Local area network exception handling method and device for home equipment

Abstract

The invention discloses a local area network exception handling method and device for home equipment. The method comprises the following steps: acquiring local area network data information of the home equipment; the home equipment local area network data information comprises a plurality of pieces of to-be-processed local area network data information; pre-processing the home equipment local area network data information to obtain first local area network data information; the first local area network data information comprises a plurality of pieces of first processing local area network data information; each piece of first processing local area network data information comprises at least one piece of first target local area network data information distributed in sequence; and carrying out detection analysis processing on the first local area network data information to obtain target anomaly detection result information.

Description

Technical Field

[0001] The present invention relates to the field of network technology, and in particular to a local area network exception processing method and device for household equipment. Background Art

[0002] In the actual communication process, there may be various exceptions, and we need to handle these exceptions, such as how to handle token expiration, message unresponsive retry mechanism, etc. For example, in the communication process, there may be a situation where the message is sent out but the other party does not respond due to network or packet loss. Therefore, a local area network exception handling method and device for home appliances are provided to monitor local area network abnormalities, improve the accuracy of network status monitoring, and thus improve the efficiency of network abnormality monitoring and repair, and improve the network communication experience. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a local area network anomaly processing method and device for household appliances, which is conducive to monitoring local area network abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0004] In order to solve the above technical problems, the first aspect of the embodiment of the present invention discloses a local area network exception processing method for home appliances, the method comprising: Acquire local area network data information of household equipment; the local area network data information of household equipment includes a plurality of local area network data information to be processed; Pre-processing the household device LAN data information to obtain first LAN data information; the first LAN data information includes a plurality of first processed LAN data information; each of the first processed LAN data information includes at least one first target LAN data information distributed in sequence; The first local area network data information is detected, analyzed and processed to obtain target anomaly detection result information.

[0005] A second aspect of an embodiment of the present invention discloses a local area network exception processing device for household equipment, the device comprising: An acquisition module, used for acquiring household equipment local area network data information; the household equipment local area network data information includes a plurality of local area network data information to be processed; A first processing module is used to pre-process the household device LAN data information to obtain first LAN data information; the first LAN data information includes a plurality of first processed LAN data information; each of the first processed LAN data information includes at least one first target LAN data information distributed in sequence; The second processing module is used to detect, analyze and process the first local area network data information to obtain target anomaly detection result information.

[0006] The third aspect of the present invention discloses another local area network exception processing device for household equipment, the device comprising: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the local area network exception handling method for home appliances disclosed in the first aspect of the embodiment of the present invention.

[0007] The fourth aspect of the present invention discloses a computer-readable storage medium, which stores computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the local area network exception handling method for home appliances disclosed in the first aspect of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 is a schematic diagram of a scenario of a local area network exception handling system for home appliances provided by an embodiment of the present invention; Figure 2 It is a flow chart of a method for handling local area network exceptions for household devices disclosed in an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a local area network exception processing device for household appliances disclosed in an embodiment of the present invention; Figure 4 It is a structural schematic diagram of another local area network exception processing device for household equipment disclosed in an embodiment of the present invention; Figure 5 It is a structural diagram of a target anomaly detection model disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0010] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or equipment.

[0012] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0013] In this application, the word "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described in this application as "exemplary" is not necessarily to be construed as being preferred or advantageous over other embodiments. The following description is given to enable any technician in the field to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present application can be implemented without using these specific details. In other instances, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present application with unnecessary details. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0014] It should be noted that since the method of the embodiment of the present application is executed in a computer device, the processing objects of each computer device exist in the form of data or information. For example, time is actually time information. It can be understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data for processing by the computer device. The details will not be repeated here.

[0015] It should be noted that the artificial intelligence related technologies that may be involved in this application are briefly described. Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making.

[0016] Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level and software-level technologies. The basic technologies of artificial intelligence generally include sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics and other technologies. Artificial intelligence software technology mainly includes computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0017] Computer Vision (CV) is a science that studies how to make machines "see". To put it more specifically, it refers to machine vision such as using cameras and computers to replace human eyes to identify and measure targets, and further perform graphic processing so that computer processing becomes an image that is more suitable for human eye observation or transmission to instrument detection. As a scientific discipline, computer vision studies related theories and technologies, and attempts to establish an artificial intelligence system that can obtain information from images or multi-dimensional data. Computer vision technology usually includes image processing, image recognition, image semantic understanding, image retrieval, OCR, video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and map construction, and other technologies, as well as common biometric recognition technologies such as face recognition and fingerprint recognition.

[0018] Unimodal information is data of only one type, such as text, image, audio, video, electromagnetic signal, etc. Multimodal information is data that includes at least two types of unimodal information. Furthermore, multimodal information is suitable for complex tasks that require the integration of multiple information sources, such as sentiment analysis, robot interaction, autonomous driving, etc. By integrating information from multiple modalities, higher performance and accuracy can usually be achieved on the task.

[0019] A large model refers to an artificial neural network model with a very large number of parameters. In the field of artificial intelligence, a large model generally refers to a model with hundreds of millions to trillions of parameters. Models usually need to be trained on large-scale data sets and require a large amount of computing resources to be optimized and adjusted. Large models are often used to solve complex tasks such as natural language processing, computer vision, and speech recognition. Generative AI is an AI that can create new content and ideas, including conversations, stories, images, videos, and music. In an embodiment of the present application, the large model may be ChatGPT, BERT, XLNet, Zhipu model, Claude, Moonshot AI model, ChatGLM model, Qianyi Tongwen model, MiniMax model, Spark model, Llama model, 360GPT model, Qwen model, Baichuan model, Skylark model, vivoLM model, and Wenxin Yiyan scale language models, which are not limited in the embodiments of the present application.

[0020] The embodiments of the present application provide a method, apparatus, computer equipment, and computer-readable storage medium for handling local area network exceptions for home appliances, which are described in detail below.

[0021] See also Figure 1 , Figure 1 The schematic diagram of the scenario of the LAN exception handling system for household appliances provided in the embodiment of the present application, the LAN exception handling system for household appliances may include a computer device 100, and the computer device 100 is integrated with a LAN exception handling device for household appliances, such as Figure 1 Computer equipment in.

[0022] In the embodiment of the present application, the computer device 100 is mainly used to obtain the local area network data information of the household equipment; the local area network data information of the household equipment includes a plurality of local area network data information to be processed; Pre-processing the household device LAN data information to obtain first LAN data information; the first LAN data information includes a plurality of first processed LAN data information; each first processed LAN data information includes at least one first target LAN data information distributed in sequence; The first local area network data information is detected, analyzed and processed to obtain target anomaly detection result information.

[0023] It can monitor abnormal conditions in the local area network, improve the accuracy of network status monitoring, thereby improving the efficiency of network abnormality monitoring and repair, and improving the network communication experience.

[0024] In the embodiment of the present application, the computer device 100 may be an independent server, or a server network or server cluster composed of servers. For example, the computer device 100 described in the embodiment of the present application includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0025] It is understandable that the computer device 100 used in the embodiments of the present application may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific computer device 100 may be a desktop terminal or a mobile terminal, and the computer device 100 may also be one of a mobile phone, a tablet computer, a laptop computer, etc.

[0026] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the present application solution and does not constitute a limitation on the application scenario of the present application solution. Other application environments may also include Figure 1 More or less computer equipment as shown in Figure 1 Only one computer device is shown. It can be understood that the local area network exception handling system for home appliances may also include one or more other services, which are not specifically limited here.

[0027] In addition, if Figure 1 As shown, the local area network exception handling system for home appliances may further include a memory 200 for storing data, such as image data, location information, and the like.

[0028] It should be noted that Figure 1 The scenario diagram of the LAN exception handling system for household appliances shown is merely an example. The LAN exception handling system and scenario for household appliances described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided in the embodiment of the present application. A person of ordinary skill in the art can appreciate that with the evolution of the LAN exception handling system for household appliances and the emergence of new business scenarios, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.

[0029] The present invention discloses a local area network anomaly processing method and device for household appliances, which is beneficial to monitoring the abnormal conditions of the local area network, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication. The following are detailed descriptions.

[0030] Embodiment 1 See also Figure 2 , Figure 2 1 is a flow chart of a method for handling local area network anomalies for home appliances disclosed in an embodiment of the present invention. Figure 2 The described LAN exception handling method for household devices is applied to a management system, such as a local server or a cloud server for management, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the local area network exception handling method for home devices may include the following operations: 101. Obtain local area network data information of home appliances.

[0031] In the embodiment of the present invention, the household device local area network data information includes a plurality of local area network data information to be processed.

[0032] 102. Pre-process the local area network data information of the home appliance to obtain first local area network data information.

[0033] In the embodiment of the present invention, the first LAN data information includes a plurality of first processing LAN data information; each first processing LAN data information includes at least one first target LAN data information distributed in sequence.

[0034] 103. Perform detection, analysis and processing on the first local area network data information to obtain target anomaly detection result information.

[0035] It should be noted that the above-mentioned LAN data information to be processed represents the message information collected under the TCP / IP / MPLS communication mechanism of the home LAN composed of smart home (smart TV, smart air conditioner, smart speaker, smart fan, etc.) and switches, routers, etc., which is not limited in the embodiment of the present invention. Further, the above-mentioned message information can be a network traffic packet crawled. Further, the above-mentioned network traffic packet can be crawled by Prob probe method or by WireShark method, which is not limited in the embodiment of the present invention.

[0036] It should be noted that the data of the above-mentioned local area network data information to be processed may be information such as network protocol and device type, which is not limited in the embodiment of the present invention.

[0037] It can be seen that implementing the LAN anomaly handling method for home appliances described in the embodiment of the present invention is beneficial to monitoring LAN abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0038] In an optional embodiment, the detection and analysis of the first local area network processing information to obtain target anomaly detection result information includes: Using the target anomaly detection model to detect and process the first local area network processing information, and obtain data detection result information; the data detection result information includes a plurality of local area network data detection result information; The data detection result information is classified, analyzed and processed to obtain the target anomaly detection result information.

[0039] It should be noted that the above-mentioned LAN data detection result information represents the data analysis category corresponding to each LAN data information to be processed, which is not limited by the embodiment of the present invention. Further, the above-mentioned data analysis categories include normal data, abnormal data, data intrusion, and unclear data, which are not limited by the embodiment of the present invention. Further, the above-mentioned normal data represents that this type of LAN data is a normally flowing data stream, which is not limited by the embodiment of the present invention. Further, the above-mentioned abnormal data represents that this type of LAN data has abnormal points, but it is not a serious data intrusion, which is not limited by the embodiment of the present invention. Further, the above-mentioned data intrusion represents that the LAN has been invaded by abnormal functions such as Trojans, which is not limited by the embodiment of the present invention. Further, the above-mentioned unclear data represents the problem of being unable to identify the data, and the user needs to perform further identification and recognition, which is not limited by the embodiment of the present invention.

[0040] It should be noted that the above target anomaly detection result information includes data detection classification result information and data detection alarm information, which is not limited in the embodiment of the present invention. Furthermore, the above data detection classification result information is a vector representing the data analysis category, which can be [x1, x2, x3, x4] (x1, x2, x3, x4 represent the data detection statistics of normal data, abnormal data, unknown data, and data intrusion, respectively), which is not limited in the embodiment of the present invention.

[0041] In this optional embodiment, as an optional implementation, the data detection result information is classified and analyzed to obtain target anomaly detection result information, including: Performing classified statistical analysis on the local area network data detection result information in the data detection result information according to the data analysis category to obtain data detection statistical result information; the data detection statistical result information includes 4 data detection statistical values; Fill the data detection statistical result information according to the preset category vector to obtain the data detection classification result information; Determine whether the data detection statistic corresponding to the fourth vector element in the data detection classification result information is greater than the first detection threshold, and obtain a first detection judgment result; When the first detection judgment result is yes, determine whether the data detection statistic corresponding to the third vector element in the data detection classification result information is greater than the second detection threshold, and obtain a second detection judgment result; When the second detection result is yes, the local area network significant abnormality is used as data detection alarm information; When the second detection judgment result is no, the intrusion warning is used as data detection alarm information; When the first detection judgment result is no, determine whether the data detection statistic corresponding to the third vector element in the data detection classification result information is greater than the second detection threshold, and obtain a third detection judgment result; When the third detection result is yes, the security detection is required as data detection alarm information; When the third detection judgment result is no, determine whether the data detection statistic corresponding to the second vector element in the data detection classification result information is greater than the third detection threshold, and obtain a fourth detection judgment result; When the fourth detection judgment result is yes, the security detection is required as data detection alarm information; When the fourth detection judgment result is no, network security is determined as data detection alarm information.

[0042] It should be noted that the first detection threshold, the second detection threshold and the third detection threshold can be set by the user, or can be a default value given by the system, or can be obtained by averaging the historical first detection threshold, the historical second detection threshold and the historical third detection threshold, or can be obtained by data regression or clustering based on empirical data, which is not limited in the embodiments of the present invention. Furthermore, the first detection threshold is smaller than the second detection threshold and the third detection threshold, which indicates that the detection of the home LAN pays more attention to the problem of data intrusion, so as to improve the detection sensitivity of data intrusion and ensure the security of the home LAN, that is, data anomalies and unclear data can be considered as smaller data problems, but data intrusion may cause serious home LAN failures, therefore, the judgment threshold is lowered to improve the detection sensitivity of the system, which is not limited in the embodiments of the present invention.

[0043] It should be noted that the above-mentioned significant abnormality of the local area network indicates that a serious data communication problem has occurred in the local area network and timely intervention is required to ensure the safe communication of the home devices, which is not limited in the embodiments of the present invention.

[0044] It should be noted that the above intrusion warning indicates that the home local area network may have been invaded by a Trojan horse and needs to be processed by anti-virus to ensure the communication security of the home local area network, which is not limited in the embodiment of the present invention.

[0045] It should be noted that the need for security detection indicates that there may be abnormal conditions in the home local area network, and that hardware device detection needs to be arranged to ensure normal communication of each device node in the home local area network, which is not limited in the embodiments of the present invention.

[0046] It should be noted that the above network security represents that the communication of the home local area network is normal, and the embodiment of the present invention does not limit this.

[0047] It can be seen that implementing the LAN anomaly handling method for home appliances described in the embodiment of the present invention is beneficial to monitoring LAN abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0048] In another optional embodiment, the target anomaly detection model includes a first feature extraction module for extracting features from a spatial dimension, a second feature extraction module for extracting features from a temporal dimension, a feature fusion module, a connection module, and an activation module; wherein, The input end of the first feature extraction module and the input end of the second feature extraction module are configured to receive model input information of the target anomaly detection model; the output end of the first feature extraction module and the output end of the second feature extraction module are both connected to the input end of the feature fusion module; the output end of the feature fusion module is connected to the input end of the connection module; the output end of the connection module is connected to the input end of the activation module; the output end of the activation module is configured to output the model output information of the target anomaly detection model.

[0049] It should be noted that the target anomaly detection model of the present application utilizes the first feature extraction module and the second feature extraction module to extract information features of the local area network data information from the two dimensions of space and time, so as to realize in-depth feature extraction of the two continuous features of the data information in the spatial and temporal dimension features, thereby avoiding the problem of insufficient information utilization caused by the traditional model only analyzing the single dimensional features, thereby further improving the representation ability of the features in the present application, and then improving the model's ability to identify local area network abnormal information, which is not limited to the embodiments of the present invention.

[0050] It should be noted that the above-mentioned first feature extraction module represents a model for feature extraction from the spatial dimension, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned first feature extraction module can be a model built on the basis of CNN (used to capture spatial correlation in sequence data, extract features from local areas by sliding the convolution kernel on the input data, which helps to detect local patterns and features in the input sequence. A pooling layer is usually added after the CNN layer to reduce the spatial dimension of the data to reduce computational complexity), or it can be a model built on the basis of a graph convolutional neural network, which is not limited in the embodiment of the present invention.

[0051] It should be noted that the second feature extraction module represents a model for feature extraction from the time dimension to mine the contextual timing information of the time series and further extract feature information, which is not limited in the embodiments of the present invention. Furthermore, the first feature extraction module can be a model constructed by LSTM (used to process the temporal correlation of sequence data, which is a recursive neural network that can remember and learn the long-term dependencies of the input sequence. The LSTM layer manages the flow and memory of information through gating units, which enables the model to adapt to input data of different time steps), or it can be a model constructed based on the autoregressive integrated moving average model (ARIMA), which is not limited in the embodiments of the present invention.

[0052] It should be noted that the above connection module is constructed based on the fully connected layer, which is not limited in the embodiment of the present invention.

[0053] It should be noted that the above activation module can be constructed based on the softmax activation function or the RELU activation function, which is not limited in the embodiment of the present invention.

[0054] It can be seen that implementing the LAN anomaly handling method for home appliances described in the embodiment of the present invention is beneficial to monitoring LAN abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0055] In another optional embodiment, the feature fusion module includes a first sampling unit, a first pooling unit, a second pooling unit, a multi-head attention unit, a first fusion unit, a second fusion unit and a third fusion unit; wherein, The input end of the first sampling unit and the input end of the second fusion unit are both connected to the output end of the first feature extraction module; the output end of the first sampling unit is connected to the input end of the first pooling unit; the output end of the first pooling unit is connected to the input end of the multi-head attention unit; the input end of the second pooling unit and the input end of the first fusion unit are both connected to the input end of the second feature extraction module; the output end of the second pooling unit is connected to the input end of the multi-head attention unit; the output end of the multi-head attention unit is respectively connected to the input end of the first fusion unit, the input end of the second fusion unit and the input end of the third fusion unit; the output end of the first fusion unit is connected to the input end of the third fusion unit; the output end of the second fusion unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the connection module.

[0056] It should be noted that the above-mentioned first fusion unit and the second fusion unit are calculation modules constructed based on element-by-element multiplication operations, which are not limited in the embodiments of the present invention.

[0057] It should be noted that the third fusion unit is a calculation module constructed based on element-by-element addition operation, which is not limited in the embodiment of the present invention.

[0058] It should be noted that the above-mentioned first pooling unit and the second pooling unit are both constructed based on the average pooling operation to facilitate the multi-head attention unit to integrate feature data of different dimensions, which is not limited in the embodiment of the present invention.

[0059] It should be noted that the above-mentioned multi-head attention unit is constructed based on the cross attention module, which is not limited in the embodiment of the present invention. Furthermore, the output end of the above-mentioned first pooling unit is connected to the K key and V key of the multi-head attention unit, and the output end of the second pooling unit is connected to the Q key of the multi-head attention unit, which is not limited in the embodiment of the present invention, thereby realizing the multi-head attention analysis of the associated semantics of the spatial dimension and the temporal dimension, which is not limited in the embodiment of the present invention.

[0060] It should be noted that the first sampling unit is constructed based on the downsampling operation, which is not limited in the embodiment of the present invention. Further, through the processing of the first sampling unit, it can be achieved that the feature data of the spatial dimension extracted by the first feature extraction module and the feature data of the time dimension extracted by the second feature extraction module are kept the same size, which is not limited in the embodiment of the present invention.

[0061] It should be noted that the above processing by the feature fusion module can realize the dynamic fusion of spatial dimension features and temporal dimension features, so as to facilitate more in-depth feature extraction and analysis of local area network data information, which is not limited in the embodiment of the present invention.

[0062] It can be seen that implementing the LAN anomaly handling method for home appliances described in the embodiment of the present invention is beneficial to monitoring LAN abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0063] In yet another optional embodiment, the target anomaly detection model is obtained by training the basic anomaly detection model based on the loss function model; Among them, the loss function model is: ; In the formula, Characterize the loss function value; and Respectively represent the first loss function coefficient and the second loss function coefficient; Represents the true labels corresponding to the training samples input into the basic anomaly detection model; Prediction labels that characterize the output of the underlying anomaly detection model.

[0064] It should be noted that the model architecture of the above-mentioned basic anomaly detection model is consistent with that of the target anomaly detection model, which is not limited in the embodiment of the present invention. Furthermore, the above-mentioned target anomaly detection model is a model obtained by optimizing the parameters of the basic anomaly detection model on the basis of model training, which is not limited in the embodiment of the present invention.

[0065] It should be noted that the above-mentioned real labels represent the actual label values ​​of the network anomalies labeled by the training samples, which is not limited in the embodiments of the present invention. Furthermore, the above-mentioned training samples can be traffic packets labeled by users themselves, or they can be composed of a general data set composed of traffic data such as Normal and U2R, such as those selected from the NSL-KDD data set, which is not limited in the embodiments of the present invention.

[0066] It should be noted that the above-mentioned prediction label represents the anomaly prediction probability of the local area network detection output by the basic anomaly detection model, and the embodiment of the present invention does not limit this.

[0067] It should be noted that the above-mentioned loss function value is used to analyze and determine whether the basic anomaly detection model has been trained. It can be analyzed and determined whether the model training is completed by whether the data composed of the loss function value and the historical loss function value converges. If the data converges, it can be determined that the model training is completed, and the target anomaly detection model can be obtained. The embodiment of the present invention does not limit this.

[0068] It should be noted that the sum of the first loss function coefficient and the second loss function coefficient is 1, which is not limited in the embodiment of the present invention. Further, the first loss function coefficient is a positive number between 0 and 0.5, which is not limited in the embodiment of the present invention.

[0069] It can be seen that implementing the LAN anomaly handling method for home appliances described in the embodiment of the present invention is beneficial to monitoring LAN abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0070] In an optional embodiment, the above-mentioned pre-processing of the household device local area network data information to obtain the first local area network processing information includes: Get the segmentation conversion threshold and conversion symbol; For any unprocessed LAN data information in the LAN data information of the household device, segment and convert the unprocessed LAN data information using the segmentation conversion threshold and the conversion symbol to obtain the unused LAN data information corresponding to the unprocessed LAN data information; the unused LAN data information includes at least one unused LAN sub-data information distributed in sequence; Vectorized processing is performed on the local area network data information to be processed to obtain first processed local area network data information corresponding to the local area network data information to be processed.

[0071] It should be noted that the above conversion symbol represents a binary symbol, which can be 0 or 1, which is set by the user according to actual needs and is not limited in the embodiment of the present invention.

[0072] It should be noted that the above segmentation conversion threshold represents the vector data length that can be processed by the target anomaly detection model, which is usually an integer multiple of 8 and is not limited in the embodiments of the present invention.

[0073] It should be noted that the above-mentioned vectorization processing of the stand-by LAN data information is to vectorize the stand-by LAN sub-data information representing the text type, which can be implemented based on the Doc2Vec model or based on a fine-tuned large model, and the embodiment of the present invention does not limit this.

[0074] It can be seen that implementing the LAN anomaly handling method for home appliances described in the embodiment of the present invention is beneficial to monitoring LAN abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0075] In another optional embodiment, the segmentation conversion threshold and the conversion symbol are used to perform segmentation conversion processing on the LAN data information to be processed to obtain the standby LAN data information corresponding to the LAN data information to be processed, including: Determine whether the length of the local area network data information corresponding to the local area network data information to be processed is less than the segmentation conversion threshold, and obtain a first length determination result; When the first length judgment result is yes, the conversion symbol is filled into the front end of the LAN data information to be processed to obtain the standby LAN sub-data information corresponding to the LAN data information to be processed; When the first length determination result is no, determining whether the length of the local area network data information is equal to the segmentation conversion threshold, and obtaining a second length determination result; When the second length judgment result is yes, determining the LAN data information to be processed as the standby LAN sub-data information corresponding to the LAN data information to be processed; When the second length judgment result is no, segmenting the to-be-processed local area network data information from left to right based on the segmentation conversion threshold value to obtain a plurality of sequentially distributed to-be-processed local area network sub-data information corresponding to the to-be-processed local area network data information; Determine whether the length of the local area network data information corresponding to the last-sorted standby local area network sub-data information is less than the segmentation conversion threshold, and obtain a third length determination result; When the third length judgment result is yes, the conversion symbol is filled to the front end of the last sorted standby local area network sub-data information to obtain new last sorted standby local area network sub-data information; When the result of the third length judgment is no, the segmentation and conversion processing flow corresponding to the LAN data information to be processed is terminated.

[0076] It should be noted that the above-mentioned segmentation and conversion processing of the LAN data information to be processed by using the segmentation conversion threshold and the conversion symbol is to convert the data into a data sequence of a specific length that can be processed by the target anomaly detection model, which is not limited in the embodiment of the present invention.

[0077] It should be noted that when the data length is less than the segmentation conversion threshold, the present application fills the conversion symbol before the data so that the data length reaches the segmentation conversion threshold so that the target anomaly detection model can perform effective processing. The embodiment of the present invention does not limit this.

[0078] It should be noted that, when the data length is greater than the segmentation conversion threshold, the data of equal length (data length = segmentation conversion threshold) is segmented one by one from left to right according to the segmentation conversion threshold to obtain the corresponding stand-by LAN sub-data information, and the data length of the last stand-by LAN sub-data information is analyzed to determine whether to add a conversion symbol, thereby ensuring that the data can be effectively processed by the target anomaly detection model, thereby achieving effective detection of abnormal conditions in the LAN, which is not limited in the embodiments of the present invention.

[0079] It can be seen that implementing the LAN anomaly handling method for home appliances described in the embodiment of the present invention is beneficial to monitoring LAN abnormal conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the network communication usage experience.

[0080] Embodiment 2 See also Figure 3 , Figure 3 1 is a schematic diagram of a structure of a local area network exception handling device for household appliances disclosed in an embodiment of the present invention. Figure 3 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 3 As shown, the device may include: The acquisition module 201 is used to acquire the local area network data information of the household equipment; the local area network data information of the household equipment includes a plurality of local area network data information to be processed; The first processing module 202 is used to pre-process the household device LAN data information to obtain first LAN data information; the first LAN data information includes a plurality of first processed LAN data information; each first processed LAN data information includes at least one first target LAN data information distributed in sequence; The second processing module 203 is used to detect, analyze and process the first local area network data information to obtain target anomaly detection result information.

[0081] It can be seen that implementation Figure 3 The described LAN anomaly processing device for household appliances is beneficial for monitoring LAN anomaly conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication.

[0082] In another optional embodiment, Figure 3 As shown, the first processing module 202 performs detection and analysis on the first local area network processing information to obtain target anomaly detection result information, including: Using the target anomaly detection model to detect and process the first local area network processing information, and obtain data detection result information; the data detection result information includes a plurality of local area network data detection result information; The data detection result information is classified, analyzed and processed to obtain the target anomaly detection result information.

[0083] It can be seen that implementation Figure 3 The described LAN anomaly processing device for household appliances is beneficial for monitoring LAN anomaly conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication.

[0084] In yet another optional embodiment, Figure 3 As shown, the target anomaly detection model includes a first feature extraction module for extracting features from the spatial dimension, a second feature extraction module for extracting features from the temporal dimension, a feature fusion module, a connection module and an activation module; wherein, The input end of the first feature extraction module and the input end of the second feature extraction module are configured to receive model input information of the target anomaly detection model; the output end of the first feature extraction module and the output end of the second feature extraction module are both connected to the input end of the feature fusion module; the output end of the feature fusion module is connected to the input end of the connection module; the output end of the connection module is connected to the input end of the activation module; the output end of the activation module is configured to output the model output information of the target anomaly detection model.

[0085] It can be seen that implementation Figure 3 The described LAN anomaly processing device for household appliances is beneficial for monitoring LAN anomaly conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication.

[0086] In yet another optional embodiment, Figure 3As shown, the feature fusion module includes a first sampling unit, a first pooling unit, a second pooling unit, a multi-head attention unit, a first fusion unit, a second fusion unit and a third fusion unit; wherein, The input end of the first sampling unit and the input end of the second fusion unit are both connected to the output end of the first feature extraction module; the output end of the first sampling unit is connected to the input end of the first pooling unit; the output end of the first pooling unit is connected to the input end of the multi-head attention unit; the input end of the second pooling unit and the input end of the first fusion unit are both connected to the input end of the second feature extraction module; the output end of the second pooling unit is connected to the input end of the multi-head attention unit; the output end of the multi-head attention unit is respectively connected to the input end of the first fusion unit, the input end of the second fusion unit and the input end of the third fusion unit; the output end of the first fusion unit is connected to the input end of the third fusion unit; the output end of the second fusion unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the connection module.

[0087] It can be seen that implementation Figure 3 The described LAN anomaly processing device for household appliances is beneficial for monitoring LAN anomaly conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication.

[0088] In yet another optional embodiment, Figure 3 As shown, the target anomaly detection model is obtained by training the basic anomaly detection model based on the loss function model; Among them, the loss function model is: ; In the formula, Characterize the loss function value; and Respectively represent the first loss function coefficient and the second loss function coefficient; Represents the true labels corresponding to the training samples input into the basic anomaly detection model; Prediction labels that characterize the output of the underlying anomaly detection model.

[0089] It can be seen that implementation Figure 3 The described LAN anomaly processing device for household appliances is beneficial for monitoring LAN anomaly conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication.

[0090] In yet another optional embodiment, Figure 3 As shown, the second processing module 203 pre-processes the household device LAN data information to obtain first LAN processing information, including: Get the segmentation conversion threshold and conversion symbol; For any unprocessed LAN data information in the LAN data information of the household device, segment and convert the unprocessed LAN data information using the segmentation conversion threshold and the conversion symbol to obtain the unused LAN data information corresponding to the unprocessed LAN data information; the unused LAN data information includes at least one unused LAN sub-data information distributed in sequence; Vectorized processing is performed on the local area network data information to be processed to obtain first processed local area network data information corresponding to the local area network data information to be processed.

[0091] It can be seen that implementation Figure 3 The described LAN anomaly processing device for household appliances is beneficial for monitoring LAN anomaly conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication.

[0092] In yet another optional embodiment, Figure 3 As shown, the second processing module 203 performs segmentation and conversion processing on the LAN data information to be processed using the segmentation and conversion threshold and the conversion symbol to obtain the standby LAN data information corresponding to the LAN data information to be processed, including: Determine whether the length of the local area network data information corresponding to the local area network data information to be processed is less than the segmentation conversion threshold, and obtain a first length determination result; When the first length judgment result is yes, the conversion symbol is filled into the front end of the LAN data information to be processed to obtain the standby LAN sub-data information corresponding to the LAN data information to be processed; When the first length determination result is no, determining whether the length of the local area network data information is equal to the segmentation conversion threshold, and obtaining a second length determination result; When the second length judgment result is yes, determining the LAN data information to be processed as the standby LAN sub-data information corresponding to the LAN data information to be processed; When the second length judgment result is no, segmenting the to-be-processed local area network data information from left to right based on the segmentation conversion threshold value to obtain a plurality of sequentially distributed to-be-processed local area network sub-data information corresponding to the to-be-processed local area network data information; Determine whether the length of the local area network data information corresponding to the last-sorted standby local area network sub-data information is less than the segmentation conversion threshold, and obtain a third length determination result; When the third length judgment result is yes, the conversion symbol is filled to the front end of the last sorted standby local area network sub-data information to obtain new last sorted standby local area network sub-data information; When the result of the third length judgment is no, the segmentation and conversion processing flow corresponding to the LAN data information to be processed is terminated.

[0093] It can be seen that implementation Figure 3 The described LAN anomaly processing device for household appliances is beneficial for monitoring LAN anomaly conditions, improving the accuracy of network status monitoring, thereby improving the efficiency of network anomaly monitoring and repair, and improving the user experience of network communication.

[0094] Embodiment 3 See also Figure 4 , Figure 4 1 is a schematic diagram of the structure of another device for handling local area network anomalies for household appliances disclosed in an embodiment of the present invention. Figure 4 The described device can be applied to a management system, such as a local server or a cloud server for management, etc., which is not limited in the embodiments of the present invention. Figure 4 As shown, the device may include: A memory 301 storing executable program codes; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the local area network exception handling method for home appliances described in the first embodiment.

[0095] Embodiment 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the local area network exception handling method for home appliances described in the first embodiment.

[0096] Embodiment 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the local area network exception handling method for home appliances described in Example 1.

[0097] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0098] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0099] Finally, it should be noted that the method and device for handling local area network anomalies for household appliances disclosed in the embodiments of the present invention only disclose the preferred embodiments of the present invention, which are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, it should be understood by those skilled in the art that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for handling local area network exceptions for household equipment, characterized in that: The method comprises: Acquire local area network data information of household equipment; the local area network data information of household equipment includes a plurality of local area network data information to be processed; Pre-processing the household device LAN data information to obtain first LAN data information; the first LAN data information includes a plurality of first processed LAN data information; each of the first processed LAN data information includes at least one first target LAN data information distributed in sequence; The first local area network data information is detected, analyzed and processed to obtain target anomaly detection result information.

2. The method for handling local area network abnormalities for household appliances according to claim 1, characterized in that: The detecting, analyzing and processing the first local area network processing information to obtain target anomaly detection result information includes: Using the target anomaly detection model to detect and process the first local area network processing information to obtain data detection result information; the data detection result information includes a plurality of local area network data detection result information; The data detection result information is classified, analyzed and processed to obtain target anomaly detection result information.

3. The method for handling local area network abnormalities for household appliances according to claim 2, characterized in that: The target anomaly detection model includes a first feature extraction module for extracting features from a spatial dimension, a second feature extraction module for extracting features from a temporal dimension, a feature fusion module, a connection module, and an activation module; wherein, The input end of the first feature extraction module and the input end of the second feature extraction module are configured to receive model input information of the target anomaly detection model; the output end of the first feature extraction module and the output end of the second feature extraction module are both connected to the input end of the feature fusion module; the output end of the feature fusion module is connected to the input end of the connection module; the output end of the connection module is connected to the input end of the activation module; the output end of the activation module is configured to output the model output information of the target anomaly detection model.

4. The method for handling local area network abnormalities for household appliances according to claim 3, characterized in that: The feature fusion module includes a first sampling unit, a first pooling unit, a second pooling unit, a multi-head attention unit, a first fusion unit, a second fusion unit and a third fusion unit; wherein, The input end of the first sampling unit and the input end of the second fusion unit are both connected to the output end of the first feature extraction module; the output end of the first sampling unit is connected to the input end of the first pooling unit; the output end of the first pooling unit is connected to the input end of the multi-head attention unit; the input end of the second pooling unit and the input end of the first fusion unit are both connected to the input end of the second feature extraction module; the output end of the second pooling unit is connected to the input end of the multi-head attention unit; the output end of the multi-head attention unit is respectively connected to the input end of the first fusion unit, the input end of the second fusion unit and the input end of the third fusion unit; the output end of the first fusion unit is connected to the input end of the third fusion unit; the output end of the second fusion unit is connected to the input end of the third fusion unit; the output end of the third fusion unit is connected to the input end of the connection module.

5. The method for handling local area network anomalies for household appliances according to claim 2, characterized in that: The target anomaly detection model is obtained by training the basic anomaly detection model based on the loss function model; Wherein, the loss function model is: ； In the formula, Characterize the loss function value; and Respectively represent the first loss function coefficient and the second loss function coefficient; Characterize the true labels corresponding to the training samples input into the basic anomaly detection model; A predicted label representing the output of the underlying anomaly detection model.

6. The method for handling local area network anomalies for household appliances according to claim 2, characterized in that: The pre-processing of the household device local area network data information to obtain first local area network processing information includes: Get the segmentation conversion threshold and conversion symbol; For any of the to-be-processed LAN data information in the LAN data information of the household device, the to-be-processed LAN data information is segmented and converted using the segmentation conversion threshold and the conversion symbol to obtain the standby LAN data information corresponding to the to-be-processed LAN data information; the standby LAN data information includes at least one standby LAN sub-data information distributed in sequence; The standby LAN data information is vectorized to obtain first processed LAN data information corresponding to the standby LAN data information.

7. The method for handling local area network abnormalities for household appliances according to claim 6, characterized in that: The segmentation conversion processing is performed on the to-be-processed LAN data information by using the segmentation conversion threshold and the conversion symbol to obtain the to-be-processed LAN data information corresponding to the to-be-processed LAN data information, including: Determine whether the length of the local area network data information corresponding to the local area network data information to be processed is less than the segmentation conversion threshold, and obtain a first length determination result; When the first length judgment result is yes, the conversion symbol is filled into the front end of the LAN data information to be processed to obtain the standby LAN sub-data information corresponding to the LAN data information to be processed; When the first length judgment result is no, judging whether the length of the local area network data information is equal to the segmentation conversion threshold, and obtaining a second length judgment result; When the second length judgment result is yes, determining the LAN data information to be processed as the standby LAN sub-data information corresponding to the LAN data information to be processed; When the second length judgment result is no, based on the segmentation conversion threshold, the to-be-processed local area network data information is segmented from left to right to obtain a plurality of sequentially distributed to-be-processed local area network sub-data information corresponding to the to-be-processed local area network data information; Determine whether the length of the local area network data information corresponding to the last-sorted unused local area network sub-data information is less than the segmentation conversion threshold, and obtain a third length determination result; When the third length judgment result is yes, the conversion symbol is filled to the front end of the last sorted standby local area network sub-data information to obtain new last sorted standby local area network sub-data information; When the result of the third length judgment is no, the segmentation and conversion processing flow corresponding to the LAN data information to be processed is terminated.

8. A local area network exception handling device for household equipment, characterized in that: The device comprises: An acquisition module, used for acquiring household equipment local area network data information; the household equipment local area network data information includes a plurality of local area network data information to be processed; A first processing module is used to pre-process the household device LAN data information to obtain first LAN data information; the first LAN data information includes a plurality of first processed LAN data information; each of the first processed LAN data information includes at least one first target LAN data information distributed in sequence; The second processing module is used to detect, analyze and process the first local area network data information to obtain target anomaly detection result information.

9. A local area network exception handling device for household equipment, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the local area network exception handling method for home appliances as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are called, they are used to execute the local area network exception handling method for home appliances as described in any one of claims 1-7.

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