Holographic communication method and system, computer equipment and storage medium

Abstract

The invention relates to the technical field of holographic images, and discloses a holographic communication method and system, computer equipment and a storage medium. The method comprises the steps that an initial holographic data stream reported by a sending end is acquired, and the initial holographic data stream is determined based on information of an environment where a target object is located; compressing the initial holographic data stream to obtain a compressed holographic data stream; acquiring a first network state of the receiving end, and decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream; and transmitting the target holographic data stream to a receiving end, so that the receiving end performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream. By implementing the technical scheme of the invention, the stability, real-time performance and resource utilization efficiency of holographic communication in multiple scenes can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of holographic imaging technology, and particularly to a holographic communication method, system, computer device and storage medium. Background Art

[0002] With the rapid development of holographic imaging technology, its applications in fields such as remote interaction, virtual reality and real-time communication are becoming increasingly widespread. Traditional holographic image transmission systems usually directly transmit the original holographic data stream using a fixed coding strategy, but this method has obvious deficiencies. First, the unoptimized holographic data stream has extremely high requirements for network bandwidth, and it is easy to cause image transmission delay or even interruption in case of network congestion. Second, traditional systems lack the ability to dynamically sense the network state of the receiving end and cannot adjust the data processing strategy according to real-time network conditions, resulting in the failure to fully utilize network resources to improve image quality in high-bandwidth scenarios, and it is difficult to ensure basic transmission fluency in low-bandwidth situations. Summary of the Invention

[0003] In view of this, the present invention provides a holographic communication method, system, computer device and storage medium to solve the systematic defects of traditional holographic image transmission systems in terms of network adaptability and resource utilization efficiency.

[0004] In a first aspect, the present invention provides a holographic communication method, including: obtaining an initial holographic data stream reported by a sending end, where the initial holographic data stream is determined based on environmental information of a target object; compressing the initial holographic data stream to obtain a compressed holographic data stream; obtaining a first network state of a receiving end and decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream; and transmitting the target holographic data stream to the receiving end so that the receiving end performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

[0005] The holographic communication method provided by the embodiments of the present invention ensures the accuracy and scene adaptability of the data source by obtaining the initial holographic data stream based on the environmental information of the target object. The data stream is optimized by using dynamic compression technology, effectively reducing the transmission bandwidth requirement and improving the transmission efficiency. Innovatively, it combines the real-time network state of the receiving end for adaptive decompression, achieving the optimal allocation of network resources and the dynamic balance of transmission quality. It can not only ensure fluency in a weak network environment but also make full use of the performance potential of high-bandwidth networks. Finally, high-fidelity images are generated through holographic imaging at the receiving end, significantly reducing communication latency while greatly enhancing the real-time and immersive experience of holographic interaction, providing a systematic solution for high-quality holographic communication under different network conditions.

[0006] In an optional embodiment, the initial holographic data stream is compressed to obtain a compressed holographic data stream, including: extracting features of the initial holographic data stream according to a preset signal frequency to obtain first holographic data and second holographic data, the signal frequency of the first holographic data being smaller than that of the second holographic data; quantizing the first holographic data and the second holographic data to obtain first quantized data corresponding to the first holographic data and second quantized data corresponding to the second holographic data; compressing the first quantized data and the second quantized data to obtain first compressed data corresponding to the first quantized data and second compressed data corresponding to the second quantized data; wherein the compressed holographic data stream includes the first compressed data and the second compressed data.

[0007] The holographic communication method provided in the embodiment of the present invention performs intelligent frequency division processing on the initial holographic data by presetting the signal frequency, separates the high-frequency details from the low-frequency basic information into the first holographic data and the second holographic data, and realizes the refined data management of layered compression. A differentiated quantization strategy is adopted to independently process the high-frequency and low-frequency data, which not only retains the fine features of the high-frequency data to support high-fidelity imaging, but also effectively reduces data redundancy through simplified quantization of the low-frequency data. A compressed stream composed of the first compressed data and the second compressed data is generated through block compression technology to form a dynamically adjustable transmission structure - in a weak network environment, low-frequency basic data is transmitted first to ensure the basic smoothness of communication, and high-frequency data is superimposed under high-quality network conditions to improve imaging quality.

[0008] In an optional implementation, the compressed holographic data stream is decompressed according to the first network state to obtain a target holographic data stream, including: analyzing network parameters corresponding to the first network state to determine network load information at the receiving end; and decompressing the compressed holographic data stream based on the network load information to obtain the target holographic data stream.

[0009] The holographic communication method provided by the embodiment of the present invention accurately identifies the network load status by real-time analysis of the network parameters of the receiving end, so that the decompression process has the ability to respond to dynamic environments. The decompression strategy is intelligently selected based on the load information - when the load is low, the high and low frequency data are fully decompressed to restore the high-precision holographic image, and when the load is high or the network fluctuates, the low-frequency basic data is preferentially decompressed to ensure the continuity of the core picture. This "network status driven" decompression mechanism opens up the closed-loop optimization of the transport layer and the application layer, which can not only avoid freezes or data loss caused by network congestion, but also maximize the use of available bandwidth resources to improve imaging quality, and realize full-link adaptive adjustment from the compression end to the decompression end, which significantly enhances the robustness and service quality of the holographic communication system in complex network environments.

[0010] In an alternative embodiment, when there are multiple transmitters, decompress the compressed holographic data stream according to the first network state to obtain the target holographic data stream, including: analyzing the network parameters corresponding to the first network state to determine the network load information of the receiver; obtaining the transmission priorities of the respective transmitters; and decompressing the compressed holographic data streams corresponding to the respective transmitters according to the transmission priorities and the network load information to obtain the target holographic data streams corresponding to the respective transmitters.

[0011] The holographic communication method provided by the embodiments of the present invention can, by perceiving the network load of the receiver in real time and comprehensively considering the transmission priorities of the respective transmitters, make an intelligent decision on the decompression order and resource allocation ratio of multiple data streams - giving priority to ensuring the complete decompression of the data of high-priority transmitters to maintain the imaging quality of the core scenario, and at the same time flexibly adjusting the decompression depth of low-priority data streams according to the remaining bandwidth. This collaborative decompression strategy based on "network state + service weight" can not only prevent systematic congestion caused by resource competition in the multi-channel concurrent scenario, but also optimize the quality of service of key services and the overall bandwidth utilization rate through differential decompression, enabling the multi-terminal holographic communication system to still maintain a stable hierarchical service ability in a complex load environment, and significantly improving the reliability and scalability of large-scale holographic collaboration scenarios.

[0012] In an alternative embodiment, obtain the second network state of the transmitter; analyze the network parameters of the second network state, and determine the optimal transmission path of the initial holographic data stream based on the analysis result.

[0013] The holographic communication method provided by the embodiments of the present invention can accurately perceive the quality and fluctuation trend of the current network environment by obtaining and analyzing the second network state parameters (such as bandwidth, delay, packet loss rate, etc.) of the transmitter in real time. Dynamically select the optimal transmission path of the initial holographic data stream based on the analysis result to ensure the continuity and low-latency transmission of the data stream. At the same time, this mechanism optimizes the bandwidth resource allocation by intelligently avoiding network congestion paths and reduces the risk of transmission interruption, thereby ensuring the efficiency and stability of holographic communication.

[0014] In an alternative embodiment, analyze the network parameters of the second network state, and determine the optimal transmission path of the initial holographic data stream based on the analysis result, including: analyzing the network parameters of the second network state to determine at least one candidate transmission path corresponding to the initial holographic data stream; performing path quality evaluation on each candidate transmission path based on the transmission parameters of the candidate transmission paths to generate a path quality evaluation result; and determining the optimal transmission path from each candidate transmission path according to the path quality evaluation result.

[0015] The holographic communication method provided by the embodiments of the present invention generates a candidate path set with both availability and redundancy by analyzing the network parameters of the sending end, laying a foundation for subsequent optimization. A multi-dimensional evaluation model is established based on transmission parameters, and the candidate paths are dynamically weighted and scored by combining the real-time network status and historical transmission efficiency to ensure that the evaluation results accurately reflect the characteristics of the current network environment. Finally, the path with the optimal comprehensive quality is selected according to the quantitative evaluation results, realizing the in-depth matching of "data characteristics - network status - service requirements". This not only avoids the limitations of single-path evaluation but also supports adaptive path switching triggered by network fluctuations during transmission, significantly improving the timeliness, reliability, and cross-network domain collaboration efficiency of holographic data stream transmission.

[0016] In an alternative embodiment, a timestamp and the location information of the target object are embedded in the initial holographic data stream, so that the receiving end extracts the timestamp from the target holographic data stream, uses the timestamp for local time calibration, and performs holographic imaging on the target holographic data stream according to the calibrated time to generate a holographic image carrying the timestamp and location information.

[0017] The holographic communication method provided by the embodiments of the present invention integrates the timestamp and the location information of the target object in the data stream, enabling the receiving end to perform local clock synchronization based on an accurate spatio-temporal reference. At the same time, the spatial coordinates of the target are restored through the location data to ensure that the spatio-temporal mapping relationship between the holographic image and the physical world is accurately aligned. Therefore, this method not only solves the problem of multi-terminal spatio-temporal misalignment caused by network jitter or path differences in cross-domain communication but also enables multi-dimensional data association of dynamic scenarios through spatio-temporal tags, significantly improving the spatio-temporal perception authenticity and collaboration accuracy of remote holographic interaction.

[0018] In a second aspect, the present invention provides a holographic communication system, including: a receiving end; a sending end, communicatively connected to the receiving end, for generating an initial holographic data stream based on the environmental information of the target object; a server cluster, communicatively connected to the sending end, for compressing the initial holographic data stream to obtain a compressed holographic data stream; and for obtaining the first network state of the receiving end and decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream; transmitting the target holographic data stream to the receiving end; the receiving end is used for performing holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

[0019] In a third aspect, the present invention provides a holographic communication device, comprising: a first acquisition module configured to acquire an initial holographic data stream reported by a sending end, the initial holographic data stream being determined based on environmental information of a target object; a compression module configured to compress the initial holographic data stream to obtain a compressed holographic data stream; a decompression module configured to acquire a first network state of a receiving end and decompress the compressed holographic data stream according to the first network state to obtain a target holographic data stream; and a transmission module configured to transmit the target holographic data stream to the receiving end so that the receiving end performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

[0020] In a fourth aspect, the present invention provides a computer device, comprising: a memory and a processor, which are communicatively connected to each other, wherein the memory stores computer instructions, and the processor executes the computer instructions to execute the holographic communication method according to the first aspect or any corresponding embodiment thereof.

[0021] In a fifth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, and the computer instructions are used to cause a computer to execute the holographic communication method according to the first aspect or any corresponding embodiment thereof.

[0022] In a sixth aspect, the present invention provides a computer program product comprising computer instructions, and the computer instructions are used to cause a computer to execute the holographic communication method according to the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for use in the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0024] Figure 1 is a schematic flowchart of a holographic communication method according to an embodiment of the present invention;

[0025] Figure 2 is a schematic flowchart of another holographic communication method according to an embodiment of the present invention;

[0026] Figure 3 is a schematic flowchart of yet another holographic communication method according to an embodiment of the present invention;

[0027] Figure 4 is a structural block diagram of a holographic communication system according to an embodiment of the present invention;

[0028] Figure 5 It is a structural block diagram of another holographic communication system according to an embodiment of the present invention;

[0029] Figure 6 It is a structural block diagram of a holographic communication device according to an embodiment of the present invention;

[0030] Figure 7 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Specific Embodiments

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] With the development of the 6th Generation Mobile Communication Technology (6G) network, holographic communication has gradually become a core application for immersive interaction and is widely used in fields such as telemedicine, enterprise meetings, and industrial collaboration. Through high-resolution 3D holographic projection, users can achieve real-time interaction across regions. The ultra-high data throughput (>10 Gbps), ultra-low latency (<1 ms), and high reliability of the 6G network provide strong support for holographic communication. However, there are still some challenges, such as unstable connections in high-mobility scenarios, poor synchronization accuracy of distributed devices, complex real-time data compression and transmission problems, and impaired holographic content consistency due to high packet loss rates in multicast communication.

[0033] The current implementation solutions mainly rely on the combination of the 5th Generation Mobile Communication Technology (5G) and edge computing to solve these problems. For example, some solutions transmit compressed holographic data through the 5G network and use edge nodes for decoding and rendering to reduce the load on the core network; others adopt multi-path transmission protocols (such as QUIC) to improve transmission reliability, but lack the ability of dynamic resource allocation; there are also solutions that use traditional light field compression algorithms (such as compression coding based on deep learning), but it is often difficult to balance compression efficiency and real-time performance. The disadvantages of these technical solutions are that the current network cannot dynamically adapt to high-speed mobile scenarios, resulting in stuttering or interruption of holographic projection; the synchronization accuracy between devices is insufficient to cope with network jitter and latency fluctuations; at the same time, the compression algorithm has high requirements for computing power, and it is difficult to meet the real-time decoding requirements of edge nodes; the multicast communication efficiency is low and cannot be dynamically adjusted according to network load, causing bandwidth waste or transmission delay.

[0034] In view of this, the technical solution of the present invention proposes a holographic communication support and multi-scenario adaptation mechanism based on the 6G system. Through multi-path transmission and dynamic bandwidth allocation, it ensures the stability of holographic communication in high-mobility scenarios; uses space-time coding and distributed clock calibration to achieve sub-millisecond synchronization of multiple terminal devices; proposes a lightweight light field compression algorithm (LFC-Algorithm) to reduce computing requirements and improve compression efficiency; dynamically adjusts the multicast strategy based on network status to achieve efficient use of bandwidth and low-latency transmission.

[0035] According to an embodiment of the present invention, there is provided an embodiment of a holographic communication method. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0036] In this embodiment, a holographic communication method is provided, which can be used in a computer device. Figure 1 It is a flowchart of the holographic communication method according to an embodiment of the present invention, as Figure 1 shown. The process includes the following steps:

[0037] Step S101, obtain the initial holographic data stream reported by the sending end, and the initial holographic data stream is determined based on the environmental information of the target object.

[0038] The sending end refers to a device or terminal that generates holographic data and uploads it to the network, such as a holographic acquisition device (such as a 3D camera, a light field sensor, etc.) in the main venue. The function of the sending end is to capture the holographic image of the target object (such as a speaker) and generate the initial holographic data stream.

[0039] The environmental information of the target object refers to the physical environmental parameters in the holographic data acquisition scenario, including but not limited to spatial coordinates, light intensity, background object distribution, dynamic interference factors (such as moving objects), etc. These information directly affect the fidelity and synchronization accuracy of the holographic projection.

[0040] The initial holographic data stream refers to the uncompressed original holographic data, which contains the complete light field information of the target object (such as light direction, intensity, depth, etc.) and its environmental information (such as lighting conditions, spatial coordinates, background details, etc.), and is used for subsequent processing and transmission. Specifically, the sending end captures the light field information (including light direction, intensity, depth, etc.) and environmental information (such as spatial coordinates, lighting conditions, background object distribution, dynamic interference factors, etc.) of the target object in real time through a holographic acquisition device. The environmental information is integrated into the initial holographic data stream through sensor fusion technology to ensure the fidelity and scene adaptability of the holographic projection. For example, in a remote meeting scenario, the actions of the speakers, background details, and lighting changes in the main venue are accurately captured to form the initial holographic data stream. The initial holographic data stream is uploaded to a computer device through a 6G network, providing high-resolution and low-latency input for subsequent processing.

[0041] Step S102: Compress the initial holographic data stream to obtain a compressed holographic data stream.

[0042] The compressed holographic data stream refers to the holographic data stream processed by a compression algorithm, such as the lightweight light field compression algorithm (LFC - Algorithm). Specifically, the light field information and environmental information are analyzed to identify the key data parts, and then these information are compressed through a compression algorithm to remove unnecessary details while retaining the key features and parameters required for the holographic image. The generated compressed holographic data stream has a smaller volume and is encapsulated as a low-latency transmission unit through a 6G network. The compression process not only reduces the amount of data, but also can be flexibly adjusted according to the network bandwidth limit to ensure efficient data transmission.

[0043] Step S103: Obtain the first network status of the receiving end and decompress the compressed holographic data stream according to the first network status to obtain the target holographic data stream.

[0044] The receiving end refers to the terminal device that receives and processes holographic data, such as the holographic projection device or user terminal in the branch venue. The first network state refers to the current network parameters of the receiving end, including real-time metrics such as bandwidth, latency, and packet loss rate. Specifically, the devices at the receiving end will obtain these network state parameters regularly or dynamically, which can be collected through dedicated network monitoring tools or protocols. For example, the receiving end uses adaptive network protocols (such as TCP, UDP, etc.) to cooperate with network devices to report information such as changes in network bandwidth, latency fluctuations, and packet loss in real time. This information can be transmitted to the computer device through a regular feedback mechanism (such as feedback messages, RTT measurements, etc.), so as to help the computer device understand the current network state and then adopt appropriate decompression strategies.

[0045] The target holographic data stream refers to the holographic data stream decompressed according to the first network state of the receiving end, which has been adapted to the current network conditions (such as bandwidth limitations) and can be directly used for holographic projection. Specifically, after obtaining the first network state, the decompression accuracy and data recovery method are determined according to the current network parameters such as bandwidth, latency, and packet loss rate. In the case of low network bandwidth or high latency, a more efficient decompression algorithm needs to be selected or only some key data is decompressed to maintain a smooth holographic display experience. That is, the decompression process of the computer device includes first initializing the decoding of the compressed holographic data stream, and then further adjusting the decompression process according to the first network state of the receiving end, retaining important light field information and environmental information, and reducing the data recovery accuracy as much as possible to adapt to the current network conditions. The target holographic data stream is the adapted decompressed data stream, which ensures that the holographic data can be correctly restored under specific network conditions and provides the required input for holographic imaging.

[0046] Step S104, transmit the target holographic data stream to the receiving end so that the receiving end performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

[0047] The holographic image refers to the three-dimensional stereoscopic image generated by the receiving end through the holographic projection device, which can vividly restore the dynamic details (such as lip movements, gestures) of the target object and the environmental scene. Specifically, the target holographic data stream is sent to the holographic projection device or user terminal at the receiving end, and these devices can process the holographic data and generate the corresponding holographic image. The receiving end uses the target holographic data stream obtained after decompression to reconstruct the light field information of the target object, and generates a high-quality three-dimensional holographic image according to the dynamic behavior (such as lip movements, gestures, etc.) of the target object and the environmental scene where it is located. Finally, the receiving end presents the three-dimensional image to the user through the holographic projection device to achieve the purpose of restoring the target object.

[0048] The holographic communication method provided by the embodiments of the present invention ensures the accuracy of the data source and the scene adaptability by obtaining the initial holographic data stream based on the environmental information of the target object. The data stream is optimized by using dynamic compression technology, effectively reducing the transmission bandwidth requirement and improving the transmission efficiency. It innovatively combines the real-time network status at the receiving end for adaptive decompression, achieving the optimal allocation of network resources and the dynamic balance of transmission quality. It can not only ensure the smoothness in a weak network environment but also make full use of the performance potential of a high-bandwidth network. Finally, high-fidelity images are generated through holographic imaging at the receiving end, significantly reducing the communication delay while greatly enhancing the real-time performance and immersion of holographic interaction, providing a systematic solution for high-quality holographic communication under different network conditions.

[0049] In this embodiment, a holographic communication method is provided, which can be used in a computer device. Figure 2 It is a flowchart of the holographic communication method according to the embodiments of the present invention, as Figure 2 shown, and the process includes the following steps:

[0050] Step S201, obtain the initial holographic data stream reported by the sending end, and the initial holographic data stream is determined based on the environmental information of the target object. For details, please refer to Figure 1 step S101 of the embodiment shown, which will not be elaborated here.

[0051] Step S202, compress the initial holographic data stream to obtain a compressed holographic data stream.

[0052] Specifically, the above step S202 includes:

[0053] Step S2021, extract features from the initial holographic data stream according to a preset signal frequency to obtain first holographic data and second holographic data, and the signal frequency of the first holographic data is less than that of the second holographic data.

[0054] The preset signal frequency refers to the hierarchical feature extraction standard preset in the lightweight light field compression algorithm (LFC - Algorithm), which is used to divide the initial holographic data stream into sub - data of different frequency bands. For example, low - frequency signals (such as overall contour, main actions) and high - frequency signals (such as texture details, shadow changes). The preset signal frequency is determined based on the typical feature distribution of the light field data and the network transmission requirements to ensure the rationality and efficiency of hierarchical compression.

[0055] The first holographic data refers to the low-frequency signal data extracted through a preset signal frequency, which contains the basic information of the holographic image (such as the main structure, dynamic actions), has a low demand for network bandwidth, but provides the core content for holographic projection. The second holographic data refers to the enhanced layer data extracted through high-frequency signals, which contains the detailed information of the holographic image (such as texture, lighting changes), and requires a higher bandwidth for transmission to improve the image quality. The two together constitute a layered compression input, adapting to dynamic network conditions. Specifically, the initial holographic data stream is segmented into a low-frequency part and a high-frequency part by a frequency-domain filter (such as wavelet transform or Fourier transform) according to a frequency threshold: the first holographic data corresponds to the low-frequency signal, and the second holographic data corresponds to the high-frequency signal. For example, the preset signal frequency threshold is set to 1 GHz, and the signals below this threshold are classified as the first holographic data, while the signals above the threshold are classified as the second holographic data. This layering method can separate the key basic information from the detailed redundant data, laying the foundation for subsequent differential compression.

[0056] In step S2022, quantization processing is performed on the first holographic data and the second holographic data to obtain the first quantization data corresponding to the first holographic data and the second quantization data corresponding to the second holographic data.

[0057] The first holographic data refers to the data obtained after quantization processing of the first holographic data (low-frequency information), using relatively coarse quantization parameters to reduce the data volume while retaining the basic features. The second holographic data refers to the data obtained after refined quantization of the second holographic data (high-frequency details), and the quantization accuracy is dynamically adjusted according to the network bandwidth (for example, more details are retained at high bandwidth, and redundant information is compressed at low bandwidth). Specifically, the quantization processing uses dynamic quantization coding technology, and different quantization parameters are assigned to data in different frequency layers. For example, for the first holographic data (low-frequency), low-precision quantization (such as 8 bits) is used to reduce the data volume while retaining the core light field structure (such as depth, light direction). For the second holographic data (high-frequency), adaptive quantization is used, and the quantization bit width is dynamically adjusted according to the network state (such as from 12 bits to 16 bits). For example, more high-frequency details are retained in high-bandwidth scenarios, and the bit width is reduced at low bandwidth to compress redundant information. Among them, an error compensation mechanism can be introduced during the quantization process to compensate for quantization losses through residual coding to ensure the reconstruction quality.

[0058] In step S2023, data compression is performed on the first quantization data and the second quantization data to obtain the first compression data corresponding to the first quantization data and the second compression data corresponding to the second quantization data.

[0059] The first compressed data refers to the compression result after entropy encoding the first quantized data, further removing statistical redundancy to form a low-bitrate data stream, which is suitable for scenarios with poor network conditions. The second compressed data refers to the compression result after entropy encoding the second quantized data, retaining high-frequency details, requiring higher bandwidth support, but can significantly improve the image fidelity. The two adapt to different network states through a hierarchical transmission strategy. Specifically, in the compression stage, entropy encoding and a hierarchical compression strategy are combined to compress the first quantized data and the second quantized data. For the first quantized data, lossless entropy encoding (such as Huffman encoding or arithmetic encoding) is used to ensure lossless compression of the base layer data. For the second quantized data, a lossy compression algorithm (such as compressive sensing technology based on sparse representation) is applied to remove spatio-temporal redundant information. For example, the sparse features of high-frequency data are extracted using K-SVD dictionary learning, and then the sparse coefficients are reconstructed through the OMP algorithm to achieve efficient compression. Finally, the first compressed data (base layer) and the second compressed data (enhancement layer) are output.

[0060] The holographic communication method provided by the embodiments of the present invention performs intelligent frequency division processing on the initial holographic data through a preset signal frequency, separates high-frequency details and low-frequency basic information into the first holographic data and the second holographic data, and realizes refined data management for hierarchical compression. A differential quantization strategy is used to independently process high- and low-frequency data, which not only retains the fine features of high-frequency data to support high-fidelity imaging, but also effectively reduces data redundancy through simplified quantization of low-frequency data. A compressed stream combined with the first compressed data and the second compressed data is generated through block compression technology, forming a dynamically adjustable transmission structure - in a weak network environment, the low-frequency basic data is preferentially transmitted to ensure the basic fluency of communication, and in a high-quality network condition, high-frequency data is superimposed to improve the imaging quality.

[0061] Step S203, obtain the first network state of the receiving end, and decompress the compressed holographic data stream according to the first network state to obtain the target holographic data stream.

[0062] Specifically, the above step S203 includes:

[0063] Step S2031, obtain the first network state of the receiving end. For details, please refer to Figure 1 Step S103 of the illustrated embodiment, which will not be elaborated here.

[0064] Step S2032, analyze the network parameters corresponding to the first network state to determine the network load information of the receiving end.

[0065] The network parameters corresponding to the first network state refer to the network performance indicators monitored in real time in the network environment where the receiving end is located, including bandwidth, latency, packet loss rate, etc. These parameters reflect the current network transmission capacity and are used to dynamically adjust the decompression strategy. Network load information refers to comprehensive indicators such as bandwidth occupancy rate and data transmission pressure analyzed based on network parameters. For example, in a high-load scenario, the network may not be able to support multiple enhancement layer data streams simultaneously, and it is necessary to downgrade the decompression level to prioritize the transmission of core content. Specifically, the network load information of the receiving end is determined through multi-dimensional parameter fusion analysis. First, key network parameters such as bandwidth, latency, packet loss rate, and base station load reported by the receiving end are collected in real time; secondly, the long short-term memory network (LSTM) model is used to predict the bandwidth fluctuation and congestion trend in the future period based on historical network data; at the same time, the dynamic load weight is calculated by combining local network topology information (such as the number of routing hops and user equipment density) to generate a load matrix that quantifies the real-time pressure of the receiving end. For example, when the bandwidth of the receiving end is lower than the threshold and the base station load exceeds 80%, it is determined that the network load is in a high-pressure state, and an adaptive decompression strategy is triggered.

[0066] Step S2033, decompress the compressed holographic data stream based on the network load information to obtain the target holographic data stream.

[0067] The decompression process adopts an adaptive bitstream reconstruction technology and is specifically processed according to different scenarios. Specifically, in a high-load scenario, the first compressed data (basic layer low-frequency information) is preferentially decompressed to generate a low-resolution hologram, and if the bandwidth permits, the second compressed data (high-frequency details) is gradually loaded to supplement the enhancement layer; in a low-load scenario, the basic layer and the enhancement layer are decompressed in parallel, and a GPU-accelerated light field reconstruction algorithm (such as the angular spectrum method) is called to restore high-fidelity holographic data; if packet loss is detected, the forward error correction (FEC) redundant packets are used to recover the missing data. For example, when the network bandwidth is 8 Gbps, the enhancement layer is fully decompressed to achieve millimeter-level detail restoration; when the bandwidth drops to 2 Gbps, only the basic layer is decompressed to ensure the continuity of the basic projection.

[0068] In some alternative embodiments, when there are multiple sending ends, the above step S203 includes:

[0069] Step a1, obtain the first network state of the receiving end. For details, please refer to Figure 2 Step S2031 of the embodiment shown, which will not be elaborated here.

[0070] Step a2, analyze the network parameters corresponding to the first network state to determine the network load information of the receiving end. For details, please refer to Figure 2 Step S2032 of the embodiment shown, which will not be elaborated here.

[0071] Step a3, obtain the transmission priorities of each sending end.

[0072] The transmission priority refers to the data transmission priorities set for different senders. For example, in a cross-regional meeting, the data stream of the main venue (the speaker) has the highest priority, and the data of the branch venues (the audience) dynamically allocates resources according to importance. Specifically, the transmission priority is dynamically set through different scenarios. For example, the main venue priority rule, in the cross-regional meeting scenario, the data stream of the speaker in the main venue is marked as the highest priority (such as priority 1) to ensure its real-time nature and integrity. For example, business type determination, the business type is identified through the QoS label in the packet header (such as remote surgery is real-time critical level, industrial inspection is high reliability level), and differential priorities are allocated. For example, user-defined configuration, the administrator manually adjusts the priority through the control interface, for example, promoting the data stream of the emergency task to the highest level. For example, in a medical holographic consultation, the operation stream of the surgeon has a higher priority than the environmental data stream of the auxiliary equipment.

[0073] Step a4, according to the transmission priority and network load information, decompress the compressed holographic data streams corresponding to each sender to obtain the target holographic data streams corresponding to each sender.

[0074] Dynamically allocate resources according to priority and load. Specifically, reserve dedicated decoding channels and GPU computing power for high-priority data streams to ensure their real-time decoding. When the network load is too high, pause the decompression of low-priority data streams (such as the background data of the branch venue), and give priority to processing high-priority data (such as the image of the speaker in the main venue). Only decompress the base layer for low-priority data (such as only showing the outline of the speaker in the branch venue), and fully decompress the enhancement layer for high-priority data (such as restoring gesture details in the main venue). For example, when the bandwidth is tight, the branch venue only receives the base layer data, while the main venue synchronously decompresses the enhancement layer to achieve sub-millisecond synchronization of lip shape and voice.

[0075] Step S204, transmit the target holographic data stream to the receiver so that the receiver performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream. For details, please refer to Figure 1 Step S104 of the embodiment shown, which will not be elaborated here.

[0076] The holographic communication method provided by the embodiment of the present invention accurately identifies the network load status by real-time analysis of the network parameters of the receiving end, so that the decompression process has the ability to respond to dynamic environments. The decompression strategy is intelligently selected based on the load information - when the load is low, the high and low frequency data are fully decompressed to restore the high-precision holographic image, and when the load is high or the network fluctuates, the low-frequency basic data is preferentially decompressed to ensure the continuity of the core picture. This "network status driven" decompression mechanism opens up the closed-loop optimization of the transport layer and the application layer, which can not only avoid freezes or data loss caused by network congestion, but also maximize the use of available bandwidth resources to improve imaging quality, and realize full-link adaptive adjustment from the compression end to the decompression end, which significantly enhances the robustness and service quality of the holographic communication system in complex network environments.

[0077] In this embodiment, a holographic communication method is provided, which can be used in computer devices. Figure 3 is a flow chart of a holographic communication method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0078] Step S301, obtaining the initial holographic data stream reported by the sender, the initial holographic data stream is determined based on the environment information where the target object is located. Figure 2 Step S201 of the illustrated embodiment will not be described in detail here.

[0079] Step S302, obtaining a second network state of the transmitting end; analyzing network parameters of the second network state, and determining an optimal transmission path of the initial holographic data stream based on the analysis result.

[0080] Specifically, the above step S302 includes:

[0081] Step S3021, obtaining the second network status of the sending end.

[0082] The second network state refers to the real-time operating status of the network environment in which the sender is located. Specifically, the second network state of the sender is obtained by real-time monitoring of the dynamic parameters of the network in which the sender is located, including indicators such as bandwidth, latency, packet loss rate, base station load and channel quality. These parameters are periodically collected by the built-in network status sensor of the sender or the probe deployed by the computer device (for example, updated every millisecond). For example, in a user mobile scenario, the signal strength of the currently connected base station and the availability of adjacent base stations are continuously monitored to ensure the real-time and accuracy of the candidate transmission path.

[0083] Step S3022: Analyze the network parameters of the second network state and determine at least one candidate transmission path corresponding to the initial holographic data stream.

[0084] The network parameters of the second network state are specific metrics that describe the network environment state where the sender is located, including bandwidth, latency, packet loss rate, etc. At least one candidate transmission path refers to multiple candidate data transmission paths existing between the sender and the receiver. For example, the sender is simultaneously connected to multiple base stations (such as base station a and base station b), and each connection forms a candidate path. Specifically, by scanning the list of base stations that the sender can access (such as base station a, base station b, base station c), and combining the base station load, coverage area, and channel quality (such as SINR value), the initial paths that meet the minimum transmission requirements are filtered out. The parameters such as bandwidth, latency, and packet loss rate of each initial path are mapped to path quality metrics (such as bandwidth weight accounting for 40%, latency accounting for 30%, and packet loss rate accounting for 30%) to generate a preliminary candidate set, that is, at least one candidate transmission path.

[0085] Among them, according to network topology changes (such as base station failures or new nodes), the candidate path list is updated in real time. For example, if the load of a certain base station exceeds the threshold (such as 90%), it will be removed from the candidate paths to avoid congestion risks.

[0086] Step S3023, based on the transmission parameters of the candidate transmission paths, perform path quality evaluation on each candidate transmission path to generate a path quality evaluation result.

[0087] The transmission parameters refer to the metrics used to evaluate the quality of candidate paths. For example, they can include the bandwidth of the path, the latency of the path, the packet loss rate of the path, network jitter, etc. The path quality evaluation result is a comprehensive score for each candidate transmission path. Specifically, a comprehensive scoring model driven by reinforcement learning can be used to perform path quality evaluation on each candidate transmission path. The bandwidth, latency, and packet loss rate of each candidate transmission path are normalized to eliminate the dimension difference. The comprehensive score is calculated according to the preset weights (such as bandwidth score × 0.4 + latency score × 0.3 + packet loss rate score × 0.3). The latency score and the packet loss rate score are calculated using an inverse proportional function (such as the lower the latency, the higher the score).

[0088] Among them, the scoring weights can be dynamically adjusted through a reinforcement learning model (such as Q-Learning) combined with historical path switching success rate and stability data. For example, paths with frequent switching failures will be deweighted, and stable paths will be preferred. Finally, a path quality evaluation result is generated and sorted from high to low according to the scores.

[0089] Step S3024, according to the path quality evaluation result, determine the optimal transmission path from each candidate transmission path.

[0090] The optimal transmission path is the candidate path with the highest score after path quality evaluation. This path can maximize the transmission efficiency (such as high bandwidth, low latency, and low packet loss rate) under the current network state, ensuring the continuity and stability of the holographic data stream. Specifically, only the paths with a comprehensive score higher than a preset threshold (such as 80 points) are retained to ensure the basic transmission quality. If the score of the current path is still the highest, the original path is maintained; if the scores of other paths are higher, seamless switching is triggered. For example, when the score of path B is 10% higher than that of the current path A, the routing table is immediately updated to switch the data stream to path B. Among them, packet redundancy caching and fast retransmission technologies are used during the switching process to avoid packet loss or interruption. For example, 5ms of data packets are pre-cached before switching to ensure continuity during the switching period. Finally, the path with the highest comprehensive score and qualified stability is selected as the optimal transmission path.

[0091] The holographic communication method provided by the embodiments of the present invention generates a set of candidate paths with both availability and redundancy by analyzing the network parameters at the sending end, laying a foundation for subsequent optimization. A multi-dimensional evaluation model is established based on transmission parameters, and the candidate paths are dynamically weighted and scored by combining the real-time network state and historical transmission efficiency, ensuring that the evaluation results accurately reflect the characteristics of the current network environment. Finally, the path with the optimal comprehensive quality is selected according to the quantitative evaluation results, realizing the deep matching of "data characteristics - network state - service requirements", avoiding the limitations of single-path evaluation, and supporting adaptive path switching according to network fluctuations during the transmission process, significantly improving the timeliness, reliability, and cross-network domain collaboration efficiency of holographic data stream transmission.

[0092] Step S303: Compress the initial holographic data stream to obtain a compressed holographic data stream. For details, please refer to Figure 2 Step S202 of the embodiment shown, which will not be elaborated here.

[0093] Step S304: Obtain the first network state of the receiving end, and decompress the compressed holographic data stream according to the first network state to obtain a target holographic data stream. For details, please refer to Figure 2 Step S203 of the embodiment shown, which will not be elaborated here.

[0094] Step S305: Transmit the target holographic data stream to the receiving end so that the receiving end performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

[0095] In some optional embodiments, a time stamp and the position information of the target object are embedded in the initial holographic data stream, so that the receiving end extracts the time stamp from the target holographic data stream, uses the time stamp to calibrate the local time, and performs holographic imaging on the target holographic data stream according to the calibrated time to generate a holographic image carrying the time stamp and position information.

[0096] A timestamp refers to an accurate time mark embedded in the initial holographic data stream, used to record the specific moment when the data is generated. The position information of the target object refers to the coordinate data of the target object in the three-dimensional space in the holographic image. This position information and the timestamp together constitute a "space-time tag" to ensure that the receiving end can perform holographic projection at the correct spatial position and time sequence. Specifically, in the data encoding stage, the timestamp (accurate to the sub-millisecond level) and the three-dimensional spatial coordinates of the target object (such as the position information based on the world coordinate system) are embedded in the header or specific data blocks of the initial holographic data stream in the form of metadata. The timestamp is generated by a high-precision clock source, and the position information captures the spatial coordinates of the target object in real time through a holographic acquisition device (such as a light field sensor). The hierarchical coding technology is adopted to fuse the space-time tag with the light field data (ray direction, intensity, depth) to ensure that the tag and the content data are synchronized during transmission. For example, the space-time tag is embedded by expanding the SEI (Supplemental Enhancement Information) field of the H.266 / VVC protocol, or a custom data packet structure is used to achieve low-overhead embedding.

[0097] The receiving end parses the timestamp and position information from the received target holographic data stream, and uses the Distributed Clock Calibration Protocol (DCCP protocol) to calibrate the local clock to eliminate the clock deviation between the sending end and the receiving end. For example, if the timestamp at the sending end is T1 and the local time at the receiving end is T2, DCCP calculates the average delay ΔT through a multi-node redundant clock source and adjusts the receiving end clock to T1 + ΔT to ensure that the multi-terminal projection synchronization error < 0.5 ms. Secondly, according to the embedded position information, combined with the local environment parameters of the receiving end (such as the projection space size, LiDAR scan results), the depth of field and viewing angle of the holographic image are dynamically adjusted to make it seamlessly integrated with the physical environment. For example, if the position coordinates of the target object are (X, Y, Z), the receiving end adjusts the wavefront phase through a Spatial Light Modulator (SLM) to ensure that the projection position is consistent with the coordinates. Based on the calibrated timestamp, the target holographic data stream is decoded and rendered in strict time sequence to generate a holographic image carrying the space-time tag. For example, in a remote conferencing scenario, the lip movements and speech of the speaker are synchronized through the timestamp, and the projection positions of each branch venue are aligned through the spatial coordinates, finally realizing a multi-terminal sub-millisecond-level synchronous holographic interaction experience.

[0098] The holographic communication method provided by the embodiments of the present invention fuses the timestamp and the position information of the target object in the data stream, enabling the receiving end to perform local clock synchronization based on an accurate space-time reference. At the same time, the spatial coordinates of the target are restored through the position data to ensure that the space-time mapping relationship between the holographic image and the physical world is accurately aligned. Therefore, this method not only solves the problem of multi-terminal space-time misalignment caused by network jitter or path differences in cross-domain communication, but also can realize multi-dimensional data association of dynamic scenes through space-time tags, significantly improving the space-time perception authenticity and collaboration accuracy of remote holographic interaction.

[0099] This embodiment provides a holographic communication system, as Figure 4 shown, including:

[0100] A receiving end 1;

[0101] A sending end 2, communicatively connected to the receiving end 1, for generating an initial holographic data stream based on the environmental information of the target object;

[0102] A server cluster 3, communicatively connected to the sending end 2, for compressing the initial holographic data stream to obtain a compressed holographic data stream; and for obtaining the first network state of the receiving end 1 and decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream, and transmitting the target holographic data stream to the receiving end 1;

[0103] The receiving end 1 is used for performing holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

[0104] The sending end 2 generates an initial holographic data stream by collecting the light field data of the target object (including information such as light direction and intensity). Among them, the time - space encoding technology can be combined to embed time stamps and spatial coordinate tags (time - space tags) in the initial holographic data stream, providing a basis for subsequent multi - terminal synchronization.

[0105] The server cluster 3 is composed of multiple servers and has a main server for central control. The server cluster 3 uses a compression algorithm to compress the initial holographic data stream to obtain a compressed holographic data stream. The server cluster 3 monitors the network state of the receiving end 1 in real - time, and decides the decompression accuracy and data recovery method according to the current network state (such as bandwidth, delay, packet loss rate) to obtain a target holographic data stream.

[0106] The receiving end 1 obtains the target holographic data stream through a 6G network, reconstructs the light field information of the target object using the target holographic data stream, and generates a high - quality three - dimensional holographic image according to the dynamic behavior of the target object (such as lip - shape, gesture, etc.) and the environmental scene where it is located.

[0107] In some alternative embodiments, the server cluster 3 includes:

[0108] A first server 31, for embedding a time stamp and the position information of the target object in the initial holographic data stream;

[0109] A second server 32, for obtaining the second network state of the sending end 2, analyzing the network parameters of the second network state, and determining the optimal transmission path of the initial holographic data stream based on the analysis result;

[0110] The third server 33 is used to compress the initial holographic data stream to obtain a compressed holographic data stream; and to obtain the first network state of the receiving end 1 and decompress the compressed holographic data stream according to the first network state to obtain a target holographic data stream.

[0111] The first server 31 encodes the timestamp (accurate to the sub-millisecond level) and the spatial coordinates of the target object (such as three-dimensional position information) into the initial holographic data stream. Using hierarchical coding technology, tags are embedded in the header or specific data segments of the data packet to ensure that the receiving end 1 can extract them quickly.

[0112] The second server 32 obtains the network parameters of the sending end 2 in real time, including key indicators such as bandwidth, delay, packet loss rate, and base station 4 signal strength, and dynamically evaluates the comprehensive quality of available transmission paths (such as base station a, base station b, or satellite link) based on these parameters through a reinforcement learning algorithm (Q-Learning). The algorithm aims to maximize the transmission rate, minimize the delay and packet loss rate, and continuously calculates the priority of each path. When it detects that the quality of the current path has decreased (for example, due to the unstable connection of base station 4 caused by the high-speed movement of the user), the system will trigger a seamless handover mechanism, automatically select the optimal path, and generate a routing table update instruction to ensure that the holographic data is transmitted to the receiving end 1 through a highly reliable path, thereby ensuring the continuity and low latency of communication.

[0113] In the compression stage (LFC - Algorithm), the third server 33 first divides the light field data into a base layer (low-frequency contour information) and an enhancement layer (high-frequency detail information) through hierarchical feature extraction. Subsequently, it dynamically adjusts the quantization accuracy according to the network bandwidth of the sending end 2: if the bandwidth is sufficient, the details of the enhancement layer are retained and a smaller quantization step is used; if the bandwidth is limited, the data of the enhancement layer is compressed and the quantization step is increased. Finally, entropy coding is used to perform lossless compression on the quantized data, achieving an increase in the compression rate and a significant reduction in the data volume. In the decompression stage, it monitors the network state of the receiving end 1 (such as bandwidth and decoding ability) in real time and adopts a dynamic decoding strategy. When the network bandwidth is sufficient and stable, the data of the base layer and the enhancement layer are completely decompressed to restore a high-precision holographic image; if the network bandwidth is insufficient or fluctuates greatly, the data of the base layer is preferentially decompressed to reduce the decoding delay, sacrificing some details to ensure real-time performance.

[0114] In some alternative embodiments, as Figure 5 shown, the above holographic communication system further includes a base station 4 and a core network 5.

[0115] The base station 4 is set between the second server 32 and the third server 33. After determining the optimal transmission path of the initial holographic data stream, the initial holographic data stream is transmitted to the third server 33 through the base station 4;

[0116] The core network 5, which is set between the base station 4 and the third server 33, transmits the initial holographic data stream transmitted by the base station 4 to the third server 33 through the core network 5.

[0117] The base station 4 is set between the second server 32 and the third server 33. Its core function is to transmit the initial holographic data stream from the sending end 2 to the third server 33 according to the optimal transmission path determined by the second server 32 (such as base station a, base station b or satellite link evaluated by the Q-Learning algorithm). As a relay node at the physical layer, the base station 4 receives path instructions through wireless or wired links to ensure seamless switching of the data stream to the optimal channel, thereby maintaining the stability and low latency of transmission in high-mobility scenarios.

[0118] The core network 5 is located between the base station 4 and the third server 33. As the backbone network infrastructure, it is responsible for efficiently routing the initial holographic data stream transmitted by the base station 4 to the third server 33.

[0119] In the following, this embodiment will take the usage scenario of a cross-regional holographic conference as an example to exemplarily illustrate the above-mentioned holographic communication method and holographic communication system.

[0120] The holographic image of the speaker in the main venue generates an initial holographic data stream through the light field acquisition device. Subsequently, the lightweight light field compression algorithm (LFC-Algorithm) is called to perform hierarchical processing on the data: the light field data is divided into a base layer (low-frequency contour information) and an enhancement layer (high-frequency detail information), and the quantization accuracy is dynamically adjusted according to the current network bandwidth. For example, if the bandwidth in the main venue is sufficient, the details of the enhancement layer (such as facial micro-expressions) are retained; if the bandwidth is limited, the enhancement layer is compressed to reduce the data volume. Finally, lossless compression is achieved through entropy coding to generate a compressed holographic data stream.

[0121] The first server embeds a timestamp (accurate to the sub-millisecond level) and the three-dimensional spatial coordinates of the speaker (such as position information) in the compressed data stream to provide a benchmark for multi-terminal synchronization. The second server real-time monitors the network status of the sending end (such as bandwidth, latency, packet loss rate), and uses the reinforcement learning algorithm (Q-Learning) to dynamically evaluate available transmission paths (such as base station a, satellite link or edge node). For example, when the user moves at high speed and causes the signal of the current base station to be unstable, it automatically switches to a base station or satellite link with a stronger signal to ensure smooth transmission of the data stream to the third server.

[0122] The third server dynamically adjusts the decompression strategy according to the network status (such as real-time bandwidth and decoding ability) of the receiving end (each branch venue). If the bandwidth of the branch venue is sufficient (such as nodes in first-tier cities), the basic layer and enhancement layer data are completely decompressed to restore high-precision holographic images; if the bandwidth of the branch venue is limited (such as nodes in remote areas), the basic layer data (such as the speaker's outline and voice) is preferentially decompressed, sacrificing some details to reduce the decoding delay. At the same time, based on the LSTM model to predict network traffic, the bandwidth of the main venue data stream is preferentially guaranteed, and the non-critical data (such as background images) of the branch venue are degraded and transmitted on demand, improving the bandwidth utilization rate.

[0123] Receiving ends of each branch venue extract timestamps in the data through the Distributed Clock Calibration Protocol (DCCP), dynamically calibrate the local clock (such as the error <0.5ms), and adjust the projection position according to the spatial coordinate tags. For example, when the speaker turns around, the holographic images of all branch venues synchronously update the actions and perspectives, ensuring that the lip movements strictly match the voice and eliminating the problem of "out-of-sync sound and picture" caused by time delay.

[0124] The holographic communication system provided by the embodiments of the present invention realizes multi-path intelligent switching through the dynamic resource allocation module combined with the reinforcement learning algorithm, significantly improving the network stability in high-speed mobile scenarios. By using the spatio-temporal coding technology and the distributed clock calibration protocol, the multi-terminal synchronization error is reduced, overcoming the defect of insufficient synchronization accuracy of traditional GPS / NTP. The lightweight light field compression algorithm is introduced. Through hierarchical feature extraction and dynamic quantization coding, while improving the compression ratio, the decoding delay is reduced, taking into account the requirements of efficient compression and real-time performance. Combining the LSTM network load prediction model and dynamic priority management, the intelligent multicast strategy is optimized, improving the bandwidth utilization rate. In addition, through the cooperation of the server cluster and the core network, spatio-temporal tags are embedded and the optimal transmission path is dynamically adapted, realizing the full-process optimization of holographic data from generation, compression, synchronization to distribution, effectively ensuring the efficiency and reliability of cross-scenario holographic communication.

[0125] In this embodiment, a holographic communication device is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0126] This embodiment provides a holographic communication device, as Figure 6 shown, including:

[0127] A first acquisition module 601, configured to acquire an initial holographic data stream reported by a sending end, where the initial holographic data stream is determined based on environmental information of a target object;

[0128] A compression module 602 for compressing an initial holographic data stream to obtain a compressed holographic data stream;

[0129] A decompression module 603 for obtaining a first network state at a receiving end and decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream;

[0130] A transmission module 604 for transmitting the target holographic data stream to the receiving end so that the receiving end performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

[0131] In some alternative embodiments, the compression module 602 includes:

[0132] An extraction sub-module for extracting features from the initial holographic data stream according to a preset signal frequency to obtain first holographic data and second holographic data, where the signal frequency of the first holographic data is less than that of the second holographic data;

[0133] A quantization sub-module for performing quantization processing on the first holographic data and the second holographic data to obtain first quantization data corresponding to the first holographic data and second quantization data corresponding to the second holographic data;

[0134] A compression sub-module for compressing the first quantization data and the second quantization data to obtain first compressed data corresponding to the first quantization data and second compressed data corresponding to the second quantization data; wherein, the compressed holographic data stream includes the first compressed data and the second compressed data.

[0135] In some alternative embodiments, the decompression module 603 includes:

[0136] A first analysis sub-module for analyzing network parameters corresponding to the first network state to determine network load information of the receiving end;

[0137] A first decompression sub-module for decompressing the compressed holographic data stream based on the network load information to obtain a target holographic data stream.

[0138] In some alternative embodiments, the decompression module 603 further includes:

[0139] An acquisition sub-module for acquiring transmission priorities of each sending end;

[0140] A second decompression sub-module for decompressing the compressed holographic data streams corresponding to each sending end according to the transmission priorities and the network load information to obtain target holographic data streams corresponding to each sending end.

[0141] In some alternative embodiments, the above device further includes:

[0142] A second acquisition module, configured to acquire a second network status of a sending end;

[0143] An analysis module, configured to analyze network parameters of the second network status and determine an optimal transmission path of an initial holographic data stream based on an analysis result.

[0144] In some alternative embodiments, the analysis module includes:

[0145] A second analysis sub-module, configured to analyze network parameters of the second network status and determine at least one candidate transmission path corresponding to the initial holographic data stream;

[0146] An evaluation sub-module, configured to perform path quality evaluation on each candidate transmission path based on transmission parameters of the candidate transmission paths and generate a path quality evaluation result;

[0147] A determination sub-module, configured to determine an optimal transmission path from each candidate transmission path according to the path quality evaluation result.

[0148] In some alternative embodiments, the above apparatus further includes:

[0149] A synchronization module, configured to embed a timestamp and location information of a target object in the initial holographic data stream, so that a receiving end extracts the timestamp from the target holographic data stream, uses the timestamp to perform local time calibration, and performs holographic imaging on the target holographic data stream according to the calibrated time to generate a holographic image carrying the timestamp and location information.

[0150] The further function descriptions of the above various modules and units are the same as those in the corresponding foregoing embodiments, and will not be elaborated herein.

[0151] The holographic communication apparatus in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0152] The holographic communication device provided by the embodiment of the present invention obtains the initial holographic data stream based on the environmental information of the target object, ensuring the accuracy of the data source and the scene adaptability. The dynamic compression technology is used to optimize the data stream, effectively reducing the transmission bandwidth requirement and improving the transmission efficiency. Innovatively, it combines with the real-time network status at the receiving end for adaptive decompression, achieving the optimal allocation of network resources and the dynamic balance of transmission quality. It can not only ensure the smoothness in a weak network environment but also make full use of the performance potential of a high-bandwidth network. Finally, through holographic imaging at the receiving end, high-fidelity images are generated, significantly reducing the communication delay while greatly enhancing the real-time performance and immersion of holographic interaction, providing a systematic solution for high-quality holographic communication under different network conditions.

[0153] The embodiment of the present invention also provides a computer device, which can be any server in the server cluster and has the above-mentioned Figure 6 holographic communication device.

[0154] Please refer to Figure 7 , Figure 7 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As shown in Figure 7 , the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphic information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (such as a server array, a set of blade servers, or a multi-processor system). Figure 7 In

[0155] one example, a processor 10 is taken.

[0156] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0157] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device and the like. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely disposed relative to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0158] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, a hard disk, or a solid-state drive; the memory 20 may further include a combination of the above types of memory.

[0159] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0160] The embodiments of the present invention also provide a computer-readable storage medium. The methods according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code originally stored in a remote storage medium or a non-transitory machine-readable storage medium and to be downloaded through a network and stored in a local storage medium, so that the methods described herein can be stored in such software processes on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memory. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0161] A part of the present invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the present invention through the operations of the computer. Those skilled in the art should understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways for a computer to execute computer program instructions include, but are not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0162] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A holographic communication method, characterized in that, The method includes: Obtaining an initial holographic data stream reported by a sending end, where the initial holographic data stream is determined based on environmental information of a target object; Compressing the initial holographic data stream to obtain a compressed holographic data stream; Obtaining a first network state of a receiving end, and decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream; Transmitting the target holographic data stream to the receiving end, so that the receiving end performs holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

2. The method according to claim 1, wherein The step of compressing the initial holographic data stream to obtain a compressed holographic data stream includes: Performing feature extraction on the initial holographic data stream according to a preset signal frequency to obtain first holographic data and second holographic data, where the signal frequency of the first holographic data is less than that of the second holographic data; Performing quantization processing on the first holographic data and the second holographic data to obtain first quantization data corresponding to the first holographic data and second quantization data corresponding to the second holographic data; Performing data compression on the first quantization data and the second quantization data to obtain first compressed data corresponding to the first quantization data and second compressed data corresponding to the second quantization data; Wherein, the compressed holographic data stream includes the first compressed data and the second compressed data.

3. The method according to claim 1 or 2, characterized in that, The step of decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream includes: Analyzing network parameters corresponding to the first network state to determine network load information of the receiving end; Decompressing the compressed holographic data stream based on the network load information to obtain the target holographic data stream.

4. The method according to claim 3, characterized in that, When there are multiple sending ends, the step of decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream includes: Analyzing network parameters corresponding to the first network state to determine network load information of the receiving end; Obtaining the transmission priorities of the respective sending ends; Decompressing the compressed holographic data streams corresponding to the respective sending ends according to the transmission priorities and the network load information to obtain target holographic data streams corresponding to the respective sending ends.

5. The method according to claim 1, wherein It further includes: Obtaining a second network state of the sending end; Analyzing network parameters of the second network state, and determining an optimal transmission path of the initial holographic data stream based on the analysis result.

6. The method according to claim 5, wherein The step of analyzing network parameters of the second network state and determining an optimal transmission path of the initial holographic data stream based on the analysis result includes: Analyzing network parameters of the second network state to determine at least one candidate transmission path corresponding to the initial holographic data stream; Performing path quality evaluation on each of the candidate transmission paths based on transmission parameters of the candidate transmission paths to generate a path quality evaluation result; Determining the optimal transmission path from each of the candidate transmission paths according to the path quality evaluation result.

7. The method according to claim 1, characterized in that It further includes: Embed a timestamp and the position information of the target object in the initial holographic data stream, so that the receiving end extracts the timestamp from the target holographic data stream, uses the timestamp for local time calibration, and performs holographic imaging on the target holographic data stream according to the calibrated time to generate the holographic image carrying the timestamp and the position information.

8. A holographic communication system, characterized in that, Comprising: A receiving end; A sending end, communicatively connected to the receiving end, for generating an initial holographic data stream based on the environmental information of the target object; A server cluster, communicatively connected to the sending end, for compressing the initial holographic data stream to obtain a compressed holographic data stream; And for obtaining the first network state of the receiving end, decompressing the compressed holographic data stream according to the first network state to obtain a target holographic data stream, and transmitting the target holographic data stream to the receiving end; The receiving end is used for performing holographic imaging on the target holographic data stream to generate a holographic image corresponding to the target holographic data stream.

9. A computer device, characterized in that, Comprising: A memory and a processor, communicatively connected to each other between the memory and the processor, the memory stores computer instructions, and the processor executes the computer instructions to execute the holographic communication method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the holographic communication method according to any one of claims 1 to 7.

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