A communication optimization method for adaptive bus topology

Abstract

The present invention provides a communication optimization method for an adaptive bus topology structure, and relates to the field of communication technology. The method includes collecting a variety of data and indicators. Node status data is analyzed to obtain status change data, and the impact on data performance indicators is obtained to obtain performance fluctuation data. External factor data and performance fluctuation data are combined to obtain correlation influence data, and a game theory algorithm is used to predict the impact of the correlation influence data on data performance indicators to obtain a resource allocation plan. A conflict detection mechanism is established and optimized according to an adaptive backoff algorithm to obtain adaptive node distribution data, and the bus topology structure is adjusted according to the adaptive node distribution data to obtain an optimized topology structure. The present invention makes resource allocation more reasonable and improves resource utilization efficiency through the application of game theory algorithms. According to the real-time status of the network and changes in external factors, the resource allocation plan is dynamically adjusted to meet the needs of different nodes and services, thereby improving the flexibility and adaptability of the network.

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 and local area networks, 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 shortcomings in communication that need to be addressed.

[0003] In a bus-topology network, there are various types of nodes (such as data processing nodes and data forwarding nodes) and different business applications (such as real-time monitoring and file transfer). These applications have varying requirements for network resources (such as bandwidth, computing resources, and storage resources). All nodes share a single transmission bus, and conflicts are very likely to occur when multiple nodes attempt to send data simultaneously. Once a conflict occurs, not only does it cause data transmission failures and require retransmissions, increasing data transmission latency, but it also consumes additional network resources and reduces overall network throughput.

[0004] Finally, network topology lacks adaptability. With the constant changes in network application scenarios and the rapid evolution of business requirements, such as the massive influx of devices in IoT applications and the stringent bandwidth and low latency requirements of high-definition video streaming, traditional bus topologies struggle to flexibly adjust and optimize to accommodate these changes. Fixed topologies are unable to dynamically adapt to changes in node status, service requirements, and external environmental influences, resulting in degraded network performance and a failure to meet user expectations for network quality of service.

[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 existing technologies, a more intelligent and adaptive communication optimization method is needed. Summary of the Invention

[0006] The present 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 the network topology structure in the prior art.

[0007] The present invention provides a communication optimization method for an adaptive bus topology structure, comprising:

[0008] Collect node status data and external factor data, and monitor data transmission in real time to obtain data performance indicators.

[0009] 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 indicators.

[0010] According to the Gini coefficient analysis method, external factor data and performance fluctuation data are combined to obtain correlation influence data. Game theory algorithm is used to predict the impact of correlation influence data on data performance indicators to obtain resource allocation plan.

[0011] The nodes of the bus topology 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 is adjusted according to the adaptive node distribution data to obtain an optimized topology.

[0012] 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 includes:

[0013] 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.

[0014] 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.

[0015] The state change data is obtained by calculating the change between the time change series and the time series.

[0016] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of obtaining performance fluctuation data includes:

[0017] The causal relationship between each state change in the state change data and each data performance indicator is analyzed to obtain the correlation relationship.

[0018] Based on 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.

[0019] 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.

[0020] 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:

[0021] Integrate state change data and corresponding data performance indicators, and perform slice processing according to preset time.

[0022] Use visualization tools to plot the processed state change data and data performance indicators, and observe to obtain preliminary relationships.

[0023] According to the Granger causality test, it is determined whether each state change is the cause of the change in data performance indicators. If so, it is added to the preliminary relationship; otherwise, it is deleted.

[0024] 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.

[0025] Each state change determined in the causal relationship is extracted from each data performance indicator to obtain the correlation relationship.

[0026] 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:

[0027] The external factor data and the performance fluctuation data are grouped according to preset standards to obtain multiple factor groups and multiple fluctuation groups.

[0028] 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.

[0029] A coordinate plane is constructed with the factor ratio as the horizontal coordinate and the fluctuation ratio as the vertical coordinate. Points are drawn on the coordinate plane according to all ratios and connected to form a ratio curve.

[0030] 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.

[0031] 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 impact data is obtained by analyzing the Gini coefficient value.

[0032] According to a communication optimization method for an adaptive bus topology structure provided by the present invention, the step of obtaining a resource allocation solution includes:

[0033] Establish a game model, define the game participants and the corresponding strategy space and payoff function.

[0034] 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.

[0035] Based on the best response strategy and associated impact data, predict the changes in each data performance indicator, and formulate resource allocation plans based on the changes and the needs of different business applications.

[0036] 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 includes:

[0037] The adaptive backoff algorithm sets the initial backoff time and maximum backoff times 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.

[0038] The adjustment direction is obtained based on the analysis of the correlation relationship, and the node configuration, resource allocation and adaptive backoff algorithm are adjusted to obtain the optimized decision.

[0039] 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.

[0040] 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:

[0041] The association relationships are quantitatively analyzed to determine the degree of influence of each factor on node configuration, resource allocation, and adaptive backoff algorithm.

[0042] The current node configuration is obtained by reallocating node performance, configuration rationality and node collaboration according to the degree of impact.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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:

[0047] The data that has an impact on topology adjustment is extracted from the adaptive node distribution data as key information data.

[0048] Analyze the current bus topology to obtain the current structural performance, and determine the performance improvement target based on key information data.

[0049] Based on the adaptive node distribution data and performance improvement goals, the node position, connection relationship, node configuration and redundancy design are adjusted and optimized to obtain the topology adjustment strategy.

[0050] According to the topology adjustment strategy and the preset adjustment sequence and time arrangement, the bus topology is updated to obtain an optimized topology.

[0051] 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:

[0052] The current bus topology is converted into a graphic, and the current structure analysis data is obtained by analyzing the node attributes, node connection relationships and communication paths.

[0053] 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.

[0054] Based on the performance difference data combined with key information data, the performance indicator improvement rate is obtained and priorities are set according to network needs.

[0055] Set performance improvement goals based on the improvement extent and priority of performance indicators.

[0056] The present invention provides a communication optimization method for an adaptive bus topology structure. By analyzing node status data to obtain status change data, the problem of how to accurately capture the dynamic changes of node status is solved. This method has the beneficial effect of being able to effectively analyze the evolution of node status over time and avoid inaccurate performance evaluation or difficult fault diagnosis caused by ignoring status changes.

[0057] The present invention provides a communication optimization method for an adaptive bus topology structure. By combining external factor data and performance fluctuation data to obtain correlation influence data, the method solves the problem of how to quantify the impact of external factors (such as electromagnetic interference, network attacks, etc.) on network performance fluctuations. The method can analyze the correlation between the two at the data level and identify key external factors and their impact on performance.

[0058] This invention provides a communication optimization method for an adaptive bus topology. By using a game theory algorithm to predict the impact of associated influencing data on data performance indicators and deriving a resource allocation plan, it addresses the problem of how to rationally allocate resources to optimize network performance under limited resources, achieving efficient resource utilization. Furthermore, by combining a conflict detection mechanism with an adaptive backoff algorithm, data transmission conflicts are effectively reduced, improving the success rate and reliability of data transmission. Even in complex network environments, this method ensures stable data transmission and reduces the risk of network failures caused by conflicts. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to 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 any creative work.

[0060] Figure 1 This is one of the flow diagrams of a communication optimization method for an adaptive bus topology structure provided by an embodiment of the present invention;

[0061] 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;

[0062] 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

[0063] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0064] The following combination Figure 1-Figure 3 The present invention describes a communication optimization method for an adaptive bus topology structure.

[0065] like Figure 1 As shown, an embodiment of the present invention provides a communication optimization method for an adaptive bus topology structure, comprising:

[0066] Collect node status data and external factor data, and monitor data transmission in real time to obtain data performance indicators. Node status data may include: node load (CPU usage, memory usage, etc.), communication traffic (data send and receive rate), battery level (for mobile or battery-powered nodes), etc. External factor data may include changes in user behavior and sudden business demands.

[0067] 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 usage, disk I / O rate, etc. These indicators will be used to measure the performance of the network or node.

[0068] 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 indicators.

[0069] The steps for analyzing and obtaining state change data include:

[0070] 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.

[0071] 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.

[0072] The state change data is obtained by calculating the change between the time change series and the time series.

[0073] like Figure 2 As shown, the steps for obtaining performance fluctuation data include:

[0074] The causal relationship between each state change in the state change data and each data performance indicator is analyzed to obtain the correlation relationship.

[0075] The steps to obtain the association relationship include:

[0076] Integrate state change data and corresponding data performance indicators, and perform slice processing according to preset time.

[0077] Use visualization tools to plot the processed state change data and data performance indicators, and observe to obtain preliminary relationships.

[0078] According to the Granger causality test, it is determined whether each state change is the cause of the change in data performance indicators. If so, it is added to the preliminary relationship; otherwise, it is deleted.

[0079] 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.

[0080] Each state change determined in the causal relationship is extracted from each data performance indicator to obtain the correlation relationship.

[0081] Based on the correlation relationships, for each data performance indicator, multiple indicator changes are calculated within the time interval of the state change. The distribution over time is then observed to obtain the corresponding performance fluctuation pattern. This allows us to determine whether the performance fluctuation is cyclical, sudden, or gradual. Cyclic 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 depletion of system resources or the gradual decline in performance.

[0082] 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.

[0083] According to the Gini coefficient analysis method, external factor data and performance fluctuation data are combined to obtain correlation influence data. Game theory algorithm is used to predict the impact of correlation influence data on data performance indicators to obtain resource allocation plan.

[0084] The steps for obtaining the associated impact data include:

[0085] The external factor data and the performance fluctuation data are grouped according to preset standards to obtain multiple factor groups and multiple fluctuation groups.

[0086] 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.

[0087] A coordinate plane is constructed with the factor ratio as the horizontal coordinate and the fluctuation ratio as the vertical coordinate. Points are drawn on the coordinate plane according to all ratios and connected to form a ratio curve.

[0088] 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.

[0089] 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 impact data is obtained by analyzing the Gini coefficient value.

[0090] When the Gini coefficient is close to 0, it indicates that changes in external factors have a relatively uniform impact on performance fluctuations, with no key external factor clearly having a dominant influence on performance. When the Gini coefficient is close to 1, it indicates that certain groups of external factors have a significant impact on performance fluctuations. Further analysis of the external factors and performance fluctuations corresponding to these groups can reveal specific correlations. By comparing the Gini coefficients of different combinations of external factors and performance fluctuations, it is possible to determine which external factors have a more critical and concentrated impact on performance fluctuations.

[0091] like Figure 3 As shown, the steps of obtaining the resource allocation plan include:

[0092] Establish a game model, define the game participants and the corresponding strategy space and profit function, and the formula is expressed as:

[0093] u i (s1,s2,…,s n )=α·T i -β·D i -γ·L i

[0094] Where, T i is the data transmission volume of node i, D i is the data transmission delay of node i, L i is the packet loss rate of node i, α, β, γ are weight coefficients, n is the number of game participants and n ≥ 2, i is a positive integer, i∈{1,2…,n} distinguishes different game entities.

[0095] For each game participant, the corresponding strategy space is initialized, and the best response strategy is calculated under the fixed strategy space of other game participants. The formula is expressed as:

[0096]

[0097] Where, For the best response strategy, is a fixed strategy space, s j ∈S j is the strategy chosen by player j, s j is the strategy space of game participant j.

[0098] Based on the optimal response strategy and associated impact data, the system predicts changes in various data performance indicators and formulates resource allocation plans based on these changes and the needs of different business applications. Based on the resource usage of network nodes and business applications under the balancing strategy and the status of external interference factors, it calculates changes in performance indicators such as overall network throughput, average latency, and packet loss rate. It allocates appropriate bandwidth resources to different services based on their latency and throughput requirements. Computing resources are also allocated based on node processing capabilities and load.

[0099] 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 the adaptive backoff algorithm.

[0100] The steps of optimizing and obtaining adaptive node distribution data include:

[0101] The adaptive backoff algorithm sets the initial backoff time and maximum backoff times 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.

[0102] The adjustment direction is obtained based on the analysis of the correlation relationship, and the node configuration, resource allocation and adaptive backoff algorithm are adjusted to obtain the optimized decision.

[0103] The steps for adjusting the direction include:

[0104] The association relationships are quantitatively analyzed to determine the degree of influence of each factor on node configuration, resource allocation, and adaptive backoff algorithm.

[0105] The current node configuration is obtained by reallocating node performance, configuration rationality and node collaboration according to the degree of impact.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The bus topology is adjusted according to the adaptive node distribution data to obtain an optimized topology.

[0111] The steps to adjust the optimized topology include:

[0112] Data that influences topology adjustments is extracted from the adaptive node distribution data as key information data. This key information data may include identifying nodes with poor performance or unreasonable resource utilization, as well as connections between nodes that frequently communicate but have conflicts or high latency.

[0113] Analyze the current bus topology to obtain the current structural performance, and determine the performance improvement target based on key information data.

[0114] The steps to determine performance improvement goals include:

[0115] The current bus topology is converted into a graphic, and the current structure analysis data is obtained by analyzing the node attributes, node connection relationships and communication paths.

[0116] 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.

[0117] Connection relationship analysis can include analyzing the connection methods between nodes, such as bandwidth, transmission medium, and signal strength. It can also determine which connections are critical and play an important role in the overall performance of the network, and which connections present potential bottlenecks or instability factors.

[0118] Communication path analysis can include: sorting out the communication path of data in the bus topology, analyzing the path length, number of hops, transmission delay, etc., and checking whether there are circuitous paths, loops, or unreasonable routing choices.

[0119] 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.

[0120] Based on the performance difference data combined with key information data, the performance indicator improvement rate is obtained and priorities are set according to network needs.

[0121] Set performance improvement goals based on the improvement extent and priority of performance indicators.

[0122] Based on the adaptive node distribution data and performance improvement goals, the node position, connection relationship, node configuration and redundancy design are adjusted and optimized to obtain the topology adjustment strategy.

[0123] According to the topology adjustment strategy and the preset adjustment sequence and time arrangement, the bus topology is updated to obtain an optimized topology.

[0124] Example 1: I. Initial data collection:

[0125] There is a bus topology network with 5 nodes (Node1-Node5) and the following data is collected in real time:

[0126] Node status data:

[0127] Node1: CPU usage is 30%, memory usage is 2 GB, and disk I / O rate is 100 KB / s.

[0128] Node2: CPU usage is 40%, memory usage is 3 GB, and disk I / O rate is 120 KB / s.

[0129] Node3: CPU usage is 20%, memory usage is 1.5 GB, and disk I / O rate is 80 KB / s.

[0130] Node4: CPU usage is 50%, memory usage is 2.5 GB, and disk I / O rate is 150 KB / s.

[0131] Node5: CPU usage is 35%, memory usage is 2.2 GB, and disk I / O rate is 110 KB / s.

[0132] External factors data:

[0133] Electromagnetic interference intensity: Currently 20dB (the larger the value, the stronger the interference).

[0134] Number of cyberattacks: 5 small attacks occurred in the past hour.

[0135] Data performance indicators:

[0136] Throughput: Currently 500Mbps.

[0137] Transmission delay: The average delay is 10ms.

[0138] Packet loss rate: Currently 2%.

[0139] 2. Differential state evolution analysis:

[0140] After a period of time (10 minutes), collect node status data again:

[0141] Node 1: CPU usage is 35%, memory usage is 2.2 GB, and disk I / O rate is 110 KB / s.

[0142] Node2: CPU usage is 45%, memory usage is 3.2 GB, and disk I / O rate is 130 KB / s.

[0143] Node3: CPU usage dropped to 25%, memory usage reached 1.6 GB, and disk I / O rate reached 90 KB / s.

[0144] Node4: CPU usage is 55%, memory usage is 2.8 GB, and disk I / O rate is 160 KB / s.

[0145] Node5: CPU usage is 40%, memory usage is 2.5 GB, and disk I / O rate is 120 KB / s.

[0146] Calculate state change data using the differential state evolution method:

[0147] Node 1: 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.

[0148] Similarly, the status change data of other nodes are calculated.

[0149] Based on the impact of state change data on data performance indicators, it was found that the increase in CPU usage was correlated with the increase in transmission delay. The performance fluctuation data was calculated as follows:

[0150] Throughput dropped to 480Mbps.

[0151] Transmission delay rises to 12ms.

[0152] The packet loss rate becomes 3%.

[0153] 3. Gini Coefficient Analysis:

[0154] 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%).

[0155] Using the Gini coefficient analysis method, we determined that the Gini coefficient for electromagnetic interference intensity and throughput was 0.4, indicating that electromagnetic interference intensity has a certain impact on throughput. The Gini coefficient for the number of network attacks and packet loss rate was 0.6, indicating that the number of network attacks has a significant impact on packet loss rate. This generated correlated impact data, clarifying the extent and direction of the impact of external factors on performance indicators.

[0156] 4. Application of Game Theory Algorithms:

[0157] There are three types of resources (bandwidth, CPU resources, and memory resources) that need to be allocated to 5 nodes.

[0158] Using a game theory algorithm, we consider the state changes of nodes, the impact of external factors, and the resource requirements of each node (Node 1 has a higher bandwidth requirement, and Node 4 has a higher CPU resource requirement). After calculation, we get the resource allocation plan:

[0159] Node 1: allocated bandwidth 120 Mbps, CPU resource usage 20%, and memory 0.8 GB.

[0160] Node2: allocated bandwidth 100 Mbps, CPU resource usage 18%, memory 0.7 GB.

[0161] Node3: allocated bandwidth 80 Mbps, CPU resource usage 15%, and memory 0.6 GB.

[0162] Node4: allocated bandwidth 150 Mbps, CPU resource usage 25%, and memory 0.9 GB.

[0163] Node5: allocated bandwidth 100 Mbps, CPU resource usage 22%, memory 0.7 GB.

[0164] 5. Node configuration and conflict detection optimization:

[0165] Configure the nodes of the bus topology according to the resource allocation plan, and adjust the node's network interface bandwidth settings, CPU resource allocation parameters, and memory allocation.

[0166] A conflict detection mechanism was established. At a certain moment, Node2 and Node4 simultaneously attempted to send a large amount of data, and the conflict detection mechanism detected a conflict. Using an adaptive backoff algorithm (assuming backoff time is calculated based on node priority and data volume), Node2 backed off for 5ms and Node4 for 8ms. After multiple conflict detection and backoff adjustments, adaptive node distribution data was obtained, determining the optimal location and data transmission strategy for each node in the bus topology.

[0167] 6. Topology Adjustment

[0168] Based on the adaptive node distribution data, the bus topology is adjusted. Node1 and Node4, which have frequent communication, are moved closer together to reduce the signal transmission distance. After the adjustment, the optimized topology is obtained and the data performance indicators are monitored again:

[0169] Throughput increased to 520Mbps.

[0170] Transmission delay is reduced to 8ms.

[0171] The packet loss rate dropped to 1%.

[0172] 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.

[0173] This embodiment provides a communication optimization method for an adaptive bus topology. This method uses a differential state evolution method to accurately analyze node state changes and, combined with a Gini coefficient analysis method, quantifies the impact of external factors on performance. This method enables a more comprehensive and accurate assessment of network performance, avoiding performance misjudgments caused by one-sided analysis. The application of game theory algorithms makes resource allocation more rational and improves resource utilization efficiency. It dynamically adjusts resource allocation based on the network's real-time status and changes in external factors to meet the needs of different nodes and services, thereby enhancing the network's overall performance and service quality. The entire technical solution monitors and analyzes network status in real time, adjusting network configuration and resource allocation in a timely manner based on external factors and performance fluctuations. This enables the adaptive bus topology to better adapt to changes in the network environment, improving network flexibility and adaptability, extending network lifespan, and providing a clear direction and specific implementation plan for further network optimization. Targeted adjustments and improvements can be made to the network, enhancing its performance and competitiveness.

[0174] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0175] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling 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 certain parts of the embodiments.

[0176] 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 various 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; The missing values ​​of the node status data were supplemented by the median filling method, and outliers were checked by the box plot method. The data of different dimensions were 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; State change data is obtained by calculating the change between the time change series and the time series, and performance fluctuation data is obtained according to the influence of the state change data on the data performance index; Combining the external factor data and the performance fluctuation data according to the Gini coefficient analysis method to obtain correlation influence data, and using a game theory algorithm to predict the impact of the correlation influence data on the data performance index to obtain a resource allocation plan; The nodes of the bus topology 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 is adjusted according to the adaptive node distribution data to obtain an optimized topology.

2. 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 includes: Analyzing the causal relationship between each state change in the state change data and each data performance indicator to obtain a correlation relationship; Based on the correlation, 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 degree of influence on each state change is determined according to the size and frequency of each performance change, and the degree of influence, the correlation relationship and multiple performance fluctuation patterns are integrated to obtain the performance fluctuation data.

3. The communication optimization method of an adaptive bus topology structure according to claim 2, 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 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 addition 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.

4. The communication optimization method of an adaptive bus topology structure according to claim 1, characterized in that: The steps of obtaining the associated impact data include: 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 abscissa and the fluctuation ratio as the ordinate, and points are drawn on the coordinate plane according to all ratios and connected to form a ratio curve; Calculating the area between the proportional curve and the diagonal line to obtain the diagonal area, and calculating 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.

5. The communication optimization method of an adaptive bus topology structure according to claim 1, characterized in that: The step of obtaining the resource allocation solution includes: Establish a game model, define the game participants and the corresponding strategy space and payoff function; For each game participant, initialize the corresponding strategy space and calculate the best response strategy under the fixed strategy spaces of other game participants; According to the optimal response strategy and 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.

6. The communication optimization method of an adaptive bus topology structure according to claim 3, characterized in that: The step of optimizing and obtaining the adaptive node distribution data includes: 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. 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.

7. The communication optimization method of an adaptive bus topology structure according to claim 6, characterized in that: The step of adjusting to obtain the adjustment direction includes: Quantitatively analyzing the association relationship to determine the degree of influence of each factor on the node configuration, the resource allocation, and the adaptive backoff algorithm; Re-allocating node performance, configuration rationality, and node collaboration according to the impact to obtain a current node configuration; The current resource allocation is obtained by reallocating the resource allocation balance and the matching degree between resources and business requirements according to the impact degree; 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.

8. 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 that has 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 structural performance, and determine the performance improvement target based on 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 adjusted and optimized to obtain a topology adjustment strategy; According to the topology adjustment strategy and the preset adjustment sequence and time arrangement, the bus topology is updated to obtain the optimized topology.

9. The communication optimization method of an adaptive bus topology structure according to claim 8, characterized in that: The steps of determining the performance improvement target include: Convert the current bus topology into a graphic, and analyze it from three aspects: node attributes, node connection relationships, and communication paths to obtain current structural analysis data; 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 combining the current structure analysis data to obtain the current structure performance; According to the performance difference data and the key information data, the performance indicator improvement range 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.

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