A wireless communication link optimization system and method

Abstract

The application discloses a wireless communication link optimization system and method, and relates to the field of wireless communication.The application makes up for the deficiency of the prior art which only depends on a single or a few performance indexes to evaluate the communication quality, and constructs a comprehensive evaluation model which considers multiple factors and can accurately reflect the task specificity in the cloud-edge collaborative environment.The application also overcomes the poor adaptability of the static QoS configuration to network environment changes in the prior art, and improves the limitation of the prior art that the QoS parameters are pre-set and difficult to adjust, and a mechanism capable of flexibly adjusting the QoS parameters is designed, so that the QoS strategy can be periodically generated and delivered without manual intervention.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wireless communication, and particularly relates to a wireless communication link optimization system and method. BACKGROUND

[0002] The statements in this section merely provide background information related to the present disclosure and can not constitute the prior art.

[0003] With the rapid development of cloud computing and edge computing technologies, cloud-edge collaboration has become an important part of modern network architecture. Cloud-edge collaboration aims to improve system performance, reduce latency, improve reliability, and save bandwidth by reasonably allocating computing, storage, and network resources between the cloud and edge devices. However, wireless communication link optimization in the cloud-edge collaboration environment is one of the key technologies to achieve these goals.

[0004] In the traditional cloud computing mode, all data processing and storage tasks are concentrated in remote cloud data centers. This centralized mode has advantages in resource utilization and maintenance costs, but in application scenarios with high real-time requirements, the delay problem of data transmission is more prominent. With the rapid increase in the number of Internet of Things devices and the diversification of application scenarios, the traditional centralized cloud computing mode is difficult to meet the demand for low latency, high bandwidth, and high reliability.

[0005] Edge computing effectively alleviates the drawbacks of centralized cloud computing by deploying computing and storage resources near the network edge close to the data source. However, pure edge computing cannot fully utilize the powerful resource integration capabilities of cloud computing. Therefore, cloud-edge collaboration emerged, combining the advantages of cloud computing and edge computing, dynamically adjusting the distribution of computing tasks between the cloud and the edge to achieve optimal resource allocation.

[0006] In the cloud-edge collaboration system, wireless communication links are the key channels connecting the cloud and edge nodes. Due to the variability and uncertainty of the wireless communication environment, fluctuations in link quality will directly affect the overall performance of the system. In order to ensure the efficient operation of the cloud-edge collaboration system, it is necessary to optimize the wireless communication link, reasonably allocate link resources for different cloud-edge collaboration operation tasks, and ensure the reliability of data transmission.

[0007] Task scheduling is crucial in cloud-edge collaborative architecture, which is complex and often requires decentralized monitoring and centralized management. First, task scheduling can rationalize resource allocation and load balancing, optimizing resource usage on cloud and edge devices and avoiding waste. Second, reasonable task scheduling can meet the needs of real-time applications, significantly reducing data transmission time and improving response speed by processing data locally. Scheduling delay-sensitive tasks to the edge and high-computing but delay-insensitive tasks to the cloud can achieve better performance. Additionally, task redundancy and fault isolation can enhance system reliability and fault tolerance, preventing single-point failures from affecting the entire system. By pre-processing data on edge nodes, reducing the amount of data transmitted to the cloud, and filtering and aggregating data to further reduce bandwidth consumption, bandwidth costs can be saved. Furthermore, dynamic scheduling can adjust task allocation based on actual conditions, improving system flexibility and scalability, making it easier to add new edge nodes or cloud resources. Finally, optimizing task scheduling can reduce unnecessary computation and transmission, reduce energy consumption, achieve green computing, balance energy consumption across nodes, and extend device life.

[0008] In cloud-edge collaborative systems, communication quality assessment and QoS (Quality of Service) management are key to ensuring efficient execution of critical tasks and improving user experience. Although there are some existing methods for communication quality assessment and QoS optimization in cloud-edge collaboration, there are still some limitations and unresolved issues in practical applications.

[0009] Existing solution one: traditional static QoS configuration

[0010] In existing cloud-edge collaborative systems, a common approach is to set fixed QoS parameters for various tasks in advance. This static configuration method usually has the following steps: roughly classify transmission tasks into several categories, and set fixed bandwidth allocation ratios, priorities, maximum delay thresholds, and other QoS parameters for each category; then configure the corresponding QoS strategy on each node in the cloud-edge network to achieve differentiated services for different categories of tasks.

[0011] However, due to the use of QoS parameter setting and network device configuration in this solution, there are the following problems: network environment, task load, and device status often change dynamically, making it difficult for static QoS parameters to accurately reflect actual needs in real time, which may result in resource waste or substandard service quality for critical tasks; in the face of unexpected events such as network congestion, device failure, and task emergency insertion, static configuration cannot quickly respond and adjust QoS strategies to adapt to changes; frequent monitoring and manual adjustments increase operational costs.

[0012] Existing Solution 2: Communication Quality Evaluation Based on Simple Indicators

[0013] Another type of existing technology focuses on monitoring and statistically analyzing basic performance indicators (such as bandwidth utilization, latency, and packet loss rate) during cloud-edge collaborative communication. This approach typically includes the following steps: deploying monitoring tools at cloud edge nodes to collect real-time performance indicator data during communication; performing statistics and calculations on the collected data to generate various performance reports, such as average latency and maximum packet loss rate; identifying whether communication quality issues exist based on preset thresholds or empirical judgments, and attempting to locate the cause of the problem; and based on the analysis results, making local adjustments to network device parameters, task scheduling strategies, and other factors in an attempt to improve communication quality.

[0014] While this approach can provide a certain level of communication quality assessment, it has the following limitations: It focuses on a single or limited set of performance indicators, ignoring the combined impact of task specificity and network dynamics on communication quality in cloud-edge collaborative environments. The evaluation results may not fully reflect the actual service quality. It relies on periodic or post-analysis, making it less responsive to sudden communication quality issues. Analysis based on simple indicators cannot provide a detailed analysis of communication quality in cloud-edge collaborative networks. Summary of the Invention

[0015] The purpose of the present invention is to: address the problems existing in the prior art, provide a wireless communication link optimization system and method, make up for the deficiency of the prior art that only relies on a single or a few performance indicators to evaluate the communication quality, and construct a comprehensive evaluation model that considers multiple factors and can accurately reflect the task specificity in the cloud-edge collaborative environment; it also overcomes the problem that the static QoS configuration in the prior art has poor adaptability to changes in the network environment; at the same time, it improves the limitations of the prior art that QoS parameters are pre-set and difficult to adjust, and designs a mechanism that can flexibly adjust QoS parameters to realize the periodic generation and issuance of QoS policies without manual intervention.

[0016] The technical solutions of the present invention are as follows:

[0017] A wireless communication link optimization system, comprising:

[0018] A link quality testing module, wherein the link quality testing module obtains real-time inter-node communication link parameters of each edge node;

[0019] Data aggregation module, all data aggregation modules aggregate the inter-node communication link parameters of all edge nodes and store them in the database for the QoS policy update module to update the QoS parameters;

[0020] a task classification module, which classifies the transmission tasks according to the task characteristics and specifies the link parameters that each transmission task focuses on, for the QoS policy updating module to update the QoS parameters;

[0021] a QoS policy updating module, which integrates the processing results of the data summarization module and the task classification module, and allocates appropriate communication links according to the task characteristics and the QoS parameters;

[0022] a link allocation scheme issuing module, which issues the allocation results of the QoS policy updating module to each edge node, and the edge node can allocate appropriate communication links to the transmission tasks according to the updated strategy.

[0023] Further, the inter-node communication link parameters include:

[0024] throughput rate, latency, signal-to-noise ratio, packet loss rate, bit error rate, and link distortion condition.

[0025] Further, the task classification module first determines the types of node transmission tasks in cloud-edge collaboration, and analyzes the requirements of each task for the performance indicators of the communication links.

[0026] Further, the types of node transmission tasks include:

[0027] sensing data, control data, state data, network management data, and log data;

[0028] The performance indicators of the communication links include:

[0029] data volume, real-time performance, large throughput, low latency, high signal-to-noise ratio, low packet loss rate, low bit error rate, and low link distortion.

[0030] Further, the QoS policy updating module is built-in with an optimization model, which calculates the mean square error of the required communication link performance and the actual performance of the communication link for each type of task through the optimization model, and allocates appropriate communication links according to the communication link allocation strategy.

[0031] Further, the optimization model is represented as follows:

[0032]

[0033] wherein:

[0034] is a set of tasks, T is the number of tasks;

[0035] is a set of communication links, L is the number of communication links;

[0036] x ij is a binary decision variable, indicating whether task i is assigned on communication link j, if yes, x ij = 1, otherwise x ij = 0;

[0037] represents the mean square error of the kth performance requirement of task i and the kth performance index of real-time communication link j; d i = (d i1 , d i2 ,..., d i6 ) is a performance requirement vector of each task, corresponding to throughput, delay, signal-to-noise ratio, packet loss rate, bit error rate and link distortion respectively; c i = (c j1 , c j2 ,..., c j6 ) is a real-time performance index vector of each communication link, also corresponding to throughput, delay, signal-to-noise ratio, packet loss rate, bit error rate and link distortion respectively; The smaller the value is, the higher the matching degree of task i and communication link j is, and the smaller the performance surplus is.

[0038] Further, the communication link allocation strategy comprises:

[0039] Compression: when the amount of data to be transmitted for an image or video information is very large, but there is no link that can meet the requirement, the cloud server requires the edge node to compress or block the data to be transmitted locally, and then transmits the compressed or blocked data through the communication link, and restores the original data by restoring the compressed picture or splicing the blocked data at the cloud server end;

[0040] Caching: for the case that the existing communication link cannot meet the task transmission requirement, the cloud server requires the edge node to place the to-be-sent data in the sending cache, and then sends the data according to the new link allocation scheme after the next QoS policy update of the cloud server;

[0041] Preemption: for particularly urgent information, the cloud server sends an interrupt to the communication link that meets the real-time requirement but is occupied, so that the communication link gives priority to serve more urgent tasks, and the interrupted task will regain the use right of the communication link after the transmission of the urgent task is completed.

[0042] Further, the application also provides a wireless communication link optimization method based on the wireless communication link optimization system.

[0043] Step S1: obtain the real-time inter-node communication link parameters of each edge node;

[0044] Step S2: Summarize the inter-node communication link parameters of all edge nodes and store them in a database;

[0045] Step S3: classify the transmission tasks according to the task characteristics and specify the link parameters that each transmission task focuses on;

[0046] Step S4: Integrate the processing results of step S2 and step S3, and allocate appropriate communication links according to task characteristics and QoS parameters;

[0047] Step S5: The allocation result is distributed to each edge node, and the edge node can allocate a suitable communication link to the transmission task according to the updated strategy.

[0048] Furthermore, the step S3 includes:

[0049] Step S31: Determine the type of node transmission task in cloud-edge collaboration;

[0050] Step S32: Analyze the performance indicator requirements of each task for the communication link.

[0051] Furthermore, the step S4 includes:

[0052] Step S41: Calculate the mean square error between the communication link performance required for each type of task and the actual performance of the communication link;

[0053] Step S42: Allocate appropriate communication links according to the communication link allocation strategy.

[0054] Compared with the existing technology, the beneficial effects of the present invention are:

[0055] 1. The present invention improves the accuracy of communication quality assessment: The link quality test module not only considers traditional performance indicators, but also incorporates packet loss rate estimation and link distortion estimation, making the assessment results more comprehensive and accurate, and able to truly reflect the actual communication service quality.

[0056] 2. The present invention improves the system's adaptability and flexibility: The present invention can regularly collect communication link quality data, automatically generate and adjust QoS policies, and avoid the limitations of traditional static configuration when facing network changes.

[0057] 3. The present invention improves the service guarantee for key tasks: By dynamically adjusting the QoS parameters and link allocation of transmission tasks, the present invention can ensure that key tasks still receive high-quality services when network resources are tight or emergencies occur.

[0058] 4. The present invention optimizes resource utilization: real-time evaluation and dynamic policy adjustment can avoid resource waste and unreasonable allocation, and improve the overall resource utilization efficiency, network performance and task execution efficiency of the cloud-edge collaborative system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a system program flowchart proposed in Example 2;

[0060] Figure 2 This is a schematic diagram of the system structure proposed in Example 2. DETAILED DESCRIPTION

[0061] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus comprising the element.

[0062] The features and performance of the present invention are further described in detail below with reference to the embodiments.

[0063] Example 1

[0064] A wireless communication link optimization system that can be used for cloud-edge collaborative operation and maintenance task scheduling, including:

[0065] Link quality testing module, which obtains the real-time inter-node communication link parameters of each edge node. It should be noted that in cloud-edge collaboration, a communication link refers to the data transmission channel between cloud services (such as data centers or cloud servers) and edge devices (such as IoT devices, edge servers, etc.), through which data collection, transmission, processing and control instruction issuance are realized, ensuring efficient, reliable and real-time communication between cloud and edge devices to support the collaborative work of distributed computing and intelligent applications. The link quality testing module can measure the performance indicators in the communication link.

[0066] Data aggregation module, all data aggregation modules aggregate the inter-node communication link parameters of all edge nodes and store them in the database for the QoS policy update module to update the QoS parameters;

[0067] A task classification module, which classifies transmission tasks according to task characteristics and specifies link parameters that each transmission task focuses on, so that the QoS policy update module can update the QoS parameters;

[0068] A QoS policy update module, which combines the processing results of the data aggregation module and the task classification module and allocates appropriate communication links according to task characteristics and QoS parameters;

[0069] The link allocation scheme issuing module issues the allocation result of the QoS policy updating module to each edge node, and the edge node can allocate a suitable communication link to the transmission task according to the updated policy.

[0070] In this embodiment, specifically, the inter-node communication link parameters include:

[0071] Throughput, latency, signal-to-noise ratio, packet loss rate, bit error rate, and link distortion;

[0072] In addition to basic communication parameters such as throughput, latency, and signal-to-noise ratio, each edge node sends a fixed number of data packets to other edge nodes and records the number of packets sent and received, as well as any transmission errors or packet loss, to estimate the overall packet loss rate. Furthermore, specific signal waveforms are transmitted between edge nodes, and the receiving end compares the received waveform with the original transmitted waveform, using methods such as cross-correlation or mean square error to assess link distortion.

[0073] Specifically, for the nth channel (i.e., the nth communication link), the data format stored in the database is as follows:

[0074] Table 1 Data format in the database

[0075] Throughput Latency Signal-to-noise ratio Packet loss rate Bit Error Rate Link distortion Channel n T L SNR PLR BER MSE

[0076] In this embodiment, specifically, the task classification module first determines the type of node transmission tasks in the cloud-edge collaboration and analyzes the requirements of each task for the performance indicators of the communication link;

[0077] Throughput, T, refers to the amount of data successfully transmitted per unit time in a communication link. High throughput ensures the rapid transmission of large amounts of data in cloud-edge collaborative systems, improves the efficiency of data processing and response, and supports complex applications and big data analysis.

[0078]

[0079] Where T is the throughput, in bits per second (bps), t is the transmission time, and D is the amount of data successfully transmitted during the transmission time, in bits.

[0080] Latency L refers to the time required for data to be transmitted from the source node to the destination node. Low latency is crucial for cloud-edge collaborative applications with high real-time requirements (such as industrial control and autonomous driving). Excessive latency may lead to untimely system responses, affecting the accuracy and security of operations.

[0081] L=T r -T t

[0082] Where L is the total delay, and the node calculation method is the timestamp T when the data is received. r The timestamp T when the data is sent t The difference is in seconds (s);

[0083] The signal-to-noise ratio (SNR) is the ratio of signal strength to noise strength, usually expressed in decibels (dB). A higher SNR means better signal quality and lower bit error rate in the communication link, thereby improving the data transmission reliability and stability of the cloud-edge collaborative system.

[0084]

[0085] Where SNR is the signal-to-noise ratio, that is, the signal strength P s With the noise intensity P n The ratio of , in decibels (dB);

[0086] The packet loss rate (PLR) refers to the ratio of data packets lost during data transmission to the total number of data packets sent. A low packet loss rate is crucial for cloud-edge collaborative systems, especially in data transmission scenarios that require high reliability. Excessive packet loss rates can lead to incomplete data, increased retransmission overhead, and poor system performance.

[0087]

[0088] Where PLR ​​is the packet loss rate, that is, sending N total In the case of data packets, the number of data packets that the receiver fails to receive is N lost And the total number of packets N total The ratio of

[0089] The bit error rate (BER) refers to the ratio of the number of bits that are erroneous during transmission to the total number of bits sent. A lower BER means higher data transmission accuracy, reducing data errors and the need for retransmissions, and improving the overall efficiency and reliability of the cloud-edge collaborative system.

[0090]

[0091] BER is the bit error rate, that is, sending N total bits, the number of bits N that the receiver recognizes incorrectly error and the total number of bits N total The ratio of

[0092] Link distortion refers to the phenomenon in which signals are distorted due to various interferences and attenuation during transmission. Low link distortion ensures accurate data transmission in cloud-edge collaborative systems, avoiding misjudgments and system performance degradation caused by information distortion, especially in application scenarios with high data integrity requirements.

[0093] The transmitter sends a standard sine wave, and the receiver receives this sine wave. Since the channel is always distorted, there must be a deviation between the received sine wave and the standard sine wave at the transmitter. The mean square error (MSE) is used to measure this deviation:

[0094]

[0095] Where MSE is the mean square error between the transmitted signal and the received signal, N is the number of sampling points for the original signal, and x i and y i are the values ​​of the original signal and the received signal at the i-th sampling point respectively.

[0096] In this embodiment, specifically, the types of node transmission tasks include:

[0097] sensor data, control data, status data, network management data, and log data;

[0098] Sensor data is generally raw data collected by sensors on nodes, typically including temperature, humidity, audio, images, video, pressure, light intensity, sound, vibration intensity, gas concentration, etc. Sensor data requires medium to high throughput from the communication link due to the frequent data transmission; medium to high latency, depending on the real-time requirements of the application; a medium signal-to-noise ratio to ensure data accuracy; low packet loss rate and bit error rate to avoid data loss and errors; and minimal link distortion to maintain data accuracy.

[0099] Control data: This is used to manage and control the operation of sensor nodes or networks, and typically includes configuration parameters, control instructions (such as sensor start / stop, mode switching, etc.), calibration commands, etc. The communication requirements for control data are mainly low throughput, because the data volume is usually small but needs to be transmitted quickly; low latency is required to ensure timely execution of commands; high signal-to-noise ratio is required to ensure accurate command transmission; low packet loss rate and bit error rate are required to avoid system loss of control or command errors; link distortion must also be kept low to prevent command misunderstanding;

[0100] Status data: reflects the current health of the node's sensors, typically including battery charge, signal strength, online / offline status, and memory usage. Status data requires low throughput and medium latency for the communication link, as the data volume is small and timeliness is required but not urgent. The signal-to-noise ratio and bit error rate must be moderate to ensure the data reflects the node's true state, but a certain degree of error is acceptable. The packet loss rate must be low; occasional packet loss is tolerable, but frequent packet loss can affect monitoring. Link distortion must be moderate; distortion can affect status judgment but is not particularly sensitive.

[0101] Network management data: This data is used to maintain the network topology and ensure normal data transmission. It typically includes routing updates, topology information, error reporting and correction information, and notifications of network node joining / leaving. Network management data requires moderate throughput, especially during topology changes; low latency to ensure timely routing updates and error reporting; a high signal-to-noise ratio to ensure the accuracy of management data; low packet loss and bit error rates to avoid network instability and management information errors; and minimal link distortion to maintain effective network management.

[0102] Log data: Log data related to node network operation and maintenance. These data are used to ensure the normal operation and optimization of the network, and usually include: fault detection and diagnosis data, maintenance logs (including node fault records, repair records, etc.), performance monitoring data (such as latency, throughput, etc.), firmware update data, security and access control logs, etc. Operation and maintenance data requires medium throughput for the communication link, especially when it comes to system logs and performance monitoring data; medium latency, most data does not require real-time but fault detection data requires low latency; medium signal-to-noise ratio and bit error rate to ensure data accuracy but allow a certain error; medium packet loss rate requirement, occasional packet loss can be tolerated but frequent packet loss will affect operation and maintenance efficiency; link distortion must also be kept at medium level, distortion will affect the accuracy of operation and maintenance data but it is not particularly sensitive;

[0103] The performance indicators of the communication link include:

[0104] Data volume, real-time performance, high throughput, low latency, high signal-to-noise ratio, low packet loss rate, low bit error rate, and low link distortion;

[0105] It should be noted that the requirements of various tasks on link performance indicators are as follows:

[0106] Table 2 Requirements of various tasks on link performance indicators

[0107] Performance indicators sensor data Control Data Status data Network management data Log data Data volume medium Lower Lower medium Higher Real-time medium Higher Lower Higher medium High throughput medium Lower Lower medium Higher Low latency medium Higher medium medium medium High signal-to-noise ratio medium Higher medium medium medium Low packet loss rate Higher Higher Lower medium medium Low bit error rate Higher Higher medium medium medium Low link distortion Higher Higher medium medium Higher

[0108] It should be noted that the classification method of the types of tasks can refer to the following table:

[0109] Table 3 Transmission task classification method

[0110]

[0111]

[0112] In this embodiment, specifically, the QoS policy updating module is built-in with an optimization model, which calculates the mean square error of the required communication link performance and the actual performance of each type of task through the optimization model, and allocates appropriate communication links according to the communication link allocation strategy.

[0113] In this embodiment, specifically, the optimization model is expressed as follows:

[0114]

[0115] Wherein:

[0116] is a set of tasks, T is the number of tasks;

[0117] is a set of communication links, L is the number of communication links;

[0118] x ij is a binary decision variable, indicating whether task i is allocated on communication link j, if yes, x ij = 1, otherwise x ij = 0;

[0119] represents the mean square error of the kth performance requirement of task i and the kth performance indicator on real-time communication link j; d i = (d i1 , d i2 ,..., d i6 ) is a performance requirement vector of each task, corresponding to six indicators of throughput, delay, signal-to-noise ratio, packet loss rate, bit error rate and link distortion respectively; c i = (c j1 , c j2 ,..., c j6) is the real-time performance indicator vector of each communication link, which also corresponds to the six indicators of throughput, delay, signal-to-noise ratio, packet loss rate, bit error rate and link distortion; The smaller the value, the better the match between task i and communication link j, and the smaller the performance overload. By calculating the mean squared deviation (MSD) between tasks and links, we can measure the link that best matches the task, maximizing resource utilization while meeting transmission requirements. For example, if the throughput requirement of a task is 1 Mbps, and the throughput performance indicators of two links are 1.2 Mbps and 2 Mbps, respectively, the link with a 1.2 Mbps throughput has a smaller MSD than the task's 1 Mbps throughput requirement, indicating that the link with a 1.2 Mbps throughput is more suitable for the task. In actual calculations, we need to comprehensively consider six indicators: throughput, latency, signal-to-noise ratio, packet loss rate, bit error rate, and link distortion to more accurately measure the match between tasks and links.

[0120] The first constraint Indicates that each task must be assigned to a link. The second constraint Indicates that each link can only carry one task.

[0121] In this embodiment, when considering link allocation strategies, it is also important to note that there may be situations where a task is not assigned to a suitable link. For example, a node needs to transmit a high-resolution image, but the throughput of the existing link cannot meet its needs. In this case, the cloud server needs to specify a "special strategy" to require the node to locally compress the image until the existing link can meet the requirements for transmitting the compressed image. This type of allocation strategy can be divided into the following categories:

[0122] Compression: When large amounts of data, such as images or videos, need to be transmitted but no link exists to meet the requirements, the cloud server will require the node to compress or segment the data locally. The compressed or segmented data is then transmitted over the link. The cloud server then restores the compressed image or splices the segmented data to recover the original data.

[0123] Cache: If the existing link cannot meet the task transmission requirements, for example, high link occupancy leads to low throughput, and the transmission task is not particularly urgent, the cloud server will require the node to place the data to be sent in the sending cache and wait for the next cloud server QoS policy update before sending the data according to the new link allocation plan.

[0124] Preemption: For particularly urgent information, such as control instructions with high real-time requirements, which must be transmitted to the cloud server within a specified time, the cloud server will interrupt the occupied link that meets the real-time requirements, giving priority to the more urgent task. The previously interrupted task will regain the right to use the link after the urgent task is transmitted.

[0125] In this embodiment, specifically, it also includes: a log recording module, which saves each step of the system's evaluation operation and the final evaluation result in a log, making it easy to check the cause of the failure when a system failure occurs.

[0126] This embodiment further proposes a wireless communication link optimization method, based on the wireless communication link optimization system described above, including:

[0127] Step S1: Obtain the real-time inter-node communication link parameters of each edge node;

[0128] Step S2: Summarize the inter-node communication link parameters of all edge nodes and store them in a database;

[0129] Step S3: classify the transmission tasks according to the task characteristics and specify the link parameters that each transmission task focuses on;

[0130] Step S4: Integrate the processing results of step S2 and step S3, and allocate appropriate communication links according to task characteristics and QoS parameters;

[0131] Step S5: The allocation result is distributed to each edge node, and the edge node can allocate a suitable communication link to the transmission task according to the updated strategy.

[0132] In this embodiment, specifically, step S3 includes:

[0133] Step S31: Determine the type of node transmission task in cloud-edge collaboration;

[0134] Step S32: Analyze the performance indicator requirements of each task for the communication link.

[0135] In this embodiment, specifically, step S4 includes:

[0136] Step S41: Calculate the mean square error between the communication link performance required for each type of task and the actual performance of the communication link;

[0137] Step S42: Allocate appropriate communication links according to the communication link allocation strategy.

[0138] Example 2

[0139] Figure 1The system flowchart of this embodiment shows the link quality test module. In addition to basic communication parameters such as throughput, latency, and signal-to-noise ratio, each node sends a fixed number of data packets to other nodes and records the number of packets sent and received, as well as any transmission errors or packet loss, to estimate the overall packet loss rate. Nodes also transmit specific signal waveforms. The receiving end compares the received waveform with the original waveform, using methods such as cross-correlation or mean squared error to assess link distortion. After statistical analysis, the data is sent to the data aggregation module, which processes the data and sends it to the QoS policy update module. The task classification module also sends the categorized and graded tasks to the QoS policy update module. The QoS policy update module integrates this information and assigns the corresponding communication links to the tasks of different categories and levels, creating a new QoS policy. The final QoS policy is then sent to the link allocation plan distribution module, which updates the plan to the edge nodes, completing the QoS policy update. This update process is periodic. To stop the QoS policy update, a "stop" command is sent to the cloud server that manages QoS policy updates.

[0140] Figure 2 The schematic diagram of the system structure of this embodiment shows that it can be generally divided into a cloud server, a master node and an edge node (i.e., the edge node in the figure). The cloud server is responsible for grading the tasks, receiving the link quality information sent by the master node, and comprehensively calculating the new link allocation scheme (QoS strategy). The master node is responsible for collecting link quality information from the edge nodes, and retrieving the new QoS strategy from the cloud server and sending it to each edge node. The edge node is responsible for collecting link quality information and providing priority communication links for the assigned tasks after updating the QoS strategy. During the entire cloud-edge collaboration process, the communication between nodes is wireless transmission.

[0141] The above-described embodiments merely represent specific implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of protection of the present application. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the technical concept of the present application, and all such variations and improvements fall within the scope of protection of the present application.

[0142] This background section is provided to generally present the context of the invention, and the work of the presently named inventors, the work to the extent described in this background section, and aspects of the description in this section that did not constitute prior art at the time of filing are neither explicitly nor implicitly admitted to be prior art to the present invention.

Claims

1. A wireless communication link optimization system, characterized in that: include: A link quality testing module, wherein the link quality testing module obtains real-time inter-node communication link parameters of each edge node; Data aggregation module, all data aggregation modules aggregate the inter-node communication link parameters of all edge nodes and store them in the database for the QoS policy update module to update the QoS parameters; A task classification module, which classifies transmission tasks according to task characteristics and specifies link parameters that each transmission task focuses on, so that the QoS policy update module can update the QoS parameters; A QoS policy update module, which combines the processing results of the data aggregation module and the task classification module and allocates appropriate communication links according to task characteristics and QoS parameters; A link allocation scheme issuing module, which distributes the allocation results of the QoS policy update module to each edge node, and the edge node allocates appropriate communication links to the transmission task according to the updated policy; The QoS policy update module has a built-in optimization model, which calculates the mean square error between the communication link performance required for each type of task and the actual performance of the communication link, and allocates the appropriate communication link according to the communication link allocation strategy; The optimization model is expressed as follows: in: For the task set, , is the number of tasks; is the set of communication links, , is the number of communication links; is a binary decision variable, representing the task Is it allocated in the communication link? If so, ,otherwise ; Indicates a task The required performance requirements and real-time communication links On the The mean square error of the performance indicators; The performance requirement vector for each task corresponds to six indicators: throughput, latency, signal-to-noise ratio, packet loss rate, bit error rate, and link distortion; is the real-time performance indicator vector of each communication link, which also corresponds to the six indicators of throughput, delay, signal-to-noise ratio, packet loss rate, bit error rate and link distortion; The smaller the number, the more tasks and communication links The higher the match, the smaller the performance excess.

2. A wireless communication link optimization system according to claim 1, characterized in that: The inter-node communication link parameters include: Throughput, latency, signal-to-noise ratio, packet loss rate, bit error rate, and link distortion.

3. A wireless communication link optimization system according to claim 1, characterized in that: The task classification module first determines the type of node transmission tasks in cloud-edge collaboration and analyzes the performance indicator requirements of each task for the communication link.

4. A wireless communication link optimization system according to claim 3, characterized in that: The types of node transmission tasks include: sensor data, control data, status data, network management data, and log data; The performance indicators of the communication link include: Data volume, real-time performance, high throughput, low latency, high signal-to-noise ratio, low packet loss rate, low bit error rate, and low link distortion.

5. A wireless communication link optimization system according to claim 1, characterized in that: The communication link allocation strategy includes: Compression: When large amounts of image or video data need to be transmitted but there is no link that meets the requirements, the cloud server requires the edge node to compress or block the data to be transmitted locally. The compressed or block data is then transmitted through the communication link. The cloud server then restores the compressed image or splices the block data to restore the original data. Cache: When the existing communication link cannot meet the task transmission requirements, the cloud server requires the edge node to place the data to be sent in the sending cache and wait for the next cloud server QoS policy update before sending the data according to the new link allocation plan; Preemption: For particularly urgent information, the cloud server sends an interrupt to the communication link that meets the real-time requirements but is occupied, allowing the communication link to serve more urgent tasks first. The previously interrupted task will regain the right to use the communication link after the urgent task is transmitted.

6. A wireless communication link optimization method, characterized in that: A wireless communication link optimization system according to any one of claims 1 to 5, comprising: Step S1: Obtain the real-time inter-node communication link parameters of each edge node; Step S2: Summarize the inter-node communication link parameters of all edge nodes and store them in a database; Step S3: classify the transmission tasks according to the task characteristics and specify the link parameters that each transmission task focuses on; Step S4: Integrate the processing results of step S2 and step S3, and allocate appropriate communication links according to task characteristics and QoS parameters; Step S5: The allocation result is distributed to each edge node, and the edge node allocates a suitable communication link to the transmission task according to the updated strategy.

7. A wireless communication link optimization method according to claim 6, characterized in that: The step S3 comprises: Step S31: Determine the type of node transmission task in cloud-edge collaboration; Step S32: Analyze the performance indicator requirements of each task for the communication link.

8. A wireless communication link optimization method according to claim 6, characterized in that: The step S4 comprises: Step S41: Calculate the mean square error between the communication link performance required for each type of task and the actual performance of the communication link; Step S42: Allocate appropriate communication links according to the communication link allocation strategy.

Images