A Multi-Channel High-Speed ​​Data Routing Method Based on FPGA

Abstract

This invention discloses a multi-channel high-speed data routing method based on FPGA. This technical solution generates a fluctuation coefficient Wf by real-time monitoring of the operating status of each data channel in the FPGA hardware device, and after evaluation, triggers a risk warning, effectively improving the reliability of data transmission. A real-time data extraction mechanism and a DSP module perform extraction, filtering, and frame format verification operations to ensure data integrity. Real-time calculation and evaluation of bandwidth utilization efficiency Bwe triggers a bandwidth optimization alarm. A bandwidth allocation model dynamically adjusts the bandwidth allocation strategy based on actual bandwidth usage Bac and available bandwidth Bav to avoid resource waste or channel overload. The calculation and evaluation of the topology adaptability index Tad triggers a topology adjustment mechanism, combined with a dynamic routing algorithm to optimize path selection. The multi-channel optimization parameter Copt dynamically adjusts the underlying extraction monitoring and preprocessing to ensure efficient system resource utilization and improved adaptability. Finally, routing performance evaluation ensures overall transmission efficiency and stability.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, and in particular to a multi-channel high-speed data routing method based on FPGA. Background Technology

[0002] Multi-channel high-speed data routing methods are designed to address the surge in data traffic in modern information systems, particularly in large-scale communication, data processing, and storage environments. With the development of network technology and the gradual increase in data transmission speeds, traditional single-channel data transmission methods are increasingly unable to meet high bandwidth demands, especially in high-performance computing, big data analytics, and cloud computing. Multi-channel high-speed data routing effectively improves data transmission efficiency and system throughput by intelligently scheduling and distributing data streams across multiple data channels. In recent years, with advancements in communication protocols and innovations in hardware technology, multi-channel high-speed data routing methods have matured and are widely used, especially in applications such as the Internet, large-scale data centers, and the Internet of Things (IoT).

[0003] However, multi-channel high-speed data routing methods often have the following technical drawbacks in practical applications:

[0004] 1. Issues related to the stability and reliability of data transmission:

[0005] This involves packet loss, erroneous transmission, and transmission instability, especially in situations of signal interference, channel congestion, or unstable network quality, which can affect the reliability of multi-channel data routing.

[0006] 2. Issues related to resource utilization and allocation efficiency:

[0007] Issues such as uneven bandwidth allocation and multi-channel overload can lead to wasted channel resources or performance bottlenecks, failing to fully utilize the overall transmission capacity of the system.

[0008] 3. Issues related to dynamic adaptability and real-time processing capabilities:

[0009] The impact of dynamic changes in network topology on routing path selection, as well as the insufficient real-time extraction monitoring and data preprocessing capabilities at the underlying level, limit the system's adaptability and real-time performance in dynamic environments. Summary of the Invention

[0010] The purpose of this invention is to provide a multi-channel high-speed data routing method based on FPGA to solve the above-mentioned problems.

[0011] This invention is achieved through the following technical solution: a multi-channel high-speed data routing method based on FPGA, comprising the following steps:

[0012] Step 1: First, monitor the operating status of each data channel in the FPGA hardware device in real time, and obtain the real-time operating status data of each channel through the high-speed data acquisition module, including the packet loss rate Dbl of the channel; generate the fluctuation coefficient Wf based on the real-time operating status data and evaluate it. If the fluctuation coefficient Wf exceeds the preset fluctuation threshold Q1, a preliminary risk warning is triggered.

[0013] Step 2: Next, based on the real-time operating status data, feature extraction of channel bandwidth utilization is performed, a bandwidth allocation model is established, and the bandwidth utilization efficiency Bwe is calculated and evaluated; if the bandwidth utilization efficiency Bwe is lower than the preset optimal utilization threshold Q2, a bandwidth optimization alarm is issued; and the bandwidth allocation of each data channel is adjusted in real time through the bandwidth allocation model.

[0014] Step 3: Next, monitor and record topology change data related to the network topology structure. Then, based on the real-time topology change data, identify the topology change factors that will cause transmission path delays, calculate and evaluate the topology adaptability index Tad. If the topology adaptability index Tad is abnormal, trigger the network topology adjustment mechanism to automatically optimize the routing path.

[0015] Step 4: Next, based on the parallel computing capabilities of the FPGA hardware, the status of multi-channel data packets is monitored and analyzed in real time, and the integrity of the data packets is quickly determined. A preprocessing buffer is preset, and key data packets are independently copied to the preprocessing buffer through a real-time extraction mechanism. Then, the FPGA's DSP module is used to perform real-time preprocessing operations on the data. Finally, combined with the real-time feedback of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad, the multi-channel optimization parameter Copt is generated.

[0016] Step 5: Finally, compare and analyze all routing adjustment and optimization parameters Copt with the preset routing performance evaluation threshold Pro to evaluate the overall transmission efficiency and stability of the multi-channel data routing system, and output the routing adjustment strategy based on the evaluation results; if the evaluation results show that the current transmission efficiency does not meet expectations, then further adjust the channel allocation and path selection.

[0017] Preferably, step one specifically includes:

[0018] The real-time operating status data of each data channel in the FPGA hardware device, which is monitored and collected in real time, includes the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs, and packet loss rate Dbl for each data channel. Then, the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs, and packet loss rate Dbl are extracted, and after dimensionless processing, the fluctuation coefficient Wf is calculated using the following formula:

[0019]

[0020] Preferably, step one further includes:

[0021] A preset volatility threshold Q1 is set, and the volatility coefficient Wf is compared and evaluated with the preset volatility threshold Q1, generating the following evaluation content:

[0022] When the volatility coefficient Wf ≤ volatility threshold Q1, the system continues to operate normally without any intervention;

[0023] When the volatility coefficient Wf > volatility threshold Q1, the system triggers a preliminary risk warning and issues a warning signal to remind the user that there is a risk of data loss and unstable transmission in the current channel.

[0024] Preferably, step two specifically includes:

[0025] Based on real-time operational status data, bandwidth utilization characteristics of each data channel are extracted. A bandwidth allocation model is then established based on these characteristics. This model considers load balancing between channels, transmission efficiency, and real-time bandwidth requirements to formulate a bandwidth allocation strategy for each channel. Finally, using the bandwidth allocation model and real-time operational status data, the bandwidth utilization efficiency (Bwe) of each data channel is calculated. The specific calculation formula is as follows:

[0026]

[0027] In the formula, Bac represents the actual bandwidth usage in the real-time running status data, Bav represents the available bandwidth in the real-time running status data, Pac represents the data packet transmission rate in the real-time running status data, and Lat represents the latency in the real-time running status data.

[0028] Preferably, step two further includes:

[0029] The evaluation was conducted by comparing the preset optimal utilization threshold Q2 with the bandwidth utilization efficiency Bwe, as detailed below:

[0030] When the bandwidth utilization efficiency Bwe is greater than or equal to the optimal utilization threshold Q2, it indicates that the current bandwidth resource allocation is reasonable and the network is operating stably.

[0031] When the bandwidth utilization efficiency Bwe is less than the optimal utilization threshold Q2, it indicates that the current bandwidth resource allocation is unreasonable and the bandwidth resources are not being used effectively. At this time, a bandwidth optimization alarm is triggered, and the administrator is notified through the visual interface to remind them of the problem of wasted bandwidth resources or unbalanced load.

[0032] Next, the bandwidth of each data channel is adjusted in real time using a bandwidth allocation model; at the same time, bandwidth resources are reallocated, including dynamically migrating the bandwidth of overloaded channels to idle channels and adjusting the bandwidth of different data channels through a load balancing algorithm.

[0033] Preferably, step three specifically includes:

[0034] Based on topology change-related data, a predictive model is constructed to identify factors that will lead to transmission path delays, including network bottlenecks, topology faults, and link quality degradation. Using historical and trend data from the topology change-related data, combined with network load and transmission requirements, impending delay changes in the network topology are identified. At this point, the topology adaptability index Tad is calculated by extracting the relevant topology change data.

[0035]

[0036] In the formula, Lin represents the link load value in the topology change related data, Nod represents the node connection stability value in the topology change related data, Lik represents the link delay fluctuation value in the topology change related data, and Lir represents the link recovery time in the topology change related data.

[0037] Preferably, step three also includes:

[0038] The topology fitness index Tad was evaluated by comparison using a preset fitness threshold Q3, as detailed below:

[0039] If the topology adaptability index Tad ≥ the adaptability threshold Q3, it means that the current network topology has normal adaptability, the current network topology can stably cope with changes in transmission paths, and the network is operating normally.

[0040] If the topology adaptability index Tad is less than the adaptability threshold Q3, it indicates that the current network topology has an adaptability anomaly. The system will trigger the network topology adjustment mechanism to automatically adjust the routing path, including recalculating the optimal data transmission path based on the current topology and traffic requirements, evaluating the links, and switching paths in real time.

[0041] Preferably, step four specifically includes:

[0042] With the support of FPGA hardware parallel computing capabilities, the status of data packets in each channel is monitored in real time, including analysis of packet loss rate Dbl, to determine data packet integrity. A real-time data extraction mechanism independently copies and distributes critical data packets to a dedicated preprocessing buffer. Then, based on the FPGA-based DSP module, real-time extraction and filtering operations are performed during high-speed data stream allocation, including high-frequency noise suppression, critical data marking, and data frame format verification. Next, dynamic feedback data of fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad are acquired in real time, and data stream priority and preprocessing resource allocation are adjusted based on this dynamic feedback data. Finally, the data stream distribution among multiple channels is adjusted using the FPGA's built-in multi-threaded routing algorithm.

[0043] Step four also includes:

[0044] Combining real-time feedback from the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad, the multi-channel optimization parameter Copt is generated using the following formula:

[0045] Copt = f(Wf, Bwe, Tad);

[0046] In the formula, f represents the calculation function used to combine the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad to calculate the multi-channel optimization parameter Copt.

[0047] Preferably, step five specifically includes:

[0048] A preset routing performance evaluation threshold Pro is set, and the routing adjustment and optimization parameter Copt is compared and analyzed with the routing performance evaluation threshold Pro. The specific content is as follows:

[0049] If the routing adjustment and optimization parameter Copt is greater than or equal to the routing performance evaluation threshold Pro, the existing routing policy should be maintained without further optimization.

[0050] If the route adjustment and optimization parameter Copt < the route performance evaluation threshold Pro: start the route optimization mechanism.

[0051] Preferably, step five also includes:

[0052] If the performance of multi-channel data routing is abnormally poor and the transmission efficiency is insufficient, resulting in underutilization of bandwidth, a targeted routing adjustment strategy will be output. Specific strategies include:

[0053] Adjust bandwidth allocation: Adjust the bandwidth usage of each channel;

[0054] Optimize route selection: Select alternative routing paths based on topology changes and real-time network conditions;

[0055] Adjust data flow allocation: Intelligently adjust data flow allocation based on the real-time load and bandwidth requirements of each channel.

[0056] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0057] 1. To address the stability and reliability issues of data transmission, the system monitors the operating status of each data channel in the FPGA hardware in real time, acquiring the transmission rate (Dcs), data packet arrival time (Dds), number of packet loss events (Dbs), and packet loss rate (Dbl), and generates a fluctuation coefficient (Wf). If the fluctuation coefficient Wf exceeds a preset fluctuation threshold (Q1), a preliminary risk warning is triggered, promptly alerting users to potential packet loss and transmission instability issues. A real-time data extraction mechanism is used to independently copy critical data packets to a dedicated preprocessing buffer, effectively preventing the loss of important data. The FPGA's DSP module performs real-time data extraction, filtering, and frame format verification to ensure data integrity and accuracy. The optimization parameter (Copt) is compared and analyzed using a routing performance evaluation threshold (Pro). If the optimization parameter Copt is lower than the performance evaluation threshold (Pro), a routing optimization mechanism is activated to further improve transmission reliability.

[0058] 2. To address resource utilization and allocation efficiency issues, the bandwidth utilization efficiency (Bwe) is calculated using real-time operational data. If Bwe falls below the optimal utilization threshold (Q2), a bandwidth optimization alarm is triggered to alert the administrator of uneven bandwidth resource allocation. A bandwidth allocation model is used to adjust the bandwidth allocation strategy based on actual bandwidth usage (Bac), available bandwidth (Bav), packet transmission rate (Pac), and latency (Lat) to avoid single-channel overload or idleness. Combining multi-threaded routing and load balancing algorithms, bandwidth resources are reallocated according to real-time network load conditions, dynamically migrating bandwidth usage from overloaded channels to idle channels and adjusting data flow distribution in real time to ensure full utilization of bandwidth resources. Feedback and analysis of the topology adaptability index (Tad) are used, combined with the multi-channel optimization parameter (Copt), to dynamically optimize system resource configuration, avoiding resource waste and performance bottlenecks.

[0059] 3. To address the issues of dynamic adaptability and real-time processing capabilities, the system monitors dynamic changes in the network topology to obtain link load value Lin, node connection stability value Nod, link latency fluctuation value Lik, and link recovery time Lir. Combining historical data with topology change trends, a topology adaptability index Tad is calculated. If the topology adaptability index Tad falls below the adaptability threshold Q3, a topology adjustment mechanism is triggered. A dynamic routing algorithm optimizes path selection based on real-time network conditions and traffic demands, recalculating the optimal transmission path or replacing links. Leveraging the parallel computing capabilities of FPGA hardware, multi-channel data packets are monitored, rapidly analyzed, and preprocessed in real-time. Optimization parameters Copt are generated through dynamic feedback of fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad, guiding the dynamic adjustment of underlying extraction, monitoring, and preprocessing. This significantly improves the system's adaptability and real-time performance in dynamic environments. Attached Figure Description

[0060] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0061] Figure 1 This is a flowchart illustrating the steps of a multi-channel high-speed data routing method based on FPGA according to the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and are not intended to limit the invention. It should be noted that this invention is already in the actual research and development stage.

[0063] Example 1

[0064] like Figure 1 As shown, this embodiment includes a multi-channel high-speed data routing method based on FPGA, comprising the following steps:

[0065] Step 1: First, monitor the operating status of each data channel in the FPGA hardware device in real time, and obtain the real-time operating status data of each channel through the high-speed data acquisition module, including the packet loss rate Dbl of the channel; generate the fluctuation coefficient Wf based on the real-time operating status data and evaluate it. If the fluctuation coefficient Wf exceeds the preset fluctuation threshold Q1, a preliminary risk warning is triggered.

[0066] Step 2: Next, based on the real-time operating status data, feature extraction of channel bandwidth utilization is performed, a bandwidth allocation model is established, and the bandwidth utilization efficiency Bwe is calculated and evaluated; if the bandwidth utilization efficiency Bwe is lower than the preset optimal utilization threshold Q2, a bandwidth optimization alarm is issued; and the bandwidth allocation of each data channel is adjusted in real time through the bandwidth allocation model.

[0067] Step 3: Next, monitor and record topology change data related to the network topology structure. Then, based on the real-time topology change data, identify the topology change factors that will cause transmission path delays, calculate and evaluate the topology adaptability index Tad. If the topology adaptability index Tad is abnormal, trigger the network topology adjustment mechanism to automatically optimize the routing path.

[0068] Step 4: Next, based on the parallel computing capabilities of the FPGA hardware, the status of multi-channel data packets is monitored and analyzed in real time, and the integrity of the data packets is quickly determined. A preprocessing buffer is preset, and key data packets are independently copied to the preprocessing buffer through a real-time extraction mechanism. Then, the FPGA's DSP module is used to perform real-time preprocessing operations on the data. Finally, combined with the real-time feedback of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad, the multi-channel optimization parameter Copt is generated.

[0069] Step 5: Finally, compare and analyze all routing adjustment and optimization parameters Copt with the preset routing performance evaluation threshold Pro to evaluate the overall transmission efficiency and stability of the multi-channel data routing system, and output the routing adjustment strategy based on the evaluation results; if the evaluation results show that the current transmission efficiency does not meet expectations, then further adjust the channel allocation and path selection.

[0070] In this embodiment, step one involves real-time monitoring of the operating status of each data channel in the FPGA hardware device to obtain the packet loss rate Dbl and generate a fluctuation coefficient Wf. When Wf exceeds a preset fluctuation threshold Q1, a preliminary risk warning is triggered, effectively improving the stability and reliability of data transmission. Step two involves extracting real-time operating status data by feature extraction, calculating the bandwidth utilization efficiency Bwe, and adjusting the bandwidth allocation in real time using a bandwidth allocation model. When Bwe is lower than the optimal utilization threshold Q2, a bandwidth optimization alarm is issued, significantly improving the utilization rate of channel resources. Step three involves monitoring network topology change-related data, calculating and evaluating the topology adaptability index Tad. If Tad is abnormal, a network topology adjustment is triggered. The entire mechanism optimizes routing path selection in dynamic network environments. Step four leverages the parallel computing capabilities of FPGAs to monitor and analyze the status of multi-channel data packets in real time. Simultaneously, a real-time extraction mechanism is used to copy key data packets to a preprocessing buffer. Real-time preprocessing operations such as filtering and extraction are performed by the DSP module. Combined with feedback from Wf, Bwe, and Tad, multi-channel optimization parameters Copt are generated, improving the underlying extraction monitoring and data preprocessing capabilities. Step five compares and analyzes the optimization parameters Copt with the routing performance evaluation threshold Pro, outputs adjustment strategies, and further optimizes channel allocation and path selection, ensuring continuous optimization of the overall transmission efficiency and stability of the system.

[0071] Example 2

[0072] Step one specifically includes:

[0073] The real-time operating status data of each data channel in the FPGA hardware device, which is monitored and collected in real time, includes the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs, and packet loss rate Dbl for each data channel. Then, the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs, and packet loss rate Dbl are extracted, and after dimensionless processing, the fluctuation coefficient Wf is calculated using the following formula:

[0074]

[0075] Step one also includes:

[0076] A preset volatility threshold Q1 is set, and the volatility coefficient Wf is compared and evaluated with the preset volatility threshold Q1, generating the following evaluation content:

[0077] When the volatility coefficient Wf ≤ volatility threshold Q1, the system continues to operate normally without any intervention;

[0078] When the volatility coefficient Wf > volatility threshold Q1, the system triggers a preliminary risk warning and issues a warning signal to remind the user that there is a risk of data loss and unstable transmission in the current channel.

[0079] Step two specifically includes:

[0080] Based on real-time operational status data, bandwidth utilization characteristics of each data channel are extracted. A bandwidth allocation model is then established based on these characteristics. This model considers load balancing between channels, transmission efficiency, and real-time bandwidth requirements to formulate a bandwidth allocation strategy for each channel. Finally, using the bandwidth allocation model and real-time operational status data, the bandwidth utilization efficiency (Bwe) of each data channel is calculated. The specific calculation formula is as follows:

[0081]

[0082] In the formula, Bac represents the actual bandwidth usage in the real-time running status data, Bav represents the available bandwidth in the real-time running status data, Pac represents the data packet transmission rate in the real-time running status data, and Lat represents the latency in the real-time running status data.

[0083] Step two also includes:

[0084] The evaluation was conducted by comparing the preset optimal utilization threshold Q2 with the bandwidth utilization efficiency Bwe, as detailed below:

[0085] When the bandwidth utilization efficiency Bwe is greater than or equal to the optimal utilization threshold Q2, it indicates that the current bandwidth resource allocation is reasonable and the network is operating stably.

[0086] When the bandwidth utilization efficiency Bwe is less than the optimal utilization threshold Q2, it indicates that the current bandwidth resource allocation is unreasonable and the bandwidth resources are not being used effectively. At this time, a bandwidth optimization alarm is triggered, and the administrator is notified through the visual interface to remind them of the problem of wasted bandwidth resources or unbalanced load.

[0087] Next, the bandwidth of each data channel is adjusted in real time using a bandwidth allocation model; at the same time, bandwidth resources are reallocated, including dynamically migrating the bandwidth of overloaded channels to idle channels and adjusting the bandwidth of different data channels through a load balancing algorithm.

[0088] Step three specifically includes:

[0089] Based on topology change-related data, a predictive model is constructed to identify factors that will lead to transmission path delays, including network bottlenecks, topology faults, and link quality degradation. Using historical and trend data from the topology change-related data, combined with network load and transmission requirements, impending delay changes in the network topology are identified. At this point, the topology adaptability index Tad is calculated by extracting the relevant topology change data.

[0090]

[0091] In the formula, Lin represents the link load value in the topology change related data, Nod represents the node connection stability value in the topology change related data, Lik represents the link delay fluctuation value in the topology change related data, and Lir represents the link recovery time in the topology change related data.

[0092] Step three also includes:

[0093] The topology fitness index Tad was evaluated by comparison using a preset fitness threshold Q3, as detailed below:

[0094] If the topology adaptability index Tad ≥ the adaptability threshold Q3, it means that the current network topology has normal adaptability, the current network topology can stably cope with changes in transmission paths, and the network is operating normally.

[0095] If the topology adaptability index Tad is less than the adaptability threshold Q3, it indicates that the current network topology has an adaptability anomaly. The system will trigger the network topology adjustment mechanism to automatically adjust the routing path, including recalculating the optimal data transmission path based on the current topology and traffic requirements, evaluating the links, and switching paths in real time.

[0096] In this embodiment, the stability and transmission efficiency of the data routing system are effectively improved through a multi-step intelligent monitoring and dynamic adjustment mechanism. Step 1 monitors and collects the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs, and packet loss rate Dbl of each data channel in real time. After dimensionless processing, the fluctuation coefficient Wf is calculated and compared with the preset fluctuation threshold Q1 to ensure that a risk warning is triggered in time when the fluctuation coefficient Wf exceeds the threshold, so as to avoid the risk of data loss and unstable transmission in the system.

[0097] Step 2 involves extracting bandwidth utilization characteristics and establishing a bandwidth allocation model. The bandwidth utilization efficiency Bwe is calculated and compared with the optimal utilization threshold Q2. When the bandwidth utilization efficiency Bwe is lower than the preset threshold, a bandwidth optimization alarm is triggered, and network load and transmission efficiency are optimized through real-time bandwidth adjustment.

[0098] Step 3 involves constructing a predictive model to analyze data related to topology changes, calculating the topology adaptability index Tad, and comparing it with the adaptability threshold Q3. If the topology adaptability index Tad is abnormal, the network topology adjustment mechanism is automatically activated. This mechanism recalculates the optimal data transmission path and adjusts the transmission path using a dynamic routing algorithm to ensure stable operation of the system when the topology changes, thus optimizing the adaptability and flexibility of data transmission. By combining these steps, the system can achieve intelligent optimization and stable operation of multi-channel data routing, ensuring efficient data transmission and load balancing.

[0099] In addition, key features such as link load (Lin), node connection stability (Nod), link latency fluctuation (Lik), and link recovery time (Lir) are extracted from topology change-related data. Next, combining historical and trend data from the topology change-related data, a time-dependent model of the topology change features is established using time series analysis to analyze the changing patterns of feature values ​​at different time points. Then, machine learning algorithms are applied, using link load, node stability, latency fluctuation, and recovery time as input variables and potential latency changes as the output target. The mapping relationship between features and latency changes is obtained through model training. Furthermore, a dynamic correlation analysis mechanism is established by combining network load and transmission demand data to predict latency changes that may be caused by the topology under specific load conditions. Finally, the prediction model is continuously optimized using real-time updated topology change data to ensure that the model can accurately identify latency factors such as network bottlenecks, topology faults, and link quality degradation, thereby providing support for dynamic routing optimization.

[0100] Example 4

[0101] Step four specifically includes:

[0102] With the support of FPGA hardware parallel computing capabilities, the status of data packets in each channel is monitored in real time, including analysis of packet loss rate Dbl, to determine data packet integrity. A real-time data extraction mechanism independently copies and distributes critical data packets to a dedicated preprocessing buffer. Then, based on the FPGA-based DSP module, real-time extraction and filtering operations are performed during high-speed data stream allocation, including high-frequency noise suppression, critical data marking, and data frame format verification. Next, dynamic feedback data of fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad are acquired in real time, and data stream priority and preprocessing resource allocation are adjusted based on this dynamic feedback data. Finally, the data stream distribution among multiple channels is adjusted using the FPGA's built-in multi-threaded routing algorithm.

[0103] Step four also includes:

[0104] Combining real-time feedback from the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad, the multi-channel optimization parameter Copt is generated using the following formula:

[0105] Copt = f(Wf, Bwe, Tad);

[0106] In the formula, f represents the calculation function used to combine the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad to calculate the multi-channel optimization parameter Copt.

[0107] Step five specifically includes:

[0108] A preset routing performance evaluation threshold Pro is set, and the routing adjustment and optimization parameter Copt is compared and analyzed with the routing performance evaluation threshold Pro. The specific content is as follows:

[0109] If the routing adjustment and optimization parameter Copt is greater than or equal to the routing performance evaluation threshold Pro, the existing routing policy should be maintained without further optimization.

[0110] If the route adjustment and optimization parameter Copt < the route performance evaluation threshold Pro: start the route optimization mechanism.

[0111] Step five also includes:

[0112] If the performance of multi-channel data routing is abnormally poor and the transmission efficiency is insufficient, resulting in underutilization of bandwidth, a targeted routing adjustment strategy will be output. Specific strategies include:

[0113] Adjust bandwidth allocation: Adjust the bandwidth usage of each channel;

[0114] Optimize route selection: Select alternative routing paths based on topology changes and real-time network conditions;

[0115] Adjust data flow allocation: Intelligently adjust data traffic allocation based on the real-time load and bandwidth requirements of each channel;

[0116] In this embodiment, in step four, based on the parallel computing capabilities of the FPGA hardware, the data packet status of each channel is monitored in real time and the packet loss rate Dbl is analyzed to effectively determine the integrity of the data packets, thereby improving the reliability of data transmission. A real-time data extraction mechanism independently copies and distributes key data packets to a dedicated preprocessing buffer, ensuring that key data is processed first and avoiding the loss of important information due to interference or packet loss. The FPGA's DSP module performs real-time extraction and filtering operations during high-speed data stream allocation. Through high-frequency noise suppression, key data marking, and data frame format verification, the accuracy and integrity of the data are significantly improved, while reducing the workload of subsequent processors. Dynamic feedback data of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad are acquired in real time, and the priority of the data stream and the allocation of preprocessing resources are adjusted based on the feedback to further optimize the system's resource utilization efficiency. Through the FPGA's built-in multi-threaded routing algorithm, the data stream distribution among multiple channels is dynamically adjusted, achieving a balanced allocation of channel resources, reducing the possibility of channel overload, and improving the overall performance and stability of the system.

[0117] Next, step four generates multi-channel optimization parameters Copt by combining real-time feedback from the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad. This ensures that the system can comprehensively evaluate various performance indicators, thereby achieving intelligent optimization of multi-channel routing. The specific functional form of the multi-channel optimization parameter Copt is as follows:

[0118] Copt=w1×Wf+w2×Bwe+w3×Tad;

[0119] Where w1, w2, and w3 are the weighting coefficients of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad, respectively, and w1+w2+w3=1, 0≤w1<1, 0≤w2<1, 0≤w3<1;

[0120] Step five compares the optimization parameter Copt with the preset routing performance evaluation threshold Pro to ensure that the system maintains the existing strategy when routing performance meets the standard, avoiding unnecessary optimization. If the evaluation results show that the performance does not meet expectations, the system automatically starts the routing optimization mechanism to adjust bandwidth allocation, optimize path selection, and adjust data flow allocation to achieve more efficient data transmission and resource utilization. Among these, the calculation of the fluctuation coefficient Wf helps to detect the instability of channel transmission, the bandwidth utilization efficiency Bwe reflects the degree of bandwidth resource utilization, and the topology adaptability index Tad evaluates the adaptability and stability of the network topology. The calculation and evaluation of these parameters provide the system with a precise basis for adjustment, ensuring the flexibility and efficiency of multi-channel data routing, and ultimately improving the network's transmission performance and stability.

[0121] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-channel high-speed data routing method based on FPGA, characterized in that: Includes the following steps: Step 1: First, monitor the operating status of each data channel in the FPGA hardware device in real time, and obtain relevant data on the real-time operating status of each channel through the high-speed data acquisition module, including the packet loss rate Dbl of the channel. A volatility coefficient Wf is generated and evaluated based on real-time operational status data. If the volatility coefficient Wf exceeds the preset volatility threshold Q1, a preliminary risk warning is triggered. Step 2: Next, based on the real-time operating status data, feature extraction of channel bandwidth utilization is performed, a bandwidth allocation model is established, and the bandwidth utilization efficiency Bwe is calculated and evaluated; if the bandwidth utilization efficiency Bwe is lower than the preset optimal utilization threshold Q2, a bandwidth optimization alarm is issued. And through a bandwidth allocation model, the bandwidth allocation of each data channel is adjusted in real time; Step 3: Next, monitor and record topology change data related to the network topology structure. Then, based on the real-time topology change data, identify the topology change factors that will cause transmission path delays, calculate and evaluate the topology adaptability index Tad. If the topology adaptability index Tad is abnormal, trigger the network topology adjustment mechanism to automatically optimize the routing path. Step 4: Next, based on the parallel computing capabilities of FPGA hardware, the status of multi-channel data packets is monitored and analyzed in real time, and the integrity of data packets is quickly determined. A preprocessing buffer is preset, and key data packets are independently copied to the preprocessing buffer through a real-time extraction mechanism; then, the FPGA's DSP module is used to perform real-time preprocessing operations on the data; finally, the multi-channel optimization parameter Copt is generated by combining the real-time feedback of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad. Step 5: Finally, compare and analyze all routing adjustment and optimization parameters Copt with the preset routing performance evaluation threshold Pro to evaluate the overall transmission efficiency and stability of the multi-channel data routing system, and output the routing adjustment strategy based on the evaluation results; if the evaluation results show that the current transmission efficiency does not meet expectations, then further adjust the channel allocation and path selection.

2. The FPGA-based multi-channel high-speed data routing method according to claim 1, characterized in that: Step one specifically includes: The real-time operating status data of each data channel in the FPGA hardware device, which is monitored and collected in real time, includes the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs, and packet loss rate Dbl for each data channel. Then, the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs, and packet loss rate Dbl are extracted, and after dimensionless processing, the fluctuation coefficient Wf is calculated using the following formula:

3. The FPGA-based multi-channel high-speed data routing method according to claim 2, characterized in that: Step one also includes: A preset volatility threshold Q1 is set, and the volatility coefficient Wf is compared and evaluated with the preset volatility threshold Q1, generating the following evaluation content: When the volatility coefficient Wf ≤ volatility threshold Q1, the system continues to operate normally without any intervention; When the volatility coefficient Wf > volatility threshold Q1, the system triggers a preliminary risk warning and issues a warning signal to remind the user that there is a risk of data loss and unstable transmission in the current channel.

4. The FPGA-based multi-channel high-speed data routing method according to claim 3, characterized in that: Step two specifically includes: Based on real-time operational status data, bandwidth utilization characteristics of each data channel are extracted. A bandwidth allocation model is then established based on these characteristics. This model considers load balancing between channels, transmission efficiency, and real-time bandwidth requirements to formulate a bandwidth allocation strategy for each channel. Finally, using the bandwidth allocation model and real-time operational status data, the bandwidth utilization efficiency (Bwe) of each data channel is calculated. The specific calculation formula is as follows: In the formula, Bac represents the actual bandwidth usage in the real-time running status data, Bav represents the available bandwidth in the real-time running status data, Pac represents the data packet transmission rate in the real-time running status data, and Lat represents the latency in the real-time running status data.

5. The FPGA-based multi-channel high-speed data routing method according to claim 4, characterized in that: Step two also includes: The evaluation was conducted by comparing the preset optimal utilization threshold Q2 with the bandwidth utilization efficiency Bwe, as detailed below: When the bandwidth utilization efficiency Bwe is greater than or equal to the optimal utilization threshold Q2, it indicates that the current bandwidth resource allocation is reasonable and the network is operating stably. When the bandwidth utilization efficiency Bwe is less than the optimal utilization threshold Q2, it indicates that the current bandwidth resource allocation is unreasonable and the bandwidth resources are not being used effectively. At this time, a bandwidth optimization alarm is triggered, and the administrator is notified through the visual interface to remind them of the problem of wasted bandwidth resources or unbalanced load. Next, the bandwidth of each data channel is adjusted in real time using a bandwidth allocation model; at the same time, bandwidth resources are reallocated, including dynamically migrating the bandwidth of overloaded channels to idle channels and adjusting the bandwidth of different data channels through a load balancing algorithm.

6. The FPGA-based multi-channel high-speed data routing method according to claim 5, characterized in that: Step three specifically includes: Based on topology change-related data, a predictive model is constructed to identify factors that will lead to transmission path delays, including network bottlenecks, topology faults, and link quality degradation. Using historical and trend data from the topology change-related data, combined with network load and transmission requirements, impending delay changes in the network topology are identified. At this point, the topology adaptability index Tad is calculated by extracting the relevant topology change data. In the formula, Lin represents the link load value in the topology change related data, Nod represents the node connection stability value in the topology change related data, Lik represents the link delay fluctuation value in the topology change related data, and Lir represents the link recovery time in the topology change related data.

7. The FPGA-based multi-channel high-speed data routing method according to claim 6, characterized in that: Step three also includes: The topology fitness index Tad was evaluated by comparison using a preset fitness threshold Q3, as detailed below: If the topology adaptability index Tad ≥ the adaptability threshold Q3, it means that the current network topology has normal adaptability, the current network topology can stably cope with changes in transmission paths, and the network is operating normally. If the topology adaptability index Tad is less than the adaptability threshold Q3, it indicates that the current network topology has an adaptability anomaly. The system will trigger the network topology adjustment mechanism to automatically adjust the routing path, including recalculating the optimal data transmission path based on the current topology and traffic requirements, evaluating the links, and switching paths in real time.

8. The FPGA-based multi-channel high-speed data routing method according to claim 7, characterized in that: Step four specifically includes: With the support of FPGA hardware parallel computing capabilities, the status of data packets in each channel is monitored in real time, including analysis of packet loss rate Dbl, to determine data packet integrity. A real-time data extraction mechanism independently copies and distributes critical data packets to a dedicated preprocessing buffer. Then, based on the FPGA-based DSP module, real-time extraction and filtering operations are performed during high-speed data stream allocation, including high-frequency noise suppression, critical data marking, and data frame format verification. Next, dynamic feedback data of fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad are acquired in real time, and data stream priority and preprocessing resource allocation are adjusted based on this dynamic feedback data. Finally, the data stream distribution among multiple channels is adjusted using the FPGA's built-in multi-threaded routing algorithm. Step four also includes: Combining real-time feedback from the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad, the multi-channel optimization parameter Copt is generated using the following formula: Copt = f(Wf, Bwe, Tad); In the formula, f represents the calculation function used to combine the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad to calculate the multi-channel optimization parameter Copt.

9. The FPGA-based multi-channel high-speed data routing method according to claim 8, characterized in that: Step five specifically includes: A preset routing performance evaluation threshold Pro is set, and the routing adjustment and optimization parameter Copt is compared and analyzed with the routing performance evaluation threshold Pro. The specific content is as follows: If the routing adjustment and optimization parameter Copt is greater than or equal to the routing performance evaluation threshold Pro, the existing routing policy should be maintained without further optimization. If the route adjustment and optimization parameter Copt < the route performance evaluation threshold Pro: start the route optimization mechanism.

10. The FPGA-based multi-channel high-speed data routing method according to claim 9, characterized in that: Step five also includes: If the performance of multi-channel data routing is abnormally poor and the transmission efficiency is insufficient, resulting in underutilization of bandwidth, a targeted routing adjustment strategy will be output. Specific strategies include: Adjust bandwidth allocation: Adjust the bandwidth usage of each channel; Optimize route selection: Select alternative routing paths based on topology changes and real-time network conditions; Adjust data flow allocation: Intelligently adjust data flow allocation based on the real-time load and bandwidth requirements of each channel.

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