CN116056133A - A network quality detection method, device, electronic equipment and storage medium - Google Patents

Abstract

The disclosure relates to a network quality detection method, a device, an electronic device and a storage medium, comprising: collecting first characteristic data and second characteristic data of each access device of the target networking; the first characteristic data are respectively related to each terminal device in the target network, and the second characteristic data are not related to any terminal device; acquiring a first characteristic weight of each first characteristic data, a terminal equipment weight of each terminal equipment, a second characteristic weight of each second characteristic data and an access equipment weight of each access equipment; and according to the first characteristic weight, the terminal equipment weight, the second characteristic weight and the access equipment weight, carrying out weighted calculation on the first characteristic data and the second characteristic data, and determining a network quality detection result of the target networking. The network quality evaluation method is low in implementation difficulty and high in operability, so that the network quality evaluation method can be suitable for service requirements of different scenes.

Description

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

technical field

The present disclosure relates to the technical field of the Internet, and in particular to a network quality detection method, device, electronic equipment and storage medium.

Background technique

Home WiFi (wireless network communication technology) is usually built by users themselves. Operators need to continuously track the network quality of WiFi networking in order to evaluate the network quality of WiFi networking.

In the existing technology, the operator can use some handheld tools to have the operation and maintenance personnel come to test the quality of the WiFi networking network, or, by embedding probes in the terminal equipment of the WiFi networking such as the user's mobile phone and computer, Perform forward and reverse delay and packet loss rate detection between source and destination addresses to evaluate the network quality of WiFi networking.

However, the door-to-door inspection is expensive, and it is only suitable for initial installation and maintenance, not for long-term automatic inspection. The method of embedding probes needs to rely on the participation of terminal equipment, and the operability is not high. Therefore, the current WiFi The network quality evaluation method of the networking is difficult to implement, and it is difficult to apply to the business needs of different scenarios.

Contents of the invention

The present disclosure provides a network quality detection system, method, device, electronic equipment, and storage medium to at least solve the problem that the WiFi network quality evaluation method in the related art is difficult to implement and is difficult to apply to the business requirements of different scenarios. The disclosed technical scheme is as follows:

According to the first aspect of the embodiments of the present disclosure, a network quality detection method is provided, including:

Collect the first characteristic data and second characteristic data of each access device of the target network; the first characteristic data are respectively related to each terminal device in the target network, and the second characteristic data are related to any - said terminal equipment is not relevant;

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 It is positively correlated with the duration of the corresponding terminal device accessing the target network, and the weight of the access device is positively correlated with the accumulated duration of the terminal device accessing the corresponding access device to access the target network ;

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 target The network quality inspection result of the networking.

Optionally, the collecting the first feature data and the second feature data of each access device of the target network includes:

The first characteristic data and the second characteristic data of each access device of the target network are collected through a preset plug-in; the preset plug-in is installed in each access device of the target network.

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

Through the preset plug-in, collect the candidate feature data of the corresponding access device;

Obtaining the installation work order information of the target networking, and based on the installation work order information, determining each access device included in the target networking;

Perform feature clustering on the candidate feature data of each access device, and respectively determine the first feature data and the second feature data of each access device.

Optionally, performing feature clustering on the candidate feature data of each access device, and respectively determining the first feature data and the second feature data corresponding to each access device, includes:

According to the preset feature dimension, perform feature clustering on the candidate feature data of each access device, and respectively determine the feature data of each preset feature dimension corresponding to each access device; the preset feature dimension includes the first feature data and the feature dimension corresponding to the second feature data;

Dimensionless processing is performed on the feature data to obtain first feature data and second feature data.

Optionally, 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 Calculating and determining the network quality detection result of the target networking, including:

determining a first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight;

determining a 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 weight of the access device to obtain a network quality detection result of the target network.

According to a second aspect of an embodiment of the present disclosure, a network quality detection device is provided, including:

A collection module, configured to collect first characteristic data and second characteristic data of each access device of the target network; the first characteristic data are respectively related to each terminal device in the target network, and the first characteristic data are respectively related to each terminal device in the target network. 2. The feature data is not related to any of the terminal devices;

An acquisition module, configured 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 weight of the terminal device is positively correlated with the duration of the corresponding terminal device accessing the target network, and the weight of the access device is related to the access time of the terminal device corresponding to the access device to access the target network The cumulative duration of is positively correlated;

An analysis module, 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 , determining the network quality detection result of the target networking.

Optionally, the collection module is used for:

The first characteristic data and the second characteristic data of each access device of the target network are collected through a preset plug-in; the preset plug-in is installed in each access device of the target network.

Optionally, the collection module is used for:

Through the preset plug-in, collect the candidate feature data of the corresponding access device;

Obtaining the installation work order information of the target networking, and based on the installation work order information, determining each access device included in the target networking;

Perform feature clustering on the candidate feature data of each access device, and respectively determine the first feature data and the second feature data of each access device.

Optionally, the collection module is used for:

According to the preset feature dimension, perform feature clustering on the candidate feature data of each access device, and respectively determine the feature data of each preset feature dimension corresponding to each access device; the preset feature dimension includes the first feature data and the feature dimension corresponding to the second feature data;

Dimensionless processing is performed on the feature data to obtain first feature data and second feature data.

Optionally, the analysis module is used for:

determining a first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight;

determining a 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 weight of the access device to obtain a network quality detection result of the target network.

According to a third aspect of the embodiments of the present disclosure, there is provided an electronic device for network quality detection, including:

processor;

memory for storing said processor-executable instructions;

Wherein, the processor is configured to execute the instructions, so as to implement any one of the network quality detection methods described above.

According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium. 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 can execute The network quality detection method described in any one of the above.

According to a fifth aspect of the embodiments of the present disclosure, a computer program product is provided, including computer programs/instructions, and when the computer programs/instructions are executed by a processor, implement the network quality detection method described in any one of the above.

The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects:

Collecting first characteristic data and second characteristic data of each access device of the target network; the first characteristic data are respectively related to each terminal device in the target network, and the second characteristic data are 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 corresponds to The duration of the terminal device accessing the target network is positively correlated, and the weight of the access device is positively correlated with the cumulative duration of the terminal device accessing the corresponding access device to access the target network; according to the first feature weight, terminal device weight, and the second Two feature weights and access device weights are used to perform weighted calculations on the first feature data and the second feature data to determine the network quality detection result of the target network.

In this way, the detection of the network quality of the target network can be realized without the need for door-to-door detection and 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. Strong, so it can be applied to the business needs of different scenarios.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

The accompanying drawings here are incorporated into the specification and constitute a part of the specification, show embodiments consistent with the disclosure, and are used together with the description to explain the principle of the disclosure, and do not constitute an improper limitation of the disclosure.

Fig. 1 is a flowchart showing a method for detecting network quality according to an exemplary embodiment.

Fig. 2 is a schematic diagram of a method for detecting network quality according to an exemplary embodiment.

Fig. 3 is a block diagram showing a device for detecting network quality according to an exemplary embodiment.

Fig. 4 is a block diagram showing an electronic device for network quality detection according to an exemplary embodiment.

Fig. 5 is a block diagram showing a device for network quality detection according to an exemplary embodiment.

Detailed ways

In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

It should be noted that the terms "first" and "second" in the description and claims of the present disclosure and the drawings are used to distinguish similar objects, and not necessarily used to describe a specific order or sequence . It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein can be practiced in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.

Fig. 1 is a flowchart of a method for detecting network quality according to an exemplary embodiment. As shown in Fig. 1, the method for detecting network quality includes:

In step S11, the first characteristic data and second characteristic data of each access device of the target network are collected; the first characteristic data are respectively related to each terminal device in the target network, and the second characteristic data are related to any End devices are not relevant.

In some scenarios, it is often necessary to detect the network quality of the target network in order to update or maintain the target network in time. For example, the target network can be home WiFi, which is usually built by the user. It is necessary to continuously track the network quality of the WiFi networking.

In the existing technology, the operator can use some handheld tools to have the operation and maintenance personnel come to test the quality of the WiFi networking network, or, by embedding probes in the terminal equipment of the WiFi networking such as the user's mobile phone and computer, Realize the evaluation of the network quality of WiFi networking.

However, the cost of on-site detection is relatively high, and it is only suitable for initial installation and maintenance, not suitable for long-term automatic detection. The method of embedding probes needs to rely on the participation of terminal equipment, and the operability is not high. It is difficult to apply to the business needs of different scenarios.

In this application, the target network is the network that requires network quality detection, and the network includes multiple access devices and multiple terminal devices.

The access device is the AP (WirelessAccessPoint, wireless access point), which is equivalent to a bridge connecting the wired network and the wireless network. Its main function is to connect various wireless network clients together, and then connect the wireless network to the Ethernet. , so as to achieve the purpose of network wireless coverage.

The terminal device is a user terminal that accesses and uses network services provided by the target network through the access device. For example, the terminal device may be a mobile phone, a tablet computer, a desktop computer, a portable notebook computer, and other terminal devices, which are not limited in this embodiment of the present application.

Wherein, the collection of the first feature data and the second feature data of each access device of the target network may be collected according to a preset time point, such as once every hour, or may be collected according to a preset time point. The collection period is collected, such as once every 10 minutes, etc., and there is no specific limit.

In an implementation manner, collecting the first feature data and second feature data of each access device of the target network includes: collecting the first feature data and second feature data of each access device of the target network through a preset plug-in. The second feature data: the preset plug-in is installed in each access device of the target network.

That is to say, the first feature data and the second feature data of each access device may be collected respectively by building a collection plug-in in the access device.

The preset plug-in is installed in each access device of the target network, so that the participation of the terminal device is not required, thereby improving the operability of network quality detection.

The first characteristic data are respectively related to each terminal device in the target network, and the second characteristic data are not related to any terminal device, that is to say, each access device has multiple At the same time, the first feature data also has second feature data that is not related to any terminal device.

For example, assuming that there are k access devices and n terminal devices in a target network, then, within a collection period, each access device can collect m parameters for each terminal device, and a total of k*mth parameters In addition, each access device also collects s parameters irrelevant to the user terminal, that is, s second characteristic data.

In an implementation manner, the first characteristic data and the second characteristic data of each access device of the target network are collected through a preset plug-in, including:

Through the preset plug-in, collect the candidate feature data of the corresponding access device; 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; Feature clustering is performed on the candidate feature data of the devices, and the first feature data and the second feature data of each access device are respectively determined.

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

The standard generation, number of spatial streams, channel bandwidth, frequency band used, signal strength of the target network, negotiation rate, backhaul link medium of the access device, backhaul link negotiation rate, backhaul link Transmission signal power (applicable to wireless backhaul and optical fiber backhaul), whether there is a mesh network between access devices, the status of terminals connected to each access device, the number of connection branches of each access device, roaming switching delay, signal-to-noise ratio , channel duty cycle, bit error rate, retransmission times information, CPU (central processing unit, central processing unit)/memory utilization rate of the access device, working time after startup, etc., are not specifically limited.

That is to say, all access devices in a target network can be locked according to the installation work order information, so as to analyze the network quality of the target network.

Further, the feature clustering may be performed on the candidate feature data of each access device, and the first feature data and the second feature data of each access device are respectively determined.

Among them, after feature clustering, the collected data can be clustered into multiple preset feature dimensions such as terminal capability, network coverage, environmental interference, device status, and network connection, forming a data table of each dimension.

For example, preset feature dimensions may include but not limited to:

i. Terminal capability: standard generation, number of spatial streams, channel bandwidth, frequency band used. For example, standard generations can include WiFi4/5/6, etc.

ii. Network coverage: WiFi signal strength, negotiation rate.

iii. Environmental interference: signal-to-noise ratio, channel duty cycle, bit error rate, retransmission times information.

iv. Device status: the CPU and memory utilization rate of the connected device, the working time after startup, etc.

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, and the connection between access devices. Whether there is a mesh network, the status of terminals attached to each access device, the number of branches connected to each access device, etc.

In an implementation manner, feature clustering is performed on the candidate feature data of each access device, and the first feature data and the second feature data corresponding to each access device are respectively determined, including:

According to the preset feature dimension, perform feature clustering on the candidate feature data of each access device, and respectively determine the feature data of each preset feature dimension corresponding to each access device; the preset feature dimension includes the first feature data and the second feature data The characteristic dimension corresponding to the second characteristic data; the dimensionless processing is performed on the characteristic data to obtain the first characteristic data and the second characteristic data.

Among them, dimensionless refers to mapping each parameter value to [0,1] while retaining the comparable relationship between the original parameter values.

In this way, the different dimensions of the collected parameters can be eliminated, which facilitates subsequent comprehensive analysis of the first characteristic data and the second characteristic data.

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 acquired; The weight of the terminal device is positively correlated with the duration of the corresponding terminal device accessing the target network, and the weight of the access device is positively correlated with the cumulative duration of accessing the target network for the terminal device connected to the corresponding access device.

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 may be further obtained. Weights. Based on these weights, the first characteristic data and the second characteristic data can be analyzed in a targeted manner, so as to obtain more accurate network quality detection results.

Among them, the weight of the terminal device is positively correlated with the duration of the corresponding terminal device accessing the target network, that is to say, the longer the duration of the terminal device accessing the target network, the greater the weight of the terminal device corresponding to the target device. Conversely, The shorter the time period for a terminal device to access the target network, the smaller the weight of the terminal device corresponding to the target device.

In an implementation manner, the weight of the terminal device is a ratio of the duration of the corresponding terminal device accessing the target network to the total duration of each terminal device accessing the target network.

A higher terminal device weight indicates that the terminal device is more important to the user's network access experience.

For example, the value of the terminal device weight of the i-th terminal device can be expressed as:

In addition, the weight of an access device is positively correlated with the accumulative duration of access to the target network for a terminal device that accesses the corresponding access device. That is to say, for each access device, each terminal that accesses the access device The longer the accumulated time for a device to access the target network, the greater the weight of the access device corresponding to the access device. Conversely, the shorter the cumulative time for each terminal device connected to the access device to access the target network, the The access device weight corresponding to the access device is smaller.

The higher the weight of the access device, the more network services the access device provides, and the greater the contribution to the overall evaluation of the target network.

In an implementation manner, the weight of the access device is a ratio of the duration of using the target network by the terminal device corresponding to the access device to the total duration of accessing the target network by each terminal device.

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

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 are not specifically limited.

In step S13, according to the first feature weight, the terminal device weight, the second feature weight and the access device weight, weight calculation is performed on the first feature data and the second feature data to determine the network quality detection result of the target network.

It can be seen from the foregoing that both the first characteristic data and the second characteristic data correspond to each access device, and the first characteristic data are respectively related to each terminal device in the target network, then, in this step, it can be According to the first feature weight, the terminal device weight, the second feature weight, and the access device weight, weighted calculation is performed on the first feature data and the second feature data to determine the network quality detection result of the target network.

In an implementation manner, weighted calculation is performed 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, include:

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 first product and the first product The sum of the two products is multiplied by the weight of the corresponding access device to obtain the network quality detection result of the target network.

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

Assuming that the first feature weights of the m first feature data are respectively W=(w ₁ w ₂ ...w _m ), the terminal device weight G(i)=(g _i1 g _i2 ... g _in )i=1, 2, ..., k, then the quality evaluation H of parameters related to all access devices and terminal devices is:

H=(G(1)A(1)W ^T ,G(2)A(2)W ^T ,...,G(k)A(k)W ^T )

Secondly, considering the s second feature data B on the access device that has nothing to do with the terminal device, it can be expressed as:

Assuming that its second feature weight is C=(c ₁ c ₂ ...c _s ), and the access device weight of the access device is expressed as P=(p ₁ p ₂ ...p _k ), then the above information is integrated, the target network The network quality evaluation results of can be expressed as:

PH ^T +PBC ^T

Among them, A(i) and B are characteristic data obtained at discrete time points through plug-in sampling installed on the access device, and A(i) and B are actually several discrete time point values within a certain time range amount of statistical analysis.

It can be seen from the above that the technical solutions provided by the embodiments of the present disclosure can realize the detection of the network quality of the target network without the need for door-to-door detection and the participation of terminal equipment. Compared with the solutions in the prior art, The network quality evaluation method is less difficult to implement and has strong operability, so it can be applied to the business needs of different scenarios.

As shown in Figure 2, the method in the embodiment of the present invention will be described below with a specific embodiment, which specifically includes:

1. Access the device plug-in acquisition module:

The built-in collection plug-in of the access device collects data related to the access device and terminal respectively.

The collected data includes:

Standard generation, number of spatial streams, channel bandwidth, used frequency band, WiFi signal strength, negotiation rate, backhaul link medium of the access device, backhaul link negotiation rate, and backhaul signal power of access devices and WiFi terminals (Applicable to wireless backhaul and optical fiber backhaul), whether there is mesh networking between access devices, the status of terminals attached to each access device, the number of branches connected to each access device, roaming switching delay, signal-to-noise ratio, channel occupation Duty ratio, bit error rate, number of retransmissions, CPU/memory utilization of connected devices, working hours after startup, etc.

2. Access device installation work order information module:

According to the access device installation work order information, lock all the access devices in a target network, so as to analyze the WiFi quality of the target network dimension.

3. Dimensional clustering module:

Cluster the collected data into terminal capabilities, network coverage, environmental interference, device status, and network connections to form data tables of each dimension. The specific clustering is as follows:

i. Terminal capabilities include:

Standard generations (such as WiFi4/5/6), number of spatial streams, channel bandwidth, and frequency bands used.

ii. Network coverage includes:

Wi-Fi signal strength, negotiation rate.

iii. Environmental disturbances include:

Signal-to-noise ratio, channel duty cycle, bit error rate, and retransmission times information.

iv. Equipment status includes:

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

v. Network connections include:

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 the access devices, The status of terminals attached to each access device, the number of branches connected to each access device, etc.

4. Dimensionless:

Map each parameter value to [0,1] while retaining the comparable relationship between the original parameter values.

5. Comprehensive analysis:

In order to model the data, it is assumed that there are k access devices and n terminal devices in a certain home. In a collection period, m parameters are collected for a terminal device, and s parameters irrelevant to the terminal device are also collected for each access device.

The specific method is as follows:

First consider the parameters related to the terminal equipment. The m parameters related to the terminal equipment collected on the i-th access device are dimensionless and expressed as A(i), as follows:

Suppose the weight value of m parameters is W=(w ₁ w ₂ ...w _m ), the weight of each terminal device on the i-th access device G(i)=(g _i1 g _i2 ...g _in )i=1 , 2,...,k, then the quality evaluation H of parameters related to all access devices and terminal devices is:

H=(G(1)A(1)W ^T ,G(2)A(2)W ^T ,...,G(k)A(k)W ^T )

Secondly, consider the s acquisition parameters on the access device that have nothing to do with the terminal device, which will be B after dimensionless:

Assuming that the parameter weight corresponding to the access device is C=(c ₁ c ₂ ...c _s ), and the weight of the access device is expressed as P=(p ₁ p ₂ ...p _k ), then based on the above information, the target networking Wi The comprehensive quality evaluation results of the -Fi network are:

PH ^T +PBC ^T

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

Wherein, for example, the value of the access device weight of the i-th access device may be expressed as:

The value of the access device weight of the i-th access device can be expressed as:

Additional notes on weights:

The terminal weight G is a statistical parameter positively related to the duration of the terminal device using the target Wi-Fi network. The higher the terminal weight, the more important the terminal is to the network access experience;

The access device weight P is a statistical parameter that is positively correlated with the total cumulative duration 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. ;

Parameter weights C and W are statistical parameters obtained by combining engineering experience and experience feedback regression.

It can be seen from the above that the technical solutions provided by the embodiments of the present disclosure can realize the detection of the network quality of the target network without the need for door-to-door detection and 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.

Fig. 3 is a block diagram of a network quality detection device according to an exemplary embodiment, including:

The collection module 201 is configured to collect first characteristic data and second characteristic data of each access device of the target network; the first characteristic data are respectively related to each terminal device in the target network, and the The second feature data is not related to any of the terminal devices;

An acquisition module 202, configured 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 weight of the terminal device is positively correlated with the duration for the corresponding terminal device to access the target network, and the weight of the access device is related to the access of the terminal device corresponding to the access device to the target group The cumulative duration of the network is positively correlated;

An analysis module 203, configured to weight 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 Calculate and determine the network quality detection result of the target network.

Optionally, the collection module 201 is configured to:

The first characteristic data and the second characteristic data of each access device of the target network are collected through a preset plug-in; the preset plug-in is installed in each access device of the target network.

Optionally, the collection module 201 is configured to:

Through the preset plug-in, collect the candidate feature data of the corresponding access device;

Obtaining the installation work order information of the target networking, and based on the installation work order information, determining each access device included in the target networking;

Perform feature clustering on the candidate feature data of each access device, and respectively determine the first feature data and the second feature data of each access device.

Optionally, the collection module 201 is configured to:

According to the preset feature dimension, perform feature clustering on the candidate feature data of each access device, and respectively determine the feature data of each preset feature dimension corresponding to each access device; the preset feature dimension includes the first feature data and the feature dimension corresponding to the second feature data;

Dimensionless processing is performed on the feature data to obtain first feature data and second feature data.

Optionally, the analysis module 203 is configured to:

determining a first product between the first feature data and the corresponding first feature weight and the corresponding terminal device weight;

determining a 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 weight of the access device to obtain a network quality detection result of the target network.

It can be seen from the above that the technical solutions provided by the embodiments of the present disclosure can realize the detection of the network quality of the target network without the need for door-to-door detection and 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.

Fig. 4 is a block diagram showing an electronic device for network quality detection according to an exemplary embodiment.

In an exemplary embodiment, there is also provided a computer-readable storage medium including instructions, such as a memory including instructions, executable by a processor of an electronic device to perform the method. Alternatively, the computer readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.

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

It can be seen from the above that the technical solutions provided by the embodiments of the present disclosure can realize the detection of the network quality of the target network without the need for door-to-door detection and the participation of terminal equipment. Compared with the solutions in the prior art, The network quality evaluation method is less difficult to implement and has strong operability, so it can be applied to the business needs of different scenarios.

Fig. 5 is a block diagram of an apparatus 800 for network quality detection according to an exemplary embodiment.

For example, apparatus 800 may be a mobile phone, computer, digital broadcast electronics, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, and the like.

Referring to FIG. 5, the 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 communication component 816 .

The processing component 802 generally controls the overall operations of the device 800, such as those associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the method.

Additionally, processing component 802 may include one or more modules that facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802 .

The memory 804 is configured to store various types of data to support operations at the device 800 . Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and the like. The memory 804 can be implemented by any type of volatile or non-volatile storage device or their combination, 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 Memory, Flash Memory, Magnetic or Optical Disk.

The power supply component 807 provides power to the various components of the device 800 . Power components 807 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for device 800 . The multimedia component 808 includes screens that provide an output interface between the device 800 and the account.

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 touch screen 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 sensor may not only sense a boundary of a touch or swipe action, but also detect duration and pressure associated with the touch or swipe action.

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

For example, the audio component 810 includes a microphone (MIC) configured to receive external audio signals when the device 800 is in operation modes, such as call mode, recording mode and voice recognition mode. Received audio signals may be further stored in memory 804 or sent via communication component 816 .

In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module, and the peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include, but are not limited to: a home button, volume buttons, start button, and lock button.

Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of device 800 . For example, the sensor component 814 can detect the open/closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800, and the sensor component 814 can also detect a change in the position of the device 800 or a component of the device 800 , accounts for the presence or absence of contact with the device 800 , the device 800 orientation or acceleration/deceleration and the temperature change of the device 800 .

Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. Sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications.

In some embodiments, the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor or a temperature sensor.

The communication component 816 is configured to facilitate wired or wireless communication between the apparatus 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof.

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

In an exemplary embodiment, the communication component 816 also 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 Wide Band (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, apparatus 800 may be programmed by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable Realized by a gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, for performing the methods described in the first aspect and the second aspect.

In an exemplary embodiment, there is also provided a non-transitory computer-readable storage medium comprising instructions, such as the memory 804 comprising instructions, executable by the processor 820 of the apparatus 800 to perform the method.

Optionally, for example, the storage medium may be a non-transitory computer-readable storage medium, for example, the non-transitory non-transitory computer-readable storage medium computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage devices, etc.

In an exemplary embodiment, there is also provided a computer program product including instructions, which, when run on a computer, cause the computer to execute the network quality detection method described in any one of the above embodiments.

It can be seen from the above that the technical solutions provided by the embodiments of the present disclosure can realize the detection of the network quality of the target network without the need for door-to-door detection and 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.

Other embodiments of the present disclosure will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, and these modifications, uses or adaptations follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed in the present disclosure . The specification and examples are to be considered exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It should be understood that the present disclosure is not limited to the precise constructions which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (13)

1. A method for detecting network quality, comprising:

collecting first characteristic data and second characteristic data of each access device of the target networking; the first characteristic data are respectively related to each terminal device in the target networking, and the second characteristic data are not related to any terminal device;

acquiring a first characteristic weight of each first characteristic data, a terminal equipment weight of each terminal equipment, a second characteristic weight of each second characteristic data and an access equipment weight of each access equipment; the weight of the terminal equipment is positively correlated with the corresponding time length of the terminal equipment accessing the target networking, and the weight of the access equipment is positively correlated with the accumulated time length of the terminal equipment accessing the corresponding access equipment accessing the target networking;

and according to the first characteristic weight, the terminal equipment weight, the second characteristic weight and the access equipment weight, carrying out weighted calculation on the first characteristic data and the second characteristic data, and determining a network quality detection result of the target networking.

2. The network quality detection method according to claim 1, wherein the collecting the first feature data and the second feature data of each access device of the target network includes:

collecting first characteristic data and second characteristic data of each access device of the target networking through a preset plug-in; the preset plug-in is installed in each access device of the target networking.

3. The network quality detection method according to claim 2, wherein the collecting, by a preset plug-in, the first feature data and the second feature data of each access device of the target network includes:

collecting candidate characteristic data of corresponding access equipment through a preset plug-in;

acquiring installation work order information of the target networking, and determining each access device included in the target networking based on the installation work order information;

and carrying out feature clustering on the candidate feature data of each access device, and respectively determining the first feature data and the second feature data of each access device.

4. The network quality detection method according to claim 3, wherein the performing feature clustering on the candidate feature data of each access device, respectively determining the first feature data and the second feature data corresponding to each access device, includes:

According to the preset feature dimension, carrying out feature clustering on the candidate feature data of each access device, and respectively determining the feature data of each preset feature dimension corresponding to each access device; the preset feature dimension comprises feature dimensions corresponding to the first feature data and the second feature data;

and carrying out dimensionless treatment on the characteristic data to obtain first characteristic data and second characteristic data.

5. The network quality detection method according to claim 1, wherein the determining the network quality detection result of the target network by performing 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 includes:

determining a first product between the first characteristic data and the corresponding first characteristic weight and the corresponding terminal equipment weight;

determining a second product between the second feature data and the corresponding second feature weight;

and determining the sum of the first product and the second product, and multiplying the sum by the corresponding weight of the access equipment to obtain a network quality detection result of the target networking.

6. A network quality detection apparatus, comprising:

the acquisition module is used for acquiring first characteristic data and second characteristic data of each access device of the target networking; the first characteristic data are respectively related to each terminal device in the target networking, and the second characteristic data are not related to any terminal device;

the acquisition module is used for acquiring the first characteristic weight of each first characteristic data, the terminal equipment weight of each terminal equipment, the second characteristic weight of each second characteristic data and the access equipment weight of each access equipment; the weight of the terminal equipment is positively correlated with the corresponding time length of the terminal equipment accessing the target networking, and the weight of the access equipment is positively correlated with the accumulated time length of the terminal equipment accessing the corresponding access equipment accessing the target networking;

and the analysis module is used for carrying out weighted calculation on the first characteristic data and the second characteristic data according to the first characteristic weight, the terminal equipment weight, the second characteristic weight and the access equipment weight, and determining a network quality detection result of the target networking.

7. The network quality detection apparatus of claim 1, wherein the acquisition module is configured to:

Collecting first characteristic data and second characteristic data of each access device of the target networking through a preset plug-in; the preset plug-in is installed in each access device of the target networking.

8. The network quality detection apparatus of claim 7, wherein the acquisition module is configured to:

collecting candidate characteristic data of corresponding access equipment through a preset plug-in;

acquiring installation work order information of the target networking, and determining each access device included in the target networking based on the installation work order information;

and carrying out feature clustering on the candidate feature data of each access device, and respectively determining the first feature data and the second feature data of each access device.

9. The network quality detection apparatus of claim 8, wherein the acquisition module is configured to:

according to the preset feature dimension, carrying out feature clustering on the candidate feature data of each access device, and respectively determining the feature data of each preset feature dimension corresponding to each access device; the preset feature dimension comprises feature dimensions corresponding to the first feature data and the second feature data;

and carrying out dimensionless treatment on the characteristic data to obtain first characteristic data and second characteristic data.

10. The network quality detection apparatus of claim 6, wherein the analysis module is configured to:

determining a first product between the first characteristic data and the corresponding first characteristic weight and the corresponding terminal equipment weight;

determining a second product between the second feature data and the corresponding second feature weight;

and determining the sum of the first product and the second product, and multiplying the sum by the corresponding weight of the access equipment to obtain a network quality detection result of the target networking.

11. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the network quality detection method of any one of claims 1 to 6.

12. A computer readable storage medium, characterized in that instructions in the computer readable storage medium, when executed by a processor of a network quality detection electronic device, enable the network quality detection electronic device to perform the network quality detection method according to any one of claims 1 to 6.

13. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the network quality detection method of any of claims 1 to 6.

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