Method, apparatus and computer storage medium for network quality determination

Abstract

The application discloses a network quality determination method, device, equipment and computer storage medium. The method filters the intensity of signals in a preset time period through a first threshold value, and uses the identification result obtained by a target spectrum diagram corresponding to the intensity of signals in the preset time period, so that the application can determine the network quality in multiple indexes according to the threshold value and the change of the signal intensity in the preset time period, thereby improving the accuracy of network quality determination.

Description

Technical Field

[0001] This field relates to wireless networks, and more particularly to a method, apparatus, device, computer storage medium, and computer program product for determining network quality. Background Technology

[0002] Currently, most households use wireless networks for internet access, so the coverage of home wireless networks directly determines the user experience.

[0003] In related technologies, gateway probe devices are typically used to collect relevant data from terminal devices, and the network quality of home wireless networks is determined by dividing the data into thresholds. However, the threshold-based method cannot be determined as an abnormal network state in certain scenarios, such as when the signal strength fluctuates frequently above the threshold, resulting in low accuracy in determining network quality. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, computer storage medium, and computer program product for determining network quality, which can improve the accuracy of network quality determination.

[0005] In a first aspect, embodiments of this disclosure provide a method for determining network quality, the method comprising:

[0006] Obtain the signal strength of the mobile terminal within a preset time period;

[0007] If the first value corresponding to the signal strength within a preset time period is greater than or equal to the first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain the target spectrum. The first value is the standard deviation or variance of the signal strength within the preset time period.

[0008] Image recognition is performed on the target spectrogram to obtain the recognition result;

[0009] If the identification result is greater than the preset judgment threshold, the network quality corresponding to the mobile terminal is determined as the first target network quality;

[0010] If the identification result is less than or equal to the preset judgment threshold, the network quality corresponding to the mobile terminal is determined as the second target network quality.

[0011] In one feasible implementation, after acquiring the signal strength of the mobile terminal within a preset time period, the method further includes:

[0012] If the first value corresponding to the signal strength within a preset time period is less than the first threshold, the second value corresponding to the signal strength within the preset time period and the second threshold are compared, and the second value is the average value of the signal strength within the preset time period.

[0013] If the second value corresponding to the signal strength within the preset time period is less than the second threshold, then the network quality corresponding to the mobile terminal is determined as the third target network quality.

[0014] In one feasible implementation, if the first value corresponding to the signal strength within a preset time period is greater than or equal to a first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain a target spectrum, including:

[0015] The signal strength within a preset time period is stitched together in chronological order to obtain the target signal strength waveform.

[0016] Slice the target signal intensity waveform to obtain multiple target image frame data;

[0017] Multiple target image frame data are processed by Fast Fourier Transform to obtain multiple frame spectral data;

[0018] The target spectrogram is obtained by combining multiple frames of spectral data.

[0019] In one feasible implementation, the signal strengths within a preset time period are stitched together in chronological order to obtain a target signal strength waveform, including:

[0020] The signal strengths within a preset time period are stitched together in chronological order to obtain the initial target signal strength waveform.

[0021] The signal intensity points in the initial target signal intensity waveform that have a difference greater than a preset screening threshold are selected as discrete points.

[0022] Delete the discrete points in the initial target signal strength waveform to obtain the target signal strength waveform.

[0023] In one feasible implementation, the target signal intensity waveform is sliced ​​to obtain multiple target image frame data, including:

[0024] The target signal intensity waveform is sliced ​​according to a preset repetition degree to obtain multiple initial image frame data. The preset repetition degree is used to extract the maximum periodic waveform.

[0025] Multiple initial image frame data are normalized to obtain multiple target image frame data.

[0026] In one feasible implementation, the first target network quality includes a fourth target network quality and a fifth target network quality. After determining that the network quality corresponding to the mobile terminal is the first target network quality when the identification result is greater than a preset judgment threshold, the method further includes:

[0027] The comparison result is obtained by comparing the second value and the second threshold corresponding to the signal strength within a preset time period;

[0028] If the second value corresponding to the signal strength within the preset time period is greater than or equal to the second threshold, the network quality corresponding to the mobile terminal is determined as the fourth target network quality.

[0029] If the second value corresponding to the signal strength within the preset time period is less than the second threshold, the network quality corresponding to the mobile terminal is determined as the fifth target network quality.

[0030] In one feasible implementation, after performing image recognition on the target spectrogram and obtaining the recognition result, the method further includes:

[0031] The target confidence information is obtained by dividing the number of mobile terminals with the same network quality type by the total number of terminals. The total number of terminals includes mobile terminals.

[0032] The user's network quality type is determined based on the target confidence information.

[0033] Secondly, embodiments of this disclosure provide an apparatus for determining network quality, the apparatus comprising:

[0034] The acquisition module is used to acquire the signal strength of the mobile terminal within a preset time period;

[0035] The conversion module is used to convert the signal intensity within a preset time period from the time domain to the frequency domain to obtain a target spectrum diagram when the first value corresponding to the signal intensity within a preset time period is greater than or equal to a first threshold. The first value is the standard deviation or variance of the signal intensity within the preset time period.

[0036] The recognition module is used to perform image recognition on the target spectrogram and obtain the recognition result.

[0037] The determination module is used to determine the network quality corresponding to the mobile terminal as the first target network quality when the identification result is greater than a preset judgment threshold.

[0038] The determination module is also used to determine the network quality corresponding to the mobile terminal as the second target network quality when the identification result is less than or equal to a preset judgment threshold.

[0039] Thirdly, embodiments of this disclosure provide a network quality determination device, the device including a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the network quality determination method as described in any of the first aspects.

[0040] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement a method for determining network quality as described in any of the first aspects.

[0041] Fifthly, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements a method for determining network quality as described in any of the first aspects.

[0042] This disclosure provides a method, apparatus, device, computer storage medium, and computer program product for determining network quality. The method acquires the signal strength of a mobile terminal within a preset time period. If a first value corresponding to the signal strength within the preset time period is greater than or equal to a first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain a target spectrum. Image recognition is performed on the target spectrum to obtain a recognition result. If the recognition result is greater than a preset judgment threshold, the network quality corresponding to the mobile terminal is determined as a first target network quality. If the recognition result is less than or equal to the preset judgment threshold, the network quality corresponding to the mobile terminal is determined as a second target network quality. This disclosure, by filtering the signal strength within a preset time period using a first threshold, can determine the network quality of the mobile terminal based on the numerical value corresponding to the signal strength. By performing image recognition on the target spectrum corresponding to the signal strength within the preset time period to obtain the recognition result, the network quality of the mobile terminal can be determined based on the time-domain and frequency-domain information corresponding to the signal strength. Therefore, this disclosure improves the accuracy of network quality determination by comprehensively determining the network quality of the mobile terminal using both the numerical value corresponding to the signal strength and the time-domain and frequency-domain information corresponding to the signal strength. Attached Figure Description

[0043] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments of the present invention will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart illustrating a method for determining network quality provided in an embodiment of this disclosure;

[0045] Figure 2 This is a flowchart illustrating another method for determining network quality provided in this embodiment of the disclosure;

[0046] Figure 3 This is a flowchart illustrating a method for obtaining a target spectrogram provided in an embodiment of this disclosure;

[0047] Figure 4This is a schematic diagram of a target signal strength waveform provided in an embodiment of this disclosure;

[0048] Figure 5 This is a schematic diagram of obtaining a target spectrum diagram based on a target signal strength waveform diagram provided in an embodiment of this disclosure;

[0049] Figure 6 This is a flowchart illustrating a method for obtaining a target signal strength waveform according to an embodiment of this disclosure;

[0050] Figure 7 This is a flowchart illustrating a method for acquiring target image frame data according to an embodiment of this disclosure;

[0051] Figure 8 This is a flowchart illustrating another method for determining network quality provided in this embodiment of the disclosure;

[0052] Figure 9 This is a flowchart illustrating a method for determining the network quality of a mobile terminal according to an embodiment of this disclosure;

[0053] Figure 10 This is a flowchart illustrating another method for determining network quality provided in this embodiment of the disclosure;

[0054] Figure 11 This is a schematic diagram of the apparatus for a method of determining network quality provided in this embodiment of the disclosure;

[0055] Figure 12 This is a schematic diagram of the device used in a method for determining network quality according to an embodiment of this disclosure. Detailed Implementation

[0056] The features and exemplary embodiments of various aspects of the present invention will now be described in detail. To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present invention and not to limit the present invention. For those skilled in the art, the present invention can be practiced without some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present invention by illustrating examples of the invention.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0058] Before describing the technical solutions provided by the embodiments of this disclosure, in order to facilitate understanding of the embodiments of this disclosure, this disclosure will specifically explain the problems existing in the related technologies:

[0059] Currently, there are generally two ways to test the coverage of home Wireless Fidelity (wifi): one is for installation and maintenance personnel to conduct point-to-point wireless tests in the home when a new broadband is installed; the other is to actively detect the signal strength of the terminal through a gateway probe device and judge the coverage of home wifi based on a threshold model.

[0060] However, the method of having installation and maintenance personnel conduct on-site wireless testing in users' homes has the following drawbacks:

[0061] 1. The limited number of tests may lead to some randomness and error in the test results. 2. After broadband installation, home users may change their network configuration, causing the test results obtained by installation and maintenance personnel to not consistently reflect the current Wi-Fi coverage in the user's home. In one example, the process of a home user changing their network configuration is as follows: The current Wi-Fi router is placed in the living room. After the user feels the signal strength is weak in the bedroom, they move the Wi-Fi router back to the bedroom.

[0062] The method of actively detecting the signal strength of terminals using gateway probes and determining the coverage of home Wi-Fi based on a threshold model also has drawbacks: If the detected terminal signal strength is above a preset threshold, according to the definition of the threshold model, the coverage of the home Wi-Fi is normal. However, if the terminal signal strength is above the preset threshold and fluctuates frequently, it can be considered that the coverage of the home Wi-Fi still has problems, and these problems cannot be detected by the threshold model method.

[0063] This disclosure provides a method, apparatus, device, computer storage medium, and computer program product for determining network quality, which can solve the aforementioned technical problems existing in related technologies.

[0064] This disclosure acquires the signal strength of a mobile terminal within a preset time period. If a first value corresponding to the signal strength within the preset time period is greater than or equal to a first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain a target spectrum. Image recognition is performed on the target spectrum to obtain a recognition result. If the recognition result is greater than a preset judgment threshold, the network quality corresponding to the mobile terminal is determined as a first target network quality. If the recognition result is less than or equal to the preset judgment threshold, the network quality corresponding to the mobile terminal is determined as a second target network quality. This disclosure, by filtering the signal strength within a preset time period using a first threshold, can determine the network quality of the mobile terminal based on the numerical value corresponding to the signal strength. By performing image recognition on the target spectrum corresponding to the signal strength within the preset time period to obtain a recognition result, the network quality of the mobile terminal can be determined based on the time-domain and frequency-domain information corresponding to the signal strength. Therefore, this disclosure improves the accuracy of network quality determination by comprehensively determining the network quality of the mobile terminal using the numerical value corresponding to the signal strength and the time-domain and frequency-domain information corresponding to the signal strength.

[0065] The method for determining network quality provided in the embodiments of this disclosure will be described below.

[0066] Figure 1 A flowchart illustrating a method for determining network quality according to an embodiment of this disclosure is shown. Figure 1 The execution subject of this method is the backend server, and the method may include the following steps S110-S150.

[0067] S110: Obtain the signal strength of the mobile terminal within a preset time period.

[0068] In this context, a mobile terminal is a portable device, such as a mobile phone, tablet, or smart robotic vacuum cleaner. In this embodiment, the backend server acquires the signal strength of the mobile terminal within a preset time period.

[0069] In one example, the mobile terminal is connected to the gateway and router. The gateway or network probe collects the signal strength of the mobile terminal within a preset time period and sends it to the backend server. In other words, the backend server obtains the signal strength of the mobile terminal within the preset time period.

[0070] S120: If the first value corresponding to the signal strength within a preset time period is greater than or equal to the first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain the target spectrum.

[0071] After obtaining the signal strength of the mobile terminal within a preset time period, a first value corresponding to the signal strength within the preset time period is calculated. If the value of the first value is greater than or equal to a first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain a target spectrum map corresponding to the signal strength within the preset time period.

[0072] The first value is the standard deviation or variance of the signal strength within a preset time period. The first threshold represents the signal strength threshold.

[0073] This embodiment of the present disclosure converts the signal intensity within a preset time period from the time domain to the frequency domain to obtain a target spectrum diagram, which can determine the energy distribution of the signal intensity at different frequencies, facilitating subsequent identification of the signal intensity characteristics.

[0074] S130: Perform image recognition on the target spectrogram to obtain the recognition result.

[0075] After obtaining the target spectrogram, image recognition is performed on the spectrum in the target spectrogram to obtain the recognition result.

[0076] In one example, an image recognition model, such as a convolutional neural network, can be used to perform image recognition on the target spectrogram.

[0077] This embodiment of the disclosure can identify spectral features by performing image recognition on the target spectrum map, thereby determining the network quality corresponding to the mobile terminal based on the spectral features.

[0078] S140: If the identification result is greater than the preset judgment threshold, determine the network quality corresponding to the mobile terminal as the first target network quality.

[0079] After obtaining the recognition result, compare the recognition result with a preset threshold. If the recognition result is greater than the preset threshold, then the network quality corresponding to the mobile terminal is determined as the first target network quality.

[0080] Among them, the preset threshold represents the probability threshold.

[0081] In one example, the first target network quality characterization shows high signal power stability.

[0082] In another example, image recognition is performed on the target spectrogram to obtain the recognition result. If the recognition result indicates a 76% probability of high signal power stability, and this is greater than a preset threshold of 50%, then the network quality corresponding to the mobile terminal is determined to have high signal power stability.

[0083] S150: If the identification result is less than or equal to the preset judgment threshold, determine the network quality corresponding to the mobile terminal as the second target network quality.

[0084] If the identification result is less than or equal to the preset threshold, the network quality corresponding to the mobile terminal is determined as the second target network quality.

[0085] In one example, the second target network quality characterization shows poor signal power stability.

[0086] This disclosure filters signal strength within a preset time period using a first threshold, enabling the determination of mobile terminal network quality based on the numerical values ​​corresponding to signal strength. By performing image recognition on the target spectrum corresponding to the signal strength within the preset time period, the identification result is obtained, allowing the determination of mobile terminal network quality based on the time-domain and frequency-domain information corresponding to signal strength. Therefore, this disclosure improves the accuracy of network quality determination by comprehensively determining the network quality of the mobile terminal using both the numerical values ​​corresponding to signal strength and the time-domain and frequency-domain information corresponding to signal strength. In one embodiment, after acquiring the signal strength of the mobile terminal within the preset time period, the number of signal strengths acquired within that preset time period is calculated. If the number of signal strengths acquired within the preset time period is less than a preset threshold, i.e., the number of signal strengths collected is insufficient, then the network quality of the mobile terminal is not analyzed. For example, if a user takes their mobile terminal on a business trip for a month and the gateway probe does not collect a sufficient number of signal strengths, then the network quality of the mobile terminal is not analyzed.

[0087] In one embodiment, such as Figure 2 As shown, after obtaining the signal strength of the mobile terminal within a preset time period, the method for determining network quality further includes steps S160 and S170.

[0088] S160: If the first value corresponding to the signal strength within a preset time period is less than the first threshold, compare the second value corresponding to the signal strength within the preset time period with the second threshold.

[0089] After obtaining the signal strength of the mobile terminal within a preset time period, a first value corresponding to the signal strength within the preset time period is calculated. If the first value is less than a first threshold, a second value corresponding to the signal strength within the preset time period is calculated, and the second value is compared with the second threshold.

[0090] The second value is the average signal strength over a preset time period. The second threshold ensures the signal strength is within a certain threshold range.

[0091] S170: If the second value corresponding to the signal strength within the preset time period is less than the second threshold, then the network quality corresponding to the mobile terminal is determined as the third target network quality.

[0092] If the second value corresponding to the signal strength within a preset time period is less than the second threshold, then the network quality corresponding to the mobile terminal is determined to be weak overall coverage.

[0093] This embodiment of the disclosure improves the accuracy of determining the network quality corresponding to a mobile terminal by comparing multiple values ​​corresponding to the signal strength within a preset time period.

[0094] In one example, such as Figure 3 As shown, if the first value corresponding to the signal strength within a preset time period is greater than or equal to the first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain the target spectrum, which may include steps S121-S124.

[0095] S121: The signal strength within a preset time period is spliced ​​together in chronological order to obtain the target signal strength waveform.

[0096] The signals within a preset time period are sorted and stitched together according to their chronological order to obtain the target signal strength waveform.

[0097] In one example, such as Figure 4 As shown, the signal strength of user household A from 6 PM to 12 AM every day from month N to month N+3 can be obtained through the gateway probe. The signal strengths of user household A from month N to month N+3 are then stitched together in chronological order to obtain the target signal strength waveform.

[0098] S122: Slice the target signal intensity waveform to obtain multiple target image frame data.

[0099] After obtaining the target signal intensity waveform, the target signal intensity waveform is sliced ​​according to a preset slicing method to obtain multiple target image frame data.

[0100] The preset slicing method can be slicing according to a preset slicing frequency or random slicing.

[0101] This embodiment of the disclosure improves time and frequency resolution, reduces spectral leakage, and enhances the accuracy of the subsequently obtained spectrum by slicing the target signal strength waveform.

[0102] S123: Perform Fast Fourier Transform on multiple target image frame data respectively to obtain multiple frame spectrum data.

[0103] After slicing to obtain multiple target image frame data, fast Fourier transform is performed on each of the multiple target image frame data to obtain frame spectrum data corresponding to the multiple target image frame data.

[0104] S124: Combine multiple frames of spectral data to obtain the target spectral map.

[0105] After obtaining multiple frames of spectral data, the multiple frames of spectral data are combined to obtain the target spectrogram.

[0106] This embodiment of the present disclosure obtains a target signal strength waveform by splicing the signal strength within a preset time period, and performs slicing, fast Fourier transform and combination processing on the target signal strength waveform to obtain a spectrum diagram with high accuracy, thereby improving the recognition accuracy of subsequent image recognition of the spectrum diagram.

[0107] In one example, the steps for obtaining the target spectrum from the target signal intensity waveform are as follows: Figure 5 As shown: The target signal intensity waveform is sliced ​​to obtain multiple target image frames. Then, a Fast Fourier Transform is performed on each target image frame sequentially to obtain the corresponding frame spectrum. Finally, the obtained frame spectrum data are combined in order to obtain a spectrogram.

[0108] In one embodiment, such as Figure 6 As shown, the signal strength within a preset time period is spliced ​​together in chronological order to obtain the target signal strength waveform, which may include steps S1211-S1213.

[0109] S1211: The signal strengths within a preset time period are spliced ​​together in chronological order to obtain the initial target signal strength waveform.

[0110] The signals within a preset time period are sorted and spliced ​​according to their chronological order to obtain the initial target signal strength waveform.

[0111] S1212: Obtain the signal strength points in the initial target signal strength waveform diagram whose difference from any signal strength point is greater than the preset screening threshold as discrete points.

[0112] After obtaining the initial target signal strength waveform, a preset detection method can be used to detect signal strength points in the initial target signal strength waveform whose difference from any signal strength point is greater than a preset screening threshold, and these points are used as discrete points.

[0113] In one example, the preset detection method may include a density-based spatial clustering algorithm (DBSCAN).

[0114] In one example, the target signal strength waveform contains 20 signal strength points. The signal strengths of these 20 points, in numerical order, are: -75, -74, -73, -74, -75, -76, -75, -74, -73, -75, -85, -90, -92, -75, -74, -73, -74, -75, -76, -75. The DBSCAN parameters are set to a neighborhood radius (eps) of 5 and a minimum number of samples (min_samples) of 5. For signal strength points numbered 1 to 10, each point's signal strength is between -73 and -76, and the difference in signal strength between these points is less than 5, so these points are core points. For signal strength point number 11, its signal strength is -85, and the difference in signal strength between it and the points before and after it is greater than 5, therefore it is not a core point. For signal strength points numbered 12 and 13, their signal strengths are -90 and -92 respectively, and the difference between their signal strengths and those of the points before and after them is greater than 5; therefore, they are not core points. For signal strength points numbered 14 to 20, their signal strengths are between -73 and -76, and the difference between the signal strengths of these points is less than 5; therefore, these points are core points. In conclusion, signal strength points numbered 11, 12, and 13 are discrete points.

[0115] S1213: Delete the discrete points in the initial target signal strength waveform diagram to obtain the target signal strength waveform diagram.

[0116] After detecting discrete points in the initial target signal strength waveform, the discrete points in the initial target signal strength waveform are deleted to obtain the target signal strength waveform.

[0117] The embodiments of this disclosure, by obtaining an initial target signal strength waveform and deleting discrete points in the initial target signal strength waveform, can remove noise from the initial target signal strength waveform, improve the quality of the target signal strength waveform, and thereby improve the accuracy of subsequent image recognition.

[0118] In one embodiment, such as Figure 7 As shown, slicing the target signal intensity waveform diagram to obtain multiple target image frame data can include steps S1221 and S1222.

[0119] S1221: Slice the target signal intensity waveform according to the preset repeatability to obtain multiple initial image frame data.

[0120] In one embodiment, after obtaining the target signal strength waveform, the target signal strength waveform is sliced ​​based on a rolling time window with a preset repetition rate to obtain multiple initial image frame data.

[0121] The preset repeatability is used to extract the waveform with the largest periodicity.

[0122] In one example, the relationship between the target signal strength waveform and the repeatability is expressed as Equation (1).

[0123]

[0124] Where S represents the total length of the target signal intensity waveform, a represents the length of the slice image frame, n represents the number of slices, and k represents the repetition rate.

[0125] S1222: Normalize multiple initial image frame data to obtain multiple target image frame data.

[0126] After obtaining multiple initial image frame data, the multiple initial image frame data are normalized to obtain multiple target image frame data.

[0127] In one example, after normalizing multiple initial image frame data, the waveform time domain is compressed to 1 second.

[0128] The embodiments of this disclosure improve the efficiency of processing initial image frame data by slicing and normalizing the target signal intensity waveform.

[0129] In one embodiment, the first target network quality includes a fourth target network quality and a fifth target network quality. After determining that the network quality corresponding to the mobile terminal is the first target network quality when the identification result is greater than a preset judgment threshold, as follows: Figure 8 As shown, the method for determining network quality also includes steps S210-S230.

[0130] S210: Compare the second value and the second threshold corresponding to the signal strength within the preset time period to obtain the comparison result.

[0131] The second value corresponding to the signal strength within a preset time period is compared with the second threshold to obtain the comparison result.

[0132] S220: If the second value corresponding to the signal strength within the preset time period, as represented by the comparison result, is greater than or equal to the second threshold, the network quality corresponding to the mobile terminal is determined as the fourth target network quality.

[0133] If the comparison result indicates that the second value corresponding to the signal strength within the preset time period is greater than or equal to the second threshold, then the network quality corresponding to the mobile terminal is determined as the fourth target network quality.

[0134] In one example, the fourth target network quality characterization signal coverage is uneven.

[0135] S230: If the second value corresponding to the signal strength within the preset time period is less than the second threshold, the network quality corresponding to the mobile terminal is determined as the fifth target network quality.

[0136] If the second value corresponding to the signal strength within the preset time period is less than the second threshold, the network quality corresponding to the mobile terminal is determined to be the fifth target network quality.

[0137] In one example, the fifth target network quality characterization signal coverage is uneven and weak.

[0138] In this embodiment of the present disclosure, after determining that the network quality corresponding to the mobile terminal is the first target network quality, the second value corresponding to the signal strength within a preset time period is compared with a second threshold. By making multiple comparisons, the network quality type corresponding to the mobile terminal is further determined, thereby improving the accuracy of network quality determination.

[0139] In one example, a flowchart illustrating the process of determining the network quality of a mobile terminal is shown below. Figure 9As shown, in step S901, the signal strength of the mobile terminal within a preset time period is acquired. In step S902, if the first value corresponding to the signal strength within the preset time period is greater than or equal to a first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain a target spectrum. Subsequently, step S903 is executed to perform image recognition on the target spectrum to obtain a recognition result. In step S904, if the recognition result is greater than a preset judgment threshold, the network quality corresponding to the mobile terminal is determined as the first target network quality. In step S905, the second value corresponding to the signal strength within the preset time period and the second threshold are compared to obtain a comparison result. In step S906, if the comparison result indicates that the second value corresponding to the signal strength within the preset time period is greater than or equal to the second threshold, the network quality corresponding to the mobile terminal is determined as the fourth target network quality. In step S907, if the comparison result indicates that the second value corresponding to the signal strength within the preset time period is less than the second threshold, the network quality corresponding to the mobile terminal is determined as the fifth target network quality. In step S908, if the recognition result is less than or equal to the preset judgment threshold, the network quality corresponding to the mobile terminal is determined as the second target network quality. In S909, if the first value corresponding to the signal strength within a preset time period is less than a first threshold, a second value corresponding to the signal strength within the preset time period and a second threshold are compared. In S910, if the second value corresponding to the signal strength within the preset time period is less than the second threshold, the network quality corresponding to the mobile terminal is determined to be the third target network quality.

[0140] In one embodiment, the signal strength of a non-mobile terminal within a preset time period can be obtained. The non-mobile terminal is a non-movable device, which may include terminals such as smart locks, smart speakers, and smart cameras. If the variance or standard deviation of the signal strength of the non-mobile terminal within the preset time period is greater than or equal to a first preset threshold, the network quality corresponding to the non-mobile terminal is determined to be poor signal power stability. If the variance or standard deviation of the signal strength of the non-mobile terminal within the preset time period is less than the first preset threshold, the average value of the signal strength of the non-mobile terminal within the preset time period and a second preset threshold are compared. If the average value of the signal strength of the non-mobile terminal within the preset time period is less than the second preset threshold, the network quality of the non-mobile terminal is determined to be overall weak coverage. In one embodiment, such as... Figure 10 As shown, after performing image recognition on the target spectrogram and obtaining the recognition result, the method for determining network quality also includes steps S310 and S320.

[0141] S310: Divide the number of mobile terminals with the same network quality type by the total number of terminals to obtain the target confidence information.

[0142] After determining the network quality of the mobile terminal, the number of mobile terminals with the same network quality type is divided by the total number of terminals to obtain the target confidence information.

[0143] The total number of terminals includes mobile terminals. Target confidence information is used to determine the user type.

[0144] S320: Determine the user's network quality type based on the target confidence information.

[0145] After calculating the target confidence information, the user's network quality type is determined based on the target confidence information.

[0146] In one example, the number of mobile terminals with uneven signal coverage is 4, and the total number of terminals is 6. The calculated target confidence level is 66.6%. Since the target confidence level of 66.6% is greater than the preset confidence threshold of 50%, the user type is determined to be a user with uneven signal coverage.

[0147] This embodiment of the disclosure obtains target confidence information by dividing the number of mobile terminals with the same network quality type by the total number of terminals. Determining the user's network quality type using this target confidence information avoids relying on the network quality types of only a subset of mobile terminals, thereby improving the accuracy of determining the user's network quality type.

[0148] like Figure 11 As shown in the embodiments of this disclosure, a network quality determination apparatus 1100 is also provided, the network quality determination apparatus 1100 comprising:

[0149] The acquisition module 1101 is used to acquire the signal strength of the mobile terminal within a preset time period;

[0150] The conversion module 1102 is used to convert the signal intensity within the preset time period from the time domain to the frequency domain to obtain a target spectrum diagram when the first value corresponding to the signal intensity within the preset time period is greater than or equal to a first threshold. The first value is the standard deviation or variance of the signal intensity within the preset time period.

[0151] The recognition module 1103 is used to perform image recognition on the target spectrogram and obtain the recognition result;

[0152] The determination module 1104 is used to determine the network quality corresponding to the mobile terminal as the first target network quality when the identification result is greater than a preset judgment threshold.

[0153] The determining module 1104 is also used to determine the network quality corresponding to the mobile terminal as the second target network quality when the identification result is less than or equal to a preset judgment threshold.

[0154] This disclosure filters the signal strength within a preset time period using a first threshold, enabling the determination of the mobile terminal's network quality based on the numerical value corresponding to the signal strength. Furthermore, by performing image recognition on the target spectrum map corresponding to the signal strength within the preset time period, the identification result is obtained, allowing the determination of the mobile terminal's network quality based on the time-domain and frequency-domain information corresponding to the signal strength. Therefore, this disclosure improves the accuracy of network quality determination by comprehensively determining the mobile terminal's network quality using both the numerical value corresponding to the signal strength and the time-domain and frequency-domain information corresponding to the signal strength. In some embodiments, the network quality determination apparatus 1100 includes a comparison module, configured to compare a second value corresponding to the signal strength within the preset time period with a second threshold when the first value corresponding to the signal strength within the preset time period is less than the first threshold. The second value is the average value of the signal strength within the preset time period.

[0155] If the second value corresponding to the signal strength within the preset time period is less than the second threshold, then the network quality corresponding to the mobile terminal is determined as the third target network quality.

[0156] In some embodiments, the network quality determination apparatus 1100 includes a splicing module, which splices the signal strengths within a preset time period in chronological order to obtain a target signal strength waveform.

[0157] Slice the target signal intensity waveform to obtain multiple target image frame data;

[0158] Multiple target image frame data are processed by Fast Fourier Transform to obtain multiple frame spectral data;

[0159] The target spectrogram is obtained by combining multiple frames of spectral data.

[0160] In some embodiments, the network quality determination apparatus 1100 includes a splicing module, which splices the signal strengths within a preset time period in chronological order to obtain an initial target signal strength waveform.

[0161] The signal intensity points in the initial target signal intensity waveform that have a difference greater than a preset screening threshold are selected as discrete points.

[0162] Delete the discrete points in the initial target signal strength waveform to obtain the target signal strength waveform.

[0163] In some embodiments, the network quality determination apparatus 1100 includes a slicing module, which is used to slice the target signal strength waveform according to a preset repetition rate to obtain multiple initial image frame data, and the preset repetition rate is used to extract the maximum periodic waveform.

[0164] Multiple initial image frame data are normalized to obtain multiple target image frame data.

[0165] In some embodiments, the network quality determination apparatus 1100 includes a comparison module, which is used to compare a second value and a second threshold corresponding to the signal strength within a preset time period to obtain a comparison result;

[0166] If the second value corresponding to the signal strength within the preset time period is greater than or equal to the second threshold, the network quality corresponding to the mobile terminal is determined as the fourth target network quality.

[0167] If the second value corresponding to the signal strength within the preset time period is less than the second threshold, the network quality corresponding to the mobile terminal is determined as the fifth target network quality.

[0168] In some embodiments, the network quality determination apparatus 1100 includes a calculation module, which is used to divide the number of mobile terminals with the same network quality type by the total number of terminals to obtain target confidence information, wherein the total number of terminals includes mobile terminals.

[0169] The user's network quality type is determined based on the target confidence information.

[0170] Figure 11 Each module / unit in the illustrated device has the ability to implement Figures 1 to 3 , Figures 6 to 10 The functions of each step performed by the application system and the corresponding technical effects it achieves are described briefly and will not be elaborated here.

[0171] Figure 12 A schematic diagram of the hardware structure for determining network quality provided in an embodiment of this disclosure is shown.

[0172] The device for determining network quality may include a processor 1201 and a memory 1202 storing computer program instructions.

[0173] Specifically, the processor 1201 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0174] Memory 1202 may include mass storage for data or instructions. For example, and not limitingly, memory 1202 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1202 may include removable or non-removable (or fixed) media. Where appropriate, memory 1202 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1202 is non-volatile solid-state memory.

[0175] Memory 1202 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0176] The processor 1201 implements any of the network quality determination methods in the above embodiments by reading and executing computer program instructions stored in the memory 1202.

[0177] In one example, the device for determining network quality may also include a communication interface 1203 and a bus 1204. For example, Figure 12 As shown, the processor 1201, memory 1202, and communication interface 1203 are connected through bus 1204 and complete communication with each other.

[0178] The communication interface 1203 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0179] Bus 1204 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1204 may include one or more buses. Although specific buses are described and illustrated in embodiments of the invention, the invention contemplates any suitable bus or interconnect. Additionally, in conjunction with the network quality determination method described in the above embodiments, embodiments of this application also provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by the processor, they implement any of the network quality determination methods described in the above embodiments.

[0180] This application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the network quality determination methods described in the above embodiments.

[0181] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0182] The functional blocks shown in the above structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0183] It should also be noted that the exemplary embodiments mentioned in this invention describe methods or systems based on a series of steps or apparatus. However, this invention is not limited to the order of the steps described above; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0184] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0185] The above description is merely a specific embodiment of the present invention. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the protection scope of the present invention.

Claims

1. A method for determining network quality, characterized in that, include: Obtain the signal strength of the mobile terminal within a preset time period; If the first value corresponding to the signal strength within the preset time period is greater than or equal to the first threshold, the signal strength within the preset time period is converted from the time domain to the frequency domain to obtain the target spectrum. The first value is the standard deviation or variance of the signal strength within the preset time period. Image recognition is performed on the target spectrogram to obtain a recognition result, which characterizes the probability of signal power stability; If the identification result is greater than a preset judgment threshold, the network quality corresponding to the mobile terminal is determined as the first target network quality. If the identification result is less than or equal to a preset judgment threshold, the network quality corresponding to the mobile terminal is determined as the second target network quality.

2. The method according to claim 1, characterized in that, After acquiring the signal strength of the mobile terminal within a preset time period, the method further includes: If the first value corresponding to the signal strength within the preset time period is less than the first threshold, the second value corresponding to the signal strength within the preset time period and the second threshold are compared, where the second value is the average value of the signal strength within the preset time period. If the second value corresponding to the signal strength within the preset time period is less than the second threshold, then the network quality corresponding to the mobile terminal is determined to be the third target network quality.

3. The method according to claim 1, characterized in that, When the first value corresponding to the signal intensity within the preset time period is greater than or equal to a first threshold, the signal intensity within the preset time period is converted from the time domain to the frequency domain to obtain a target spectrum, including: The signal strengths within the preset time period are stitched together in chronological order to obtain the target signal strength waveform. The target signal intensity waveform is sliced ​​to obtain multiple target image frame data; The multiple target image frame data are processed by Fast Fourier Transform to obtain multiple frame spectral data; The target spectrum map is obtained by combining the multiple frame spectrum data.

4. The method according to claim 3, characterized in that, The step of stitching together the signal strengths within the preset time period in chronological order to obtain the target signal strength waveform includes: The signal strengths within the preset time period are spliced ​​together in chronological order to obtain the initial target signal strength waveform. The signal strength points in the initial target signal strength waveform whose difference from any signal strength point is greater than a preset screening threshold are taken as discrete points; Delete the discrete points in the initial target signal strength waveform to obtain the target signal strength waveform.

5. The method according to claim 3, characterized in that, The process of slicing the target signal intensity waveform to obtain multiple target image frame data includes: The target signal intensity waveform is sliced ​​according to a preset repetition degree to obtain multiple initial image frame data. The preset repetition degree is used to extract the maximum periodic waveform. The initial image frame data is normalized to obtain multiple target image frame data.

6. The method according to claim 1, characterized in that, The first target network quality includes a fourth target network quality and a fifth target network quality. After determining that the network quality corresponding to the mobile terminal is the first target network quality when the identification result is greater than a preset judgment threshold, the method further includes: The second value and the second threshold corresponding to the signal strength within the preset time period are compared to obtain a comparison result, whereby the second value represents the average value of the signal strength within the preset time period. If the second value corresponding to the signal strength within the preset time period, as indicated by the comparison result, is greater than or equal to the second threshold, the network quality corresponding to the mobile terminal is determined as the fourth target network quality. If the second value corresponding to the signal strength within the preset time period, as indicated by the comparison result, is less than the second threshold, the network quality corresponding to the mobile terminal is determined as the fifth target network quality.

7. The method according to claim 1, characterized in that, After performing image recognition on the target spectrogram to obtain the recognition result, the method further includes: The target confidence information is obtained by dividing the number of mobile terminals with the same network quality type by the total number of terminals, where the total number of terminals includes the mobile terminals. The user's network quality type is determined based on the target confidence information.

8. A device for determining network quality, characterized in that, The device includes An acquisition module is used to acquire the signal strength of the mobile terminal within a preset time period; A conversion module is used to convert the signal intensity within the preset time period from the time domain to the frequency domain to obtain a target spectrum when the first value corresponding to the signal intensity within the preset time period is greater than or equal to a first threshold. The identification module is used to perform image recognition on the target spectrogram to obtain an identification result, wherein the identification result characterizes the probability of signal power stability; The determining module is used to determine the network quality corresponding to the mobile terminal as the first target network quality when the identification result is greater than a preset judgment threshold. The determining module is further configured to determine the network quality corresponding to the mobile terminal as the second target network quality when the identification result is less than or equal to a preset judgment threshold.

9. A device for determining network quality, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method for determining network quality as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the method for determining network quality as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method for determining network quality as described in any one of claims 1-7.

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