including the frame rate and data transmission speed, optimize the playback freeze and screen jump, and generate image frame processing information.

[0026] As a further solution of the present invention, the Kalman filter algorithm is according to the formula:

[0027] x k|k =x k|k-1 +K k (zk -H k x k|k-1 )+α k (P k +Q k -R k )

[0028] Calculate the frame time error correction value, where x k|k is the state estimate at time k, x k|k-1 is the predicted state estimate, K k is the Kalman gain, z k is the actual observed value at time k, H k is the observation model, α k is the weight coefficient adjusted according to the noise level, P k is the forecast error covariance, Q k is the process noise covariance, R k is the observation noise covariance, and k is the index in the time series.

[0029] As a further solution of the present invention, according to the image frame processing information, by analyzing the content changes and dynamic characteristics of multiple pictures, evaluating the criticality of pictures at multiple time points, adjusting the compression parameters to optimize the clarity of the key content, the steps of generating the compression parameter adjustment record are specifically as follows:

[0030] S401: Based on the image frame processing information, an optical flow algorithm is used to analyze the image content at multiple time points in the video data, analyze the motion pattern and scene changes, evaluate the criticality of multiple images, and generate key frame recognition records;

[0031] S402: Based on the key frame recognition record, according to the content complexity and visual importance of the key frame, taking into account the picture quality and transmission efficiency, adjusting the picture compression parameters to generate compression ratio matching information;

[0032] S403: Based on the compression rate matching information, apply compression parameters to the video data, optimize the clarity and smoothness of the video, and generate a compression parameter adjustment record.

[0033] As a further solution of the present invention, the optical flow algorithm is according to the formula:

[0034] u=-(αA+γI) -1 (b+βc)

[0035] Calculate the motion vector, where u is the displacement vector of the pixel between consecutive frames, α is the ambient light influence coefficient, A is the matrix composed of the gradient of the image, γ is the spatial continuity parameter, I is the unit matrix, b is the time derivative vector, β is the dynamic blur coefficient, c is the brightness change vector caused by dynamic blur, -(αA+γI)-1 is the adjusted inverse matrix, and (b+βc) is the brightness change vector after considering dynamic blur.

[0036] As a further solution of the present invention, based on the compression parameter adjustment record, by real-time monitoring of changes in video content and user needs, adjusting the server-side processor and storage resource allocation parameters, and generating the resource scheduling optimization configuration step are specifically:

[0037] S501: Based on the compression parameter adjustment record, monitor the change of video content and the user's playback demand in real time, analyze the real-time resource usage data, and generate resource usage analysis data;

[0038] S502: Based on the resource usage analysis data, adjust the configuration parameters of the server processor allocation and storage resources, optimize the data processing capability and storage efficiency, and generate resource configuration adjustment information;

[0039] S503: Based on the resource configuration adjustment information, the resource allocation parameters of the server are updated in real time, the matching degree between the resource configuration and the real-time demand is optimized, and the resource scheduling optimization configuration is generated.

[0040] As a further solution of the present invention, based on the resource scheduling optimization configuration, combined with the real-time network status, the video buffer size and data packet capacity parameters are adjusted to optimize the smoothness and continuity of video playback. The steps of generating video data transmission parameters are specifically as follows:

[0041] S601: Based on the resource scheduling optimization configuration, monitor the network status in real time, evaluate the bandwidth utilization and network delay, analyze the buffer size and data packet capacity that need to be adjusted, and generate adjustment demand evaluation data;

[0042] S602: Based on the adjustment demand evaluation data, combined with network bandwidth changes and playback requirements, the buffer size and data packet sending interval are adjusted, and a buffer adjustment execution record is generated;

[0043] S603: Based on the buffer adjustment execution record, adjust the transmission parameters of the video data stream, optimize the smoothness and continuity of the video playback, and generate video data transmission parameters.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by matching timestamps and sequence numbers, data packets are marked and checked at transmission nodes, thereby improving the security and reliability of data transmission, reducing errors and losses of data packets during transmission, analyzing video data, identifying abnormal image frames and adjusting transmission parameters, optimizing video quality, dynamically adjusting compression parameters and server resource allocation based on video content and in combination with real-time user needs, maintaining video quality under limited bandwidth, adjusting buffer size and data packet capacity according to network conditions, and improving the smoothness and continuity of video playback. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0047] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0048] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0049] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0050] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0051] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0052] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0055] See also Figure 1The present invention provides a technical solution, a video data transmission method, comprising the following steps:

[0056] S1: Based on the video transmission data packet, analyze the sending time point of the video data packet, record the sending time and sequence number, and verify the time stamp at multiple transmission nodes to generate a time stamp data packet;

[0057] S2: Based on the time-stamped data packet, the timestamp and sequence information of the video transmission data packet are verified by the receiving end, including the timestamp and sequence information of the data packet, and a time data verification result is generated;

[0058] S3: Based on the time data verification result, the video data is analyzed and abnormal image frames are identified, data transmission parameters are adjusted to correct frame anomalies, and image frame processing information is generated;

[0059] S4: according to the image frame processing information, by analyzing the content changes and dynamic characteristics of multiple pictures, evaluating the criticality of the pictures at multiple time points, adjusting the compression parameters to optimize the clarity of the key content, and generating a compression parameter adjustment record;

[0060] S5: Based on the compression parameter adjustment record, the server-side processor and storage resource allocation parameters are adjusted by real-time monitoring of changes in video content and user needs to generate resource scheduling optimization configuration;

[0061] S6: Based on resource scheduling optimization configuration and combined with real-time network status, adjust the video buffer size and data packet capacity parameters, optimize the smoothness and continuity of video playback, and generate video data transmission parameters.

[0062] The time stamp data includes the sending timestamp of the data packet, sequence number information, and time consistency verification records. The time data verification results include time sequence verification results, time window compliance verification records, and timestamp integrity comparison records. The image frame processing information includes abnormal image frame information, data transmission parameter adjustment records, and abnormal frame correction records. The compression parameter adjustment records include compression ratio adjustment parameters for key scenes, compression parameter adjustment data for dynamic scenes, and compression efficiency optimization records for static scenes. The resource scheduling optimization configuration includes the load distribution parameters of the server-side processor, storage resource allocation records, and video data processing records. The video data transmission parameters include buffer capacity adjustment records, data packet capacity configuration, and video fluency assessment scores.

[0063] See also Figure 2 ,Based on the video transmission data packet, analyze the sending time point of the video data packet, record the sending time and sequence number, and verify the time stamp at multiple transmission nodes. The steps of generating the time stamp data packet are as follows:

[0064] S101: Based on the video transmission data packet, by analyzing the sending time of multiple data packets, matching timestamp information for multiple video data packets, and generating a timestamp record, the specific process is as follows;

[0065] In sub-step S101, based on the video transmission data packet, by analyzing the sending time of multiple data packets, timestamp information is matched for multiple video data packets, the time synchronization algorithm is used to match the timestamps, and the differential time method is used to calculate the time difference between the actual sending time and the expected sending time of each data packet. The formula is: in, is the time deviation of the i0th data packet, is the actual sending time, t 0z is the sending time of the first data packet, δt z For the ideal time interval between two consecutive data packets, the time deviation of each data packet is calculated, the timestamp is corrected, and the timestamp record is generated.

[0066] S102: Based on the timestamp record, the sequence numbers are matched for multiple data packets according to the sending time, the consistency of the transmission order is optimized, and the process of generating the sequence number allocation result is specifically as follows;

[0067] In sub-step S102, based on the timestamp record, multiple data packets are matched with sequence numbers according to the sending time, the sequence of the data packets is optimized using a sequence rearrangement algorithm, and the data packet sequence is numbered using an optimization model, and the formula is: in, is the sequence number of data packet o0, is the sending time of data packet o0, t minz is the earliest data packet sending time, ΔT z For the time window length, the consistency of the transmission order is optimized and the sequence number allocation result is generated.

[0068] S103: Based on the sequence number allocation result, the time stamp of the data packet is verified at multiple transmission nodes, and the process of generating the time stamp data packet is specifically as follows;

[0069] In sub-step S103, based on the sequence number allocation result, the time stamp of the data packet is verified at multiple transmission nodes, the time stamp of each data packet is verified using a verification algorithm, and the accuracy of the time stamp is evaluated using a standard deviation calculation formula, which is: Among them, σ z is the standard deviation of the time stamp, t pz A timestamp for each packet. is the average value of all packet time stamps, N 0zTo ensure the accuracy of time stamps, generate time stamped packets for the total number of packets.

[0070] See also Figure 3 ,Based on the time-stamped data packet, the timestamp and sequence information of the video transmission data packet are verified by the receiving end, including the timestamp and sequence information of the data packet. The steps for generating the time data verification result are as follows:

[0071] S201: Based on the time-stamped data packet, the receiving end collects the time stamp information of multiple data packets, analyzes the time and sequence information, and generates the sequence information comparison result. The specific process is as follows;

[0072] In sub-step S201, based on the time-stamped data packet, the timestamp information of multiple data packets is collected by the receiving end, the time and sequence information are analyzed, the timestamps of the received data packets are collected, the receiving order and the sending order of the data packets are compared and analyzed using the timestamp data, and the deviation between the actual receiving timestamp and the sending timestamp is compared using the timestamp comparison algorithm. The formula is: in, is the time deviation of the i1th data packet, is the actual timestamp of the data packet at the receiving end, It is the timestamp of the sending end, identifies the time delay or disorder of the data packet during the transmission process, optimizes the network transmission path and adjusts the sending strategy, and generates sequence information comparison results.

[0073] S202: Based on the sequence information comparison result, the timestamp of the data packet is compared with the preset time window, the consistency between the actual receiving time and the preset time is evaluated, and the specific process of generating the time window verification record is as follows;

[0074] In sub-step S202, based on the sequence information comparison result, the timestamp of the data packet is compared with the preset time window, the consistency between the actual receiving time and the preset time is evaluated, the deviation between the receiving time of each data packet and the preset time window is calculated, and the overall time alignment accuracy is evaluated by averaging. The time window comparison model is used to evaluate the consistency between the actual receiving time and the preset time. The formula is: Among them, γ x is the time window consistency evaluation index, is the actual receiving timestamp, is the timestamp in the preset time window, N 1x Generate time window validation records to evaluate the total number of packets, identify time synchronization issues, optimize data transmission efficiency and reduce data latency.

[0075] S203: Based on the time window verification record, by comparing the timestamp information of the target data packet and the previous data packet, the continuity of the time sequence is evaluated, and the process of generating the time data verification result is specifically as follows;

[0076] In sub-step S203, based on the time window verification record, by comparing the timestamp information of the target data packet p1 and the previous data packet p0, the continuity of the time sequence is evaluated, the time difference between each data packet and its previous data packet is checked, and the changing trend of the time difference is analyzed to identify data transmission errors or equipment failures. The sequential consistency verification model is adopted, and the formula is: in, is the time difference of consecutive data packets, is the timestamp of the target data packet, It is the timestamp of the previous data packet, ensuring the consistency of the timestamp sequence of the data packet, reducing data loss and errors, improving the reliability of network communication, and generating time data verification results.

[0077] See also Figure 4 ,Based on the time data verification results, the video data is analyzed and abnormal image frames are identified, and the data transmission parameters are adjusted to correct the frame anomalies. The steps of generating image frame processing information are as follows:

[0078] S301: Based on the time data verification result, the multiple frames of images in the video data stream are analyzed in real time, abnormal frames with time deviation are identified, and the specific process of generating a time abnormal frame record is as follows;

[0079] In sub-step S301, based on the time data verification result, multiple frames in the video data stream are analyzed in real time to identify abnormal frames caused by time deviation, which involves checking the timestamps of the video frames one by one, identifying frames that do not match the preset playback schedule, combining time stamps and image processing to identify abnormal frames, and using the time deviation threshold for judgment. The formula is: in, is the time deviation of the i2th frame, is the actual receiving timestamp of frame i2, By identifying and recording the abnormal frames of time deviation for the expected timestamp, a time anomaly frame record is generated.

[0080] S302: Based on the abnormal frame record, analyze the abnormal frame, identify data errors and information loss, use the Kalman filter algorithm, use the adjacent frame data for correction, and generate the data error processing result. The specific process is as follows;

[0081] In sub-step S302, based on the time abnormal frame record, the abnormal frames are analyzed to identify data errors and information loss. The Kalman filter algorithm is used to correct the abnormal frames using the data of adjacent frames, predict and compensate for the loss or error in the video data, improve the overall quality of the data stream, process data errors, and generate data error processing results.

[0082] Kalman filter algorithm, according to the formula:

[0083] x k|k =x k|k-1 +K k (z k -H k x k|k-1 )+α k (P k +Q k -R k )

[0084] Calculate the frame time error correction value, where x k|k is the state estimate at time k, which is used to represent the corrected frame time data, x k|k-1 is the predicted state estimate, which is used to represent the observed z k The previous prediction frame time, K k is the Kalman gain, which is used to adjust the weight of the difference between the predicted state and the actual observation, z k is the actual observation value at time k, which is used to update the state estimate, H k is the observation model, which maps the state space to the observation space and is used to calculate the expected observation value, α k is the weight coefficient adjusted according to the noise level to enhance the adaptability and robustness of the model. k is the forecast error covariance, indicating the uncertainty of the forecast state, Q k is the process noise covariance, which represents the random interference in the state transition process, R k is the observation noise covariance, which indicates the noise level during the observation process. k is the index in the time series, which represents the target time point and frame number and is used to mark the position of the data in the time series.

[0085] The specific execution process of the formula is as follows:

[0086] Obtain z through current and historical observation data k Parameters, according to the prediction model F k and the historical state x k|k-1 , using the Kalman filter algorithm, we get the updated state estimate x k|k-1 and the Kalman gain K k , Substitute the parameters into the formula to calculate the frame time error correction value x k|k, used to ensure the temporal accuracy and consistency of video data frames.

[0087] S303: Based on the data error processing result, the video data transmission parameters including the frame rate and data transmission speed are adjusted to optimize the playback freeze and screen jump, and the process of generating the image frame processing information is as follows;

[0088] In sub-step S303, based on the data error processing result, the video data transmission parameters, including the frame rate and data transmission speed, are adjusted to optimize the problems of playback freeze and screen jump. This is implemented by analyzing the time data and playback quality of the corrected frames to ensure the continuity and visual experience of the video playback. The adjustment formula is: in, is the adjusted data transmission speed, is the original data transmission speed, α c is the adjustment coefficient, Δv c In order to adjust the speed requirements based on the error processing results, the control transmission parameters are adjusted to optimize the playback quality and generate image frame processing information.

[0089] See also Figure 5 According to the image frame processing information, by analyzing the content changes and dynamic characteristics of multiple pictures, evaluating the criticality of the pictures at multiple time points, adjusting the compression parameters to optimize the clarity of the key content, the steps of generating the compression parameter adjustment record are as follows:

[0090] S401: Based on the image frame processing information, the optical flow algorithm is used to analyze the picture content at multiple time points in the video data, analyze the motion pattern and scene changes, evaluate the criticality of multiple pictures, and generate key frame recognition records. The specific process is as follows;

[0091] In sub-step S401, based on the image frame processing information, the optical flow algorithm is used to analyze the picture content at multiple time points in the video data, evaluate the motion pattern and scene changes, identify the key frames by measuring the motion of the pixels in the picture, determine the continuity and changes of the actions in the picture, track the dynamic changes in the video in real time, evaluate the criticality of multiple pictures by analyzing the optical flow data, and generate key frame recognition records.

[0092] Optical flow algorithm, according to the formula:

[0093] u=-(αA+γI) -1 (b+βc)

[0094] Calculate the motion vector, where u is the displacement vector of the pixel between consecutive frames, which is used to represent the motion direction and speed of the object in the image. α is the ambient light influence coefficient, which adjusts the influence of ambient light changes on motion estimation. A is a matrix composed of the gradient of the image, which expresses the spatial change of each point in the image in the time series. γ is the spatial continuity parameter, which ensures the consistency of the motion vectors of adjacent pixels. I is the unit matrix, which is used to enhance the influence of spatial continuity. b is the time derivative vector, which reflects the rate of change of image brightness. β is the dynamic blur coefficient, which considers the image blur caused by fast movement or camera shake. c is the brightness change vector caused by dynamic blur, -(αA+γI) -1 is the adjusted inverse matrix that integrates the effects of ambient light and spatial continuity, and (b+βc) is the brightness change vector after considering dynamic blur.

[0095] The specific execution process of the formula is as follows:

[0096] By analyzing the ambient lighting and dynamic blur of the video frame, we can obtain the parameters α, β, γ, the image gradient A, and the brightness changes b and c. Based on the parameters, we use the matrix operation model to get the displacement vector u parameter. Substituting the parameters into the formula, we can calculate the motion vector of each pixel to accurately identify the key frames in the image sequence, enhance the storage and processing efficiency of video data, retain important visual information, and improve the performance of the analysis and monitoring system.

[0097] S402: Based on the key frame recognition record, according to the content complexity and visual importance of the key frame, taking into account the picture quality and transmission efficiency, adjusting the picture compression parameters, and generating the compression ratio matching information specifically includes:

[0098] In sub-step S402, based on the key frame recognition record, according to the content complexity and visual importance of the key frame, the compression parameters of the picture are adjusted, and the picture quality and transmission efficiency are considered. Through complexity analysis and compression algorithm selection, it is ensured that the visual quality of the key frame is not damaged by excessive compression. The compression rate adjustment formula is: in, is the compression parameter of the i3th frame, is the basic quality coefficient, β v is the adjustment factor, is the content complexity of the frame, Generate compression rate matching information for minimum quality requirements, ensuring picture quality and optimizing data transmission.

[0099] S403: Based on the compression rate matching information, compression parameters are applied to the video data to optimize the clarity and fluency of the video. The specific process of generating a compression parameter adjustment record is as follows:

[0100] In sub-step S403, based on the compression rate matching information, compression parameters are applied to the video data to optimize the clarity and smoothness of the video. By applying compression parameters to multiple parts of the video, the viewing experience is improved. The formula is: in, is the compressed parameter of the i3th frame, is the initial parameter value, λ v is the attenuation coefficient, is the compression rate, e represents the base of the natural logarithm, which realizes dynamic compression of video data, ensures the clarity and smoothness of video playback, and generates compression parameter adjustment records.

[0101] See also Figure 6 Based on the compression parameter adjustment record, the server-side processor and storage resource allocation parameters are adjusted by real-time monitoring of changes in video content and user needs. The steps to generate resource scheduling optimization configuration are as follows:

[0102] S501: Based on the compression parameter adjustment record, monitor the change of video content and the user's playback demand in real time, and analyze the real-time resource usage data. The specific process of generating resource usage analysis data is as follows;

[0103] In sub-step S501, based on the compression parameter adjustment record, the changes in video content and the user's playback needs are monitored in real time, and the real-time resource usage of the server is analyzed. The usage data of various resources, including CPU occupancy, memory usage, and bandwidth consumption, are collected and analyzed. Real-time analysis helps determine the peak and valley values ​​of resource usage, and provides data support for responding to sudden playback needs. The monitoring formula is: in, Indicates the resource usage rate in the i4th analysis cycle, Indicates the usage data of the n4th resource, where N4 is the total number of monitored resources. Generate resource usage analysis data for theoretical maximum resource capacity.

[0104] S502: Based on the resource usage analysis data, the configuration parameters of the server processor allocation and storage resources are adjusted to optimize the data processing capability and storage efficiency. The specific process of generating resource configuration adjustment information is as follows;

[0105] In sub-step S502, based on the resource usage analysis data, the configuration parameters of the server processor allocation and storage resources are adjusted, and the changes in data processing and storage requirements are considered. The algorithm is adjusted to dynamically allocate processing power and storage space according to the analysis results to improve the system's response speed and data processing capability. The resource configuration adjustment formula is: in, is the adjusted configuration parameter, α b and βb is the adjustment factor, and They represent processor demand and storage usage respectively, optimize data processing capabilities and storage efficiency, and generate resource configuration adjustment information.

[0106] S503: Based on the resource configuration adjustment information, the resource allocation parameters of the server are updated in real time, the matching degree between the resource configuration and the real-time demand is optimized, and the process of generating the resource scheduling optimization configuration is specifically as follows;

[0107] In sub-step S503, based on the resource configuration adjustment information, the resource allocation parameters of the server are updated in real time, the matching degree between resource configuration and real-time demand is optimized, and the server resources are dynamically adjusted using the resource scheduling algorithm to ensure the optimal balance between user demand and system resources. The scheduling optimization formula is: in, is the adjusted resource scheduling parameter, γ b and δ b is the adjustment factor, Configure parameters for resources. Through this dynamic adjustment, real-time service needs can be met to generate optimal resource scheduling configuration for real-time resource needs.

[0108] See also Figure 7 ,Based on resource scheduling optimization configuration, combined with real-time network status, adjust the video buffer size and data packet capacity parameters to optimize the smoothness and continuity of video playback. The specific steps for generating video data transmission parameters are as follows:

[0109] S601: Based on resource scheduling optimization configuration, real-time monitoring of network status, evaluation of bandwidth utilization and network delay, analysis of buffer size and data packet capacity that need to be adjusted, and generation of adjustment demand evaluation data are specifically as follows;

[0110] In sub-step S601, based on resource scheduling optimization configuration, the network status is monitored in real time, and bandwidth utilization and network latency are evaluated, including continuous measurement of network throughput and response time, identification of potential bottlenecks and performance degradation, analysis of buffer size and data packet capacity that need to be adjusted, matching changes in network conditions, and ensuring the stability of data flow. The monitoring formula is: in, Indicates the bandwidth utilization in the i5th measurement cycle, Indicates the bandwidth weight of the nth data packet, Indicates the length of the data packet, N5 is the total number of data packets, Generate adjustment needs assessment data to measure cycle time.

[0111] S602: Based on the adjustment demand evaluation data, combined with the network bandwidth change and the playback demand, the buffer size and the data packet sending interval are adjusted, and the specific process of generating the buffer adjustment execution record is as follows;

[0112] In sub-step S602, based on the adjustment demand evaluation data, combined with the network bandwidth change and the playback demand, the buffer size and the packet sending interval are adjusted, the real-time network status and the predicted data flow demand are considered, the buffer behavior and the transmission efficiency are optimized, and the buffer adjustment formula is: in, is the adjusted buffer size, is the initial buffer size, α q and β q is the adjustment factor, is the bandwidth variation, Generate buffer adjustment execution records for latency variation, matching network conditions.

[0113] S603: Based on the buffer adjustment execution record, the transmission parameters of the video data stream are adjusted to optimize the smoothness and continuity of the video playback. The specific process of generating the video data transmission parameters is as follows;

[0114] In sub-step S603, based on the buffer adjustment execution record, the transmission parameters of the video data stream are adjusted to optimize the smoothness and continuity of video playback. By evaluating the network performance and user experience requirements, the seamless playback of the video stream is ensured and the transmission parameters are adjusted. The formula is: in, is the adjusted transmission parameter, is the basic transmission parameter, λ q To adjust the sensitivity coefficient, is the parameter obtained from the adjustment of the buffer, e represents the base of the natural logarithm, which ensures the optimal transmission performance of video data under various network conditions and generates video data transmission parameters.

[0115] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A video data transmission method, characterized in that: The following steps are involved: Based on the video transmission data packet, analyze the sending time point of the video data packet, record the sending time and sequence number, and verify the time mark at multiple transmission nodes to generate a time mark data packet; Based on the time-stamped data packet, the time stamp and sequence information of the video transmission data packet are verified by the receiving end, including the time stamp and sequence information of the data packet, to generate a time data verification result; Based on the time data verification result, analyzing the video data and identifying abnormal image frames, adjusting data transmission parameters to correct frame anomalies, and generating image frame processing information; According to the image frame processing information, by analyzing the content changes and dynamic characteristics of multiple pictures, evaluating the criticality of pictures at multiple time points, adjusting compression parameters to optimize the clarity of key content, and generating a compression parameter adjustment record; Based on the compression parameter adjustment record, by real-time monitoring of changes in video content and user needs, the server-side processor and storage resource allocation parameters are adjusted to generate a resource scheduling optimization configuration; Based on the resource scheduling optimization configuration and combined with the real-time network status, the video buffer size and data packet capacity parameters are adjusted to optimize the smoothness and continuity of video playback and generate video data transmission parameters.

2. The video data transmission method according to claim 1, characterized in that: The time-stamp data includes the sending timestamp of the data packet, sequence number information, and time consistency verification record; the time data verification result includes the time sequence verification result, the time window compliance verification record, and the timestamp integrity comparison record; the image frame processing information includes abnormal image frame information, data transmission parameter adjustment record, and abnormal frame correction record; the compression parameter adjustment record includes the compression ratio adjustment parameters of key scenes, the compression parameter adjustment data of dynamic scenes, and the compression efficiency optimization record of static scenes; the resource scheduling optimization configuration includes the load distribution parameters of the server-side processor, the storage resource allocation record, and the video data processing record; the video data transmission parameters include the buffer capacity adjustment record, the data packet capacity configuration, and the video fluency evaluation score.

3. The video data transmission method according to claim 1, characterized in that: Based on the video transmission data packet, the sending time point of the video data packet is analyzed, the sending time and sequence number are recorded, and the time stamp is verified at multiple transmission nodes. The steps of generating the time stamp data packet are as follows: Based on the video transmission data packets, by analyzing the sending time of multiple data packets, matching timestamp information for multiple video data packets, and generating timestamp records; Based on the timestamp record, matching sequence numbers for multiple data packets according to the sending time, optimizing the consistency of the transmission order, and generating a sequence number allocation result; Based on the sequence number allocation result, the time stamp of the data packet is verified at multiple transmission nodes to generate a time stamp data packet.

4. The video data transmission method according to claim 1, characterized in that: Based on the time-stamped data packet, the timestamp and sequence information of the video transmission data packet are verified by the receiving end, including the timestamp and sequence information of the data packet, and the steps of generating the time data verification result are specifically as follows: Based on the time-stamped data packet, the receiving end collects the time stamp information of multiple data packets, analyzes the time and sequence information, and generates a sequence information comparison result; Based on the sequence information comparison result, the timestamp of the data packet is compared with the preset time window, the consistency between the actual receiving time and the preset time is evaluated, and a time window verification record is generated; Based on the time window verification record, by comparing the timestamp information of the target data packet and the previous data packet, the continuity of the time sequence is evaluated and the time data verification result is generated.

5. The video data transmission method according to claim 1, characterized in that: Based on the time data verification result, the steps of analyzing the video data and identifying abnormal image frames, adjusting the data transmission parameters to correct the frame abnormalities, and generating the image frame processing information are specifically as follows: Based on the time data verification result, multiple frames of images in the video data stream are analyzed in real time, abnormal frames with time deviation are identified, and time abnormal frame records are generated; Based on the abnormal frame record, the abnormal frame is analyzed to identify data errors and information loss, and a Kalman filter algorithm is used to correct the adjacent frame data to generate a data error processing result; Based on the data error processing result, video data transmission parameters including frame rate and data transmission speed are adjusted to optimize playback freezes and screen jumps, and image frame processing information is generated.

6. The video data transmission method according to claim 5, characterized in that: The Kalman filter algorithm is based on the formula: x k|k =x k|k-1 +K k (z k -H k x k|k-1 )+α k (P k +Q k -R k ) Calculate the frame time error correction value, where x k|k is the state estimate at time k, x k|k-1 is the predicted state estimate, K k is the Kalman gain, z k is the actual observed value at time k, H k is the observation model, α k is the weight coefficient adjusted according to the noise level, P k is the forecast error covariance, Q k is the process noise covariance, R k is the observation noise covariance, and k is the index in the time series.

7. The video data transmission method according to claim 1, characterized in that: According to the image frame processing information, by analyzing the content changes and dynamic features of multiple pictures, evaluating the criticality of pictures at multiple time points, adjusting compression parameters to optimize the clarity of key content, and generating compression parameter adjustment records are specifically as follows: Based on the image frame processing information, an optical flow algorithm is used to analyze the image content at multiple time points in the video data, analyze the motion pattern and scene changes, evaluate the criticality of multiple images, and generate key frame recognition records; Based on the key frame recognition record, according to the content complexity and visual importance of the key frame, taking into account the picture quality and transmission efficiency, adjusting the compression parameters of the picture to generate compression ratio matching information; Based on the compression rate matching information, compression parameters are applied to the video data to optimize the clarity and smoothness of the video and generate a compression parameter adjustment record.

8. The video data transmission method according to claim 7, characterized in that: The optical flow algorithm is based on the formula: u=-(αA+γI) -1 (b+βc) Calculate the motion vector, where u is the displacement vector of the pixel between consecutive frames, α is the ambient light influence coefficient, A is the matrix composed of the gradient of the image, γ is the spatial continuity parameter, I is the unit matrix, b is the time derivative vector, β is the dynamic blur coefficient, c is the brightness change vector caused by dynamic blur, -(αA+γI) -1 is the adjusted inverse matrix, and (b+βc) is the brightness change vector after considering dynamic blur.

9. The video data transmission method according to claim 1, characterized in that: Based on the compression parameter adjustment record, by real-time monitoring of changes in video content and user needs, adjusting the server-side processor and storage resource allocation parameters, and generating the resource scheduling optimization configuration steps are specifically as follows: Based on the compression parameter adjustment record, monitor the changes of video content and the user's playback needs in real time, analyze the real-time resource usage data, and generate resource usage analysis data; Based on the resource usage analysis data, adjust the configuration parameters of server processor allocation and storage resources, optimize data processing capacity and storage efficiency, and generate resource configuration adjustment information; Based on the resource configuration adjustment information, the resource allocation parameters of the server are updated in real time, the matching degree between the resource configuration and the real-time demand is optimized, and the resource scheduling optimization configuration is generated.

10. The video data transmission method according to claim 1, characterized in that: Based on the resource scheduling optimization configuration, combined with the real-time network status, the video buffer size and data packet capacity parameters are adjusted to optimize the smoothness and continuity of video playback. The steps of generating video data transmission parameters are specifically as follows: Based on the resource scheduling optimization configuration, real-time monitoring of network status, evaluation of bandwidth utilization and network latency, analysis of buffer sizes and data packet capacities that need to be adjusted, and generation of adjustment demand evaluation data; Based on the adjustment demand assessment data, combined with network bandwidth changes and playback requirements, the buffer size and data packet sending interval are adjusted to generate a buffer adjustment execution record; Based on the buffer adjustment execution record, the transmission parameters of the video data stream are adjusted to optimize the smoothness and continuity of the video playback, and the video data transmission parameters are generated.

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