Microwave transmission networking method suitable for ultra-high-definition television signal transmission

Abstract

The invention relates to the technical field of communication, in particular to a microwave transmission networking method suitable for ultra-high-definition television signal transmission. Comprising the following steps: constructing a heterogeneous multi-path network: constructing a microwave network comprising at least two main transmission paths and a standby transmission path between a signal transmitting end and a signal receiving end; a cross-path clock synchronization mechanism is adopted to realize signal cooperative transmission, and an annular or mesh topology structure is constructed. According to the invention, priority marking and reliability enhancement processing technologies are combined, so that priority transmission and time delay control of core data are ensured, and strict requirements of ultra-high-definition signals on high capacity and real-time performance are met; the main path starts an interference offset technology, and an interference component is separated through a dual-polarized antenna and an adaptive algorithm, so that the multipath fading resistance is improved, and the continuity and stability of signal transmission are guaranteed; periodically starting a global topological optimization mechanism; and the network resource utilization rate and the flexibility of coping with diversified services are improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a microwave transmission networking method suitable for ultra-high-definition television signal transmission. Background Art

[0002] With the widespread adoption of ultra-high-definition television (UHDTV) technology, higher requirements are being placed on the bandwidth, real-time performance, and reliability of signal transmission. Traditional microwave transmission networks struggle to meet the high-quality transmission requirements of UHDTV signals due to bandwidth limitations, weak anti-interference capabilities, and insufficient multipath coordination. For example, the high resolution (e.g., 4K / 8K) and high frame rate (e.g., 120fps) of UHD video signals require a single-channel signal bandwidth of tens of Gbps. Traditional microwave links typically only support a few Gbps of capacity and lack multipath redundancy and dynamic routing optimization mechanisms, making them prone to signal freezes and image quality loss due to link interruptions or interference. Furthermore, existing microwave equipment suffers from protocol incompatibility and clock asynchrony when used in hybrid networks, further restricting the flexibility and scalability of transmission networks.

[0003] To address the above-mentioned issues, existing technologies attempt to improve performance by improving transmission architecture and signal processing technology. For example, Chinese patent CN201510552857.9 discloses a signal transmission system that attempts to achieve multi-service converged transmission by merging network signals and television signals into a mixed signal and distributing the output. However, this solution only stops at simple multiplexing at the signal level, and does not perform differentiated processing for the layered characteristics of ultra-high-definition video (such as video layer, audio layer, and metadata layer). It lacks key technologies such as IP packet encapsulation and QoS priority management, and cannot solve problems such as uneven bandwidth allocation and delay jitter in ultra-high-definition signal transmission. Another Chinese patent, CN202210686303.8, proposes an end-to-end access method for wired transmission of DTMB ultra-high-definition signals, which achieves controllable access to video services by optimizing the access layer protocol. However, this solution is limited to wired transmission scenarios and does not address problems such as multipath fading and cross-polarization interference unique to microwave transmission. It also does not build a heterogeneous multipath redundancy mechanism, making it difficult to deal with link instability problems caused by factors such as weather and terrain in microwave transmission.

[0004] Existing microwave networking technology used for ultra-high-definition television signal transmission is limited by traditional homogeneous architecture, and has problems such as prominent single-node failure risk, significant bandwidth bottleneck, and poor compatibility between new and old equipment. These problems restrict the reliability, real-time performance and network scalability of ultra-high-definition signal transmission, making it difficult to meet the radio and television industry's urgent needs for secure broadcasting and IP-based transformation. In view of this, we propose a microwave transmission networking method suitable for ultra-high-definition television signal transmission. Summary of the Invention

[0005] The purpose of the present invention is to provide a microwave transmission networking method suitable for ultra-high-definition television signal transmission to solve the problems raised in the above background technology.

[0006] To solve the above technical problems, the present invention provides a microwave transmission networking method suitable for ultra-high-definition television signal transmission, comprising the following steps:

[0007] S1. Heterogeneous multi-path network construction: A microwave network consisting of at least two primary transmission paths and one backup transmission path is constructed between the signal transmitter and receiver. A cross-path clock synchronization mechanism is used to achieve coordinated signal transmission, creating a ring or mesh topology.

[0008] S2, signal layer processing: cut and process the input ultra-high-definition signal in layers;

[0009] S3, layered signal encapsulation and transmission: The layered processed UHD signal is encapsulated in IP packets and transmitted end-to-end via a packet switching network;

[0010] S4, Interference cancellation: Enable cross-polarization interference cancellation technology on each primary path;

[0011] S5. Monitoring, early warning, and path switching: Deploy real-time monitoring modules, build evaluation models, and establish hot backup mechanisms;

[0012] S6. Compatibility modification and global optimization: Perform compatibility modification on existing microwave transmission equipment to achieve hybrid networking of new and old equipment; periodically activate the global topology optimization mechanism to dynamically adjust network routing based on node load and business needs.

[0013] As a further improvement to this technical solution, the cross-path clock synchronization mechanism in S1 is implemented by deploying clock modules containing atomic clocks or GPS synchronization units at each transmission path node, with each node periodically exchanging clock synchronization messages according to the IEEE 1588v2 protocol. By deploying clock modules containing specific units and exchanging messages according to the protocol, the cross-path clock synchronization mechanism achieves precise synchronization of the clocks at each node, ensuring the timing consistency of coordinated signal transmission.

[0014] As a further improvement of the present technical solution, the layered cutting and processing of the input ultra-high-definition signal in S2 includes the following steps:

[0015] S2.1. Signal layer cutting: Separate the UHD signal into independent video, audio, and metadata layers. The video layer contains image pixel data, the audio layer contains sound coding data, and the metadata layer contains auxiliary information such as subtitles and timestamps.

[0016] S2.2, pre-processing of each layer:

[0017] The BM3D algorithm is used to perform denoising and resolution unification on the video layer to ensure format consistency of video signals from different sources.

[0018] The Wiener filter algorithm is used to reduce noise and standardize the sampling rate of the audio layer to eliminate background noise and unify audio parameters;

[0019] A cyclic redundancy check algorithm is used to perform format verification and remove redundant data on the metadata layer to ensure the accuracy and integrity of auxiliary information;

[0020] S2.3 Timing Synchronization Marking: Timestamps and frame numbers are added to each layer of data to establish a time-series correspondence between the video, audio, and metadata layers, providing a synchronization benchmark for subsequent layered packaging. Through signal layer segmentation and processing, the UHD signal is layered, pre-processed, and time-series marked, standardizing the data format of each layer to ensure information accuracy and completeness, providing a reliable synchronization benchmark for subsequent packaging.

[0021] As a further improvement of the present technical solution, the IP packet encapsulation in S3 includes the following steps:

[0022] S3.1, Protocol Adaptation and Encapsulation: Select an encapsulation protocol based on the signal characteristics of each layer, and encapsulate the video layer, audio layer, and metadata layer into IP packets respectively;

[0023] S3.2, QoS priority marking: inserting a quality of service field into the IP packet header to mark the priority level of each layer signal;

[0024] S3.3 Reliability Enhancement: Cyclic redundancy check (CRC) is added to encapsulated IP packets, and forward error correction (FEC) redundant data is embedded to improve transmission reliability. IP packet encapsulation uses protocol adaptation, priority marking, and reliability enhancement to achieve layered signal transmission, ensuring priority transmission of core data and improving error resilience.

[0025] As a further improvement of this technical solution, the cross-polarization interference cancellation technology in S4 specifically includes:

[0026] Dual-polarized antennas are deployed at the transmission nodes of the primary path to receive two signals with the same frequency and orthogonal polarizations.

[0027] The two received signals are sampled and digitized, and an interference estimation model is constructed based on an adaptive filtering algorithm;

[0028] The cross-polarization interference component in the target signal is separated by an interference estimation model, and the interference component is eliminated from the received signal.

[0029] As a further improvement of the present technical solution, when the adaptive filtering algorithm in S4 performs interference elimination through the interference estimation model, the processing process is as follows:

[0030] First, the error signal is calculated based on the real-time sampling data of the received signal. The error signal e(n) is expressed as: e(n) = d(n) - y(n); where d(n) is the desired signal, y(n) is the actual output signal, and n is the sampling time.

[0031] Next, the filter coefficients are dynamically adjusted based on the error signal. The filter coefficient update formula is: w(n+1)=w(n)+x(n)e(n), where w(n) is the filter coefficient vector at time n and x(n) is the input signal vector. Cross-polarization interference cancellation technology utilizes dual-polarized antennas and an adaptive algorithm to separate and eliminate interference components, effectively reducing interference from co-frequency orthogonal signals and improving signal purity and transmission stability on the primary path.

[0032] As a further improvement of this technical solution, the construction of the evaluation model in S5 specifically includes the following steps:

[0033] S5.1. Dynamic weight configuration: Configure dynamic weight coefficients for each transmission parameter. The dynamic weight coefficients are adaptively adjusted based on signal transmission period, network load status, and weather conditions.

[0034] S5.2. Multi-parameter fusion evaluation: Generate a path quality evaluation value Q based on a multi-parameter fusion algorithm. The multi-parameter fusion algorithm includes a weighted sum model, a fuzzy logic reasoning model, or a neural network evaluation model. The calculation formula of the improved weighted sum model is: Q = ∑ [w i (t)×p i ], where p i is the standardized parameter value, w i (t) is the dynamic weight of the i-th parameter at time t;

[0035] w i (t) is calculated as follows: Where: w i0 is the initial weight of the i-th parameter, γ is the weight adjustment coefficient, which is set according to the degree of influence of the parameter on ultra-high-definition signal transmission, and p i (t-1) is the value of the i-th parameter at the previous moment t-1, μ i is the historical mean of the i-th parameter, reflecting the long-term average level of the parameter, σ i is the historical standard deviation of the i-th parameter, which is used to reflect the degree of fluctuation of the parameter.

[0036] As a further improvement of the present technical solution, the specific method of establishing the hot backup mechanism in S5 includes the following steps:

[0037] S5.1. Heterogeneous backup path construction: Use a different transmission technology from the primary path to build a backup path. For example, use microwave transmission technology or optical fiber transmission technology with a different frequency band to ensure that the backup path has different transmission characteristics from the primary path.

[0038] S5.2 Automatic Rerouting and Switching: When the quality of the primary path is detected to be below the preset standard, the automatic rerouting algorithm is activated. Based on real-time path quality assessment data, the optimal backup path is calculated and a lossless switch is performed. This algorithm, using real-time updated path quality assessment data, rapidly calculates and switches to the optimal backup path, ensuring the continuity of UHDTV signal transmission. The evaluation model and hot backup mechanism utilize dynamic configuration weights, multi-parameter evaluation, and automatic switching of heterogeneous paths to accurately assess path quality in real time and rapidly switch to the backup path, ensuring transmission continuity.

[0039] As a further improvement of this technical solution, the compatibility modification in S6 includes the following steps:

[0040] S6.1.1. Hardware Adaptation and Modification: Upgrade the RF front-end modules of existing equipment to support the new equipment modulation method, modify the power adapter unit to be compatible with the new equipment power supply specifications, and expand the physical interface types to enable physical connection and signal interaction between new and old equipment;

[0041] S6.1.2, Software Protocol Adaptation: Update device firmware to support the new communication protocol, embed a protocol conversion module in the control plane to parse and convert control instructions and data formats between new and old devices, and establish a unified configuration management interface to centrally monitor the parameters and status of new and old devices;

[0042] S6.1.3. Joint commissioning test verification: Test the consistency of signal modulation and demodulation between old and new equipment, verify the stability of the power module in a mixed power supply environment, test the delay and reliability of the protocol conversion process, and ensure that new and old equipment can be mixed and networked.

[0043] As a further improvement of the present technical solution, the periodic initiation of the global topology optimization mechanism in S6 specifically includes the following implementation steps:

[0044] S6.2.1, Cycle and trigger condition configuration: Set a fixed optimization cycle, the cycle length can be configured according to network service characteristics;

[0045] S6.2.2 Real-time monitoring and triggering: When it is detected that the load of any node exceeds the preset threshold, a new high-priority service is added, or the quality parameters of key links deteriorate, real-time routing optimization is immediately initiated;

[0046] S6.2.3. Routing Optimization Algorithm Execution: Based on the current network topology, node load data, and service requirements, generate an optimal routing solution that includes path priorities using a shortest path algorithm, a multi-objective optimization algorithm, or an intelligent optimization algorithm. The multi-objective optimization algorithm can be parameterized using the objective function F = w1·L + w2·(1 / B) + w3·D, where L represents path length, B is available bandwidth, and D is latency. w1, w2, and w3 are weight coefficients to achieve multi-dimensional optimization of path length, available bandwidth, and latency.

[0047] S6.2.4. Routing Table Synchronization and Adjustment: Generate instructions for the optimized routing solution and synchronize them to each network node to update the routing table. The global topology optimization mechanism optimizes routing through periodic configuration, real-time monitoring triggers, and intelligent algorithms, dynamically adjusting network paths to improve node load balancing and business response flexibility.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. This invention significantly improves link transmission capacity through IP packet encapsulation and multi-channel aggregation technology, supporting the simultaneous transmission of multiple UHD signals. A layered signal processing mechanism separates UHD signals into independent layers and pre-processes them. Combined with priority marking and reliability enhancement processing technology, this ensures priority transmission of core data and delay control, meeting the stringent high-capacity and real-time requirements of UHD signals.

[0050] 2. This invention addresses the risk of single node failure by building a heterogeneous network with multiple primary and backup paths, using a cross-path collaborative transmission mechanism to create a ring or mesh topology. At the same time, interference cancellation technology is enabled on the primary path, using dual-polarized antennas and adaptive algorithms to separate interference components, improving resistance to multipath fading and ensuring the continuity and stability of signal transmission.

[0051] 3. This invention uses hardware adaptation and software protocol conversion technology to upgrade the RF and power modules of existing equipment, expand physical interfaces, update firmware, and embed protocol conversion modules. This establishes a unified management interface, enabling hybrid networking and centralized monitoring of new and old equipment. This avoids resource waste caused by device iteration and provides a smooth transition solution for system upgrades.

[0052] 4. This invention deploys a real-time monitoring module and a multi-parameter fusion assessment model to collect network status data in real time and dynamically adjust path weights, accurately identifying faults and triggering early warnings. It also periodically activates a global topology optimization mechanism to dynamically adjust routes based on node load and service demand, supporting rapid route switching and lossless switching, improving network resource utilization and flexibility in responding to diverse services. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 The figure is a flow chart of an exemplary method of the present invention. DETAILED DESCRIPTION

[0054] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] like Figure 1 As shown, this embodiment provides a microwave transmission networking method suitable for ultra-high-definition television signal transmission, including the following steps:

[0056] S1. Heterogeneous multi-path network construction: A microwave network consisting of at least two primary transmission paths and one backup transmission path is constructed between the signal transmitter and receiver. A cross-path clock synchronization mechanism is used to achieve coordinated signal transmission, creating a ring or mesh topology.

[0057] In this step, the cross-path clock synchronization mechanism in S1 is implemented by deploying clock modules containing atomic clocks or GPS synchronization units at each transmission path node. These nodes periodically exchange clock synchronization messages based on the IEEE 1588v2 protocol. The combination of atomic clocks / GPSDO and the IEEE 1588v2 protocol enables high-precision clock synchronization, meeting the timing alignment requirements for layered transmission of ultra-high-definition signals. Furthermore, physical layer timestamping improves synchronization message processing efficiency and reduces synchronization errors.

[0058] In this step, the primary path meets the following conditions:

[0059] Spectrum resources in different frequency bands are utilized, with band spacing complying with spectrum allocation rules and the available bandwidth within a single band meeting the requirements for UHD signal transmission. For example, primary path A uses band E (71-76 GHz), while primary path B uses band V (59-64 GHz). Band spacing strictly adheres to spectrum allocation rules established by organizations such as the International Telecommunication Union (ITU) to avoid co-channel and adjacent channel interference. The available bandwidth within a single band has been professionally calculated to meet the stringent bandwidth requirements (typically ≥1 Gbps) for UHD signals (such as 8K / 60fps video), ensuring unobstructed high-bitrate transmission of video, audio, and metadata.

[0060] Microwave transmission equipment equipped with different communication standards may employ different modulation and demodulation methods or channel coding schemes. This creates differentiation between devices in modulation and demodulation or channel coding. For example, path A uses 256QAM modulation + LDPC coding to improve spectral efficiency and enhance error correction capabilities; path B uses 64QAM modulation + Turbo coding, demonstrating robust fading resistance in complex channel environments. This design avoids the limitations of a single standard. For example, 256QAM is prone to bit errors under strong interference, while 64QAM ensures link connectivity by reducing the modulation order. The two standards complement each other, enhancing network adaptability.

[0061] Hardware platforms from different manufacturers are selected to form technical heterogeneous redundancy for baseband processing and RF transmission; this design forms technical heterogeneous redundancy for baseband and RF. For example, the baseband of path A uses Xilinx FPGA to implement flexible algorithm processing, and the RF front-end uses GaN power amplifiers to enhance signal transmission; the baseband of path B uses Broadcom ASIC to implement efficient fixed logic operations, and the RF front-end uses LDMOS power amplifiers (with strong burn-out resistance). If the hardware of a certain manufacturer fails due to process defects or specific interference (such as the performance of GaN devices degrading under abnormally high temperatures), the other path can still maintain transmission to ensure network reliability. Through the above design, the main path is significantly enhanced in anti-interference, adaptability to complex environments and fault tolerance, meeting the stringent requirements of ultra-high-definition signal transmission for stability and low latency, while avoiding the risks of a single technology, in line with the development trend of "heterogeneous redundancy and autonomous control" in modern communication networks.

[0062] As a further illustration of this step, this clock synchronization mechanism offers technical flexibility and scalability. In addition to the current implementation, a hybrid synchronization solution combining SyncE (G.8261) and IEEE1588v2 can also be used. This solution, which uses a phase-locked loop (PLL) for frequency synchronization, can dynamically adapt to network environment changes under varying network topologies and transmission media conditions, improving the reliability and adaptability of clock synchronization and providing diverse technical support for the stable operation of ultra-high-definition signal transmission networks in complex scenarios.

[0063] It should be added that the calculation of clock deviation adopts the formula Δt=(t rec -t sent )-(t resp -t delay ); where t rec With t sent Represents the receiving and sending timestamps respectively, t resp With t delayThis formula, based on the timestamp processing logic of the IEEE 1588v2 protocol standard, accurately calculates clock deviations between nodes, providing a quantitative basis for adjusting clock modules and achieving high-precision synchronization. This ensures that ultra-high-definition signals maintain timing alignment during multipath transmission, avoiding signal distortion or loss caused by clock deviation.

[0064] S2, signal layer processing: cut and process the input ultra-high-definition signal in layers;

[0065] In this step, the layered cutting and processing of the input ultra-high-definition signal in S2 includes the following steps:

[0066] S2.1. Signal layer cutting: The UHD signal is separated into independent video, audio, and metadata layers. The video layer contains image pixel data, the audio layer contains sound coding data, and the metadata layer contains auxiliary information such as subtitles and timestamps. By separating the UHD signal into independent video, audio, and metadata layers, a clear division of business functions is achieved, facilitating optimized processing for the characteristics of different layers, avoiding mutual interference between data processing layers, and improving processing efficiency. For example, taking an 8K / 60fps UHD signal as an example, the video layer data volume is approximately 12Gbps (based on H.265 encoding compression), containing image data of 33 million pixels per frame; if the audio layer is 8-channel LPCM encoding with a sampling rate of 48kHz and a bit depth of 24bit, the data volume is approximately 9.2Mbps; the metadata layer (such as subtitles and timestamps) has a relatively small data volume of approximately a few hundred kbps.

[0067] S2.2, pre-processing of each layer:

[0068] Perform denoising and resolution unification on the video layer to ensure format consistency of video signals from different sources;

[0069] Perform noise reduction and sampling rate standardization on the audio layer to eliminate background noise and unify audio parameters;

[0070] Perform format verification and remove redundant data on the metadata layer to ensure the accuracy and completeness of auxiliary information; through preprocessing of each layer (denoising, standardization, format verification, etc.), improve signal quality and format consistency, eliminate the impact of noise, parameter inconsistency and other problems on subsequent transmission and decoding, and ensure high-quality restoration of ultra-high-definition signals.

[0071] As a further illustration of this step, the BM3D (Block-Matching and 3D filtering) algorithm can be used to denoise the video layer. By jointly filtering similar blocks in the video frame, it is effective in removing Gaussian noise, salt and pepper noise, and other aspects. For example, for a video frame containing 2% Gaussian noise (mean 0, variance 0.01), the peak signal-to-noise ratio (PSNR) can be improved by 5-8dB after denoising. At the same time, the bilinear interpolation formula can be used for the resolution uniformity processing in this video layer step, and its calculation formula is: Where I(x, y) represents the interpolated pixel value of the pixel at coordinates (x, y) in the target resolution image; I(i, j) represents the pixel values of the four pixels adjacent to (x, y) in the original image ((i, j) takes the value 0 or 1); x and y are the coordinates of the target pixel during interpolation (usually normalized to the interval [0, 1] to determine the distance weights with respect to the neighboring pixels); i and j are the coordinate indices of the neighboring pixels in the original image (by traversing the values of (i, j) from 0 to 1, a weighted calculation is performed on the four neighboring pixels). By taking a weighted sum of the pixel values of these four neighboring pixels (the weight is determined by (1-|xi|)·(1-|yj|), with the closer the pixel is to the target pixel, the higher the weight), this formula achieves unified resolution processing for videos of different resolutions (such as 3840×2160 and 7680×4320), reducing pixel distortion during image scaling and ensuring image clarity after scaling.

[0072] As a further explanation of this step, in the audio layer processing, noise reduction can use the Wiener filtering algorithm, which designs filters based on the statistical characteristics of the audio signal (such as power spectral density). For audio signals with background noise (SNR 10dB), after processing with this algorithm, the SNR can be increased to more than 25dB after noise reduction, which can effectively eliminate background noise and significantly improve audio quality. In the audio layer sampling rate standardization processing, the resampling formula can be used Where: y(n) represents the audio signal sequence at the target sampling rate after resampling, which is the standardized audio data output after processing; x(m) is the audio signal sequence at the original sampling rate, which represents the input unstandardized audio data; h(n) is the low-pass filter coefficient, which is used for anti-aliasing or interpolation filtering during the resampling process to ensure the quality of the audio signal during sampling rate conversion; F s Represents the original sampling rate (such as the common 44.1kHz), which is the initial sampling rate of the audio signal; F snεwThe target sampling rate (e.g., 48kHz) is the desired sampling rate to which the audio is standardized. This formula can be used to standardize audio with different sampling rates to the target sampling rate. For example, 44.1kHz and 48kHz audio can be standardized to 48kHz, ensuring the consistency of audio parameters. This lays a standardized foundation for subsequent processing and transmission, and avoids audio playback anomalies or processing errors caused by sampling rate differences.

[0073] As a further illustration of this step, the format verification of the metadata layer can use a cyclic redundancy check (CRC) algorithm (such as CRC-32), which can effectively ensure the integrity of the metadata. For example, for subtitle data containing 1000 characters, the CRC check can detect more than 99.99% of bit errors, avoiding display or processing anomalies caused by data errors and ensuring the accuracy of auxiliary information. The metadata layer also eliminates redundancy through lexical and semantic analysis. For example, for consecutively repeated "00:00:00" timestamps, only one valid instance is retained. This processing eliminates duplicate timestamps or invalid characters, improves the simplicity and effectiveness of metadata, and optimizes metadata quality and transmission efficiency.

[0074] S2.3 Timing Synchronization Marker: Timestamps and frame numbers are added to each layer of data to establish a timing correspondence between the video, audio, and metadata layers, providing a synchronization benchmark for subsequent layered encapsulation. By adding timestamps and frame numbers to each layer of data, a strict timing correspondence is established, ensuring the synchronization of video, audio, and metadata during transmission and decoding. This prevents problems such as audio and video asynchrony and subtitle misalignment, improving the user experience.

[0075] As a further explanation of this step, in the timing synchronization marking step, the timestamp is generated based on a high-precision system clock (such as a 10MHz crystal oscillator with an accuracy of ±1ppm) in milliseconds, and its calculation formula is T=t system ×1000. T represents the final generated timestamp in milliseconds, which accurately marks the time position of each frame of data; t system The number of seconds in the system clock is the basis for timestamp calculation. By multiplying the number of seconds by 1000, the conversion from seconds to milliseconds is achieved, ensuring that the accuracy of the timestamp meets the timing requirements of ultra-high-definition signals. The frame number starts at 0 and increases by 1 for each frame output, that is, FrameNo(n) = FrameNo(n-1) + 1. Here, FrameNo(n) represents the sequence number of the nth frame, and FrameNo(n-1) represents the sequence number of the previous frame (the nth frame). Through this recursive formula, the sequential relationship between frames is clearly established, providing a clear benchmark for the timing correspondence of each layer of data.

[0076] In addition to locally generated timestamps and frame numbers, a synchronized time source can also be obtained through the Network Time Protocol (NTP) or Precision Time Protocol (PTP, such as IEEE1588v2). For example, in a distributed UHD production network, each node uses PTP to maintain time synchronization error within 100ns. PTP precisely measures and adjusts each node's clock to ensure global timing consistency, guaranteeing uniform timing for processed signals across nodes. This prevents issues such as audio and video asynchrony or metadata misalignment caused by time deviation, providing a solid timing guarantee for the high-quality transmission and processing of UHD signals.

[0077] S3, layered signal encapsulation and transmission: The layered processed UHD signal is encapsulated in IP packets and transmitted end-to-end via a packet switching network;

[0078] In this step, the IP packet encapsulation in S3 includes the following steps:

[0079] S3.1. Protocol Adaptation and Encapsulation: The encapsulation protocol is selected based on the signal characteristics of each layer, and the video layer, audio layer, and metadata layer are encapsulated into IP packets respectively. This design selects the encapsulation protocol based on the signal characteristics of each layer to achieve differentiated and precise encapsulation, avoiding the inefficiency or compatibility issues caused by a "one-size-fits-all" encapsulation method, and improving the transmission adaptability of each layer of signals in the network.

[0080] As a further explanation of this step, the video layer in this step uses RTP (Real-time Transport Protocol) encapsulation due to the large amount of data and high real-time requirements. For example, after the 8K video stream (about 12Gbps) is encapsulated by RTP, it can ensure the orderly transmission of the frame sequence; the audio layer uses UDP (User Datagram Protocol) encapsulation based on low latency requirements, such as 5.1-channel audio (about 1Mbps) is quickly transmitted through UDP; the metadata layer contains management information (such as subtitles and timestamps), so it uses SNMP (Simple Network Management Protocol) encapsulation to facilitate network equipment identification and processing.

[0081] S3.2, QoS priority marking: Insert the quality of service field into the IP data packet header to mark the priority level of each layer of signal; by inserting the quality of service field into the IP data packet header to mark the priority, network devices can schedule data packets according to priority, ensuring that key layers (such as the video layer) are forwarded first when the network is congested, thereby ensuring the viewing experience of the core content of the ultra-high-definition signal.

[0082] As a further illustration of this step, the DSCP (Differentiated Services Code Point) field can also be used to implement priority marking. For example, the DSCP value for the video layer is set to EF (46, representing expedited forwarding class 41), the audio layer is set to AF41 (34, representing assured forwarding class 41), and the metadata layer is set to BE (0, representing best effort). Clear numerical marking facilitates rapid identification and processing by network devices such as routers and switches, optimizing resource allocation.

[0083] S3.3 Reliability Enhancement: This design improves transmission reliability by adding a cyclic redundancy check (CRC) to encapsulated IP packets and embedding redundant forward error correction (FEC) data. This creates a dual "detection + error correction" mechanism, effectively addressing common interference and noise issues in microwave transmission links, reducing bit error rates and improving transmission reliability.

[0084] As a further illustration of this step, in order to improve transmission reliability, a double protection mechanism can be adopted for the encapsulated IP data packets:

[0085] First, CRC can use CRC-32 algorithm, and its generating polynomial is G(x)=x 32 +x 26 +x 23 +x 22 +x 16 +x 12 +x 11 +x 10 +x 8 +x 7 +x 5 +x 4 +x 2 +x+1, this algorithm can detect more than 99.99% of bit errors, providing strong detection protection for data integrity;

[0086] Secondly, FEC can use Reed-Solomon coding (such as RS (255, 239)), and its coding formula is: C (x) = (x n- k M(x))modG(x), where M(x) is the information polynomial, G(x) is the generator polynomial, and nk is the number of redundant symbols (here n=255, k=239, providing 16 redundant symbols), which makes it possible to correct burst errors within a certain range. For example, when the bit error rate is 10 -4 In the link, the bit error rate can be reduced to 10 after RS coding. -7 The following effectively improves the anti-interference ability and accuracy of data in complex transmission environments;

[0087] Through the application of these specific algorithms and parameters, the technical details of reliability enhancement processing are clarified, the problem of insufficient disclosure is avoided, the feasibility of the application of the technical solution in actual complex network environments is expanded, and the patent content is made more complete and convincing in terms of ensuring transmission reliability.

[0088] S4. Interference cancellation: Enabling cross-polarization interference cancellation technology on each primary path can specifically solve the common co-frequency cross-polarization interference problem in microwave transmission, effectively improve the purity and transmission quality of the primary path signal, ensure the stable transmission of ultra-high-definition signals in complex electromagnetic environments, reduce the bit error rate, and enhance the anti-interference capability of the entire microwave transmission network.

[0089] In this step, the cross-polarization interference cancellation technology in S4 specifically includes:

[0090] Dual-polarized antennas are deployed at transmission nodes on the primary path to receive two co-frequency, orthogonally polarized signals. By deploying dual-polarized antennas to receive two co-frequency, orthogonally polarized signals, the target signal and interference signal are separated by using their polarization characteristics, improving the targeted nature of interference cancellation.

[0091] The two received signals are sampled and digitized, and an interference estimation model is constructed based on an adaptive filtering algorithm. After sampling and digitizing the signals, an interference estimation model is constructed based on an adaptive filtering algorithm to achieve dynamic tracking and accurate estimation of interference.

[0092] The interference estimation model is used to separate and eliminate cross-polarization interference components from the target signal, thus eliminating them from the received signal. This allows for real-time signal purification and ensures high-quality transmission of ultra-high-definition signals.

[0093] In this step, when the adaptive filtering algorithm performs interference elimination through the interference estimation model, the processing process is as follows:

[0094] First, the error signal is calculated based on the real-time sampling data of the received signal. The error signal e(n) is expressed as: e(n) = d(n) - y(n); where d(n) is the desired signal (which can be extracted from the received signal through prior knowledge or preliminary filtering and approximately represents a mixture of the target signal and interference), y(n) is the actual output signal, and n is the sampling time (obtained by processing the input signal vector using the current filter coefficients and used to estimate the interference component).

[0095] Next, the filter coefficient is dynamically adjusted according to the error signal. The filter coefficient update formula is: w(n+1)=w(n)+x(n)e(n), where w(n) is the filter coefficient vector at time n, and its dimension needs to be determined according to the interference characteristics and signal bandwidth (for example, for scenarios with more complex multipath interference, it can be set to 64 dimensions to capture the interference correlation of more historical sampling points), and x(n) is the input signal vector (usually the received signal in the orthogonal polarization direction is selected, such as the other signal of the dual-polarization antenna, which is digitized to form a time series vector for extracting interference characteristics).

[0096] As a further explanation of this step, the dual-polarized antenna can be a parabolic antenna, horn antenna, or microstrip antenna with orthogonal polarization. The appropriate type should be selected based on the actual scenario (e.g., a parabolic antenna for long-distance transmission or a microstrip antenna for space-constrained environments).

[0097] S5. Monitoring, early warning, and path switching: Deploy real-time monitoring modules, build evaluation models, and establish a hot backup mechanism. By deploying real-time monitoring modules, building evaluation models, and establishing a hot backup mechanism, dynamic monitoring, accurate evaluation, and reliable backup of transmission paths can be achieved, ensuring the stability and continuity of ultra-high-definition signal transmission in complex network environments, reducing the risk of transmission interruption due to path failures or quality deterioration, and improving the reliability and robustness of the entire microwave transmission network.

[0098] In this step, the construction of the evaluation model in S5 specifically includes the following steps:

[0099] S5.1. Dynamic weight configuration: Configure a dynamic weight coefficient for each transmission parameter. The dynamic weight coefficient is adaptively adjusted based on the signal transmission period, network load status, and weather conditions. By configuring a dynamic weight coefficient for each transmission parameter, the evaluation model can adapt to different transmission scenarios (period, load, weather), improve the accuracy and flexibility of path quality assessment, and avoid evaluation lags or deviations caused by static weights.

[0100] S5.2. Multi-parameter fusion evaluation: Generate a path quality evaluation value Q based on a multi-parameter fusion algorithm. The multi-parameter fusion algorithm includes a weighted sum model, a fuzzy logic reasoning model, or a neural network evaluation model. The calculation formula of the improved weighted sum model is: Q = ∑ [w i (t)×p i ], where p i is the standardized parameter value, w i (t) is the dynamic weight of the i-th parameter at time t;

[0101] w i (t) is calculated as follows: Where: w i0is the initial weight of the i-th parameter, γ is the weight adjustment coefficient, which is set according to the degree of influence of the parameter on ultra-high-definition signal transmission, and p i (t-1) is the value of the i-th parameter at the previous moment t-1, μ i is the historical mean of the i-th parameter, reflecting the long-term average level of the parameter, σ i is the historical standard deviation of the i-th parameter, reflecting the degree of parameter fluctuation. This design generates path quality assessments based on a multi-parameter fusion algorithm, comprehensively considering multiple transmission parameters (such as bandwidth, latency, and bit error rate), avoiding the one-sidedness of single-parameter assessments and providing a comprehensive reflection of path quality. The improved weighted summation model further enhances the real-time performance and accuracy of the assessment through dynamic weight adjustment.

[0102] As a further explanation of this step, the transmission parameters may specifically include bandwidth (unit: Mbps), delay (unit: ms), bit error rate (unit: %), signal-to-noise ratio (unit: dB), etc.

[0103] In this step, the specific method of establishing the hot backup mechanism in S5 includes the following steps:

[0104] S5.1. Heterogeneous Backup Path Construction: A backup path is constructed using a different transmission technology than the primary path. For example, microwave transmission technology or fiber optic transmission technology with a different frequency band is used to ensure that the transmission characteristics of the backup path are different from those of the primary path. This design uses a different transmission technology to construct the backup path, leveraging the heterogeneity of the technology to avoid similar failures on the primary path (such as interference in the same frequency band or defects in equipment of the same standard). This ensures that the backup path can stably take over if the primary path fails, thereby improving the system's fault tolerance.

[0105] For example, the primary path uses E-band (71-76 GHz) microwave transmission technology (bandwidth 1.25 GHz, line-of-sight transmission), while the backup path can use fiber optic transmission technology (such as single-mode fiber, transmission distance ≥ 100 km, bandwidth ≥ 10 Gbps), or lower-frequency microwave technology (such as C-band, 4-8 GHz, with strong diffraction capability, suitable for non-line-of-sight scenarios). When the primary path is interrupted due to severe rain attenuation in the high-frequency band (such as rainfall > 50 mm / h, signal attenuation > 20 dB), the backup path can be immediately activated.

[0106] S5.2 Automatic Rerouting and Switching: When the quality of the primary path is detected to be below the preset standard, the automatic rerouting algorithm is activated. Based on real-time path quality assessment data, the optimal backup path is calculated and lossless switching is performed. When the quality of the primary path is below the preset standard, the automatic rerouting algorithm is activated and switches to the optimal backup path based on real-time assessment data. This enables fast and lossless switching of transmission paths, ensuring the continuity of UHD signal transmission and reducing user-perceived interruption time.

[0107] As a further explanation of this step, when the quality of the primary path is detected to be lower than the preset standard (such as packet loss rate > 5% or delay > 100ms), the system immediately triggers the automatic rerouting algorithm. Taking the classic Dijkstra algorithm as an example, this algorithm is based on a greedy strategy and aims to find the path with the lowest cost in the directed graph from the source node to the target node. In this scenario, we use the real-time path quality evaluation value Q(t) (generated by a multi-parameter fusion evaluation model, the higher the value, the better the path quality) as the reverse mapping indicator of the link cost, that is, the link cost cost = 1 / Q(t), so that the path with high Q(t) is given priority due to its low cost;

[0108] The specific execution process of the algorithm is as follows: First, all backup paths and their connecting nodes are abstracted into a directed graph, where nodes represent network devices (such as routers and switches), edges represent links, and the cost of each edge is converted from the Q(t) of the corresponding path. Next, starting from the source node (signal transmitter), traversal is carried out, and the node with the lowest current cost is continuously selected for expansion using a greedy strategy, and the cumulative cost from the source node to each node is calculated. Finally, the cumulative costs of the endpoints (signal receivers) of all backup paths are compared, and the path with the lowest cost (i.e., the largest Q(t)) is selected as the optimal backup path.

[0109] After determining the optimal backup path, the lossless switching process begins: through the software-defined network (SDN) controller or network management system, establish a control layer connection with each node of the optimal backup path in advance (such as sending OpenFlow pre-configuration instructions) to ensure that the path is unobstructed; quickly update the routing table of the source node and the intermediate node to point the ultra-high-definition signal transmission path to the optimal backup path; use packet-by-packet forwarding or fast rerouting mechanism to seamlessly migrate the main path traffic to the backup path within ≤50ms. For example, when the bit error rate of a main path suddenly rises to 10 due to sudden interference, -3 When the system detects the quality degradation, the automatic rerouting algorithm immediately calculates the backup path. Assume that the backup path A uses optical fiber transmission (strong anti-interference performance), its tQ A (t) = 0.95, which is higher than other paths. The algorithm selects this path and performs the switch. Through pre-connection and fast update of routing table, the signal is migrated within 30ms, and the bit error rate is reduced to 10 -6 , ensuring stable transmission of ultra-high-definition signals, avoiding user perception of freezes or screen distortion;

[0110] In summary, by introducing the Dijkstra algorithm and combining it with real-time path quality evaluation values, not only can efficient calculation of the optimal backup path be achieved, but the continuity of ultra-high-definition signal transmission is also guaranteed through a standardized switching process, making the entire hot backup mechanism more reliable and practical in complex network environments.

[0111] S6. Compatibility Retrofit and Global Optimization: Existing microwave transmission equipment undergoes compatibility retrofits to enable hybrid networking of new and legacy equipment. Global topology optimization mechanisms are periodically activated to dynamically adjust network routing based on node load and service demand. This allows for seamless hybrid networking of new and legacy microwave transmission equipment, resolving the issue of device heterogeneity during existing network upgrades. Routing is dynamically adjusted based on real-time load and service demand, optimizing network resource allocation, improving overall transmission efficiency and reliability, extending the lifespan of legacy equipment, and reducing network upgrade costs.

[0112] In this step, the compatibility modification in S6 includes:

[0113] S6.1.1. Hardware adaptation and modification: Upgrade the RF front-end module of existing equipment to support the modulation mode of the new equipment, modify the power adapter unit to be compatible with the power supply specifications of the new equipment, and expand the physical interface types to achieve physical connection and signal interaction between old and new equipment; by upgrading the RF front-end, power adapter unit and expanding the physical interface, the old equipment can support the modulation mode, power supply specifications and physical connection of the new equipment at the hardware level, thereby achieving physical layer interoperability between old and new equipment.

[0114] As a further explanation of this step, the RF front-end upgrade in this step can adopt a modular design, such as replacing the transceiver module that supports higher-order modulation (such as 256QAM / 1024QAM) to be compatible with the modulation methods of old and new equipment (such as old equipment supports 64QAM and new equipment supports 1024QAM). The power adapter unit modification in this step can adopt a wide voltage input design (such as 90-264VAC) to support different power supply specifications of old and new equipment (such as new equipment requires 48VDC and old equipment requires 24VDC), and realize voltage conversion through DC-DC converters. The physical interface expansion in this step can add high-speed optical interfaces such as SFP+ and QSFP28, which are compatible with the RJ45 electrical ports of old equipment, and realize signal format conversion through optical-to-electrical converters.

[0115] S6.1.2, Software Protocol Adaptation: Update device firmware to support new communication protocols, embed a protocol conversion module in the control plane to parse and convert control instructions and data formats between old and new devices, and establish a unified configuration management interface to centrally monitor the parameters and status of new and old devices; through firmware updates, protocol conversion modules and a unified configuration interface, resolve the differences in communication protocols, control instructions and data formats between old and new devices, and achieve interoperability between the control plane and the management plane.

[0116] As a further illustration of this step, the protocol conversion module in this step can adopt a microservices architecture to support multi-protocol conversion in parallel (e.g., converting the SNMP protocol of legacy devices to the NETCONF / YANG protocol of new devices). Furthermore, the unified configuration management interface in this step can be implemented based on a REST API, allowing centralized configuration of both new and legacy devices through a standardized interface (e.g., modifying device IP addresses and bandwidth parameters through a POST request).

[0117] S6.1.3. Joint commissioning and verification: Test the signal modulation and demodulation consistency of new and old equipment, verify the stability of the power module in a mixed power supply environment, and test the latency and reliability of the protocol conversion process to ensure the mixed networking of new and old equipment. Through systematic testing and verification, the performance and reliability of the mixed networking of new and old equipment are guaranteed to avoid transmission failures caused by compatibility issues.

[0118] As a further illustration of this step,

[0119] In this step, the periodic start of the global topology optimization mechanism in S6 specifically includes the following implementation steps:

[0120] S6.2.1. Cycle and trigger condition configuration: Set a fixed optimization cycle, and the cycle length can be configured according to network service characteristics. By flexibly configuring the optimization cycle and trigger conditions, balance network stability and optimization timeliness to avoid network oscillation caused by over-optimization.

[0121] S6.2.2 Real-time monitoring and triggering: When it is detected that the load of any node exceeds the preset threshold, a new high-priority service is added, or the quality parameters of key links deteriorate, real-time routing optimization is immediately initiated. This design monitors key indicators in real time and triggers optimization immediately when the network is abnormal, ensuring business continuity.

[0122] As a further supplement to this step, the monitoring indicators in this step include: CPU, memory, bandwidth, packet loss rate, delay jitter, QoS queue length and other parameters;

[0123] S6.2.3. Routing optimization algorithm execution: Based on the current network topology, node load data, and business requirements, an optimal routing solution including path priority is generated through a shortest path algorithm, a multi-objective optimization algorithm, or an intelligent optimization algorithm. The multi-objective optimization algorithm can be parameterized with the aid of the objective function F = w1·L+w21(1 / B)+w3·D, where L represents the path length, B is the available bandwidth, D is the latency, and w1, w2, and w3 are weight coefficients to achieve multi-dimensional optimization configuration of path length, available bandwidth, and latency. This design generates an optimal routing solution based on multi-dimensional data, taking into account the shortest path, load balancing, and business priority, thereby improving resource utilization.

[0124] As a further complement to this step, when applying routing optimization algorithms, it is necessary to select an adaptation solution based on network characteristics. Shortest path algorithms (such as Dijkstra's algorithm) are suitable for scenarios with simple network topologies and consistent service priorities. Their core goal is to simply and efficiently find the shortest transmission path. Multi-objective optimization algorithms (such as NSGA-II) focus on comprehensive optimization in complex scenarios. Through the objective function F = w1·L+w2·(1 / B)+w3·D, where L is the path length, B is the available bandwidth, D is the delay, and w1, w2, and w3 are weight coefficients, factors such as path length L, available bandwidth B, and delay D are taken into consideration at the same time. For example, the weight coefficients are (w1=0.4), w2=0.3), and (w3=0.3). The weights of each factor can be flexibly adjusted to achieve a balance in multi-dimensional optimization. Intelligent optimization algorithms (such as the ant colony algorithm) are designed for networks with frequent dynamic changes. With the help of the pheromone update mechanism, they imitate the adaptive characteristics of ants foraging and adjust routes in real time to ensure that the routing solution always maintains the optimality during dynamic changes in network topology, effectively responding to complex and changing network environments.

[0125] S6.2.4. Routing Table Synchronization and Adjustment: Generate instructions for the optimized routing solution and synchronize them to each network node to update the routing table. Synchronizing the routing table through a standardized mechanism ensures that the optimized solution takes effect quickly across the entire network, reducing routing convergence time.

[0126] As a further supplement to this step, the synchronization protocol in this step can use the BGP-LS (BGP Link State) protocol to transmit topology information, IS-IS or OSPF protocol to synchronize routing tables, and support incremental updates (such as the IS-IS extension defined in RFC7354).

[0127] Those skilled in the art will appreciate that the process of implementing all or part of the steps of the above embodiments may be accomplished by hardware, or by instructing related hardware through a program, which may be stored in a computer-readable storage medium.

[0128] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A microwave transmission networking method suitable for ultra-high-definition television signal transmission, characterized in that: The steps include: S1. Heterogeneous multi-path network construction: A microwave network consisting of at least two primary transmission paths and one backup transmission path is constructed between the signal transmitter and receiver. A cross-path clock synchronization mechanism is used to achieve coordinated signal transmission, creating a ring or mesh topology. S2, signal layer processing: cut and process the input ultra-high-definition signal in layers; S3, layered signal encapsulation and transmission: The layered processed UHD signal is encapsulated in IP packets and transmitted end-to-end via a packet switching network; S4, Interference cancellation: Enable cross-polarization interference cancellation technology on each primary path; S5. Monitoring, early warning, and path switching: Deploy real-time monitoring modules, build evaluation models, and establish hot backup mechanisms; S6. Compatibility modification and global optimization: Perform compatibility modification on existing microwave transmission equipment to achieve hybrid networking of new and old equipment; periodically activate the global topology optimization mechanism to dynamically adjust network routing based on node load and business needs.

2. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The cross-path clock synchronization mechanism in S1 is implemented in the following manner: a clock module containing an atomic clock or GPS synchronization unit is deployed at each transmission path node, and clock synchronization messages are periodically exchanged between nodes according to the IEEE1588v2 protocol.

3. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The layered cutting and processing of the input ultra-high-definition signal in S2 includes the following steps: S2.1, Signal layer cutting: Separate the UHD signal into independent video layer, audio layer and metadata layer; S2.2, pre-processing of each layer: The BM3D algorithm is used to denoise the video layer and unify the resolution; The Wiener filter algorithm is used to reduce noise and standardize the sampling rate of the audio layer; Use cyclic redundancy check algorithm to check the format of metadata layer and remove redundant data; S2.

3. Timing synchronization mark: Add timestamps and frame numbers to each layer of data to establish a timing correspondence between the video layer, audio layer, and metadata layer, providing a synchronization benchmark for subsequent layered encapsulation.

4. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The IP packet encapsulation in S3 includes the following steps: S3.1, Protocol Adaptation and Encapsulation: Select an encapsulation protocol based on the signal characteristics of each layer, and encapsulate the video layer, audio layer, and metadata layer into IP packets respectively; S3.2, QoS priority marking: inserting a quality of service field into the IP packet header to mark the priority level of each layer signal; S3.3, Reliability enhancement processing: Add cyclic redundancy check code to the encapsulated IP data packet and embed forward error correction redundant data.

5. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The cross-polarization interference cancellation technology in S4 specifically includes: Deploy dual-polarized antennas at the transmission nodes on the primary path to receive two signals with the same frequency and orthogonal polarizations. The two received signals are sampled and digitized, and an interference estimation model is constructed based on an adaptive filtering algorithm; The cross-polarization interference component in the target signal is separated by an interference estimation model, and the interference component is eliminated from the received signal.

6. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 5, characterized in that: When the adaptive filtering algorithm in S4 performs interference elimination through the interference estimation model, the processing process is as follows: First, the error signal is calculated based on the real-time sampling data of the received signal. The error signal e(n) is expressed as: e(n) = d(n) - y(n); where d(n) is the desired signal, y(n) is the actual output signal, and n is the sampling time. Next, the filter coefficient is dynamically adjusted according to the error signal. The filter coefficient update formula is: w(n+1)=w(n)+x(n)e(n), where w(n) is the filter coefficient vector at time n and x(n) is the input signal vector.

7. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The construction of the evaluation model in S5 specifically includes the following steps: S5.

1. Dynamic weight configuration: Configure dynamic weight coefficients for each transmission parameter. The dynamic weight coefficients are adaptively adjusted based on signal transmission period, network load status, and weather conditions. S5.

2. Multi-parameter fusion evaluation: Generate a path quality evaluation value Q based on a multi-parameter fusion algorithm. The multi-parameter fusion algorithm includes a weighted sum model, a fuzzy logic reasoning model, or a neural network evaluation model. The calculation formula of the improved weighted sum model is: Q = ∑ [w i (t)×p i ], where p i is the standardized parameter value, w i (t) is the dynamic weight of the i-th parameter at time t; w i (t) is calculated as follows: Where: w i0 is the initial weight of the i-th parameter, γ is the weight adjustment coefficient, which is set according to the degree of influence of the parameter on ultra-high-definition signal transmission, and p i (t-1) is the value of the i-th parameter at the previous moment t-1, μ i is the historical mean of the i-th parameter, σ i is the historical standard deviation of the i-th parameter.

8. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The specific method of establishing the hot backup mechanism in S5 includes the following steps: S5.

1. Heterogeneous backup path construction: Use a different transmission technology from the primary path to build a backup path; S5.

2. Automatic rerouting and switching: When the quality of the primary path is detected to be lower than the preset standard, the automatic rerouting algorithm is started, and the optimal backup path is calculated based on the real-time path quality evaluation data and a lossless switching is performed.

9. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The compatibility transformation in S6 includes the following steps: S6.1.

1. Hardware Adaptation and Modification: Upgrade the RF front-end modules of existing equipment to support the new equipment modulation method, modify the power adapter unit to be compatible with the new equipment power supply specifications, and expand the physical interface types to enable physical connection and signal interaction between new and old equipment; S6.1.2, Software Protocol Adaptation: Update device firmware to support the new communication protocol, embed a protocol conversion module in the control plane to parse and convert control instructions and data formats between new and old devices, and establish a unified configuration management interface to centrally monitor the parameters and status of new and old devices; S6.1.

3. Joint commissioning test verification: Test the consistency of signal modulation and demodulation between old and new equipment, verify the stability of the power module in a mixed power supply environment, test the delay and reliability of the protocol conversion process, and ensure that new and old equipment can be mixed and networked.

10. The microwave transmission networking method for ultra-high-definition television signal transmission according to claim 1, characterized in that: The periodic start of the global topology optimization mechanism in S6 specifically includes the following implementation steps: S6.2.1, Cycle and trigger condition configuration: Set a fixed optimization cycle, the cycle length can be configured according to network service characteristics; S6.2.2 Real-time monitoring and triggering: When it is detected that the load of any node exceeds the preset threshold, a new high-priority service is added, or the quality parameters of key links deteriorate, real-time routing optimization is immediately initiated; S6.2.

3. Routing Optimization Algorithm Execution: Based on the current network topology, node load data, and service requirements, generate an optimal routing solution that includes path priorities using a shortest path algorithm, a multi-objective optimization algorithm, or an intelligent optimization algorithm. The multi-objective optimization algorithm can be parameterized using the objective function F = w1·L + w2·(1 / B) + w3·D, where L represents path length, B is available bandwidth, and D is latency. w1, w2, and w3 are weight coefficients to achieve multi-dimensional optimization of path length, available bandwidth, and latency. S6.2.

4. Routing table synchronization and adjustment: Generate instructions for the optimized routing solution and synchronize them to each network node to update the routing table.

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