Fault Analysis Method and Device, Equipment and Medium for FTTR Equipment

Abstract

This application provides a method and apparatus, device, and medium for analyzing FTTR equipment failures, relating to the field of artificial intelligence technology. First, multiple fault-related data are acquired through data collection on a target FTTR device. Second, semantic mining is performed on the multiple fault-related data to form a semantic representation of the target failure. Finally, based on the semantic representation of the target failure, a fault analysis is performed on the target FTTR device, outputting fault prediction data corresponding to the target FTTR device. This approach can improve the existing difficulty in effectively analyzing LAN port failures.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and more specifically, to a method and apparatus for analyzing a fault of an FTTR device, a device, and a medium. Background Art

[0002] Fiber to the Room (FTTR) equipment is a network access technology that lays optical fiber directly into user rooms, aiming to improve network bandwidth and stability. FTTR equipment typically involves hardware such as optical fiber modems (ONUs) and wireless routers. In FTTR networks, LAN ports (Local Area Network) are key interfaces for connecting local devices such as computers, televisions, and printers. If FTTR equipment damages a LAN port, it can have a range of consequences. Therefore, LAN port damage or failures need to be analyzed or predicted to ensure timely maintenance and ensure effective equipment operation. However, existing technologies typically only perform maintenance after a LAN port failure has occurred, making effective analysis of LAN port failures difficult. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a fault analysis method and apparatus, equipment and medium for FTTR equipment, so as to improve the problem in the prior art that it is difficult to effectively analyze LAN port faults.

[0004] To achieve the above objectives, this application adopts the following technical solutions:

[0005] A fault analysis method for FTTR equipment, comprising:

[0006] Acquire multiple fault-related data obtained by collecting data from a target FTTR device, wherein the fault-related data refers to data that contributes to the failure of a LAN port of the target FTTR device, and the multiple fault-related data include at least one time series data;

[0007] Performing semantic mining on the plurality of fault-related data to form a target fault semantic representation, wherein, in the semantic mining process, for each of the time series data, data fluctuation semantics in the time series data are mined from at least two time change directions;

[0008] Based on the target fault semantic representation, a fault analysis is performed on the target FTTR device, and fault prediction data corresponding to the target FTTR device is output, wherein the fault prediction data is used to reflect whether a LAN port of the target FTTR device will fail.

[0009] In a preferred embodiment of the present application, in the above-mentioned fault analysis method for FTTR equipment, the step of performing semantic mining on the plurality of fault-related data to form a semantic representation of the target fault includes:

[0010] For each time series data in the plurality of fault-related data, mining the data fluctuation semantics in the time series data from at least two time change directions, and outputting a corresponding local fault semantic representation;

[0011] When there is at least one fault-related data that does not belong to time series data among the plurality of fault-related data, performing semantic mining on each of the at least one fault-related data and outputting a corresponding local fault semantic representation;

[0012] The local fault semantic representation corresponding to each of the fault-related data is fused to form a target fault semantic representation, wherein the target fault semantic representation is used to characterize the global semantic information possessed by the multiple fault-related data.

[0013] In a preferred embodiment of the present application, in the above-mentioned FTTR equipment fault analysis method, the step of mining the data fluctuation semantics in each time series data of the plurality of fault-related data from at least two time change directions and outputting the corresponding local fault semantic representation includes:

[0014] Loading the time series data into corresponding semantic mining branches, wherein each time series data corresponds to one semantic mining branch;

[0015] For each sequence sub-data in the time series data, semantic space mapping is performed on the sequence sub-data to form a corresponding first fault semantic representation, and starting from the sequence sub-data, a first sequence data segment corresponding to the sequence sub-data is cut out from the time series data according to a first time change direction through a target window carried by the semantic mining branch. Also, starting from the sequence sub-data, a second sequence data segment corresponding to the sequence sub-data is cut out from the time series data according to a second time change direction through the target window. The size of the target window is formed during training as a network parameter of the semantic mining branch.

[0016] Performing semantic space mapping on the first sequence of data segments and the second sequence of data segments respectively to form corresponding second fault semantic representations and third fault semantic representations;

[0017] The first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to each sequence sub-data are fused to form a corresponding local fault semantic representation.

[0018] In a preferred embodiment of the present application, in the above-mentioned fault analysis method for FTTR equipment, the step of performing semantic space mapping on the first sequence of data segments and the second sequence of data segments to form corresponding second fault semantic representations and third fault semantic representations includes:

[0019] performing a dispersion calculation on the first sequence of data segments to obtain a first dispersion, and performing a dispersion calculation on the second sequence of data segments to obtain a second dispersion, wherein the time series data is time numerical series data, including temperature, voltage, or network traffic;

[0020] Semantic space mapping is performed on the first discreteness and the second discreteness respectively to form corresponding second fault semantic representation and third fault semantic representation, wherein the semantic space mapping includes word embedding processing.

[0021] In a preferred embodiment of the present application, in the above-mentioned fault analysis method for FTTR equipment, the step of fusing the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to each sequence sub-data to form a corresponding local fault semantic representation includes:

[0022] For each sequence sub-data, concatenate the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to the sequence sub-data to form a concatenated fault semantic representation corresponding to the sequence sub-data;

[0023] Splicing the spliced fault semantic representations corresponding to each of the sequence sub-data to form a spliced fault semantic representation corresponding to the time series data, and performing convolution and pooling on the spliced fault semantic representations corresponding to the time series data to extract different semantic features to form corresponding convolutional fault semantic representations and pooled fault semantic representations, wherein the spliced fault semantic representations, the convolutional fault semantic representations, and the pooled fault semantic representations corresponding to each of the sequence sub-data have the same size;

[0024] For each sequence sub-data, based on the spliced fault semantic representation corresponding to the sequence sub-data, the convolutional fault semantic representation and the pooled fault semantic representation are respectively focused and mined to form a corresponding first focused semantic representation and a second focused semantic representation, and the first focused semantic representation and the second focused semantic representation are superimposed to form a corresponding fused focused semantic representation;

[0025] The fused focused semantic representation corresponding to each of the sequence sub-data is spliced to form a spliced focused semantic representation, and a local fault semantic representation is determined based on the spliced focused semantic representation.

[0026] In a preferred embodiment of the present application, in the above-mentioned fault analysis method for FTTR equipment, when at least one fault-related data is not time series data among the plurality of fault-related data, the step of performing semantic mining on each of the at least one fault-related data and outputting a corresponding local fault semantic representation includes:

[0027] When at least one fault-related data is not time series data among the plurality of fault-related data, performing semantic space mapping on each fault-related data among the at least one fault-related data to obtain a corresponding fault mapping semantic representation;

[0028] When the number of obtained fault mapping semantic representations is greater than a preset number, clustering the fault mapping semantic representations to form at least one corresponding semantic representation cluster;

[0029] For each of the fault mapping semantic representations, focused mining is performed on the fault mapping semantic representation according to the cluster center of the semantic representation cluster corresponding to the fault mapping semantic representation to form a local fault semantic representation corresponding to the fault mapping semantic representation.

[0030] In a preferred embodiment of the present application, in the above-mentioned method for analyzing the failure of the FTTR device, the method for analyzing the failure of the FTTR device further comprises:

[0031] Using a semantic mining unit included in a candidate fault analysis network, semantic mining is performed on a plurality of training fault-related data to form a training fault semantic representation, wherein the candidate fault analysis network is a neural network, and the plurality of training fault-related data includes at least one training time series data. During the semantic mining process, for each training time series data, data fluctuation semantics in the training time series data is mined from at least two time change directions;

[0032] Utilizing the semantic analysis unit included in the candidate fault analysis network, performing fault analysis based on the training fault semantic representation, and outputting training fault prediction data;

[0033] Based on a training loss indicator between the training fault prediction data and the fault label data corresponding to the plurality of training fault-related data, network parameters of the candidate fault analysis network are updated to form a target fault analysis network.

[0034] The present application also provides a fault analysis device for FTTR equipment, comprising:

[0035] a fault-related data acquisition module, configured to acquire a plurality of fault-related data obtained by collecting data from a target FTTR device, wherein the fault-related data refers to data that contributes to the failure of a LAN port of the target FTTR device, and the plurality of fault-related data includes at least one time series data;

[0036] a data semantic mining module, configured to perform semantic mining on the plurality of fault-related data to form a target fault semantic representation, wherein, during the semantic mining process, for each of the time series data, the data fluctuation semantics in the time series data are mined from at least two time change directions;

[0037] A fault analysis module is used to perform fault analysis on the target FTTR device based on the target fault semantic representation, and output fault prediction data corresponding to the target FTTR device, wherein the fault prediction data is used to reflect whether the LAN port of the target FTTR device will fail.

[0038] Based on the above, the present application further provides an electronic device, including:

[0039] Memory for storing computer programs;

[0040] The processor connected to the memory is used to execute the computer program stored in the memory to implement the above-mentioned fault analysis method for FTTR equipment.

[0041] On the basis of the above, the present application further provides a computer-readable storage medium, in which a computer program is stored. When the computer program is run, each step of the above-mentioned fault analysis method for FTTR equipment is executed.

[0042] The fault analysis method, apparatus, device, and medium for FTTR equipment provided in this application first obtain multiple fault-related data obtained by data collection on the target FTTR equipment; secondly, semantic mining is performed on the multiple fault-related data to form a target fault semantic representation; then, based on the target fault semantic representation, fault analysis is performed on the target FTTR equipment to output fault prediction data corresponding to the target FTTR equipment. Based on the above content, on the one hand, because semantic mining is performed on multiple fault-related data, the semantic information carried by the formed target fault semantic representation can be richer. On the other hand, because in the process of semantic mining, the data fluctuation semantics are mined from at least two time change directions for each time series data, the representation reliability of the mined semantic information is higher, thereby ensuring the reliability of the fault prediction data output based on the target fault semantic representation, thereby improving the problem of difficulty in effectively analyzing LAN port faults in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings.

[0044] Figure 1 This is a structural block diagram of the electronic device provided in an embodiment of the present application.

[0045] Figure 2 A flowchart of a fault analysis method for FTTR equipment provided in an embodiment of the present application.

[0046] Figure 3 A schematic diagram of the fusion of semantic representations of each sequence sub-data provided in an embodiment of the present application.

[0047] Figure 4 A block diagram of a fault analysis device for FTTR equipment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for protection, but merely represents selected embodiments of the present application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in the present application without creative work are within the scope of protection of the present application.

[0050] like Figure 1 As shown, an embodiment of the present application provides an electronic device, wherein the electronic device may include a memory, a processor, and a fault analysis device for an FTTR device.

[0051] Specifically, the memory and the processor are electrically connected, directly or indirectly, to enable data transmission or interaction. For example, the memory and the processor may be electrically connected via one or more communication buses or signal lines. The fault analysis device for the FTTR device includes at least one software function module stored in the memory in the form of software or firmware. The processor is configured to execute an executable computer program stored in the memory, such as the software function module and computer program included in the fault analysis device for the FTTR device, to implement the fault analysis method for the FTTR device provided in the embodiments of the present application.

[0052] Optionally, the memory may be, but is not limited to, random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), etc.

[0053] Furthermore, the processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0054] I understand. Figure 1The structure shown is for illustration only. The electronic device may also include Figure 1 More or fewer components than shown, or with Figure 1 The different configurations shown may, for example, further include a communication unit for exchanging information with other devices.

[0055] Combine Figure 2 The present application also provides a method for analyzing the failure of an FTTR device applicable to the above-mentioned electronic device. The method steps defined in the process related to the method for analyzing the failure of the FTTR device can be implemented by the electronic device. Figure 2 The specific process shown is explained in detail.

[0056] Step S110: Acquire a plurality of fault-related data obtained by collecting data from a target FTTR device.

[0057] In an embodiment of the present application, the electronic device can acquire multiple fault-related data obtained by collecting data from a target FTTR device. The fault-related data refers to data that may have contributed to the failure of the LAN port of the target FTTR device (i.e., factors that may have caused the LAN port failure). The multiple fault-related data include at least one time series data item. For example, some or all of the multiple fault-related data items may be time series data items. Furthermore, time series data items refer to sets of data collected sequentially in chronological order, such as data at time point 1, data at time point 2, and data at time point 3. Furthermore, the specific application scenario of the target FTTR device may be FTTR-B (Business FTTR), an all-optical networking solution for businesses and enterprises. This is a Wi-Fi solution specifically designed for businesses and enterprises. This all-optical networking solution utilizes optical fiber access, optical fiber composite cable, and Wi-Fi 6 to provide Wi-Fi coverage to every corner of the enterprise, providing network services for employees' online activities such as live streaming and online meetings.

[0058] Step S120 : performing semantic mining on the plurality of fault-related data to form a semantic representation of a target fault.

[0059] In an embodiment of the present application, after obtaining the multiple fault-related data, the electronic device can perform semantic mining on the multiple fault-related data to form a target fault semantic representation. In the process of semantic mining, for each of the time series data, the data fluctuation semantics in the time series data are mined from at least two time change directions. In addition, semantic mining refers to mining potential semantic information from the multiple fault-related data and representing it in the form of a vector or matrix, that is, obtaining a corresponding semantic representation.

[0060] Step S130 : performing fault analysis on the target FTTR device based on the target fault semantic representation, and outputting fault prediction data corresponding to the target FTTR device.

[0061] In an embodiment of the present application, after obtaining the target fault semantic representation, the electronic device can perform a fault analysis on the target FTTR device based on the target fault semantic representation and output fault prediction data corresponding to the target FTTR device. The fault prediction data is used to reflect whether the LAN port of the target FTTR device will fail. For example, the target fault semantic representation can be fully connected to obtain a corresponding fully connected fault semantic representation. Then, the fully connected fault semantic representation can be processed by a classification function (such as softmax) to obtain a corresponding probability distribution, such as (a, b), where a can refer to the probability that the LAN port of the target FTTR device fails, and b can refer to the probability that the LAN port of the target FTTR device will not fail. Then, the type with the larger probability value can be determined as the corresponding fault prediction data. If a is greater than b, a fault will occur.

[0062] Based on the above content, on the one hand, due to the semantic mining of multiple fault-related data, the semantic information carried by the formed target fault semantic representation can be richer. On the other hand, since in the process of semantic mining, for each time series data, the data fluctuation semantics are mined from at least two time change directions, the representation reliability of the mined semantic information is higher, thereby ensuring the reliability of the fault prediction data output based on the target fault semantic representation, and thus improving the problem of difficulty in effectively analyzing LAN port faults in the existing technology.

[0063] It should be noted that for step S120 , the specific method of performing semantic mining on the plurality of fault-related data is not limited and can be selected accordingly according to actual needs.

[0064] For example, in an alternative implementation, in order to improve the efficiency of semantic mining, the multiple fault-related data can be separately mapped into semantic spaces (such as word embedding), and then the semantic space mapping results corresponding to each fault-related data can be spliced. Then, convolution, pooling, activation, attention and other processing can be performed to obtain the corresponding target fault semantic information.

[0065] For example, in another alternative embodiment, in order to improve the accuracy of semantic mining so that the obtained target fault semantic information can represent more semantic information, the above-mentioned step S120 can further include step S121, step S122 and step S123, the specific contents of which are described below.

[0066] Step S121 : for each time series data in the plurality of fault-related data, mining the data fluctuation semantics in the time series data from at least two time change directions, and outputting a corresponding local fault semantic representation.

[0067] In this embodiment of the present application, for each time series data item in the plurality of fault-related data items, data fluctuation semantics within the time series data can be mined from at least two temporal change directions, outputting a corresponding local fault semantic representation. It should be noted that data fluctuations generally play an important role in time series data, at least in characterizing equipment failures, and therefore, corresponding semantic mining can be performed.

[0068] Step S122: When there is at least one fault-related data that does not belong to time series data in the multiple fault-related data, semantic mining is performed on each fault-related data in the at least one fault-related data, and a corresponding local fault semantic representation is output.

[0069] In an embodiment of the present application, when there is at least one fault-related data that does not belong to time series data among the multiple fault-related data, semantic mining is performed on each fault-related data among the at least one fault-related data, and the corresponding local fault semantic representation is output, that is, the semantic information of the fault-related data itself is mined.

[0070] Step S123 , fusing the local fault semantic representation corresponding to each of the fault-related data to form a target fault semantic representation.

[0071] In an embodiment of the present application, after obtaining the local fault semantic representation corresponding to each of the fault-related data (including the local fault semantic representation corresponding to each of the time series data), the local fault semantic representation corresponding to each of the fault-related data can be fused to form a target fault semantic representation. The target fault semantic representation is used to characterize the global semantic information possessed by the multiple fault-related data. Exemplarily, the local fault semantic representation corresponding to each of the fault-related data can be spliced to obtain a corresponding spliced semantic representation, and then the spliced semantic representation can be convolutionally, pooled, and activated to obtain target fault semantic information that can characterize the global semantic information of the multiple fault-related data.

[0072] It can be understood that in the above-mentioned embodiment, the implementation method of step S121 is not restricted, that is, the specific method of mining the data fluctuation semantics in time series data is not restricted. For example, in an alternative embodiment, in order to fully mine the data fluctuation semantic information and ensure the semantic richness of the obtained local fault semantic representation, the above-mentioned step S121 can further include the following steps S121a, S121b, S121c and S121d.

[0073] Step S121a: Load the time series data into the corresponding semantic mining branch.

[0074] In an embodiment of the present application, the time series data can be loaded into a corresponding semantic mining branch. Each time series data corresponds to a semantic mining branch, and the semantic mining branch is part of a corresponding neural network model, such as a part of the network structure of the target fault analysis network described later, for semantic mining.

[0075] Step S121b: For each sequence sub-data in the time series data, perform semantic space mapping on the sequence sub-data to form a corresponding first fault semantic representation, and take the sequence sub-data as the starting point, through the target window carried by the semantic mining branch, in the time series data according to the first time change direction, cut out the first sequence data segment corresponding to the sequence sub-data, and, take the sequence sub-data as the starting point, through the target window carried by the semantic mining branch, in the time series data according to the second time change direction, cut out the second sequence data segment corresponding to the sequence sub-data.

[0076] In an embodiment of the present application, for each sequence sub-data in the time series data (e.g., data at a time point), a semantic space mapping (e.g., word embedding processing) is performed on the sequence sub-data to form a corresponding first fault semantic representation. Starting from the sequence sub-data, the target window carried by the semantic mining branch is used to extract the first sequence data segment corresponding to the sequence sub-data from the time series data in a first time change direction (e.g., from early to late). Furthermore, starting from the sequence sub-data, the target window is used to extract the second sequence data segment corresponding to the sequence sub-data from the time series data in a second time change direction (e.g., from late to early). The size of the target window serves as a network parameter of the semantic mining branch and is formed during training. For example, when the size of the target window is 4, it indicates that the number of sequence sub-data included in the first sequence data segment is less than or equal to 4, and the number of sequence sub-data included in the second sequence data segment is less than or equal to 4. For example, for the first sequence sub-data, the first sequence data segment includes the first sequence sub-data, the second sequence sub-data, the third sequence sub-data, and the fourth sequence sub-data, and the second sequence data segment includes the first sequence sub-data. For the second sequence sub-data, the first sequence data segment includes the second sequence sub-data, the third sequence sub-data, the fourth sequence sub-data, and the fifth sequence sub-data, and the second sequence data segment includes the first sequence sub-data and the second sequence sub-data. For the fourth sequence sub-data, the first sequence data segment includes the first sequence sub-data, the second sequence sub-data, the third sequence sub-data, and the fourth sequence sub-data, and the second sequence data segment includes the fourth sequence sub-data, the fifth sequence sub-data, the sixth sequence sub-data, and the seventh sequence sub-data. The same applies to the other sequence data segments, and no further examples are given here.

[0077] Step S121c: Perform semantic space mapping on the first sequence of data segments and the second sequence of data segments respectively to form corresponding second fault semantic representation and third fault semantic representation.

[0078] In an embodiment of the present application, after obtaining the first sequence of data segments and the second sequence of data segments, semantic space mapping can be performed on the first sequence of data segments and the second sequence of data segments respectively to form corresponding second fault semantic representations and third fault semantic representations.

[0079] Step S121d: Fusing the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to each sequence sub-data to form a corresponding local fault semantic representation.

[0080] In an embodiment of the present application, after obtaining the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to each sequence sub-data, the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to each sequence sub-data can be fused to form a corresponding local fault semantic representation. In this way, the local fault semantic representation carries not only the semantic information of the sequence sub-data itself, but also the semantic information of the fluctuation situation with the previous sequence sub-data, and also the semantic information of the fluctuation situation with the subsequent sequence sub-data, and therefore has better semantic representation capabilities. In addition, since the target window is formed during training, an adapted target window can be determined for different time series data, thereby achieving reliable capture of the potential semantic information in the fluctuation situation.

[0081] It is understood that in the above embodiment, the implementation of step S121c is not limited, that is, the specific manner of performing semantic space mapping on the first sequence of data segments and the second sequence of data segments is not limited. For example, in an alternative embodiment, in order to reliably capture the potential semantic information in the corresponding fluctuation situation, the above step S121c may further include the following:

[0082] First, a dispersion calculation can be performed on the first sequence data segment to obtain a first dispersion, and a dispersion calculation can be performed on the second sequence data segment to obtain a second dispersion, wherein the time series data is a time numerical series data, including temperature, voltage, or network traffic, such as the temperature at time point 1, the temperature at time point 2, and the temperature at time point 3. In addition, the dispersion can be calculated by first calculating the mean of each sequence sub-data in the sequence data segment, then calculating the absolute difference between each sequence sub-data and the mean, and finally calculating the mean of each absolute difference to obtain the corresponding dispersion.

[0083] Secondly, semantic space mapping is performed on the first discreteness and the second discreteness respectively to form corresponding second fault semantic representation and third fault semantic representation, wherein the semantic space mapping includes word embedding processing, that is, word embedding processing is performed on the corresponding discreteness to obtain corresponding semantic representation, and the word embedding processing can be implemented through the corresponding word embedding model.

[0084] It is understood that in the above embodiment, the embodiment of step S121d is not limited, that is, the specific method of forming the corresponding local fault semantic representation is not limited. For example, in an alternative embodiment, in order to achieve full integration of multiple semantic representations of each sequence sub-data, the above step S121d may further include the following contents (combined with Figure 3 shown):

[0085] First, for each sequence sub-data, the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to the sequence sub-data are concatenated to form a concatenated fault semantic representation corresponding to the sequence sub-data;

[0086] Secondly, the spliced fault semantic representations corresponding to each of the sequence sub-data are spliced to form a spliced fault semantic representation corresponding to the time series data, and the spliced fault semantic representations corresponding to the time series data are convolved and pooled (which can be implemented by corresponding convolutional networks and pooling networks, respectively) to achieve different semantic feature extraction, forming corresponding convolutional fault semantic representations and pooled fault semantic representations. The spliced fault semantic representations, the convolutional fault semantic representations, and the pooled fault semantic representations corresponding to each of the sequence sub-data have the same size, that is, important semantic features are extracted through convolution and pooling, and the semantic representations are compressed so that they have the same size as the spliced fault semantic representations corresponding to each of the sequence sub-data. In this way, while capturing important semantic features, subsequent processing is also facilitated.

[0087] Then, for each sequence sub-data, based on the spliced fault semantic representation corresponding to the sequence sub-data, the convolution fault semantic representation and the pooled fault semantic representation are focused and mined respectively to form a corresponding first focused semantic representation and a second focused semantic representation, and the first focused semantic representation and the second focused semantic representation are superimposed to form a corresponding fused focused semantic representation; that is, on the one hand, semantic information associated with the convolution fault semantic representation can be mined from the spliced fault semantic representation corresponding to the sequence sub-data, and on the other hand, semantic information associated with the pooled fault semantic representation can be mined from the spliced fault semantic representation corresponding to the sequence sub-data. In this way, since the convolution fault semantic representation and the pooled fault semantic representation are obtained by convolution and pooling the spliced fault semantic representation that can represent global semantic information, the convolution fault semantic representation and the pooled fault semantic representation can also focus on different semantic features when representing global semantic information. Therefore, by performing focused mining separately, different associated semantic features can be mined, that is, more associated semantic features are mined, so that the semantics of the superimposed fused focused semantic representation is richer.

[0088] Secondly, the fused focused semantic representation corresponding to each of the sequence sub-data can be spliced to form a spliced focused semantic representation, and the local fault semantic representation can be determined based on the spliced focused semantic representation; exemplarily, the spliced focused semantic representation can be directly used as the corresponding local fault semantic representation, or the spliced focused semantic representation can be convolved, pooled and activated to obtain the corresponding local fault semantic representation.

[0089] It is understood that in the above embodiment, the implementation method of step S122 is not limited, that is, the specific method of performing semantic mining on fault-related data is not limited. For example, in an alternative embodiment, in order to improve the semantic representation capability of the mined local fault semantic representation, the above step S122 may further include the following:

[0090] First, when there is at least one fault-related data that does not belong to time series data among the multiple fault-related data, for each fault-related data in the at least one fault-related data, semantic space mapping is performed on the fault-related data to obtain a corresponding fault mapping semantic representation. Exemplarily, word embedding processing can be performed through a word embedding model to obtain the corresponding semantic space mapping. In addition, the fault-related data may refer to the number of times electrostatic protection is not performed during port connection operations, such as not wearing an anti-static wristband. The reason is that electrostatic discharge usually occurs when connecting or operating a device. When the LAN port of the device comes into contact with an electrostatic source, it may cause hardware damage to a certain extent. Alternatively, the fault-related data may also refer to electromagnetic interference conditions, such as whether it is in a strong magnetic environment, such as being placed near high-power electrical appliances (such as microwave ovens, air conditioners, etc.). The reason is that frequent electromagnetic interference will affect the operation of the LAN port and may cause damage.

[0091] Secondly, when the number of obtained fault mapping semantic representations is greater than a preset number (e.g., 3, 5, 7, 10, etc.), clustering is performed on each fault mapping semantic representation (the specific clustering algorithm is not limited, such as KNN (K-Nearest Neighbors) clustering algorithm), forming at least one corresponding semantic representation cluster (i.e., obtaining at least one cluster center);

[0092] Then, for each of the fault mapping semantic representations, focused mining is performed on the fault mapping semantic representation according to the cluster center of the semantic representation cluster corresponding to the fault mapping semantic representation to form a local fault semantic representation corresponding to the fault mapping semantic representation.

[0093] Based on this, by clustering first and then performing focused mining, the accuracy of focused mining can be guaranteed, avoiding the problem of low accuracy of mined semantic representations caused by focused mining based on irrelevant semantic representations. In addition, the specific implementation method of focused mining can adopt cross attention.

[0094] It should also be noted that, in order to ensure the reliable execution of steps S120 and S130, they can be implemented using a corresponding neural network model, such as a target fault analysis network. Based on this, the fault analysis method for FTTR equipment can also include the step of training and forming the target fault analysis network, as follows:

[0095] First, a semantic mining unit included in the candidate fault analysis network can be used to perform semantic mining on multiple training fault-related data to form a training fault semantic representation, wherein the candidate fault analysis network is a neural network, and the multiple training fault-related data include at least one training time series data. During the semantic mining process, for each training time series data, the data fluctuation semantics in the training time series data are mined from at least two time change directions. For details, please refer to the relevant explanation of step S120 above.

[0096] Secondly, the semantic analysis unit included in the candidate fault analysis network can be used to perform fault analysis based on the training fault semantic representation and output training fault prediction data. Please refer to the relevant explanation of step S130 above.

[0097] Then, based on a training loss indicator (such as cross entropy loss) between the training fault prediction data and the fault label data corresponding to the multiple training fault-related data (i.e., information that indicates whether a fault will occur, such as manual labeling or identification using other neural network models), the network parameters of the candidate fault analysis network can be updated to form a target fault analysis network. For example, the network parameters can be updated in a direction that reduces the training loss indicator until the training loss indicator converges, thereby forming a target fault analysis network.

[0098] Combine Figure 4 The present application also provides a fault analysis device for an FTTR device applicable to the above-mentioned electronic device. The fault analysis device for the FTTR device may include a fault-related data acquisition module, a data semantic mining module, and a fault analysis module.

[0099] In detail, the fault-related data acquisition module can be used to acquire multiple fault-related data obtained by collecting data from the target FTTR device, wherein the fault-related data refers to data that may contribute to the failure of the LAN port of the target FTTR device, and there is at least one time series data in the multiple fault-related data. In the embodiment of the present application, the fault-related data acquisition module can be used to perform Figure 2 As shown in step S110 , for the relevant content of the fault-related data acquisition module, reference may be made to the above description of step S110 .

[0100] In detail, the data semantic mining module can be used to perform semantic mining on the multiple fault-related data to form a target fault semantic representation, wherein, in the process of semantic mining, for each of the time series data, the data fluctuation semantics in the time series data are mined from at least two time change directions. In the embodiment of the present application, the data semantic mining module can be used to perform Figure 2 As shown in step S120, for the relevant content of the data semantic mining module, please refer to the description of step S120 above.

[0101] In detail, the fault analysis module can be used to perform fault analysis on the target FTTR device based on the target fault semantic representation, and output fault prediction data corresponding to the target FTTR device, wherein the fault prediction data is used to reflect whether the LAN port of the target FTTR device will fail. In the embodiment of the present application, the fault analysis module can be used to perform Figure 2 As shown in step S130, for the relevant content of the fault analysis module, reference may be made to the above description of step S130.

[0102] In an embodiment of the present application, corresponding to the aforementioned method for analyzing FTTR equipment failures applied to the electronic device, a computer-readable storage medium is also provided. The computer-readable storage medium stores a computer program that, when executed, executes each step of the method for analyzing FTTR equipment failures. The steps executed by the aforementioned computer program are not described in detail here; reference is made to the aforementioned explanation of the method for analyzing FTTR equipment failures.

[0103] In summary, the fault analysis method and apparatus, equipment, and medium for FTTR equipment provided in this application first obtain multiple fault-related data obtained by data collection for the target FTTR equipment; secondly, semantic mining is performed on the multiple fault-related data to form a target fault semantic representation; then, based on the target fault semantic representation, fault analysis is performed on the target FTTR equipment to output fault prediction data corresponding to the target FTTR equipment. Based on the above content, on the one hand, because semantic mining is performed on multiple fault-related data, the semantic information carried by the formed target fault semantic representation can be richer; on the other hand, because in the process of semantic mining, for each time series data, the data fluctuation semantics are mined from at least two time change directions, the representation reliability of the mined semantic information is higher, thereby ensuring the reliability of the fault prediction data output based on the target fault semantic representation, thereby improving the problem of difficulty in effectively analyzing LAN port faults in the existing technology.

[0104] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0106] If the functions are implemented in the form of software modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. It should be noted that, in this document, the terms "comprise," "include," or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article, or device. Without further constraints, an element defined by the phrase "comprises a..." does not preclude the existence of additional identical elements in the process, method, article or apparatus that includes the element.

[0107] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for analyzing a fault of an FTTR device, characterized in that: include: Acquire multiple fault-related data obtained by collecting data from a target FTTR device, wherein the fault-related data refers to data that contributes to the failure of a LAN port of the target FTTR device, and the multiple fault-related data include at least one time series data; For each time series data in the plurality of fault-related data, data fluctuation semantics in the time series data are mined from at least two time change directions, and a corresponding local fault semantic representation is output; when there is at least one fault-related data in the plurality of fault-related data that does not belong to the time series data, semantic mining is performed on each fault-related data in the at least one fault-related data, and a corresponding local fault semantic representation is output; the local fault semantic representation corresponding to each of the fault-related data is fused to form a target fault semantic representation, wherein the target fault semantic representation is used to characterize the global semantic information possessed by the plurality of fault-related data; Based on the target fault semantic representation, a fault analysis is performed on the target FTTR device, and fault prediction data corresponding to the target FTTR device is output, wherein the fault prediction data is used to reflect whether a LAN port of the target FTTR device will fail.

2. The fault analysis method of FTTR equipment according to claim 1, characterized in that: The step of mining data fluctuation semantics in each time series data in the plurality of fault-related data from at least two time change directions and outputting corresponding local fault semantic representations includes: Loading the time series data into corresponding semantic mining branches, wherein each time series data corresponds to one semantic mining branch; For each sequence sub-data in the time series data, semantic space mapping is performed on the sequence sub-data to form a corresponding first fault semantic representation, and starting from the sequence sub-data, a first sequence data segment corresponding to the sequence sub-data is cut out from the time series data according to a first time change direction through a target window carried by the semantic mining branch. Also, starting from the sequence sub-data, a second sequence data segment corresponding to the sequence sub-data is cut out from the time series data according to a second time change direction through the target window. The size of the target window is formed during training as a network parameter of the semantic mining branch. Performing semantic space mapping on the first sequence of data segments and the second sequence of data segments respectively to form corresponding second fault semantic representations and third fault semantic representations; The first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to each sequence sub-data are fused to form a corresponding local fault semantic representation.

3. The fault analysis method of FTTR equipment according to claim 2, characterized in that: The step of performing semantic space mapping on the first sequence data segments and the second sequence data segments respectively to form corresponding second fault semantic representations and third fault semantic representations includes: performing a dispersion calculation on the first sequence of data segments to obtain a first dispersion, and performing a dispersion calculation on the second sequence of data segments to obtain a second dispersion, wherein the time series data is time numerical series data, including temperature, voltage, or network traffic; Semantic space mapping is performed on the first discreteness and the second discreteness respectively to form corresponding second fault semantic representation and third fault semantic representation, wherein the semantic space mapping includes word embedding processing.

4. The fault analysis method of FTTR equipment according to claim 2, characterized in that: The step of fusing the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to each sequence sub-data to form a corresponding local fault semantic representation includes: For each sequence sub-data, concatenate the first fault semantic representation, the second fault semantic representation, and the third fault semantic representation corresponding to the sequence sub-data to form a concatenated fault semantic representation corresponding to the sequence sub-data; Splicing the spliced fault semantic representations corresponding to each of the sequence sub-data to form a spliced fault semantic representation corresponding to the time series data, and performing convolution and pooling on the spliced fault semantic representations corresponding to the time series data to extract different semantic features to form corresponding convolutional fault semantic representations and pooled fault semantic representations, wherein the spliced fault semantic representations, the convolutional fault semantic representations, and the pooled fault semantic representations corresponding to each of the sequence sub-data have the same size; For each sequence sub-data, based on the spliced fault semantic representation corresponding to the sequence sub-data, the convolutional fault semantic representation and the pooled fault semantic representation are respectively focused and mined to form a corresponding first focused semantic representation and a second focused semantic representation, and the first focused semantic representation and the second focused semantic representation are superimposed to form a corresponding fused focused semantic representation; The fused focused semantic representation corresponding to each of the sequence sub-data is spliced to form a spliced focused semantic representation, and a local fault semantic representation is determined based on the spliced focused semantic representation.

5. The fault analysis method of FTTR equipment according to claim 1, characterized in that: When at least one fault-related data is not time series data among the plurality of fault-related data, the step of performing semantic mining on each of the at least one fault-related data and outputting a corresponding local fault semantic representation includes: When at least one fault-related data is not time series data among the plurality of fault-related data, performing semantic space mapping on each fault-related data among the at least one fault-related data to obtain a corresponding fault mapping semantic representation; When the number of obtained fault mapping semantic representations is greater than a preset number, clustering the fault mapping semantic representations to form at least one corresponding semantic representation cluster; For each of the fault mapping semantic representations, focused mining is performed on the fault mapping semantic representation according to the cluster center of the semantic representation cluster corresponding to the fault mapping semantic representation to form a local fault semantic representation corresponding to the fault mapping semantic representation.

6. The fault analysis method for FTTR equipment according to any one of claims 1 to 5, characterized in that: The fault analysis method of the FTTR device further includes: Using a semantic mining unit included in a candidate fault analysis network, semantic mining is performed on a plurality of training fault-related data to form a training fault semantic representation, wherein the candidate fault analysis network is a neural network, and the plurality of training fault-related data includes at least one training time series data. During the semantic mining process, for each training time series data, data fluctuation semantics in the training time series data is mined from at least two time change directions; Utilizing the semantic analysis unit included in the candidate fault analysis network, performing fault analysis based on the training fault semantic representation, and outputting training fault prediction data; Based on a training loss indicator between the training fault prediction data and the fault label data corresponding to the plurality of training fault-related data, network parameters of the candidate fault analysis network are updated to form a target fault analysis network.

7. A fault analysis device for FTTR equipment, characterized in that: include: a fault-related data acquisition module, configured to acquire a plurality of fault-related data obtained by collecting data from a target FTTR device, wherein the fault-related data refers to data that contributes to the failure of a LAN port of the target FTTR device, and the plurality of fault-related data includes at least one time series data; A data semantic mining module is configured to mine the data fluctuation semantics in each time series data of the plurality of fault-related data from at least two time change directions, and output a corresponding local fault semantic representation; when at least one fault-related data that does not belong to the time series data is included in the plurality of fault-related data, perform semantic mining on each of the at least one fault-related data, and output a corresponding local fault semantic representation; fuse the local fault semantic representation corresponding to each of the fault-related data to form a target fault semantic representation, wherein the target fault semantic representation is used to characterize the global semantic information possessed by the plurality of fault-related data; A fault analysis module is used to perform fault analysis on the target FTTR device based on the target fault semantic representation, and output fault prediction data corresponding to the target FTTR device, wherein the fault prediction data is used to reflect whether the LAN port of the target FTTR device will fail.

8. An electronic device, characterized in that: include: Memory for storing computer programs; A processor connected to the memory is used to execute the computer program stored in the memory to implement the fault analysis method of the FTTR equipment according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed, executes the fault analysis method for FTTR equipment according to any one of claims 1 to 6.

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