Network quality evaluation method and device, network monitoring system and electronic device

Abstract

The invention discloses a network quality evaluation method and device, a network monitoring system and an electronic device, and relates to the technical field of smart home / smart home, and the method comprises the steps: receiving air interface data and service data of a target wireless communication environment obtained based on two wireless network cards, the air interface data comprising a channel occupancy ratio, a retransmission rate and a signal-to-noise ratio, and the service data comprising the channel occupancy ratio, the retransmission rate and the signal-to-noise ratio; the service data comprises a timestamp, a packet loss rate and an average time delay. And performing association processing on the air interface data and the service data to obtain a plurality of network quality indexes. And generating a network quality evaluation result based on the plurality of network quality indexes, wherein the network quality evaluation result is used for indicating the performance state of the target wireless communication environment. According to the method, the performance state of the wireless communication environment can be comprehensively and accurately reflected, and a basis is provided for technical personnel to check network problems and optimize the network.

Description

Technical Field

[0001] This application relates to the field of smart home / intelligent home technology, and more specifically, to a network quality assessment method, apparatus, network monitoring system, and electronic device. Background Technology

[0002] With the widespread adoption of IoT devices, the number of devices connected to home networks is growing exponentially. Communication between devices depends on network stability, and network quality directly impacts user experience. Real-time monitoring and analysis of network quality are crucial for ensuring device interconnectivity and business continuity.

[0003] Existing network testing methods rely on basic tools to acquire signal strength and simple connectivity data. Alternatively, specialized hardware can be used to collect multi-dimensional data for network quality analysis. Smart routers can also be employed, utilizing their built-in network quality monitoring functions to analyze signal strength in real time.

[0004] However, basic tools suffer from limited data collection dimensions, making it difficult to quickly locate network problems. They also lack real-time and comprehensive data capabilities and historical data analysis. Specialized hardware equipment is expensive and complex to operate, requiring operator training, which is costly and reliant on manual intervention, resulting in low automation. Summary of the Invention

[0005] This application provides a network quality assessment method, apparatus, network monitoring system, and electronic device to address the problems of limited network performance data acquisition dimensions and poor network quality analysis capabilities in the prior art.

[0006] Firstly, this application provides a network quality assessment method, including:

[0007] Receive air interface data and service data of the target wireless communication environment obtained by two wireless network cards respectively. The air interface data includes channel occupancy ratio, retransmission rate and signal-to-noise ratio. The service data includes timestamp, packet loss rate and average delay.

[0008] The air interface data and service data are correlated to obtain multiple network quality indicators;

[0009] A network quality assessment result is generated based on multiple network quality indicators, and the network quality assessment result is used to indicate the performance status of the target wireless communication environment.

[0010] Optionally, the two wireless network cards are a first wireless network card and a second wireless network card, and the step of obtaining air interface data based on the first wireless network card includes:

[0011] Set the first wireless network card to monitoring mode, and after setting, obtain the monitoring interface name of the first wireless network card;

[0012] Based on the monitoring interface name, obtain wireless LAN standard frame data;

[0013] The wireless LAN standard frame data is parsed and processed to obtain the air interface data.

[0014] Optionally, the step of acquiring service data based on the second wireless network card includes:

[0015] Obtain the Internet Protocol address and preset test parameters of the target device, which is connected to the target wireless communication environment. The preset test parameters include: ping packet size range, adjustment step size, number of transmissions, timeout time, and interval time.

[0016] According to the preset test parameters, multiple ping request packets are generated, and the ping request packets are sent to the target device according to the Internet Protocol address;

[0017] Receive network performance metrics for each ping request packet;

[0018] Based on the aforementioned network performance metrics, the service data is determined.

[0019] Optionally, the step of associating the air interface data with service data to obtain multi-dimensional network quality indicators includes:

[0020] The air interface data and the service data are stored in a time series database, which includes the acquisition time of the air interface data and the service data.

[0021] According to the collection time, the air interface data and the service data are sorted separately to obtain a sorting result, which is used to indicate the time order of the air interface data and the service data;

[0022] Based on the sorting results, the air interface data and the service data are correlated and matched to obtain multiple network quality indicators.

[0023] Optionally, generating network quality assessment results based on multiple network quality indicators includes:

[0024] Determine the information entropy of multiple network quality metrics;

[0025] Based on the information entropy, determine the weight of each of the network quality indicators;

[0026] The network quality indicators are weighted according to the stated weights to obtain the network quality assessment results.

[0027] Optionally, after generating the network quality assessment results, the method further includes:

[0028] When the network quality assessment result indicates poor network quality, the network load for a preset period is determined based on the time series model and the data in the time series database. The time series model is trained based on historical network data.

[0029] Based on the network load, adjust the channel allocation strategy and sampling frequency of the target wireless communication environment.

[0030] Optionally, the method further includes:

[0031] If the network quality assessment result indicates poor network quality, acquire at least one target network device in the target wireless communication environment;

[0032] The device type of the target network device is determined based on the device fingerprint database, which is constructed by collecting the physical addresses and corresponding device type information of network devices in the target wireless communication environment.

[0033] Based on the device type, the network quality priority of the target network device is determined, and the network quality priority is used to indicate the maximum bandwidth value of the target network device.

[0034] Secondly, this application provides a network quality assessment device, comprising:

[0035] The receiving module is used to receive air interface data and service data of the target wireless communication environment obtained by two wireless network cards respectively. The air interface data includes channel occupancy ratio, retransmission rate and signal-to-noise ratio. The service data includes timestamp, packet loss rate and average delay.

[0036] The processing module is used to correlate the air interface data with the service data to obtain multiple network quality indicators;

[0037] A generation module is used to generate network quality assessment results based on multiple network quality indicators, the network quality assessment results being used to indicate the performance status of the target wireless communication environment.

[0038] Optionally, the two wireless network cards are a first wireless network card and a second wireless network card, and the receiving module is specifically used to set the first wireless network card to monitoring mode, and after setting, to obtain the monitoring interface name of the first wireless network card.

[0039] The receiving module is specifically used to obtain wireless local area network standard frame data based on the monitoring interface name;

[0040] The processing module is specifically used to parse and calculate the wireless local area network standard frame data to obtain the air interface data.

[0041] Optionally, a receiving module is specifically used to obtain the Internet Protocol address and preset test parameters of the target device, wherein the target device is connected to the target wireless communication environment, and the preset test parameters include: ping packet size range, adjustment step size, number of transmissions, timeout time and interval time;

[0042] The generation module is specifically used to generate multiple ping request packets according to the preset test parameters, and send the ping request packets to the target device according to the Internet Protocol address;

[0043] The receiving module is specifically used to receive network performance metrics for each ping request packet; and to determine service data based on the network performance metrics.

[0044] Optionally, the processing module is specifically used to store the air interface data and the service data in a time series database, wherein the time series database includes the acquisition time of the air interface data and the service data;

[0045] The processing module is specifically used to sort the air interface data and the service data according to the collection time, respectively, to obtain a sorting result, which is used to indicate the time order of the air interface data and the service data;

[0046] The processing module is specifically used to associate and match the air interface data and the service data according to the sorting results to obtain multiple network quality indicators.

[0047] Optionally, the generation module is specifically used to determine the information entropy of multiple network quality indicators; and to determine the weight of each network quality indicator based on the information entropy.

[0048] The processing module is specifically used to perform weighted processing on the network quality indicators according to the weights to obtain the network quality assessment results.

[0049] Optionally, the device further includes: a determining module and an adjusting module;

[0050] The determination module is used to determine the network load for a preset period based on a time series model and data in the time series database when the network quality assessment result indicates poor network quality. The time series model is trained based on historical network data.

[0051] The adjustment module is used to adjust the channel allocation strategy and sampling frequency of the target wireless communication environment based on the network load.

[0052] Optionally, the receiving module is further configured to acquire at least one target network device in the target wireless communication environment when the network quality assessment result indicates poor network quality;

[0053] The determination module is further configured to determine the device type of the target network device based on a device fingerprint database, wherein the device fingerprint database is constructed by collecting the physical addresses and corresponding device type information of network devices in the target wireless communication environment;

[0054] The determining module is further configured to determine the network quality priority of the target network device based on the device type, wherein the network quality priority is used to indicate the maximum bandwidth value of the target network device.

[0055] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0056] The memory stores computer-executed instructions;

[0057] The processor executes computer execution instructions stored in the memory to implement the network quality assessment method as described in the first aspect and various possible implementations of the first aspect above.

[0058] Fourthly, this application provides a network monitoring system, which includes at least two wireless network cards and a server;

[0059] The wireless network card is used to collect air interface data and service data of the target wireless communication environment;

[0060] The server is connected to the wireless network card and is used to perform the network quality assessment method as described in the first aspect and various possible implementations of the first aspect.

[0061] Fifthly, this application provides a computer-readable storage medium storing computer-executable instructions thereon, which, when executed by a processor, are used to implement the network quality assessment method as described in the first aspect and various possible implementations of the first aspect.

[0062] Sixthly, this application provides a program product including a computer program that, when executed by a processor, implements the network quality assessment method described above.

[0063] The network quality assessment method, apparatus, network monitoring system, and electronic device provided in this application involve using two wireless network cards to collect air interface data (including channel occupancy rate, retransmission rate, and signal-to-noise ratio) and service data (including timestamps, packet loss rate, and average latency) of the target wireless communication environment. After correlating and processing the air interface data and service data, multiple network quality indicators are obtained, and an assessment result is generated based on these indicators. This method can comprehensively and accurately reflect the performance status of the wireless communication environment, providing a basis for technicians to troubleshoot problems and optimize the network. It also helps users select a network that suits their service needs based on the assessment results, effectively improving network user experience and efficiency. Attached Figure Description

[0064] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0065] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a schematic diagram of the hardware environment for a network quality assessment method according to an embodiment of this application;

[0067] Figure 2 A schematic diagram of the structure of a network monitoring system provided in this application;

[0068] Figure 3 A flowchart illustrating a network quality assessment method provided in this application embodiment. Figure 1 ;

[0069] Figure 4 A flowchart illustrating a network quality assessment method provided in this application embodiment. Figure 2 ;

[0070] Figure 5 A flowchart illustrating a network quality assessment method provided in this application embodiment. Figure 3 ;

[0071] Figure 6 A schematic diagram of the network quality assessment device provided in this application;

[0072] Figure 7 A schematic diagram of the electronic device provided in this application. Detailed Implementation

[0073] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0074] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0075] According to one aspect of the embodiments of this application, a network quality assessment method is provided. This network quality assessment method is widely used in whole-house intelligent digital control application scenarios such as smart homes, smart home ecosystems, and intelligence house ecosystems. Figure 1 This is a schematic diagram of the hardware environment for a network quality assessment method according to an embodiment of this application. Optionally, in this embodiment, the above-described network quality assessment method can be applied to, for example... Figure 1 The hardware environment shown consists of terminal device 102 and server 104. For example... Figure 1 As shown, server 104 is connected to terminal device 102 via a network and can be used to provide services (such as application services) to the terminal or clients installed on the terminal. A database can be set up on the server or independently of the server to provide data storage services for server 104. Cloud computing and / or edge computing services can be configured on the server or independently of the server to provide data processing services for server 104.

[0076] The aforementioned network may include, but is not limited to, at least one of the following: wired network, wireless network. The aforementioned wired network may include, but is not limited to, at least one of the following: wide area network, metropolitan area network, local area network. The aforementioned wireless network may include, but is not limited to, at least one of the following: Wi-Fi (Wireless Fidelity), Bluetooth. The terminal device 102 may not be limited to PC, mobile phone, tablet computer, smart air conditioner, smart range hood, smart refrigerator, smart oven, smart stove, smart washing machine, smart water heater, smart washing equipment, smart dishwasher, smart projector, smart TV, smart clothes rack, smart curtains, smart audio-visual equipment, smart socket, smart speaker, smart speaker box, smart fresh air equipment, smart kitchen and bathroom equipment, smart bathroom equipment, smart robot vacuum cleaner, smart window cleaning robot, smart mopping robot, smart air purifier, smart steam oven, smart microwave oven, smart water heater, smart air purifier, smart water dispenser, smart door lock, etc.

[0077] With the widespread adoption of IoT devices, the number of devices connected to home networks is growing exponentially (such as smart appliances, security equipment, and voice assistants). Communication between devices depends on the stability of the wireless network, and network quality directly affects user experience (such as video stuttering, latency, and slow response). Real-time monitoring and analysis of network quality are crucial for ensuring device interconnection and business continuity.

[0078] Existing network testing methods rely on basic tools to obtain signal strength and simple connectivity data. Alternatively, they use specialized hardware to collect multi-dimensional data for network quality analysis. Smart routers can also be used, leveraging their built-in network quality monitoring functions to analyze signal strength in real time.

[0079] However, basic tools suffer from limited data collection dimensions, making it difficult to quickly locate network problems. They lack real-time and comprehensive data, and lack historical data analysis capabilities, failing to support trend analysis. Specialized hardware is expensive and complex to operate, requiring operator training, resulting in excessive costs and reliance on manual intervention with low automation. Smart routers cannot provide unified management across devices and have limited data analysis capabilities.

[0080] To address the aforementioned issues, this application provides a network quality assessment method. By integrating multi-source data from the air interface layer (e.g., channel occupancy rate, retransmission rate) and the service layer (e.g., packet loss rate, latency), a lightweight and automated monitoring system is constructed. This system enables real-time acquisition of network quality indicators, in-depth analysis of historical data, and generation of dynamic optimization strategies. This method collects data from multiple dimensions, comprehensively reflecting network quality. Comparative analysis with historical data improves the efficiency of network quality optimization, and the use of wireless network cards significantly reduces costs.

[0081] Figure 2A schematic diagram of the structure of a network monitoring system provided in this application is shown below. Figure 2 As shown, the network detection system includes at least two wireless network cards, a server, and a visualization display.

[0082] At least two wireless network cards can simultaneously collect and acquire wireless network signals from different frequency bands and channels, comprehensively gathering data for network quality assessment. Through the collaborative work of multiple wireless network cards, the network detection system can expand the signal monitoring range and improve coverage of the wireless network environment.

[0083] The network testing system can also include a wired network card, which can determine the ideal upper limit of the network infrastructure, verify whether the wireless performance meets expectations, and determine whether the signal problem is caused by an anomaly in the wireless test.

[0084] The server serves as the data processing and storage center for the network monitoring system. It receives signal data collected from wireless network cards and analyzes the data using pre-set algorithms and programs. The server can also identify the characteristics of different network devices, such as their MAC (Media Access Control) addresses. Furthermore, the server can save the collected data and evaluation results for subsequent querying and further analysis.

[0085] A visualization display serves as the interactive page between the network monitoring system and the user, presenting data collected by the network interface card (NIC) and network quality assessment results in an intuitive manner. It can display network-related information analyzed and processed by the server through various visualization formats such as graphs, charts, and line graphs. For example, a line graph can show the trend of signal strength changes over time, helping users understand the dynamic changes in network status.

[0086] The visual display also provides interactive functions, allowing users to filter and select data and view detailed information on the interface, helping them easily understand the results of network testing and evaluation and grasp the network's operating efficiency.

[0087] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0088] Figure 3 A flowchart illustrating a network quality assessment method provided in this application embodiment. Figure 1 .like Figure 3As shown, the network quality assessment method provided in this embodiment includes:

[0089] S101: Receive air interface data and service data of the target wireless communication environment obtained from the two wireless network cards respectively.

[0090] The air interface data includes channel occupancy rate, retransmission rate and signal-to-noise ratio, while the service data includes timestamp, packet loss rate and average latency.

[0091] Understandably, two wireless network cards can independently collect data from the target wireless communication environment. Two wireless network cards can acquire data from different angles and locations, avoiding the limitations of a single network card that could lead to incomplete or inaccurate data collection.

[0092] The data acquired through these two wireless network cards can be divided into two main categories: air interface data and service data. Air interface data can be collected using Kismet, an open-source wireless air interface monitoring tool that passively monitors wireless signals and collects air interface layer data such as signal strength and channel occupancy to assess the quality of the wireless environment.

[0093] Business data can be collected using ping. Ping is a network connectivity testing tool based on the ICMP (Internet Control Message Protocol). By sending echo request packets and measuring round-trip time, it can collect business layer data such as end-to-end latency and packet loss rate, which can be used to evaluate network transmission quality.

[0094] Air interface data refers to the underlying communication data of the wireless interface, reflecting the physical characteristics and transmission efficiency of the wireless channel, and directly affecting signal quality and connection stability. Channel occupancy ratio represents the proportion of a specific channel actually occupied within a given time period to the total time period, reflecting the channel's activity level. When the channel occupancy ratio is too high, indicating frequent communication activity on that channel, it may lead to channel congestion and affect communication quality.

[0095] The retransmission rate refers to the proportion of data packets that need to be retransmitted due to errors or collisions during data transmission. A high retransmission rate usually indicates that the wireless signal has been interfered with or that the signal strength is insufficient during transmission. The signal-to-noise ratio (SNR) is the ratio of useful signal power to noise power. A higher SNR indicates that the signal is stronger relative to the noise and the signal quality is better; conversely, a lower SNR may lead to increased data transmission errors and affect transmission reliability.

[0096] Business data refers to data related to the user's actual business experience, reflecting end-to-end service quality. Among them, timestamps record the precise time of data packet transmission or reception, and are used to analyze the data transmission order, timing relationships, and changes in network latency.

[0097] Packet loss rate refers to the proportion of data packets lost during data transmission out of the total number of data packets sent. Packet loss rate reflects network reliability; an excessively high packet loss rate can severely impact the continuity and integrity of services. Average latency refers to the average time it takes for a data packet to travel from the sender to the receiver. A longer average latency slows down service response and degrades user experience, especially in services with high real-time requirements, such as online games.

[0098] S102: Correlate air interface data with service data to obtain multiple network quality indicators.

[0099] Understandably, after acquiring both air interface data and service data, the actual situation of the target wireless communication environment can be comprehensively analyzed based on the correlation between the two types of data.

[0100] Correlation analysis can determine network quality indicators by analyzing the mutual influence between two types of data. For example, the relationship between channel occupancy and packet loss rate reveals that when the channel occupancy is too high, the packet loss rate tends to increase. This is because excessive communication activity leads to channel resource strain, increasing the probability of data collisions, which causes some data packets to be dropped and unable to be transmitted correctly.

[0101] Association processing can also involve data fusion, extracting and summarizing data from the same point in time, and analyzing the relationships between data from different points in time.

[0102] Correlation analysis yields multiple network quality metrics. These metrics cover various aspects from the wireless link layer to the service application layer, providing a comprehensive and objective reflection of network performance. For example, combining channel occupancy ratio, retransmission rate, and packet loss rate can derive an metric reflecting network congestion; a higher metric value indicates more severe congestion and poorer communication quality. Furthermore, combining signal-to-noise ratio (SNR) and average latency can construct an metric to measure network transmission efficiency; a higher SNR and shorter average latency mean the network can transmit data efficiently and stably.

[0103] S103: Generate network quality assessment results based on multiple network quality indicators.

[0104] Among them, the network quality assessment results are used to indicate the performance status of the target wireless communication environment.

[0105] Understandably, after obtaining multiple network quality indicators, they can be comprehensively analyzed and evaluated to generate a network quality assessment result. Network quality assessment can employ evaluation models and algorithms that weight different network quality indicators based on their importance. By comprehensively calculating each network quality indicator according to its corresponding weight, a comprehensive network quality score can be obtained.

[0106] Network quality assessment results can intuitively indicate the performance status of a target wireless communication environment. They can be categorized into multiple levels, such as excellent, good, average, and poor. An excellent assessment result indicates that the target wireless communication environment performs very well, can meet the needs of various services, and users will rarely encounter communication problems during use.

[0107] When the evaluation result is poor, it indicates that there are serious performance problems in the network, which may lead to business interruptions, frequent data transmission errors, etc., and timely measures need to be taken to optimize and improve the network.

[0108] Network quality assessment results can help technicians identify and resolve network problems in a timely manner, and can also provide users with a reference, allowing them to understand the current network performance status and thus choose a network environment that suits their business needs.

[0109] This embodiment provides a network quality assessment method. The method receives air interface data and service data of the target wireless communication environment acquired by two wireless network cards (NICs). The air interface data includes channel occupancy ratio, retransmission rate, and signal-to-noise ratio (SNR), while the service data includes timestamps, packet loss rate, and average latency. The air interface data and service data are correlated to obtain multiple network quality indicators. A network quality assessment result is generated based on these indicators, indicating the performance status of the target wireless communication environment. This method can comprehensively and accurately reflect the performance status of the wireless communication environment, providing a basis for technicians to troubleshoot network problems and optimize the network.

[0110] Figure 4 A flowchart illustrating a network quality assessment method provided in this application embodiment. Figure 2 .like Figure 4 As shown, in Figure 3 Based on the embodiments, a possible implementation method for acquiring air interface data and service data is described in detail, including:

[0111] S201: Set the first wireless network card to monitoring mode. After setting, obtain the monitoring interface name of the first wireless network card.

[0112] As is understandable, monitoring mode is a special operating mode of a wireless network card. It allows the card to capture all 802.11 wireless frame signals within its operating frequency band, regardless of whether these frames are related to the access point associated with the card. In other words, it can receive all transmitted data in the wireless environment. Setting monitoring mode allows for comprehensive acquisition of information about the target wireless environment.

[0113] The methods for setting monitoring mode vary depending on the server and wireless network card driver. Taking a Linux server as an example, you can usually use network configuration and management commands in conjunction with the wireless network card interface name to configure it.

[0114] S202: Obtain wireless LAN standard frame data based on the monitoring interface name.

[0115] Understandably, the monitoring interface name is a specific interface identifier assigned by the server after the wireless network card is in monitoring mode. Through this interface, the server can communicate with the wireless network card and obtain comprehensive wireless signal information collected by the wireless network card in monitoring mode.

[0116] Wireless LAN standard frame data is the data format used by devices in a wireless LAN to communicate. Wireless LAN standard frame data is also known as 802.11 frame data. "802.11" is a series of wireless LAN standards developed by electrical engineering associations. It covers various wireless communication technologies and specifications, used to regulate the communication methods, frequency usage, data transmission rates, and security mechanisms of wireless devices.

[0117] S203: Performs parsing and calculation on the standard frame data of the wireless local area network to obtain air interface data.

[0118] Understandably, 802.11 frame data contains a variety of information. Wireless LAN standard frame data exists in a specific binary format, containing multiple fields, each with its own specific meaning and function. The parsing process involves analyzing this binary data according to the standard protocol to obtain the value of each field. For example, the frame header can determine which device the data was sent from; the frame type field can determine whether the frame is a data frame, a management frame, or a control frame, thus understanding the frame's function and purpose.

[0119] Computational processing involves further processing the data based on the parsed data. For example, channel occupancy can be determined based on data frames, management frames, or control frames. Beacon frames in management frames are sent periodically to announce the existence and parameters of the wireless network; data frames and control frames represent actual wireless data transmission and control operations. By analyzing the transmission time and duration of these frames, the channel occupancy time can be determined.

[0120] The collected wireless LAN standard frame data is sorted and analyzed according to the order of transmission to determine the retransmission status of each data frame. The retransmission rate is determined by calculating the ratio of the number of retransmitted frames to the total number of transmitted frames.

[0121] The physical layer header information of standard WLAN frame data typically includes fields related to signal strength. By analyzing these fields, the signal strength value can be obtained. A higher value indicates a stronger signal and potentially better signal transmission quality.

[0122] S204: Obtain the Internet Protocol address and preset test parameters of the target device.

[0123] The target device is connected to a target wireless communication environment, which could be, for example, a wireless network coverage area in a home. Preset test parameters include: ping packet size range, adjustment step size, number of transmissions, timeout time, and interval time.

[0124] Understandably, the target device's Internet Protocol address (IP address) is a crucial identifier for ensuring accurate data transmission. The target device's IP address can be obtained using network scanning tools or the device's own network configuration information viewing function.

[0125] The preset test parameters are the relevant parameters for acquiring business data that are set in advance. Among them, the ping packet size range determines the size of the data packet sent each time. Different sizes will have different effects on network performance. Smaller packets can test network connection stability and response speed, while larger packets can test the network's ability to transmit large amounts of data.

[0126] Adjusting the step size allows you to gradually change the size of ping packets during testing, enabling you to observe how network performance changes with packet size. The number of sends specifies the total number of ping request packets to be sent to the target device. The timeout is the maximum time you can wait for a response from the target device; if this time is exceeded, the request is considered failed. The interval is the time between sending two ping request packets; frequent sending of request packets can unnecessarily burden the network.

[0127] S205: Generate multiple ping request packets according to preset test parameters, and send ping request packets to the target device according to Internet Protocol addresses.

[0128] Understandably, the process of generating ping request packets needs to follow preset test parameters such as the ping packet size range and adjustment step size. For example, if the ping packet size range is set to 32 bytes to 1500 bytes, and the adjustment step size is 32 bytes, then ping request packets of 32 bytes, 64 bytes, 96 bytes...1500 bytes will be generated sequentially.

[0129] After generating a ping request packet, it can be sent based on the target device's IP address. The wireless network card can send individual ping request packets over the network to the target device's IP address. During the sending process, it is crucial to ensure the accuracy and integrity of the data packets to avoid inaccurate test results due to data errors.

[0130] At the same time, ping request packets should be sent in an orderly manner according to the preset number of transmissions and intervals. For example, if the preset number of transmissions is 10 and the interval is 1 second, then a ping request packet will be sent every 1 second for a total of 10 times to ensure the standardization and scientific nature of the test.

[0131] S206: Network performance metrics for receiving each ping request packet.

[0132] Understandably, after sending a ping request packet to the target device, it needs to receive the corresponding network performance metrics. Upon receiving the ping request packet, the target device can process it and return a response packet. The wireless network card, acting as the receiver, can accurately receive these response packets.

[0133] The received response packet contains a wealth of network performance metrics. Failure to receive the packet indicates a network failure, while successful reception indicates a normal network connection. The response time is also available; it represents the time from sending the ping request packet to receiving the response packet. A shorter response time indicates a faster network transmission speed. Furthermore, other information may be obtained, such as the data packet transmission path and network congestion levels. This information can be used for subsequent network performance evaluation.

[0134] S207: Determine service data based on network performance metrics.

[0135] Understandably, after receiving network performance metrics for each ping request packet, business data can be determined based on these metrics. Timestamps record the specific time each ping request packet is sent and received. These timestamps allow us to understand how network performance changes over time; for example, whether network response time and packet loss rate fluctuate in different time periods.

[0136] Packet loss rate is a measure of network reliability; it represents the proportion of ping request packets that fail to receive a response. A higher packet loss rate indicates that more data packets are lost during transmission, and therefore, the network is less reliable.

[0137] Average latency measures network transmission efficiency; it is the average response time of all ping request packets. A shorter average latency indicates faster data transmission and a better user experience. By calculating and analyzing service data, the network performance of the target device's wireless communication environment can be quantitatively evaluated, providing a strong basis for subsequent network optimization and troubleshooting.

[0138] This embodiment provides a network quality assessment method. The method involves setting a first wireless network card to monitoring mode and obtaining its monitoring interface name. A tool is used to capture standard wireless LAN frame data through this interface, which is then parsed to obtain air interface data. By obtaining the target device's IP address and preset test parameters, multiple ping request packets are generated and sent to the target device according to the parameters. After receiving network performance indicators, the service data is determined. This method can comprehensively acquire raw wireless communication information and service data, quantitatively assess network performance through data, accurately locate network problems, assist in network optimization and troubleshooting, and improve the operational quality of wireless networks and user experience.

[0139] Figure 5 A flowchart illustrating a network quality assessment method provided in this application embodiment. Figure 3 .like Figure 5 As shown, in Figure 3 Based on the embodiments, a possible implementation of generating network quality assessment results is described in detail, including:

[0140] S301: Store air interface data and business data in a time-series database.

[0141] The time-series database includes the collection times of air interface data and business data.

[0142] Understandably, time-series databases possess the capability to process time-series data, allowing for the storage and rapid retrieval of data arranged chronologically. When data is stored in a time-series database, each data point is recorded with its corresponding acquisition time.

[0143] S302: Sort the air interface data and service data according to the collection time to obtain the sorting results.

[0144] Understandably, to accurately analyze the relationships between data, these two types of data can be sorted according to their collection time. The sorting result indicates the temporal order of the air interface data and the business data. The sorting result provides an ordered dataset for data correlation and analysis, helping the server analyze the inherent connections between air interface data and business data, and avoiding analytical errors caused by disordered data order.

[0145] S303: Based on the sorting results, the air interface data and service data are correlated and matched to obtain multiple network quality indicators.

[0146] Understandably, after the data is sorted, it can be correlated and fused according to the sorting results. The matching and fusion principle used here is to fuse data based on time, that is, to associate air interface data and service data at the same or similar time points. Air interface data reflects the physical layer characteristics of the wireless network, such as signal strength and signal-to-noise ratio; while service data reflects the network's performance when carrying specific services, such as packet loss rate and retransmission rate.

[0147] Air interface data and service data are interdependent in time. For example, poor air interface signal quality can lead to service data lag and packet loss. Merging these two different but closely related types of data can comprehensively reflect the actual network operation from multiple dimensions, thereby obtaining a series of representative network quality indicators.

[0148] S304: Determine the information entropy of multiple network quality metrics.

[0149] Understandably, after obtaining multiple network quality indicators, the entropy weight method can be used to determine the network quality assessment result through a weighted comprehensive score. The entropy weight method is an objective weighting method based on information entropy, which can automatically determine the weights according to the degree of variation of the indicators.

[0150] Specifically, the first step is to determine the information entropy of each indicator. Information entropy measures the uncertainty or amount of information contained in an indicator. For example, if a network quality indicator has a wide range of values ​​and a relatively dispersed distribution, then its information entropy is relatively high, meaning that the indicator contains rich information and has strong uncertainty. Indicators with high information entropy may be more sensitive to changes in network quality and can provide more information about the network state.

[0151] S305: Determine the weight of each network quality indicator based on information entropy.

[0152] Understandably, once the information entropy of each network quality indicator is obtained, the weight of each indicator can be determined based on the magnitude of the information entropy. Generally, the higher the information entropy of an indicator, the higher its weight. This is because indicators with higher information entropy contain more uncertainty and information, and can more comprehensively and sensitively reflect changes in network quality.

[0153] By rationally determining the weight of each network quality indicator, a more scientific and reasonable network quality assessment model can be constructed, enabling the assessment results to more accurately reflect the actual quality status of the network and avoiding deviations in the assessment results due to the excessive influence or neglect of a certain indicator.

[0154] S306: The network quality indicators are weighted according to their weights to obtain the network quality assessment results.

[0155] As is understandable, the weighted processing involves multiplying the value of each indicator by its corresponding weight, and then summing the weighted results of all indicators. Weighted processing can comprehensively consider the impact of each indicator on network quality, making the evaluation results more comprehensive and accurate in reflecting the overall quality of the network.

[0156] This embodiment provides a network quality assessment method. This method stores air interface data and service data in a time-series database containing collection timestamps. After sorting by collection time, multiple network quality indicators are obtained through correlation and matching. Weights are then determined based on the information entropy of each indicator and weighted averaged to obtain the final network quality assessment result. This method integrates air interface and service data, comprehensively analyzes their time-based correlation, and accurately quantifies the impact of each indicator on network quality. This provides a scientific basis for network optimization and effectively improves network operation quality and user experience.

[0157] In some embodiments, after generating network quality assessment results, if the network quality assessment results indicate poor network quality, adjustments can be made to the target wireless communication environment to improve network quality. The adjustment methods include:

[0158] Optionally, the network load for a preset time period can be determined based on the time series model and data in the time series database.

[0159] Understandably, time series models are trained based on historical network data. Through in-depth analysis of historical data, the model can capture the changing patterns of network load at different times and in different scenarios. Based on the air interface data and business data stored in the time series database, combined with the patterns of historical data, the time series model can accurately infer the network load situation for a preset period (i.e., a future period).

[0160] Based on network load, adjust the channel allocation strategy and sampling frequency for the target wireless communication environment.

[0161] Understandably, after obtaining network load information for a preset time period, the server can make targeted adjustments to the target wireless communication environment based on the predicted information to optimize network quality. One such adjustment method is adjusting the channel allocation strategy. Through a dynamic channel switching strategy, the server can utilize the occupancy ratios of each channel indicated in the prediction results. If it detects that a channel may experience high congestion during a predicted time period, the server can switch communication to a low-congestion channel. This effectively reduces the retransmission rate because data transmission on low-congestion channels is smoother, reducing data collisions and retransmissions caused by channel congestion, thereby improving network stability and allowing users to enjoy a smoother network service.

[0162] The sampling frequency can also be adjusted adaptively based on network load. Under high load, such as when processor utilization exceeds 80%, the server uses a higher sampling frequency, sampling once every 10 seconds. This is because network conditions change rapidly under high load, requiring more frequent data collection to promptly grasp network dynamics and make quick adjustments. Under low load, the server can reduce the sampling frequency to once per minute. This aims to reduce server resource consumption, avoid wasting system resources due to excessively frequent sampling, and enable the system to operate more efficiently while maintaining network quality.

[0163] In addition, the ping packet size can be adaptively adjusted based on predicted network load or current load conditions. In high-bandwidth scenarios, such as a 5GHz wireless environment, if the packet loss rate is less than 1%, it indicates good network transmission, and the ping packet size can be gradually increased. Larger ping packets can more comprehensively test the network's transmission capabilities and stability, further exploring the network's potential.

[0164] In congested scenarios, if the packet loss rate exceeds 5% or the round-trip time fluctuates by more than 30%, it indicates network congestion. Reducing the ping packet size is crucial in such situations. Smaller ping packets reduce the burden on network transmission, minimizing data loss and transmission delays caused by excessively large packets, thereby alleviating network congestion and improving network availability.

[0165] The adaptive adjustment strategy described above can optimize and adjust in advance after obtaining the predicted information for a preset time period, or it can optimize and adjust the network quality in a timely and rapid manner when the network status changes are monitored in real time.

[0166] Optionally, after determining network quality, differentiated quality of service (QoS) assurance can be provided for specific equipment types, including:

[0167] Acquire at least one target network device in the target wireless communication environment.

[0168] Understandably, the target wireless communication environment can be a wireless network area within a home, covering multiple rooms. Within this environment, there are various types of network devices, such as smartphones, computers, and other mobile terminal devices that can wirelessly access the network for convenient internet access; and smart routers, which act as the core hub of the network, responsible for data forwarding and distribution.

[0169] Network devices can be identified using network scanning tools. These tools actively send probe packets to the target network and analyze the returned response information to identify the devices present in the network and their IP addresses. Alternatively, network management software can be used to discover connected devices and their basic information, thereby identifying the network devices.

[0170] Based on the device fingerprint database, determine the device type of the target network device.

[0171] Understandably, a device fingerprint database is built by collecting the physical addresses and corresponding device type information of network devices in a target wireless communication environment. The database records detailed information about the physical addresses and corresponding device types of network devices. The physical address is a unique hardware identifier for a network device, allowing it to distinguish between different network devices.

[0172] After obtaining the physical address of the target network device, this address can be entered into the device fingerprint database for precise matching. Once a matching record is found, the device type information corresponding to that device can be returned. This device fingerprint database-based determination method is efficient and accurate, allowing for rapid identification of the target network device's type.

[0173] Determine the network quality priority of the target network device based on the device type.

[0174] Understandably, network quality priority is used to indicate the maximum bandwidth value of a target network device. Different device types have different network quality requirements due to their different use cases and functions. For example, video devices, whose main function is to play high-definition video content, require a stable and high-speed network to ensure smooth video playback and avoid problems such as stuttering and buffering. Therefore, video devices are given a higher network quality priority and allocated a higher maximum bandwidth value, such as ensuring their minimum bandwidth is 20Mbps (Million bits per second), to ensure that the transmission requirements of video data can be met.

[0175] For some simple IoT devices, such as temperature and humidity sensors, they only need to send detection data periodically, and their requirements for network bandwidth and real-time performance are relatively low. Therefore, these devices can be given a lower network quality priority, and their corresponding maximum bandwidth values ​​are also relatively small, for example, limited to a maximum bandwidth of 2Mbps.

[0176] By prioritizing network quality based on device type, network resources can be allocated rationally, avoiding waste and over-utilization. For example, in a home network, assigning higher network quality priority to video devices ensures a good user experience. For less critical devices, bandwidth usage can be appropriately limited, ensuring that the network needs of critical devices are met. This effectively identifies and resolves potential network bottlenecks caused by inappropriate resource allocation, further optimizing network quality.

[0177] Figure 6 A schematic diagram of the network quality assessment device provided in this application. Figure 6 As shown, this application provides a network quality assessment device, the network quality assessment device 600 including:

[0178] The receiving module 601 is used to receive air interface data and service data of the target wireless communication environment obtained by two wireless network cards respectively. The air interface data includes channel occupancy ratio, retransmission rate and signal-to-noise ratio, and the service data includes timestamp, packet loss rate and average delay.

[0179] Processing module 602 is used to correlate air interface data with service data to obtain multiple network quality indicators;

[0180] The generation module 603 is used to generate network quality assessment results based on multiple network quality indicators. The network quality assessment results are used to indicate the performance status of the target wireless communication environment.

[0181] Optionally, the two wireless network cards are a first wireless network card and a second wireless network card. The receiving module 601 is specifically used to set the first wireless network card to monitoring mode and, after setting, to obtain the monitoring interface name of the first wireless network card.

[0182] The receiving module 601 is specifically used to obtain wireless local area network standard frame data based on the monitoring interface name;

[0183] The processing module 602 is specifically used to parse and calculate the standard frame data of the wireless local area network to obtain the air interface data.

[0184] Optionally, the receiving module 601 is specifically used to obtain the Internet Protocol address and preset test parameters of the target device. The target device is connected to the target wireless communication environment. The preset test parameters include: ping packet size range, adjustment step size, number of transmissions, timeout time and interval time.

[0185] The generation module 603 is specifically used to generate multiple ping request packets according to preset test parameters, and send the ping request packets to the target device according to the Internet Protocol address;

[0186] The receiving module 601 is specifically used to receive network performance metrics for each ping request packet; and to determine service data based on the network performance metrics.

[0187] Optionally, the processing module 602 is specifically used to store air interface data and business data into a time series database, the time series database including the acquisition time of air interface data and business data;

[0188] The processing module 602 is specifically used to sort the air interface data and service data according to the acquisition time, and obtain the sorting result. The sorting result is used to indicate the time order of the air interface data and service data.

[0189] The processing module 602 is specifically used to correlate and match air interface data and service data according to the sorting results to obtain multiple network quality indicators.

[0190] Optionally, the generation module 603 is specifically used to determine the information entropy of multiple network quality indicators; and to determine the weight of each network quality indicator based on the information entropy.

[0191] The processing module 602 is specifically used to perform weighted processing on the network quality indicators according to the weights to obtain the network quality assessment results.

[0192] Optionally, the device may also include: a determining module 604 and an adjusting module 605;

[0193] The determination module 604 is used to determine the network load for a preset period based on the time series model and data in the time series database when the network quality assessment result indicates poor network quality. The time series model is trained based on historical network data.

[0194] The adjustment module 605 is used to adjust the channel allocation strategy and sampling frequency of the target wireless communication environment based on network load.

[0195] Optionally, the receiving module 601 is further configured to acquire at least one target network device in the target wireless communication environment when the network quality assessment result indicates poor network quality;

[0196] The determination module 604 is also used to determine the device type of the target network device based on the device fingerprint database, which is constructed by collecting the physical address and corresponding device type information of the network devices in the target wireless communication environment;

[0197] The determination module 604 is also used to determine the network quality priority of the target network device based on the device type. The network quality priority is used to indicate the maximum bandwidth value of the target network device.

[0198] The network quality assessment device provided in this application embodiment has a similar implementation principle and technical effect to the implementation of each part of the aforementioned network quality assessment method, and will not be described again here.

[0199] Figure 7 A schematic diagram of the electronic device provided in this application. Figure 7 As shown, this application provides an electronic device 700, which includes a receiver 701, a transmitter 702, a processor 703, and a memory 704.

[0200] Receiver 701 is used to receive commands and data;

[0201] Transmitter 702 is used to send commands and data;

[0202] Memory 704 is used to store instructions executed by the computer;

[0203] The processor 703 is used to execute computer execution instructions stored in the memory 704 to implement the various steps of the network quality assessment method in the above embodiments. For details, please refer to the relevant descriptions in the foregoing embodiments of the network quality assessment method.

[0204] Optionally, the memory 704 can be either standalone or integrated with the processor 703.

[0205] When the memory 704 is set up independently, the electronic device also includes a bus for connecting the memory 704 and the processor 703.

[0206] The implementation principle and technical effects of the electronic device provided in this embodiment can be found in the foregoing embodiments, and will not be repeated here.

[0207] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method of any of the foregoing embodiments.

[0208] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method of any of the foregoing embodiments.

[0209] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0210] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor. The memory may include high-speed RAM, and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0211] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0212] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0213] 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 apparatus 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 apparatus. 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 apparatus that includes that element.

[0214] The above are merely preferred embodiments of this application. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A network quality evaluation method characterized by comprising: Applied to a server, the method comprises: Receiving air interface data and service data of a target wireless communication environment acquired by two wireless network cards respectively, the air interface data comprising channel occupancy ratio, retransmission rate and signal-to-noise ratio, and the service data comprising timestamp, packet loss rate and average delay; Associating the air interface data and the service data to obtain multiple network quality indicators; Generating a network quality evaluation result based on the multiple network quality indicators, the network quality evaluation result being used to indicate the performance state of the target wireless communication environment.

2. The method of claim 1, wherein, The two wireless network cards are a first wireless network card and a second wireless network card, and the air interface data acquired based on the first wireless network card comprises: Setting the first wireless network card to a monitoring mode, and acquiring the monitoring interface name of the first wireless network card after the setting is completed; Acquiring wireless local area network standard frame data based on the monitoring interface name; Analyzing and calculating the wireless local area network standard frame data to obtain the air interface data.

3. The method of claim 2, wherein, The service data acquired based on the second wireless network card comprises: Acquiring the Internet protocol address of a target device and preset test parameters, the target device being connected in the target wireless communication environment, and the preset test parameters comprising ping packet size range, adjustment step, sending times, timeout time and interval time; Generating multiple ping request packets according to the preset test parameters, and delivering the ping request packets to the target device according to the Internet protocol address; Receiving network performance indicators of each ping request packet; Determining service data based on the network performance indicators.

4. The method of claim 3, wherein, The association processing of the air interface data and the service data to obtain multiple network quality indicators comprises: Storing the air interface data and the service data to a time series database, the time series database comprising the collection time of the air interface data and the service data; Sorting the air interface data and the service data according to the collection time to obtain sorting results, the sorting results being used to indicate the time sequence of the air interface data and the service data; Associating and matching the air interface data and the service data according to the sorting results to obtain multiple network quality indicators.

5. The method of claim 4, wherein, The generation of the network quality evaluation result based on the multiple network quality indicators comprises: Determining the information entropy of the multiple network quality indicators; Determining the weight of each network quality indicator according to the information entropy; Weighting the network quality indicators according to the weight to obtain the network quality evaluation result.

6. The method of claim 5, wherein, After the network quality evaluation result is generated, the method further comprises: In the case that the network quality evaluation result indicates poor network quality, determining the network load of a preset period based on a time series model and the data in the time series database, the time series model being trained based on historical network data; Adjusting the channel allocation strategy and sampling frequency of the target wireless communication environment based on the network load.

7. The method of claim 6, wherein, The method further comprises: In a case where the network quality evaluation result indicates poor network quality, at least one target network device in the target wireless communication environment is acquired; According to a device fingerprint library, a device type of the target network device is determined, the device fingerprint library being constructed by collecting physical addresses of network devices in the target wireless communication environment and corresponding device type information; According to the device type, a network quality priority of the target network device is determined, the network quality priority being used to indicate a maximum bandwidth value of the target network device.

8. A network quality assessment apparatus characterized by comprising: Comprise: A receiving module is configured to receive air interface data and service data of a target wireless communication environment acquired based on two wireless network cards respectively, the air interface data comprising channel occupation ratio, retransmission rate and signal-to-noise ratio, and the service data comprising timestamp, packet loss rate and average time delay; A processing module is configured to perform associated processing on the air interface data and the service data to obtain a plurality of network quality indexes; A generating module is configured to generate a network quality evaluation result based on the plurality of network quality indexes, the network quality evaluation result being used to indicate a performance state of the target wireless communication environment.

9. A network monitoring system, characterized by The network monitoring system comprises at least two wireless network cards and a server; The wireless network cards are configured to collect air interface data and service data of a target wireless communication environment; The server is connected with the wireless network cards and is configured to execute the method according to any one of claims 1-7. 10.An electronic device comprising a memory and a processor, the electronic device characterized by, The memory stores a computer program, and the processor is configured to execute the method according to any one of claims 1-7 through the computer program.

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