Abnormal network device identification method and device

Abstract

The application discloses an abnormal network device identification method and device. The method comprises the following steps: acquiring performance index parameter sets of a first network device newly connected to a passive optical network and a plurality of second network devices connected to the network within a preset time period; determining at least one second target network device of the same model and / or the same version as the first network device according to the performance index parameter sets of the first network device and each second network device; determining a specification boundary value according to the performance index parameter set of the first network device, and determining a process capability index of each second target network device according to the specification boundary value and the performance index parameter set of each second target network device; and determining whether each second target network device has quality abnormality according to the process capability index of each second target network device. The application solves the technical problem that related technologies cannot accurately identify abnormal network devices with degraded performance index parameters but not reaching the quality standard.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of network security, in particular to an abnormal network device identification method and device. BACKGROUND

[0002] In the current PON (Passive Optical Network) operation and maintenance process, the operator mainly relies on whether the performance index parameter of the monitored network device is lower than the preset threshold to determine whether the network device has a quality problem. However, this method can only identify the case where the device performance has decreased significantly. For those network devices that have not yet reached the quality standard but the performance index stability is gradually deteriorating, the existing technology often fails to provide early warning. This technical problem leads to hidden faults in the network, affecting user experience but not easy to be discovered and handled in time, especially in a network with various device models and complex operating environment, it is difficult to effectively predict and manage the aging and performance fluctuation of the device, thereby increasing the difficulty and cost of network maintenance.

[0003] In view of the above problems, an intelligent algorithm for early identification and evaluation of network device performance indicators is currently proposed to realize accurate identification of network devices that are about to deteriorate in performance and reduce the occurrence of network quality problems. SUMMARY

[0004] The embodiments of the present application provide an abnormal network device identification method and device to at least solve the technical problem that related technologies cannot accurately identify abnormal network devices whose performance index parameters deteriorate but do not reach the quality standard.

[0005] According to an aspect of an embodiment of the present application, an abnormal network device identification method is provided, comprising: obtaining a set of performance index parameters of a first network device newly entered into a passive optical network and a plurality of second network devices already entered into the network within a preset time period, wherein the set of performance index parameters is a parameter set of a preset performance index of the network device within the preset time period; determining at least one second target network device of the same model and / or the same version as the first network device according to the set of performance index parameters of the first network device and each second network device; determining a specification boundary value according to the set of performance index parameters of the first network device, and determining a process capability index of each second target network device according to the specification boundary value and the set of performance index parameters of each second target network device; for each second target network device, determining whether the second target network device has a quality anomaly according to the process capability index of the second target network device.

[0006] Optionally, the performance index parameter set of the first network device and the plurality of second network devices in the PON within a preset time period is acquired, including: collecting the performance index parameter set of the preset performance index within the preset time period by the monitoring probe deployed on the first network device and each second network device in the PON, wherein the type of the preset performance index includes: transmit power, received power or received power signal-to-noise ratio.

[0007] Optionally, at least one second target network device of the same model and / or the same version as the first network device is determined according to the performance index parameter set of the first network device and each second network device, including: determining the first mean square error of the performance of the first network device on the preset performance index according to the performance index parameter set of the first network device, and determining the second mean square error of the performance of each second network device on the preset performance index according to the performance index parameter set of each second network device; comparing the first mean square error of the performance of the first network device on the preset performance index with the second mean square error of the performance of each second network device on the preset performance index by using the hypothesis testing method, to determine at least one second target network device of the same model and / or the same version as the first network device.

[0008] Optionally, at least one second target network device of the same model and / or the same version as the first network device is determined according to the performance index parameter set of the first network device and each second network device, including: for each second network device, calculating the quotient of the first mean square error of the performance of the first network device on the preset performance index and the second mean square error of the performance of the second network device on the preset performance index to obtain a corresponding check statistic; in the case that the check statistic is not greater than a preset check limit value, determining the second network device as a second target network device of the same model and / or the same version as the first network device.

[0009] Optionally, the specification boundary value is determined according to the performance index parameter set of the first network device, and the process capability index of each second target network device is determined according to the specification boundary value and the performance index parameter set of each second target network device, including: determining the first mean and the first mean square error of the performance of the first network device on the preset performance index according to the performance index parameter set of the first network device, and determining the specification boundary value according to the first mean and the first mean square error, wherein the specification boundary value includes: an upper specification limit value and a lower specification limit value; determining the second mean and the second mean square error of the performance of the second target network device on the corresponding performance index according to the performance index parameter set of the second target network device; and determining the process capability index of the second target network device according to the specification boundary value, the second mean and the second mean square error of the second target network device, and according to the following formula: wherein μ represents a first mean value, σ represents a first mean square deviation, μ+4σ represents an upper specification limit value, μ-4σ represents a lower specification limit value, μ f,i represents a second mean value of the ith second target network device, σ f,i represents a second mean square deviation of the ith second target network device.

[0010] Optionally, determining whether the second target network device has a quality abnormality according to the process capability index of the second target network device comprises: in a case where the process capability index of the second target network device is less than a preset threshold value, determining that the second target network device has a quality abnormality; and in a case where the process capability index of the second target network device is not less than the preset threshold value, determining that the second target network device does not have a quality abnormality.

[0011] Optionally, after determining that the second target network device has a quality abnormality, the method further comprises: determining a gap size between the process capability index of the second target network device having a quality abnormality and the threshold value; determining a quality abnormality level of the second target network device having a quality abnormality according to the gap size; determining a management priority of the second target network device having a quality abnormality according to the quality abnormality level, and sequentially maintaining the second target network device having a quality abnormality according to the management priority.

[0012] According to another aspect of the embodiments of the present application, an abnormal network device identification apparatus is further provided, comprising: an acquisition module, configured to acquire a performance index parameter set of a first network device newly entering a passive optical network and a plurality of second network devices having entered the network respectively within a preset time period, wherein the performance index parameter set is a parameter set of a preset performance index of a network device within a preset time period; a first determination module, configured to determine at least one second target network device of the same model and / or the same version as the first network device according to the performance index parameter set of the first network device and the performance index parameter set of each second network device; a second determination module, configured to determine a specification boundary value according to the performance index parameter set of the first network device, and determine a process capability index of each second target network device according to the specification boundary value and the performance index parameter set of each second target network device; and an abnormality determination module, configured to determine, for each second target network device, whether the second target network device has a quality abnormality according to the process capability index of the second target network device.

[0013] According to another aspect of the embodiments of the present application, a computer program product is further provided, comprising: a computer program, wherein the computer program is executed by a processor to implement the above-mentioned abnormal network device identification method.

[0014] According to another aspect of the embodiments of the present application, an electronic device is also provided, which includes a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-mentioned abnormal network device identification method through the computer program.

[0015] In the embodiments of the present application, the performance index parameter set of the first network device newly entered into the passive optical network and the performance index parameter set of each of the plurality of second network devices already entered into the network within a preset time period are acquired, wherein the performance index parameter set is a parameter set of a preset performance index of the network device within the preset time period; at least one second target network device of the same model and / or the same version as the first network device is determined according to the performance index parameter set of the first network device and the performance index parameter set of each of the second network devices; a specification boundary value is determined according to the performance index parameter set of the first network device, and a process capability index of each of the second target network devices is determined according to the specification boundary value and the performance index parameter set of each of the second target network devices; for each of the second target network devices, whether the second target network device has quality abnormality is determined according to the process capability index of the second target network device. That is, by adopting the combination of big data analysis and statistical methods, the technical effect of intelligent identification and evaluation of quality abnormality of network devices in the network is achieved, early warning and accurate positioning of network devices with performance problems are achieved, the purpose of optimizing the quality of network services is achieved, and the technical problem that related technologies cannot accurately identify abnormal network devices with deteriorating performance index parameters but not reaching the quality standard is solved. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of the present application and illustrate the illustrative embodiments of the present application and its description, and do not constitute improper limitations to the present application. In the drawings:

[0017] Figure 1 is a flow diagram of an optional abnormal network device identification method according to an embodiment of the present application;

[0018] Figure 2 is a structural diagram of an optional abnormal network device identification device according to an embodiment of the present application;

[0019] Figure 3 is a structural diagram of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application, so that those skilled in the art can better understand the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should be within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0022] In order to better understand the embodiments of the present application, first, the translation explanation of some nouns or terms appearing in the process of describing the embodiments of the present application is as follows:

[0023] PON (Passive Optical Network): It is a pure medium network, which transmits and accesses through point-to-multipoint optical fiber technology, adopts broadcast mode in downlink and time division multiple access mode in uplink, flexibly forms tree, star, bus and other topological structures, and only needs to install a simple optical branch device at the optical branch point. Therefore, it has the advantages of saving optical cable resources, sharing bandwidth resources, saving machine room investment, fast network building speed and low comprehensive network building cost.

[0024] Mean: or average, is the average of a group of data, which is obtained by adding the data and then dividing by the number of data. Its calculation formula is as follows:

[0025]

[0026] In the formula, x i represents the i-th data, and n represents the total number of data.

[0027] Mean Squared Error (MSE): is the average of the square sum of the distance of each data from the true value, that is, the average of the square sum of the error, whose calculation formula is as follows:

[0028]

[0029] where x GT represents the true value, x i represents the ith data, and n represents the total number of data.

[0030] Hypothesis Testing: also known as statistical hypothesis testing, is a statistical inference method for determining whether the difference between samples and samples, or samples and the population, is caused by sampling error or essential difference. Significance test is the most commonly used method in hypothesis testing, and is also the most basic form of statistical inference. The basic principle is to make a certain assumption about the characteristics of the population, and then make an inference on whether the hypothesis should be rejected or accepted through statistical reasoning of sampling research. Common hypothesis testing methods include Z-test, T-test, F-test, and Chi-square test.

[0031] F-Test: also known as joint hypothesis test, variance ratio test, and variance homogeneity test. It is a test under the null hypothesis (H0) that the statistical value follows the F-distribution. It is usually used to analyze statistical models with more than one parameter to determine whether all or part of the parameters in the model are suitable for the statistical population.

[0032] Process Capability Index: also known as process capability index, is the actual processing capacity of the process in a certain period of time under the control state (stable state). It is the inherent capability of the process, or it is the capability of the process guarantee instruction. Here, the process refers to the process of the five basic quality factors of operators, machines, raw materials, process methods and production environment, that is, the production process of product quality. Product quality is the comprehensive performance of the role of each quality factor in the process.

[0033] Embodiment 1

[0034] According to the embodiments of the present application, a method for identifying abnormal network equipment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0035] Figure 1 is a flowchart of a method for identifying abnormal network equipment according to an embodiment of the present application, as Figure 1 shown, the method comprises the following steps S102-S108, wherein:

[0036] In step S102, a performance index parameter set of each of the first network device newly registered in the PON and the plurality of second network devices registered in the PON within a preset time period is acquired.

[0037] In the technical solution provided in step S102, the first network device newly registered in the PON can be understood as a user terminal device such as an optical modem or a router that is first accessed and registered to the PON within a recent time period, wherein the time period of the newly registered network device can be defined by an operator according to actual conditions, for example, within a month, within three months, etc. The plurality of second network devices registered in the PON can be understood as network devices that have been operated in the network for a period of time, and their registration time is earlier than the defined time period of the newly registered network device. Generally, the number of the second network devices registered in the PON is usually much more than that of the newly registered network device, and the performance index parameters of the second network devices registered in the PON can reflect the long-term performance of the network devices in the actual operating environment.

[0038] In addition, the performance index parameter set of each of the first network device and the plurality of second network devices registered in the PON within a preset time period can be a parameter set of a preset performance index of the network device within a preset time period, wherein the preset performance index can be one of a plurality of performance indexes.

[0039] In step S104, at least one second target network device of the same model and / or the same version as the first network device is determined according to the performance index parameter set of the first network device and each of the second network devices.

[0040] In the technical solution provided in step S104, since the network devices of the same model usually use the same hardware configuration, and the network devices of the same version run the same version of software or firmware. This means that the network devices of the same model and / or the same version should have similar performance characteristics and parameter ranges in design, such as transmit power, receive power, signal-to-noise ratio, etc. Therefore, by analyzing the distribution between the performance index parameter sets of each of the first network device and the second network devices, at least one second target network device with similar performance index parameter set distribution to the first network device is identified, and it is inferred that the identified second target network device belongs to the same model or version as the first network device.

[0041] In step S106, a specification boundary value is determined according to the performance index parameter set of the first network device, and a process capability index of each of the second target network devices is determined according to the specification boundary value and the performance index parameter set of each of the second target network devices.

[0042] In the technical solution provided in step S106, since the first network device newly entering the network is usually in the initial stage of performance testing and stability, the performance index parameter set of the first network device can be used to establish a "healthy" benchmark performance index, i.e., the specification boundary value of the second target network device of the same model and / or the same version, as the normal range of the device performance parameter. Since the second target network devices have entered the network, their performance may change due to aging, damage, software problems, etc., and therefore, by combining the specification boundary value and the performance index parameter set of each second target network device, the process capability index of each second target network device can be determined, which can measure the degree to which the device process capability meets the specification range.

[0043] In step S108, for each second target network device, it is determined whether the second target network device has a quality abnormality according to the process capability index of the second target network device.

[0044] In the technical solution provided in step S108, the process capability index, as a quantitative index, can measure the degree of compliance between the performance parameter of the device and the predetermined specification boundary value, which reflects the stability and consistency of the network device in the manufacturing and running process. Therefore, according to the process capability index of the second target network device, it can be determined whether the performance parameter of the second target network device meets the expected stability and consistency standard, so as to analyze whether the quality abnormality is caused by device aging, failure, software problems or other factors related thereto.

[0045] Based on the technical solutions provided in steps S102-S108, in the embodiments of the present application, the technical effect of intelligent identification and evaluation of the quality abnormality of the network device in the network is achieved by combining big data analysis and statistical methods, the network device with performance problems is accurately located and warned in advance, and the purpose of optimizing the network service quality is achieved.

[0046] The steps of the abnormal network device identification method will be described in detail in combination with a specific implementation process.

[0047] As an optional implementation, in the technical solution provided in step S102, the performance index parameter set of the network device can be obtained by the following steps, including: collecting the performance index parameter set of the preset performance index of the first network device and each second network device in the passive optical network within the preset time period by the monitoring probe deployed on the first network device and each second network device.

[0048] Specifically, the probe deployed on the network device detects the performance index parameter set of the preset performance index of the network device within the preset time period. The type of the preset performance index includes: transmit power, received power, or received power signal-to-noise ratio.

[0049] Since transmit power determines the strength of the signal sent by network devices, if the transmit power is too low, it may cause signal attenuation, affecting the data transmission rate and quality, and thus affecting the user's network experience. At the same time, abnormal changes in transmit power are often early signs of device aging, hardware damage, or software problems. Therefore, when identifying abnormal network devices, the embodiments of the present application can mainly identify potential faulty devices in advance by continuously monitoring and analyzing the performance indicator of transmit power, providing early warning for network maintenance, helping to take timely measures to prevent faults and reduce the risk of network interruption.

[0050] After collecting the performance indicator parameter sets for each network device, it is necessary to determine whether the distribution of these performance indicator parameters follows a normal distribution (also known as a Gaussian distribution). This is because hypothesis testing methods presuppose a normal data distribution. If the data deviates from a normal distribution, the effectiveness of these testing methods will be reduced, and the conclusions drawn may be unreliable. Generally, methods for determining whether data distribution follows a normal distribution include graphical testing (i.e., plotting a histogram or QQ plot of the data to observe whether it exhibits the typical bell-shaped curve of a normal distribution), statistical testing (i.e., using statistical methods such as normality tests to test whether the data conforms to the normal distribution assumption), and empirical rules (i.e., observing whether the skewness and kurtosis of the data approach the normal distribution's skewness of 0 and kurtosis of 3).

[0051] In addition, if the data distribution is not normal, it can be made close to normal distribution through data transformation (such as logarithmic transformation, square root transformation, etc.), or non-parametric statistical methods can be used for analysis, such as Mann-Whitney U test, Kruskal-Wallis test, etc.

[0052] As an optional implementation, in the technical solution provided in the above step S104, the method may include:

[0053] Step S1041, determining a first mean square error of the performance of the first network device on a preset performance indicator based on a performance indicator parameter set of the first network device, and determining a second mean square error of the performance of each second network device on the preset performance indicator based on a performance indicator parameter set of each second network device;

[0054] Step S1042: Using a hypothesis testing method, the first mean square error of the first network device on the preset performance indicator is compared with the second mean square error of each second network device on the preset performance indicator to determine at least one second target network device of the same model and / or version as the first network device.

[0055] Specifically, the above-mentioned hypothesis checking methods include, but are not limited to, F-check (used to compare whether the variances of two groups of data are equal), T-check (used to compare whether the average values of two groups of data are significantly different, suitable for small sample data of normal distribution), chi-square check (used to check whether there is a significant correlation between two or more classification variables, suitable for count data), Mann-Whitney U test (used to compare whether the medians of two independent samples are the same, suitable for data that are not normally distributed), or Kruskal-Wallis test (used to compare whether the medians of three or more independent samples are the same, which can be used when the data is not normally distributed or the variances are not equal), etc.

[0056] Taking F-check as an example, the technical solution provided in step S1042 can be implemented according to the following steps:

[0057] First, for each second network device, the quotient of the first mean square error of the first network device in the preset performance indicator and the second mean square error of the second network device in the preset performance indicator is calculated to obtain the corresponding checking statistic;

[0058] In the case where the checking statistic is not greater than the preset checking threshold, it is determined that the second network device is a second target network device of the same model and / or the same version as the first network device.

[0059] In the case where the checking statistic is greater than the preset checking threshold, it is determined that the second network device is a second target network device of the same model and / or the same version as the first network device.

[0060] That is, the first mean square error of the first network device in the preset performance indicator is denoted as The second mean square error of the second network device in the preset performance indicator is denoted as Then the statistic can be expressed as:

[0061]

[0062] In addition, the general significance level (i.e. the probability of making a mistake when estimating the population parameter to fall within a certain interval) is 0.05, and the degrees of freedom is the number of sampling points of the collected network device in the preset time period minus one. Therefore, by consulting the corresponding F-check threshold table, the corresponding checking threshold can be obtained. If the F statistic is greater than the F threshold, it means that the checking result has a significant difference; otherwise, if the F statistic is not greater than the F threshold, it means that the checking result does not have a significant difference.

[0063] In addition to the above-mentioned several implementation solutions, based on the basic concept of identifying other devices of the same model and / or the same version as the new network device according to the embodiments of the present application, those skilled in the art can also implement the determination of the second target network device of the same model and / or the same version as the first network device through other technical solutions, for example, those skilled in the art can transform the above-mentioned implementation solutions, and all of them should be within the protection scope of the present application.

[0064] As an optional implementation, in the technical solution provided in the above-mentioned step S106, the method can comprise:

[0065] Step S1061, determining a first mean value and a first mean square error of the performance of the first network device on the preset performance index according to the performance index parameter set of the first network device, and determining a specification boundary value according to the first mean value and the first mean square error, wherein the specification boundary value comprises: a specification upper limit value and a specification lower limit value.

[0066] Wherein, the first mean value of the performance of the first network device on the preset performance index is denoted as μ, and the first mean square error is denoted as σ, then the specification boundary value of other network devices of the same model and / or the same version as the first network device can be denoted as: the specification upper limit value (Upper Spec Limit, USL) is μ+4σ, and the specification lower limit value (Lower Spec Limit, USL) is μ-4σ.

[0067] Step S1062, determining a second mean value and a second mean square error of the performance of the second target network device on the corresponding performance index according to the performance index parameter set of the second target network device.

[0068] Wherein, the second mean value of the performance of the i-th second target network device on the corresponding performance index can be denoted as μ f,i , and the second mean square error can be denoted as σ f,i .

[0069] Step S1063, determining the process capability index of the second target network device according to the specification boundary value, the second mean value and the second mean square error of the second target network device, and according to the following formula:

[0070]

[0071] In the formula, μ represents the first mean value, σ represents the first mean square error, μ+4σ represents the specification upper limit value, μ-4σ represents the specification lower limit value, μ f,i represents the second mean value of the i-th second target network device, and σ f,i represents the second mean square error of the i-th second target network device.

[0072] It should be noted that the above-mentioned method of setting the specification boundary value is considering that in the normal distribution, Theoretically, it can cover data points closer to 100% in the normal distribution to strictly screen out network devices with abnormal performance, ensure network stability and service quality, and further reduce the impact of outliers. However, if the network environment has very high performance requirements for equipment, or the cost allows for a higher maintenance frequency, you can consider using a larger standard deviation multiple (such as ) to further reduce potential abnormal devices. Therefore, the method for determining the specification boundary value based on the first mean and the first mean square deviation can be dynamically adjusted based on the actual operation of the device, changes in the network environment, or the operator's policy.

[0073] As an optional implementation, in the technical solution provided in the above step S108, the method may include:

[0074] When the process capability index of the second target network device is less than a preset threshold value, determining that the second target network device has a quality abnormality;

[0075] When the process capability index of the second target network device is not less than a preset threshold value, it is determined that the second target network device does not have quality abnormality.

[0076] Specifically, the threshold value can typically be set to 1.33, for example. Generally, the higher the threshold value, the smaller the deviation between the mean and mean square error of the transmit power of other devices and the reference value, and the higher the number of unqualified products. Secondary target network devices with a process capability index below the threshold value are considered to have unstable or abnormal performance. This is because a low process capability index indicates that the performance parameters of the device deviate significantly from the specification boundaries, meaning that the device's performance indicators are unstable or fail to meet expected performance standards.

[0077] Furthermore, after determining that the second target network device has quality abnormality, the second target network device with quality abnormality may be managed according to the following method, including:

[0078] Step 1: Determine the gap between the process capability index of the second target network device with quality anomalies and the threshold value. This gap reflects the degree of deviation between the stability of the device's transmit power and the network standard and is an important basis for judging the device's quality status.

[0079] Step 2: Determine the quality abnormality level of the second target network device with quality abnormality based on the size of the gap. That is, classify the second target network device with quality abnormality into different quality abnormality levels, and the level classification can be based on a fixed interval.

[0080] For example, the devices with a gap less than 0.3 can be marked as slight abnormality, the devices with a gap between 0.3 and 0.6 are marked as moderate abnormality, and the devices with a gap greater than 0.6 are marked as severe abnormality. It should be noted that the division of abnormality levels can be determined according to network performance requirements and device maintenance costs.

[0081] Step 3: Determine the management priority of the second target network device with quality abnormality according to the quality abnormality level, and maintain the second target network device with quality abnormality in turn according to the management priority.

[0082] Wherein, the higher the quality abnormality level of the second target network device, the higher the corresponding management priority; conversely, the lower the quality abnormality level of the second target network device, the lower the corresponding management priority. Therefore, maintaining the second target network device with quality abnormality in turn according to the management priority can ensure that the devices with higher priority will be maintained before the devices with lower priority, thereby ensuring that the network stability and user experience are not significantly affected, and the maintenance content includes but is not limited to device parameter adjustment, software upgrade, hardware replacement or on-site inspection, etc.

[0083] Through the above abnormal network device identification method, not only the devices whose performance indicators have fallen below the quality threshold can be identified, but also the devices whose performance indicators have started to decline but have not yet reached the quality threshold can be found in advance, thereby effectively preventing network problems and improving the initiative and accuracy of network maintenance. At the same time, different abnormality levels of different abnormal devices are managed in a targeted and refined manner to ensure that the key problems of different abnormal devices are solved in time, and resource allocation is optimized to avoid unnecessary intervention on devices with normal performance, thereby achieving the efficiency and pertinence of network maintenance.

[0084] Embodiment 2

[0085] According to the embodiments of the present application, an abnormal network device identification apparatus for implementing the abnormal network device identification method in Embodiment 1 is also provided, as shown in Figure 2 The abnormal network device identification apparatus at least includes an acquisition module 22, a first determination module 24, a second determination module 26 and an abnormality determination module 28, wherein:

[0086] The acquisition module 22 is configured to acquire a performance indicator parameter set of each of a first network device newly entered into a passive optical network and a plurality of second network devices already entered into the passive optical network within a preset time period, wherein the performance indicator parameter set is a parameter set of a preset performance indicator of a network device within a preset time period.

[0087] The first determining module 24 is configured to determine at least one second target network device of the same model and / or the same version as the first network device according to the performance index parameter set of the first network device and the performance index parameter set of each second network device.

[0088] The second determining module 26 is configured to determine a specification boundary value according to the performance index parameter set of the first network device, and determine a process capability index of each second target network device according to the specification boundary value and the performance index parameter set of each second target network device.

[0089] The abnormality discriminating module 28 is configured to determine, for each second target network device, whether the second target network device has quality abnormality according to the process capability index of the second target network device.

[0090] In addition, the device can further include a management module configured to determine a gap between the process capability index of the second target network device having quality abnormality and a threshold value after determining that the second target network device has quality abnormality, determine a quality abnormality level of the second target network device having quality abnormality according to the gap, determine a management priority of the second target network device having quality abnormality according to the quality abnormality level, and maintain the second target network device having quality abnormality according to the management priority.

[0091] It should be noted that each module in the abnormal network device identification device in the embodiments of the present application corresponds to each implementation step of the abnormal network device identification method in Embodiment 1. Since Embodiment 1 has been described in detail, the details not embodied in this embodiment can be referred to Embodiment 1, and will not be described in detail here.

[0092] Embodiment 3

[0093] According to the embodiments of the present application, a computer program product is also provided, which includes a computer program. When the computer program is executed by a processor, the abnormal network device identification method in Embodiment 1 is implemented.

[0094] According to the embodiments of the present application, a non-volatile storage medium is also provided, which includes a stored computer program. The device in which the non-volatile storage medium is located executes the abnormal network device identification method in Embodiment 1 by running the computer program.

[0095] According to the embodiments of the present application, a processor is also provided, which is used to run a computer program. When the computer program is run, the abnormal network device identification method in Embodiment 1 is executed.

[0096] According to the embodiment of the present application, an electronic device is also provided, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the abnormal network device identification method in embodiment 1 through the computer program.

[0097] Specifically, the computer program runs to implement the following steps: obtaining a performance index parameter set of a first network device newly entered into a passive optical network and a plurality of second network devices already entered into the passive optical network within a preset time period, wherein the performance index parameter set is a parameter set of a preset performance index of the network device within the preset time period; determining at least one second target network device of the same model and / or the same version as the first network device according to the performance index parameter set of the first network device and each second network device; determining a specification boundary value according to the performance index parameter set of the first network device, and determining a process capability index of each second target network device according to the specification boundary value and the performance index parameter set of each second target network device; and determining whether the second target network device has quality abnormality according to the process capability index of the second target network device for each second target network device.

[0098] As an optional implementation, the electronic device can exist in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 3 A hardware structure block diagram of an electronic device for implementing the abnormal network device identification method is shown. As shown in the figure, Figure 3 The electronic device 30 can include one or more (shown in the figure as 302a, 302b, …, 302n) processors 302 (the processor 302 can include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 304 for storing data, and a transmission device 306 for communication function. In addition, it can also include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. Those skilled in the art can understand that, Figure 3 The structure shown in the figure is only schematic, and it does not limit the structure of the above-mentioned electronic device. For example, the electronic device 30 can also include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure. Figure 3 For example, the electronic device 30 can also include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure. Figure 3 For example, the electronic device 30 can also include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure.

[0099] It should be noted that the one or more processors 302 and / or other data processing circuitry described above can be referred to herein generically as "data processing circuitry". The data processing circuitry can be embodied in whole or in part as software, hardware, firmware, or any combination thereof. In addition, the data processing circuitry can be a single standalone processing module, or it can be incorporated in whole or in part within any one of the other elements of the electronic device 30. As referred to in the embodiments herein, the data processing circuitry acts as a processor to control, for example, the selection of the variable resistance terminal path connected to the interface.

[0100] The memory 304 can be used to store software programs of application software and modules, such as program instructions / data storage means corresponding to the abnormal network device identification method in the embodiments of the present application. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, i.e. implements the vulnerability detection method of the application program described above. The memory 304 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 304 can further include a memory remotely arranged with respect to the processor 302, which can be connected to the electronic device 30 through a network. Examples of the network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0101] The transmission device 306 is used to receive or send data via a network. Specific examples of the network can include a wireless network provided by a communication provider of the electronic device 30. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

[0102] The display can be, for example, a touch screen type liquid crystal display (LCD), which can enable a user to interact with the user interface of the electronic device 30.

[0103] The above-mentioned embodiment numbers are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0104] In the above-described embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0105] In several embodiments provided in the present application, it should be understood that the disclosed technology can be implemented by other ways. Among them, the above-described device embodiments are only schematic, for example, the division of units can be a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed units can be indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0106] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e. they can be located in one place or distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0107] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0108] If the integrated unit is realized in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the technical solutions or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0109] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. An abnormal network device identification method, characterized by, include: Obtaining a performance indicator parameter set of each of a newly connected first network device and a plurality of connected second network devices within a preset time period in the passive optical network, wherein the performance indicator parameter set is a parameter set of a preset performance indicator of the network device within the preset time period; Determine, based on the performance indicator parameter sets of the first network device and each of the second network devices, at least one second target network device of the same model and / or same version as the first network device; Determining a specification boundary value based on a performance indicator parameter set of the first network device, and determining a process capability index of each second target network device based on the specification boundary value and the performance indicator parameter set of each second target network device, including: determining a first mean and a first mean square deviation of the first network device on a preset performance indicator based on the performance indicator parameter set of the first network device, and determining the specification boundary value based on the first mean and the first mean square deviation, wherein the specification boundary value includes: an upper specification limit value and a lower specification limit value; determining a second mean and a second mean square deviation of the second target network device on a corresponding performance indicator based on the performance indicator parameter set of the second target network device; determining the process capability index of the second target network device based on the specification boundary value, the second mean and the second mean square deviation of the second target network device, and according to the following formula: ; In the formula denotes the first mean value, denotes the first mean square deviation, denotes the upper specification limit value, denotes the lower specification limit value, denotes the second mean value of the i-th second target network device, denotes the second mean square deviation of the i-th second target network device; For each second target network device, it is determined whether the second target network device has a quality abnormality according to a process capability index of the second target network device.

2. The method of claim 1, wherein, Obtaining a performance indicator parameter set of a newly connected first network device and a plurality of connected second network devices within a preset time period in a passive optical network, including: A set of performance indicator parameters of preset performance indicators within a preset time period collected by monitoring probes deployed on the first network device and each second network device in the passive optical network, wherein the types of the preset performance indicators include: transmission power, received power or received power signal-to-noise ratio.

3. The method of claim 1, wherein, Determining, based on the performance indicator parameter sets of the first network device and each of the second network devices, at least one second target network device of the same model and / or same version as the first network device includes: Determining a first mean square error of the performance of the first network device on a preset performance indicator based on the performance indicator parameter set of the first network device, and determining a second mean square error of the performance of each of the second network devices on the preset performance indicator based on the performance indicator parameter set of each of the second network devices; A hypothesis testing method is used to compare the first mean square deviation of the first network device on the preset performance indicator with the second mean square deviation of each second network device on the preset performance indicator to determine at least one second target network device of the same model and / or version as the first network device.

4. The method of claim 3, wherein, The first mean square deviation of the first network device in the preset performance index is compared with the second mean square deviation of each second network device in the preset performance index by using hypothesis testing method, and at least one second target network device of the same model and / or the same version as the first network device is determined, comprising: For each second network device, the quotient value of the first mean square deviation of the first network device in the preset performance index and the second mean square deviation of the second network device in the preset performance index is calculated to obtain a corresponding check statistic; In the case that the check statistic is not greater than a preset check threshold, the second network device is determined as a second target network device of the same model and / or the same version as the first network device.

5. The method of claim 1, wherein, According to the process capability index of the second target network device, it is determined whether the second target network device has quality abnormality, comprising: In the case that the process capability index of the second target network device is less than a preset threshold value, it is determined that the second target network device has quality abnormality; In the case that the process capability index of the second target network device is not less than the preset threshold value, it is determined that the second target network device does not have quality abnormality.

6. The method of claim 5, wherein, After it is determined that the second target network device has quality abnormality, the method further comprises: Determining the gap size between the process capability index of the second target network device with quality abnormality and the threshold value; According to the gap size, the quality abnormality level of the second target network device with quality abnormality is determined; According to the quality abnormality level, the management priority of the second target network device with quality abnormality is determined, and the second target network device with quality abnormality is sequentially maintained according to the management priority.

7. An abnormal network device identification apparatus characterized by comprising: Comprising: An acquisition module is configured to acquire a performance index parameter set of a first network device newly entered into a passive optical network and a plurality of second network devices already entered into the network within a preset time period, wherein the performance index parameter set is a parameter set of a preset performance index of a network device within the preset time period; A first determination module is configured to determine at least one second target network device of the same model and / or the same version as the first network device according to the performance index parameter set of the first network device and each second network device; The second determining module is configured to determine a specification boundary value according to the performance index parameter set of the first network device, and determine a process capability index of each second target network device according to the specification boundary value and the performance index parameter set of each second target network device, including: determining a first mean value and a first mean square error of the performance of the first network device on a preset performance index according to the performance index parameter set of the first network device, and determining the specification boundary value according to the first mean value and the first mean square error, wherein the specification boundary value includes an upper limit value and a lower limit value of the specification; determining a second mean value and a second mean square error of the performance of the second target network device on a corresponding performance index according to the performance index parameter set of the second target network device; and determining the process capability index of the second target network device according to the specification boundary value, the second mean value and the second mean square error of the second target network device, and according to the following formula: ; In the formula denotes the first mean value, denotes the first mean square deviation, denotes the upper specification limit value, denotes the lower specification limit value, denotes the second mean value of the i-th second target network device, denotes the second mean square deviation of the i-th second target network device; The abnormality determining module is configured to determine, for each second target network device, whether the second target network device has quality abnormality according to the process capability index of the second target network device.

8. A computer program product, characterised in that, The computer program, wherein the computer program is executed by a processor to implement the abnormal network device identification method in any one of claims 1 to 6. The memory and the processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the abnormal network device identification method in any one of claims 1 to 6.

9. An electronic device, comprising: The memory and the processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the abnormal network device identification method in any one of claims 1 to 6. ​

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