Device test method and apparatus, and readable medium

Abstract

The present disclosure provides a device test method, comprising: acquiring sampling data of target objects, the sampling data comprising input optical powers, optical module receiving sensitivities, output optical powers, and optical module overload optical powers of the target objects within a target period; for each target object, performing weak-light hazard identification on the target object on the basis of the input optical power, the optical module receiving sensitivity, and a weak-light threshold deviation of the target object in the target period, so as to obtain a weak-light hazard identification result of the target object; and for each target object, performing strong-light hazard identification on the target object on the basis of the output optical power, the optical module overload optical power, and a strong-light threshold deviation of the target object in the target period, so as to obtain a strong-light hazard identification result of the target object. According to the embodiments of the present disclosure, strong-light hazards and weak-light hazards can be identified, thereby improving the accuracy and timeliness of fault identification in a transmission network. The present disclosure further provides a device test apparatus and a readable medium.

Description

Equipment testing methods, apparatus and readable media

[0001] Relevant publicly available cross-references

[0002] This disclosure claims priority to Chinese Patent Application No. CN202411534637.9, filed on October 30, 2024, entitled "Equipment Testing Method, Apparatus, and Readable Medium Connector and Electronic Device Including the Connector", the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to, but are not limited to, the field of communication technology, and particularly to a device detection method, apparatus, and readable medium. Background Technology

[0004] Currently, transmission networks primarily use optical fiber for high-capacity, long-distance communication. The quality of the optical signal in the fiber is mainly characterized by the optical power at the device ports. Excessive or insufficient optical power will affect the quality of optical communication, so it is necessary to ensure that the optical power is within a certain range. The optical power value can be obtained by the network management system by collecting data from each device port. Maintenance personnel set the overload optical power and receiver sensitivity for each type of optical module according to the requirements of each device port and perform fault monitoring. When the optical power exceeds the limit, an alarm is generated, notifying maintenance personnel to handle the issue. This method of fault detection and handling has several drawbacks: faults can only be detected after they occur, and it cannot proactively identify and address potential problems before a fault occurs. Summary of the Invention

[0005] This disclosure provides a device testing method, apparatus, and readable medium.

[0006] In a first aspect, embodiments of this disclosure provide a device detection method, comprising: acquiring sampling data of a target object, the sampling data including the input optical power, optical module receiving sensitivity, output optical power, and optical module overload optical power of the target object in a target period; the target object including a transmission network device port or at least one channel of the transmission network device port; for each target object, performing weak light hazard identification on the target object based on the input optical power, the optical module receiving sensitivity, and the weak light threshold deviation of the target object in the target period, to obtain a weak light hazard identification result for the target object; and performing strong light hazard identification on the target object based on the output optical power, the optical module overload optical power, and the strong light threshold deviation of the target object in the target period, to obtain a strong light hazard identification result for the target object.

[0007] Secondly, embodiments of this disclosure provide a device detection apparatus, including a memory and a processor; the memory stores a computer program that can be executed by the processor, and when the computer program is executed by the processor, it implements the device detection method.

[0008] Thirdly, embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed, it implements the device detection method as described above. Attached Figure Description

[0009] In the accompanying drawings of the embodiments disclosed herein:

[0010] Figure 1 is a flowchart of a device testing method according to an embodiment of the present disclosure;

[0011] Figure 2 is a schematic diagram of the functional modules of a device testing apparatus according to an embodiment of the present disclosure;

[0012] Figure 3 is a flowchart of another device detection method according to an embodiment of the present disclosure;

[0013] Figure 4 is a flowchart of determining the weak light threshold deviation and the strong light threshold deviation according to an embodiment of the present disclosure;

[0014] Figure 5 is a schematic diagram of the module composition of the equipment testing device according to an embodiment of the present disclosure. Detailed Implementation

[0015] To enable those skilled in the art to better understand the technical solutions of this disclosure, the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings.

[0016] The present disclosure will be described more fully below with reference to the accompanying drawings; however, the embodiments shown may be embodied in different forms, and the present disclosure should not be construed as limited to the embodiments set forth below. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will enable those skilled in the art to fully understand the scope of the disclosure.

[0017] The accompanying drawings of the embodiments disclosed herein are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the detailed embodiments to explain this disclosure and do not constitute a limitation thereof. The above and other features and advantages will become more apparent to those skilled in the art from the description of the detailed embodiments with reference to the accompanying drawings.

[0018] This disclosure may be described with reference to plan and / or cross-sectional views using the ideal schematic diagrams of this disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances.

[0019] Where there is no conflict, the various embodiments of this disclosure and the features thereof in the embodiments may be combined with each other.

[0020] The terminology used in this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the disclosure. The term "and / or" as used in this disclosure includes any and all combinations of one or more of the associated enumerated entries. The singular forms "a" and "the" as used in this disclosure are also intended to include the plural forms, unless the context clearly indicates otherwise. The terms "comprising," "made of," etc., as used in this disclosure specify the presence of the stated feature, integral, step, operation, element, and / or component, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof.

[0021] Unless otherwise specified, all terms used in this disclosure (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning, unless expressly so defined in this disclosure.

[0022] This disclosure is not limited to the embodiments shown in the accompanying drawings, but includes modifications to the configuration based on the manufacturing process. Therefore, the areas illustrated in the drawings are schematic, and the shapes of the areas shown illustrate specific shapes of the areas of an element, but are not intended to be limiting.

[0023] In related technologies, the fault detection and handling methods for transmission networks have the following drawbacks:

[0024] 1. Faults can only be detected after they occur, making it impossible to identify and address potential problems before they occur.

[0025] 2. Fault diagnosis is based solely on the received and transmitted optical power, resulting in a single dimension and basis for fault diagnosis.

[0026] 3. It heavily relies on the capabilities of on-site maintenance personnel, and there is a high degree of uncertainty in fault detection, troubleshooting, and handling.

[0027] 4. In scenarios with complex network topologies or large amounts of data, manual identification involves a large workload and takes a long time.

[0028] 5. Key parameters are set based on human experience and cannot be adaptively adjusted according to network conditions, resulting in low accuracy in fault identification.

[0029] To address the aforementioned problems, this disclosure provides a device testing method, which is applied to a device testing apparatus. Figure 2 is a functional module diagram of the device testing apparatus according to an embodiment of this disclosure. As shown in Figure 2, the device testing apparatus includes a front-end display module, a setting module, a data acquisition module, a strong and weak light hazard management module, a business impact analysis module, and a light power trend chart management module.

[0030] The front-end presentation module is used for interface display and configuration;

[0031] The configuration module is designed to set the period, initial threshold deviation value, and filtering conditions to complete the configuration of strong and weak light analysis strategies. It can also automatically adjust the initial values ​​of weak light threshold deviation and strong light threshold deviation based on network conditions.

[0032] The data acquisition module is configured to collect sampled data from all network device ports or each channel of a port according to the acquisition granularity.

[0033] The strong and weak light hazard management module is designed to initiate strong and weak light hazard analysis tasks, periodically read sampling data from all network device ports or port channels, filter the sampling data based on filtering conditions, calculate strong / weak light anomalies in the filtered sampling data, determine the type and severity level of strong / weak light hazards according to the proportion of strong / weak light anomalies within a period and the presence of CRC (Cyclic Redundancy Check) error packets, and determine the hit or miss status of strong / weak light hazards in multiple historical periods. Based on this, the module automatically adjusts the corresponding strong light threshold deviation and weak light threshold deviation.

[0034] The impact analysis module is configured to query the database of the business knowledge graph for the port to see which L3VPN (Layer 3 Virtual Private Network), L2VPN (Layer 2 Virtual Private Network), or TDM (Time Division Multiplexing Optical Network) services are affected by the port. It outputs the type of service that may be affected, the service name, and the A-end and Z-end information. You can navigate to the corresponding service manager by the service name to view the service details.

[0035] The optical power trend graph management module is designed to query the database for optical power information of transmission network equipment ports with potential strong / weak light hazards according to the user-defined query period. It can present indicators such as input optical power, maximum input optical power, minimum input optical power, output optical power, maximum output optical power, and minimum output optical power in line graphs of different colors, generating and displaying optical power trend graphs.

[0036] Figure 1 is a flowchart of a device testing method according to an embodiment of the present disclosure. Referring to Figures 1 and 2, the device testing method includes the following steps:

[0037] Step S11: Obtain sampling data of the target object. The sampling data includes the input optical power, optical module receiving sensitivity, output optical power and optical module overload optical power of the target object within the target period. The target object includes a transmission network device port or at least one channel of the transmission network device port.

[0038] The data acquisition module can collect data from the port of the transmission network device or each channel of the port according to a preset acquisition granularity to obtain sampled data. In some embodiments, the acquisition granularity can be set to 5 minutes or 15 minutes. As shown in Figure 2, the acquisition granularity can be preset through the setting module.

[0039] In this embodiment of the disclosure, the strong / weak light hazard management module identifies strong / weak light hazards according to a period, where the target period refers to the current period. The period can be preset through a setting module, and the period is much larger than the data acquisition granularity of the data acquisition module. For example, the period can be one day, while the acquisition granularity is at the minute level.

[0040] The sampling data includes, but is not limited to, the target object's input optical power, maximum received optical power, minimum received optical power, output optical power, maximum output optical power, minimum output optical power, overload optical power, and maximum receiver sensitivity within the target period. The sampling data may also include basic information such as device port name and speed.

[0041] Step S12: For each target object, weak light hazard identification is performed on the target object based on the input optical power, the optical module receiving sensitivity and the weak light threshold deviation of the target object in the target period, and the weak light hazard identification result of the target object is obtained.

[0042] Step S13: For each target object, identify the strong light hazard based on the output optical power, the optical module overload optical power, and the strong light threshold deviation of the target object in the target period, and obtain the strong light hazard identification result of the target object.

[0043] When the target object has potential risks of weak light and / or strong light, the link transmission quality is lower than that under normal link conditions, but higher than that under link failure conditions. In other words, potential risks of strong light and weak light refer to poor link transmission quality, but not yet reaching the level of link failure.

[0044] The target object can be either the port of the transmission network device or the channel of the transmission network device port. If the transmission network device port includes multiple channels, the strong and weak light hazard management module will identify strong light hazards and weak light hazards for each channel respectively; if the transmission network device port does not include channels, the strong and weak light hazard management module will identify strong light hazards and weak light hazards for each port respectively.

[0045] The device detection method in this embodiment includes: acquiring sampling data of a target object, the sampling data including the input optical power, optical module receiving sensitivity, output optical power, and optical module overload optical power of the target object in a target period; for each target object, identifying weak light hazards based on the input optical power, optical module receiving sensitivity, and weak light threshold deviation of the target object in the target period, and obtaining a weak light hazard identification result for the target object; and identifying strong light hazards based on the output optical power, optical module overload optical power, and strong light threshold deviation of the target object in the target period, and obtaining a strong light hazard identification result for the target object. This embodiment can identify weak light hazards based on the input optical power, optical module receiving sensitivity, and weak light threshold deviation of the target object in the target period, and can identify strong light hazards based on the output optical power, optical module overload optical power, and strong light threshold deviation of the target object in the target period, so as to enable timely processing and improve the accuracy and timeliness of transmission network fault identification.

[0046] To further improve the accuracy of identifying strong light hazards and weak light hazards, in some embodiments, after obtaining the sampling data of the target object (i.e., step S11) and before performing weak light hazard identification and strong light hazard identification (i.e., steps S12 and S13), the device detection method may further include the following steps: for each target object, the sampling data is filtered according to preset filtering conditions to obtain filtered sampling data, and the filtered sampling data is used to perform strong light hazard identification and weak light hazard identification within the target period.

[0047] Filtering conditions can be configured in the settings module according to the actual operation of the transmission network. Single or multiple filtering conditions can be configured. In some embodiments, filtering conditions include, but are not limited to:

[0048] Maximum output optical power ≠ set value;

[0049] Maximum output optical power <= set value;

[0050] Minimum input optical power ≠ setting value;

[0051] Minimum input optical power >= set value.

[0052] The following describes the process of identifying potential hazards in low-light conditions for a target object.

[0053] In some embodiments, the result of weak light hazard identification may include the number of weak light anomaly data. The step of identifying weak light hazards in the target object based on the input optical power, the optical module receiving sensitivity, and the weak light threshold deviation of the target object in the target period to obtain the result of weak light hazard identification of the target object (i.e., step S12) includes the following steps:

[0054] Step S121: At each acquisition moment of the target period, calculate the sum of the acquired optical module receiving sensitivity and the weak light threshold deviation of the target object in the target period.

[0055] Step S122: If the input optical power of the target object is less than or equal to the sum of the optical module's receiving sensitivity and the weak light threshold deviation of the target object in the target period, the sampled data is determined to be a weak light abnormal data.

[0056] If the input optical power of the target object is less than or equal to the receiving sensitivity of the optical module plus the weak light threshold deviation A of the target object in the target period, then the sampled data can be identified as a weak light anomaly.

[0057] Step S123: Count the number of weak light anomaly data of the target object within the target period.

[0058] In some embodiments, the low-light hazard identification result may further include the type of low-light hazard. After the number of low-light abnormal data of the target object within the target period is obtained (i.e., step S123), the device detection method may further include the following steps:

[0059] Step S124: Determine the proportion of weak light abnormal data based on the number of weak light abnormal data and the total number of all sampled data within the target period.

[0060] Step S125: Determine the type of low light hazard based on the proportion of low light anomaly data and the first preset threshold. The type of low light hazard is used to indicate the degree of low light anomaly.

[0061] In some embodiments, determining the type of low light hazard based on the proportion of low light abnormal data and a first preset threshold (i.e., step S125) includes the following steps: determining the frequency of low light abnormality based on the proportion of low light abnormal data and the first preset threshold; and determining the type of low light hazard based on the frequency of low light abnormality.

[0062] In some embodiments, determining the type of low-light hazard based on the proportion of low-light abnormal data and a first preset threshold (i.e., step S125) includes the following steps: obtaining alarm information of the target object within a target period, the alarm information including CRC error alarm information; determining the low-light abnormality frequency based on the proportion of low-light abnormal data and the first preset threshold; and determining the type of low-light hazard based on the CRC error alarm information and the low-light abnormality frequency.

[0063] The alarm information for the target object may include, but is not limited to, receiving error check frames, signal loss alarms, signal degradation alarms, input optical power exceeding limits alarms, output optical power exceeding limits alarms, optical module failure alarms, exceeding limits for the number of transmitted error frames alarms, exceeding limits for the number of received error frames alarms, and exceeding limits for the number of received CRC error packets alarms. If CRC error alarm information (i.e., receiving error check frames) is obtained within the target period, it indicates the presence of CRC error packets, and the type of weak light hazard can be determined based on this CRC error alarm information and the frequency of weak light anomalies; if no CRC error alarm information is obtained within the target period, the type of weak light hazard can be determined based on the frequency of weak light anomalies.

[0064] In this embodiment of the disclosure, the following six types of low light hazards can be identified: continuous low light (with CRC error packets), frequent low light (with CRC error packets), occasional low light (with CRC error packets), continuous low light, frequent low light, and occasional low light.

[0065] In some embodiments, the first preset threshold includes a first lower threshold L1 and a first upper threshold M1, wherein the first lower threshold L1 is less than the first upper threshold M1. In some embodiments, determining the frequency of weak light anomalies based on the proportion of weak light anomaly data and the first preset threshold includes the following steps: when the proportion of weak light anomaly data is less than the first lower threshold L1, determining the frequency of weak light anomalies as occasional anomalies; when the proportion of weak light anomaly data is greater than the first lower threshold L1 and less than the first upper threshold M1, determining the frequency of weak light anomalies as frequent anomalies; when the proportion of weak light anomaly data is greater than or equal to the first upper threshold M1, determining the frequency of weak light anomalies as continuous anomalies.

[0066] In some embodiments, after determining the type of low-light hazard, the device detection method may further include the following steps: determining the low-light hazard level corresponding to the type of low-light hazard based on a preset mapping relationship between hazard type and hazard level.

[0067] For example, in this embodiment of the disclosure, the hazards are divided into four levels of strong and weak light hazards, and the preset mapping relationship between hazard types and hazard levels is as follows:

[0068] Severe low light hazard types include: continuous low light (with CRC error packets) and continuous low light;

[0069] The types of low-light hazards of a high importance level include: frequent low-light occurrences (with CRC error packets);

[0070] The main types of low-light hazards include: frequent low light and occasional low light (with CRC error packets);

[0071] Secondary level low light hazards include: occasional low light.

[0072] The following describes the process of identifying potential hazards caused by strong light on target objects.

[0073] In some embodiments, the strong light hazard identification result includes the number of strong light anomaly data. The step of identifying the strong light hazard of the target object based on the output optical power, the overload optical power of the optical module, and the strong light threshold deviation of the target object in the target period to obtain the strong light hazard identification result of the target object (i.e., step S12) includes the following steps:

[0074] Step S121': At each acquisition moment of the target period, calculate the difference between the acquired optical module overload optical power and the strong light threshold deviation of the target object in the target period.

[0075] Step S122': If the output optical power of the target object is greater than or equal to the difference between the overload optical power of the optical module and the strong light threshold deviation of the target object in the target period, the sampled data is determined to be a strong light anomaly data.

[0076] If the output optical power of the target object is greater than or equal to the overload optical power of the optical module minus the strong light threshold deviation B of the target object in the target period, then the sampled data can be identified as a strong light anomaly.

[0077] Step S123': Count the number of strong light anomaly data of the target object within the target period.

[0078] In some embodiments, the strong light hazard identification result may further include the type of strong light hazard. After the number of strong light anomaly data of the target object within the target period is obtained (i.e., step S123'), the device detection method may further include the following steps:

[0079] Step S124': Determine the proportion of strong light anomaly data based on the number of strong light anomaly data and the total number of all sampled data within the target period.

[0080] Step S125': Determine the type of strong light hazard based on the proportion of strong light anomaly data and the second preset threshold. The type of strong light hazard is used to indicate the degree of strong light anomaly.

[0081] In some embodiments, determining the type of strong light hazard based on the proportion of strong light anomaly data and the second preset threshold (i.e., step S125') includes the following steps: determining the frequency of strong light anomalies based on the proportion of strong light anomaly data and the second preset threshold; and determining the type of strong light hazard based on the frequency of strong light anomalies.

[0082] In some embodiments, determining the type of strong light hazard based on the proportion of strong light anomaly data and a second preset threshold includes: acquiring alarm information of the target object within the target period, the alarm information including cyclic redundancy check (CRC) error alarm information; determining the frequency of strong light anomalies based on the proportion of strong light anomaly data and the second preset threshold; and determining the type of strong light hazard based on the CRC error alarm information and the frequency of strong light anomalies.

[0083] In this embodiment of the disclosure, the following six types of strong light hazards can be identified: continuous strong light (with CRC error packets), frequent strong light (with CRC error packets), occasional strong light (with CRC error packets), continuous strong light, frequent strong light, and occasional strong light.

[0084] The alarm information for the target object may include, but is not limited to, receiving error check frames, signal loss alarms, signal degradation alarms, input optical power exceeding limits alarms, output optical power exceeding limits alarms, optical module failure alarms, exceeding limits for the number of transmitted error frames alarms, exceeding limits for the number of received error frames alarms, and exceeding limits for the number of received CRC error packets alarms. If CRC error alarm information (i.e., receiving error check frames) is obtained within the target period, it indicates the presence of CRC error packets, and the type of strong light hazard can be determined based on this CRC error alarm information and the frequency of strong light anomalies; if no CRC error alarm information is obtained within the target period, the type of strong light hazard can be determined based on the frequency of strong light anomalies.

[0085] In some embodiments, the second preset threshold includes a second lower threshold L2 and a second upper threshold M2, wherein the second lower threshold L2 is less than the second upper threshold M2. In some embodiments, determining the frequency of strong light anomalies based on the proportion of strong light anomaly data and the second preset threshold includes the following steps: when the proportion of strong light anomaly data is less than the second lower threshold L2, the frequency of strong light anomalies is determined to be an occasional anomaly; when the proportion of strong light anomaly data is greater than the second lower threshold L2 and less than the second upper threshold M2, the frequency of strong light anomalies is determined to be a frequent anomaly; when the proportion of strong light anomaly data is greater than or equal to the second upper threshold M2, the frequency of strong light anomalies is determined to be a continuous anomaly.

[0086] In some embodiments, after determining the type of strong light hazard, the device detection method may further include the following steps: determining the strong light hazard level corresponding to the type of strong light hazard based on a preset mapping relationship between hazard type and hazard level.

[0087] For example, in this embodiment of the disclosure, the hazard level of strong light is divided into four levels, and the preset mapping relationship between hazard types and hazard levels is as follows:

[0088] Severe-level hazard types of strong light include: continuous strong light (with CRC error packets) and continuous strong light;

[0089] The types of high-intensity light hazards of a critical level include: frequent strong light incidents (with CRC error packets);

[0090] The main types of strong light hazards include: frequent strong light and occasional strong light (with CRC error packets);

[0091] Minor level hazard types of strong light include: occasional strong light.

[0092] Figure 3 is a flowchart of another device detection method according to an embodiment of the present disclosure. In some embodiments, as shown in Figure 3, before identifying weak light hazards of each target object based on input optical power, optical module receiving sensitivity, and weak light threshold deviation of the target object in the target period, the device detection method may further include the following steps:

[0093] Step S11': Determine the strong light threshold deviation of the target object in the target period and the weak light threshold deviation of the target object in the target period.

[0094] Figure 4 is a flowchart of determining the weak light threshold deviation and the strong light threshold deviation according to an embodiment of the present disclosure. As shown in Figure 4, determining the strong light threshold deviation of the target object in the target period and the weak light threshold deviation of the target object in the target period (i.e., step S11') includes the following steps:

[0095] Step S21: Determine the initial value of the weak light threshold deviation of the target object in the target period and the initial value of the strong light threshold deviation of the target object in the target period.

[0096] The initial values ​​of the weak light threshold deviation of the target object in the target period and the initial values ​​of the strong light threshold deviation of the target object in the target period can be preset values ​​or calculated based on historical sampling data.

[0097] Step S22: Determine whether there are omissions in identifying weak light hazards and strong light hazards in the preset number of cycles before the target cycle, and obtain the judgment results of the omissions in identifying weak light hazards and strong light hazards.

[0098] The system acquires alarm information from the target object within a preset number of D periods prior to the target period, where D ≥ 1. If alarm information is acquired but no strong / weak light hazard is identified, it is marked as a missed strong / weak light hazard identification. Alarm information may include one or more of the following: signal loss, signal degradation, input optical power exceeding the limit, output optical power exceeding the limit, optical module failure, exceeding the limit for the number of transmitted error frames, exceeding the limit for the number of received error frames, and exceeding the limit for the number of received CRC error messages.

[0099] Step S23: Based on the results of the omission judgment of the low light hazard identification and the initial value of the low light threshold deviation, determine the low light threshold deviation of the target object in the target period; and based on the results of the omission judgment of the high light hazard identification and the initial value of the high light threshold deviation, determine the high light threshold deviation of the target object in the target period.

[0100] It should be noted that for the first D cycles, the values ​​of the strong light threshold deviation and the weak light threshold deviation of each target object are the initial values. Starting from the (D+1)th cycle, it is necessary to redetermine the strong light threshold deviation and the weak light threshold deviation of each target object in the current cycle.

[0101] The following is a detailed explanation of the process for determining the weak light threshold deviation A of the target object in the target period.

[0102] In some embodiments, when the result of the weak light hazard identification omission judgment is that there is no weak light hazard identification omission, the weak light hazard identification rate E1 is calculated based on the number of known weak light hazards and the number of identified weak light hazards of the target object within a preset number of cycles before the target cycle; based on the comparison result of the weak light hazard identification rate E1 and the preset weak light identification rate threshold F1, as well as the preset first weak light offset C1 and the initial value of the weak light threshold deviation, the weak light threshold deviation A of the target object in the target cycle is determined.

[0103] In some embodiments, determining the weak light threshold deviation A of the target object in the target period based on the comparison result between the weak light hazard identification rate E1 and the preset weak light identification rate threshold F1, as well as the preset first weak light offset C1 and the initial value of the weak light threshold deviation, includes the following steps: when the weak light hazard identification rate E1 is less than the preset weak light identification rate threshold F1, calculating the difference between the initial value of the weak light threshold deviation and the preset first weak light offset C1 to obtain the weak light threshold deviation A of the target object in the target period; when the weak light hazard identification rate E1 is greater than or equal to the preset weak light identification rate threshold F1, determining the weak light threshold deviation A of the target object in the target period as the initial value of the weak light threshold deviation.

[0104] In other words, if there are no omissions in identifying low-light hazards, the known number of low-light hazards M1 and the total number of identified low-light hazards N1 within the previous preset number of cycles are counted. The known number of low-light hazards M1 can be the number of low-light alarm messages or the number of manually marked low-light hazards. The ratio of the known number of low-light hazards M1 to the total number of identified low-light hazards N1 is calculated to obtain the low-light hazard identification rate E1. If the low-light hazard identification rate E1 < the preset low-light identification rate threshold F1, then the low-light threshold deviation A of the target object in the target cycle is A = A_initial value - C1; if the low-light hazard identification rate E1 ≥ the preset low-light identification rate threshold F1, then the low-light threshold deviation A of the target object in the target cycle is A = A_initial value.

[0105] The following is a detailed explanation of the process for determining the strong light threshold deviation B of the target object during the target period.

[0106] In some embodiments, when the result of the strong light hazard identification omission judgment is that there is no strong light hazard identification omission, the strong light hazard identification rate E2 is calculated based on the number of known strong light hazards and the number of identified strong light hazards of the target object within a preset number of cycles before the target cycle; based on the comparison result of the strong light hazard identification rate E2 and the preset strong light identification rate threshold F2, as well as the preset first strong light offset C2 and the initial value of the strong light threshold deviation, the strong light threshold deviation B of the target object in the target cycle is determined.

[0107] In some embodiments, determining the strong light threshold deviation B of the target object in the target period based on the comparison result between the strong light hazard identification rate E2 and the preset strong light identification rate threshold F2, as well as the preset first strong light offset C2 and the initial value of the strong light threshold deviation, includes the following steps: when the strong light hazard identification rate E2 is less than the preset strong light identification rate threshold F2, calculating the difference between the initial value of the strong light threshold deviation and the preset first strong light offset C2 to obtain the strong light threshold deviation B of the target object in the target period; when the strong light hazard identification rate E2 is greater than or equal to the preset strong light identification rate threshold F2, determining the strong light threshold deviation B of the target object in the target period as the initial value of the strong light threshold deviation.

[0108] In other words, if there are no missed strong light hazards, the known number of strong light hazards M2 and the total number of identified strong light hazards N2 within the previous preset number of cycles are calculated. Here, the known number of strong light hazards M2 can be the number of strong light alarm messages or the number of manually marked strong light hazards. The ratio of the known number of strong light hazards M2 to the total number of identified strong light hazards N2 is calculated to obtain the strong light hazard identification rate E2. If the strong light hazard identification rate E2 < the preset strong light identification rate threshold F2, then the strong light threshold deviation B of the target object in the target cycle is B = initial value B - C2; if the strong light hazard identification rate E2 ≥ the preset strong light identification rate threshold F2, then the strong light threshold deviation B of the target object in the target cycle is B = initial value B.

[0109] It should be noted that F1 and F2, C1 and C2 can be the same or different. In some embodiments, the preset first weak light offset C1 and the preset first strong light offset C2 can be in the range of 0.1-0.5.

[0110] In some embodiments, determining the initial value of the weak light threshold deviation of the target object in the target period (i.e., step S21) includes the following steps: if the target object receives an alarm message in the previous period of the target period, calculate the difference between the input optical power of the target object and the receiving sensitivity of the optical module in the previous period of the target period to obtain the initial value of the weak light threshold deviation of the target object in the target period; if the target object does not receive an alarm message in the previous period of the target period, set the initial value of the weak light threshold deviation of the target object in the target period to a first preset value.

[0111] In other words, if the target object receives an alarm message in the previous cycle of the current cycle, the initial value of the weak light threshold deviation of the target object in the target cycle is equal to the input optical power of the target object in the previous cycle minus the receiving sensitivity of the optical module in the previous cycle; if the target object does not receive an alarm message in the previous cycle of the current cycle, the initial value of the weak light threshold deviation of the target object in the target cycle is equal to the first preset value A0.

[0112] In some embodiments, determining the initial value of the strong light threshold deviation of the target object in the target period (i.e., step S21) includes the following steps: if the target object receives an alarm message in the previous period of the target period, calculate the difference between the overload optical power of the optical module of the target object and the output optical power of the target object in the previous period of the target period to obtain the initial value of the strong light threshold deviation of the target object in the target period; if the target object does not receive an alarm message in the previous period of the target period, set the initial value of the strong light threshold deviation of the target object in the target period to a second preset value.

[0113] In other words, if the target object receives an alarm message in the previous cycle of the current cycle, the initial value of the strong light threshold deviation of the target object in the target cycle is equal to the overload optical power of the optical module in the previous cycle minus the output optical power of the target object in the previous cycle; if the target object does not receive an alarm message in the previous cycle of the current cycle, the initial value of the strong light threshold deviation of the target object in the target cycle is equal to the second preset value B0.

[0114] It should be noted that both the first preset value A0 and the second preset value B0 are set through the settings module and can be obtained from the database.

[0115] In some embodiments, determining the weak light threshold deviation of the target object in the target period based on the weak light hazard identification omission judgment result and the initial value of the weak light threshold deviation; and determining the strong light threshold deviation of the target object in the target period based on the strong light hazard identification omission judgment result and the initial value of the strong light threshold deviation (i.e., step S23) includes the following steps: when the weak light hazard identification omission judgment result indicates that there is a weak light hazard identification omission, determining the weak light threshold deviation A of the target object in the target period based on the initial value of the weak light threshold deviation and the preset second weak light offset G1; when the strong light hazard identification omission judgment result indicates that there is a strong light hazard identification omission, determining the strong light threshold deviation B of the target object in the target period based on the initial value of the strong light threshold deviation and the preset second strong light offset G2.

[0116] In some embodiments, determining the weak light threshold deviation of the target object in the target period based on the initial value of the weak light threshold deviation and the preset second weak light offset G1 includes: calculating the sum of the initial value of the weak light threshold deviation and the preset second weak light offset G1 to obtain the weak light threshold deviation of the target object in the target period.

[0117] The step of determining the strong light threshold deviation of the target object in the target period based on the initial value of the strong light threshold deviation and the preset second strong light offset G2 includes: calculating the sum of the initial value of the strong light threshold deviation and the preset second strong light offset G2 to obtain the strong light threshold deviation of the target object in the target period.

[0118] In other words, if there is a missed identification of low light hazards, it means that the current low light threshold deviation of the target object is inaccurate, and it needs to be adjusted. The adjustment method is as follows: the low light threshold deviation of the target object in the target period A = the initial value of A + the preset second low light offset G1; if there is a missed identification of strong light hazards, it means that the current strong light threshold deviation of the target object is inaccurate, and it needs to be adjusted. The adjustment method is as follows: the strong light threshold deviation of the target object in the target period B = the initial value of B + the preset second strong light offset G2.

[0119] A large AI (Artificial Intelligence) model can be used, trained with empirical data, to automatically adjust the first preset value A0, second preset value B0, first weak light offset C1, first strong light offset C2, weak light recognition rate threshold F1, strong light recognition rate threshold F2, second weak light offset G1, and second strong light offset G2 for each target object in each period, thereby improving the accuracy of strong / weak light hazard identification. The first preset value A0, second preset value B0, first weak light offset C1, first strong light offset C2, weak light recognition rate threshold F1, strong light recognition rate threshold F2, second weak light offset G1, and second strong light offset G2 for each target object in each period can also be manually set, imported, or adjusted in batches.

[0120] In some embodiments, after obtaining the low-light hazard identification result and the high-light hazard identification result of the target object (i.e., steps S12 and S13), the device detection method may further include the following steps:

[0121] Step S14: Determine the target ports of the transmission network devices corresponding to the low light hazard and the high light hazard.

[0122] The impact analysis module determines the target ports of the transmission network equipment corresponding to the strong / weak light hazard.

[0123] Step S15: Determine the service information corresponding to the target port based on the preset mapping relationship between port information and service information.

[0124] The impact analysis module retrieves a preset mapping relationship between port information and service information from the database, and queries this mapping relationship based on the target port to obtain the service information affected by strong / weak light hazards. In this embodiment of the disclosure, the service information may include service transmission network information, for example, L3VPN, L2VPN, TDM, and the service information may also include at least one of the following: service name, service type, A-end, and Z-end information.

[0125] In some embodiments, after acquiring the sampling data of the target object (i.e., step S11), the device detection method may further include the following steps:

[0126] Step S12': Store the sampled data.

[0127] Step S13': Obtain the stored historical sampling data, and generate an optical power trend chart for each port of the transmission network device based on the historical sampling data.

[0128] The sampling data collected by the data acquisition module can be stored in a database. The optical power trend chart management module can retrieve historical sampling data from the database, such as historical optical power information, and generate optical power trend charts for each network device port. For example, one or more of the following can be used to generate line graphs of different colors for input optical power, maximum input optical power, minimum input optical power, output optical power, maximum output optical power, and minimum output optical power, thus obtaining the optical power trend chart, which is then displayed on the front-end display module. Hovering the mouse over each point displays the corresponding time and optical power information. In the optical power trend chart, the horizontal axis represents time, and the vertical axis represents optical power information (unit: dBm), with 0dBm as the default center line.

[0129] This disclosure addresses the challenges of predicting optical power faults across a vast number of network devices in complex network topologies or with large data volumes, such as numerous network elements and alarm performance data. These challenges stem from the difficulty in pre-identifying optical power faults, the reliance on a single judgment dimension, and the reliance on manual investigation of strong / weak light hazards, leading to low efficiency in identification and handling. This disclosure enables the timely detection of potential strong / weak light hazards even before an optical power fault occurs. Based on massive amounts of port optical power and alarm information from the transmission network, this disclosure periodically analyzes port or channel data and automatically adjusts threshold deviations for each port or channel. This allows for the automatic identification of ports potentially exhibiting strong / weak light hazards in the transmission network, aiding in service impact analysis and optical power trend graph presentation. It eliminates the influence of maintenance personnel's personal experience, improving the accuracy and efficiency of handling strong / weak light hazards, preventing potential faults, and reducing the impact on network services.

[0130] The embodiments disclosed herein have the following technical effects:

[0131] 1. Automatically collect and analyze historical optical power information and alarm information of transmission network equipment, and can automatically identify strong light hazards and weak light hazards in advance, automatically classify the severity level of strong / weak light hazards and provide possible root causes and handling suggestions.

[0132] 2. Strong light threshold deviation and weak light threshold deviation are set independently and dynamically and adaptively adjusted according to network operation status.

[0133] 3. Based on the ports with potential strong / weak light hazards, automatically analyze and identify the services that may be affected, and output the types of services that may be affected, service names, A-end and Z-end information. Users can navigate to the corresponding service manager in the network management system by service name to view further service details.

[0134] 4. Based on the query period set by the user, query the optical power information of the port of the device with potential strong / weak light hazards from the database, and present the optical power trend chart of the input optical power, maximum input optical power, minimum input optical power, output optical power, maximum output optical power, minimum output optical power, etc. in line graphs of different colors.

[0135] 5. Supports single-channel and multi-channel optical modules. For multi-channel optical modules, it supports the identification of potential light hazards in each channel.

[0136] [Correction 06.01.2026 based on Rule 91] This disclosure also provides a device detection apparatus, as shown in FIG5, including a memory and a processor; the memory stores a computer program that can be executed by the processor, and when the computer program is executed by the processor, it implements any one of the device detection methods of this disclosure.

[0137] Among them, the processor is a device with data processing capabilities, including but not limited to the central processing unit (CPU); the memory is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read-write interface) is connected between the processor and the memory, enabling information exchange between the memory and the processor, including but not limited to the data bus (Bus).

[0138] This disclosure also provides a computer-readable medium having a computer program stored thereon, wherein the program, when executed, implements the device detection method as described above.

[0139] Those skilled in the art will understand that all or some of the steps, systems, and devices disclosed above, as functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0140] In hardware implementations, the division between functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be executed by several physical components working together.

[0141] Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit (CPU), digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software may be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technique for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH) or other disk storage; read-only optical disc (CD-ROM), digital versatile disc (DVD) or other optical disc storage; magnetic cartridges, magnetic tapes, disk storage or other magnetic storage; and any other media that can be used to store desired information and can be accessed by a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0142] This disclosure has disclosed exemplary embodiments, and although specific terminology has been used, it is for general illustrative purposes only and should not be construed as limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of this disclosure as set forth by the appended claims.

Claims

1. A method for testing equipment, comprising: Acquire sampling data of the target object, the sampling data including the input optical power, optical module receiving sensitivity, output optical power and optical module overload optical power of the target object in the target period, wherein the target object includes a transmission network device port or at least one channel of the transmission network device port; For each target object, based on the input optical power, the receiving sensitivity of the optical module, and the weak light threshold deviation of the target object in the target period, a weak light hazard identification result for the target object is obtained; and, For each target object, a strong light hazard identification is performed on the target object based on the output optical power, the overload optical power of the optical module, and the strong light threshold deviation of the target object in the target period, so as to obtain the strong light hazard identification result of the target object.

2. The method according to claim 1, wherein, The low-light hazard identification result includes the number of low-light anomaly data. The low-light hazard identification of the target object is performed based on the input optical power, the optical module's receiving sensitivity, and the target object's low-light threshold deviation during the target period, resulting in the low-light hazard identification result for the target object, including: At each acquisition moment of the target period, the sum of the acquired optical module receiving sensitivity and the weak light threshold deviation of the target object in the target period is calculated; If the input optical power of the target object is less than or equal to the sum of the receiving sensitivity of the optical module and the weak light threshold deviation of the target object in the target period, the sampled data is determined to be a weak light anomaly data. The number of weak light anomaly data of the target object within the target period is obtained by statistics.

3. The method according to claim 2, wherein, The low-light hazard identification result also includes the type of low-light hazard. After statistically obtaining the number of low-light anomaly data for the target object within the target period, it also includes: The proportion of weak light anomaly data is determined based on the number of weak light anomaly data and the total number of all sampled data within the target period; The type of low light hazard is determined based on the proportion of low light anomaly data and a first preset threshold. The type of low light hazard is used to indicate the degree of low light anomaly.

4. The method according to claim 3, wherein, The step of determining the type of low-light hazard based on the proportion of low-light abnormal data and a first preset threshold includes: The frequency of weak light anomalies is determined based on the proportion of weak light anomaly data and the first preset threshold. The type of potential low-light hazard is determined based on the frequency of low-light anomalies.

5. The method according to claim 3, wherein, The step of determining the type of low-light hazard based on the proportion of low-light abnormal data and a first preset threshold includes: Obtain alarm information of the target object within the target period, the alarm information including cyclic redundancy check (CRC) error alarm information; The frequency of weak light anomalies is determined based on the proportion of weak light anomaly data and the first preset threshold. The type of potential low-light hazard is determined based on the CRC error alarm information and the frequency of low-light anomalies.

6. The method according to claim 1, wherein, The strong light hazard identification result includes the number of strong light anomaly data. The strong light hazard identification of the target object is performed based on the output optical power, the overload optical power of the optical module, and the strong light threshold deviation of the target object in the target period, resulting in the strong light hazard identification result for the target object, including: At each acquisition moment of the target period, the difference between the acquired overload optical power of the optical module and the strong light threshold deviation of the target object in the target period is calculated; If the output optical power of the target object is greater than or equal to the difference between the overload optical power of the optical module and the strong light threshold deviation of the target object in the target period, the sampled data is determined to be a strong light anomaly data. The number of strong light anomaly data of the target object within the target period is obtained by statistics.

7. The method according to claim 6, wherein, The strong light hazard identification result also includes the type of strong light hazard. After statistically obtaining the number of strong light anomaly data of the target object within the target period, it also includes: The proportion of strong light anomaly data is determined based on the number of strong light anomaly data and the total number of all sampled data within the target period; The type of strong light hazard is determined based on the proportion of strong light anomaly data and a second preset threshold. The type of strong light hazard is used to indicate the degree of strong light anomaly.

8. The method according to claim 7, wherein, The determination of the type of hazard caused by strong light based on the proportion of abnormal strong light data and a second preset threshold includes: The frequency of strong light anomalies is determined based on the proportion of strong light anomaly data and the second preset threshold. The type of hazard caused by strong light is determined based on the frequency of the strong light anomalies.

9. The method according to claim 7, wherein, The determination of the type of hazard caused by strong light based on the proportion of abnormal strong light data and a second preset threshold includes: Obtain alarm information of the target object within the target period, the alarm information including cyclic redundancy check (CRC) error alarm information; The frequency of strong light anomalies is determined based on the proportion of strong light anomaly data and the second preset threshold. The type of hazard caused by strong light is determined based on the CRC error alarm information and the frequency of abnormal strong light.

10. The method according to claim 1, wherein, Before identifying potential low-light hazards for each target object based on the input optical power, the optical module's receiving sensitivity, and the target object's low-light threshold deviation during the target period, the method further includes: Determine the strong light threshold deviation of the target object in the target period and the weak light threshold deviation of the target object in the target period.

11. The method according to claim 10, wherein, Determining the strong light threshold deviation of the target object in the target period and the weak light threshold deviation of the target object in the target period includes: Determine the initial value of the weak light threshold deviation of the target object in the target period and the initial value of the strong light threshold deviation of the target object in the target period; Determine whether there are omissions in identifying weak light hazards and strong light hazards in the preset number of cycles before the target cycle, and obtain the judgment results of the omissions in identifying weak light hazards and strong light hazards. Based on the results of the low light hazard identification omission judgment and the initial value of the low light threshold deviation, the low light threshold deviation of the target object in the target period is determined; and based on the results of the high light hazard identification omission judgment and the initial value of the high light threshold deviation, the high light threshold deviation of the target object in the target period is determined.

12. The method according to claim 11, wherein, The method is to determine the weak light threshold deviation of the target object in the target period based on the result of the weak light hazard identification omission judgment and the initial value of the weak light threshold deviation; And, based on the results of the strong light hazard identification omission judgment and the initial value of the strong light threshold deviation, the strong light threshold deviation of the target object in the target period is determined, including: If the result of the low light hazard identification omission judgment is that there is no low light hazard identification omission, the low light hazard identification rate is calculated based on the number of known low light hazards and the number of identified low light hazards of the target object within the preset number of periods before the target period. Based on the comparison result between the low-light hazard identification rate and the preset low-light identification rate threshold, and the preset first low-light offset and the initial value of the low-light threshold deviation, the low-light threshold deviation of the target object in the target period is determined; and, If the result of the strong light hazard identification omission judgment is that there is no strong light hazard identification omission, the strong light hazard identification rate is calculated based on the number of known strong light hazards and the number of identified strong light hazards of the target object within the preset number of periods before the target period. Based on the comparison result between the strong light hazard identification rate and the preset strong light identification rate threshold, as well as the preset first strong light offset and the initial value of the strong light threshold deviation, the strong light threshold deviation of the target object in the target period is determined.

13. The method according to claim 12, wherein, The step of determining the weak light threshold deviation of the target object in the target period based on the comparison result between the weak light hazard identification rate and the preset weak light identification rate threshold, as well as the preset first weak light offset and the initial value of the weak light threshold deviation, includes: If the low light hazard identification rate is less than the preset low light identification rate threshold, the difference between the initial value of the low light threshold deviation and the preset first low light offset is calculated to obtain the low light threshold deviation of the target object in the target period. If the low light hazard identification rate is greater than or equal to the preset low light identification rate threshold, the low light threshold deviation of the target object in the target period is determined as the initial value of the low light threshold deviation.

14. The method according to claim 12, wherein, The step of determining the strong light threshold deviation of the target object in the target period based on the comparison result between the strong light hazard identification rate and the preset strong light identification rate threshold, as well as the preset first strong light offset and the initial value of the strong light threshold deviation, includes: If the strong light hazard identification rate is less than the preset strong light identification rate threshold, the difference between the initial value of the strong light threshold deviation and the preset first strong light offset is calculated to obtain the strong light threshold deviation of the target object in the target period. If the strong light hazard identification rate is greater than or equal to the preset strong light identification rate threshold, the strong light threshold deviation of the target object in the target period is determined as the initial value of the strong light threshold deviation.

15. The method according to claim 11, wherein, Determining the initial value of the weak light threshold deviation of the target object in the target period includes: If the target object receives an alarm message in the previous period of the target period, the difference between the input optical power of the target object and the receiving sensitivity of the optical module in the previous period of the target period is calculated to obtain the initial value of the weak light threshold deviation of the target object in the target period. If the target object does not receive an alarm message in the previous period of the target period, the initial value of the weak light threshold deviation of the target object in the target period is set to a first preset value.

16. The method according to claim 11, wherein, Determining the initial value of the intensity threshold deviation of the target object in the target period includes: If the target object receives an alarm message in the previous period of the target period, the difference between the overload optical power of the optical module of the target object and the output optical power of the target object in the previous period of the target period is calculated to obtain the initial value of the strong light threshold deviation of the target object in the target period. If the target object does not receive an alarm message in the previous cycle of the target cycle, the initial value of the strong light threshold deviation of the target object in the target cycle is set to a second preset value.

17. The method according to claim 11, wherein, The method is to determine the weak light threshold deviation of the target object in the target period based on the result of the weak light hazard identification omission judgment and the initial value of the weak light threshold deviation; And, based on the results of the strong light hazard identification omission judgment and the initial value of the strong light threshold deviation, the strong light threshold deviation of the target object in the target period is determined, including: If the result of the low light hazard identification omission judgment is that there is a low light hazard identification omission, the low light threshold deviation of the target object in the target period is determined according to the initial value of the low light threshold deviation and the preset second low light offset. as well as, If the result of the strong light hazard identification omission is that there is a strong light hazard identification omission, the strong light threshold deviation of the target object in the target period is determined according to the initial value of the strong light threshold deviation and the preset second strong light offset.

18. The method according to claim 17, wherein, The step of determining the weak light threshold deviation of the target object in the target period based on the initial value of the weak light threshold deviation and the preset second weak light offset includes: The sum of the initial value of the weak light threshold deviation and the preset second weak light offset is calculated to obtain the weak light threshold deviation of the target object in the target period; and, The step of determining the intensity threshold deviation of the target object in the target period based on the initial value of the intensity threshold deviation and the preset second intensity offset includes: The sum of the initial value of the strong light threshold deviation and the preset second strong light offset is calculated to obtain the strong light threshold deviation of the target object in the target period.

19. The method according to any one of claims 1-18, wherein, After obtaining the low-light hazard identification result and the high-light hazard identification result of the target object, the method further includes: Determine the target ports of the transmission network equipment corresponding to the low-light and high-light hazards; Based on the preset mapping relationship between port information and service information, the service information corresponding to the target port is determined.

20. The method according to any one of claims 1-18, wherein, After obtaining the sampling data of the target object, the method further includes: Store the sampled data; Obtain the stored historical sampling data, and generate an optical power trend chart for each port of the transmission network device based on the historical sampling data.

21. A device testing apparatus, comprising a memory and a processor; the memory storing a computer program executable by the processor, wherein the computer program, when executed by the processor, implements the device testing method according to any one of claims 1 to 20.

22. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed, it implements the device detection method as described in any one of claims 1-20.

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