A user traffic allocation management method and system for a traffic pool

Abstract

The present invention discloses a user traffic allocation management method and system for a traffic pool, which relates to the field of traffic allocation management. The main solutions are as follows: generating a process number according to order data; calculating the process priority weight through process characteristic parameters; calculating the channel quality attenuation factor and the allocated bandwidth through network performance parameters; calculating the load index value of the device by obtaining device status parameters; performing comprehensive analysis through the above parameters to obtain the dynamic bandwidth allocation amount; and allocating the total bandwidth of the traffic pool according to the dynamic bandwidth allocation amount; judging whether to execute the shunt evaluation instruction through the load balancing evaluation threshold of the device status parameters; calculating the shunt benefit evaluation value according to the shunt benefit data; comparing the shunt benefit evaluation value with the shunt benefit evaluation threshold to judge whether to execute the shunt instruction, so as to solve the problems that the order data is not associated with the production equipment, resulting in extensive resource allocation, and the traffic allocation management lacks flexibility and dynamic adaptability, etc.

Description

Technical Field

[0001] The present invention relates to the field of traffic allocation management, and specifically to a method and system for user traffic allocation management in a traffic pool. Background Art

[0002] With the continuous expansion of industrial production scale and the increasing improvement of automation degree, the data interaction between production devices and the demand for network traffic in various production processes have shown an explosive growth. As a key resource for centralized management and allocation of network traffic, the traffic pool plays a crucial role in the industrial production field. An efficient user traffic allocation management method can ensure that each production device can obtain the required network traffic in a timely and stable manner at different production stages, thereby improving production efficiency, optimizing production processes, and reducing production costs. This has broad application prospects in emerging fields such as intelligent manufacturing and industrial Internet of Things.

[0003] During the industrial production process, there are significant differences in the workload, priority, and network bandwidth requirements of different processes. At the same time, the operating status of production devices will also change dynamically, affecting their traffic processing capabilities and demand levels. In addition, the performance and stability of the network itself will also fluctuate over time. In order to ensure the efficient and stable operation of the entire production system, it is necessary to adopt a user traffic allocation management method that comprehensively considers various factors to achieve dynamic and precise allocation of the traffic pool.

[0004] Existing user traffic allocation management methods for traffic pools have defects. For example, most methods fail to fully combine the relevance between order data and production devices when allocating traffic, and cannot accurately match devices according to the production requirements of orders and reasonably plan the initial traffic allocation, resulting in extensive resource allocation and inability to meet the refined traffic requirements of order production processes. In terms of network bandwidth allocation, existing methods usually do not fully consider the channel quality attenuation factor and cannot accurately calculate the reasonable allocated bandwidth of each channel based on real-time network performance, thus leading to waste of bandwidth resources or overload of some channels from time to time, affecting the stability and efficiency of network transmission. Existing methods have deficiencies in load balancing and shunt evaluation after traffic allocation, lacking effective dynamic evaluation mechanisms and shunt benefit evaluation means, unable to timely detect load imbalance problems caused by unreasonable traffic allocation, nor accurately judge the actual necessity and benefits of shunt operations, making traffic allocation management lack flexibility and dynamic adaptability and difficult to cope with the complex and changeable traffic demand changes in the industrial production environment. Summary of the Invention

[0005] (I) Technical Problems to be Solved

[0006] In view of the deficiencies of the prior art, the present invention provides a method for managing user traffic allocation in a traffic pool, which at least solves the problems in the prior art that order data has no association with production equipment, resulting in extensive resource allocation, and the traffic allocation management lacks flexibility and dynamic adaptability.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for managing user traffic allocation in a traffic pool, including:

[0009] Step 1: Obtain order data according to the order management system, match the corresponding production equipment according to the order data, and generate a work order traffic label;

[0010] Step 2: Obtain process characteristic parameters corresponding to the process number through the manufacturing execution system, and calculate the process priority weight through the process characteristic parameters;

[0011] Step 3: Obtain network performance parameters through the network management system, calculate the channel quality attenuation factor according to the network performance parameters, and calculate the allocated bandwidth of the channel based on the channel quality attenuation factor;

[0012] Step 4: Obtain the equipment status parameters of the production equipment corresponding to the process number through the industrial monitoring system; and calculate the load index value of the equipment by analyzing the equipment status parameters;

[0013] Step 5: Through comprehensive analysis of the allocated bandwidth, load index value, and process priority weight, obtain the dynamic bandwidth allocation amount; and allocate the total bandwidth of the traffic pool according to the dynamic bandwidth allocation amount;

[0014] Step 6: Set the load balancing evaluation threshold; collect the equipment status parameters in real time, and judge whether to execute the shunt evaluation instruction through the equipment status parameter load balancing evaluation threshold;

[0015] Step 7: When it is judged to execute the shunt evaluation instruction, obtain the shunt benefit data, and calculate the shunt benefit evaluation value according to the shunt benefit data; set the shunt benefit evaluation threshold, compare the shunt benefit evaluation value with the shunt benefit evaluation threshold, and judge whether to execute the shunt instruction.

[0016] In the above method for managing user traffic allocation in a traffic pool, the process characteristic parameters include a delay sensitivity coefficient, a data volume weight, and a process stability factor;

[0017] Calculate the process priority weight W i , according to the following formula:

[0018] ;

[0019] Among them, W i represents the process priority weight of the i-th production process; α i represents the delay sensitivity coefficient of the i-th production process; β i represents the data volume level weight of the i-th production process; γ i represents the process stability factor of the i-th production process, i represents the process number of the production process, and n represents the total number of production processes.

[0020] In the above user traffic allocation and management method for a traffic pool, the network performance parameters include the end-to-end delay δ and the square of the jitter variance σ 2 , the end-to-end delay δ is obtained by analyzing the timestamps of the transmitted data through a client-server model; the square of the jitter variance σ 2 captures the timestamp sequence through a functional network probe, then calculates the jitter variance of the packet arrival time, and obtains it through square calculation.

[0021] In the above user traffic allocation and management method for a traffic pool, the channel quality attenuation factor is calculated according to the network performance parameters, and the formula is as follows:

[0022] ;

[0023] Among them, Q ch represents the channel quality attenuation factor of channel ch; δ ch represents the end-to-end delay of channel ch, represents the square of the jitter variance of channel ch, and ch represents the serial number of the channel;

[0024] Based on the channel quality attenuation factor Q ch calculate the allocated bandwidth B ch of the channel, and the formula is: ; Among them, B ch represents the allocated bandwidth of channel ch, Btotal represents the total bandwidth, and K represents the total number of channels.

[0025] In the above user traffic allocation and management method for a traffic pool, the device status parameters include CPU utilization rate, TCP retransmission rate, and concurrent connection number; among them, the CPU utilization rate is collected through the gateway built in the production device; the TCP retransmission rate is obtained by calculating the ratio of the number of retransmitted packets to the total number of sent packets through a network traffic monitoring tool, and the formula is: TCP retransmission rate = number of retransmitted packets / total number of sent packets * 100%; the concurrent connection number can be obtained by counting the current active TCP / UDP connection numbers in real time;

[0026] And calculate the load index value based on the CPU utilization rate, TCP retransmission rate, and concurrent connection number.

[0027] In the above user traffic allocation management method for a traffic pool, the dynamic bandwidth allocation amount is calculated by combining the allocated bandwidth, the load index value, and the process priority weight. The formula is as follows:

[0028] ;

[0029] where B i (t) represents the dynamic bandwidth allocation amount of the device corresponding to the i-th production process at time t; B ch represents the reference value of the bandwidth allocation amount; λ i (t) represents the load index value of the device corresponding to the i-th production process at time t; η ch (t) represents the bandwidth occupancy rate of the ch-th channel at time t.

[0030] In the above user traffic allocation management method for a traffic pool, by analyzing the bandwidth occupancy rate η ch (t) and the load index value λ i (t), the formula for obtaining the load balancing index L(t) is as follows:

[0031] ;

[0032] where L(t) represents the load balancing index at time t, λ threshold is the preset upper limit of device load; η threshold is the upper limit of bandwidth occupancy rate;

[0033] The load balancing index L(t) is compared with the load balancing evaluation threshold. When the load balancing index L(t) ≥ the load balancing evaluation threshold, the shunt evaluation instruction is executed; when the load balancing index L(t) < the load balancing evaluation threshold, the shunt evaluation instruction is not executed.

[0034] In the above user traffic allocation management method for a traffic pool, the shunt benefit data includes the original broadband demand Bor i and the saved bandwidth Bsa i ; among them, the original broadband demand is set according to the device specifications; the saved bandwidth is obtained by comparing the port traffic statistics before and after shunting.

[0035] In the above user traffic allocation management method for a traffic pool, according to the original broadband demand Bor i and the saved bandwidth Bsa i the shunt benefit evaluation value E of is calculated. The formula is as follows:

[0036] ;

[0037] Among them, E of represents the shunt benefit evaluation value, and Bsa i represents the bandwidth required for the data block of the device corresponding to the i-th production process before shunting; Bor i represents the bandwidth released by the main channel after the data block of the device corresponding to the i-th production process is shunted to the edge node; Qt ch is the target channel quality value; Qe ch is the edge node quality value;

[0038] Compare the shunt benefit evaluation value E of with the shunt benefit evaluation threshold. When the shunt benefit evaluation value E of ≥ the shunt benefit evaluation threshold, it is determined that the shunt instruction is valid, and the shunt instruction is executed; when the shunt benefit evaluation value E of < the shunt benefit evaluation threshold, it is determined that the shunt instruction is invalid, and the current allocation is maintained.

[0039] (III) Beneficial effects

[0040] The present invention provides a user traffic allocation management method for a traffic pool, having the following beneficial effects:

[0041] (1) By accurately matching orders with production equipment, the generated work order traffic label is like a personalized traffic ID card, providing a reliable basis for subsequent targeted traffic allocation. Compared with the traditional relatively general traffic allocation method, this matching based on order data can effectively avoid the disconnection between traffic allocation and production requirements, and improve production efficiency.

[0042] (2) By collecting the device status parameters to calculate the load index value, this provides a real-time reference for the device operation status for dynamic traffic allocation. When the device is running at high load, the traditional traffic allocation method may cause the device to malfunction due to overload. However, with the device load index value, the traffic can be reasonably allocated according to the device's tolerance, avoiding device failures, extending the service life of the device, and at the same time optimizing the production process to make the entire production system more stable and efficient.

[0043] (3) Obtaining network performance parameters and calculating the channel quality attenuation factor and channel allocation bandwidth can achieve precise bandwidth allocation in a complex network environment. By calculating the channel quality attenuation factor, the transmission quality of each channel can be quantified, and then the bandwidth can be reasonably allocated. This is like allocating vehicle flow according to the road conditions of different roads in traffic management, avoiding the situation where some channels are congested while others are idle. In an automated production workshop, this bandwidth allocation method based on network performance can ensure that key production links such as the instruction transmission of the automated control system and real-time data collection can obtain sufficient bandwidth preferentially, ensuring the continuity and precision of production, and reducing problems such as production delays and product quality degradation caused by network congestion.

[0044] (4) Comprehensive analysis of the allocated bandwidth, load index value, and process priority weight to obtain the dynamic bandwidth allocation amount and perform total bandwidth allocation based on this is a highly intelligent traffic management strategy. In modern industrial production, the production process is complex and changeable, and the priority between processes will change dynamically according to various factors such as order urgency and production progress. At the same time, the device load and network bandwidth also fluctuate in real time. Through this comprehensive analysis method, the traffic allocation can be adjusted dynamically in real time to meet the production needs of different processes at different times.

[0045] (5) The load balancing evaluation and traffic splitting evaluation mechanism further enhances the flexibility and reliability of traffic allocation. When abnormal situations occur in device load or network bandwidth, they can be detected in time and adjusted through measures such as traffic splitting, avoiding local problems from affecting the normal operation of the entire production system, thereby improving the stability and efficiency of the entire industrial production and reducing the risks of increased production costs and decreased production efficiency caused by unreasonable traffic allocation. Brief Description of the Drawings

[0046] Figure 1 It is a schematic diagram of the steps of a method for managing user traffic allocation in a traffic pool according to the present invention. Detailed Embodiment

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment 1

[0049] Please refer to Figure 1 , the present invention provides a method for managing user traffic allocation in a traffic pool, including:

[0050] Step 1: Obtain order data from the order management system, match the corresponding production equipment according to the order data; and generate process numbers based on the order data.

[0051] Specifically: The order management system obtains order data, which at least includes product process requirements and process routes, selects the corresponding production equipment according to the process requirements, determines the sequence of each production process and the corresponding required equipment according to the process route, and generates process numbers based on each production process.

[0052] It should be noted that the precise connection between order production and process numbers is realized. The process numbers provide a clear basis for subsequent flow allocation, enabling each process to obtain corresponding flow resources according to its specific role and requirements in order production, improving production efficiency, ensuring product quality, optimizing the production process, and reducing production costs; solving the problem that traditional flow allocation methods are difficult to accurately allocate flow according to the specific production requirements of orders, resulting in the disconnection between flow allocation and actual production needs, and possible problems such as insufficient flow in key processes or excessive flow in non-key processes, which affect production efficiency and product quality.

[0053] Step 2: Obtain the process characteristic parameters of the production process corresponding to the process number through the manufacturing execution system, and calculate the process priority weight W through the process characteristic parameters i 。

[0054] It should be noted that in the process of calculating the process priority weight W i In the process, it is necessary to perform normalized parameter preprocessing on all parameters involved in each calculation step to eliminate the dimension of different parameters for subsequent formula calculation.

[0055] It should be noted that the process characteristic parameters include delay sensitivity coefficient, data magnitude weight, and process stability factor.

[0056] Step 201: The MES system calculates the delay sensitivity coefficient α of different production processes by parsing the work order process file and integrating PLC control data i 。The formula based on is: , where e is the base of the natural logarithm, JZ is the reference delay value, that is, the maximum allowable delay preset in the process file; SZ is the actual delay, that is, the actual delay value statistically obtained through the PLC control cycle or historical flow log, and k is an adjustment coefficient with a value range of 0-1; GY is the process sensitivity coefficient, that is, the sensitivity coefficient preset according to the process type, with a value range of (0,1], for example, precision assembly can take a value of 1, and ordinary spraying takes a value of 0.5, etc.

[0057] It should be noted that in the formula is an S-shaped function (Logistic function) with an output range from 0 to 1. When SZ < JZ, the output approaches 0, indicating that the delay is within the allowable range; when SZ > JZ, the output approaches 1, indicating that the delay exceeds the threshold. The larger the k value, the steeper the S-shaped curve, and the more significant the impact of the delay on α n For example, when k = 1, for every 0.1-second increase in SZ, the change in α n may jump from 0.1 to 0.9; when k = 0.3, the change is more gradual. As a multiplier factor, GY directly amplifies or shrinks the range of α n For example, when GY = 1 for precision assembly, the value of α n is completely determined by the S-shaped function; when GY = 0.5 for ordinary spraying, the maximum value of α n is only 0.5 at most, reflecting a higher tolerance for delay. By adjusting k and GY, different production scenarios can be adapted to solve the problem that traditional methods rely on manual experience to judge delays and lack a scientific basis. For example, manual workers may only have a vague description like "long delay" and cannot distinguish the specific impacts of a 1-second delay and a 2-second delay. Fixed thresholds cannot meet the sensitivity requirements of different processes. For example, precision assembly requires strict control of delays, while ordinary spraying has a higher tolerance for delays, etc., showing insufficient dynamic adaptability.

[0058] In step 202, the statistical analysis function of the MES system historical traffic log is used to calculate the data volume weight βi, and the calculation formula is as follows: ; where HD is the moving window mean, which is extracted from the historical traffic log through the MES system and sampled every 5 seconds to calculate the average data volume of the current process; BD is the data fluctuation coefficient, and the calculation method is: data fluctuation coefficient BD = moving window standard deviation / moving window mean * 100%; ZS represents the reference data volume, and the average data volume of the same process in the past normal working state for a period of time can be automatically counted by the MES system as the reference data volume.

[0059] It should be noted that: in the formula reflects the impact of data volatility on the weight. For example, when BD = 0% (no volatility), 1 + 0 / 100 = 1, β i = HD / ZS; when BD = 20% (high volatility), 1 + 20 / 100 = 1.2, β i= 1.2×(HD / ZS); Avoid the problem that the impact of volatility on resource allocation is ignored; Through automated cleaning and multi-window averaging, ZS is closer to the true stable value. Avoiding the susceptibility of manually set ZS to empirical biases, through the synergistic effects of dynamic weight calculation, volatility sensitivity adjustment, and benchmark data automation, the core problems in traditional flow allocation such as weight distortion, resource rigidity, and benchmark subjectivity are solved. HD in the formula reflects the real-time load, BD quantifies the volatility risk, and ZS provides a standardized benchmark. The combination of the three realizes the scientificity, flexibility, and stability of resource allocation.

[0060] Step 203: The MES system directly associates with production quality data and generates a process stability factor γ by comparing the yield rate with the benchmark value i , and the calculation method is γ = current process yield rate / production line benchmark yield rate.

[0061] It should be noted that the stability of flow processing is dynamically reflected by γi. For example, when γi = 1, it indicates that the current quality meets the standard; when γi < 1, it prompts that the processing strategy needs to be optimized

[0062] Step 204: Calculate the process priority weight W through process characteristic parameters i , and the formula is as follows:

[0063] ;

[0064] Among them, W i represents the process priority weight of the i-th production process; α i represents the delay sensitivity coefficient of the i-th production process; β i represents the data magnitude weight of the i-th production process; γ i represents the process stability factor of the i-th production process, i represents the process number of the production process, taking positive integer values, and n represents the total number of production processes.

[0065] It should be noted that α in the numerator i can be used to adjust the weight of delay-sensitive traffic, perform a logarithmic transformation on βi to alleviate the excessive impact of large traffic on the weight. For example, when βi = 10, ln(11) = 2.4; when βi = 100, ln(101) = 4.6, to avoid small traffic being completely ignored due to data magnitude differences; the denominator normalization compresses the weights of all traffic into the [0,1] interval for direct comparison. For example, if the total sum is 100, then W i = 0.1 of a certain traffic indicates that it occupies 10% of the resource quota. α i , β i , γ iCorresponding to latency sensitivity, data volume, and stability respectively, through logarithmic transformation and normalization processing, dynamic and precise traffic scheduling is achieved.

[0066] Step 3: Obtain network performance parameters through the network management system, and calculate the channel quality attenuation factor Q based on the network performance parameters ch , and based on the channel quality attenuation factor Q ch calculate the allocated bandwidth B of the channel ch .

[0067] It should be noted that during the process of calculating the allocated bandwidth B ch , it is necessary to perform normalization parameter preprocessing on all parameters involved in each calculation step to eliminate the dimensions of different parameters for subsequent formula calculations.

[0068] It should be noted that the network performance parameters include the end-to-end delay δ and the square of the jitter variance σ 2 .

[0069] Step 301: The end-to-end delay δ represents the end-to-end delay of the two-way transmission delay from the control end to the device end. The end-to-end delay δ can be calculated through the client-server model of the TWAMP protocol (RFC5357) by calculating four timestamps. The formula is: ; where t1 is the sending time of the Sender, t1' is the receiving time of the Reflector, t2' is the response time of the Reflector, and t2 is the receiving time of the Sender. The calculation of the difference between the four timestamps eliminates the one-way delay error and is applicable to scenarios such as dynamic routing and load balancing to ensure the stability and reliability of the delay measurement results.

[0070] Step 302: Use a functional network probe (such as PLC-Recorder), the TWAMP or UDP protocol to capture the timestamp sequence, and calculate the mean μ of the packet arrival time intervals within the time period; and calculate the square of the jitter variance σ of the packet arrival time 2 , and the formula is: ; where t m represents the arrival timestamp of the mth packet, m represents the packet sequence number, taking positive integer values, and M represents the total number of packets.

[0071] Step 303: Calculate the channel quality attenuation factor Q based on the network performance parameters ch , and the formula is as follows:

[0072] ;

[0073] where Q ch represents the channel quality attenuation factor of channel ch; δ ch represents the end-to-end delay of channel ch, It represents the square of the jitter variance of channel ch, where ch represents the serial number of the channel and takes positive integer values. According to Q ch Dynamically adjust the traffic allocation weight.

[0074] The solution solves the core problems such as inaccurate evaluation of channel quality in the traffic pool, strong subjectivity in resource allocation, and unquantified influence of jitter through the three-stage linkage of end-to-end delay measurement, jitter quantization analysis, and comprehensive channel quality evaluation. δ, σ², and Q in the formula ch correspond to delay, jitter, and quality score respectively. By combining the exponential function and the fractional function to achieve dynamic weight allocation, it can improve the accuracy of channel quality evaluation and optimize resource utilization.

[0075] Step 304: Based on the channel quality decay factor Q ch Calculate the allocated bandwidth B of the channel ch The formula is as follows: ; where B ch represents the allocated bandwidth of channel ch, Btotal represents the total bandwidth, and K represents the total number of channels.

[0076] Step Four: Obtain the device status parameters of the production equipment corresponding to the process number through the industrial monitoring system; and calculate the load index value of the device by analyzing the device status parameters λ i .

[0077] It should be noted that in the process of calculating the load index value λ i of each calculation step, the parameters involved need to be preprocessed with normalized parameters respectively to eliminate the dimensions of different parameters for the subsequent formula calculation.

[0078] It should be noted that: the device status parameters include CPU utilization rate, TCP retransmission rate, and concurrent connection number;

[0079] Step 401: The CPU utilization rate can be collected through sensors built into the production equipment or industrial gateways (such as PLCs, industrial PCs); the TCP retransmission rate can be calculated by counting the number of retransmitted packets and the total number of sent packets through the logs of network traffic monitoring tools (such as Wireshark, NetFlow) or network devices (such as switches, routers). The formula is: TCP retransmission rate = number of retransmitted packets / total number of sent packets * 100%; Use the NetFlow or sFlow protocol to count the current active TCP / UDP connection number in real time.

[0080] Step 402: Calculate the load index value according to the CPU utilization rate, TCP retransmission rate, and concurrent connection number. The formula is as follows: λ= 0.4 * CPU utilization + 0.3 * TCP retransmission rate + 0.3 * number of concurrent connections.

[0081] Step Five: By analyzing the allocated bandwidth B ch , the load index value λ i and the process priority weight W i comprehensively, the dynamic bandwidth allocation amount B i (t) is obtained, and the total bandwidth of the traffic pool is allocated according to the dynamic bandwidth allocation amount B i .

[0082] It should be noted that in the process of calculating the dynamic bandwidth allocation amount B i (t), the parameters involved in each calculation step need to be preprocessed with normalized parameters respectively to eliminate the dimensions of different parameters for subsequent formula calculations.

[0083] Specifically: Combining the allocated bandwidth B ch , the load index value λ i and the process priority weight W i , the dynamic bandwidth allocation amount is obtained through comprehensive calculation, and the formula is:

[0084] ;

[0085] where B i (t) represents the dynamic bandwidth allocation amount of the device corresponding to the i-th production process at time t; B ch represents the reference value of the bandwidth allocation amount; λ i (t) represents the load index value of the device corresponding to the i-th production process at time t; η ch (t) represents the bandwidth occupancy rate of the ch-th channel at time t.

[0086] It should be noted that each production process i corresponds to one of the channels ch; for example, the channel ch is divided into a low-latency channel L0, a high-bandwidth channel L1, and an edge channel L2; when the delay sensitivity coefficient α i is greater than the set threshold, such as α i ≥ 0.9, select the low-latency channel L0; when the data volume weight is greater than that of the medium-delay channel and the high-delay channel, such as β i ≥ 0.8, select the high-bandwidth channel L1; otherwise, select the edge channel L2; map all processes to the corresponding channels so that the production process i and the channel number ch form a mapping relationship; in the calculation process, when the production process i takes the value of 1, it corresponds to the robotic arm control instruction, which is a highly sensitive process and is associated with the serial number channel 1 of the low-latency channel, and select the B of the low-latency channel L0ch Value

[0087] It should be noted that through the three - stage linkage of the dynamic bandwidth allocation formula, parameter normalization processing, and real - time comprehensive analysis, problems such as rigid resource allocation in the traffic pool, priority - load conflict, and calculation distortion are solved. B in the formula ch 、W i 、η ch (t), λ i (t) correspond to the reference bandwidth, priority, remaining bandwidth, and load index respectively. Combining square - root and normalization processing, flexible and accurate bandwidth management is achieved.

[0088] Step Six: Set the load - balancing evaluation threshold; collect the bandwidth occupancy rate η ch (t) and the load - index value λ i (t) in real - time, and analyze the bandwidth occupancy rate η ch (t) and the load - index value λ i (t) to obtain the load - balancing index L(t); compare the load - balancing index L(t) with the load - balancing evaluation threshold to determine whether to execute the shunt - evaluation instruction.

[0089] It should be noted that during the calculation of L(t), it is necessary to perform pre - processing of normalized parameters on all parameters involved in each calculation step to eliminate the dimension of different parameters for subsequent formula calculations.

[0090] Step 601: Set the load - balancing evaluation threshold. The load - balancing index L(t) is used to represent the dual - over - limit risk index of device load and bandwidth occupancy. When L(t)>1, it means that the load - index value λ i (t) or the bandwidth occupancy rate η i (t) of any index exceeds the preset safety upper limit. The system needs to immediately determine whether to shunt to avoid risks. Therefore, the load - balancing evaluation threshold can be set to 1, and a safety factor φ can be superimposed on the basis of the threshold 1. φ can take values of 0.1~0.2, and the shunt execution condition = L(t)>1 + φ to prevent the risk of false triggering due to instantaneous fluctuations.

[0091] Step 602: Through the analysis of the bandwidth occupancy rate η ch (t) and the load - index value λ i (t), the formula for obtaining the load - balancing index L(t) is:

[0092] ;

[0093] where L(t) represents the load balancing index at time t, λ threshold is the preset upper limit of device load; η threshold is the upper limit of bandwidth occupancy rate.

[0094] Step 603: Compare the load balancing index L(t) with the load balancing evaluation threshold. When the load balancing index L(t) ≥ the load balancing evaluation threshold, execute the shunt evaluation instruction; when the load balancing index L(t) < the load balancing evaluation threshold, do not execute the shunt evaluation instruction.

[0095] Step Seven, when it is judged to execute the shunt evaluation instruction, obtain the shunt benefit data, and calculate the shunt benefit evaluation value E of and set the shunt benefit evaluation threshold, and compare the shunt benefit evaluation value E of with the shunt benefit evaluation threshold to judge whether to execute the shunt instruction.

[0096] It should be noted that calculate the average load index of all n devices to reflect the overall device load status. Divide by λ threshold represents the degree to which the device load approaches the threshold. For example, if the average load is 70% and the threshold is 80%, the ratio is 0.875, indicating that the device load is approaching overload. Calculate the average bandwidth occupancy rate of all K channels to reflect the overall channel load status. Divide by η threshold represents the degree to which the channel bandwidth approaches the threshold. For example, if the average bandwidth is 65% and the threshold is 70%, the ratio is 0.928, indicating that the channel is approaching overload. By taking the maximum ratio of device load and channel bandwidth, it is ensured that any dimension overload can trigger adjustment. For example, when the device load ratio is 0.9 (close to the threshold) and the channel bandwidth ratio is 0.8, L(t)=0.9, indicating that the device load needs to be optimized first. L(t) directly reflects the critical state of the system load and provides a quantitative basis for subsequent shunt decisions. For example, when L(t)≥1, it indicates that at least one dimension is overloaded and shunting needs to be executed.

[0097] The comparison of the load balancing index L(t) with the evaluation threshold and the execution of the shunt evaluation instruction solve the problems that the fixed allocation rule cannot cope with dynamic load changes, over-reservation of resources reduces utilization, and insufficient resources lead to a decline in service quality. Dynamically respond to load fluctuations. When L(t)≥1, immediately start the shunt evaluation (such as directing traffic to standby channels or low-load devices) to avoid system crashes. For example, the bandwidth ratio of a certain channel is 1.2 (exceeding the threshold), and after shunting, the bandwidth ratio drops to 0.9. When L(t)<1, maintain the current allocation strategy to reduce unnecessary resource consumption (such as avoiding energy consumption waste of idle channels).

[0098] Through the linkage of the calculation of the load balancing index L(t) and dynamic shunt decision-making, the solution addresses issues such as incomplete evaluation of device / channel loads in the traffic pool and rigid resource allocation. The device load ratio and channel bandwidth ratio in the formula quantify the load status of two key dimensions respectively, and the maximum value logic ensures the highest system risk priority. The dynamic comparison mechanism realizes "adjustment on demand", shunts traffic in a timely manner under high load, and maintains efficient utilization under low load, ultimately enhancing system stability and service quality.

[0099] It should be noted that during the calculation of the shunt benefit evaluation value E of the parameters involved in each calculation step need to be preprocessed with normalized parameters respectively to eliminate the dimensions of different parameters for subsequent formula calculations.

[0100] It should be noted that the shunt benefit data includes the original broadband demand Bor i and the saved bandwidth Bsa i ;

[0101] Step 701: The original broadband demand Bor i can be set according to the device specifications; the saved bandwidth Bsa i can be obtained by comparing the port traffic statistics before and after shunting, and the calculation formula is: Bsa g =Bor g - (1 - the occupancy rate of the main channel after shunting); where, the occupancy rate of the main channel after shunting = (original bandwidth - cache bandwidth) / original bandwidth.

[0102] Step 702: Calculate the shunt benefit evaluation value E i and the saved bandwidth Bsa i according to the original broadband demand Bor of , and the formula used is:

[0103] ;

[0104] where, E of represents the shunt benefit evaluation value, Bsa i represents the bandwidth required for the data block of the device corresponding to the i-th production process without shunting; Bor i represents the bandwidth released by the main channel after the data block of the device corresponding to the i-th production process is shunted to the edge node; Qt ch is the target channel quality value, which can be expressed as the minimum quality benchmark that the channel ch needs to reach, and the value range is (0, 1], and the calculation formula is: the target channel quality value Qt ch = benchmark good product rate / current good product rate; Qe ch is the edge node quality value, which is used to quantify the effectiveness of the edge node cache service, and the calculation method is: Qech = Amount of successfully transmitted data / Total amount of edge cache data.

[0105] It should be noted that E of The formula combines bandwidth savings (Bsa i ), channel quality (Qt ch ) and edge node efficiency (Qe ch ), providing multi-dimensional evaluation. The solution solves the core problems of fuzzy resource allocation in the traffic pool, difficulty in quantifying the diversion effect, and lack of multi-dimensional benefit evaluation through the coordination of the original broadband demand, bandwidth saving calculation, and comprehensive evaluation of diversion benefits. Through Bor and Bsa, the scientific setting of bandwidth demand and accurate quantification of savings are achieved. The Eof formula comprehensively considers bandwidth savings, channel quality, and edge node efficiency to ensure that the diversion strategy saves resources while ensuring service quality.

[0106] Step 703: Set the diversion benefit evaluation threshold. When the diversion benefit evaluation value is 1, the balance point is reached. To ensure that the diversion operation has a significant net benefit, a safety factor θ can be set. The diversion benefit evaluation threshold = 1.0 + θ. The safety factor θ can be (0, 1) and can be adjusted according to actual needs, but cannot be less than 0.

[0107] Step 704: The diversion benefit evaluation value E of Compared with the diversion benefit evaluation threshold, when the diversion benefit evaluation value E of ≥ the diversion benefit evaluation threshold, the diversion instruction is judged to be valid and the diversion instruction is executed; when the diversion benefit evaluation value E of When the value is less than the diversion benefit evaluation threshold, the diversion instruction is judged to be invalid and the current allocation is maintained.

[0108] It should be noted that the formula is used to quantify the diversion benefit benchmark to ensure that the decision is based on objective data. For example, when the threshold is 1.2, only E of Only when the value of θ is ≥1.2 will the diversion operation be executed, avoiding invalid diversion of "barely balanced". The introduction of θ raises the profit threshold of diversion operation and reduces the risk of misjudgment caused by small fluctuations. For example, in the scenario of network jitter, θ=0.1 can filter out low-efficiency diversion caused by short-term jitter. The adjustability of θ enables the solution to adapt to different business needs. For example, during peak hours, θ=0.3 (threshold=1.3) is set to ensure that the diversion operation significantly improves resource utilization; during off-peak hours, θ=0.1 (threshold=1.1) is set to allow more flexible diversion. The scientific nature of the diversion operation is ensured through quantitative comparison. For example, a diversion operation E of =1.5 (higher than the threshold of 1.2), indicating that its benefits are significant and can be executed first; if E of = 1.05 (below the threshold), avoid execution. ofWhen it is ≥ the threshold value, perform flow diversion to maximize the resource utilization efficiency. Maintain the current allocation strategy to avoid low-benefit operations and reduce system jitter. Through the linkage of the flow diversion benefit evaluation threshold setting and the benefit value comparison mechanism, the solution solves the three core problems of subjective decision-making, resource waste, and system instability in the traffic pool allocation.

[0109] Example 2

[0110] A user traffic allocation management system for a traffic pool is used to implement the above-mentioned user traffic allocation management method for a traffic pool.

[0111] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0112] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A user traffic allocation management method for a traffic pool, characterized in that Including: Step 1: Obtain order data according to the order management system, match the corresponding production equipment based on the order data, and generate a work order traffic label; Step 2: Obtain the process characteristic parameters corresponding to the process number through the manufacturing execution system, and calculate the process priority weight through the process characteristic parameters; Step 3: Obtain the network performance parameters through the network management system, calculate the channel quality attenuation factor based on the network performance parameters, and calculate the allocated bandwidth of the channel based on the channel quality attenuation factor; Step 4: Obtain the equipment status parameters of the production equipment corresponding to the process number through the industrial monitoring system; and calculate the load index value of the equipment by analyzing the equipment status parameters; Step 5: Through comprehensive analysis of the allocated bandwidth, load index value, and process priority weight, obtain the dynamic bandwidth allocation amount; and allocate the total bandwidth of the traffic pool according to the dynamic bandwidth allocation amount; Step 6: Set the load balancing evaluation threshold; Collect the equipment status parameters in real time, and determine whether to execute the traffic splitting evaluation instruction through the equipment status parameter load balancing evaluation threshold; Step 7: When it is determined to execute the traffic splitting evaluation instruction, obtain the traffic splitting benefit data, and calculate the traffic splitting benefit evaluation value based on the traffic splitting benefit data; Set the traffic splitting benefit evaluation threshold, compare the traffic splitting benefit evaluation value with the traffic splitting benefit evaluation threshold, and determine whether to execute the traffic splitting instruction.

2. The user traffic allocation management method for a traffic pool according to claim 1, wherein, The process characteristic parameters include a delay sensitivity coefficient, a data volume weight, and a process stability factor; A user traffic allocation management method for a traffic pool, characterized in that the process characteristic parameters include a delay sensitivity coefficient, a data volume weight, and a process stability factor; Calculate the process priority weight W based on process characteristic parameters i , and the formula is as follows: ； Among them, W i represents the process priority weight of the i-th production process; α i represents the delay sensitivity coefficient of the i-th production process; β i represents the data volume weight of the i-th production process; γ i represents the process stability factor of the i-th production process, where i represents the process number of the production process and n represents the total number of production processes.

3. A user traffic allocation management method for a traffic pool according to claim 2, characterized in that, The network performance parameters include the end-to-end delay δ and the square of the jitter variance σ 2 , the end-to-end delay δ is obtained by analyzing the timestamps of the transmitted data through the client-server model; the square of the jitter variance σ 2 is obtained by capturing the timestamp sequence through the functional network probe, then calculating the jitter variance of the packet arrival time, and performing a square calculation.

4. A user traffic allocation management method for a traffic pool according to claim 3, characterized in that Calculate the channel quality attenuation factor according to the network performance parameters, and the formula is as follows: Among them, Q ch represents the channel quality attenuation factor of channel ch; δ ch represents the end-to-end delay of channel ch, represents the square of the jitter variance of channel ch, where ch represents the channel number; Based on the channel quality attenuation factor Q ch Calculate the allocated bandwidth B of the channel ch , and the formula used is: ; Among them, B ch represents the allocated bandwidth of channel ch, Btotal represents the total bandwidth, and K represents the total number of channels.

5. The user traffic allocation management method for a traffic pool according to claim 4, characterized in that The equipment status parameters include CPU utilization rate, TCP retransmission rate, and concurrent connection number; Among them, the CPU utilization rate is collected through the gateway built in the production equipment; The TCP retransmission rate is calculated through the formula after counting the number of retransmitted packets and the total number of sent packets by the network traffic monitoring tool. The formula is: TCP retransmission rate = number of retransmitted packets / total number of sent packets * 100%; The concurrent connection number can be obtained by counting the current active TCP / UDP connection numbers in real time; Calculate the load index value based on the CPU utilization rate, TCP retransmission rate, and concurrent connection number.

6. The user traffic allocation management method for a traffic pool according to claim 5, wherein, Combine the allocated bandwidth, load index value, and process priority weight to calculate the dynamic bandwidth allocation amount. The formula is: ； Among them, B i (t) represents the dynamic bandwidth allocation amount of the device corresponding to the i-th production process at time t; B ch represents the reference value of the bandwidth allocation amount; λ i (t) represents the load index value of the device corresponding to the i-th production process at time t; η ch (t) represents the bandwidth occupancy rate of the ch-th channel at time t.

7. A method for user traffic allocation management in a traffic pool according to claim 6, characterized in that By analyzing the bandwidth occupancy rate η ch (t) and the load index value λ i (t), the formula for obtaining the load balancing index L(t) is as follows: ； where \(L(t)\) represents the load balancing index at time \(t\), λ threshold is the preset upper limit of device load; \(\eta\) threshold is the upper limit of bandwidth occupancy rate; Compare the load balancing index L(t) with the load balancing evaluation threshold. When the load balancing index L(t) ≥ the load balancing evaluation threshold, execute the traffic splitting evaluation instruction; When the load balancing index L(t) < the load balancing evaluation threshold, do not execute the traffic splitting evaluation instruction.

8. A user traffic allocation management method for a traffic pool according to claim 7, characterized in that, The shunt benefit data includes the original broadband demand Bor i and the bandwidth savings Bsa i ; among which, the original broadband demand is set according to the device specifications; the bandwidth savings are obtained by comparing the port traffic statistics before and after shunting.

9. A user traffic allocation management method for a traffic pool according to claim 8, characterized in that, According to the original broadband requirement Bor i and the bandwidth savings Bsa i calculate the shunt benefit evaluation value E of , and the formula is as follows: ； Among them, E of represents the shunt benefit evaluation value, Bsa i represents the bandwidth required for the data block of the device corresponding to the i-th production process before shunting; Bor i represents the bandwidth released by the main channel after the data block of the device corresponding to the i-th production process is shunted to the edge node; Qt ch is the target channel quality value; Qe ch is the edge node quality value; Compare the shunt benefit evaluation value E of with the shunt benefit evaluation threshold. When the shunt benefit evaluation value E of ≥ the shunt benefit evaluation threshold, it is determined that the shunt instruction is valid and the shunt instruction is executed; when the shunt benefit evaluation value E of < the shunt benefit evaluation threshold, it is determined that the shunt instruction is invalid and the current allocation is maintained.

10. A user traffic allocation and management system for a traffic pool, characterized in that, A user traffic allocation management method for a traffic pool for implementing any one of the above claims 1-9.

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