Method, device and equipment for detecting quality of bearer network and computer storage medium

Abstract

The application discloses a method, device and equipment for detecting quality difference of a bearing network and a computer storage medium. The method comprises the following steps: determining the degrees of nodes in the bearing network, determining key performance indicators of the bearing network which affect Internet TV services, determining objective weights corresponding to the key performance indicators, performing secondary weighting correction on the objective weights according to preset subjective estimation weights to obtain modified weights, determining overall deviation degrees of the key performance indicators corresponding to a current node in each node, determining a target quality evaluation value of the current node according to the modified weights and the overall deviation degrees, and determining a comprehensive evaluation value of the current node on the quality difference of the Internet TV services according to the degrees of the nodes in the bearing network and the target quality evaluation value. The application can effectively evaluate the influence of the bearing network quality on the Internet TV services while saving costs.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, device, and computer storage medium for detecting poor network quality. Background Technology

[0002] The quality of Internet TV services is mainly determined by the quality of both ends and the quality of the intermediate bearer network. Since the user access network, content source network and bearer network are three completely different types of networks with significant differences in their network element composition, network topology and functions, different methods need to be adopted for different networks when studying the impact of the network on the service.

[0003] Currently, the deployment relies heavily on hardware equipment. For a massive network system like a carrier network, the number of provincial Internet TV users can reach millions. The number of end-to-end nodes that the service passes through will be even greater. Deploying probe devices to all nodes is impractical and extremely costly, making it impossible to effectively assess the impact of the network quality on Internet TV services. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, device, and computer storage medium for detecting poor network quality, aiming to solve the technical problem of how to effectively assess the impact of network quality on Internet television services while saving costs.

[0005] To achieve the above objectives, this application provides a method for detecting poor quality in a bearer network, comprising the following steps:

[0006] Determine the degree of each node in the bearer network, and determine the key performance indicators of the bearer network that affect Internet TV services;

[0007] Determine the objective weights corresponding to the key performance indicators, and perform a second weighting correction on the objective weights based on the preset subjective estimation weights to obtain the corrected weights;

[0008] Determine the overall deviation of the key performance indicators corresponding to the current node in each of the nodes, and determine the target quality assessment value of the current node based on the correction weight and the overall deviation.

[0009] Based on the degree of each node in the bearer network and the target quality assessment value, the comprehensive assessment value of the quality impact of the current node on the Internet TV service is determined.

[0010] Optionally, the step of determining the objective weights corresponding to the key performance indicators includes:

[0011] Extract the key performance indicator data from the communication network corresponding to the bearer network within a preset time period, wherein the preset time period includes at least one of daily off-peak time period, daily peak time period, and holidays;

[0012] All extracted indicator data are normalized, and the objective weights corresponding to the key performance indicators are calculated based on the normalized indicator data.

[0013] Optionally, the step of normalizing all the extracted indicator data includes:

[0014] If the key performance indicator includes at least one of port bandwidth utilization, device CPU utilization, device memory utilization, and port bit error rate, then target indicator data belonging to the same key performance indicator among all the indicator data is determined, and all target indicator data are normalized based on the maximum and minimum values ​​among the target indicator data.

[0015] If the key performance indicators include port optical power, then a center value is determined based on the network device model of the node, the absolute difference between each indicator data and the center value is determined, and the indicator data is normalized based on the maximum and minimum values ​​among the absolute differences.

[0016] Optionally, the step of calculating the objective weights corresponding to the key performance indicators based on the normalized indicator data includes:

[0017] If there are multiple different types of the key performance indicators, then determine the probability of occurrence of all the indicator data under each key performance indicator;

[0018] The amount of information corresponding to each indicator data under the same key performance indicator is determined based on the probability of occurrence.

[0019] For each of the multiple key performance indicators, the degree of consistency of the impact on the key performance indicator is determined based on the amount of information corresponding to the data of each indicator under the key performance indicator, and the objective weight corresponding to the key performance indicator is determined based on the degree of consistency.

[0020] Optionally, the step of determining the overall deviation of the key performance indicators corresponding to the current node among the nodes includes:

[0021] Determine the actual index data and baseline value of the key performance indicators corresponding to the current node in each of the aforementioned nodes;

[0022] The overall deviation of the key performance indicators corresponding to the current node is determined based on the deviation between the actual indicator data and the benchmark value.

[0023] Optionally, the step of determining the comprehensive evaluation value of the quality impairment impact of the current node on the Internet TV service based on the degree of each node in the bearer network and the target quality evaluation value includes:

[0024] The degree of the current node is determined based on the degree of each node in the bearer network. Based on the degree of the current node and the target quality assessment value, the comprehensive assessment value of the quality impact of the current node on the Internet TV service is determined.

[0025] Optionally, the step of determining the degree of each node in the bearer network includes:

[0026] Construct an adjacency matrix based on the connectivity of each node in the network;

[0027] Determine the network flow rate of each node, and perform weighted processing on the matrix elements in the adjacency matrix based on the network flow rate to obtain a directed matrix, wherein the matrix elements include the connection relationships between each node;

[0028] The degree of each node in the bearer network is determined based on the directed matrix.

[0029] In addition, to achieve the above objectives, this application also provides a bearer network poor quality detection device, comprising:

[0030] The determination module is used to determine the degree of each node in the bearer network and to determine the key performance indicators of the bearer network that affect Internet TV services.

[0031] The correction module is used to determine the objective weights corresponding to the key performance indicators, and to perform a second weighted correction on the objective weights based on the preset subjective estimated weights to obtain the corrected weights.

[0032] The quality assessment module is used to determine the overall deviation of the key performance indicators corresponding to the current node in each of the nodes, and to determine the target quality assessment value of the current node based on the correction weight and the overall deviation.

[0033] The quality assessment module is used to determine the comprehensive assessment value of the quality impact of the current node on Internet TV services based on the degree of each node in the bearer network and the target quality assessment value.

[0034] In addition, to achieve the above objectives, this application also provides a poor network quality detection device, which includes a memory, a processor, and a poor network quality detection program stored in the memory and executable on the processor. When the poor network quality detection program is executed by the processor, it implements the steps of the poor network quality detection method described above.

[0035] In addition, to achieve the above objectives, this application also provides a computer storage medium storing a poor network quality detection program, which, when executed by a processor, implements the steps of the poor network quality detection method described above.

[0036] This application determines key performance indicators (KPIs) and performs a secondary weighted adjustment on objective weights based on subjective estimation weights to obtain corrected weights. It then determines the overall deviation of the KPIs of the current node, determines the target quality assessment value of the current node based on the corrected weights and the overall deviation, and finally determines the comprehensive assessment value of the current node's impact on the quality of the Internet TV service based on the node's degree and the target quality assessment value. This allows for the direct determination of KPIs, followed by the combination of subjective estimation weights and objective weights to obtain corrected weights, resulting in more accurate weights for the KPIs and thus more accurate target quality assessment values ​​for the current node. Furthermore, it incorporates the node's degree to obtain the comprehensive assessment value of the current node's impact on the quality of the Internet TV service, achieving a comprehensive consideration of all nodes in the bearer network, making the final comprehensive assessment value of the impact on quality more comprehensive and effective. Moreover, since it does not require specific data collection sources, it is more flexible and cost-effective compared to methods that require specific probe hardware support. This achieves effective assessment of the impact of bearer network quality on the Internet TV service while saving costs. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the terminal / device structure of the hardware operating environment involved in the embodiments of this application;

[0038] Figure 2 This is a flowchart illustrating the first embodiment of the poor network quality detection method of this application;

[0039] Figure 3 This is a flowchart illustrating the second embodiment of the poor network quality detection method of this application;

[0040] Figure 4 This is a schematic diagram of the matrix corresponding to the index data extracted in the second embodiment of the poor quality detection method for bearer networks in this application;

[0041] Figure 5 This is a schematic diagram of the matrix after normalization in the second embodiment of the poor quality detection method for bearer networks in this application;

[0042] Figure 6 This is a schematic diagram of the device unit of the poor network quality detection device of this application.

[0043] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0044] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0045] like Figure 1 As shown, Figure 1 This is a schematic diagram of the terminal structure of the hardware operating environment involved in the embodiments of this application.

[0046] The terminal in this embodiment is a network quality poor detection device.

[0047] like Figure 1 As shown, the terminal may include: a processor 1001, such as a CPU; a network interface 1004; a user interface 1003; a memory 1005; and a communication bus 1002. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be high-speed RAM or non-volatile memory, such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0048] Optionally, the terminal may also include a camera, RF (Radio Frequency) circuitry, sensors, audio circuitry, a WiFi module, and so on. These sensors may include light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor. The ambient light sensor can adjust the brightness of the display screen according to the ambient light level, while the proximity sensor can turn off the display screen and / or backlight when the terminal device is moved to the ear. Of course, the terminal device may also be equipped with other sensors such as a gyroscope, barometer, hygrometer, thermometer, and infrared sensor, which will not be elaborated upon here.

[0049] Those skilled in the art will understand that Figure 1 The terminal structure shown does not constitute a limitation on the terminal and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0050] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a network quality detection program.

[0051] exist Figure 1In the terminal shown, network interface 1004 is mainly used to connect to the backend server and communicate with it; user interface 1003 is mainly used to connect to the client (user terminal) and communicate with it; while processor 1001 can be used to call the network quality detection program stored in memory 1005 and perform the following operations:

[0052] Determine the degree of each node in the bearer network, and determine the key performance indicators of the bearer network that affect Internet TV services;

[0053] Determine the objective weights corresponding to the key performance indicators, and perform a second weighting correction on the objective weights based on the preset subjective estimation weights to obtain the corrected weights;

[0054] Determine the overall deviation of the key performance indicators corresponding to the current node in each of the nodes, and determine the target quality assessment value of the current node based on the correction weight and the overall deviation.

[0055] Based on the degree of each node in the bearer network and the target quality assessment value, the comprehensive assessment value of the quality impact of the current node on the Internet TV service is determined.

[0056] This application provides a method for detecting poor quality of a bearer network.

[0057] Currently, determining the impact of the network on services relies heavily on the deployment of hardware. For massive network systems like those of telecom operators, with provincial internet TV users potentially reaching millions, the number of end-to-end nodes traversed by the service is even greater. Deploying probe devices to all nodes is impractical and extremely costly. Furthermore, this approach relies on the premise that the internet TV service has malfunctioned, thus limiting the definition and location of faults. This limitation necessitates more precise settings for preconditions and comparison criteria. In reality, the concept of "fault" is quite broad. A fault can be severe, making it completely impossible for a user to watch TV, or it can be mild, allowing viewing but with a poor user experience, such as stuttering or screen tearing. Therefore, the premise of identifying and detecting faults is somewhat vague. This makes it difficult to establish a unified standard when setting preset conditions to determine whether a fault has occurred, because different fault symptoms should have different preset conditions. For example, complete inability to watch TV might mean that all network equipment is blocked, requiring a link utilization rate as low as 0% in the preset conditions. However, if the user experience is poor, the link utilization rate might be above 70%, leading to congestion and packet loss, requiring a preset condition above 70%. Currently, there is no mention of setting different preset conditions for different fault symptoms. Furthermore, the specific comparison method for setting preset conditions is not explained.

[0058] Therefore, in this embodiment of the application, based on a scale-free complex network, the degree of each node in the Internet service carrying network is calculated by weighting the network node traffic to obtain the degree of influence of each node on the service. Then, the different performance indicators of the nodes are weighted by the entropy method and the subjective experience secondary weighting method to obtain the quality impact assessment of the nodes on the Internet TV service. Combined with the previous degree, the final assessment of the quality of the Internet TV service carrying network is obtained.

[0059] Furthermore, this application's embodiments do not start from Internet TV faults, but directly measure and evaluate the current network quality, calculate a score based on the degree of deviation from the benchmark value, and directly score it, rather than qualitatively determining whether there is a fault. The goal is optimization rather than fault location, reducing prerequisite restrictions and increasing flexibility and the scope of applicable scenarios.

[0060] Furthermore, this embodiment can evaluate the performance of a single node. Since there are often multiple poor-quality nodes in a network, and the poor quality of Internet TV services is often due to the combined effect of multiple nodes, it is necessary to evaluate the quality of the entire network after evaluating the quality of a single node. Because communication networks are scale-free networks and complex networks with "supernodes" following a power-law distribution, this embodiment establishes an evaluation model based on scale-free complex networks. It evaluates the degree of influence of each node on the entire network by calculating the degree of each node; the higher the degree, the greater the influence.

[0061] Furthermore, referring to Figure 2 This application provides a method for detecting poor quality of a bearer network. In a first embodiment of the method, the method includes the following steps:

[0062] Step S10: Determine the degree of each node in the bearer network and determine the key performance indicators that affect Internet TV services in the bearer network.

[0063] In this embodiment, the degree of a node can be the number of its neighboring edges, that is, the number of connections to the node. Key performance indicators can be port bandwidth utilization, device CPU utilization, device memory utilization, port optical power, and port bit error rate.

[0064] Optionally, all nodes in the bearer network can be identified first, with each node corresponding to a network element. Then, the degree of each node in the bearer network can be determined based on the connection relationships between the nodes.

[0065] Since the main issues affecting the user experience of Internet TV services are screen flickering and stuttering, these problems are caused by packet loss. This packet loss could occur during source code encoding, network transmission, or decoding on the set-top box side. This embodiment primarily addresses packet loss caused by network transmission issues. The causes of network packet loss include high port bandwidth utilization, high device CPU utilization, high memory utilization, weak port light, and port bit errors. Therefore, key performance indicators affecting the quality of Internet TV services can be: port bandwidth utilization, device CPU utilization, device memory utilization, port optical power, and port bit error rate. However, key performance indicators are not limited to those mentioned in this embodiment and can include other key performance indicators.

[0066] Step S20: Determine the objective weights corresponding to the key performance indicators, and perform a second weighted correction on the objective weights based on the preset subjective estimation weights to obtain the corrected weights;

[0067] Optionally, since the devices at each node in the bearer network generate a large amount of data every day, data sampling can be performed to obtain representative basic data, namely the indicator data corresponding to the key performance indicators, and then determine the objective weights corresponding to the key performance indicators.

[0068] Optionally, it is necessary to sample the indicator data corresponding to each key performance indicator (KPI). That is, there are multiple KPI data points for each KPI. Then, the KPI data for each KPI can be normalized. The entropy method can be used to calculate the objective weight for each KPI after normalization.

[0069] Optionally, the subjective estimation weights corresponding to key performance indicators can be determined based on actual communication network and Internet TV service maintenance experience. Optionally, the subjective estimation weights corresponding to different key performance indicators can be the same or different.

[0070] Optionally, when performing a secondary weighting adjustment on the objective weights based on the subjectively estimated weights, the following formula can be used for calculation:

[0071]

[0072] The number of key performance indicators can be multiple. j * It can be the objective weight of the j-th key performance indicator, λ j W can be the subjectively estimated weight of the j-th key performance indicator. j This can be the adjusted weight of the j-th key performance indicator. The maximum value n of j is equal to the number of key performance indicators, for example, 5.

[0073] Optionally, the ratio of subjectively estimated weights among port error rate, port bandwidth utilization, port optical power, device CPU utilization, and device memory utilization can be 4:2:2:1:1.

[0074] Step S30: Determine the overall deviation of the key performance indicators corresponding to the current node in each of the nodes, and determine the target quality assessment value of the current node based on the correction weight and the overall deviation.

[0075] Optionally, a comprehensive quality assessment can be performed on individual nodes within the bearer network. Therefore, a benchmark value can be set for each key performance indicator to evaluate its quality, and then a comprehensive quality assessment of each individual node can be conducted based on this benchmark value.

[0076] Optionally, a comprehensive quality assessment can be performed on the current node among all nodes. The current node can be any node among all nodes. Optionally, the difference between the actual value and the benchmark value of each key performance indicator of the current node can be calculated based on the standard deviation to represent the degree of dispersion, and the overall deviation of the key performance indicators can be determined based on this difference.

[0077] Then, the target quality assessment value of the current node is calculated based on the overall deviation and correction weight of each key performance indicator in the current node.

[0078] Step S40: Based on the degree of each node in the bearer network and the target quality assessment value, determine the comprehensive assessment value of the quality impact of the current node on the Internet TV service.

[0079] Optionally, for each node, the impact of that node on the quality of the Internet TV service can be determined.

[0080] Optionally, the degree of the current node can be selected from the degrees of each node in the bearer network, and then the comprehensive evaluation value of the quality impact of the current node on the Internet TV service can be calculated based on the degree of the current node and the target quality evaluation value.

[0081] Optionally, this embodiment can perform quality assessments on network elements (i.e., nodes) and links that are still providing services without disruption, excluding assessments of disruption situations. For disruption situations, the preset condition should be set to 0. The judgment method is simple: when any one of the key performance indicators—port bit error rate, port bandwidth utilization, port optical power, device CPU utilization, and device memory utilization—reaches a quality degradation impact comprehensive evaluation value that is 0, it is judged as a disruption fault. Optionally, disruption faults do not necessarily cause service quality degradation. Because communication networks have very scientific and reasonable disaster recovery mechanisms, when equipment failure or link disruption occurs, services are usually switched to backup equipment or links. If quality degradation actually occurs, it is a problem with the backup link carrying the service. In this case, the quality degradation of the carrying network can be determined based on this embodiment.

[0082] Optionally, in this embodiment, a data calculation model is established without any requirements on the data collection source. This is more flexible than the method that requires specific probe hardware support. The equipment and technology used in the current bearer network are very mature, and the data collected from communication equipment such as routers and switches are also very comprehensive and accurate. Therefore, the existing network resources can fully meet the usage requirements of this embodiment, which can greatly save costs.

[0083] Optionally, this embodiment evaluates the quality of each node and calculates a comprehensive score to provide data support for fault location and quality optimization, but does not make a qualitative judgment on its quality. Therefore, there is no need to set preconditions such as faults or quality defects. The results calculated in this way are more flexible and accurate, and applicable to a wider range of scenarios.

[0084] Optionally, this embodiment uses methods such as scale-free complex networks, entropy methods, and data normalization to perform correlation calculations on different indicators and network elements, making a comprehensive evaluation of each node in the bearer network. This is more comprehensive, integrated, and reliable than calculations based on a single indicator, a single node, or a local network.

[0085] Optionally, this embodiment specifies a concrete comparison method for the baseline value (preset conditions). Deviation is calculated by weighting objective entropy values ​​and subjective experience values ​​to evaluate the performance of the current node. Furthermore, the calculation process for the baseline value in this embodiment uses two parameters: the maximum and minimum values ​​from historical data. Therefore, the baseline value changes with the maximum and minimum values ​​of the current network status, making it more flexible and reliable. Since the maximum and minimum values ​​typically do not change frequently, the baseline value is relatively stable while maintaining flexibility and reliability, and the calculation results from different calculations can be used for mutual reference.

[0086] In this embodiment, key performance indicators (KPIs) are determined, and objective weights are adjusted using a secondary weighting method based on subjective estimation weights to obtain corrected weights. The overall deviation of the KPIs of the current node is then determined. Based on the corrected weights and the overall deviation, the target quality assessment value of the current node is determined. Finally, based on the node's degree and the target quality assessment value, the comprehensive assessment value of the current node's impact on the quality of the Internet TV service is determined. This allows for the direct determination of KPIs, followed by the combination of subjective estimation weights and objective weights to obtain corrected weights, making the determined KPI weights more accurate. Consequently, the target quality assessment value of the current node determined based on the corrected weights is also more accurate. Furthermore, the node's degree is considered when obtaining the comprehensive assessment value of the current node's impact on the quality of the Internet TV service. This allows for a comprehensive consideration of all nodes in the bearer network, resulting in a more comprehensive and effective comprehensive assessment value of the impact on quality. Moreover, since there are no requirements for the data collection source, this method is more flexible and cost-effective compared to methods requiring specific probe hardware. It achieves effective assessment of the impact of bearer network quality on the Internet TV service while saving costs.

[0087] Furthermore, based on the first embodiment of this application described above, a second embodiment of the bearer network quality poor detection method of this application is proposed, referring to... Figure 3 In this embodiment, step S20 of the above embodiment, the step of determining the objective weights corresponding to the key performance indicators, includes:

[0088] Step a, extract the key performance indicator data of the communication network corresponding to the bearer network within a preset time period, wherein the preset time period includes at least one of daily off-peak time period, daily peak time period and holidays;

[0089] In this embodiment, the communication network constructed based on communication between various nodes in the bearer network allows for the extraction of key performance indicator data for different time periods. The preset time period can be a user-defined time period, such as a regular off-peak time period, a regular peak time period, or holidays. For example, a regular peak time period could be from 7 PM to 11 PM.

[0090] Optionally, a stratified sampling method can be used, suitable for sampling from several levels with significant differences in the overall population and relatively small differences within each level. Data is sampled from different time periods with significant differences in user behavior, such as daily off-peak hours, peak hours, and holidays (including weekends). The sampling ratio is calculated according to the duration of each time period. Optionally, step a can be performed for each time period.

[0091] Optionally, if there are 5 key performance indicators, the extracted indicator data can be constructed into an n x 5 matrix. For example, ... Figure 4As shown, it includes the indicator data corresponding to 5 key performance indicators, namely A. 11 A 21 ... A n1 A 12 A 22 ... A n2 A 13 A 23 ... A n3 A 14 A 24 ... A n4 A 15 A 25 ... A n5 .

[0092] Step b: Normalize all the extracted indicator data, and calculate the objective weights corresponding to the key performance indicators based on the normalized indicator data.

[0093] Optionally, the matrix constructed from the indicator data for each time period can be normalized separately. When normalizing, the indicator data in the matrix is ​​normalized to obtain a new matrix. Then, the objective weight of each key performance indicator is calculated based on the normalized indicator data in the new matrix.

[0094] Optionally, the objective weights of key performance indicators may differ for different time periods.

[0095] In this embodiment, by extracting the key performance indicator data within a preset time period in the communication network corresponding to the bearer network, and then performing normalization processing, the objective weights corresponding to the key performance indicators are calculated based on the normalized indicator data. This eliminates the differences in data size between the indicator data and enables comprehensive calculation to obtain the objective weights.

[0096] Further, the step of normalizing all the extracted indicator data includes:

[0097] Step c: If the key performance indicator includes at least one of port bandwidth utilization, device CPU utilization, device memory utilization, and port bit error rate, then determine the target indicator data belonging to the same key performance indicator among all the indicator data, and normalize all the target indicator data according to the maximum and minimum values ​​in the target indicator data.

[0098] Step d: If the key performance indicators include port optical power, then determine the center value based on the network device model of the node, determine the absolute difference between each indicator data and the center value, and normalize each indicator data based on the maximum and minimum values ​​among the absolute differences.

[0099] In this embodiment, range standardization can be used to normalize the data of each key performance indicator. Furthermore, the normalization process is the same when the key performance indicator is port bandwidth utilization, device CPU utilization, device memory utilization, or port bit error rate growth rate. That is, it can be calculated using the following formula:

[0100]

[0101] in, This could refer to the index data obtained after normalizing the data of the i-th index under the j-th key performance indicator. For example... min(A ij Max(A) can be the minimum value among all the data for the j-th key performance indicator. ij ) can be the maximum value among all the data for the j-th key performance indicator. A ij It can be the data of the i-th indicator under the j-th key performance indicator.

[0102] Optionally, when the key performance indicator is port optical power, since port optical power can be negative and does not increase or decrease around "0" in either direction, a center value needs to be determined before normalizing the port optical power data using range standardization. For example, the center value could be 'a'. Optionally, the center value can be determined based on the network device model of the node. For example, different center values ​​can be pre-defined for different network device models, and then the corresponding center value can be determined based on the actual network device model used. Furthermore, the port optical power indicator data can be calculated using the following formula:

[0103]

[0104] Among them, |A ij -a| can be the absolute value of the difference between the i-th indicator data and the center value a under the j-th key performance indicator.

[0105] For example, such as Figure 5 As shown, if there are 5 key performance indicators, then for Figure 4 After normalizing the index data under each key performance indicator, we can obtain... Figure 5The normalized matrix shown includes the normalized index data, such as...

[0106] In this embodiment, the target index data of key performance indicators such as port bandwidth utilization, device CPU utilization, device memory utilization, and port bit error rate are normalized based on their maximum and minimum values. For port optical power, the absolute difference between the index data and the center value is determined, and normalization is performed based on the maximum and minimum values ​​of the absolute difference. This avoids the phenomenon of negative values ​​after port optical power is normalized, and the effectiveness of the normalization process is also guaranteed because it is based on the maximum and minimum values.

[0107] Further, the step of calculating the objective weights corresponding to the key performance indicators based on the normalized indicator data includes:

[0108] Step e: If there are multiple different types of the key performance indicators, then determine the probability of occurrence of all the indicator data under each key performance indicator;

[0109] Step f: Determine the amount of information corresponding to each indicator data under the same key performance indicator based on the probability of occurrence;

[0110] Step g: For each of the multiple key performance indicators, determine the degree of consistency of the impact on the key performance indicator based on the amount of information corresponding to the data of each indicator under the key performance indicator, and determine the objective weight corresponding to the key performance indicator based on the degree of consistency.

[0111] In this embodiment, the key performance indicators can be of multiple types, such as port bandwidth utilization, device CPU utilization, device memory utilization, port bit error rate, and port optical power.

[0112] For each type of key performance indicator, the entropy method can be used for calculation, which can be calculated according to the following formula:

[0113]

[0114]

[0115] d j =1-E j ;

[0116]

[0117] Among them, P ijP can be the probability of the occurrence of the i-th indicator data for the j-th key performance indicator, i.e., the uncertainty. ij The product of its logarithm and the weight can be used to assess the amount of information, and the amount of information is strongly correlated with the weight. E j It can be P ij The information content is represented by the product of its logarithm and k, where k is a constant. When the probability of occurrence of each indicator data is the same, that is, P = 1 / n, the probability of occurrence can be predicted, the information content is the smallest, and the weight is also the smallest. At this time, E j =1, and by taking the derivative, we can know that E j It is a monotonically decreasing function, since E j Within the interval [0,1], therefore use 1-E. j We obtain the information content expression that is positively correlated with the weight, i.e., d j . d j d can represent the degree of consistency in the impact of the sampled data of each indicator on the j-th key performance indicator. j The larger the value, the lower the consistency, the greater the amount of information, and the greater the weight. j * It can be an objective weight.

[0118] In this embodiment, by determining the probability of occurrence of all indicator data under each key performance indicator, the amount of information corresponding to each indicator data under the same key performance indicator is determined, thereby determining the degree of consistency of the impact on the key performance indicator, and then determining the objective weight corresponding to the key performance indicator, thus ensuring the effectiveness of the obtained objective weight.

[0119] Further, the step of determining the overall deviation of the key performance indicators corresponding to the current node in each of the aforementioned nodes includes:

[0120] Step h: Determine the actual index data and benchmark value of the key performance indicators corresponding to the current node in each of the nodes;

[0121] Step i: Determine the overall deviation of the key performance indicators corresponding to the current node based on the deviation between the actual indicator data and the benchmark value.

[0122] In this embodiment, the calculation can be performed in the same way for each node to determine the overall deviation of the key performance indicators corresponding to each node. Optionally, a baseline value can be set for each key performance indicator, such as setting the baseline value for the port bit error rate growth rate to 0, the baseline value for the port bandwidth utilization rate to 60%, the baseline value for the port optical power to 10, the baseline value for the device CPU utilization rate to 40%, and the baseline value for the device memory utilization rate to 60%.

[0123] Optionally, the deviation between the actual index data and the benchmark value of each key performance indicator of the current node can be calculated by using the standard deviation to represent the degree of dispersion, and the overall degree of deviation can be determined based on this deviation value.

[0124] Optionally, since high-quality indicators cannot offset the quality degradation caused by low-quality indicators, high-quality indicators can be zeroed out during the calculation process. High-quality indicators can be key performance indicators with good quality assessment values. Low-quality indicators can be key performance indicators with poor quality assessment values.

[0125] Optionally, for the four key performance indicators—port error rate, port bandwidth utilization, device CPU utilization, and device memory utilization—the deviation value can be determined using the following formula:

[0126]

[0127] For port optical power, the deviation value can be determined according to the following formula:

[0128]

[0129] Among them, Q j It can be a deviation value. It can be the normalized value of the current indicator data of the j-th key performance indicator of the current node. This could be the normalized value of the j-th key performance indicator. In this embodiment, the normalization process is used to eliminate differences in the basic data size between the key performance indicators, thereby enabling comprehensive calculations.

[0130] Optionally, the deviation value of the key performance indicator corresponding to the current node can be directly used as the overall deviation of the key performance indicator.

[0131] In this embodiment, the overall deviation of the key performance indicators of the current node is determined by the deviation between the actual indicator data and the benchmark value of the key performance indicators corresponding to the current node, thereby ensuring the effectiveness of determining the overall deviation.

[0132] Further, the step of determining the comprehensive evaluation value of the quality impairment impact of the current node on the Internet TV service based on the degree of each node in the bearer network and the target quality evaluation value includes:

[0133] Step j: Determine the degree of the current node based on the degree of each node in the bearer network, and determine the comprehensive evaluation value of the quality impact of the current node on the Internet TV service based on the degree of the current node and the target quality evaluation value.

[0134] After determining the overall deviation, the overall quality of the current node can be calculated using the following formula:

[0135]

[0136] Among them, Q n It could be the overall quality of the nth node, Q n The larger the value, the worse the quality of the node. N is a constant.

[0137] After determining the overall quality status of the current node, the comprehensive assessment value of the quality difference impact can be calculated based on the overall quality status and the degree of the current node. This can be determined using the following formula:

[0138]

[0139] That is, it is necessary to Q n and K n Perform normalization standard processing to obtain the corresponding and Among them, E n This is the comprehensive assessment value of the quality impact of the nth node on the Internet TV service.

[0140] In this embodiment, the effectiveness of the determined comprehensive evaluation value of the quality difference impact is ensured by determining the comprehensive evaluation value of the quality difference impact based on the degree of the current node and the target quality evaluation value.

[0141] Furthermore, the steps for determining the degree of each node in the bearer network include:

[0142] Step x: Construct an adjacency matrix based on the connectivity of each node in the bearer network;

[0143] Step y: Determine the network flow rate of each node, and perform weighted processing on the matrix elements in the adjacency matrix according to the network flow rate to obtain a directed matrix, wherein the matrix elements include the connection relationships between each node;

[0144] Step z: Determine the degree of each node in the bearer network based on the directed matrix.

[0145] In this embodiment, an adjacency matrix can be listed based on the connectivity of each node in the Internet TV service bearer network topology, with N nodes corresponding to an N-row, N-column matrix. Using B... mn This represents the elements in the matrix, indicating the connection between node m and node n. A value of 1 is assigned to a connected relationship, and a value of 0 is assigned to a disconnected relationship. For example, if node 1 is connected to node 2, then B... 12 =B 21 =1, if node 1 and node 3 are not connected, then B13 =B 31 =0.

[0146] After the adjacency matrix is ​​established, the nodes are weighted by the network flow rates between them, creating a weighted network. This weighted approach yields a degree that more accurately reflects the actual network conditions, rather than relying solely on theoretical calculations based on connections. Clearly, higher flow rates indicate more important nodes with greater impact on the overall network; similarly, more connections also increase node importance. The weighted matrix elements are as follows: Where S mn Let B represent the flow velocity from m to n. Since the upstream and downstream flow velocities are different, after weighting, the matrix becomes a directed matrix, i.e., B. mn =B nm ,but

[0147] Then, the degree of each node in the directed matrix can be calculated using the same method as for calculating the degree in complex network models. The degree can be calculated using the following formula:

[0148]

[0149] Among them, K n Let be the degree of the nth node.

[0150] In this embodiment, an adjacency matrix is ​​constructed based on the connection status of each node in the bearer network. The matrix elements are weighted according to the network flow rate of the nodes to obtain a directed matrix. The degree of each node in the bearer network is determined based on the directed matrix, thereby comprehensively considering the actual capabilities and influence of the network nodes, making the obtained node degrees more realistic and effective.

[0151] In addition, refer to Figure 6 This application also provides a poor quality detection device for a bearer network, comprising:

[0152] The determination module A10 is used to determine the degree of each node in the bearer network and to determine the key performance indicators of the bearer network that affect Internet TV services.

[0153] The correction module A20 is used to determine the objective weights corresponding to the key performance indicators, and to perform a second weighted correction on the objective weights based on the preset subjective estimated weights to obtain the corrected weights.

[0154] The quality assessment module A30 is used to determine the overall deviation of the key performance indicators corresponding to the current node among all the nodes, and to determine the target quality assessment value of the current node based on the correction weight and the overall deviation.

[0155] The quality assessment module A40 is used to determine the comprehensive assessment value of the quality impact of the current node on Internet TV services based on the degree of each node in the bearer network and the target quality assessment value.

[0156] Optionally, the correction module A20 is used for:

[0157] Extract the key performance indicator data from the communication network corresponding to the bearer network within a preset time period, wherein the preset time period includes at least one of daily off-peak time period, daily peak time period, and holidays;

[0158] All extracted indicator data are normalized, and the objective weights corresponding to the key performance indicators are calculated based on the normalized indicator data.

[0159] Optionally, the correction module A20 is used for:

[0160] If the key performance indicator includes at least one of port bandwidth utilization, device CPU utilization, device memory utilization, and port bit error rate, then target indicator data belonging to the same key performance indicator among all the indicator data is determined, and all target indicator data are normalized based on the maximum and minimum values ​​among the target indicator data.

[0161] If the key performance indicators include port optical power, then a center value is determined based on the network device model of the node, the absolute difference between each indicator data and the center value is determined, and the indicator data is normalized based on the maximum and minimum values ​​among the absolute differences.

[0162] Optionally, the correction module A20 is used for:

[0163] If there are multiple different types of the key performance indicators, then determine the probability of occurrence of all the indicator data under each key performance indicator;

[0164] The amount of information corresponding to each indicator data under the same key performance indicator is determined based on the probability of occurrence.

[0165] For each of the multiple key performance indicators, the degree of consistency of the impact on the key performance indicator is determined based on the amount of information corresponding to the data of each indicator under the key performance indicator, and the objective weight corresponding to the key performance indicator is determined based on the degree of consistency.

[0166] Optionally, the quality assessment module A30 is used for:

[0167] Determine the actual index data and baseline value of the key performance indicators corresponding to the current node in each of the aforementioned nodes;

[0168] The overall deviation of the key performance indicators corresponding to the current node is determined based on the deviation between the actual indicator data and the benchmark value.

[0169] Optionally, the poor quality assessment module A40 is used for:

[0170] The degree of the current node is determined based on the degree of each node in the bearer network. Based on the degree of the current node and the target quality assessment value, the comprehensive assessment value of the quality impact of the current node on the Internet TV service is determined.

[0171] Optionally, module A10 is determined for:

[0172] Construct an adjacency matrix based on the connectivity of each node in the network;

[0173] Determine the network flow rate of each node, and perform weighted processing on the matrix elements in the adjacency matrix based on the network flow rate to obtain a directed matrix, wherein the matrix elements include the connection relationships between each node;

[0174] The degree of each node in the bearer network is determined based on the directed matrix.

[0175] The steps for implementing each functional module of the poor network quality detection device can be referred to in the various embodiments of the poor network quality detection method of this application, and will not be repeated here.

[0176] Furthermore, this application also provides a poor network quality detection device, the terminal comprising: a memory, a processor, a communication bus, and a poor network quality detection program stored in the memory.

[0177] The communication bus is used to enable communication between the processor and the memory;

[0178] The processor is used to execute the poor quality detection program of the bearer network to implement the steps of the above embodiments of the poor quality detection method of the bearer network.

[0179] This application also provides a computer storage medium storing one or more programs, which can be executed by one or more processors to implement the steps of the above-described embodiments of the poor quality detection method for bearer networks.

[0180] The specific implementation of the computer-readable storage medium in this application is basically the same as the embodiments of the above-described poor quality detection method for bearer networks, and will not be repeated here.

[0181] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system 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 system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0182] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0183] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0184] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for detecting poor quality in a bearer network, characterized in that, The method for detecting poor quality of the bearer network includes the following steps: The process involves determining the degree of each node in a bearer network and identifying key performance indicators affecting Internet TV services within the bearer network. The step of determining the degree of each node in the bearer network includes: constructing an adjacency matrix based on the connectivity of each node in the bearer network; determining the network flow rate of each node; weighting the matrix elements in the adjacency matrix based on the network flow rate to obtain a directed matrix, wherein the matrix elements include the connectivity relationships between the nodes; and determining the degree of each node in the bearer network based on the directed matrix. Determine the objective weights corresponding to the key performance indicators, and perform a second weighting correction on the objective weights based on the preset subjective estimation weights to obtain the corrected weights; Determine the overall deviation of the key performance indicators corresponding to the current node in each of the nodes, and determine the target quality assessment value of the current node based on the correction weight and the overall deviation. Based on the degree of each node in the bearer network and the target quality assessment value, the comprehensive assessment value of the quality impact of the current node on the Internet TV service is determined.

2. The method for detecting poor quality of a bearer network as described in claim 1, characterized in that, The step of determining the objective weights corresponding to the key performance indicators includes: Extract the key performance indicator data from the communication network corresponding to the bearer network within a preset time period, wherein the preset time period includes at least one of daily off-peak time period, daily peak time period, and holidays; All extracted indicator data are normalized, and the objective weights corresponding to the key performance indicators are calculated based on the normalized indicator data.

3. The method for detecting poor quality of a bearer network as described in claim 2, characterized in that, The step of normalizing all the extracted indicator data includes: If the key performance indicator includes at least one of port bandwidth utilization, device CPU utilization, device memory utilization, and port bit error rate, then target indicator data belonging to the same key performance indicator among all the indicator data is determined, and all target indicator data are normalized based on the maximum and minimum values ​​among the target indicator data. If the key performance indicators include port optical power, then a center value is determined based on the network device model of the node, the absolute difference between each indicator data and the center value is determined, and the indicator data is normalized based on the maximum and minimum values ​​among the absolute differences.

4. The method for detecting poor quality of a bearer network as described in claim 2, characterized in that, The step of calculating the objective weights corresponding to the key performance indicators based on the normalized indicator data includes: If there are multiple different types of the key performance indicators, then determine the probability of occurrence of all the indicator data under each key performance indicator; The amount of information corresponding to each indicator data under the same key performance indicator is determined based on the probability of occurrence. For each of the multiple key performance indicators, the degree of consistency of the impact on the key performance indicator is determined based on the amount of information corresponding to the data of each indicator under the key performance indicator, and the objective weight corresponding to the key performance indicator is determined based on the degree of consistency.

5. The method for detecting poor quality of a bearer network as described in claim 1, characterized in that, The step of determining the overall deviation of the key performance indicators corresponding to the current node in each of the nodes includes: Determine the actual index data and baseline value of the key performance indicators corresponding to the current node in each of the aforementioned nodes; The overall deviation of the key performance indicators corresponding to the current node is determined based on the deviation between the actual indicator data and the benchmark value.

6. The method for detecting poor quality of a bearer network as described in claim 1, characterized in that, The step of determining the comprehensive evaluation value of the quality impact of the current node on the Internet TV service based on the degree of each node in the bearer network and the target quality evaluation value includes: The degree of the current node is determined based on the degree of each node in the bearer network. Based on the degree of the current node and the target quality assessment value, the comprehensive assessment value of the quality impact of the current node on the Internet TV service is determined.

7. A device for detecting poor network quality, characterized in that, The poor quality detection device for the bearer network includes: A determination module is used to determine the degree of each node in the bearer network and to determine the key performance indicators affecting Internet TV services in the bearer network. The determination of the degree of each node in the bearer network includes: constructing an adjacency matrix based on the connectivity of each node in the bearer network; determining the network flow rate of each node; weighting the matrix elements in the adjacency matrix based on the network flow rate to obtain a directed matrix, wherein the matrix elements include the connection relationships between each node; and determining the degree of each node in the bearer network based on the directed matrix. The correction module is used to determine the objective weights corresponding to the key performance indicators, and to perform a second weighted correction on the objective weights based on the preset subjective estimated weights to obtain the corrected weights. The quality assessment module is used to determine the overall deviation of the key performance indicators corresponding to the current node in each of the nodes, and to determine the target quality assessment value of the current node based on the correction weight and the overall deviation. The quality assessment module is used to determine the comprehensive assessment value of the quality impact of the current node on Internet TV services based on the degree of each node in the bearer network and the target quality assessment value.

8. A network quality poor detection device, characterized in that, The poor quality detection device for the bearer network includes: a memory, a processor, and a poor quality detection program for the bearer network stored in the memory and executable on the processor. When the poor quality detection program for the bearer network is executed by the processor, it implements the steps of the poor quality detection method for the bearer network as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that, The computer storage medium stores a poor network quality detection program, which, when executed by a processor, implements the steps of the poor network quality detection method as described in any one of claims 1 to 6.

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