Multi-channel high-speed data routing method based on FPGA

Abstract

The invention discloses a multi-channel high-speed data routing method based on an FPGA (Field Programmable Gate Array), and the method comprises the steps: monitoring the operation state of each data channel in FPGA hardware equipment in real time, generating a fluctuation coefficient Wf, carrying out the evaluation, triggering risk early warning, and effectively improving the reliability of data transmission; extraction, filtering and frame format verification operations are executed through a real-time data extraction mechanism and a DSP module, and data integrity is ensured. Calculating and evaluating the bandwidth utilization efficiency Bwe in real time to trigger a bandwidth optimization alarm, and dynamically adjusting a bandwidth allocation strategy based on the actual bandwidth usage amount Bac and the available bandwidth amount Bav by using a bandwidth allocation model to avoid resource waste or channel overload; a topology adjustment mechanism is triggered by calculating and evaluating a topology adaptability index Tad, and path selection is optimized in combination with a dynamic routing algorithm; the multi-channel optimization parameter Copt is utilized to dynamically adjust bottom layer extraction monitoring and preprocessing, efficient utilization and adaptability improvement of system resources are ensured, and finally the overall transmission efficiency and stability are ensured through routing performance evaluation.

Description

Technical Field

[0001] The present 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 Art

[0002] Multi-channel high-speed data routing methods are designed to solve the problem of surging data traffic in modern information systems, especially in large-scale communication, data processing and storage environments. With the development of network technology, data transmission speeds have gradually increased, and traditional single-channel data transmission methods have gradually been unable to meet high bandwidth requirements, especially in the fields of high-performance computing, big data analysis and cloud computing. Multi-channel high-speed data routing effectively improves data transmission efficiency and system throughput by intelligently scheduling and diverting data flows between multiple data channels. In recent years, with the advancement of communication protocols and the innovation of hardware technology, multi-channel high-speed data routing methods have gradually matured, especially in applications such as the Internet, large-scale data centers and the Internet of Things.

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

[0004] 1. Data transmission stability and reliability issues:

[0005] Involving packet loss, erroneous transmission, and transmission instability, especially in the case of signal interference, channel congestion, or unstable network quality, may affect the reliability of multi-channel data routing.

[0006] 2. Resource utilization and allocation efficiency issues:

[0007] These include uneven bandwidth allocation and multi-channel overload issues, which may lead to waste of certain channel resources or performance bottlenecks, and fail to fully utilize the overall transmission capacity of the system.

[0008] 3. Dynamic adaptability and real-time processing capability issues:

[0009] These include the impact of dynamic changes in network topology on routing path selection, as well as insufficient underlying real-time extraction monitoring and data preprocessing capabilities, which limit the system's adaptability and real-time performance in a dynamic environment. Summary of the invention

[0010] The present invention aims to provide a multi-channel high-speed data routing method based on FPGA to solve the above problems.

[0011] The present invention is implemented by 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 related data of each channel through the high-speed data acquisition module, including the packet loss rate Dbl of the channel; generate and evaluate the fluctuation coefficient Wf based on the real-time operating status related data. If the fluctuation coefficient Wf exceeds the preset fluctuation threshold Q1, a preliminary risk warning is triggered;

[0013] Step 2: Next, extract the characteristics of channel bandwidth usage rate based on the real-time operation status related data, establish a bandwidth allocation model, and calculate and evaluate the bandwidth utilization efficiency Bwe; if the bandwidth utilization efficiency Bwe is lower than the preset optimal utilization threshold Q2, issue a bandwidth optimization alarm; and adjust the bandwidth allocation of each data channel in real time through the bandwidth allocation model;

[0014] Step 3: Then, monitor and record the topology change related data of the network topology structure, and then identify the topology change factors that will cause transmission path delay based on the real-time topology change related data, and calculate and evaluate the topology adaptability index Tad; if the topology adaptability index Tad is abnormal, the network topology adjustment mechanism is triggered to automatically optimize the routing path;

[0015] Step 4: Next, based on the parallel computing capability of FPGA hardware, the multi-channel data packet status is monitored and analyzed in real time, and the integrity of the data packet is quickly determined; a pre-processing buffer is preset, and the key data packets are independently copied to the pre-processing buffer through a real-time extraction mechanism; then the DSP module of FPGA is used to perform real-time pre-processing 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;

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

[0017] Preferably, step one specifically includes:

[0018] The real-time monitoring and collection of the real-time operation status related data of each data channel in the FPGA hardware device includes the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs and packet loss rate Dbl of 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 dimensionless processed, and the fluctuation coefficient Wf is calculated by the following formula:

[0019]

[0020] Preferably, step one specifically further includes:

[0021] The fluctuation threshold Q1 is preset, and the fluctuation coefficient Wf is compared and evaluated with the preset fluctuation threshold Q1, and the following evaluation content is generated:

[0022] When the fluctuation coefficient Wf ≤ the fluctuation threshold Q1, the system maintains normal operation without any intervention;

[0023] When the fluctuation coefficient Wf>the fluctuation threshold Q1, the system triggers a preliminary risk warning and sends out 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 2 specifically includes:

[0025] According to the real-time operation status related data, the bandwidth utilization characteristics of each data channel are extracted; and based on the bandwidth utilization characteristics, a bandwidth allocation model is established; wherein, the bandwidth allocation model considers the load balance, transmission efficiency and real-time bandwidth demand between channels to form a bandwidth allocation strategy for each channel; then, through the bandwidth allocation model, based on the real-time operation status related data, the bandwidth utilization efficiency Bwe of each data channel is calculated, and the specific calculation formula is as follows:

[0026]

[0027] Wherein, Bac represents the actual bandwidth usage in the real-time operation status related data, Bav represents the available bandwidth in the real-time operation status related data, Pac represents the data packet transmission rate in the real-time operation status related data, and Lat represents the delay duration in the real-time operation status related data.

[0028] Preferably, step 2 specifically further includes:

[0029] The preset optimal utilization threshold Q2 is compared with the bandwidth utilization efficiency Bwe for evaluation. The specific contents are as follows:

[0030] When the bandwidth utilization efficiency Bwe ≥ the optimal utilization threshold Q2, it means that the current bandwidth resource allocation is reasonable and the network operation is stable;

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

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

[0033] Preferably, step three specifically includes:

[0034] Based on the topology change related data, a prediction model is built to identify factors that will cause transmission path delays, including network bottlenecks, topology failures, and link quality degradation. Based on the historical data and topology change trend data in the topology change related data, combined with network load and transmission requirements, the upcoming delay changes in the network topology are identified. At this time, the topology change related data is extracted to calculate the topology adaptability index Tad:

[0035]

[0036] Wherein, 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 duration in the topology change related data.

[0037] Preferably, step three specifically further includes:

[0038] The topology adaptability index Tad is compared and evaluated by the preset adaptability threshold Q3. The specific contents are as follows:

[0039] If the topology adaptability index Tad ≥ adaptability threshold Q3, it means that the current network topology structure has normal adaptability, the current network topology structure is stable to cope with changes in the transmission path, and the network operation status is normal;

[0040] If the topology adaptability index Tad is less than the adaptability threshold Q3, it means that the current network topology has an adaptability anomaly; the system will trigger the network topology adjustment mechanism and automatically adjust the routing path, including recalculating the optimal data transmission path according to the current topology and traffic demand, evaluating the link, and switching the path in real time.

[0041] Preferably, step 4 specifically includes:

[0042] With the support of FPGA hardware parallel computing capabilities, the data packet status of each channel is monitored in real time, including the analysis of the packet loss rate Dbl to determine the integrity of the data packet; through the real-time data extraction mechanism, the key data packets are independently copied and distributed to the dedicated pre-processing buffer; then based on the FPGA-based DSP module, real-time extraction and filtering operations are performed during the high-speed data flow distribution process to suppress high-frequency noise, mark key data, and verify the data frame format; secondly, the dynamic feedback data of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad are obtained in real time, and the data flow priority and pre-processing resource allocation are adjusted based on the dynamic feedback data; the data flow distribution between multiple channels is adjusted through the built-in multi-threaded routing algorithm of the FPGA;

[0043] Step 4 specifically includes:

[0044] Combining 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 by the following formula:

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

[0046] Where f represents the calculation function, which is used to comprehensively consider 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] The routing performance evaluation threshold Pro is preset, and the routing adjustment and optimization parameter Copt is compared and analyzed with the routing performance evaluation threshold Pro. The specific contents are as follows:

[0049] If the routing adjustment and optimization parameter Copt ≥ routing performance evaluation threshold Pro, the existing routing strategy is maintained without further optimization;

[0050] If the route adjustment and optimization parameter Copt is less than the route performance evaluation threshold Pro: the route optimization mechanism is started.

[0051] Preferably, step five specifically further includes:

[0052] If the performance of multi-channel data routing is abnormal and the transmission efficiency is abnormal, and the bandwidth utilization is insufficient, a targeted routing adjustment strategy will be output; the specific strategies include:

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

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

[0055] Adjust data flow distribution: Intelligently adjust data flow distribution 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. In order to solve the stability and reliability problems of data transmission, the running status of each data channel in the FPGA hardware device is monitored in real time to obtain the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs and packet loss rate Dbl, and generate the fluctuation coefficient Wf. If the fluctuation coefficient Wf exceeds the preset fluctuation threshold Q1, a preliminary risk warning is triggered to promptly remind users of potential packet loss and transmission instability problems; the real-time data extraction mechanism is used to independently copy key data packets to a dedicated pre-processing buffer to effectively avoid the loss of important data; the DSP module of the FPGA is combined to perform real-time extraction, filtering and frame format verification operations on the data to ensure data integrity and accuracy; the optimization parameter Copt is compared and analyzed through the routing performance evaluation threshold Pro. If the optimization parameter Copt is lower than the performance evaluation threshold Pro, the routing optimization mechanism is started to further improve the reliability of transmission.

[0058] 2. In response to the problem of resource utilization and allocation efficiency, the bandwidth utilization efficiency Bwe is calculated through real-time operation status related data. If Bwe is lower than the optimal utilization threshold Q2, a bandwidth optimization alarm is triggered to remind the administrator of uneven bandwidth resource allocation. Through the bandwidth allocation model, the bandwidth allocation strategy is adjusted based on the actual bandwidth usage Bac, available bandwidth Bav, packet transmission rate Pac and delay time Lat to avoid single channel overload or idleness. Combined with multi-threaded routing algorithm and load balancing algorithm, bandwidth resources are reallocated according to real-time network load conditions, and the bandwidth usage of overloaded channels is dynamically migrated to idle channels. The data flow distribution is adjusted in real time to ensure full utilization of bandwidth resources. Through feedback and analysis of the topology adaptability index Tad, combined with the multi-channel optimization parameter Copt, the system resource configuration is dynamically optimized to avoid resource waste and performance bottlenecks.

[0059] 3. In order to solve the problems of dynamic adaptability and real-time processing capability, the dynamic changes of the network topology structure are monitored to obtain the link load value Lin, the node connection stability value Nod, the link delay fluctuation value Lik and the link recovery time Lir. The topology adaptability index Tad is calculated by combining historical data and topology change trends. If the topology adaptability index Tad is lower than the adaptability threshold Q3, the topology adjustment mechanism is triggered. Through the dynamic routing algorithm, the path selection is optimized according to the real-time network status and traffic demand, and the optimal transmission path is recalculated or the link is replaced. Combined with the parallel computing capability of FPGA hardware, multi-channel data packets are monitored in real time, quickly analyzed and preprocessed. The optimization parameter Copt is generated by dynamically feedbacking the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe and topology adaptability index Tad to guide the dynamic adjustment of the underlying extraction monitoring and preprocessing, which significantly improves the system's adaptability and real-time performance in a dynamic environment. BRIEF DESCRIPTION OF THE DRAWINGS

[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 A schematic diagram of the steps of a multi-channel high-speed data routing method based on FPGA of the present invention; DETAILED DESCRIPTION

[0062] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation modes and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use 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 related data of each channel through the high-speed data acquisition module, including the packet loss rate Dbl of the channel; generate and evaluate the fluctuation coefficient Wf based on the real-time operating status related data. If the fluctuation coefficient Wf exceeds the preset fluctuation threshold Q1, a preliminary risk warning is triggered;

[0066] Step 2: Next, extract the characteristics of channel bandwidth usage rate based on the real-time operation status related data, establish a bandwidth allocation model, and calculate and evaluate the bandwidth utilization efficiency Bwe; if the bandwidth utilization efficiency Bwe is lower than the preset optimal utilization threshold Q2, issue a bandwidth optimization alarm; and adjust the bandwidth allocation of each data channel in real time through the bandwidth allocation model;

[0067] Step 3: Then, monitor and record the topology change related data of the network topology structure, and then identify the topology change factors that will cause transmission path delay based on the real-time topology change related data, and calculate and evaluate the topology adaptability index Tad; if the topology adaptability index Tad is abnormal, the network topology adjustment mechanism is triggered to automatically optimize the routing path;

[0068] Step 4: Next, based on the parallel computing capability of FPGA hardware, the multi-channel data packet status is monitored and analyzed in real time, and the integrity of the data packet is quickly determined; a pre-processing buffer is preset, and the key data packets are independently copied to the pre-processing buffer through a real-time extraction mechanism; then the DSP module of FPGA is used to perform real-time pre-processing 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;

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

[0070] In this embodiment, step 1 obtains the packet loss rate Dbl and generates the fluctuation coefficient Wf by real-time monitoring of the operating status of each data channel in the FPGA hardware device. When Wf exceeds the preset fluctuation threshold Q1, a preliminary risk warning is triggered, which effectively improves the stability and reliability of data transmission; step 2 extracts real-time operating status data through features, calculates the bandwidth utilization efficiency Bwe, and adjusts the bandwidth allocation in real time in combination with the bandwidth allocation model. When Bwe is lower than the optimal utilization threshold Q2, a bandwidth optimization alarm is issued, which significantly improves the utilization rate of channel resources; step 3 calculates and evaluates the topology adaptability index Tad by monitoring the relevant data of network topology changes. If Tad is abnormal, the network topology adjustment is triggered. The whole mechanism is used to optimize the routing path selection in a dynamic network environment; Step 4 is based on the FPGA parallel computing capability to monitor and analyze the multi-channel data packet status in real time, and at the same time use the real-time extraction mechanism to copy the key data packets to the preprocessing buffer, and perform real-time preprocessing operations such as filtering and extraction through the DSP module. Combined with the feedback of Wf, Bwe and Tad, the multi-channel optimization parameter Copt is generated, which improves the underlying extraction monitoring and data preprocessing capabilities; Step 5 compares and analyzes the optimization parameter Copt with the routing performance evaluation threshold Pro, outputs the adjustment strategy and further optimizes the channel allocation and path selection, ensuring the continuous optimization of the overall transmission efficiency and stability of the system.

[0071] Example 2

[0072] Step 1 specifically includes:

[0073] The real-time monitoring and collection of the real-time operation status related data of each data channel in the FPGA hardware device includes the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs and packet loss rate Dbl of 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 dimensionless processed, and the fluctuation coefficient Wf is calculated by the following formula:

[0074]

[0075] Step 1 specifically includes:

[0076] The fluctuation threshold Q1 is preset, and the fluctuation coefficient Wf is compared and evaluated with the preset fluctuation threshold Q1, and the following evaluation content is generated:

[0077] When the fluctuation coefficient Wf ≤ the fluctuation threshold Q1, the system maintains normal operation without any intervention;

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

[0079] Step 2 specifically includes:

[0080] According to the real-time operation status related data, the bandwidth utilization characteristics of each data channel are extracted; and based on the bandwidth utilization characteristics, a bandwidth allocation model is established; wherein, the bandwidth allocation model considers the load balance, transmission efficiency and real-time bandwidth demand between channels to form a bandwidth allocation strategy for each channel; then, through the bandwidth allocation model, based on the real-time operation status related data, the bandwidth utilization efficiency Bwe of each data channel is calculated, and the specific calculation formula is as follows:

[0081]

[0082] Wherein, Bac represents the actual bandwidth usage in the real-time operation status related data, Bav represents the available bandwidth in the real-time operation status related data, Pac represents the data packet transmission rate in the real-time operation status related data, and Lat represents the delay duration in the real-time operation status related data.

[0083] Step 2 specifically includes:

[0084] The preset optimal utilization threshold Q2 is compared with the bandwidth utilization efficiency Bwe for evaluation. The specific contents are as follows:

[0085] When the bandwidth utilization efficiency Bwe ≥ the optimal utilization threshold Q2, it means that the current bandwidth resource allocation is reasonable and the network operation is stable;

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

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

[0088] Step three specifically includes:

[0089] Based on the topology change related data, a prediction model is built to identify factors that will cause transmission path delays, including network bottlenecks, topology failures, and link quality degradation. Based on the historical data and topology change trend data in the topology change related data, combined with network load and transmission requirements, the upcoming delay changes in the network topology are identified. At this time, the topology change related data is extracted to calculate the topology adaptability index Tad:

[0090]

[0091] Wherein, 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 duration in the topology change related data.

[0092] Step three specifically includes:

[0093] The topology adaptability index Tad is compared and evaluated by the preset adaptability threshold Q3. The specific contents are as follows:

[0094] If the topology adaptability index Tad ≥ adaptability threshold Q3, it means that the current network topology structure has normal adaptability, the current network topology structure is stable to cope with changes in the transmission path, and the network operation status is normal;

[0095] If the topology adaptability index Tad is less than the adaptability threshold Q3, it means that the current network topology has an adaptability anomaly; the system will trigger the network topology adjustment mechanism and automatically adjust the routing path, including recalculating the optimal data transmission path according to the current topology and traffic demand, evaluating the link, and switching the path 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, calculates the fluctuation coefficient Wf after dimensionless processing and compares it with the preset fluctuation threshold Q1, ensures that the risk warning is triggered in time when the fluctuation coefficient Wf exceeds the threshold, and avoids the risk of data loss and transmission instability in the system;

[0097] Step 2: By extracting bandwidth usage characteristics and establishing a bandwidth allocation model, the bandwidth utilization efficiency Bwe is calculated and compared with the optimal utilization threshold Q2 for evaluation. 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: Analyze the data related to topology changes by building a prediction model, calculate the topology adaptability index Tad and compare it with the adaptability threshold Q3. If the topology adaptability index Tad is abnormal, the network topology adjustment mechanism is automatically started, and the transmission path is adjusted by recalculating the optimal data transmission path and the dynamic routing algorithm to ensure that the system can operate stably when the topology structure changes, and optimize the adaptability and flexibility of data transmission. Combining these steps, the system can realize intelligent optimization and stable operation of multi-channel data routing, ensuring efficient data transmission and load balancing.

[0099] In addition, based on the topology change related data, key features such as link load value Lin, node connection stability value Nod, link delay fluctuation value Lik and link recovery time Lir are extracted; then, combined with the historical data and topology change trend data in the topology change related data, the time dependence model of the topology change characteristics is established using the time series analysis method, and the change law of the characteristic values ​​at different time points is analyzed; then, the machine learning algorithm is applied to take the link load, node stability, delay fluctuation and recovery time as input variables, and the potential delay change as the output target, and the mapping relationship between the features and the delay change is obtained through model training; further combined with the network load and transmission demand data, a dynamic correlation analysis mechanism is established to predict the delay changes that may be caused by the topology structure under specific load conditions; finally, the prediction model is continuously optimized through the real-time updated topology change data to ensure that the model can accurately identify delay factors such as network bottlenecks, topology failures and link quality degradation, thereby providing support for dynamic routing optimization.

[0100] Example 4

[0101] Step 4 specifically includes:

[0102] With the support of FPGA hardware parallel computing capabilities, the data packet status of each channel is monitored in real time, including the analysis of the packet loss rate Dbl to determine the integrity of the data packet; through the real-time data extraction mechanism, the key data packets are independently copied and distributed to the dedicated pre-processing buffer; then based on the FPGA-based DSP module, real-time extraction and filtering operations are performed during the high-speed data flow distribution process to suppress high-frequency noise, mark key data, and verify the data frame format; secondly, the dynamic feedback data of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad are obtained in real time, and the data flow priority and pre-processing resource allocation are adjusted based on the dynamic feedback data; the data flow distribution between multiple channels is adjusted through the built-in multi-threaded routing algorithm of the FPGA;

[0103] Step 4 specifically includes:

[0104] Combining 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 by the following formula:

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

[0106] Where f represents the calculation function, which is used to comprehensively consider the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe and topology adaptability index Tad to calculate the multi-channel optimization parameter Copt.

[0107] Step 5 specifically includes:

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

[0109] If the routing adjustment and optimization parameter Copt ≥ routing performance evaluation threshold Pro, the existing routing strategy is maintained without further optimization;

[0110] If the route adjustment and optimization parameter Copt is less than the route performance evaluation threshold Pro: the route optimization mechanism is started.

[0111] Step 5 specifically includes:

[0112] If the performance of multi-channel data routing is abnormal and the transmission efficiency is abnormal, and the bandwidth utilization is insufficient, a targeted routing adjustment strategy will be output; the specific strategies include:

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

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

[0115] Adjust data flow distribution: intelligently adjust data flow distribution according to the real-time load and bandwidth requirements of each channel;

[0116] In this embodiment, in step 4, based on the parallel computing capability of FPGA hardware, the data packet status of each channel is monitored in real time and the packet loss rate Dbl is analyzed, and the integrity of the data packet is effectively determined, thereby improving the reliability of data transmission; the key data packets are independently copied and distributed to the dedicated pre-processing buffer through the real-time data extraction mechanism to ensure that the key data is processed first and avoid the loss of important information due to interference or packet loss; the DSP module of FPGA is used to perform real-time extraction and filtering operations in the high-speed data stream distribution process, and the accuracy and integrity of the data are significantly improved through high-frequency noise suppression, key data marking and data frame format verification, while reducing the workload of subsequent processors; the dynamic feedback data of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe and topology adaptability index Tad are obtained in real time, and the priority of the data stream and the allocation of pre-processing resources are adjusted according to the feedback, so as to further optimize the resource utilization efficiency of the system; the data stream distribution between multiple channels is dynamically adjusted through the built-in multi-threaded routing algorithm of FPGA, so as to achieve balanced allocation of channel resources, reduce the possibility of channel overload, and improve the overall performance and stability of the system;

[0117] Then, in step 4, by combining the real-time feedback of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe and topology adaptability index Tad, a multi-channel optimization parameter Copt is generated to ensure that the system can comprehensively evaluate various performance indicators, thereby realizing intelligent optimization of multi-channel routing; wherein, the specific function form of the multi-channel optimization parameter Copt is as follows:

[0118] Copt=w1×Wf+w2×Bwe+w3×Tad;

[0119] Among them, w1, w2 and w3 are weight 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 5 compares the optimization parameter Copt with the preset routing performance evaluation threshold Pro to ensure that the system can maintain the existing strategy when the routing performance meets the standard and avoid unnecessary optimization. If the evaluation result shows that the performance does not meet expectations, the system automatically starts the routing optimization mechanism to adjust bandwidth allocation, path selection optimization and data flow allocation to achieve more efficient data transmission and resource utilization. Among them, the calculation of the fluctuation coefficient Wf helps to detect the instability of channel transmission, the bandwidth utilization efficiency Bwe reflects the utilization degree of bandwidth resources, 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 an accurate adjustment basis, ensuring the flexibility and efficiency of multi-channel data routing, and ultimately improving the transmission performance and stability of the network.

[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: The following steps are involved: 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 related data of each channel through the high-speed data acquisition module, including the packet loss rate Dbl of the channel; Generate and evaluate the fluctuation coefficient Wf based on the real-time operation status related data. If the fluctuation coefficient Wf exceeds the preset fluctuation threshold Q1, a preliminary risk warning is triggered; Step 2: Next, extract the characteristics of channel bandwidth usage rate based on the real-time operation status related data, establish a bandwidth allocation model, and calculate and evaluate the bandwidth utilization efficiency Bwe; if the bandwidth utilization efficiency Bwe is lower than the preset optimal utilization threshold Q2, issue a bandwidth optimization alarm; And through the bandwidth allocation model, the bandwidth allocation of each data channel is adjusted in real time; Step 3: Then, monitor and record the topology change related data of the network topology structure, and then identify the topology change factors that will cause transmission path delay based on the real-time topology change related data, and calculate and evaluate the topology adaptability index Tad; if the topology adaptability index Tad is abnormal, the network topology adjustment mechanism is triggered to automatically optimize the routing path; Step 4: Next, based on the parallel computing capability of FPGA hardware, the multi-channel data packet status is monitored and analyzed in real time, and the integrity of the data packet is quickly determined; Preset the preprocessing buffer and independently copy the key data packets to the preprocessing buffer through the real-time extraction mechanism; then use the DSP module of the FPGA to perform real-time preprocessing operations on the data; finally, combine the real-time feedback of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe and topology adaptability index Tad to generate the multi-channel optimization parameter Copt; Step 5: Finally, all routing adjustment and optimization parameters Copt are compared and analyzed with the preset routing performance evaluation threshold Pro to evaluate the overall transmission efficiency and stability of the multi-channel data routing system, and the routing adjustment strategy is output based on the evaluation results; if the evaluation results show that the current transmission efficiency does not meet expectations, the channel allocation and path selection are further adjusted.

2. The FPGA-based multi-channel high-speed data routing method according to claim 1, characterized in that: Step 1 specifically includes: The real-time monitoring and collection of the real-time operation status related data of each data channel in the FPGA hardware device includes the transmission rate Dcs, data packet arrival time Dds, number of packet loss events Dbs and packet loss rate Dbl of 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 dimensionless processed, and the fluctuation coefficient Wf is calculated by the following formula:

3. The FPGA-based multi-channel high-speed data routing method according to claim 2, characterized in that: Step 1 specifically includes: The fluctuation threshold Q1 is preset, and the fluctuation coefficient Wf is compared and evaluated with the preset fluctuation threshold Q1, and the following evaluation content is generated: When the fluctuation coefficient Wf ≤ the fluctuation threshold Q1, the system maintains normal operation without any intervention; When the fluctuation coefficient Wf>the fluctuation threshold Q1, the system triggers a preliminary risk warning and sends out a warning signal to remind the user that there is a risk of data loss and unstable transmission in the current channel.

4. The multi-channel high-speed data routing method based on FPGA according to claim 3, characterized in that: Step 2 specifically includes: According to the real-time operation status related data, the bandwidth utilization characteristics of each data channel are extracted; and based on the bandwidth utilization characteristics, a bandwidth allocation model is established; wherein, the bandwidth allocation model considers the load balance, transmission efficiency and real-time bandwidth demand between channels to form a bandwidth allocation strategy for each channel; then, through the bandwidth allocation model, based on the real-time operation status related data, the bandwidth utilization efficiency Bwe of each data channel is calculated, and the specific calculation formula is as follows: Wherein, Bac represents the actual bandwidth usage in the real-time operation status related data, Bav represents the available bandwidth in the real-time operation status related data, Pac represents the data packet transmission rate in the real-time operation status related data, and Lat represents the delay duration in the real-time operation status related data.

5. The FPGA-based multi-channel high-speed data routing method according to claim 4, characterized in that: Step 2 specifically includes: The preset optimal utilization threshold Q2 is compared with the bandwidth utilization efficiency Bwe for evaluation. The specific contents are as follows: When the bandwidth utilization efficiency Bwe ≥ the optimal utilization threshold Q2, it means that the current bandwidth resource allocation is reasonable and the network operation is stable; When the bandwidth utilization efficiency Bwe is less than the optimal utilization threshold Q2, it means that the current bandwidth resource allocation is unreasonable and the bandwidth resources are not effectively utilized. At this time, the bandwidth optimization alarm is triggered and the administrator is notified through the visual interface to remind the administrator of the problem of bandwidth resource waste or load imbalance. Then, the bandwidth of each data channel is adjusted in real time through the bandwidth allocation model; at the same time, bandwidth resources are reallocated, including dynamically migrating the bandwidth allocation of overloaded channels to idle channels and adjusting the bandwidth of different data channels through load balancing algorithms.

6. The FPGA-based multi-channel high-speed data routing method according to claim 5, characterized in that: Step three specifically includes: Based on the topology change related data, a prediction model is built to identify factors that will cause transmission path delays, including network bottlenecks, topology failures, and link quality degradation. Based on the historical data and topology change trend data in the topology change related data, combined with network load and transmission requirements, the upcoming delay changes in the network topology are identified. At this time, the topology change related data is extracted to calculate the topology adaptability index Tad: Wherein, 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 duration 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 specifically includes: The topology adaptability index Tad is compared and evaluated by the preset adaptability threshold Q3. The specific contents are as follows: If the topology adaptability index Tad ≥ adaptability threshold Q3, it means that the current network topology structure has normal adaptability, the current network topology structure is stable to cope with changes in the transmission path, and the network operation status is normal; If the topology adaptability index Tad is less than the adaptability threshold Q3, it means that the current network topology has an adaptability anomaly; the system will trigger the network topology adjustment mechanism and automatically adjust the routing path, including recalculating the optimal data transmission path according to the current topology and traffic demand, evaluating the link, and switching the path in real time.

8. The FPGA-based multi-channel high-speed data routing method according to claim 7, characterized in that: Step 4 specifically includes: With the support of FPGA hardware parallel computing capabilities, the data packet status of each channel is monitored in real time, including the analysis of the packet loss rate Dbl to determine the integrity of the data packet; through the real-time data extraction mechanism, the key data packets are independently copied and distributed to the dedicated pre-processing buffer; then based on the FPGA-based DSP module, real-time extraction and filtering operations are performed during the high-speed data flow distribution process to suppress high-frequency noise, mark key data, and verify the data frame format; secondly, the dynamic feedback data of the fluctuation coefficient Wf, bandwidth utilization efficiency Bwe, and topology adaptability index Tad are obtained in real time, and the data flow priority and pre-processing resource allocation are adjusted based on the dynamic feedback data; the data flow distribution between multiple channels is adjusted through the built-in multi-threaded routing algorithm of the FPGA; Step 4 specifically includes: Combining 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 by the following formula: Copt = f(Wf, Bwe, Tad); Where f represents the calculation function, which is used to comprehensively consider 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 5 specifically includes: The routing performance evaluation threshold Pro is preset, and the routing adjustment and optimization parameter Copt is compared and analyzed with the routing performance evaluation threshold Pro. The specific contents are as follows: If the routing adjustment and optimization parameter Copt ≥ routing performance evaluation threshold Pro, the existing routing strategy is maintained without further optimization; If the route adjustment and optimization parameter Copt is less than the route performance evaluation threshold Pro: the route optimization mechanism is started.

10. The FPGA-based multi-channel high-speed data routing method according to claim 9, characterized in that: Step 5 specifically includes: If the performance of multi-channel data routing is abnormal and the transmission efficiency is abnormal, and the bandwidth utilization is insufficient, a targeted routing adjustment strategy will be output; the specific strategies include: Adjust bandwidth allocation: adjust bandwidth usage of each channel; Optimize path selection: select alternative routing paths based on topology changes and real-time network conditions; Adjust data flow distribution: Intelligently adjust data flow distribution based on the real-time load and bandwidth requirements of each channel.

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