Wireless network adaptive optimization method and system based on health topological graph, terminal and storage medium

Abstract

The invention relates to the technical field of communication matching, and discloses a wireless network adaptive optimization method and system based on a health topological graph, a terminal and a storage medium, and the method comprises the steps: constructing a logic network topological graph, obtaining a link quality index according to a preset frequency, and adding the link quality index into the logic network topological graph; calculating a link health score as a weight to be added into the logic network topological graph to obtain a health topological graph; matching a plurality of links according to the task type of the target transmission task, and selecting an optimal transmission link; and when the optimal transmission link is unstable, the optimal transmission link is converted into an alternative link. According to the method, a dynamic healthy network topological graph with weights is established, link health scores of wireless links between devices are reflected in real time, intelligent and predictive scheduling and migration of distributed tasks are achieved, smooth switching of services is completed before the wireless links completely fail, non-inductive network fault self-healing of an application layer is achieved, and the network reliability is improved. And the communication experience of the user is improved.

Description

Technical Field

[0001] This invention relates to the field of communication matching analysis technology, and in particular to a wireless network adaptive optimization method, system, terminal, and computer-readable storage medium based on a health topology map. Background Technology

[0002] Distributed soft buses can shield the complexity of underlying heterogeneous networks, providing unified and transparent cross-device communication capabilities for upper-layer applications.

[0003] In current distributed systems or IoT scenarios, device interconnection primarily relies on wireless technologies such as Wi-Fi and Bluetooth. These technologies suffer from node access and data forwarding issues at the network protocol layer. However, when the underlying wireless link experiences jitter, roaming handover, or momentary interruption, the upper-layer application perceives a "connection loss," triggering reconnection or device migration mechanisms. This process can lead to application lag, task failures, or a significant degraded user experience. Furthermore, during migration, the distributed task scheduler cannot determine which wireless path is optimal and can only blindly invoke it, resulting in limited task execution efficiency and stability.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide a wireless network adaptive optimization method, system, terminal, and computer-readable storage medium based on a health topology map, aiming to solve the problems of service interruption, low communication efficiency, and poor user experience caused by the use of passive response mechanisms in the prior art when dealing with communication fluctuations.

[0006] To achieve the above objectives, the present invention provides a wireless network adaptive optimization method based on a health topology map, the method comprising the following steps: Obtain the connection relationships with multiple target devices, construct a logical network topology based on all the connection relationships, obtain multiple link quality indicators for each target device at a preset frequency, and add all the link quality indicators to the logical network topology. Based on all the link quality indicators, calculate the link health score between each target device, and add all the link health scores as weights to the logical network topology graph to obtain the health topology graph. Based on the task type of the target transmission task, select multiple matching links in the health topology graph; Select the link with the highest health score from all the links as the transmission link; When the link health score of the transmission link is lower than the health threshold, the optimal alternative node is selected from all the links. The service of the target transmission task is switched to the link of the optimal alternative node.

[0007] Optionally, in the wireless network adaptive optimization method based on a health topology map, the link quality indicators include: signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate.

[0008] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein obtaining the connection relationships with multiple target devices, constructing a logical network topology map based on all the connection relationships, obtaining multiple link quality indicators for each target device at a preset frequency, and adding all the link quality indicators to the logical network topology map specifically includes: The system acquires connection relationships with multiple target devices. Based on these connection relationships, it interacts with all target devices via Wi-Fi and acquires the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of each target device at a preset frequency. Define all the target devices as corresponding nodes, and add each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate to the corresponding node to construct a logical network topology.

[0009] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein calculating the link health score between each target device according to all the link quality indicators, and adding all the link health scores as weights to the logical network topology map to obtain the health topology map, specifically includes: Based on the logical network topology diagram, each of the signal strength, physical connection rate, data transmission count, round-trip delay, and packet loss rate is fused to obtain a link health score between each target device. The link health score is added as a weight to the edge between each node to obtain a health topology graph.

[0010] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein selecting multiple matching links in the health topology map according to the task type of the target transmission task specifically includes: For the target transmission task of the current target device, multiple links matching the target transmission task are determined in the health topology map; The corresponding target device is determined based on all the links.

[0011] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein selecting the link with the highest health score among all links as the transmission link specifically includes: Based on the task type, determine the relative importance of the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the target device for the task. Update the link health score between the target device and all target devices according to the importance level; Based on the updated link health scores, all target devices are sorted to obtain the optimal target device; The link between the target device and the optimal target device is used as a transmission link to execute the target transmission task.

[0012] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein selecting the optimal candidate node from all links when the link health score of the transmission link is lower than a health threshold, specifically includes: The target transmission task is monitored in real time, and it is determined in real time whether the link health score of the transmission link is lower than the health threshold. If so, then update the link health score between the task target device and all target devices, obtain the best alternative target device with the highest current link health score, and send the task execution instruction to the best alternative target device; The task execution instruction is used to start the task program in the optimal alternative target device to execute the target transmission task.

[0013] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein updating the link health scores between the target device and all target devices to obtain the optimal candidate target device with the highest current link health score specifically includes: If the link health score of the transmission link is determined to be lower than the health threshold, the user input demand instruction is obtained, and the importance of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of the target device is updated according to the demand instruction. The link health score between the target device and all target devices is updated using the updated importance level to obtain the optimal alternative target device with the highest current link health score.

[0014] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein the step of transferring the service of the target transmission task to the link of the optimal candidate node specifically includes: Based on the execution status of the target transmission task on the transmission link, the execution information of the target transmission task is synchronized to the optimal alternative target device through a distributed data management method; The target transmission task will continue to be executed using the link between the target device and the optimal alternative target device as the transmission link.

[0015] Optionally, the wireless network adaptive optimization method based on a health topology map, wherein the step of transferring the service of the target transmission task to the link of the optimal candidate node further includes: Obtain the execution status of the target transmission task on the current transmission link; The transmission requirements of the target transmission task are determined based on the execution level, and multiple alternative links that meet the transmission requirements are identified in the health topology map based on the transmission requirements. A task migration request is sent to each alternative node corresponding to the alternative link. When a target alternative node sends a receive request according to the task migration request, the alternative link between the target device and the target alternative node is used as the transmission link to continue executing the target transmission task.

[0016] Furthermore, to achieve the above objectives, the present invention also provides a wireless network adaptive optimization system based on a health topology map, wherein the wireless network adaptive optimization system based on a health topology map includes: The data acquisition module is used to acquire the connection relationships with multiple target devices, construct a logical network topology map based on all the connection relationships, acquire multiple link quality indicators for each target device at a preset frequency, and add all the link quality indicators to the logical network topology map. The topology graph construction module is used to calculate the link health score between each target device based on all the link quality indicators, and add all the link health scores as weights to the logical network topology graph to obtain a health topology graph; The weight evaluation module is used to select multiple matching links in the health topology graph based on the task type of the target transmission task. The link selection module is used to select the link with the highest link health score from all the links as the transmission link; The link monitoring module is used to select the optimal alternative node among all the links when the link health score of the transmission link is lower than the health threshold. The link optimization module is used to transfer the service of the target transmission task to the link of the optimal alternative node.

[0017] Optionally, in the aforementioned wireless network adaptive optimization system based on a health topology map, the data acquisition module includes: The indicator acquisition unit is used to acquire the connection relationship with multiple target devices, and based on the connection relationship, interact with all target devices through Wi-Fi drive, and acquire the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of each target device at a preset frequency; The logical network topology construction unit is used to define all the target devices as corresponding nodes, and add each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate to the corresponding nodes to construct a logical network topology.

[0018] Optionally, in the aforementioned wireless network adaptive optimization system based on a health topology map, the topology map construction module includes: The health assessment unit is used to perform fusion processing on each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate according to the logical network topology diagram to obtain a link health score between each of the target devices. A health topology graph construction unit is used to add the link health score as a weight to the edges between each node to obtain a health topology graph.

[0019] Optionally, in the aforementioned wireless network adaptive optimization system based on a health topology map, the weight evaluation module includes: The link matching unit is used to determine multiple links that match the target transmission task in the health topology map for the target transmission task of the current task target device. A device matching unit is used to determine the corresponding target device based on all the links.

[0020] Optionally, in the aforementioned wireless network adaptive optimization system based on a health topology map, the link selection module includes: The importance assessment unit is used to determine the degree of importance among the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the target device of the task, based on the task type. A health score update unit is used to update the link health score between the task target device and all the target devices according to the importance level; The sorting unit is used to sort all the target devices according to the updated link health scores to obtain the best target device; The task execution unit is used to use the link between the target device and the optimal target device as a transmission link to execute the target transmission task.

[0021] Optionally, in the aforementioned wireless network adaptive optimization system based on a health topology map, the link monitoring module includes: The threshold judgment unit is used to monitor the target transmission task in real time and determine whether the link health score of the transmission link is lower than the health threshold in real time. The target device candidate unit is used to update the link health score between the task target device and all the target devices if the link health score is the highest, obtain the optimal candidate target device with the highest current link health score, and send the task execution instruction to the optimal candidate target device. The task execution instruction is used to start the task program in the optimal alternative target device to execute the target transmission task.

[0022] Optionally, in the aforementioned wireless network adaptive optimization system based on a health topology map, the target device candidate unit includes: The importance update unit is used to obtain a user-inputted demand instruction if it is determined that the link health score of the transmission link is lower than the health threshold, and update the importance of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of the target device according to the demand instruction; The device update unit is used to update the link health score between the task target device and all the target devices using the updated importance level, and obtain the optimal alternative target device with the highest current link health score.

[0023] Optionally, in the aforementioned wireless network adaptive optimization system based on a health topology map, the link optimization module includes: The task migration unit is used to synchronize the execution information of the target transmission task to the optimal alternative target device through a distributed data management method, based on the execution status of the target transmission task on the transmission link. The task re-execution unit is used to continue executing the target transmission task by using the link between the target device and the optimal alternative target device as a transmission link.

[0024] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a wireless network adaptive optimization program based on a health topology map stored in the memory and executable on the processor, wherein when the wireless network adaptive optimization program based on the health topology map is executed by the processor, it implements the steps of the wireless network adaptive optimization method based on the health topology map as described above.

[0025] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a wireless network adaptive optimization program based on a health topology map, and when the wireless network adaptive optimization program based on a health topology map is executed by a processor, it implements the steps of the wireless network adaptive optimization method based on a health topology map as described above.

[0026] In this invention, connection relationships with multiple target devices are acquired, and a logical network topology is constructed based on all these connections. Multiple link quality indicators for each target device are acquired at a preset frequency, and all these indicators are added to the logical network topology. Based on these indicators, a link health score is calculated between each target device, and these scores are added as weights to the logical network topology to obtain a health topology. Multiple matching links are selected from the health topology based on the task type of the target transmission task. The link with the highest health score is selected as the transmission link. When the health score of the transmission link is lower than a health threshold, an optimal alternative node is selected from all the links. The service of the target transmission task is then transferred to the link of the optimal alternative node. This invention establishes a dynamic, weighted health network topology that reflects the link health scores of wireless links between devices in real time. This enables intelligent and predictive scheduling and migration of distributed tasks, achieving smooth service switching before complete wireless link failure. It also achieves application-layer-insensitive network fault self-healing, improving the user's communication experience. Attached Figure Description

[0027] Figure 1 This is a first flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 2 This is a second flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 3 This is a third flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 4 This is the fourth flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 5 This is the fifth flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 6 This is the sixth flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 7 This is the seventh flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 8 This is the eighth flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 9 This is a closed-loop flowchart of a preferred embodiment of the wireless network adaptive optimization method based on a health topology graph of the present invention; Figure 10 This is a specific principle structure diagram of a preferred embodiment of the wireless network adaptive optimization system based on health topology graph of the present invention; Figure 11 This is another specific principle structure diagram of a preferred embodiment of the wireless network adaptive optimization system based on a health topology graph of the present invention; Figure 12 This is an overall flowchart of a preferred embodiment of the wireless network adaptive optimization system based on a health topology graph of the present invention; Figure 13 This is a structural diagram of the topology graph construction module of a preferred embodiment of the wireless network adaptive optimization system based on health topology graph of the present invention; Figure 14 This is a structural diagram of the link optimization module of a preferred embodiment of the wireless network adaptive optimization system based on a health topology graph of the present invention; Figure 15 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0029] In current distributed systems or IoT scenarios, device interconnection primarily relies on wireless technologies such as Wi-Fi and Bluetooth. To expand coverage and improve stability, the industry commonly employs technologies such as Wi-Fi Mesh (Wireless Mesh Network), WDS (Wireless Distribution System, mainly used to extend wireless network coverage or deploy operating systems in batches), and fast roaming protocols. These technologies mainly address node access and data forwarding issues at the network protocol layer (the data link layer and network layer in the OSI model (Open System Interconnect)).

[0030] For example, simultaneous communication by a high density of devices may lead to network congestion and affect performance; wireless signals are easily interfered with in dense environments, resulting in unstable connections; traditional roaming protocols have high latency when switching access points, affecting real-time performance; when connecting multiple devices in a traditional wireless network, battery-powered devices need to optimize power consumption to extend their lifespan, and wireless communication may face threats of data leakage or attacks.

[0031] Furthermore, traditional network optimization is a "black box" for upper-layer applications. When the underlying wireless link experiences jitter, roaming switching, or momentary interruption, upper-layer applications (such as video calls and collaborative control) perceive only a "connection loss," which triggers a reconnection mechanism. This process can lead to application lag, task failure, or a severe decline in user experience.

[0032] Existing roaming and node selection mechanisms mainly rely on the single indicator of Received Signal Strength Indicator (RSSI), which cannot fully reflect the true quality of the link (such as real-time latency, packet loss rate, and channel congestion) or perceive the service requirements of upper-layer applications (e.g., whether low latency or high bandwidth is required).

[0033] Most existing technologies rely on passive response mechanisms, meaning that network repair or switching only begins after connection quality has deteriorated to a certain extent or even been interrupted. This "post-event remedy" approach cannot prevent service interruptions.

[0034] Finally, the lack of fine-grained awareness of the underlying wireless physical network topology and connection quality at the operating system level means that the distributed task scheduler cannot know which wireless path is optimal when performing cross-device task migration or remote calls, and can only make calls blindly, resulting in limited task execution efficiency and stability.

[0035] To address the aforementioned issues, this invention discloses a wireless network adaptive optimization method based on a health topology graph. By establishing a dynamic, weighted health network topology graph, it reflects the link health scores of wireless links between devices in real time, enabling intelligent and predictive scheduling and migration of distributed tasks. This allows for smooth service switching before complete wireless link failure, achieving application-layer-insensitive network fault self-healing and improving the user's communication experience.

[0036] The preferred embodiment of the wireless network adaptive optimization method based on health topology graph described in this invention, such as... Figure 1 As shown, the wireless network adaptive optimization method based on the health topology map includes the following steps: Step S10: Obtain the connection relationships with multiple target devices, construct a logical network topology diagram based on all the connection relationships, obtain multiple link quality indicators for each target device at a preset frequency, and add all the link quality indicators to the logical network topology diagram.

[0037] First, after the system starts, it can detect the online status of each device, that is, obtain the connection relationship with each target device. Based on these established relationships, a preliminary logical network topology map can be obtained. Through this logical network topology map, the link channels between all different nodes can be obtained, and based on the connection relationship, the quality of the link can be obtained at a preset frequency, thereby determining the data packets that the link is suitable for transmission.

[0038] Specifically, such as Figure 2 As shown, the connection relationship with multiple target devices is obtained. Based on the connection relationship, the device interacts with all target devices through Wi-Fi driver and obtains the signal strength, physical connection rate, number of data transmissions, round-trip delay and packet loss rate of each target device at a preset frequency. Define all the target devices as corresponding nodes, and add each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate to the corresponding node to construct a logical network topology.

[0039] The link quality metrics include: signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate.

[0040] Specifically, the HDF driver framework (Hardware Driver Foundation) interacts with the Wi-Fi driver at the kernel level, periodically collecting raw data related to network connectivity from the underlying driver, including but not limited to signal strength, physical connection rate, number of data transmissions, round-trip latency, and packet loss rate. This raw data will be used as a standard indicator for evaluating link health scores in the future.

[0041] As an example, the system registers a kernel network connection socket to listen for changes in the status of network devices, and then uses the ioctl system (Input / Output Control, a system call function used for device input / output control) to periodically query network interface statistics and read relevant files to obtain network traffic data.

[0042] Furthermore, a timer thread is created to perform a data acquisition task every 5 seconds. A selection mechanism or polling mechanism is used to monitor the readiness status of multiple data sources, enabling dynamic adjustment of the acquisition interval (configurable from 1 to 60 seconds). This step realizes the process of periodically scheduling data.

[0043] Furthermore, the raw data is collected and then verified and standardized. The data is then timestamped to ensure the accuracy of the data sequence and compressed before being stored in a circular buffer.

[0044] Furthermore, for abnormal data, such as when the driver is unresponsive, the data can be re-collected through a retry mechanism. For data that fails to be verified, it is discarded or filtered out and recorded in the log. Finally, the log can be used for preventive checks on resource leaks, providing real-time and accurate network connection status data support for upper-layer applications.

[0045] Furthermore, based on different target devices, corresponding nodes are constructed to build a preliminary logical network topology. The link quality indicators obtained in the above steps, such as signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate, are added to the nodes to construct the logical network topology.

[0046] Step S20: Calculate the link health score between each target device based on all the link quality indicators, and add all the link health scores as weights to the logical network topology diagram to obtain a health topology diagram.

[0047] The algorithm employs a pre-defined method to weight and fuse the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the links between each node, thereby calculating the corresponding link health score. The link health score is periodically updated based on periodically acquired data to determine the link quality stability and better evaluate each link. Each link health score is then added to the corresponding edge of the link to generate a health topology graph.

[0048] Specifically, such as Figure 3As shown, based on the logical network topology diagram, the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate are fused to obtain a link health score between each target device; The link health score is added as a weight to the edge between each node to obtain a health topology graph.

[0049] The algorithm employs a pre-defined weighted algorithm to aggregate multi-dimensional link quality metrics (signal strength, physical connection rate, data transmission frequency, round-trip latency, and packet loss rate) into a single, quantifiable link health score (e.g., 0-100). This calculated link health score is then used as the weight of each edge, updating the logical topology graph of the soft bus to create a dynamic and visualized network health status graph. This algorithm can be adjusted according to the business scenario; for example, latency has a higher weight in real-time control scenarios. For instance, the configuration library for the metric weights can be represented in code as follows: { "video_streaming": { "signal_strength": 0.25, "physical_rate": 0.30, "tx_attempts": 0.10, "rtt": 0.20, "packet_loss": 0.15 }, "voip": { "signal_strength": 0.20, "physical_rate": 0.15, "tx_attempts": 0.10, "rtt": 0.35, "packet_loss": 0.20 } } ".

[0050] Furthermore, in the embodiments disclosed in this invention, the weight changes are described through the following scenarios: For video streaming media scenarios, since high bandwidth is required to ensure video quality, there is a certain tolerance for slight packet loss. Therefore, physical connection rate and signal strength can be defined as high weights, at 30% and 25% respectively; round-trip delay can be defined as medium weight, at 20%; and packet loss rate and number of data transmissions can be defined as low weights, at 15% and 10% respectively.

[0051] For voice call scenarios, latency and real-time performance requirements are extremely high, while bandwidth requirements are relatively low. Therefore, physical connection rate and data transmission frequency can be set to high weights, at 35% and 25% respectively; signal strength can be defined as medium weight, at 20%; and packet loss rate and round-trip latency can be set to low weights, at 15% and 10% respectively.

[0052] For file transfer scenarios, the goal is to maximize throughput and is not sensitive to latency. Therefore, physical connection rate and number of data transmissions can be given high weights, at 35% and 25% respectively; signal strength can be given a medium weight of 20%; and packet loss rate and round-trip delay can be given low weights, at 15% and 5% respectively.

[0053] For real-time gaming scenarios, extremely low latency and stable connections are crucial. Therefore, round-trip latency and packet loss rate can be set to high weights, at 40% and 25% respectively; signal strength can be defined as medium weight at 20%; and physical connection rate and data transmission count can be set to low weights, at 10% and 5% respectively.

[0054] Regardless of the scenario, the system collects data metrics at preset intervals, calculates the link quality score for the current business scenario, and triggers weight adjustment decisions based on the score. When a change in application type is detected, it smoothly transitions to the new weight configuration, sets a transition period for weight changes to avoid frequent switching, and retains historical weight records for performance analysis. For abnormal data, it can trigger a weight balancing strategy for multiple concurrent business scenarios. Through intelligent weight adjustment, it optimizes the user experience in different business scenarios and achieves optimal allocation of network resources.

[0055] Step S30: Select multiple matching links in the health topology map according to the task type of the target transmission task.

[0056] Specifically, such as Figure 4 As shown, for the target transmission task of the current task target device, multiple links matching the target transmission task are determined in the health topology map; The corresponding target device is determined based on all the links.

[0057] Before executing any cross-device operation, the distributed task scheduler queries the latest health topology and selects the optimal path.

[0058] Step S40: Select the link with the highest health score from all the links as the transmission link.

[0059] Specifically, such as Figure 5As shown, based on the task type, the importance of the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the target device of the task is determined; Update the link health score between the target device and all target devices according to the importance level; Based on the updated link health scores, all target devices are sorted to obtain the optimal target device; The link between the target device and the optimal target device is used as a transmission link to execute the target transmission task.

[0060] In one embodiment of the present invention, a certain application type is first identified by deep packet inspection technology, and then its traffic characteristics are analyzed to obtain its data packet size, sending frequency and protocol type, etc., and multiple links that match the type are selected based on these characteristics.

[0061] Furthermore, based on the aforementioned weight evaluation results, the decision-maker matches links based on scenario requirements and link status. It's important to note that during link matching and selection, it doesn't simply choose one link for the target transmission task; rather, it simultaneously selects an optimal alternative link to address potential issues with the current transmission link.

[0062] Before the link selection module schedules the target transmission task, it queries the weight evaluation module for the link health score of all matching links, thereby selecting the link with the highest link health score for communication and executing the target transmission task.

[0063] For real-time video conferencing, 5G network is preferred, with Wi-Fi 6 as the backup link. The key evaluation indicators are latency and jitter. When the packet loss rate is greater than 3%, the link will be switched to the backup link.

[0064] For large file transfer scenarios, the preferred link is Gigabit Ethernet, and the alternative link is Wi-Fi 6. The key evaluation indicators are bandwidth and stability, requiring bandwidth greater than 500Mbps and stability greater than 99%. During transmission, multiple links can be selected for parallel transmission and interrupted transmission can be resumed.

[0065] For IoT sensor data acquisition, the preferred link is LoRaWAN (Low Power Wide Area Network), while the alternative link is NB-IoT (Narrow Band Internet of Things, an IoT communication technology built on cellular networks). The key evaluation indicators are power consumption and coverage distance. Power consumption should be less than 10mW, and the coverage distance should be more than 1 kilometer. When performing batch compressed transmission, it is necessary to report regularly.

[0066] For game streaming scenarios, Wi-Fi 6 is the preferred link, with 5G millimeter wave as the alternative. The key indicators are latency and bandwidth, with latency not exceeding 20ms and bandwidth not less than 100Mbps. Quality must be guaranteed during transmission, and bandwidth should be reserved for priority scheduling.

[0067] Step S50: When the link health score of the transmission link is lower than the health threshold, the optimal alternative node is selected from all the links.

[0068] During the transmission of a target transmission task, the scheduler continuously checks the transport link and determines whether its health score is below the health threshold. If it determines that the health score is below the health threshold, the scheduler will migrate the target transmission task to the optimal alternative link for transmission.

[0069] Specifically, such as Figure 6 As shown, the target transmission task is monitored in real time, and it is determined in real time whether the link health score of the transmission link is lower than the health threshold. If so, then update the link health score between the task target device and all target devices, obtain the best alternative target device with the highest current link health score, and send the task execution instruction to the best alternative target device; The task execution instruction is used to start the task program in the optimal alternative target device to execute the target transmission task.

[0070] The system continuously monitors the link health score of the link running the target transmission task. When the score is consistently lower than the preset health threshold (e.g., below 60 points) but has not been completely interrupted (e.g., above 20 points), the scheduler will select the link with the highest link health score from all other matching links as a backup link to continue executing the target transmission task. It will also proactively trigger the task migration mechanism to smoothly migrate the task to another backup node in the network with a higher link health score without interrupting the service.

[0071] In this context, the backup node is the alternative device between the initiating device of the target transmission task and the alternative link. Through this predictive, proactive, and distributed task migration mechanism based on real-time network health, application-layer-insensitive network fault self-healing is achieved. The responsibility for network optimization is innovatively shifted from the network protocol layer or application layer to the system service layer of the operating system, realizing system-level intelligent network scheduling.

[0072] Furthermore, such as Figure 7 As shown, if the link health score of the transmission link is determined to be lower than the health threshold, the user input demand instruction is obtained, and the importance of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of the target device of the task is updated according to the demand instruction; The link health score between the target device and all target devices is updated using the updated importance level to obtain the optimal alternative target device with the highest current link health score.

[0073] When the target transmission task needs to be replaced with an alternative link, it can be directly migrated to the node of the optimal alternative device to continue executing the target transmission task.

[0074] Furthermore, if the user has other needs, the system can directly obtain the user's input requirements and recalculate the weights of all links in the health topology graph that match the target transmission task based on these requirements. For example: For media scenarios involving video streaming, high bandwidth is required to ensure video quality, and there is a certain tolerance for slight packet loss. By default, physical connection rate and signal strength are defined as high weights. However, users have higher requirements for the integrity of the video stream, so packet loss rate is set as the highest weight, and latency is defined as low weight. At this time, the weight fusion is recalculated according to the user's needs, and the link health score of all matching links is obtained to determine the optimal alternative link.

[0075] Step S60: Transfer the service of the target transmission task to the link of the optimal alternative node.

[0076] If the current link is still available, the scheduler will find an optimal backup node in the healthy topology, start the task migration process, and smoothly switch the business over.

[0077] Specifically, such as Figure 8 As shown, based on the execution status of the target transmission task on the transmission link, the execution information of the target transmission task is synchronized to the optimal alternative target device through a distributed data management method; The target transmission task will continue to be executed using the link between the target device and the optimal alternative target device as the transmission link.

[0078] Among them, such as Figure 9 As shown in the embodiments disclosed in this invention, the transmission link is monitored in real time. When the link health score is qualified, the transmission process is not changed. Once the link health score is lower than the health threshold, the target transmission task is migrated to the optimal alternative link for transmission. It should be noted that if the target transmission task needs to be transmitted in parallel, multiple transmission links will be selected to execute the target transmission task, and multiple optimal alternative links will be selected. The number of optimal alternative links can be less than or more than the transmission links. It is only necessary to replace one or more problematic links with optimal alternative links when a problem occurs in a parallel transmission link.

[0079] When performing link matching and selection (this only describes the target transmission task with a single transmission link), bandwidth resources are reserved on the alternative link, a temporary transmission channel is established, and new IP session resources are allocated; then the two links (i.e., the original transmission link and the optimal alternative link) transmit in parallel, and the MPTCP (Multi-Path TCP) multi-path transmission protocol is started. The main link continues to transmit real-time data, and the alternative link begins to receive new data streams.

[0080] Furthermore, the transmission session state is migrated to the alternative link, the sequence number and window size are synchronized, and a buffer is established to handle out-of-order packets. After the above steps are completed, the traffic load on the main link is gradually reduced, and the transmission ratio of the alternative link is increased accordingly, for example, from 50%-50% to 20%-80%, and finally to 0%-100%. Furthermore, the TCP (Transmission Control Protocol, a transport layer protocol) or UDP (User Datagram Protocol, a transport layer protocol) session is completely migrated, the connection state remains unchanged, and the routing table is updated to point to the optimal alternative link.

[0081] Finally, the reserved resources on the main link are released, the temporary transmission channels of the previous transmission links are closed, and the network topology information is further updated, thereby realizing the process of migrating the target transmission task from the transmission link to the optimal alternative link.

[0082] Furthermore, the execution status of the target transmission task on the current transmission link is obtained; The transmission requirements of the target transmission task are determined based on the execution level, and multiple alternative links that meet the transmission requirements are identified in the health topology map based on the transmission requirements. A task migration request is sent to each alternative node corresponding to the alternative link. When a target alternative node sends a receive request according to the task migration request, the alternative link between the target device and the target alternative node is used as the transmission link to continue executing the target transmission task.

[0083] In this process, when a problem occurs in the transmission link, its original weight can be updated based on the execution status of the target transmission task, thereby updating the previously proposed optimal alternative link (i.e., the optimal alternative link that was determined when the transmission link was determined).

[0084] For example, in game streaming scenarios, to consider the user's gaming experience, Wi-Fi 6 is the preferred link when constructing a healthy topology map, with 5G millimeter wave as the backup. Key metrics are latency and bandwidth, with latency not exceeding 20ms and bandwidth not less than 100Mbps. Quality must be guaranteed during transmission, and bandwidth is reserved for priority scheduling. For games where a good gaming experience is crucial regardless of progress, the most weighted key metrics (latency and bandwidth) cannot be modified, and the optimal backup link remains unchanged. For large file transfer scenarios, Gigabit Ethernet is the preferred link, with Wi-Fi 6 as the backup. Key evaluation metrics are bandwidth and stability, requiring bandwidth greater than 500Mbps and stability greater than 99%. Multiple links can be used for parallel transmission with resume capability. If multiple transmission links fail, the original optimal backup link should be used as a replacement. However, when large file transfers are frequent and only a small amount of content needs to be transmitted, the weight of bandwidth is appropriately reduced, while other metrics such as packet loss rate are prioritized.

[0085] This invention effectively avoids upper-layer application interruptions caused by underlying network fluctuations through predictive task migration, providing users with a stable "never-disconnected" experience, and is especially suitable for scenarios with extremely high requirements for connection reliability, such as industrial control and telemedicine.

[0086] Furthermore, this allows the distributed operating system to no longer blindly schedule tasks, but instead navigate in real time, selecting the optimal path based on real-time "road conditions" (link health score). This improves the overall operating efficiency of the distributed system and pushes complex network anomaly handling logic down to the system layer. Upper-layer application developers no longer need to write complex disconnection reconnection and state recovery code, thereby reducing development difficulty and improving development efficiency.

[0087] This invention establishes a dynamic, weighted healthy network topology map that reflects the link health score of wireless links between devices in real time, enabling intelligent and predictive scheduling and migration of distributed tasks. It completes smooth service switching before wireless links completely fail, achieving application-layer-insensitive network fault self-healing and improving the user's communication experience.

[0088] Furthermore, such as Figure 10 As shown, based on the above-described adaptive optimization method for wireless networks based on health topology maps, this invention also provides a corresponding adaptive optimization system for wireless networks based on health topology maps, wherein the adaptive optimization system for wireless networks based on health topology maps includes: Data acquisition module 10 is used to acquire connection relationships with multiple target devices, construct a logical network topology diagram based on all the connection relationships, acquire multiple link quality indicators for each target device at a preset frequency, and add all the link quality indicators to the logical network topology diagram; The topology graph construction module 20 is used to calculate the link health score between each target device based on all the link quality indicators, and add all the link health scores as weights to the logical network topology graph to obtain a health topology graph; The weight evaluation module 30 is used to select multiple matching links in the health topology map according to the task type of the target transmission task. Link selection module 40 is used to select the link with the highest link health score from all the links as the transmission link; The link monitoring module 50 is used to select the optimal alternative node among all the links when the link health score of the transmission link is lower than the health threshold. The link optimization module 60 is used to transfer the service of the target transmission task to the link of the optimal alternative node.

[0089] like Figure 11 As shown, another specific embodiment of the wireless network adaptive optimization method and system based on health topology map in this invention includes: a data acquisition module 10, a topology map construction module 20, a weight evaluation module 30, a link selection module 40, a link monitoring module 50, and a link optimization module 60.

[0090] Among them, such as Figure 12As shown, this invention upgrades the distributed soft bus of a system from a simple data transmission channel to a "neural network" with network status awareness and intelligent routing decision-making capabilities through a distributed soft bus topology-aware wireless network self-healing mechanism. By constructing a model at the system service layer that can evaluate the health of wireless links in real time and deeply integrating it with distributed task scheduling, the system service layer achieves refined and multi-dimensional awareness of the underlying wireless network connection quality. A dynamic, weighted logical network topology is established, which reflects the "health" of wireless links between devices in real time. Based on this health topology, intelligent and predictive scheduling and migration of distributed tasks are realized, and smooth service switching is completed before the wireless link completely fails, thereby achieving a "never-disconnect" self-healing effect for upper-layer applications.

[0091] Specifically, the data acquisition module 10 includes: an indicator acquisition unit 101 and a logical network topology diagram construction unit 102; The indicator acquisition unit 101 is used to acquire the connection relationship with multiple target devices. Based on the connection relationship, it interacts with all target devices through Wi-Fi driving and acquires the signal strength, physical connection rate, number of data transmissions, round-trip delay and packet loss rate of each target device at a preset frequency. The logical network topology construction unit 102 is used to define all the target devices as corresponding nodes, and add each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate to the corresponding nodes to construct a logical network topology.

[0092] Furthermore, the topology graph construction module 20 includes: a health assessment unit 201 and a health topology graph construction unit 202; The health assessment unit 201 is used to perform fusion processing on each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate according to the logical network topology diagram to obtain a link health score between each of the target devices. The health topology graph construction unit 202 is used to add the link health score as a weight to the edges between each node to obtain a health topology graph.

[0093] Among them, such as Figure 13As shown, the topology graph construction module uses a preset algorithm to perform weighted fusion of the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the links between each node, thereby calculating the corresponding link health score. The link health score is periodically updated based on periodically acquired data to determine the link quality stability and better evaluate each link. By adding each link health score to the edge corresponding to the link, a healthy topology graph can be generated.

[0094] Furthermore, data metrics are collected at preset intervals to calculate the link quality score for the current business scenario, and weight adjustment decisions are triggered based on the score. When a change in application type is detected, a smooth transition to the new weight configuration is achieved, a transition period for weight changes is set to avoid frequent switching, and historical weight records are retained for performance analysis. For abnormal data, a weight balancing strategy can be triggered when multiple services are running concurrently. Through intelligent weight adjustment, user experience is optimized in different business scenarios, and optimal allocation of network resources is achieved.

[0095] Furthermore, the weight evaluation module 30 includes: a link matching unit 301 and a device matching unit 302; The link matching unit 301 is used to determine multiple links that match the target transmission task in the health topology map for the target transmission task of the current task target device. The device matching unit 302 is used to determine the corresponding target device based on all the links.

[0096] Furthermore, the link selection module 40 includes: an importance assessment unit 401, a health score update unit 402, a sorting unit 403, and a task execution unit 404; The importance assessment unit 401 is used to determine the degree of importance among the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the target device of the task, based on the task type. The health score update unit 402 is used to update the link health score between the task target device and all the target devices according to the importance level; The sorting unit 403 is used to sort all the target devices according to the updated link health scores to obtain the best target device; The task execution unit 404 is used to use the link between the task target device and the optimal target device as a transmission link to execute the target transmission task.

[0097] Furthermore, the link monitoring module 50 includes: a threshold judgment unit 501 and a target device candidate unit 502; The threshold judgment unit 501 is used to monitor the target transmission task in real time and judge in real time whether the link health score of the transmission link is lower than the health threshold. The target device candidate unit 502 is used to update the link health score between the task target device and all the target devices, obtain the optimal candidate target device with the highest current link health score, and send the task execution instruction to the optimal candidate target device; The task execution instruction is used to start the task program in the optimal alternative target device to execute the target transmission task.

[0098] A lightweight machine learning model, such as a decision tree or recurrent neural network, can be used to replace the link health score disclosed in this invention. This model can be deployed on an RK3588 chip equipped with an NPU (Neural Processing Unit). By continuously learning from historical link metrics data (signal strength, round-trip latency, packet loss rate, etc.) and the subsequent connection interruptions, the model can predict the probability of link failure within the next N seconds. The distributed task scheduler is no longer based on a static "early warning threshold," but rather on this dynamic "failure probability" to trigger task migration in advance. This upgrades from "responding based on the current state" to "predicting based on future trends," providing greater predictability and allowing for earlier and more accurate intervention, further reducing or even eliminating latency during business migration.

[0099] Furthermore, the target device candidate unit 502 includes: an importance update unit 5024 and a device update unit 5022; The importance update unit 5024 is used to obtain a user-input demand instruction if it is determined that the link health score of the transmission link is lower than the health threshold, and update the importance of the signal strength, physical connection rate, number of data transmissions, round-trip delay and packet loss rate of the target device according to the demand instruction. The device update unit 5022 is used to update the link health score between the task target device and all the target devices using the updated importance level, so as to obtain the optimal alternative target device with the highest current link health score.

[0100] Furthermore, the link optimization module 60 includes: a task migration unit 601 and a task re-execution unit 602; The task migration unit 601 is used to synchronize the execution information of the target transmission task to the optimal alternative target device through a distributed data management method, based on the execution status of the target transmission task on the transmission link. The task re-execution unit 602 is used to continue executing the target transmission task by using the link between the target device and the optimal alternative target device as a transmission link.

[0101] Among them, such as Figure 14 As shown, when the link matching module performs link matching and selection (here only describes the target transmission task with a single transmission link), it reserves bandwidth resources on the alternative link, establishes a temporary transmission channel, and allocates new IP session resources; then the two links (i.e., the original transmission link and the optimal alternative link) transmit in parallel, and the MPTCP (Multi-Path TCP) multi-path transmission protocol is started. The main link continues to transmit real-time data, and the alternative link begins to receive new data streams.

[0102] The link optimization module migrates the transmission session state to the alternative link, synchronizes the sequence number and window size, and establishes a buffer to handle out-of-order packets. After performing the above steps, it gradually reduces the traffic load on the main link and correspondingly increases the transmission ratio of the alternative link, for example, from 50%-50% to 20%-80%, and finally to 0%-100%. Furthermore, it completely migrates the TCP (Transmission Control Protocol, a transport layer protocol) or UDP (User Datagram Protocol, a transport layer protocol) session, keeps the connection state unchanged, and updates the routing table to point to the optimal alternative link.

[0103] Finally, the reserved resources on the main link are released, the temporary transmission channels of the previous transmission links are closed, and the network topology information is further updated, thereby realizing the process of migrating the target transmission task from the transmission link to the optimal alternative link.

[0104] When a link quality degradation is detected, the scheduler can migrate only the subtasks most sensitive to network bandwidth or latency (such as "result rendering") to nodes with better links, while computationally intensive but network-insensitive subtasks (such as "model inference") remain on their original nodes. This allows for finer-grained migration, lower overhead, and faster response times. For large services or stateful services that cannot be fully migrated, this approach offers greater flexibility and feasibility.

[0105] Furthermore, the present invention discloses a detailed embodiment to illustrate the above-described adaptive optimization method and system for wireless networks based on health topology graphs: This invention describes this embodiment based on the RK3588 or DAYU200 development board. When deploying the scenario, three DAYU200 development boards (nodes A, B, and C) are used, all of which have the standard system of the present invention burned into them and connected to the same Wi-Fi AP (Access Point). Node A is used as the central control and client, and the same distributed video playback service FA (Feature Ability, the application interface part that users can directly perceive and interact with, mainly manifested as an independent UI page) is deployed on nodes B and C.

[0106] Initially, the distributed task scheduler of node A selects to start the video service on node B and pulls the video stream to node A for playback. At this time, the link health scores of AB and AC are both 95, indicating good network conditions.

[0107] The trigger condition is: using a signal jammer, gradually approaching node B, and artificially interfering with its Wi-Fi signal.

[0108] During the execution process, when node A performs link quality awareness, it detects that the signal strength of the AB link drops from -40dBm to -75dBm, the round-trip delay increases from 5ms to 80ms, and the packet loss rate increases from 0% to 15%.

[0109] Furthermore, based on the link health assessment algorithm, the health score of link AB was quickly lowered from 95 to 55, which is below the set warning threshold of 60; while the health score of link AC remained at 95.

[0110] Furthermore, after detecting this situation, the link monitoring module determines that link AB will soon become unavailable.

[0111] Furthermore, the system does not wait for the video playback to freeze, but immediately sends a command to node C to start the video service FA on it, and synchronizes the playback status (such as playback progress) through distributed data management.

[0112] Finally, the scheduler seamlessly switches the video stream request from node A to node C.

[0113] Through the above process, users on node A only experience a 1-2 second buffer (task migration and state synchronization time) before the video resumes normal playback, without experiencing the long black screen, spinning screen, and manual reconnection process of traditional solutions. This successfully achieves self-healing of services under the condition of underlying network deterioration.

[0114] Furthermore, in the main scheme, the task initiator (client A) centrally collects all information and makes migration decisions. In this alternative scheme, the decision-making process is distributed. When node A detects a decline in the health of its link with node B, which is executing the task, it broadcasts a "task migration request" to all alternative nodes in the network (such as nodes C, D, and E). The request contains the resource requirements and context information of the current task.

[0115] Upon receiving the request, each candidate node responds with a "bid response" based on its current load (CPU and memory usage) and the health of its detected link with node A. This response includes a comprehensive score. Node A selects the node with the highest score as the new task executor. This makes the decision-making process more robust and efficient, avoiding bottlenecks caused by single-point decisions. Furthermore, by comprehensively considering network conditions and the real-time load of candidate nodes, the selected target node is theoretically globally optimal.

[0116] Furthermore, such as Figure 15 As shown, based on the above-mentioned wireless network adaptive optimization method and system based on health topology map, the present invention also provides a terminal, which includes a processor 100, a memory 200 and a display 300. Figure 15 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0117] In some embodiments, the memory 200 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 200 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 200 may include both internal and external storage devices. The memory 200 is used to store application software and various types of data installed on the terminal, such as program code installed on the terminal. The memory 200 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 200 stores a wireless network adaptive optimization program 400 based on a health topology map, which can be executed by the processor 100 to implement the wireless network adaptive optimization method based on a health topology map in this application.

[0118] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 200 or process data, such as executing the wireless network adaptive optimization method based on the health topology map.

[0119] In some embodiments, the display 300 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 300 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0120] In one embodiment, when processor 100 executes wireless network adaptive optimization program 400 based on health topology map in memory 200, the following steps are performed: Obtain the connection relationships with multiple target devices, construct a logical network topology based on all the connection relationships, obtain multiple link quality indicators for each target device at a preset frequency, and add all the link quality indicators to the logical network topology. Based on all the link quality indicators, calculate the link health score between each target device, and add all the link health scores as weights to the logical network topology graph to obtain the health topology graph. Based on the task type of the target transmission task, select multiple matching links in the health topology graph; Select the link with the highest health score from all the links as the transmission link; When the link health score of the transmission link is lower than the health threshold, the optimal alternative node is selected from all the links. The service of the target transmission task is switched to the link of the optimal alternative node.

[0121] The link quality metrics include: signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate.

[0122] The steps of acquiring connection relationships with multiple target devices, constructing a logical network topology based on all the connection relationships, acquiring multiple link quality indicators for each target device at a preset frequency, and adding all the link quality indicators to the logical network topology specifically include: The system acquires connection relationships with multiple target devices. Based on these connection relationships, it interacts with all target devices via Wi-Fi and acquires the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of each target device at a preset frequency. Define all the target devices as corresponding nodes, and add each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate to the corresponding node to construct a logical network topology.

[0123] Specifically, the step of calculating a link health score between each target device based on all the link quality indicators, and adding all the link health scores as weights to the logical network topology graph to obtain a health topology graph, includes: Based on the logical network topology diagram, each of the signal strength, physical connection rate, data transmission count, round-trip delay, and packet loss rate is fused to obtain a link health score between each target device. The link health score is added as a weight to the edge between each node to obtain a health topology graph.

[0124] Specifically, selecting multiple matching links in the health topology map based on the task type of the target transmission task includes: For the target transmission task of the current target device, multiple links matching the target transmission task are determined in the health topology map; The corresponding target device is determined based on all the links.

[0125] Specifically, selecting the link with the highest health score from all the links as the transmission link includes: Based on the task type, determine the relative importance of the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the target device for the task. Update the link health score between the target device and all target devices according to the importance level; Based on the updated link health scores, all target devices are sorted to obtain the optimal target device; The link between the target device and the optimal target device is used as a transmission link to execute the target transmission task.

[0126] Specifically, when the link health score of the transmission link is lower than the health threshold, the optimal candidate node is selected from all the links, including: The target transmission task is monitored in real time, and it is determined in real time whether the link health score of the transmission link is lower than the health threshold. If so, then update the link health score between the task target device and all target devices, obtain the best alternative target device with the highest current link health score, and send the task execution instruction to the best alternative target device; The task execution instruction is used to start the task program in the optimal alternative target device to execute the target transmission task.

[0127] Specifically, updating the link health score between the target device and all target devices to obtain the optimal candidate target device with the highest current link health score includes: If the link health score of the transmission link is determined to be lower than the health threshold, the user input demand instruction is obtained, and the importance of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of the target device is updated according to the demand instruction. The link health score between the target device and all target devices is updated using the updated importance level to obtain the optimal alternative target device with the highest current link health score.

[0128] Specifically, the step of transferring the service of the target transmission task to the link of the optimal candidate node includes: Based on the execution status of the target transmission task on the transmission link, the execution information of the target transmission task is synchronized to the optimal alternative target device through a distributed data management method; The target transmission task will continue to be executed using the link between the target device and the optimal alternative target device as the transmission link.

[0129] The step of transferring the service of the target transmission task to the link of the optimal candidate node further includes: Obtain the execution status of the target transmission task on the current transmission link; The transmission requirements of the target transmission task are determined based on the execution level, and multiple alternative links that meet the transmission requirements are identified in the health topology map based on the transmission requirements. A task migration request is sent to each alternative node corresponding to the alternative link. When a target alternative node sends a receive request according to the task migration request, the alternative link between the target device and the target alternative node is used as the transmission link to continue executing the target transmission task.

[0130] This solution enhances the existing system framework without requiring hardware replacement, is compatible with standard Wi-Fi protocols, and has good feasibility and scalability. The health assessment model can also be flexibly customized according to different business needs.

[0131] The present invention also provides a computer-readable storage medium storing a wireless network adaptive optimization program based on a health topology map, wherein the wireless network adaptive optimization program based on a health topology map, when executed by a processor, implements the steps of the wireless network adaptive optimization method based on a health topology map as described above.

[0132] In summary, this invention provides a wireless network adaptive optimization method and related equipment based on a health topology map. The method includes: acquiring connection relationships with multiple target devices; constructing a logical network topology map based on all the connection relationships; acquiring multiple link quality indicators for each target device at a preset frequency and adding all the link quality indicators to the logical network topology map; calculating link health scores between each target device based on all the link quality indicators and adding all the link health scores as weights to the logical network topology map to obtain a health topology map; selecting multiple matching links in the health topology map according to the task type of the target transmission task; selecting the link with the highest link health score as the transmission link among all the links; when the link health score of the transmission link is lower than a health threshold, selecting the optimal alternative node among all the links; and switching the service of the target transmission task to the link of the optimal alternative node. This invention establishes a dynamic, weighted health network topology map that reflects the link health scores of wireless links between devices in real time, enabling intelligent and predictive scheduling and migration of distributed tasks, completing smooth service switching before the wireless link completely fails, achieving application-layer-insensitive network fault self-healing, and improving the user's communication experience.

[0133] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0134] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0135] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A wireless network adaptive optimization method based on a health topology graph, characterized in that, The wireless network adaptive optimization method based on health topology map includes: Obtain the connection relationships with multiple target devices, construct a logical network topology based on all the connection relationships, obtain multiple link quality indicators for each target device at a preset frequency, and add all the link quality indicators to the logical network topology. Based on all the link quality indicators, calculate the link health score between each target device, and add all the link health scores as weights to the logical network topology graph to obtain the health topology graph. Based on the task type of the target transmission task, select multiple matching links in the health topology graph; Select the link with the highest health score from all the links as the transmission link; When the link health score of the transmission link is lower than the health threshold, the optimal alternative node is selected from all the links. The service of the target transmission task is switched to the link of the optimal alternative node.

2. The adaptive optimization method for wireless networks based on a health topology map according to claim 1, characterized in that, The link quality metrics include: signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate.

3. The wireless network adaptive optimization method based on health topology map according to claim 2, characterized in that, The steps of acquiring connection relationships with multiple target devices, constructing a logical network topology based on all the connection relationships, acquiring multiple link quality indicators for each target device at a preset frequency, and adding all the link quality indicators to the logical network topology specifically include: The system acquires connection relationships with multiple target devices. Based on these connection relationships, it interacts with all target devices via Wi-Fi and acquires the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of each target device at a preset frequency. Define all the target devices as corresponding nodes, and add each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate to the corresponding node to construct a logical network topology.

4. The wireless network adaptive optimization method based on health topology map according to claim 2, characterized in that, The step of calculating the link health score between each target device based on all the link quality indicators, and adding all the link health scores as weights to the logical network topology graph to obtain a health topology graph, specifically includes: Based on the logical network topology diagram, each of the signal strength, physical connection rate, data transmission count, round-trip delay, and packet loss rate is fused to obtain a link health score between each target device. The link health score is added as a weight to the edge between each node to obtain a health topology graph.

5. The wireless network adaptive optimization method based on health topology map according to claim 2, characterized in that, The step of selecting multiple matching links in the health topology map based on the task type of the target transmission task specifically includes: For the target transmission task of the current target device, multiple links matching the target transmission task are determined in the health topology map; The corresponding target device is determined based on all the links.

6. The wireless network adaptive optimization method based on health topology map according to claim 5, characterized in that, The step of selecting the link with the highest health score from all the links as the transmission link specifically includes: Based on the task type, determine the relative importance of the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the target device for the task. Update the link health score between the target device and all target devices according to the importance level; Based on the updated link health scores, all target devices are sorted to obtain the optimal target device; The link between the target device and the optimal target device is used as a transmission link to execute the target transmission task.

7. The wireless network adaptive optimization method based on a health topology map according to claim 6, characterized in that, When the link health score of the transmission link is lower than the health threshold, the optimal candidate node is selected from all the links, specifically including: The target transmission task is monitored in real time, and it is determined in real time whether the link health score of the transmission link is lower than the health threshold. If so, then update the link health score between the task target device and all target devices, obtain the best alternative target device with the highest current link health score, and send the task execution instruction to the best alternative target device; The task execution instruction is used to start the task program in the optimal alternative target device to execute the target transmission task.

8. The wireless network adaptive optimization method based on health topology map according to claim 7, characterized in that, The step of updating the link health score between the target device and all target devices to obtain the optimal candidate target device with the highest current link health score specifically includes: If the link health score of the transmission link is determined to be lower than the health threshold, the user input demand instruction is obtained, and the importance of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of the target device of the task is updated according to the demand instruction; The link health score between the target device and all target devices is updated using the updated importance level to obtain the optimal alternative target device with the highest current link health score.

9. The wireless network adaptive optimization method based on health topology map according to claim 7, characterized in that, The step of transferring the service of the target transmission task to the link of the optimal candidate node specifically includes: Based on the execution status of the target transmission task on the transmission link, the execution information of the target transmission task is synchronized to the optimal alternative target device through a distributed data management method; The target transmission task will continue to be executed using the link between the target device and the optimal alternative target device as the transmission link.

10. The adaptive optimization method for wireless networks based on a health topology map according to claim 1, characterized in that, The step of transferring the service of the target transmission task to the link of the optimal candidate node further includes: Obtain the execution status of the target transmission task on the current transmission link; The transmission requirements of the target transmission task are determined based on the execution level, and multiple alternative links that meet the transmission requirements are identified in the health topology map based on the transmission requirements. A task migration request is sent to each alternative node corresponding to the alternative link. When a target alternative node sends a receive request according to the task migration request, the alternative link between the target device and the target alternative node is used as the transmission link to continue executing the target transmission task.

11. A wireless network adaptive optimization system based on a health topology map, characterized in that, The wireless network adaptive optimization system based on the health topology map includes: The data acquisition module is used to acquire the connection relationships with multiple target devices, construct a logical network topology map based on all the connection relationships, acquire multiple link quality indicators for each target device at a preset frequency, and add all the link quality indicators to the logical network topology map. The topology graph construction module is used to calculate the link health score between each target device based on all the link quality indicators, and add all the link health scores as weights to the logical network topology graph to obtain a health topology graph. The weight evaluation module is used to select multiple matching links in the health topology graph based on the task type of the target transmission task. The link selection module is used to select the link with the highest health score from all the links as the transmission link; The link monitoring module is used to select the optimal alternative node among all the links when the link health score of the transmission link is lower than the health threshold. The link optimization module is used to transfer the service of the target transmission task to the link of the optimal alternative node.

12. The wireless network adaptive optimization system based on health topology map according to claim 11, characterized in that, The data acquisition module includes: The indicator acquisition unit is used to acquire the connection relationship with multiple target devices, and based on the connection relationship, interact with all target devices through Wi-Fi drive, and acquire the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of each target device at a preset frequency; The logical network topology construction unit is used to define all the target devices as corresponding nodes, and add each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate to the corresponding nodes to construct a logical network topology.

13. The wireless network adaptive optimization system based on a health topology map according to claim 12, characterized in that, The topology graph construction module includes: The health assessment unit is used to perform fusion processing on each of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate according to the logical network topology diagram to obtain a link health score between each of the target devices. A health topology graph construction unit is used to add the link health score as a weight to the edges between each node to obtain a health topology graph.

14. The wireless network adaptive optimization system based on health topology map according to claim 12, characterized in that, The weight evaluation module includes: The link matching unit is used to determine multiple links that match the target transmission task in the health topology map for the target transmission task of the current task target device. A device matching unit is used to determine the corresponding target device based on all the links.

15. The wireless network adaptive optimization system based on a health topology map according to claim 14, characterized in that, The link selection module includes: The importance assessment unit is used to determine the degree of importance among the signal strength, physical connection rate, number of data transmissions, round-trip delay, and packet loss rate of the target device of the task, based on the task type. A health score update unit is used to update the link health score between the task target device and all the target devices according to the importance level; The sorting unit is used to sort all the target devices according to the updated link health scores to obtain the best target device; The task execution unit is used to use the link between the target device and the optimal target device as a transmission link to execute the target transmission task.

16. The wireless network adaptive optimization system based on health topology map according to claim 12, characterized in that, The link monitoring module includes: The threshold judgment unit is used to monitor the target transmission task in real time and judge in real time whether the link health score of the transmission link is lower than the health threshold. The target device candidate unit is used to update the link health score between the task target device and all the target devices if the link health score is the highest, obtain the optimal candidate target device with the highest current link health score, and send the task execution instruction to the optimal candidate target device. The task execution instruction is used to start the task program in the optimal alternative target device to execute the target transmission task.

17. The wireless network adaptive optimization system based on a health topology map according to claim 16, characterized in that, The target device candidate unit includes: The importance update unit is used to obtain a user-input demand instruction if it is determined that the link health score of the transmission link is lower than the health threshold, and update the importance of the signal strength, physical connection rate, data transmission count, round-trip delay and packet loss rate of the target device according to the demand instruction; The device update unit is used to update the link health score between the task target device and all the target devices using the updated importance level, and to obtain the optimal alternative target device with the highest current link health score.

18. The wireless network adaptive optimization system based on a health topology map according to claim 11, characterized in that, The link optimization module includes: The task migration unit is used to synchronize the execution information of the target transmission task to the optimal alternative target device through a distributed data management method, based on the execution status of the target transmission task on the transmission link. The task re-execution unit is used to continue executing the target transmission task by using the link between the target device and the optimal alternative target device as a transmission link.

19. A terminal, characterized in that, The terminal includes: a memory, a processor, and a wireless network adaptive optimization program based on a health topology map stored in the memory and executable on the processor. When the wireless network adaptive optimization program based on a health topology map is executed by the processor, it implements the steps of the wireless network adaptive optimization method based on a health topology map as described in any one of claims 1-7.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a wireless network adaptive optimization program based on a health topology map, which, when executed by a processor, implements the steps of the wireless network adaptive optimization method based on a health topology map as described in any one of claims 1-7.

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