Communication optimization method of adaptive bus topological structure

Abstract

The invention provides a communication optimization method of a self-adaptive bus topological structure, and relates to the technical field of communication. The method includes collecting a variety of data and metrics. And analyzing the node state data to obtain state change data, and obtaining performance fluctuation data according to the influence on the data performance index. And combining the external factor data and the performance fluctuation data to obtain association influence data, and predicting the influence of the association influence data on the data performance index by using a game theory algorithm to obtain a resource allocation scheme. Establishing a conflict detection mechanism, performing optimization according to an adaptive backoff algorithm to obtain adaptive node distribution data, and adjusting the bus topology structure according to the adaptive node distribution data to obtain an optimized topology structure. Through application of the game theory algorithm, the resource allocation is more reasonable, the utilization efficiency of the resource is improved, the resource allocation scheme is dynamically adjusted according to the real-time state of the network and the change of external factors, the requirements of different nodes and services are met, and the flexibility and adaptability of the network are improved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a communication optimization method for an adaptive bus topology structure. Background Art

[0002] In modern communication networks, bus topology is widely used in various scenarios, such as industrial automation control networks, local area networks, etc., due to its advantages such as simple structure, easy wiring and maintenance. However, with the continuous expansion of network scale and the increasing complexity of business needs, the traditional adaptive bus topology has exposed some defects in communication that need to be solved urgently.

[0003] In a bus topology network, there are many types of nodes (such as data processing nodes, data forwarding nodes, etc.) and different business applications (such as real-time monitoring services, file transfer services, etc.), and their requirements for network resources (such as bandwidth, computing resources, storage resources, etc.) are different. All nodes share a transmission bus. When multiple nodes try to send data at the same time, conflicts are very likely to occur. Once a conflict occurs, it will not only cause data transmission failure and require retransmission, thereby increasing data transmission delays, but also occupy additional network resources and reduce the overall throughput of the network.

[0004] Finally, the network topology lacks adaptability. With the continuous changes in network application scenarios and the rapid evolution of business requirements, such as the access of a large number of devices in IoT applications and the strict requirements of high bandwidth and low latency for high-definition video streaming services, the traditional bus topology is difficult to flexibly adjust and optimize to adapt to these changes. The fixed topology cannot be dynamically adjusted according to the state changes of nodes, differences in business requirements, and the influence of the external environment, resulting in a decline in network performance and failure to meet users' expectations for network service quality.

[0005] In order to solve the problems of unreasonable resource allocation, data transmission conflicts and lack of adaptability of network topology in the above-mentioned prior art, a more intelligent and adaptive communication optimization method is needed. Summary of the invention

[0006] The invention provides a communication optimization method for an adaptive bus topology structure, which is used to solve the defects of unreasonable resource allocation, data transmission conflict and lack of adaptability of network topology structure in the prior art.

[0007] The present invention provides a communication optimization method for an adaptive bus topology structure, comprising: Collect node status data and external factor data, and monitor data transmission in real time to obtain data performance indicators.

[0008] The node state data is analyzed according to the differential state evolution method to obtain the state change data, and the performance fluctuation data is obtained according to the impact of the state change data on the data performance index.

[0009] According to the Gini coefficient analysis method, the external factor data and performance fluctuation data are combined to obtain the associated influence data. The game theory algorithm is used to predict the impact of the associated influence data on the data performance indicators to obtain the resource allocation plan.

[0010] The nodes of the bus topology structure are configured according to the resource allocation scheme, and a conflict detection mechanism is established to optimize the adaptive node distribution data according to the adaptive backoff algorithm, and the bus topology structure is adjusted according to the adaptive node distribution data to obtain the optimized topology structure.

[0011] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of analyzing and obtaining state change data comprises: The median filling method was used to supplement the missing values ​​of the node status data, and the outliers were checked by the box plot method. The Z-score standardization method was used to convert data of different dimensions into a unified standard.

[0012] The processed node status data are arranged in chronological order to form a time series, and the average value within the preset time window is calculated and multi-order differences are performed to obtain the time change series.

[0013] The state change data is obtained by calculating the change between the time change series and the time series.

[0014] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of obtaining performance fluctuation data comprises: The causal relationship between each state change in the state change data and each data performance indicator is analyzed to obtain the correlation relationship.

[0015] According to the correlation relationship, for each data performance indicator, multiple indicator changes are calculated within the time interval when the state change occurs, and the distribution over time is observed to obtain the corresponding performance fluctuation pattern.

[0016] The impact degree of each state change is determined according to the size and frequency of each performance change, and the impact degree, correlation relationship and multiple performance fluctuation patterns are integrated to obtain performance fluctuation data.

[0017] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of obtaining the association relationship includes: Integrate state change data and corresponding data performance indicators, and perform slice processing according to preset time.

[0018] Use visualization tools to plot the processed state change data and data performance indicators, and observe them to obtain preliminary relationships.

[0019] According to the Granger causality test, it is determined whether each state change is the cause of the change in the data performance indicator. If so, it is added to the preliminary relationship, otherwise it is deleted.

[0020] The preliminary relationship after addition is analyzed from the perspective of network principles, and the causal relationship is verified by increasing the amount of data.

[0021] Each state change determined in the causal relationship is extracted from each data performance indicator to obtain a correlation relationship.

[0022] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of obtaining associated impact data includes: The external factor data and the performance fluctuation data are grouped according to preset standards to obtain a plurality of factor groups and a plurality of fluctuation groups.

[0023] Calculate the proportion of each factor group in all factor groups, and calculate the cumulative proportion of all proportions to get the factor proportion; calculate the proportion of each fluctuation group in the fluctuation group, and calculate the cumulative proportion of all proportions to get the fluctuation proportion.

[0024] The coordinate plane is constructed with the factor proportion as the horizontal coordinate and the fluctuation proportion as the vertical coordinate. Points are drawn on the coordinate plane according to all proportions and connected to form a proportion curve.

[0025] Calculate the area between the proportional curve and the diagonal to get the diagonal area, and calculate the area of ​​the triangle enclosed by the diagonal and the coordinate axes of the coordinate plane.

[0026] According to the calculation formula of the Gini coefficient, the diagonal area is divided by the triangle area to get the Gini coefficient value, and the associated impact data is obtained by analyzing the Gini coefficient value.

[0027] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of obtaining a resource allocation scheme comprises: Establish a game model, define the game participants and the corresponding strategy space and profit function.

[0028] For each game participant, the corresponding strategy space is initialized, and the best response strategy is calculated given the fixed strategy spaces of other game participants.

[0029] According to the best response strategy combined with the associated impact data, the changes in each data performance indicator are predicted, and a resource allocation plan is formulated based on the changes and the needs of different business applications.

[0030] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of optimizing and obtaining adaptive node distribution data comprises: The initial backoff time and maximum backoff times are set for the adaptive backoff algorithm according to the bus bandwidth, number of nodes and data flow. When the conflict detection mechanism detects a conflict, the node performs corresponding processing according to the set adaptive backoff algorithm.

[0031] The adjustment direction is obtained by analyzing the correlation relationship, and the node configuration, resource allocation and adaptive backoff algorithm are adjusted to obtain the optimized decision.

[0032] According to the optimization decision, the position of the node in the bus topology is adjusted and integrated with the node status data to obtain the adaptive node distribution data.

[0033] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of adjusting to obtain an adjustment direction includes: The association relationships are quantitatively analyzed to determine the impact of each factor on node configuration, resource allocation, and adaptive backoff algorithm.

[0034] The current node configuration is obtained by reallocating node performance, configuration rationality and node collaboration according to the degree of impact.

[0035] The current resource allocation is obtained by reallocating the resource allocation balance and the matching degree between resources and business needs according to the degree of impact.

[0036] The performance and parameter configuration of the adaptive backoff algorithm are allocated according to the degree of influence to obtain an optimized adaptive backoff algorithm.

[0037] The current node configuration, current resource allocation and optimized adaptive backoff algorithm are comprehensively considered, and the adjustment direction is determined in combination with the association relationship.

[0038] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the steps of adjusting to obtain an optimized topology structure include: The data that has an impact on the topology adjustment is extracted from the adaptive node distribution data as key information data.

[0039] Analyze the current bus topology to obtain the current structural performance, and determine the performance improvement target based on key information data.

[0040] According to the adaptive node distribution data and performance improvement goals, the node position, connection relationship, node configuration and redundant design are adjusted and optimized to obtain the topology adjustment strategy.

[0041] According to the topology adjustment strategy and the preset adjustment sequence and time schedule, the bus topology is updated to obtain an optimized topology.

[0042] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of determining a performance improvement target includes: The current bus topology is converted into a graphic structure, and the current structural analysis data is obtained by analyzing the node attributes, node connection relationships and communication paths.

[0043] The performance parameter indicators of the current bus topology are collected, and the performance difference data is obtained by comparing the performance parameter indicators of different time periods and different nodes. The current structure performance is obtained by combining the current structure analysis data.

[0044] Based on the performance difference data and key information data, the performance indicator improvement extent is obtained, and priorities are set according to network requirements.

[0045] Set performance improvement goals based on the improvement extent and priority of performance indicators.

[0046] The present invention provides a communication optimization method for an adaptive bus topology structure, which solves the problem of how to accurately capture the dynamic changes of node states by analyzing node state data to obtain state change data, and achieves the beneficial effect of being able to effectively analyze the evolution of node states over time and avoid inaccurate performance evaluation or difficult fault diagnosis caused by ignoring state changes.

[0047] The present invention provides a communication optimization method for an adaptive bus topology structure, which obtains correlation influence data by combining external factor data and performance fluctuation data, thereby solving the problem of how to quantify the influence of external factors (such as electromagnetic interference, network attacks, etc.) on network performance fluctuations. It can analyze the correlation between the two at the data level and find out the key external factors and their influence on performance.

[0048] The present invention provides a communication optimization method for an adaptive bus topology structure. By using a game theory algorithm to predict the impact of associated influencing data on data performance indicators, a resource allocation scheme is obtained, which solves the problem of how to reasonably allocate resources to optimize network performance under limited resources and achieves efficient use of resources. In addition, the combination of a conflict detection mechanism and an adaptive backoff algorithm effectively reduces data transmission conflicts and improves the success rate and reliability of data transmission. Even in a complex network environment, stable data transmission can be guaranteed, reducing the risk of network failures caused by conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 This is one of the flow charts of a communication optimization method for an adaptive bus topology structure provided by an embodiment of the present invention; Figure 2 This is a second flow chart of a communication optimization method for an adaptive bus topology structure provided by an embodiment of the present invention; Figure 3 This is the third flow chart of a communication optimization method for an adaptive bus topology structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Combine the following Figure 1-Figure 3 The present invention describes a communication optimization method for an adaptive bus topology structure.

[0053] like Figure 1 As shown, an embodiment of the present invention provides a communication optimization method for an adaptive bus topology structure, comprising: Collect node status data and external factor data, and monitor data transmission in real time to obtain data performance indicators. Node status data can include: node load (CPU usage, memory usage, etc.), communication traffic (data sending and receiving rate), battery power (for mobile or battery-powered nodes), etc. External factor data can include changes in user behavior, sudden business needs, etc.

[0054] Data performance indicators can include: data transmission throughput, transmission delay, packet loss rate, error rate, etc., as well as node-related performance indicators such as CPU utilization, memory occupancy, disk I / O rate, etc. These indicators will be used to measure the performance of the network or node.

[0055] The node state data is analyzed according to the differential state evolution method to obtain the state change data, and the performance fluctuation data is obtained according to the impact of the state change data on the data performance index.

[0056] The steps of analyzing and obtaining state change data include: The median filling method was used to supplement the missing values ​​of the node status data, and the outliers were checked by the box plot method. The Z-score standardization method was used to convert data of different dimensions into a unified standard.

[0057] The processed node status data are arranged in chronological order to form a time series, and the average value within the preset time window is calculated and multi-order differences are performed to obtain the time change series.

[0058] The state change data is obtained by calculating the change between the time change series and the time series.

[0059] like Figure 2 As shown, the steps of obtaining performance fluctuation data include: The causal relationship between each state change in the state change data and each data performance indicator is analyzed to obtain the correlation relationship.

[0060] The steps to obtain the association relationship include: Integrate state change data and corresponding data performance indicators, and perform slice processing according to preset time.

[0061] Use visualization tools to plot the processed state change data and data performance indicators, and observe them to obtain preliminary relationships.

[0062] According to the Granger causality test, it is determined whether each state change is the cause of the change in the data performance indicator. If so, it is added to the preliminary relationship, otherwise it is deleted.

[0063] The preliminary relationship after addition is analyzed from the perspective of network principles, and the causal relationship is verified by increasing the amount of data.

[0064] Each state change determined in the causal relationship is extracted from each data performance indicator to obtain a correlation relationship.

[0065] According to the association relationship, for each data performance indicator, multiple indicator changes are calculated within the time interval when the state change occurs, and the distribution over time is observed to obtain the corresponding performance fluctuation pattern. It can be determined whether the performance fluctuation is periodic, sudden or gradual. Periodic fluctuations are related to certain fixed business activities or system tasks. Sudden fluctuations are caused by external events (such as network attacks and equipment failures). Gradual fluctuations reflect the gradual consumption of system resources or the gradual decline of performance.

[0066] The impact degree of each state change is determined according to the size and frequency of each performance change, and the impact degree, correlation relationship and multiple performance fluctuation patterns are integrated to obtain performance fluctuation data.

[0067] According to the Gini coefficient analysis method, the external factor data and performance fluctuation data are combined to obtain the associated influence data. The game theory algorithm is used to predict the impact of the associated influence data on the data performance indicators to obtain the resource allocation plan.

[0068] The steps of combining and obtaining the associated impact data include: The external factor data and the performance fluctuation data are grouped according to preset standards to obtain a plurality of factor groups and a plurality of fluctuation groups.

[0069] Calculate the proportion of each factor group in all factor groups, and calculate the cumulative proportion of all proportions to get the factor proportion; calculate the proportion of each fluctuation group in the fluctuation group, and calculate the cumulative proportion of all proportions to get the fluctuation proportion.

[0070] The coordinate plane is constructed with the factor proportion as the horizontal coordinate and the fluctuation proportion as the vertical coordinate. Points are drawn on the coordinate plane according to all proportions and connected to form a proportion curve.

[0071] Calculate the area between the proportional curve and the diagonal to get the diagonal area, and calculate the area of ​​the triangle enclosed by the diagonal and the coordinate axes of the coordinate plane.

[0072] According to the calculation formula of the Gini coefficient, the diagonal area is divided by the triangle area to get the Gini coefficient value, and the associated impact data is obtained by analyzing the Gini coefficient value.

[0073] When the Gini coefficient is close to 0, it means that the impact of changes in external factors on performance fluctuations is relatively uniform, and there is no obvious key external factor that has a dominant impact on performance. When the Gini coefficient is close to 1, it indicates that there are certain specific groups of external factors that have a very significant impact on performance fluctuations. Further analysis of the external factors and performance fluctuations corresponding to these groups can be performed to find out the specific correlation. You can also compare the Gini coefficients of different combinations of external factors and performance fluctuations to determine which external factors have a more critical and concentrated impact on performance fluctuations.

[0074] like Figure 3 As shown, the steps of obtaining the resource allocation solution include: Establish a game model, define the game participants and the corresponding strategy space and profit function, and the formula is expressed as:

[0075] In the formula, Is a node The amount of data transfer, Is a node Data transmission delay, Is a node The packet loss rate, , , is the weight coefficient.

[0076] For each game participant, the corresponding strategy space is initialized, and the best response strategy is calculated under the condition of fixed strategy spaces of other game participants. The formula is expressed as:

[0077] In the formula, For the best response strategy, is a fixed strategy space, A participant in the game The strategy of choice, The game participants are .

[0078] According to the best response strategy combined with the associated impact data, the changes in the performance indicators of each data are predicted, and a resource allocation plan is formulated based on the changes and the needs of different business applications. According to the resource occupancy of network nodes and business applications under the balanced strategy and the status of external interference factors, the changes in performance indicators such as the overall throughput, average delay, and packet loss rate of the network are calculated. According to the business requirements for delay and throughput, corresponding bandwidth resources are allocated to different businesses. According to the processing capacity and load of the node, computing resources are allocated.

[0079] The nodes of the bus topology are configured according to the resource allocation scheme, and a conflict detection mechanism is established to obtain adaptive node distribution data through optimization based on an adaptive backoff algorithm.

[0080] The steps of optimizing and obtaining adaptive node distribution data include: The initial backoff time and maximum backoff times are set for the adaptive backoff algorithm according to the bus bandwidth, number of nodes and data flow. When the conflict detection mechanism detects a conflict, the node performs corresponding processing according to the set adaptive backoff algorithm.

[0081] The adjustment direction is obtained by analyzing the correlation relationship, and the node configuration, resource allocation and adaptive backoff algorithm are adjusted to obtain the optimized decision.

[0082] The steps of adjusting the adjustment direction include: The association relationships are quantitatively analyzed to determine the impact of each factor on node configuration, resource allocation, and adaptive backoff algorithm.

[0083] The current node configuration is obtained by reallocating node performance, configuration rationality and node collaboration according to the degree of impact.

[0084] The current resource allocation is obtained by reallocating the resource allocation balance and the matching degree between resources and business needs according to the degree of impact.

[0085] The performance and parameter configuration of the adaptive backoff algorithm are allocated according to the degree of influence to obtain an optimized adaptive backoff algorithm.

[0086] The current node configuration, current resource allocation and optimized adaptive backoff algorithm are comprehensively considered, and the adjustment direction is determined in combination with the association relationship.

[0087] According to the optimization decision, the position of the node in the bus topology is adjusted and integrated with the node status data to obtain the adaptive node distribution data.

[0088] The bus topology is adjusted according to the adaptive node distribution data to obtain an optimized topology.

[0089] The steps to adjust the optimized topology include: Extracting data that has an impact on topology adjustment from the adaptive node distribution data as key information data. The key information data may include: finding out nodes with poor performance or unreasonable resource utilization, and connection relationships between nodes that have frequent communications but have conflicts or high delays.

[0090] Analyze the current bus topology to obtain the current structural performance, and determine the performance improvement target based on key information data.

[0091] The steps to determine performance improvement goals include: The current bus topology is converted into a graphic structure, and the current structural analysis data is obtained by analyzing the node attributes, node connection relationships and communication paths.

[0092] Node attribute analysis can include: studying the attributes of each node, including the node's hardware configuration (such as processor performance, memory capacity, storage capacity, etc.), software configuration (such as operating system, network protocol stack, etc.), functional positioning (such as data processing node, forwarding node, etc.) and role and importance in the network.

[0093] Connection relationship analysis can include: analyzing the connection mode between nodes, such as the connection bandwidth, transmission medium, signal strength, etc. Determining which connections are critical connections and play an important role in the overall performance of the network. Which connections have potential bottlenecks or unstable factors.

[0094] Communication path analysis can include: combing the communication path of data in the bus topology, analyzing the path length, number of hops, transmission delay, etc. Checking whether there are circuitous paths, loops or unreasonable routing choices.

[0095] The performance parameter indicators of the current bus topology are collected, and the performance difference data is obtained by comparing the performance parameter indicators of different time periods and different nodes. The current structure performance is obtained by combining the current structure analysis data.

[0096] Based on the performance difference data combined with key information data, the performance indicator improvement extent is obtained, and priorities are set according to network requirements.

[0097] Set performance improvement goals based on the improvement extent and priority of performance indicators.

[0098] According to the adaptive node distribution data and performance improvement goals, the node position, connection relationship, node configuration and redundant design are adjusted and optimized to obtain the topology adjustment strategy.

[0099] According to the topology adjustment strategy and the preset adjustment sequence and time schedule, the bus topology is updated to obtain an optimized topology.

[0100] Example 1: 1. Initial data collection: There is a bus topology network with 5 nodes (Node1-Node5), and the following data is collected in real time: Node status data: Node1: CPU usage is 30%, memory usage is 2 GB, and disk I / O rate is 100 KB / s.

[0101] Node2: CPU usage is 40%, memory usage is 3 GB, and disk I / O rate is 120 KB / s.

[0102] Node3: CPU usage is 20%, memory usage is 1.5 GB, and disk I / O rate is 80 KB / s.

[0103] Node4: CPU usage is 50%, memory usage is 2.5 GB, and disk I / O rate is 150 KB / s.

[0104] Node5: CPU usage is 35%, memory usage is 2.2 GB, and disk I / O rate is 110 KB / s.

[0105] External factors data: Electromagnetic interference intensity: Currently 20dB (the larger the value, the stronger the interference).

[0106] Number of cyber attacks: 5 minor attacks occurred in the past hour.

[0107] Data performance indicators: Throughput: Currently 500Mbps.

[0108] Transmission delay: The average delay is 10ms.

[0109] Packet loss rate: Currently 2%.

[0110] 2. Differential state evolution analysis: After a period of time (10 minutes), collect node status data again: Node1: CPU usage is 35%, memory usage is 2.2 GB, and disk I / O rate is 110 KB / s.

[0111] Node2: CPU usage is 45%, memory usage is 3.2 GB, and disk I / O rate is 130 KB / s.

[0112] Node3: CPU usage is reduced to 25%, memory usage is 1.6 GB, and disk I / O rate is 90 KB / s.

[0113] Node4: CPU usage is 55%, memory usage is 2.8 GB, and disk I / O rate is 160 KB / s.

[0114] Node5: CPU usage is 40%, memory usage is 2.5 GB, and disk I / O rate is 120 KB / s.

[0115] Calculate state change data using the differential state evolution method: Node1: CPU usage change = 35%-30% = 5%, memory usage change = 2.2 GB-2 GB = 0.2 GB, disk I / O rate change = 110 KB / s-100 KB / s = 10 KB / s.

[0116] The state change data of other nodes is calculated in the same way.

[0117] According to the impact of state change data on data performance indicators, it is found that the increase in CPU usage is related to the increase in transmission delay. The performance fluctuation data is calculated: Throughput dropped to 480Mbps.

[0118] The transmission delay rises to 12ms.

[0119] The packet loss rate becomes 3%.

[0120] 3. Gini coefficient analysis: Combine external factor data (electromagnetic interference intensity 20dB, number of network attacks 5 times) and performance fluctuation data (throughput 480Mbps, transmission delay 12ms, packet loss rate 3%).

[0121] Through the Gini coefficient analysis method, the Gini coefficient of electromagnetic interference intensity on throughput is 0.4, indicating that electromagnetic interference intensity has a certain degree of influence on throughput; the Gini coefficient of the number of network attacks on packet loss rate is 0.6, indicating that the number of network attacks has a significant impact on packet loss rate. Thus, the associated impact data is obtained, and the degree and direction of the impact of external factors on performance indicators are clarified.

[0122] 4. Application of Game Theory Algorithms: There are three types of resources (bandwidth, CPU resources, and memory resources) that need to be allocated to five nodes.

[0123] Using the game theory algorithm, taking into account the state changes of nodes, the associated impact of external factors, and the resource requirements of each node (Node1 has a higher bandwidth requirement, and Node4 has a higher CPU resource requirement), the resource allocation plan is obtained after calculation: Node1: allocated bandwidth 120 Mbps, CPU resource usage 20%, memory 0.8 GB.

[0124] Node2: allocated bandwidth 100 Mbps, CPU resource usage ratio 18%, memory 0.7 GB.

[0125] Node3: allocated bandwidth 80 Mbps, CPU resource usage 15%, memory 0.6 GB.

[0126] Node4: allocated bandwidth 150 Mbps, CPU resource usage ratio 25%, memory 0.9 GB.

[0127] Node5: allocated bandwidth 100 Mbps, CPU resource usage ratio 22%, memory 0.7 GB.

[0128] 5. Node configuration and conflict detection optimization: Configure the nodes of the bus topology structure according to the resource allocation plan, and adjust the node's network interface bandwidth settings, CPU resource allocation parameters, and memory allocation.

[0129] A conflict detection mechanism is established. At a certain moment, Node2 and Node4 try to send a large amount of data at the same time, and the conflict detection mechanism detects a conflict. According to the adaptive backoff algorithm (assuming that the backoff time is calculated based on the node priority and the amount of data), the backoff time of Node2 is 5ms and the backoff time of Node4 is 8ms. After multiple conflict detections and backoff adjustments, the adaptive node distribution data is obtained, and the optimal position and data transmission strategy of each node in the bus topology are determined.

[0130] 6. Topology Adjustment According to the adaptive node distribution data, the bus topology is adjusted. Node1 and Node4, which have frequent communications, are moved closer to each other to reduce the signal transmission distance. After the adjustment, the optimized topology is obtained and the data performance indicators are monitored again: Throughput increased to 520Mbps.

[0131] Transmission delay is reduced to 8ms.

[0132] The packet loss rate dropped to 1%.

[0133] Through the above series of steps, the communication optimization of the adaptive bus topology is achieved, and the performance and reliability of the network are improved.

[0134] The communication optimization method of an adaptive bus topology structure provided in this embodiment accurately analyzes the node state changes through the differential state evolution method, and quantifies the impact of external factors on performance by combining the Gini coefficient analysis method, which can evaluate the network performance more comprehensively and accurately, and avoid performance misjudgment caused by one-sided analysis. The application of game theory algorithm makes resource allocation more reasonable and improves the efficiency of resource utilization. It can dynamically adjust the resource allocation scheme according to the real-time state of the network and the changes of external factors to meet the needs of different nodes and services, and improve the overall performance and service quality of the network. The entire technical solution can monitor and analyze the network status in real time, and adjust the network configuration and resource allocation in time according to external factors and performance fluctuations. The adaptive bus topology structure can better adapt to changes in the network environment, improve the flexibility and adaptability of the network, extend the service life of the network, and provide a clear direction and specific implementation plan for further optimization of the network. It can adjust and improve the network in a targeted manner to improve the performance and competitiveness of the network.

[0135] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0136] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A communication optimization method for an adaptive bus topology structure, characterized in that: include: Collect node status data and external factor data, and monitor data transmission in real time to obtain data performance indicators; Analyzing the node state data according to a differential state evolution method to obtain state change data, and obtaining performance fluctuation data according to the influence of the state change data on the data performance index; The external factor data and the performance fluctuation data are combined according to the Gini coefficient analysis method to obtain the associated influence data, and the impact of the associated influence data on the data performance index is predicted using the game theory algorithm to obtain the resource allocation plan; The nodes of the bus topology structure are configured according to the resource allocation scheme, and a conflict detection mechanism is established to optimize the adaptive node distribution data according to the adaptive backoff algorithm, and the bus topology structure is adjusted according to the adaptive node distribution data to obtain an optimized topology structure.

2. The communication optimization method of an adaptive bus topology structure according to claim 1, characterized in that: The step of analyzing and obtaining the state change data comprises: The missing values ​​of the node status data are supplemented by the median filling method, and the outliers are checked by the box plot method. The data of different dimensions are converted into a unified standard using the Z-score standardization method; Arrange the processed node status data in chronological order to form a time series, calculate the average value within the preset time window and perform multi-order differences to obtain the time change series; The state change data is obtained by calculating the time change series and the change amount between the time series.

3. The communication optimization method of an adaptive bus topology structure according to claim 1, characterized in that: The step of obtaining the performance fluctuation data comprises: Analyze the causal relationship between each state change in the state change data and each data performance indicator to obtain a correlation relationship; According to the association relationship, for each data performance indicator, multiple indicator changes are calculated within the time interval when the state change occurs, and the distribution over time is observed to obtain the corresponding performance fluctuation pattern; The influence degree on each state change is determined according to the size and frequency of each performance change, and the influence degree, the correlation relationship and multiple performance fluctuation patterns are integrated to obtain the performance fluctuation data.

4. The communication optimization method of an adaptive bus topology structure according to claim 3, characterized in that: The steps of obtaining the association relationship include: Integrate the state change data and corresponding data performance indicators, and perform slicing processing according to a preset time; Use visualization tools to plot the processed state change data and data performance indicators, and observe to obtain preliminary relationships; According to the Granger causality test, it is determined whether each state change is the cause of the change in the data performance indicator. If so, it is added to the preliminary relationship, otherwise it is deleted; The initial relationship after adding is analyzed from the perspective of network principles, and the causal relationship is verified by increasing the amount of data; Each state change determined in the causal relationship is extracted from each data performance indicator to obtain the association relationship.

5. The communication optimization method of an adaptive bus topology structure according to claim 1, characterized in that: The step of obtaining the associated impact data comprises: Grouping the external factor data and the performance fluctuation data according to a preset standard to obtain a plurality of factor groups and a plurality of fluctuation groups; Calculate the proportion of each factor group in all factor groups, and calculate the cumulative proportion of all proportions to obtain the factor proportion; calculate the proportion of each fluctuation group in the fluctuation group, and calculate the cumulative proportion of all proportions to obtain the fluctuation proportion; A coordinate plane is formed with the factor ratio as the horizontal coordinate and the fluctuation ratio as the vertical coordinate, and points are drawn on the coordinate plane according to all ratios and connected to form a ratio curve; Calculate the area between the proportional curve and the diagonal line to obtain the diagonal area, and calculate the area of ​​the triangle enclosed by the diagonal line and the coordinate axis of the coordinate plane; According to the calculation formula of the Gini coefficient, the diagonal area is divided by the triangle area to obtain the Gini coefficient value, and the associated influence data is obtained by analyzing the Gini coefficient value.

6. The communication optimization method of an adaptive bus topology structure according to claim 1, characterized in that: The step of obtaining the resource allocation scheme comprises: Establish a game model, define game participants and corresponding strategy space and profit function; For each game participant, initialize the corresponding strategy space and calculate the best response strategy under the condition of fixed strategy spaces of other game participants; According to the best response strategy combined with the associated impact data, the change of each data performance indicator is predicted, and the resource allocation plan is formulated according to the change and the needs of different business applications.

7. The communication optimization method of an adaptive bus topology structure according to claim 4, characterized in that: The step of optimizing and obtaining the adaptive node distribution data comprises: The adaptive backoff algorithm is configured with an initial backoff time and a maximum backoff number according to the bus bandwidth, the number of nodes and the data flow, and when the conflict detection mechanism detects a conflict, the node performs corresponding processing according to the configured adaptive backoff algorithm; According to the adjustment direction obtained by analyzing the association relationship, node configuration, resource allocation and the adaptive backoff algorithm are adjusted to obtain an optimized decision; According to the optimization decision, the position of the node in the bus topology is adjusted and integrated with the node status data to obtain the adaptive node distribution data.

8. The communication optimization method of an adaptive bus topology structure according to claim 7, characterized in that: The step of adjusting to obtain the adjustment direction comprises: Quantitatively analyzing the association relationship to determine the influence of each factor on the node configuration, the resource allocation and the adaptive backoff algorithm; According to the impact degree, node performance, configuration rationality and node collaboration are reallocated to obtain the current node configuration; According to the impact degree, the resource allocation balance and the matching degree between resources and business requirements are reallocated to obtain the current resource allocation; The cabinet allocates the performance and parameter configuration of the adaptive backoff algorithm according to the degree of influence to obtain an optimized adaptive backoff algorithm; The adjustment direction is determined by comprehensively considering the current node configuration, the current resource allocation and the optimized adaptive backoff algorithm, and combining the association relationship.

9. The communication optimization method of an adaptive bus topology structure according to claim 1, characterized in that: The steps to adjust the optimized topology include: Extracting data having an impact on topology structure adjustment from the adaptive node distribution data as key information data; Analyze the current bus topology to obtain the current structure performance, and determine the performance improvement target in combination with the key information data; According to the adaptive node distribution data and the performance improvement target, the node position, connection relationship, node configuration and redundancy design are designed, adjusted and optimized to obtain a topology adjustment strategy; According to the topology adjustment strategy and the preset adjustment sequence and time schedule, the bus topology is updated to obtain the optimized topology.

10. The communication optimization method of an adaptive bus topology structure according to claim 9, characterized in that: The steps of determining the performance improvement target include: Convert the current bus topology into a graphic structure, and analyze the current structure analysis data from three aspects: node attributes, node connection relationship and communication path; Collecting performance parameter indicators of the current bus topology structure, and obtaining performance difference data by comparing the performance parameter indicators of different time periods and different nodes, and obtaining the current structure performance in combination with the current structure analysis data; According to the performance difference data and the key information data, the improvement range of the performance indicator is obtained, and the priority is set according to the network demand; A performance improvement goal is formulated based on the performance indicator improvement extent and the priority.

Images