Method and apparatus for determining detection frequency of olt device, and electronic device

Abstract

The application discloses a detection frequency determination method and device of an OLT equipment and electronic equipment, and the method comprises the following steps: acquiring a topological structure diagram of an OLT equipment system; determining a historical abnormal data set transmitted from a target OLT equipment according to the topological structure diagram; determining key parameters of the target OLT equipment and key parameter abnormal frequencies according to the historical abnormal data set; respectively calculating correlation coefficients between each key parameter and other key parameters according to the correlation between different key parameters, and determining the weight of each key parameter according to the correlation coefficients; and determining the key parameter detection frequency of the target OLT equipment according to the key parameter abnormal frequencies and the weight of each key parameter. The key parameter detection frequency is determined through the key parameter abnormal frequencies and the weight, so that real-time detection data is avoided, the detection data amount is greatly reduced, and the detection pressure is reduced.

Description

Technical Field

[0001] This invention relates to the field of network equipment testing technology, specifically to a method, apparatus, and electronic device for determining the detection frequency of an OLT device. Background Technology

[0002] An OLT (Optical Line Terminal) is a core central office device in a fiber optic access network, primarily used for converting optical signals to electrical signals and managing user terminals. OLT equipment employs a dual-controller board / dual-power-board redundancy mechanism to support fault switching.

[0003] However, due to the redundant design of the OLT device itself, the OLT device needs to process a lot of data. Therefore, when the OLT device is tested, a large amount of data needs to be processed. As the terminal of photoelectric conversion, the OLT device has a very large data flow. Therefore, in the process of testing the OLT device, there is a problem that it is difficult to obtain reliable test results in a timely and effective manner due to the large amount of data processed. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and medium for determining the detection frequency of an OLT device, aiming to reduce the amount of data processing during the detection process by setting the detection frequency of the key parameter of the target OLT device, thereby quickly obtaining reliable detection results.

[0005] To address the aforementioned technical problems, the embodiments of the present invention provide the following technical solutions:

[0006] A method for determining the detection frequency of an OLT device, comprising:

[0007] Obtain the topology diagram of the OLT device system;

[0008] Based on the topology diagram, determine the historical abnormal dataset transmitted from the target OLT device;

[0009] Based on the historical anomaly dataset, determine the key parameters of the target OLT device and the frequency of anomalies in the key parameters;

[0010] Calculate the correlation coefficient between each key parameter and other key parameters based on the correlation relationships between different key parameters, and determine the weight of each key parameter based on the correlation coefficients.

[0011] The key parameter detection frequency of the target OLT device is determined based on the abnormal frequency of the key parameters and the weight of each key parameter.

[0012] Optionally, determining the key parameters and key parameter anomaly frequency of the target OLT device based on the historical anomaly dataset includes:

[0013] Based on a preset threshold range, determine multiple key parameters that are abnormal in the historical abnormal dataset and the initial abnormal frequency of each key parameter.

[0014] Obtain the time when the historical abnormal state occurred for each key parameter;

[0015] The time decay coefficient of each key parameter is determined based on the time when the abnormal state occurs in historical abnormal data.

[0016] The initial abnormal frequency of each key parameter is weighted and adjusted according to the time decay coefficient to obtain the key parameter abnormal frequency of each key parameter.

[0017] Optionally, determining the frequency of anomalous key parameters of the target OLT device based on the historical anomaly dataset includes:

[0018] An initial mathematical model is constructed, which includes a time series prediction module and a supervised learning algorithm module;

[0019] The historical dataset is input into the mathematical model, and the abnormal frequency of the historical data is output. The dynamic dependency of the historical dataset is obtained through the time series prediction module, and the dependency between features of the historical dataset is obtained through the supervised learning algorithm module. The model is trained iteratively until a fully trained target mathematical model is obtained.

[0020] The frequency of critical parameter anomalies is determined based on the dynamic dependencies and the dependencies between features.

[0021] Optionally, determining the key parameter detection frequency of the target OLT device based on the abnormal frequency of the key parameters and the weight of each key parameter includes:

[0022] The target abnormal frequency of the key parameter is obtained by weighted summing of the abnormal frequency of the key parameter and the corresponding weight of the key parameter.

[0023] The target anomaly frequency of the key parameter is determined as the detection frequency of the key parameter.

[0024] Optionally, determining the historical abnormal dataset transmitted from the target OLT device based on the topology diagram includes:

[0025] Based on the topology diagram, determine the upstream and downstream devices of the target OLT device;

[0026] The first historical dataset of the upstream device and the second historical dataset of the downstream device are obtained respectively.

[0027] The data that is determined to be abnormal in the first historical dataset and the second historical dataset and transmitted from the target OLT device is the historical abnormal dataset.

[0028] Optionally, after determining the detection frequency of the key parameters of the target OLT device, the method further includes:

[0029] When the detection status of a key parameter is determined to be abnormal, the detection device that detects that key parameter is obtained;

[0030] An abnormal topology diagram of the target OLT device is generated based on the topological relationship between the detection device and the target OLT device.

[0031] Optionally, after determining the detection frequency of the key parameters of the target OLT device, the method further includes:

[0032] When it is determined that the detection status of a key parameter is abnormal, the target OLT device is switched to a replaceable OLT device;

[0033] The replaceable OLT device is a redundant device of the target OLT device in the OLT device system.

[0034] A detection frequency determination device for an OLT device, comprising:

[0035] The topology diagram acquisition module is used to acquire the topology diagram of the OLT device system;

[0036] The historical anomaly dataset determination module is used to determine the historical anomaly dataset transmitted from the target OLT device based on the topology diagram.

[0037] The key parameter determination module is used to determine the key parameters and key parameter anomaly frequency of the target OLT device based on the historical anomaly dataset.

[0038] The weight calculation module is used to calculate the correlation coefficient between each key parameter and other key parameters according to the correlation relationship between different key parameters, and to determine the weight of each key parameter according to the correlation coefficient.

[0039] The key parameter detection frequency determination module is used to determine the key parameter detection frequency of the target OLT device based on the abnormal frequency of the key parameters and the weight of each key parameter.

[0040] An electronic device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps:

[0041] Obtain the topology diagram of the OLT device system;

[0042] Based on the topology diagram, determine the historical abnormal dataset transmitted from the target OLT device;

[0043] Based on the historical anomaly dataset, determine the key parameters of the target OLT device and the frequency of anomalies in the key parameters;

[0044] Calculate the correlation coefficient between each key parameter and other key parameters based on the correlation relationships between different key parameters, and determine the weight of each key parameter based on the correlation coefficients.

[0045] The key parameter detection frequency of the target OLT device is determined based on the abnormal frequency of the key parameters and the weight of each key parameter.

[0046] A computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the method for determining the detection frequency of an OLT device as described above.

[0047] In this embodiment of the invention, the topology diagram of the OLT device system facilitates the selection of devices and data only related to the target OLT device, reducing the amount of data processing. By analyzing the target OLT device's real historical anomaly dataset, the frequency of key parameter anomalies is dynamically determined, enabling objective data-based analysis of the target OLT device's operation. The Pearson coefficient reveals the linear relationship between parameters, allowing for precise weight allocation and enhancing the accuracy and timeliness of device detection. Determining the key parameter detection frequency of the target OLT device based on the frequency of key parameter anomalies and the weight of each key parameter allows for the customization of the most suitable detection frequency for each target OLT device and even for each key parameter, avoiding real-time detection data, significantly reducing the amount of detection data, and lowering the detection pressure. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of a scenario of an embodiment of the detection frequency determination system for an OLT device provided in this invention.

[0050] Figure 2 This is a schematic diagram of another embodiment of the detection frequency determination system for OLT devices provided in this invention.

[0051] Figure 3 This is a flowchart illustrating an embodiment of the method for determining the detection frequency of an OLT device provided in this invention.

[0052] Figure 4 A schematic diagram showing the results of an embodiment of key parameters of the OLT device provided in this invention;

[0053] Figure 5 Abnormal topology of OLT device provided in embodiments of the present invention Figure 1 A schematic diagram of the framework of the embodiment;

[0054] Figure 6 This is a schematic diagram of an embodiment of the detection frequency determination device for an OLT device provided in this invention.

[0055] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the steps and operations described below can also be implemented in hardware.

[0058] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. The different components, modules, engines, and services described herein can be considered as implementation objects on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.

[0059] This invention provides a method, apparatus, and electronic device for determining the detection frequency of an OLT device.

[0060] Please see Figure 1 , Figure 1 This is a schematic diagram of a scenario illustrating an embodiment of the OLT device detection frequency determination system provided in this invention. The OLT device detection frequency determination system may include a client 100 and a server 200, which are connected via a network. The server 200 integrates an OLT device detection frequency determination device. The server 200 may be a work platform server (i.e., a server loaded with a work platform), such as... Figure 1 In this embodiment of the invention, server 200 is mainly used to obtain the topology diagram of the OLT device system; determine the historical abnormal dataset transmitted from the target OLT device based on the topology diagram; determine the key parameters and abnormal frequency of the key parameters of the target OLT device based on the historical abnormal dataset; calculate the correlation coefficient between each key parameter and other key parameters based on the correlation relationship between different key parameters, and determine the weight of each key parameter based on the correlation coefficient; determine the key parameter detection frequency and service state sequence length of the target OLT device based on the abnormal frequency of the key parameters and the weight of each key parameter; perform backtracking analysis on the initial service state to obtain the target service state sequence of the system.

[0061] In this embodiment of the invention, the server 200 can be a standalone server, a server network, or a server cluster. For example, the server 200 described in this embodiment includes, but is not limited to, a computer, a network host, a single network server, a set of multiple network servers, or a cloud server composed of multiple servers. The cloud server is composed of a large number of computers or network servers based on cloud computing. In this embodiment of the invention, communication between the server and the client can be achieved through any communication method, including but not limited to, mobile communication based on the 3rd Generation Partnership Project (3GPP), Long Term Evolution (LTE), and Worldwide Interoperability for Microwave Access (WiMAX), or computer network communication based on the TCP / IP Protocol Suite (TCP / IP) and User Datagram Protocol (UDP).

[0062] It is understood that the client 100 used in this embodiment of the invention can be understood as a client device. A client device includes both receiving and transmitting hardware, that is, a device with receiving and transmitting hardware capable of performing bidirectional communication on a bidirectional communication link. Such a client device may include cellular or other communication devices, having a single-line display, a multi-line display, or a cellular or other communication device without a multi-line display. Specifically, the client 100 may be a desktop terminal or a mobile terminal, specifically a mobile phone, tablet computer, laptop computer, etc.

[0063] Those skilled in the art will understand that Figure 1 The application environment shown is merely one application scenario of the solution in this application and does not constitute a limitation on the application scenario of the solution in this application. Other application environments may include those that are more specific to this application. Figure 1 The number of more or fewer servers shown, or the server network connectivity relationships, for example... Figure 1 Only one server and two clients are shown in the diagram. It is understood that the detection frequency determination system of this OLT device may also include one or more other servers, and / or one or more clients connected to the server network, which is not limited here.

[0064] In some embodiments of the present invention, the working platform may be an enterprise office platform, such as WeChat for Business. Taking server 200 as an example, it may further include an enterprise office platform contact server, an enterprise office platform configuration management server, and a web management server. Enterprise users or developers can access the web management server using a web browser terminal to configure the field configuration information on the enterprise office platform configuration management server, and set and store the enterprise user information of enterprise employees of the enterprise office platform on the enterprise office platform contact server.

[0065] In addition, such as Figure 2 As shown, Figure 2 This is a schematic diagram of another embodiment of the detection frequency determination system for OLT devices provided in this invention. The detection frequency determination system for OLT devices may further include a storage terminal 300 for storing data, such as a storage object database. The object database stores object data, which may include application templates (such as approval templates, attendance templates, and other application templates), file data (such as Word files, Excel files, or PPT files, and other files in various formats), image data (such as images in various formats such as JPG, PNG, and BMP), and other types of data. Correspondingly, the object database may also be divided into multiple types of data, such as application databases, file databases, or image databases.

[0066] It should be noted that, Figure 1-2 The schematic diagram of the detection frequency determination system for OLT devices shown is merely an example. The detection frequency determination system and scenario for OLT devices described in this embodiment of the invention are for the purpose of more clearly illustrating the technical solutions of this embodiment of the invention, and do not constitute a limitation on the technical solutions provided by this embodiment of the invention. As those skilled in the art will know, with the evolution of the detection frequency determination system for OLT devices and the emergence of new business scenarios, the technical solutions provided by this embodiment of the invention are also applicable to similar technical problems.

[0067] The following detailed description is based on specific embodiments.

[0068] In this embodiment, the description will focus on the detection frequency determination device of the OLT device, which can be integrated into the server 200.

[0069] This invention provides a method for determining the detection frequency of an OLT device. Please refer to [link / reference]. Figure 3 , Figure 3 This is a flowchart illustrating an embodiment of the method for determining the detection frequency of an OLT device provided by the present invention, including:

[0070] S301: Obtain the topology diagram of the OLT device system;

[0071] In one specific embodiment, the OLT (Optical Line Terminal) is a core device in an optical fiber communication network, responsible for signal conversion, control, and management between user terminal equipment (ONU / ONT) and the core network. OLT devices have high functional integration, supporting unified transmission of data, voice, and video streams (e.g., GPON technology can simultaneously support IPTV, VoIP, and other services). Using GPON technology, downlink speeds reach 2.5Gbps and uplink speeds 1.25Gbps; 10G-PON can further boost this to the 10Gbps level, offering fast transmission speeds and low latency.

[0072] The OLT device system is an intelligent operation and maintenance management system built around OLT hardware. It includes not only OLT devices, but also other modules that support the operation of OLT devices, such as configuration management modules, link adaptation modules, dynamic detection engines, early warning and notification modules, etc. The number and types of devices in the OLT device system are not limited here.

[0073] The topology of an OLT equipment system is a typical tree-structured passive optical network (PON) architecture, consisting of three parts: OLT, ODN (Optical Distribution Network), and ONU / ONT. It achieves point-to-multipoint signal transmission through optical fibers and passive splitters.

[0074] Among them, the OLT (central office equipment) is the core control node, located in the operator's equipment room. It connects to the metropolitan area network / core network uplink (through GE / 10GE interface) and connects to the ODN downlink through the PON port, broadcasting data to the ONU. It also uses TDMA (Time Division Multiple Access) to dynamically allocate uplink time slots to avoid conflicts.

[0075] Optical Distribution Networks (ODNs) are used to distribute and aggregate optical signals.

[0076] The ONU / ONT (user terminal equipment) is used to receive OLT signals and provide user interfaces (such as Wi-Fi, IPTV, VoIP), and send uplink data within the specified time slot of the OLT to avoid collisions.

[0077] In this embodiment, the topology diagram of the OLT device system can intuitively reflect the signal connection relationship between various devices in the OLT device system, so as to directly obtain other devices that directly transmit information with a target OLT device.

[0078] S302: Determine the historical abnormal dataset transmitted from the target OLT device based on the topology diagram;

[0079] In one specific embodiment, the historical anomaly dataset refers to a set of anomaly operation data related to the target OLT device and its directly associated devices, selected from the historical operation data of the target OLT device system.

[0080] In this embodiment, by taking the target OLT device as the entry point, abnormal data related to the target OLT device can be specifically filtered out, reducing the amount of data processing, ensuring the reliability of abnormal data, and improving the accuracy of data.

[0081] S303: Determine the key parameters of the target OLT device and the frequency of key parameter anomalies based on historical anomaly datasets;

[0082] In one specific embodiment, key parameters refer to the core indicators affecting the stable operation of OLT equipment, such as optical power and bit error rate, determined through analysis of historical anomaly datasets.

[0083] The frequency of critical parameter anomalies refers to the statistical frequency of a specific critical parameter occurring in a historical anomaly dataset. By statistically analyzing the frequency of critical parameter anomalies, we can determine the anomalies of critical parameters in the historical operation of OLT equipment.

[0084] The key parameter detection frequency refers to the time interval for periodic detection of key parameters. The higher the key parameter detection frequency, the more frequently the key parameters of the OLT equipment are detected, and the more timely potential problems can be detected.

[0085] S304: Calculate the correlation coefficient between each key parameter and other key parameters based on the correlation relationships between different key parameters, and determine the weight of each key parameter based on the correlation coefficient;

[0086] Different key parameters are interconnected, but for the same key parameter, the degree of linear correlation between it and other key parameters varies. For a specific key parameter, the more parameters it is associated with and the higher the degree of linear correlation, the greater the influence of the key parameter and the higher its weight should be.

[0087] S305: Determine the detection frequency of key parameters for the target OLT device based on the abnormal frequency of key parameters and the weight of each key parameter.

[0088] In this embodiment, the detection frequency of key parameters is adjusted according to the frequency of key parameters in the historical abnormal dataset and the weight of each key parameter. This effectively utilizes historical data, accurately locates potential risks, and ensures timely detection of potential faults.

[0089] In this embodiment of the invention, the topology diagram of the OLT device system facilitates the selection of devices and data only related to the target OLT device, reducing the amount of data processing. By analyzing the target OLT device's real historical anomaly dataset, the frequency of key parameter anomalies is dynamically determined, enabling objective data-based analysis of the target OLT device's operation. The Pearson coefficient reveals the linear relationship between parameters, allowing for precise weight allocation and enhancing the accuracy and timeliness of device detection. Determining the key parameter detection frequency of the target OLT device based on the frequency of key parameter anomalies and the weight of each key parameter allows for the customization of the most suitable detection frequency for each target OLT device and even for each key parameter, avoiding real-time detection data, significantly reducing the amount of detection data, and lowering the detection pressure.

[0090] In one specific embodiment, in S302, determining the historical abnormal dataset transmitted from the target OLT device based on the topology diagram includes:

[0091] Based on the topology diagram, determine the upstream and downstream devices of the target OLT device; obtain the first historical dataset of the upstream device and the second historical dataset of the downstream device respectively; determine the data transmitted from the target OLT device that contains anomalies in the first and second historical datasets as historical anomaly datasets.

[0092] It should be noted that due to the large amount of historical data, the "topology" can only directly identify the "upstream and downstream devices" related to the "target OLT device". However, the "upstream and downstream devices" may also interact with other devices. Therefore, by further limiting the data transmitted from the target OLT device to the "historical abnormal dataset", the amount of irrelevant data is further reduced and the data processing efficiency is improved.

[0093] In one specific embodiment, in S303, the key parameters of the target OLT device and the frequency of key parameter anomalies are determined based on the historical anomaly dataset, specifically including:

[0094] Based on a preset threshold range, multiple key parameters with anomalies in the historical abnormal dataset and the initial abnormal frequency of each key parameter are determined; the time when the historical abnormal data of each key parameter shows an abnormal state is obtained; the time decay coefficient of each key parameter is determined based on the time when the historical abnormal data shows an abnormal state; the initial abnormal frequency of each key parameter is weighted and adjusted according to the time decay coefficient to obtain the key parameter abnormal frequency of each key parameter.

[0095] In this embodiment, the preset threshold range is different for different parameters. Generally, each parameter has a normal operating range in the target OLT device. Therefore, the preset threshold needs to be set according to the characteristics of each parameter and the operating experience of the device, specifically to meet the normal operating needs of the target OLT device, and is not limited here.

[0096] In one specific embodiment, please refer to Figure 4 , Figure 4 This is a schematic diagram showing the results of an embodiment of the key parameters of the OLT device provided in this invention. The key parameters specifically include temperature, memory usage, CPU utilization, port status, flow rate trend, number of dropped packets, number of packet errors, etc., which will not be elaborated here.

[0097] The time decay coefficient is a mathematical parameter used to quantify how a variable gradually weakens over time.

[0098] In this embodiment, by introducing a time decay coefficient, a weight based on time distance (time decay coefficient) is assigned to the key parameters, and a dynamically adjusted index of the abnormal frequency of key parameters is creatively generated. This makes the calculated abnormal frequency of key parameters more realistic and dynamic in reflecting the current abnormality probability of the parameters, avoiding excessive interference from old data, effectively improving the dynamism and accuracy of the assessment of the abnormal frequency of key parameters, thereby optimizing the allocation of detection resources of OLT equipment.

[0099] Furthermore, due to the large volume of historical anomaly datasets, in order to determine the frequency of anomalies in the key parameters of the target OLT device based on these datasets, the following specific steps are required:

[0100] An initial mathematical model is constructed, which includes a time series prediction module and a supervised learning algorithm module. Historical datasets are input into the mathematical model, and the frequency of anomalies in the historical data is used as the output. The dynamic dependencies of the historical dataset are obtained through the time series prediction module, and the dependencies between features in the historical dataset are obtained through the supervised learning algorithm module. The model is iteratively trained until a fully trained target mathematical model is obtained. The frequency of anomalies in key parameters is determined based on the dynamic dependencies and the dependencies between features.

[0101] Mathematical models are tools used in mathematical language (symbols, formulas, diagrams, etc.) to abstract and quantify real-world phenomena, systems, or processes, aiming to reveal their inherent laws, predict development trends, or optimize decision-making.

[0102] Time series forecasting is a statistical forecasting method that uses historical observation data to establish mathematical models based on the regularity of changes over time. It takes historical data as its foundation, acknowledges the continuity of development, and considers the interference of random factors to infer future trends.

[0103] Supervised learning algorithms learn the mapping relationship between input features and output results through labeled training data, thereby building a predictive model for classifying or numerically predicting unknown data.

[0104] In this embodiment, the time series prediction module specifically processes the trends, periodicity, and potential anomalies of historical datasets over time, enabling the model to accurately capture the historical variation patterns of key parameters and their abnormal behavior patterns in time series, thus obtaining dynamic dependencies. The supervised learning algorithm module analyzes the complex internal relationships and mutual influences of multiple key parameters at the same point in time, which helps to identify "combined failure" signals (even if individual parameters are within a reasonable range, a specific combination of anomalies indicates a problem), thus obtaining dependencies between features. By combining dynamic (time series) and static / contextual (between features) relationships, a more comprehensive and in-depth understanding of failure modes and triggering conditions is achieved, thereby more accurately determining the frequency of anomalies, reducing false alarms and false negatives, and improving the reliability of key parameter anomaly frequency assessment.

[0105] In addition, the frequency of key parameter anomalies can be directly determined through mathematical models. Since the mathematical models do not require human intervention during data processing, human error is avoided. Furthermore, since the determination of the frequency of key parameter anomalies is based on the characteristics of the parameter in historical data, the results of the frequency of key parameter anomalies are reliable. Moreover, no additional data input is required during the prediction process, which simplifies the operation process, improves the prediction efficiency, and ensures the accuracy and timeliness of the detection results.

[0106] Furthermore, in a specific situation, since the number of abnormal parameters in the historical abnormal dataset is huge, while the key parameters affecting the stable operation of OLT equipment are relatively limited, the key parameters that have a greater impact on the stable operation of the equipment, such as optical power and bit error rate, can be selected as needed for focused monitoring.

[0107] In this embodiment, the system automatically locks the key parameters to be detected for high-frequency faults by using historical anomaly data, avoiding low-risk parameters, greatly reducing the number of meaningless detections, avoiding resource waste, realizing the key detection of high-failure-rate parameters and the relaxed inspection of low-failure-rate parameters, maximizing the coverage of risk points with limited resources, realizing the on-demand allocation of detection resources, not only compressing invalid detection traffic, but also improving the fault response speed.

[0108] In one specific embodiment, in S305, after determining the abnormal frequency of the key parameters and the weight of each key parameter, in order to determine the key parameter detection frequency of the target OLT device, the steps include: first, weighting and summing the abnormal frequency of the key parameters with the corresponding weights of the key parameters to obtain the target abnormal frequency of the key parameters; then, determining the target abnormal frequency of the key parameters as the key parameter detection frequency.

[0109] In this embodiment, by weighting and summing the "critical parameter anomaly frequency" (reflecting the actual anomaly risk of the parameter) and the "critical parameter weight" (reflecting the importance of the parameter to the overall performance and stability of the OLT equipment), the target anomaly frequency (i.e., the final detection frequency) simultaneously considers both the anomaly risk level and the importance level of the parameter. For critical parameters with high weight (important) and high anomaly frequency (high risk), the target anomaly frequency increases after weighting, and the corresponding detection frequency increases, allowing for priority and timely detection of anomalies. For parameters with low weight (minor) and low anomaly frequency (low risk), the target anomaly frequency decreases after weighting, and the detection frequency decreases, avoiding ineffective detection. This mechanism comprehensively considers the real-time anomaly risk and inherent importance of the parameter, making the adjustment of the detection frequency more dynamic, adaptable, and scientifically reasonable. It avoids the imbalance of detection strategies caused by a single factor, tilts detection resources towards core risk points, and significantly improves the targeting and accuracy of the detection strategy.

[0110] Furthermore, after determining the detection frequency of the key parameters of the target OLT equipment, in order to facilitate the timely detection of abnormal equipment by staff and to ensure the normal operation of the OLT equipment system, when the detection status of a key parameter is determined to be abnormal, the detection equipment that detects the key parameter is obtained; and an abnormal topology diagram of the target OLT equipment is generated based on the topological relationship between the detection equipment and the target OLT equipment.

[0111] An abnormal topology diagram refers to the physical / logical connection relationships (such as star, bus, ring, tree, mesh, etc.) between devices with abnormalities (such as routers, switches, and terminals). Since the abnormal topology diagram only includes devices with abnormalities, it greatly simplifies the range of data that staff need to search during maintenance and greatly improves maintenance efficiency.

[0112] Please see Figure 5 , Figure 5 Abnormal topology of OLT device provided in embodiments of the present invention Figure 1 The schematic diagram of the embodiment is shown in the figure. In the figure, A represents the target OLT device, and 1, 2, 3 and 4 represent devices with abnormalities. Obviously, the abnormal topology diagram also includes devices that are not directly connected to the target OLT device.

[0113] Furthermore, since the OLT equipment system is redundantly configured, that is, in order to ensure the normal operation of the OLT equipment system, generally, when each OLT device fails, there is at least one device that can take over the work of that OLT device. Therefore, when it is determined that the detection status of a key parameter is abnormal, the target OLT device is switched to a replaceable OLT device; where the replaceable OLT device is the redundant device of the target OLT device in the OLT equipment system.

[0114] To facilitate better implementation of the detection frequency determination method for OLT devices provided in the embodiments of the present invention, the present invention also provides an apparatus based on the above-described detection frequency determination method for OLT devices. The meanings of the terms used are the same as in the above-described detection frequency determination method for OLT devices, and specific implementation details can be found in the descriptions in the method embodiments.

[0115] Please see Figure 6 , Figure 6 This is a schematic diagram of an embodiment of the detection frequency determination device for an OLT device provided in this invention. The detection frequency determination device 600 for the OLT device may include:

[0116] Topology diagram acquisition module 601 is used to acquire the topology diagram of the OLT device system;

[0117] Historical anomaly dataset determination module 602 is used to determine the historical anomaly dataset transmitted from the target OLT device based on the topology diagram.

[0118] The key parameter determination module 603 is used to determine the key parameters of the target OLT device and the frequency of key parameter anomalies based on historical anomaly datasets.

[0119] The weight calculation module 604 is used to calculate the correlation coefficient between each key parameter and other key parameters according to the correlation relationship between different key parameters, and to determine the weight of each key parameter based on the correlation coefficient.

[0120] The key parameter detection frequency determination module 605 is used to determine the key parameter detection frequency of the target OLT device based on the abnormal frequency of key parameters and the weight of each key parameter.

[0121] This invention also provides an electronic device, such as... Figure 7 As shown, Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention, specifically:

[0122] The electronic device may include components such as a processor 701 with one or more processing cores, a memory 702 with one or more computer-readable storage media, a power supply 703, and an input unit 704. Those skilled in the art will understand that... Figure 7 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein:

[0123] The processor 701 is the control center of the electronic device. It connects various parts of the electronic device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 702, and by calling data stored in the memory 702, it performs various functions and processes data, thereby performing overall detection of the electronic device. Optionally, the processor 701 may include one or more processing cores; preferably, the processor 701 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operation of the storage medium, user interface, and application programs, while the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 701.

[0124] The memory 702 can be used to store software programs and modules. The processor 701 executes various functional applications and data processing by running the software programs and modules stored in the memory 702. The memory 702 may mainly include a program storage area and a data storage area. The program storage area may store application programs required for operating the storage medium and at least one function (such as sound playback function, image playback function, etc.); the data storage area may store data created according to the use of the electronic device. In addition, the memory 702 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 702 may also include a memory controller to provide the processor 701 with access to the memory 702.

[0125] The electronic device also includes a power supply 703 that supplies power to various components. Preferably, the power supply 703 can be logically connected to the processor 701 via a power management storage medium, thereby enabling functions such as charging, discharging, and power consumption management through the power management storage medium. The power supply 703 may also include one or more DC or AC power supplies, recharge storage media, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0126] The electronic device may also include an input unit 704, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0127] Although not shown, the electronic device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 701 in the electronic device loads the executable files corresponding to the processes of one or more application programs into the memory 702 according to the following instructions, and the processor 701 runs the application programs stored in the memory 702 to realize various functions, as follows:

[0128] Obtain the topology diagram of the OLT device system; determine the historical abnormal dataset transmitted from the target OLT device based on the topology diagram; determine the key parameters and abnormal frequencies of the key parameters of the target OLT device based on the historical abnormal dataset; calculate the correlation coefficient between each key parameter and other key parameters based on the correlation relationship between different key parameters, and determine the weight of each key parameter based on the correlation coefficient; determine the detection frequency of the key parameters of the target OLT device based on the abnormal frequency of the key parameters and the weight of each key parameter.

[0129] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0130] Therefore, embodiments of the present invention provide a computer-readable storage medium storing a computer program thereon, the computer program being loaded by a processor to execute the steps in the detection frequency determination method for any OLT device provided in the embodiments of the present invention. For example, the computer program, when loaded by a processor, can execute the following steps:

[0131] Obtain the topology diagram of the OLT device system; determine the historical abnormal dataset transmitted from the target OLT device based on the topology diagram; determine the key parameters and abnormal frequencies of the key parameters of the target OLT device based on the historical abnormal dataset; calculate the correlation coefficient between each key parameter and other key parameters based on the correlation coefficient, and determine the weight of each key parameter based on the correlation coefficient; determine the key parameter detection frequency and service state sequence length of the target OLT device based on the abnormal frequency of key parameters and the weight of each key parameter, perform backtracking analysis on the initial service state, and obtain the target service state sequence of the system.

[0132] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0133] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0134] Since the computer program stored in the computer-readable storage medium can execute the steps in the detection frequency determination method of any OLT device provided in the embodiments of the present invention, the beneficial effects that the detection frequency determination method of any OLT device provided in the embodiments of the present invention can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0135] The above provides a detailed description of the detection frequency determination method, apparatus, and electronic device for an OLT device provided by embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for determining the detection frequency of an OLT device, characterized in that, include: Obtain the topology diagram of the OLT device system; Based on the topology diagram, determine the historical abnormal dataset transmitted from the target OLT device; Based on the historical anomaly dataset, determine the key parameters of the target OLT device and the frequency of anomalies in the key parameters; Calculate the correlation coefficient between each key parameter and other key parameters based on the correlation relationships between different key parameters, and determine the weight of each key parameter based on the correlation coefficients. The key parameter detection frequency of the target OLT device is determined based on the abnormal frequency of the key parameters and the weight of each key parameter.

2. The method for determining the detection frequency of an OLT device according to claim 1, characterized in that, The step of determining the key parameters and the frequency of key parameter anomalies of the target OLT device based on the historical anomaly dataset includes: Based on a preset threshold range, determine multiple key parameters that are abnormal in the historical abnormal dataset and the initial abnormal frequency of each key parameter. Obtain the time when the historical abnormal state occurred for each key parameter; The time decay coefficient of each key parameter is determined based on the time when the abnormal state occurs in historical abnormal data. The initial abnormal frequency of each key parameter is weighted and adjusted according to the time decay coefficient to obtain the key parameter abnormal frequency of each key parameter.

3. The method for determining the detection frequency of an OLT device according to claim 1, characterized in that, Based on the historical anomaly dataset, the frequency of anomalies in key parameters of the target OLT device is determined, including: An initial mathematical model is constructed, which includes a time series prediction module and a supervised learning algorithm module; The historical dataset is input into the mathematical model, and the abnormal frequency of the historical data is output. The dynamic dependency of the historical dataset is obtained through the time series prediction module, and the dependency between features of the historical dataset is obtained through the supervised learning algorithm module. The model is trained iteratively until a fully trained target mathematical model is obtained. The frequency of critical parameter anomalies is determined based on the dynamic dependencies and the dependencies between features.

4. The method for determining the detection frequency of an OLT device according to claim 1, characterized in that, The step of determining the key parameter detection frequency of the target OLT device based on the abnormal frequency of the key parameters and the weight of each key parameter includes: The target abnormal frequency of the key parameter is obtained by weighted summing of the abnormal frequency of the key parameter and the corresponding weight of the key parameter. The target anomaly frequency of the key parameter is determined as the detection frequency of the key parameter.

5. The method for determining the detection frequency of an OLT device according to claim 1, characterized in that, The step of determining the historical abnormal dataset transmitted from the target OLT device based on the topology diagram includes: Based on the topology diagram, determine the upstream and downstream devices of the target OLT device; The first historical dataset of the upstream device and the second historical dataset of the downstream device are obtained respectively. The data that is determined to be abnormal in the first historical dataset and the second historical dataset and transmitted from the target OLT device is the historical abnormal dataset.

6. The method for determining the detection frequency of an OLT device according to claim 5, characterized in that, After determining the detection frequency of the key parameters of the target OLT device, the process also includes: When the detection status of a key parameter is determined to be abnormal, the detection device that detects that key parameter is obtained; An abnormal topology diagram of the target OLT device is generated based on the topological relationship between the detection device and the target OLT device.

7. The method for determining the detection frequency of an OLT device according to claim 1, characterized in that, After determining the detection frequency of the key parameters of the target OLT device, the process also includes: When it is determined that the detection status of a key parameter is abnormal, the target OLT device is switched to a replaceable OLT device; The replaceable OLT device is a redundant device of the target OLT device in the OLT device system.

8. A detection frequency determination device for an OLT device, characterized in that, include: The topology diagram acquisition module is used to acquire the topology diagram of the OLT device system; The historical anomaly dataset determination module is used to determine the historical anomaly dataset transmitted from the target OLT device based on the topology diagram. The key parameter determination module is used to determine the key parameters and key parameter anomaly frequency of the target OLT device based on the historical anomaly dataset. The weight calculation module is used to calculate the correlation coefficient between each key parameter and other key parameters according to the correlation relationship between different key parameters, and to determine the weight of each key parameter according to the correlation coefficient. The key parameter detection frequency determination module is used to determine the key parameter detection frequency of the target OLT device based on the abnormal frequency of the key parameters and the weight of each key parameter.

9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the following steps: Obtain the topology diagram of the OLT device system; Based on the topology diagram, determine the historical abnormal dataset transmitted from the target OLT device; Based on the historical anomaly dataset, determine the key parameters of the target OLT device and the frequency of anomalies in the key parameters; Calculate the correlation coefficient between each key parameter and other key parameters based on the correlation relationships between different key parameters, and determine the weight of each key parameter based on the correlation coefficients. The key parameter detection frequency of the target OLT device is determined based on the abnormal frequency of the key parameters and the weight of each key parameter.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps in the method for determining the detection frequency of the OLT device according to any one of claims 1 to 7.

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