Load balancing method and system for wireless mesh network

Abstract

The invention provides a load balancing method and system for a wireless mesh network. The method comprises the following steps: firstly, acquiring real-time load data, link quality data, position data and movement historical data of a plurality of nodes in a network; generating a current load distribution condition of the network according to the real-time load data and the link quality data, and identifying a node set of which the load is higher than a preset range from the current load distribution condition; the node set is analyzed based on the position data and the movement historical data, and future position changes are predicted; determining an alternative path according to future position change in combination with link quality data; and finally, part of flow of the node set is transmitted through the alternative path. According to the technical scheme provided by the invention, the problem of path quality abrupt change caused by node mobility is solved, and the long-term stability and service continuity of load transfer in the dynamic wireless mesh network are also improved.

Description

Technical Field

[0001] This application relates to the field of wireless communication network technology, and in particular to a load balancing method and system for wireless mesh networks. Background Technology

[0002] Mesh networks are widely used in emergency communications, mobile vehicle networks, and smart IoT scenarios due to their self-organizing, multi-hop transmission, and flexible deployment characteristics. The nodes in these networks are usually mobile, which leads to dynamic changes in the network topology. This results in an extremely uneven distribution of service load in the network, and some nodes become congestion bottlenecks due to excessive connections or being on the critical path, which seriously affects the overall transmission efficiency and service reliability of the network.

[0003] Existing technical solutions make decisions based on real-time network link quality data and instantaneous node load data. The core of these solutions is to monitor the current quality indicators of each communication link and the real-time load rate of the nodes. Once it is found that the load of a node exceeds a preset threshold, one or more transmission paths are reselected for its service traffic based on the optimal link quality information at the current moment to share the load pressure.

[0004] However, this scheme has inherent flaws. It is essentially a passive response mechanism based on the current instantaneous state of the network and fails to fully consider the future impact of node mobility on the network topology. Since the node positions in the wireless mesh network are constantly changing, the path with the best quality at the current moment may deteriorate or even be interrupted in a short period of time due to node movement, causing the load migration based on this path to fail, thereby causing service jitter and additional path switching overhead. The lack of a mechanism to predict the stability of future connections makes it difficult for the load balancing effect to be sustained and stable in dynamic environments. Summary of the Invention

[0005] This application provides a load balancing method and system for wireless mesh networks to solve the problems in the prior art where load balancing decisions are based solely on the instantaneous state of the network and fail to effectively address dynamic topology changes caused by node mobility, resulting in the selected path potentially failing in a short period of time, unsustainable load migration effects, and service jitter.

[0006] In a first aspect, this application provides a load balancing method for a wireless mesh network, comprising: Acquire real-time load data, link quality data, location data, and mobility history data of multiple nodes in a wireless mesh network; The current network load distribution is generated based on the real-time load data and the link quality data. Identify the set of nodes whose load is higher than a preset range from the load distribution data; The node set is analyzed based on the location data and the historical movement data to obtain future location changes; Based on the future location changes and the link quality data, alternative paths are determined; A portion of the traffic from the node set is transmitted through the alternative path to achieve a smooth load migration.

[0007] Optionally, generating the current network load distribution based on the real-time load data and the link quality data includes: Extract the node load value corresponding to each node from the real-time load data; Extract the link reliability value corresponding to each wireless communication link from the link quality data; The link adjustment value for each node is obtained by comprehensively calculating the reliability values ​​of the links directly connected to each node. The node load value of each node is combined with the corresponding link adjustment value to generate the comprehensive load value of each node; By integrating the combined load values ​​of all nodes, the current load distribution of the network can be obtained.

[0008] Optionally, identifying a set of nodes with loads exceeding a preset range from the load distribution includes: Extract the combined load value of all nodes from the load distribution; Based on the combined load values ​​of all nodes, multiple reference nodes are selected; Calculate the preset range based on the comprehensive load value of the reference node; The overall load value of each node is compared with the preset range, and nodes with an overall load value higher than the preset range are selected to form a node set.

[0009] Optionally, the node set is analyzed based on the location data and the historical movement data to obtain future location changes, including: Obtain the current location coordinates of each node in the node set from the location data; Extract the movement record sequence of each node in the node set from the movement history data; Based on the time and coordinate information of consecutive positions in the movement record sequence, calculate the movement trend parameters of each node; By combining the current location coordinates of each node with the corresponding movement trend parameters, we can obtain the future location changes.

[0010] Optionally, alternative paths are determined based on the future location changes and the link quality data, including: Obtain the link reliability value for each wireless communication link from the link quality data; Based on the future location changes, calculate the connection stability of the wireless communication links between nodes; The connection stability is combined with the corresponding link reliability value to generate a comprehensive reliability. Based on the overall reliability, multiple candidate paths are generated in the wireless mesh network; Paths with quality higher than a preset standard are selected from the candidate paths as alternative paths.

[0011] Optionally, the connection stability is combined with the corresponding link reliability value to generate a comprehensive reliability, including: Obtain the connection stability and link reliability values ​​for each wireless communication link; The first binding factor and the second binding factor are determined based on the mobility intensity of the nodes in the wireless mesh network. The connection stability is adjusted according to the first binding factor to obtain a stability component; The link reliability value is adjusted according to the second combination factor to obtain the reliability value component; The stability component and the reliability value component are used to calculate the overall reliability.

[0012] Optionally, a portion of the traffic from the node set is transmitted via the alternative path to achieve a smooth load migration, including: Based on the overall load value of the node set and the path quality of the alternative paths, the amount of traffic to be transferred is calculated. Based on the required flow rate, the required flow rate is divided into multiple flow rate units. Based on the path quality of each alternative path, the traffic unit is allocated to different alternative paths and transmitted to the target node through the alternative paths to complete the smooth migration of the load.

[0013] Secondly, this application provides a load balancing system for a wireless mesh network, comprising: The acquisition module is used to acquire real-time load data, link quality data, location data, and mobility history data of multiple nodes in a wireless mesh network. The generation module is used to generate the current load distribution of the network based on the real-time load data and the link quality data; An identification module is used to identify a set of nodes whose load is higher than a preset range from the load distribution data; The analysis module is used to analyze the set of nodes based on the location data and the historical movement data to obtain future location changes; The determination module is used to determine alternative paths based on the future location changes and the link quality data; The transmission module is used to transmit a portion of the traffic of the node set through the alternative path to complete the smooth migration of the load.

[0014] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a load balancing method for a wireless mesh network as described in the first aspect above.

[0015] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a load balancing method for a wireless mesh network as described in the first aspect.

[0016] This application, by comprehensively acquiring and analyzing real-time load, link quality, location, and historical mobility data of nodes, can not only accurately assess the current load distribution of the network, but also further predict the future mobility trends of high-load node sets. By screening alternative paths based on predictions of future network topology changes, the load balancing decision-making becomes forward-looking, thereby effectively overcoming the problem of path quality abrupt changes caused by node mobility and improving the long-term stability and service continuity of load transfer in dynamic wireless mesh networks.

[0017] Furthermore, by dynamically calculating and precisely allocating the traffic to be transferred based on the specific load pressure of high-load nodes and the real-time quality of each alternative path, fine-grained traffic scheduling is achieved. The traffic to be transferred is divided into multiple units and allocated differentially to paths of different quality, avoiding the problems of single path overload or uneven traffic distribution that may occur in traditional methods. Thus, while completing load balancing, the overall network resource utilization efficiency is optimized, and the overall performance of load balancing operations is enhanced.

[0018] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1A flowchart of a load balancing method for a wireless mesh network provided in this application is shown; Figure 2 A schematic diagram of the structure of a load balancing system for a wireless mesh network provided in this application is shown; Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0022] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0023] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1 A flowchart of a load balancing method for a wireless mesh network is provided in this application, as shown below. Figure 1 As shown, the method includes: Step 101: Obtain real-time load data, link quality data, location data, and movement history data of multiple nodes in the wireless mesh network.

[0025] In the above steps, a wireless mesh network refers to a network structure in which multiple wireless nodes form multi-hop communication through self-organization, which is used to achieve wide coverage and flexible networking, and is obtained through wireless links between nodes. Real-time load data refers to dynamic information that reflects the current business processing pressure of network nodes. It is used to measure the degree of node congestion and is obtained by monitoring the data cache queue length and throughput of nodes. Link quality data refers to parameters that characterize the reliability of wireless communication link transmission. They are used to evaluate path stability and are obtained by measuring signal strength, bit error rate, and transmission delay. Location data refers to the coordinate information of nodes in three-dimensional space, which is used to determine network topology and is obtained through GPS modules or positioning algorithms; Historical movement data refers to information that records the continuous time trajectory of nodes, used to analyze movement patterns. It is obtained by periodically recording and storing position coordinates.

[0026] In this embodiment, data collection agents are first deployed at each network node to continuously monitor and record the service processing volume and cache status of the node to form real-time load data. Secondly, the signal-to-noise ratio and packet loss rate of the signal used in communication with neighboring nodes are measured using a wireless transceiver, and combined to generate link quality data. At the same time, the positioning module is invoked to obtain latitude and longitude coordinates as location data, and the periodic location information is stored as movement history data in a time series.

[0027] For example, in a smart transportation wireless mesh network in a city, the node device installed on bus A uses a processor to count the number of data packets forwarded per second to form real-time load data, uses a wireless network card to measure the signal strength between the device and roadside unit B to form link quality data, uses an onboard GPS module to obtain latitude and longitude coordinates as location data, and stores the location coordinates recorded every 30 seconds in chronological order as movement history data.

[0028] This step systematically collects multi-dimensional dynamic parameters, providing a comprehensive and real-time data foundation for load balancing decisions. It effectively overcomes decision-making biases caused by incomplete information in traditional methods and builds reliable data support for subsequent intelligent analysis.

[0029] Step 102: Generate the current network load distribution based on the real-time load data and the link quality data.

[0030] Optionally, step 102 may specifically include the following steps: Step 1021: Extract the node load value corresponding to each node from the real-time load data; Step 1022: Extract the link reliability value corresponding to each wireless communication link from the link quality data; Step 1023: Perform a comprehensive calculation based on the link reliability values ​​directly connected to each node to obtain the link adjustment value for each node; Step 1024: Combine the node load value of each node with the corresponding link adjustment value to generate the comprehensive load value of each node; Step 1025: Integrate the combined load values ​​of all nodes to obtain the current load distribution of the network.

[0031] In the above steps, the current load distribution of the network refers to the overall situation map reflecting the comprehensive load-bearing pressure of all nodes in the network, which is used to identify regional load unevenness and is obtained by integrating the comprehensive load values ​​of each node. Node load value refers to the amount of data transmission currently undertaken by a single node. It is used to measure the node's own pressure level and is obtained by counting the amount of data to be forwarded. A wireless communication link refers to a radio transmission channel established between two nodes for data transmission between nodes, which is established through the interaction of wireless signals. Link reliability value refers to a quantitative evaluation of the transmission stability of a single link, used to predict link availability, and is obtained by analyzing the degree of signal attenuation; The link adjustment value refers to the correction factor for load pressure based on the quality of links around the node. It is used to reflect the topology impact and is obtained by calculating the reliability of adjacent links. The overall load value refers to the node stress evaluation value that combines its own load and link environment. It is used to accurately assess the node status and is obtained by comprehensively calculating the node load value and link adjustment value.

[0032] In this embodiment, the data packet queue length of each node is first extracted from the real-time load data by the data parsing module and quantified into a node load value. Secondly, the signal-to-noise ratio and packet loss rate parameters of each wireless communication link are extracted from the link quality data, and the link reliability value of each wireless communication link is calculated using a weighted scoring method. Next, for each node, all directly connected wireless communication links are found, and the link reliability value of these wireless communication links is calculated using a weighted average algorithm to obtain the link adjustment value for each node. Then, the node load value is multiplied by the corresponding link adjustment value through a multiplier to generate a comprehensive load value that reflects both its own pressure and connection status. Finally, topology mapping technology is used to mark the comprehensive load values ​​of all nodes on the network topology graph to obtain the current load distribution of the network.

[0033] Following the implementation case of the previous step, we first extract the current data packet queue length from the real-time load data of bus A, and use it as the node load value of node E. At the same time, the wireless communication link signal scores between node E and roadside unit B, and between node E and roadside unit C are extracted from the link quality data and used as the link reliability values ​​for these two wireless communication links, respectively. Next, the decision node calculates the link adjustment value of node E by weighted averaging the link reliability values ​​of the two wireless communication links directly connected to node E. Then, the node load value of node E is combined with the corresponding link adjustment value to calculate the overall load value of node E; Finally, the combined load values ​​of all nodes are integrated and marked on the corresponding locations on the electronic map to obtain the real-time load distribution of the entire traffic area.

[0034] This step incorporates link quality factors into the load assessment system, enabling the generated load distribution to reflect not only the node's own pressure but also the impact of the network topology on the load. This effectively improves the accuracy of load status assessment and lays a solid foundation for the subsequent accurate identification of high-load areas.

[0035] Step 103: Identify the set of nodes whose load is higher than a preset range from the load distribution.

[0036] Optionally, step 103 may specifically include the following steps: Step 1031: Extract the comprehensive load value of all nodes from the load distribution; Step 1032: Select multiple reference nodes based on the comprehensive load value of all nodes; Step 1033: Calculate the preset range based on the comprehensive load value of the reference node; Step 1034: Compare the overall load value of each node with the preset range, and filter out nodes whose overall load value is higher than the preset range to form a node set.

[0037] In the above steps, the preset range refers to the dynamic threshold used to determine whether a node is in a high-load state. It is used to accurately filter overloaded nodes and is calculated by analyzing the load level of the reference node. Reference nodes refer to representative baseline nodes selected from the network to determine load judgment criteria. They are selected by comprehensively considering the load value distribution characteristics. The node set refers to the group of all nodes identified as overloaded, which is used for subsequent targeted load balancing operations and is obtained through comparison and screening.

[0038] In this embodiment, the comprehensive load value of all nodes is first extracted from the load distribution through the data extraction interface; Secondly, a stratified sampling technique is adopted. Based on the distribution characteristics of the comprehensive load value, representative nodes are selected as reference nodes from different load ranges of high, medium and low. Next, statistical analysis algorithms are used to process the comprehensive load values ​​of these reference nodes, calculate the central trend value of the load level, and determine the preset range based on the central trend value. Then, a comparator compares the overall load value of each node with the upper boundary value of the preset range one by one, and filters out all nodes whose overall load value exceeds the upper boundary to form a node set.

[0039] Following the implementation case from the previous step, we first extract the combined load value of all buses and roadside units; Next, based on the distribution characteristics of these comprehensive load values, several typical nodes were selected as reference nodes from the three levels of light, moderate and heavy load. After performing a central tendency analysis on the comprehensive load values ​​of these reference nodes, a preset range is determined; Then, the combined load value of all nodes is compared with the preset range to identify the nodes of buses that exceed the preset range, forming a node set.

[0040] This step introduces a dynamic threshold calculation mechanism based on the actual network state, enabling the identification process of high-load nodes to adapt to changes in the overall network load level. This avoids misjudgments or omissions that may be caused by fixed thresholds, thereby ensuring that the load balancing operation can accurately locate the bottleneck nodes that truly need to be addressed, and improving the adaptability and reliability of the entire method.

[0041] Step 104: Analyze the node set based on the location data and the historical movement data to obtain future location changes.

[0042] Optionally, step 104 may specifically include the following steps: Step 1041: Obtain the current position coordinates of each node in the node set from the position data; Step 1042: Extract the movement record sequence of each node in the node set from the movement history data; Step 1043: Calculate the movement trend parameters of each node based on the time and coordinate information of consecutive positions in the movement record sequence; Step 1044: Combine the current position coordinates of each node with the corresponding movement trend parameters to obtain the future position change.

[0043] In the above steps, future position change refers to the predicted displacement of a node relative to its current position within a specific time period in the future. It is used to predict changes in network topology and is calculated by combining the current position coordinates and movement trend parameters. The current location coordinates refer to the real-time geographical location of the node in three-dimensional space, which is used to determine the starting point of the movement trajectory and is obtained in real time through the positioning system; A movement record sequence refers to a set of historical position coordinates of nodes arranged in chronological order, used to analyze movement patterns, and is formed by continuously recording position information; Time information refers to the time point at which each location coordinate is collected, which is used to calculate the moving speed and is obtained through the timestamp of the positioning system. Coordinate information refers to the specific spatial value of the location, which is used to calculate the displacement distance and is obtained through the coordinate output of the positioning system. The movement trend parameter refers to the characteristic value that represents the direction and speed of node movement. It is used to predict future movement trajectories and is derived by analyzing the spatiotemporal relationships in the movement record sequence.

[0044] In this embodiment, the latest latitude and longitude coordinates of each node in the node set are first read from the location data through the positioning data interface as the current location coordinates; Secondly, retrieve the continuous location records of each node in the node set from the migration history database within a recent period, and arrange them in chronological order to form a migration record sequence. Next, trajectory analysis technology is used to process the motion record sequence, extract the directional changes and movement speed between adjacent points in the motion record sequence, and obtain the movement trend parameters through linear regression calculation; Then, using the current position coordinates of each node as the starting point, and based on the direction and speed indicated by the corresponding movement trend parameters, the predicted position coordinates of the node at a specific point in the future are calculated. By comparing the difference between the predicted position coordinates and the current position coordinates, the future position change of each node is generated.

[0045] Following the implementation example from the previous step, we first obtain the latest coordinates of each node from the set of nodes identified as having high load as its current location coordinates; Next, retrieve the position coordinates of these nodes that have been periodically recorded over a period of time, and arrange them in chronological order to form a moving record sequence; Then, by analyzing the time and coordinate information of consecutive positions in the movement record sequence, the average movement direction and speed of each node are calculated as movement trend parameters. Finally, the position coordinates of each node at a specific future time are predicted, and the position coordinates are combined with the corresponding movement trend parameters to obtain the future position change.

[0046] This step predicts future location changes by scientifically analyzing the historical movement patterns of nodes, enabling load balancing decisions to take into account the dynamic evolution of the network topology in advance. This effectively overcomes the problem of lagging response to node mobility in traditional methods and provides crucial time foresight for selecting persistent and stable communication paths.

[0047] Step 105: Determine alternative paths based on the future location changes and the link quality data.

[0048] Optionally, step 105 may specifically include the following steps: Step 1051: Obtain the link reliability value of each wireless communication link from the link quality data; Step 1052: Calculate the connection stability of the wireless communication link between nodes based on the future location changes; Step 1053: Combine the connection stability with the corresponding link reliability value to generate a comprehensive reliability. Optionally, step 1053 may specifically include the following steps: The connection stability and link reliability values ​​of each wireless communication link are obtained; a first combination factor and a second combination factor are determined based on the mobility intensity of nodes in the wireless mesh network; the connection stability is adjusted according to the first combination factor to obtain a stability component; the link reliability value is adjusted according to the second combination factor to obtain a reliability component; the stability component and the reliability component are calculated to obtain a comprehensive reliability. Step 1054: Based on the comprehensive reliability, generate multiple candidate paths in the wireless mesh network; Step 1055: Select paths with higher quality than a preset standard from the candidate paths as alternative paths.

[0049] In the above steps, the alternative path refers to the data transmission route that is ultimately selected to share the traffic of high-load nodes, in order to achieve load redirection, and is obtained by filtering candidate paths; Link reliability score refers to the transmission quality score of a single wireless communication link at the current moment. It is used to reflect real-time communication conditions and is obtained by measuring signal parameters. Connection stability refers to the probability that a link will remain connected within a predicted future period of time. It is used to assess link continuity and is calculated by analyzing future location changes. Overall reliability refers to a comprehensive score of the link that combines current quality and future stability, used for path selection decisions. It is obtained by fusing connection stability and link reliability values. Node mobility intensity refers to the degree of activity in changing the location of nodes in the network. It is used to adjust evaluation weights and is obtained by statistically analyzing node movement speed. The first binding factor is a coefficient used to adjust the weights of connectivity stability, which controls the influence of future predictions and is determined based on mobility strength. The second combining factor is a coefficient used to adjust the weight of the link reliability value, which is used to control the impact of the current quality and is determined based on the mobility intensity. The stability component refers to the weighted value of the connection stability, which is used for comprehensive evaluation calculation and is obtained by adjusting the first bonding factor. The reliability component refers to the weighted value of the link reliability, which is used for comprehensive evaluation calculation and is obtained by adjusting the second combination factor. Candidate paths refer to possible paths initially identified based on comprehensive reliability, which are used for further screening and generated through path discovery algorithms; Path quality refers to the overall transmission capacity evaluation of candidate paths, used for allocation decisions, and is obtained by analyzing the comprehensive reliability of the paths; The preset standard refers to the minimum quality requirements that a path must meet to be selected as a candidate path. It is used to ensure path availability and is set according to business needs.

[0050] In this embodiment, the real-time quality score of all active wireless communication links is first read from the link quality data by the data query module as the link reliability value; Secondly, by using future location changes to calculate the relative distance and direction of movement between nodes at a specific future time, the connection stability is predicted by using a distance decay model to forecast the link's connectivity probability in the future period. Next, the connection stability and link reliability values ​​of each wireless communication link are obtained; the mobility strength is determined based on the average moving speed of nodes in the wireless mesh network, and then the first combination factor and the second combination factor are determined; the connection stability is multiplied by the first combination factor using a multiplication operation to obtain the stability component; at the same time, the link reliability value is multiplied by the second combination factor to obtain the reliability component; the stability component and the reliability component are added together to obtain the overall reliability. Then, an improved path discovery algorithm is used, with comprehensive reliability as the link metric, to search for multiple candidate paths from high-load nodes to the target node in the network topology. Finally, the overall reliability of all wireless communication links on each candidate path is accumulated and calculated, and compared with a preset standard to select alternative paths.

[0051] Following the implementation case from the previous step, the real-time signal score of each wireless communication link between bus A and roadside unit B and roadside unit C is obtained from the link quality data as the link reliability value. Secondly, based on the future location changes of the node, the relative distance between the node and roadside unit B and roadside unit C at future times is analyzed to calculate the connection stability of each wireless communication link. Next, the weighting factor is determined based on the average moving speed of vehicles in the current network area, and the connection stability and link reliability values ​​are weighted and fused to generate a comprehensive reliability. Then, based on overall reliability, multiple candidate paths from bus A to the gateway node are searched in the network topology; Finally, two candidate paths with a quality higher than the preset standard were selected from multiple candidate paths as alternative paths.

[0052] This step combines future link stability prediction with current quality assessment, enabling path selection to consider not only instantaneous communication conditions but also the impact of topology changes. This significantly improves the persistent availability of the selected path in dynamic environments, effectively avoids frequent path switching caused by node movement, and provides more reliable transmission guarantees for load balancing.

[0053] Step 106: Transmit a portion of the traffic from the node set through the alternative path to complete a smooth load migration.

[0054] Optionally, step 106 may specifically include the following steps: Step 1061: Calculate the amount of traffic to be transferred based on the overall load value of the node set and the path quality of the alternative paths; Step 1062: Divide the flow value to be transferred into multiple flow units according to the flow value to be transferred. Step 1063: Based on the path quality of each alternative path, the traffic unit is allocated to different alternative paths and transmitted to the target node through the alternative paths to complete the smooth migration of the load.

[0055] In the above steps, "partial traffic" refers to the network data flow that needs to be transferred out from high-load nodes to reduce node pressure. The specific amount of data is determined by calculation. The required transfer volume refers to the total amount of data to be migrated, which is used to guide the migration operation and is determined by analyzing load pressure and path capacity. A traffic unit refers to a standardized data block into which the traffic to be transferred is divided, used for fine-grained allocation, and is obtained through packet packetization technology.

[0056] In this embodiment, the comprehensive load value of each node in the node set is first obtained through the load analysis module. At the same time, the comprehensive reliability of each alternative path is extracted from the path database as the path quality. The capacity matching algorithm is used to calculate the amount of data exceeding the normal load level of each node, and the traffic value that needs to be transferred is summarized. Secondly, based on the maximum transmission unit standard of the network transmission protocol, the traffic to be transferred is divided into multiple consecutive data blocks of a fixed size as traffic units; Finally, through the load distribution algorithm, multiple traffic units are dynamically allocated to different alternative paths according to the path quality ratio of each alternative path, triggering the multipath transmission protocol to transmit each traffic unit to the target node along the allocated alternative paths, so as to complete the smooth migration of the load.

[0057] Following the implementation case from the previous step, we first analyze the overall load value of the node and combine it with the path quality of the two alternative paths to calculate the specific amount of data that needs to be migrated from the node as the traffic value that needs to be transferred. Next, the traffic volume to be transferred is divided into multiple traffic units according to the standard data packet size; Then, based on the path quality of the two alternative paths, most of the traffic units are allocated to the higher-quality path one, and the remaining traffic units are allocated to path two. The node simultaneously sends the allocated traffic units to the target node of the gateway node through these two paths to complete the smooth migration of the load.

[0058] This step achieves precise and stable migration of network traffic through refined traffic calculation and standardized segmentation, combined with a differentiated allocation strategy based on path quality. While effectively alleviating the pressure on high-load nodes, it fully utilizes the transmission capacity of each alternative path, avoids the risk of congestion on a single path, significantly improves the overall network resource utilization efficiency and load balancing effect, and ensures the continuity of data transmission.

[0059] Figure 2 This application provides a schematic diagram of the structure of a load balancing system for a wireless mesh network, as shown below. Figure 2 As shown, the system includes: The acquisition module 21 is used to acquire real-time load data, link quality data, location data and mobility history data of multiple nodes in the wireless mesh network; Generation module 22 is used to generate the current load distribution of the network based on the real-time load data and the link quality data; The identification module 23 is used to identify the set of nodes whose load is higher than a preset range from the load distribution. Analysis module 24 is used to analyze the node set based on the location data and the movement history data to obtain future location changes; The determination module 25 is used to determine alternative paths based on the future location changes and the link quality data; The transmission module 26 is used to transmit a portion of the traffic of the node set through the alternative path to complete the smooth migration of the load.

[0060] Figure 2 The aforementioned load balancing system for a wireless mesh network can perform... Figure 1 The implementation principle and technical effects of the load balancing method for a wireless mesh network described in the illustrated embodiment will not be repeated here. The specific methods by which each module and unit performs its operations in the load balancing system for a wireless mesh network in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0061] In one possible design, Figure 2 The load balancing system for a wireless mesh network shown in the embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0062] The processing component 32 is used for the above Figure 1 The embodiment describes a load balancing method for a wireless mesh network.

[0063] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0064] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0065] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0066] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0067] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0068] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0069] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The embodiment shown illustrates a load balancing method for a wireless mesh network.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0072] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A load balancing method for a wireless mesh network, characterized in that, include: Acquire real-time load data, link quality data, location data, and mobility history data of multiple nodes in a wireless mesh network; The current network load distribution is generated based on the real-time load data and the link quality data. Identify the set of nodes whose load is higher than a preset range from the load distribution data; The node set is analyzed based on the location data and the historical movement data to obtain future location changes; Based on the future location changes and the link quality data, alternative paths are determined; A portion of the traffic from the node set is transmitted through the alternative path to achieve a smooth load migration.

2. The method according to claim 1, characterized in that, The current network load distribution is generated based on the real-time load data and the link quality data, including: Extract the node load value corresponding to each node from the real-time load data; Extract the link reliability value corresponding to each wireless communication link from the link quality data; The link adjustment value for each node is obtained by comprehensively calculating the reliability values ​​of the links directly connected to each node. The node load value of each node is combined with the corresponding link adjustment value to generate the comprehensive load value of each node; By integrating the combined load values ​​of all nodes, the current load distribution of the network can be obtained.

3. The method according to claim 1, characterized in that, The set of nodes whose load is higher than a preset range is identified from the load distribution, including: Extract the combined load value of all nodes from the load distribution; Based on the combined load values ​​of all nodes, multiple reference nodes are selected; Calculate the preset range based on the comprehensive load value of the reference node; The overall load value of each node is compared with the preset range, and nodes with an overall load value higher than the preset range are selected to form a node set.

4. The method according to claim 1, characterized in that, Based on the location data and the historical movement data, the node set is analyzed to obtain future location changes, including: Obtain the current location coordinates of each node in the node set from the location data; Extract the movement record sequence of each node in the node set from the movement history data; Based on the time and coordinate information of consecutive positions in the movement record sequence, calculate the movement trend parameters of each node; By combining the current location coordinates of each node with the corresponding movement trend parameters, we can obtain the future location changes.

5. The method according to claim 1, characterized in that, Based on the future location changes and the link quality data, alternative paths are determined, including: Obtain the link reliability value for each wireless communication link from the link quality data; Based on the future location changes, calculate the connection stability of the wireless communication links between nodes; The connection stability is combined with the corresponding link reliability value to generate a comprehensive reliability. Based on the overall reliability, multiple candidate paths are generated in the wireless mesh network; Paths with quality higher than a preset standard are selected from the candidate paths as alternative paths.

6. The method according to claim 5, characterized in that, The connection stability is combined with the corresponding link reliability value to generate a comprehensive reliability, including: Obtain the connection stability and link reliability values ​​for each wireless communication link; The first binding factor and the second binding factor are determined based on the mobility intensity of the nodes in the wireless mesh network. The connection stability is adjusted according to the first binding factor to obtain a stability component; The link reliability value is adjusted according to the second combination factor to obtain the reliability value component; The stability component and the reliability value component are used to calculate the overall reliability.

7. The method according to claim 1, characterized in that, Transmitting a portion of the traffic from the node set through the alternative path to achieve a smooth load migration includes: Based on the overall load value of the node set and the path quality of the alternative paths, the amount of traffic to be transferred is calculated. Based on the required flow rate, the required flow rate is divided into multiple flow rate units. Based on the path quality of each alternative path, the traffic unit is allocated to different alternative paths and transmitted to the target node through the alternative paths to complete the smooth migration of the load.

8. A load balancing system for a wireless mesh network, characterized in that, include: The acquisition module is used to acquire real-time load data, link quality data, location data, and mobility history data of multiple nodes in a wireless mesh network. The generation module is used to generate the current load distribution of the network based on the real-time load data and the link quality data; An identification module is used to identify a set of nodes whose load is higher than a preset range from the load distribution data; The analysis module is used to analyze the set of nodes based on the location data and the historical movement data to obtain future location changes; The determination module is used to determine alternative paths based on the future location changes and the link quality data; The transmission module is used to transmit a portion of the traffic of the node set through the alternative path to complete the smooth migration of the load.

9. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a load balancing method for a wireless mesh network as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a load balancing method for a wireless mesh network as described in any one of claims 1 to 7.

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