A network quality detection method and device, electronic equipment and storage medium

Abstract

The present disclosure relates to a network quality detection method and device, electronic equipment and storage medium, comprising: collecting first characteristic data and second characteristic data of each access device of a target networking; the first characteristic data is respectively related to each terminal device in the target networking, and the second characteristic data is not related to any terminal device; obtaining a first characteristic weight of each first characteristic data, a terminal device weight of each terminal device, a second characteristic weight of each second characteristic data, and an access device weight of each access device; according to the first characteristic weight, the terminal device weight, the second characteristic weight and the access device weight, the first characteristic data and the second characteristic data are weighted and calculated to determine the network quality detection result of the target networking. The network quality evaluation method has low implementation difficulty and strong operability, and can be applied to different scene business requirements.

Description

Technical Field

[0001] This disclosure relates to the field of Internet technology, and in particular to a method, apparatus, electronic device and storage medium for network quality detection. Background Technology

[0002] Home WiFi (wireless network communication technology) is usually built by the user. In order to evaluate the network quality of WiFi networks, operators need to continuously track the network quality of WiFi networks.

[0003] In existing technologies, operators can use certain handheld tools to have maintenance personnel conduct on-site testing of WiFi network quality. Alternatively, they can embed probes in user mobile phones, computers, and other WiFi-connected terminal devices to detect forward and reverse latency and packet loss rates between source and destination addresses, thereby assessing the WiFi network quality.

[0004] However, on-site testing is costly and only suitable for initial installation and repair, not for long-term automatic testing. Embedded probes rely on the participation of terminal devices and are not very practical. Therefore, current WiFi network quality evaluation methods are difficult to implement and cannot be applied to the business needs of different scenarios. Summary of the Invention

[0005] This disclosure provides a network quality detection system, method, apparatus, electronic device, and storage medium to at least address the problem that WiFi network quality evaluation methods in related technologies are difficult to implement and hard to apply to the business needs of different scenarios. The technical solution of this disclosure is as follows:

[0006] According to a first aspect of the present disclosure, a network quality detection method is provided, comprising:

[0007] Collect first feature data and second feature data of each access device in the target network; the first feature data is related to each terminal device in the target network, and the second feature data is not related to any of the terminal devices.

[0008] Obtain the first feature weight of each first feature data, the terminal device weight of each terminal device, the second feature weight of each second feature data, and the access device weight of each access device; the terminal device weight is positively correlated with the duration of the corresponding terminal device accessing the target network, and the access device weight is positively correlated with the cumulative duration of the terminal device accessing the target network.

[0009] Based on the first feature weight, the terminal device weight, the second feature weight, and the access device weight, the first feature data and the second feature data are weighted and calculated to determine the network quality detection result of the target network.

[0010] Optionally, the collection of first and second feature data of each access device in the target network includes:

[0011] The first and second feature data of each access device in the target network are collected through a preset plugin; the preset plugin is installed in each access device of the target network.

[0012] Optionally, the step of collecting first and second feature data of each access device in the target network through a preset plug-in includes:

[0013] By using preset plugins, candidate feature data of the corresponding access devices can be collected;

[0014] Obtain the installation work order information of the target network, and determine each access device included in the target network based on the installation work order information;

[0015] For each access device, the candidate feature data are clustered to determine the first feature data and the second feature data of each access device.

[0016] Optionally, the step of performing feature clustering on the candidate feature data of each access device to determine the first feature data and the second feature data corresponding to each access device includes:

[0017] Based on preset feature dimensions, feature clustering is performed on the candidate feature data of each access device to determine the feature data of each preset feature dimension corresponding to each access device; the preset feature dimensions include the feature dimensions corresponding to the first feature data and the second feature data.

[0018] The feature data is dimensionless to obtain the first feature data and the second feature data.

[0019] Optionally, the step of weighting the first feature data and the second feature data according to the first feature weight, the terminal device weight, the second feature weight, and the access device weight to determine the network quality detection result of the target network includes:

[0020] Determine the first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight;

[0021] Determine the second product between the second feature data and the corresponding second feature weight;

[0022] The sum of the first product and the second product is determined, and the sum is multiplied by the corresponding weight of the access device to obtain the network quality detection result of the target network.

[0023] According to a second aspect of the present disclosure, a network quality detection apparatus is provided, comprising:

[0024] The acquisition module is used to acquire first feature data and second feature data of each access device in the target network; the first feature data is related to each terminal device in the target network, and the second feature data is not related to any of the terminal devices.

[0025] The acquisition module is used to acquire the first feature weight of each first feature data, the terminal device weight of each terminal device, the second feature weight of each second feature data, and the access device weight of each access device; the terminal device weight is positively correlated with the duration of the corresponding terminal device accessing the target network, and the access device weight is positively correlated with the cumulative duration of the terminal device accessing the target network.

[0026] The analysis module is used to perform weighted calculations on the first feature data and the second feature data based on the first feature weight, the terminal device weight, the second feature weight, and the access device weight, to determine the network quality detection result of the target network.

[0027] Optionally, the acquisition module is used for:

[0028] The first and second feature data of each access device in the target network are collected through a preset plugin; the preset plugin is installed in each access device of the target network.

[0029] Optionally, the acquisition module is used for:

[0030] By using preset plugins, candidate feature data of the corresponding access devices can be collected;

[0031] Obtain the installation work order information of the target network, and determine each access device included in the target network based on the installation work order information;

[0032] For each access device, the candidate feature data are clustered to determine the first feature data and the second feature data of each access device.

[0033] Optionally, the acquisition module is used for:

[0034] Based on preset feature dimensions, feature clustering is performed on the candidate feature data of each access device to determine the feature data of each preset feature dimension corresponding to each access device; the preset feature dimensions include the feature dimensions corresponding to the first feature data and the second feature data.

[0035] The feature data is dimensionless to obtain the first feature data and the second feature data.

[0036] Optionally, the analysis module is used for:

[0037] Determine the first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight;

[0038] Determine the second product between the second feature data and the corresponding second feature weight;

[0039] The sum of the first product and the second product is determined, and the sum is multiplied by the corresponding weight of the access device to obtain the network quality detection result of the target network.

[0040] According to a third aspect of the present disclosure, a network quality detection electronic device is provided, comprising:

[0041] processor;

[0042] Memory used to store the processor's executable instructions;

[0043] The processor is configured to execute the instructions to implement the network quality detection method described in any one of the claims.

[0044] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein when instructions in the computer-readable storage medium are executed by a processor of a network quality detection electronic device, the network quality detection electronic device is enabled to perform the network quality detection method described in any one of the present invention.

[0045] According to a fifth aspect of the present disclosure, a computer program product is provided, including a computer program / instructions that, when executed by a processor, implement the network quality detection method described in any one of the present invention.

[0046] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects:

[0047] Collect first feature data and second feature data for each access device in the target network; the first feature data is related to each terminal device in the target network, and the second feature data is not related to any terminal device; obtain the first feature weight of each first feature data, the terminal device weight of each terminal device, the second feature weight of each second feature data, and the access device weight of each access device; the terminal device weight is positively correlated with the duration of the corresponding terminal device's access to the target network, and the access device weight is positively correlated with the cumulative duration of the terminal device's access to the target network; based on the first feature weight, the terminal device weight, the second feature weight, and the access device weight, perform a weighted calculation on the first feature data and the second feature data to determine the network quality detection result of the target network.

[0048] In this way, the network quality of the target network can be tested without on-site inspection or the participation of terminal equipment. Compared with the solutions in the existing technology, the network quality evaluation method is less difficult to implement and more operable, so it can be applied to the business needs of different scenarios.

[0049] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0051] Figure 1 This is a flowchart illustrating a network quality detection method according to an exemplary embodiment.

[0052] Figure 2 This is a logical schematic diagram illustrating a network quality detection method according to an exemplary embodiment.

[0053] Figure 3 This is a block diagram illustrating a network quality detection device according to an exemplary embodiment.

[0054] Figure 4 This is a block diagram illustrating an electronic device for network quality detection according to an exemplary embodiment.

[0055] Figure 5 This is a block diagram illustrating an apparatus for network quality detection according to an exemplary embodiment. Detailed Implementation

[0056] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0057] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure 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 disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0058] Figure 1 This is a flowchart illustrating a network quality detection method according to an exemplary embodiment, such as... Figure 1 As shown, the network quality detection method includes:

[0059] In step S11, first feature data and second feature data of each access device in the target network are collected; the first feature data is related to each terminal device in the target network, and the second feature data is not related to any terminal device.

[0060] In some scenarios, it is often necessary to test the network quality of the target network in order to update or maintain it in a timely manner. For example, the target network may be a home WiFi network, which is usually built by the user. The operator needs to continuously track the network quality of the WiFi network.

[0061] In existing technologies, operators can use certain handheld tools to have maintenance personnel conduct on-site tests of WiFi network quality, or they can assess WiFi network quality by embedding probes in user's mobile phones, computers, and other WiFi-connected terminal devices.

[0062] However, on-site inspection is costly and only suitable for initial installation and maintenance, not for long-term automatic inspection. Embedded probes rely on the participation of terminal devices and are not very operable. Therefore, none of the above methods are suitable for business needs in different scenarios.

[0063] In this application, the target network is the network that requires network quality testing, and the network includes multiple access devices and multiple terminal devices.

[0064] The access device is called an AP (Wireless Access Point), which acts as a bridge connecting wired and wireless networks. Its main function is to connect various wireless network clients together and then connect the wireless network to the Ethernet, thereby achieving the purpose of wireless network coverage.

[0065] Terminal devices are user terminals that access and use the network services provided by the target network through access devices. For example, terminal devices can be various forms of terminal devices such as mobile phones, tablets, desktop computers, and portable laptops, and this application embodiment does not limit them.

[0066] The first and second characteristic data of each access device in the target network can be collected at preset time points, such as once every hour, or at preset collection cycles, such as once every 10 minutes, etc., without any specific limitation.

[0067] In one implementation, collecting first characteristic data and second characteristic data of each access device in the target network includes: collecting first characteristic data and second characteristic data of each access device in the target network through a preset plug-in; the preset plug-in is installed in each access device of the target network.

[0068] In other words, the first feature data and the second feature data of each access device can be collected by building a data acquisition plugin into the access device.

[0069] The pre-installed plugins are installed on each access device in the target network, thus eliminating the need for terminal devices and improving the operability of network quality testing.

[0070] The first feature data is associated with each terminal device in the target network, while the second feature data is not associated with any terminal device. In other words, each access device has multiple first feature data corresponding to each terminal device, and also has second feature data that is not associated with any terminal device.

[0071] For example, assuming there are k access devices and n terminal devices in a target network, then in one collection cycle, each access device can collect m parameters from each terminal device, for a total of k*m first feature data. In addition, each access device also collects s ​​parameters that are unrelated to the user terminal, i.e., s second feature data.

[0072] In one implementation, a pre-set plugin is used to collect first and second characteristic data of each access device in the target network, including:

[0073] The system collects candidate feature data of the corresponding access devices through a preset plugin; obtains the installation work order information of the target network, and determines each access device included in the target network based on the installation work order information; performs feature clustering on the candidate feature data of each access device, and determines the first feature data and second feature data of each access device respectively.

[0074] For example, candidate feature data may include any one or more of the following:

[0075] The specific details of access devices and terminal devices include: standard generation, number of spatial streams, channel bandwidth, frequency band used, signal strength of the target network, negotiation rate, backhaul link medium of access devices, backhaul link negotiation rate, backhaul signal power (applicable to wireless backhaul and fiber optic backhaul), whether there is mesh networking between access devices, terminal status of each access device, number of connected branches of each access device, roaming handover delay, signal-to-noise ratio, channel duty cycle, bit error rate, retransmission count, CPU (central processing unit) / memory utilization of access devices, and working time after startup.

[0076] In other words, based on the installation work order information, all access devices within a target network can be identified to perform network quality analysis on the target network.

[0077] Furthermore, feature clustering can be performed on the candidate feature data of each access device to determine the first feature data and the second feature data of each access device.

[0078] Feature clustering can categorize the collected data into multiple preset feature dimensions such as terminal capabilities, network coverage, environmental interference, device status, and network connectivity, forming data tables for each dimension.

[0079] For example, the preset feature dimensions may include, but are not limited to:

[0080] i. Terminal capabilities: standard generation, number of spatial streams, channel bandwidth, and frequency band used. For example, standard generation can include WiFi 4 / 5 / 6, etc.

[0081] ii. Network coverage: WiFi signal strength and negotiation rate.

[0082] iii. Environmental interference: signal-to-noise ratio, channel duty cycle, bit error rate, and retransmission count information.

[0083] iv. Device status: CPU and memory utilization of connected devices, and working time after startup, etc.

[0084] v. Network Connection: The connection relationship between multiple access devices in the target network, the backhaul link medium of each access device, the backhaul link negotiation rate, the backhaul signal power, whether there is a mesh network between access devices, the terminal status of each access device, and the number of connection branches of each access device, etc.

[0085] In one implementation, feature clustering is performed on the candidate feature data of each access device to determine the first feature data and the second feature data corresponding to each access device, including:

[0086] Based on the preset feature dimensions, the candidate feature data of each access device are clustered to determine the feature data of each preset feature dimension corresponding to each access device. The preset feature dimensions include the feature dimensions corresponding to the first feature data and the second feature data. The feature data is then processed to be dimensionless to obtain the first feature data and the second feature data.

[0087] Dimensionless transformation refers to mapping each parameter value to [0,1] while preserving the comparability between the original parameter values.

[0088] This eliminates the difference in dimensions among the collected parameters, facilitating subsequent comprehensive analysis of the first and second feature data.

[0089] In step S12, the first feature weight of each first feature data, the terminal device weight of each terminal device, the second feature weight of each second feature data, and the access device weight of each access device are obtained. The terminal device weight is positively correlated with the duration of the corresponding terminal device accessing the target network, and the access device weight is positively correlated with the cumulative duration of the terminal device accessing the target network.

[0090] In this step, the first feature weight of each first feature data, the terminal device weight of each terminal device, the second feature weight of each second feature data, and the access device weight of each access device can be further obtained. Based on these weights, the first feature data and the second feature data can be analyzed in a targeted manner, which makes it easier to obtain more accurate network quality detection results.

[0091] The weight of a terminal device is positively correlated with the duration of its access to the target network. In other words, the longer the terminal device is accessed by the target network, the greater its weight; conversely, the shorter the access time, the smaller its weight.

[0092] In one implementation, the terminal device weight is the ratio of the duration of the corresponding terminal device accessing the target network to the total duration of all terminal devices accessing the target network.

[0093] A higher weight for a terminal device indicates that the terminal device is more important to the user's network access experience.

[0094] For example, the terminal weight of the i-th terminal device can be represented as:

[0095]

[0096] In addition, the weight of an access device is positively correlated with the cumulative time that the terminal devices connected to that access device have been connected to the target network. In other words, for each access device, the longer the cumulative time that the terminal devices connected to that access device have been connected to the target network, the greater the weight of that access device. Conversely, the shorter the cumulative time that the terminal devices connected to that access device have been connected to the target network, the smaller the weight of that access device.

[0097] A higher weight for an access device indicates that the device provides more network services and contributes more to the overall evaluation of the target network.

[0098] In one implementation, the access device weight is the ratio of the duration of the terminal device using the target network to the total duration of all terminal devices accessing the target network.

[0099] For example, the access device weight of the i-th access device can be represented as:

[0100]

[0101] In addition, the first feature weight of each first feature data and the second feature weight of each second feature data are statistical parameters obtained by combining engineering experience and user experience feedback regression, and there are no specific limitations.

[0102] In step S13, the first feature data and the second feature data are weighted and calculated according to the first feature weight, the terminal device weight, the second feature weight, and the access device weight to determine the network quality detection result of the target network.

[0103] As mentioned above, both the first feature data and the second feature data are associated with each access device. Moreover, the first feature data is associated with each terminal device in the target network. Therefore, in this step, the first feature data and the second feature data can be weighted according to the first feature weight, the terminal device weight, the second feature weight, and the access device weight to determine the network quality detection result of the target network.

[0104] In one implementation, the network quality detection result of the target network is determined by weighting the first feature data and the second feature data according to the first feature weight, the terminal device weight, the second feature weight, and the access device weight, including:

[0105] Determine the first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight; determine the second product between the second feature data and the corresponding second feature weight; determine the sum of the first product and the second product, and multiply the sum by the corresponding access device weight to obtain the network quality detection result of the target network.

[0106] For example, if the m first feature data related to each terminal device collected on the i-th access device are represented by A(i), the details are as follows:

[0107]

[0108] Suppose the weights of the first features of m first feature data are W = (w1 w2 … w m ), the terminal device weight G(i) of each terminal device on the i-th access device is = (g i1 g i2 …g in If i = 1, 2, ..., k, then the quality evaluation H of all parameters related to access devices and terminal devices is:

[0109] H=(G(1)A(1)W T ,G(2)A(2)W T ,…,G(k)A(k)W T )

[0110] Secondly, considering the s second feature data B on the access device that are unrelated to the terminal device, it can be expressed as:

[0111]

[0112] Assume its second feature weight is C = (c1 c2…c s The access device weights are represented as P = (p1p2…p ... k Based on the above information, the network quality evaluation result of this target network can be expressed as:

[0113] PH T +PBC T

[0114] Here, A(i) and B are feature data obtained by sampling through the plug-in installed on the access device at discrete time points. A(i) and B are actually statistical analysis quantities of several discrete time point values ​​within a certain time range.

[0115] As can be seen from the above, the technical solution provided by the embodiments of this disclosure can realize the detection of the network quality of the target network without the need for on-site testing or the participation of terminal equipment. Compared with the solutions in the prior art, the network quality evaluation method is less difficult to implement and more operable, so it can be applied to the business needs of different scenarios.

[0116] like Figure 2 As shown, the method in this embodiment of the invention will be described below with reference to a specific example, which specifically includes:

[0117] 1. Connect the device plug-in data acquisition module:

[0118] The access device has a built-in data acquisition plugin to collect data related to the access device and data related to the terminal.

[0119] The collected data includes:

[0120] Information includes the standard generation, number of spatial streams, channel bandwidth, frequency band used, WiFi signal strength, negotiation rate, backhaul link medium of access devices and WiFi terminals, backhaul link negotiation rate, backhaul signal power (applicable to wireless backhaul and fiber optic backhaul), whether there is mesh networking between access devices, terminal status of each access device, number of connected branches of each access device, roaming handover delay, signal-to-noise ratio, channel duty cycle, bit error rate, retransmission count, CPU / memory utilization of access devices, and working time after startup.

[0121] 2. Access the equipment installation work order information module:

[0122] Based on the installation work order information of the access devices, all access devices within a target network are identified in order to perform WiFi quality analysis at the target network level.

[0123] 3. Dimensional Clustering Module:

[0124] The collected data is clustered into terminal capabilities, network coverage, environmental interference, device status, and network connectivity, forming data tables for each dimension. The specific clustering is as follows:

[0125] i. Terminal capabilities include:

[0126] Standard generation (e.g., WiFi 4 / 5 / 6), number of spatial streams, channel bandwidth, and frequency band used.

[0127] ii. Network coverage includes:

[0128] Wi-Fi signal strength and negotiation rate.

[0129] iii. Environmental disturbances include:

[0130] Signal-to-noise ratio, channel duty cycle, bit error rate, and retransmission count information.

[0131] iv. Equipment status includes:

[0132] The CPU and memory utilization of the connected device, and its working time after startup, etc.

[0133] v. Network connections include:

[0134] The target network includes the connection relationships between multiple access devices, the backhaul link medium, backhaul link negotiation rate, backhaul signal power, whether there is a mesh network between access devices, the terminal status of each access device, and the number of connection branches of each access device.

[0135] 4. Dimensionlessization:

[0136] Each parameter value is mapped to [0,1] while preserving the comparability between the original parameter values.

[0137] 5. Comprehensive Analysis:

[0138] To perform model-based analysis of the data, assume a household has k access devices and n terminal devices. Within one data collection period, m parameters are collected for each terminal device, and s parameters unrelated to the terminal device are also collected for each access device.

[0139] The specific method is as follows:

[0140] First, consider the parameters related to the terminal device. The m parameters related to the terminal device collected from the i-th access device are dimensionless and denoted as A(i), as follows:

[0141]

[0142] Assume the weights of the m parameters are W = (w1 w2 … w m ), the weight G(i) of each terminal device on the i-th access device is = (g i1 g i2 …g in If i = 1, 2, ..., k, then the quality evaluation H of all parameters related to access devices and terminal devices is:

[0143] H=(G(1)A(1)W T ,G(2)A(2)W T ,…,G(k)A(k)W T )

[0144] Secondly, consider the s acquisition parameters on the access device that are independent of the terminal device, which, after dimensionless transformation, are B:

[0145]

[0146] Assume the parameter weights corresponding to the access devices are C = (c1 c2…c s The weights of the access devices are represented as P = (p1p2…p) k Based on the above information, the overall quality evaluation result of the target Wi-Fi network is as follows:

[0147] PH T +PBC T

[0148] Additional explanation for A(i) and B: Both A(i) and B are parameters obtained by sampling through the access device plug-in at discrete time points. A(i) and B are actually statistical analysis quantities of several discrete time point values ​​within a certain time range.

[0149] For example, the access device weight of the i-th access device can be expressed as:

[0150]

[0151] The access device weight of the i-th access device can be represented as:

[0152]

[0153] Additional explanation regarding weights:

[0154] Terminal weight G is a statistical parameter that is positively correlated with the duration of time the terminal device uses the target Wi-Fi network. The higher the terminal weight, the more important the terminal is to the network access experience.

[0155] The access device weight P is a statistical parameter that is positively correlated with the total cumulative time of all terminal devices connected to the access device. The higher the access device weight, the more network services it provides and the greater its contribution to the overall evaluation of the wireless network.

[0156] The parameter weights C and W are statistical parameters obtained by combining engineering experience and experiential feedback regression.

[0157] As can be seen from the above, the technical solution provided by the embodiments of this disclosure can realize the detection of the network quality of the target network without the need for on-site testing or the participation of terminal equipment. Compared with the solutions in the prior art, the network quality evaluation method is less difficult to implement and more operable, so it can be applied to the business needs of different scenarios.

[0158] Figure 3 This is a block diagram of a network quality detection device according to an exemplary embodiment, comprising:

[0159] The acquisition module 201 is used to acquire first feature data and second feature data of each access device in the target network; the first feature data is related to each terminal device in the target network, and the second feature data is not related to any of the terminal devices.

[0160] The acquisition module 202 is used to acquire the first feature weight of each first feature data, the terminal device weight of each terminal device, the second feature weight of each second feature data, and the access device weight of each access device; the terminal device weight is positively correlated with the duration of the corresponding terminal device accessing the target network, and the access device weight is positively correlated with the cumulative duration of the terminal device accessing the target network.

[0161] The analysis module 203 is used to perform weighted calculations on the first feature data and the second feature data according to the first feature weight, the terminal device weight, the second feature weight and the access device weight, to determine the network quality detection result of the target network.

[0162] Optionally, the acquisition module 201 is used for:

[0163] The first and second feature data of each access device in the target network are collected through a preset plugin; the preset plugin is installed in each access device of the target network.

[0164] Optionally, the acquisition module 201 is used for:

[0165] By using preset plugins, candidate feature data of the corresponding access devices can be collected;

[0166] Obtain the installation work order information of the target network, and determine each access device included in the target network based on the installation work order information;

[0167] For each access device, the candidate feature data are clustered to determine the first feature data and the second feature data of each access device.

[0168] Optionally, the acquisition module 201 is used for:

[0169] Based on preset feature dimensions, feature clustering is performed on the candidate feature data of each access device to determine the feature data of each preset feature dimension corresponding to each access device; the preset feature dimensions include the feature dimensions corresponding to the first feature data and the second feature data.

[0170] The feature data is dimensionless to obtain the first feature data and the second feature data.

[0171] Optionally, the analysis module 203 is used for:

[0172] Determine the first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight;

[0173] Determine the second product between the second feature data and the corresponding second feature weight;

[0174] The sum of the first product and the second product is determined, and the sum is multiplied by the corresponding weight of the access device to obtain the network quality detection result of the target network.

[0175] As can be seen from the above, the technical solution provided by the embodiments of this disclosure can realize the detection of the network quality of the target network without the need for on-site testing or the participation of terminal equipment. Compared with the solutions in the prior art, the network quality evaluation method is less difficult to implement and more operable, so it can be applied to the business needs of different scenarios.

[0176] Figure 4 This is a block diagram illustrating an electronic device for network quality detection according to an exemplary embodiment.

[0177] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory including instructions that can be executed by a processor of an electronic device to perform the method. Optionally, the computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0178] In an exemplary embodiment, a computer program product is also provided that, when run on a computer, enables the computer to implement the method for network quality detection.

[0179] As can be seen from the above, the technical solution provided by the embodiments of this disclosure can realize the detection of the network quality of the target network without the need for on-site testing or the participation of terminal equipment. Compared with the solutions in the prior art, the network quality evaluation method is less difficult to implement and more operable, so it can be applied to the business needs of different scenarios.

[0180] Figure 5 This is a block diagram illustrating an apparatus 800 for network quality detection according to an exemplary embodiment.

[0181] For example, device 800 can be a mobile phone, computer, digital broadcasting electronic device, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0182] Reference Figure 5The device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0183] Processing component 802 typically controls the overall operation of device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described.

[0184] Furthermore, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0185] Memory 804 is configured to store various types of data to support the operation of device 800. Examples of this data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 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.

[0186] Power supply component 807 provides power to various components of device 800. Power supply component 807 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 800. Multimedia component 808 includes a screen that provides an output interface between device 800 and an account.

[0187] In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from an account. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of a touch or swipe action but also the duration and pressure associated with the touch or swipe operation.

[0188] In some embodiments, the multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera can receive external multimedia data. Each front-facing camera and rear-facing camera can be a fixed optical lens system or have focal length and optical zoom capabilities. The audio component 810 is configured to output and / or input audio signals.

[0189] For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816.

[0190] In some embodiments, the audio component 810 further includes a speaker for outputting audio signals.

[0191] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to, a home button, volume buttons, a power button, and a lock button.

[0192] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 800. For example, sensor assembly 814 can detect the on / off state of device 800, the relative positioning of components such as the display and keypad of device 800, changes in the position of device 800 or a component of device 800, the presence or absence of contact between an account and device 800, the orientation or acceleration / deceleration of device 800, and temperature changes of device 800.

[0193] Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.

[0194] In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0195] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access wireless networks based on communication standards, such as WiFi, carrier networks (such as 2G, 3G, 4G, or 5G), or combinations thereof.

[0196] In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel.

[0197] In one exemplary embodiment, the communication component 816 further includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0198] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described in the first and second aspects.

[0199] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions that can be executed by a processor 820 of the device 800 to perform the method.

[0200] Optionally, for example, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0201] In an exemplary embodiment, a computer program product including instructions is also provided, which, when run on a computer, causes the computer to perform any of the network quality detection methods described in the embodiments.

[0202] As can be seen from the above, the technical solution provided by the embodiments of this disclosure can realize the detection of the network quality of the target network without the need for on-site testing or the participation of terminal equipment. Compared with the solutions in the prior art, the network quality evaluation method is less difficult to implement and more operable, so it can be applied to the business needs of different scenarios.

[0203] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0204] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A network quality detection method characterized by, The method comprises the following steps: Collecting first characteristic data and second characteristic data of each access device of a target network group; The first characteristic data is respectively related to each terminal device in the target network group, and the second characteristic data is not related to any terminal device; Obtaining a first characteristic weight of each first characteristic data, a terminal device weight of each terminal device, a second characteristic weight of each second characteristic data, and an access device weight of each access device; the terminal device weight is positively correlated with the time length of the corresponding terminal device accessing the target network group, and the access device weight is positively correlated with the cumulative time length of the terminal device accessing the corresponding access device in the target network group; According to the first characteristic weight, the terminal device weight, the second characteristic weight and the access device weight, the first characteristic data and the second characteristic data are weighted and calculated to determine the network quality detection result of the target network group.

2. The network quality detection method of claim 1, wherein, The method comprises the following steps: Collecting first characteristic data and second characteristic data of each access device of a target network group through a preset plug-in; the preset plug-in is installed in each access device of the target network group.

3. The network quality detection method of claim 2, wherein, The method comprises the following steps: Collecting candidate characteristic data of the corresponding access device through the preset plug-in; Obtaining installation work order information of the target network group, and determining each access device included in the target network group based on the installation work order information; Performing characteristic clustering on the candidate characteristic data of each access device to determine the first characteristic data and the second characteristic data of each access device respectively.

4. The network quality detection method of claim 3, wherein, The method comprises the following steps: According to a preset characteristic dimension, performing characteristic clustering on the candidate characteristic data of each access device to determine the characteristic data of each preset characteristic dimension corresponding to each access device respectively; the preset characteristic dimension includes the characteristic dimension corresponding to the first characteristic data and the second characteristic data; Performing non-dimensional processing on the characteristic data to obtain the first characteristic data and the second characteristic data.

5. The network quality detection method of claim 1, wherein, The method comprises the following steps: Determining a first product between the first characteristic data and the corresponding first characteristic weight and the corresponding terminal device weight; Determining a second product between the second characteristic data and the corresponding second characteristic weight; Determining the sum of the first product and the second product, multiplying the sum by the corresponding access device weight to obtain the network quality detection result of the target network group.

6. A network quality detection apparatus characterized by comprising: The method comprises the following steps: A collection module is configured to collect first characteristic data and second characteristic data of each access device of a target network group; The first feature data is respectively related to each terminal device in the target networking, and the second feature data is not related to any terminal device; The acquisition module is configured to acquire a first feature weight of each first feature data, a terminal device weight of each terminal device, a second feature weight of each second feature data, and an access device weight of each access device; the terminal device weight is positively correlated with a time length during which the corresponding terminal device accesses the target networking, and the access device weight is positively correlated with a cumulative time length during which a terminal device accessing the corresponding access device accesses the target networking; The analysis module is configured to perform weighted calculation on the first feature data and the second feature data according to the first feature weight, the terminal device weight, the second feature weight, and the access device weight, and determine a network quality detection result of the target networking.

7. The network quality detection apparatus according to claim 6, wherein The acquisition module is configured to: Collect, through a preset plug-in, first feature data and second feature data of each access device of the target networking; the preset plug-in is installed in each access device of the target networking.

8. The network quality detection apparatus according to claim 7, wherein The acquisition module is configured to: Collect, through a preset plug-in, candidate feature data of the corresponding access device; Obtain installation work order information of the target networking, and determine, based on the installation work order information, each access device included in the target networking; Perform feature clustering on the candidate feature data of each access device, and respectively determine first feature data and second feature data of each access device.

9. The network quality detection apparatus according to claim 8, wherein The acquisition module is configured to: Perform feature clustering on the candidate feature data of each access device according to a preset feature dimension, and respectively determine feature data of each preset feature dimension corresponding to each access device; the preset feature dimension includes feature dimensions corresponding to the first feature data and the second feature data; Perform non-dimensional processing on the feature data to obtain the first feature data and the second feature data.

10. The network quality detection apparatus of claim 6, wherein The analysis module is configured to: Determine a first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight; Determine a second product between the second feature data and the corresponding second feature weight; Determine a sum of the first product and the second product, multiply the sum by the corresponding access device weight to obtain the network quality detection result of the target networking.

11. An electronic device, comprising: Comprise: A processor; A memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the network quality detection method of any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that, When the instructions in the computer readable storage medium are executed by the processor of the network quality detection electronic device, the network quality detection electronic device can execute the network quality detection method of any one of claims 1 to 5.

13. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the network quality detection method of any one of claims 1 to 5.

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