A data management method, system, device, and medium

Abstract

A data management method, system, device and medium provided by the present invention relate to the technical field of data management. The method includes determining the value of K in the K-means clustering algorithm based on the historical operation data of multiple circuit boards using an operation data processing model; clustering the historical operation data of multiple circuit boards based on the K-means clustering algorithm and the value of K in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters; determining the abnormal operation data of multiple circuit boards to be retained and the normal operation data of multiple circuit boards to be deleted by processing the circuit board graph structure based on a graph convolutional network model; and performing data management based on the abnormal historical operation data of multiple circuit boards to be retained and the normal historical operation data of multiple circuit boards to be deleted. This method can quickly and accurately screen and manage the abnormal data and normal data of circuit boards.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to a data management method, system, device and medium. Background Art

[0002] As the core component of modern electronic devices, a large amount of historical operation data will be generated during the operation of a circuit board, including temperature, current, voltage sequence data, operation logs, etc. However, the amount of data generated during the long-term operation of the circuit board is extremely large. Among the operation data of the circuit board, most of the data is normal, representing the state of the circuit board operating under normal conditions. However, what really deserves attention are those abnormal data, which may be early warning signals of circuit board failures or signs of performance degradation. The identification and analysis of abnormal data are crucial for preventing failures and optimizing the performance of the circuit board. In contrast, although normal data accounts for the majority, they usually do not attract attention in most cases. Storing and analyzing too much normal data not only consumes a large amount of resources but may also obscure truly important abnormal information. Traditional data management methods, such as rule-based screening or simple statistical analysis, are difficult to effectively distinguish normal data from abnormal data in the vast amount of data. This is mainly because abnormal data often has low frequency and uncertainty, and its pattern may change over time or be generated due to external factors.

[0003] Therefore, how to quickly and accurately screen and manage the abnormal data and normal data of the circuit board is an urgent problem to be solved currently. Summary of the Invention

[0004] The main technical problem to be solved by the present invention is how to quickly and accurately screen and manage the abnormal data and normal data of the circuit board.

[0005] According to a first aspect, the present invention provides a data management method, including: obtaining historical operation data of a plurality of circuit boards; determining a K value in the K-means clustering algorithm based on the historical operation data of the plurality of circuit boards using an operation data processing model; clustering the historical operation data of the plurality of circuit boards based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters, where each of the K clusters includes the historical operation data of the plurality of circuit boards after clustering; constructing a circuit board graph structure, the circuit board graph structure including K cluster nodes and multiple edges between the K cluster nodes, each cluster node representing a cluster, the node feature of each cluster node including the historical operation data of the plurality of circuit boards in each cluster, and the edges between each cluster node representing the distance between the clustering centers of each cluster; determining abnormal operation data of a plurality of circuit boards to be retained and normal operation data of a plurality of circuit boards to be deleted based on processing the circuit board graph structure using a graph convolutional network model; performing data management based on the abnormal historical operation data of the plurality of circuit boards to be retained and the normal historical operation data of the plurality of circuit boards to be deleted.

[0006] Further, the historical operation data of the circuit board includes historical temperature sequence data, historical current sequence data, historical voltage sequence data, and historical operation logs.

[0007] Further, the operation data processing model is a long short-term neural network model, the input of the operation data processing model is the historical operation data of the plurality of circuit boards, and the output of the operation data processing model is the K value in the K-means clustering algorithm.

[0008] Further, the constructing of the circuit board graph structure further includes:

[0009] determining the stability of each circuit historical operation data in each cluster node based on the historical operation data of the plurality of circuit boards in each cluster node; determining the weight of each cluster node based on the stability of each circuit historical operation data in each cluster; and assigning the weight of each cluster node to each cluster node.

[0010] Further, the input of the graph convolutional network model is the circuit board graph structure, and the output of the graph convolutional network model is abnormal operation data of a plurality of circuit boards to be retained and normal operation data of a plurality of circuit boards to be deleted.

[0011] According to a second aspect, the present invention provides a data management system, including:

[0012] an obtaining module, configured to obtain historical operation data of a plurality of circuit boards;

[0013] A K-value determination module for determining the K value in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model;

[0014] A clustering module for clustering the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters, where each of the K clusters includes the historical operation data of the multiple circuit boards after clustering;

[0015] A construction module for constructing a circuit board graph structure, where the circuit board graph structure includes K cluster nodes and multiple edges between the K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node includes the historical operation data of the multiple circuit boards in each cluster, and the edges between each cluster node represent the distances between the clustering centers of each cluster;

[0016] A graph convolution module for processing the circuit board graph structure based on a graph convolution network model to determine the abnormal operation data of the multiple circuit boards to be retained and the normal operation data of the multiple circuit boards to be deleted;

[0017] A management module for performing data management based on the abnormal historical operation data of the multiple circuit boards to be retained and the normal historical operation data of the multiple circuit boards to be deleted.

[0018] Furthermore, the historical operation data of the circuit board includes historical temperature sequence data, historical current sequence data, historical voltage sequence data, and historical operation logs.

[0019] Furthermore, the construction is also used for:

[0020] Determining the stability of each circuit historical operation data in each cluster node based on the historical operation data of the multiple circuit boards in each cluster node;

[0021] Determining the weight of each cluster node based on the stability of each circuit historical operation data in each cluster;

[0022] Assigning the weight of each cluster node to each cluster node.

[0023] According to a third aspect, an embodiment of the present invention provides an electronic device, including: a processor; a memory; and a computer program; wherein, the computer program is stored in the memory and configured to be executed by the processor to implement the method as described above, and the method includes: obtaining historical operation data of multiple circuit boards; determining the value of K in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model; clustering the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the value of K in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters, and each of the K clusters includes the historical operation data of the multiple circuit boards after clustering; constructing a circuit board graph structure, the circuit board graph structure includes K cluster nodes and multiple edges between the K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node includes the historical operation data of the multiple circuit boards in each cluster, and the edges between each cluster node represent the distances between the clustering centers of each cluster; determining the abnormal operation data of the multiple circuit boards that need to be retained and the normal operation data of the multiple circuit boards that need to be deleted based on processing the circuit board graph structure by a graph convolutional network model; performing data management based on the abnormal historical operation data of the multiple circuit boards that need to be retained and the normal historical operation data of the multiple circuit boards that need to be deleted.

[0024] According to a fourth aspect, the present embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the data management method provided above, and the method includes: obtaining historical operation data of multiple circuit boards; determining the value of K in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model; clustering the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the value of K in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters, and each of the K clusters includes the historical operation data of the multiple circuit boards after clustering; constructing a circuit board graph structure, the circuit board graph structure includes K cluster nodes and multiple edges between the K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node includes the historical operation data of the multiple circuit boards in each cluster, and the edges between each cluster node represent the distances between the clustering centers of each cluster; determining the abnormal operation data of the multiple circuit boards that need to be retained and the normal operation data of the multiple circuit boards that need to be deleted based on processing the circuit board graph structure by a graph convolutional network model; performing data management based on the abnormal historical operation data of the multiple circuit boards that need to be retained and the normal historical operation data of the multiple circuit boards that need to be deleted.

[0025] A data management method, system, device, and medium provided by the present invention. The method includes obtaining historical operation data of multiple circuit boards; determining the value of K in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model; clustering the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the value of K in the K-means clustering algorithm to obtain K clusters and the clustering center of each of the K clusters, where each of the K clusters includes the historical operation data of the multiple circuit boards after clustering; constructing a circuit board graph structure, the circuit board graph structure including K cluster nodes and multiple edges between the K cluster nodes, each cluster node representing a cluster, the node feature of each cluster node including the historical operation data of the multiple circuit boards in each cluster, and the edge between each cluster node representing the distance between the clustering centers of each cluster; determining the abnormal operation data of the multiple circuit boards to be retained and the normal operation data of the multiple circuit boards to be deleted by processing the circuit board graph structure based on a graph convolutional network model; and performing data management based on the abnormal historical operation data of the multiple circuit boards to be retained and the normal historical operation data of the multiple circuit boards to be deleted. This method can quickly and accurately screen and manage the abnormal data and normal data of the circuit boards. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic flowchart of a data management method provided by an embodiment of the present invention;

[0027] Figure 2 It is a schematic flowchart of a method for determining the weight of each cluster node provided by an embodiment of the present invention;

[0028] Figure 3 It is a schematic diagram of a data management system provided by an embodiment of the present invention;

[0029] Figure 4 It is a schematic diagram of an electronic device provided by an embodiment of the present invention;

[0030] Figure 5 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The present invention will be further described in detail below in conjunction with the specific embodiments and the accompanying drawings. Similar elements in different embodiments are labeled with related similar reference numerals. In the following embodiments, many detailed descriptions are provided to enable a better understanding of the present invention. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, or methods. In some cases, some operations related to the present invention are not shown or described in the specification to avoid overwhelming the core part of the present invention with excessive descriptions. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the descriptions in the specification and the general technical knowledge in the art.

[0032] In addition, the features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. At the same time, the steps or actions in the method description can also be reordered or adjusted in an obvious manner by those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for clearly describing a certain embodiment and do not mean that they are the necessary sequences, unless it is stated that a certain sequence must be followed.

[0033] The serial numbers assigned to the components in this article, such as "first", "second", etc., are only used to distinguish the described objects and do not have any sequential or technical meaning. And the "connection" and "coupling" mentioned in the present invention, unless otherwise specified, both include direct and indirect connection (coupling).

[0034] In an embodiment of the present invention, a data management method as shown in Figure 1 is provided. The data management method includes steps S1 to S6:

[0035] Step S1, obtaining the historical operation data of multiple circuit boards.

[0036] A circuit board is a substrate used in an electronic device to carry and connect electronic components. It is usually made of an insulating material and has copper foil circuits and pads for electrical connection and support of electronic components. For example, a computer motherboard, a network interface board in a router, a main control board in a mobile phone, etc.

[0037] The historical operation data of the circuit board includes historical temperature sequence data, historical current sequence data, historical voltage sequence data, and historical operation logs.

[0038] The historical temperature sequence data refers to the temperature change data recorded during the operation of the circuit board and is usually stored in the form of a time series. For example, the temperature of the circuit board is recorded every 1 minute to form a series of temperature data points.

[0039] The historical current series data records the changes in the current flowing through the internal circuit of the circuit board during operation, and is also stored in the form of a time series. For example, the current value of a power supply line on the circuit board is recorded and collected every 5 seconds.

[0040] The historical voltage series data shows how the voltage at each point on the circuit board changes over time. For example, the power supply voltage and signal line voltage on the circuit board are recorded, and the voltage is read every 10 seconds.

[0041] The historical operation log records the operation history of the circuit board, including but not limited to power on / off time, software update records, error reports, maintenance activities, etc. For example, it records when the circuit board has undergone a firmware upgrade, or records the specific time and error code of a system crash.

[0042] Step S2, determining a K value in a K-means clustering algorithm using an operation data processing model based on the historical operation data of the plurality of circuit boards.

[0043] The operation data processing model is a long-term and short-term neural network model, the input of the operation data processing model is the historical operation data of the multiple circuit boards, and the output of the operation data processing model is the K value in the K-means clustering algorithm. The long-term and short-term neural network model includes a long-term and short-term neural network (LSTM, Long Short-Term Memory). The long-term and short-term neural network model can process sequence data, capture sequence information, and output results based on the correlation relationship between the previous and next data in the sequence. When analyzing the temperature, current, and voltage sequence data of the circuit board, the long-term and short-term neural network can capture the trend and pattern of these data changing over time. The historical operation data of the circuit board is time series data, which contains information such as temperature, current, and voltage that change over time. These data are not only complex but also dynamic. The long-term and short-term neural network model can effectively process such data, capture the correlation relationship between the previous and next data in the sequence, and thus better understand the internal structure and pattern of the data. The choice of K value directly affects the clustering effect. If the K value is not selected properly, the clustering may be too detailed or too rough, affecting subsequent data analysis and management. The long-term and short-term neural network model can predict the optimal K value based on the intrinsic pattern of the data, thereby optimizing the clustering effect and ensuring that the data points within each cluster are highly similar, while the data points between different clusters are significantly different.

[0044] The K-means clustering algorithm is an unsupervised learning algorithm that is used to divide a data set into K clusters. The data points within each cluster are similar to each other, while the data points between different clusters are quite different. The goal of the algorithm is to find K cluster centers so that the sum of the squared distances from each data point to the center of the cluster to which it belongs is minimized. The choice of K value is crucial to the clustering effect.

[0045] In some embodiments, running a data processing model includes an individual difference determination layer, an overall difference determination layer, and a K-value determination layer. The input of the individual difference determination layer is the historical operation data of multiple circuit boards. The output of the individual difference determination layer is the degree of difference between the operation data of each circuit board and the operation data of each of the remaining circuit boards. The input of the overall difference determination layer is the degree of difference between the operation data of each circuit board and the operation data of each of the remaining circuit boards. The output of the overall difference determination layer is the degree of difference in the overall operation data of the circuit boards. The input of the K-value determination layer is the degree of difference in the overall operation data of the circuit boards. The output of the K-value determination layer is the K value in the K-means clustering algorithm.

[0046] Based on the analysis of individual differences, the overall difference determination layer focuses on the macroscopic level. By aggregating all individual difference information, it calculates the degree of difference in the overall operation data of the circuit boards.

[0047] The K-value determination layer is based on the degree of difference in the overall operation data of the circuit boards. The task of this layer is to determine the optimal K value in the K-means clustering algorithm. The selection of the K value is crucial for the clustering result. An overly small K value may lead to information loss, while an overly large K value may lead to overfitting. By analyzing the overall difference, the model can more accurately estimate the natural clustering structure of the data, thereby selecting a K value that can capture both the data diversity and maintain the simplicity of the clustering.

[0048] Step S3: Cluster the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters and the cluster centers of each of the K clusters. Each of the K clusters includes the historical operation data of multiple circuit boards after clustering.

[0049] In some embodiments, the following steps can be used to obtain K clusters and the cluster centers of each of the K clusters. 1. Initialize the cluster centers: Randomly select K data points as the initial cluster centers, or use other initialization strategies such as K-means++. 2. Assign data points: Assign the historical operation data of each circuit board to the nearest cluster center to form preliminary clusters. 3. Update the cluster centers: For each cluster, calculate the average value of all its data points as the new cluster center. 4. Repeat the iteration: Repeat steps 2 and 3 until the cluster centers no longer change significantly or reach a preset number of iterations. 5. Output the results: Obtain K clusters, the cluster centers of each cluster, and the historical operation data of the circuit boards included in each cluster. Through clustering, complex historical operation data can be simplified into several representative clusters, facilitating further analysis and management.

[0050] Step S4, construct a circuit board graph structure, which includes K cluster nodes and multiple edges between the K cluster nodes. Each cluster node represents a cluster, and the node features of each cluster node include the historical operation data of multiple circuit boards in each cluster. The edges between each pair of cluster nodes represent the distance between the cluster centers of each cluster.

[0051] The circuit board graph structure is a data structure based on graph theory, used to represent the organization and relationship of circuit board operation data. In this graph, nodes represent clusters of circuit board operation data, and edges represent the distance between the cluster centers of each cluster.

[0052] In some embodiments, constructing the circuit board graph structure may include: Cluster analysis: First, use the K-means clustering algorithm described in step S3 to divide the historical operation data of the circuit boards into multiple clusters, and each cluster represents a group of circuit boards with similar operation characteristics. Node creation: Create a cluster node for each cluster, and the features of each node include the summary information of all the historical operation data of the circuit boards in that cluster. Definition of edges: Calculate the Euclidean distance or Manhattan distance between the cluster centers of each cluster as the weight of the edge, representing the similarity or distance between these two clusters. Graph structure construction: Organize all the cluster nodes and the edges between them into a graph structure to form the circuit board graph structure.

[0053] In the graph structure, each node represents a cluster, that is, a set of circuit board data with similar operation characteristics. For example, assume that through the clustering algorithm, the circuit board data is divided into five different clusters, then there will be five cluster nodes in the circuit board graph structure. The node features include the historical operation data of multiple circuit boards in each cluster, such as historical temperature sequence data, historical current sequence data, historical voltage sequence data, and historical operation logs.

[0054] In some embodiments, the weight of each cluster node can also be determined and assigned to each cluster node.

[0055] In some embodiments, Figure 2 is a schematic flowchart of a process for determining the weight of each cluster node provided by an embodiment of the present invention. The determination of the weight of each cluster node includes steps S21 to S23:

[0056] Step S21, determine the stability of each circuit historical operation data in each cluster node based on the historical operation data of multiple circuit boards in each cluster node.

[0057] In some embodiments, the stability of each circuit historical operation data in each cluster node can be determined by a deep neural network model. The input of the deep neural network model is the historical operation data of multiple circuit boards in each cluster node, and the output of the deep neural network model is the stability of each circuit historical operation data in each cluster node.

[0058] Step S22: Determine the weight of each cluster node based on the stability of each circuit historical operation data in each cluster.

[0059] In some embodiments, the weight of each cluster node can be determined based on a preset relationship between the stability of each circuit historical operation data in each cluster and the weight of each cluster node. The preset relationship can be set artificially in advance. A cluster node with a high weight value means that the circuit boards in the cluster operate relatively stably, which may represent the normal operation mode of the circuit boards. Therefore, when performing anomaly detection based on the graph convolutional network model, the model may give these nodes a lower anomaly sensitivity to avoid misjudging normal fluctuations as anomalies. On the contrary, for a cluster node with a low weight value, its circuit boards may have more operation problems, and the model will give a higher anomaly sensitivity during detection to more carefully identify potential anomaly patterns.

[0060] Step S23: Assign the weight of each cluster node to each cluster node.

[0061] Assign the weight value calculated in step S22 to the corresponding cluster node as an additional attribute of the node.

[0062] Step S5: Based on the graph convolutional network model, process the circuit board graph structure to determine the abnormal operation data of multiple circuit boards to be retained and the normal operation data of multiple circuit boards to be deleted.

[0063] The graph convolutional network model includes a graph convolutional network. The Graph Convolutional Network (GCN) is a deep learning model for processing graph-structured data.

[0064] The input of the graph convolutional network model is the circuit board graph structure, and the output of the graph convolutional network model is the abnormal operation data of multiple circuit boards to be retained and the normal operation data of multiple circuit boards to be deleted.

[0065] The abnormal operation data of multiple circuit boards to be retained are those in the circuit board graph structure. Through analysis by the graph convolutional network model, it can be identified which circuit boards' operation data deviate from the normal mode, which may indicate faults or performance degradation. These data need to be retained for in-depth analysis and troubleshooting.

[0066] The normal operation data of multiple circuit boards to be deleted is opposite to the abnormal data. Normal operation data refers to those data that do not show any abnormal patterns. In big data management, in order to save storage space and computing resources, normal data may not need to be retained for a long time, especially after they have been fully analyzed and confirmed as normal.

[0067] The reason why the graph convolutional network model can process the circuit board graph structure to determine abnormal and normal operation data is that it can effectively utilize the adjacency information in the graph structure, learn the complex patterns in the dataset, and thus achieve accurate identification and classification of the circuit board operation status. This ability has significant advantages in circuit board fault prediction, performance monitoring, and resource optimization.

[0068] In the circuit board graph structure, the graph convolutional network model can learn the difference between normal operation data and abnormal operation data. Since abnormal operation data usually exhibits distinctive features in the graph, such as connection patterns with other nodes, outliers of node features, etc., the graph convolutional network model can capture these abnormal features and distinguish abnormal data from normal data through the model output.

[0069] Step S6, perform data management based on the abnormal historical operation data of the multiple circuit boards to be retained and the normal historical operation data of the multiple circuit boards to be deleted.

[0070] In some embodiments, the operation data of the circuit board is divided into two parts: abnormal data and normal data. Abnormal data should be marked as to be retained, while normal data can be marked as deletable.

[0071] Based on the same inventive concept, Figure 3 A schematic diagram of a data management system provided by an embodiment of the present invention, the data management system includes:

[0072] An acquisition module 31, configured to acquire the historical operation data of multiple circuit boards;

[0073] A K value determination module 32, configured to determine the K value in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model;

[0074] A clustering module 33, configured to cluster the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the K value in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters, and each of the K clusters includes the historical operation data of multiple circuit boards after clustering;

[0075] A building module 34 for building a circuit board diagram structure, the circuit board diagram structure including K cluster nodes and multiple edges between the K cluster nodes, each cluster node representing a cluster, the node feature of each cluster node including the historical operation data of multiple circuit boards in each cluster, and the edge between each cluster node representing the distance between the clustering centers of each cluster;

[0076] A graph convolution module 35 for processing the circuit board diagram structure based on a graph convolution network model to determine the abnormal operation data of multiple circuit boards to be retained and the normal operation data of multiple circuit boards to be deleted;

[0077] A management module 36 for performing data management based on the abnormal historical operation data of the multiple circuit boards to be retained and the normal historical operation data of the multiple circuit boards to be deleted.

[0078] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, as Figure 4 shown, including:

[0079] Including: a processor 41; a memory 42; and a computer program; wherein, the computer program is stored in the memory 42 and configured to be executed by the processor 41 to implement the data management method provided as described above, the method including: obtaining the historical operation data of multiple circuit boards; determining the value of K in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model; performing clustering on the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the value of K in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters, each of the K clusters including the historical operation data of the multiple circuit boards after clustering; building a circuit board diagram structure, the circuit board diagram structure including K cluster nodes and multiple edges between the K cluster nodes, each cluster node representing a cluster, the node feature of each cluster node including the historical operation data of multiple circuit boards in each cluster, and the edge between each cluster node representing the distance between the clustering centers of each cluster; processing the circuit board diagram structure based on a graph convolution network model to determine the abnormal operation data of multiple circuit boards to be retained and the normal operation data of multiple circuit boards to be deleted; performing data management based on the abnormal historical operation data of the multiple circuit boards to be retained and the normal historical operation data of the multiple circuit boards to be deleted.

[0080] Based on the same inventive concept, this embodiment provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor 41, it implements the data management method provided above. The method includes: obtaining historical operation data of multiple circuit boards; determining the value of K in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model; clustering the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the value of K in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters. Each of the K clusters includes the historical operation data of multiple circuit boards after clustering; constructing a circuit board graph structure, the circuit board graph structure includes K cluster nodes and multiple edges between the K cluster nodes. Each cluster node represents a cluster, and the node feature of each cluster node includes the historical operation data of multiple circuit boards in each cluster. The edges between each cluster node represent the distance between the clustering centers of each cluster; processing the circuit board graph structure based on a graph convolutional network model to determine the abnormal operation data of multiple circuit boards to be retained and the normal operation data of multiple circuit boards to be deleted; performing data management based on the abnormal historical operation data of the multiple circuit boards to be retained and the normal historical operation data of the multiple circuit boards to be deleted.

[0081] The data management method provided in the embodiments of this application can be applied to electronic devices such as terminal devices (such as mobile phones), tablet computers, laptop computers, ultra-mobile personal computers (UMPCs), handheld computers, netbooks, personal digital assistants (PDAs), wearable devices (such as smart watches, smart glasses, or smart helmets, etc.), augmented reality (AR) / virtual reality (VR) devices, smart home devices, in-vehicle computers, etc. The embodiments of this application do not make any restrictions on this.

[0082] Taking the mobile phone 100 as an example of the above electronic device, Figure 4 shows a schematic structural diagram of the mobile phone 100.

[0083] As Figure 4As shown in the figure, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0084] Among them, the above-mentioned sensor module 180 may include sensors such as a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, and an ambient light sensor.

[0085] It can be understood that the structure illustrated in this embodiment does not constitute a specific limitation on the mobile phone 100. In other embodiments of the present application, the mobile phone 100 may include more or fewer components than those shown in the figure, or combine certain components, or split certain components, or have different component arrangements. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0086] The processing module 110 may include one or more processing units. For example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Among them, different processing units may be independent devices or integrated in one or more processors.

[0087] Among them, the controller may be the nerve center and command center of the mobile phone 100, and is the decision-maker that commands each component of the mobile phone 100 to work in coordination according to instructions. The controller may generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching instructions and executing instructions.

[0088] An operating system of the mobile phone 100 can be installed on the application processor, which is used to manage the hardware and software resources of the mobile phone 100. For example, it manages and configures the memory, determines the priority order of system resource supply and demand, manages the file system, manages the driver, etc. The operating system can also be used to provide an operation interface for users to interact with the system. Among them, various software can be installed in the operating system, such as drivers, application programs (App), etc. Exemplarily, the operating system of the mobile phone 100 can be an Android system, a Linux system, etc.

[0089] A memory can also be set in the processing module 110 for storing instructions and data. In some embodiments, the memory in the processing module 110 is a cache memory. This memory can save the instructions or data that the processing module 110 has just used or recycled. If the processing module 110 needs to use the instruction or data again, it can directly call it from the memory. This avoids repeated accesses, reduces the waiting time of the processing module 110, and thus improves the efficiency of the system.

[0090] In some embodiments, the processing module 110 may include one or more interfaces. The interfaces can include an inter-integrated circuit (I2C) interface, an inter-integrated circuit sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0091] The processing module 110 can be used to: obtain the historical operation data of multiple circuit boards; determine the value of K in the K-means clustering algorithm based on the historical operation data of the multiple circuit boards using an operation data processing model; perform clustering on the historical operation data of the multiple circuit boards based on the K-means clustering algorithm and the value of K in the K-means clustering algorithm to obtain K clusters and the clustering centers of each of the K clusters, where each of the K clusters includes the historical operation data of multiple circuit boards after clustering; construct a circuit board graph structure, the circuit board graph structure including K cluster nodes and multiple edges between the K cluster nodes, each cluster node representing a cluster, the node feature of each cluster node including the historical operation data of multiple circuit boards in each cluster, and the edges between each cluster node representing the distance between the clustering centers of each cluster; determine the abnormal operation data of multiple circuit boards to be retained and the normal operation data of multiple circuit boards to be deleted based on processing the circuit board graph structure using a graph convolutional network model; perform data management based on the abnormal historical operation data of multiple circuit boards to be retained and the normal historical operation data of multiple circuit boards to be deleted.

[0092] The charging management module 140 is used to receive a charging input from a charger. Among them, the charger can be a wireless charger or a wired charger. In some embodiments of wired charging, the charging management module 140 can receive the charging input from a wired charger through the USB interface 130. In some embodiments of wireless charging, the charging management module 140 can receive the wireless charging input through the wireless charging coil of the mobile phone 100. While charging the battery 142, the charging management module 140 can also supply power to the electronic device through the power management module 141.

[0093] The power management module 141 is used to connect the battery 142, the charging management module 140, and the processing module 110. The power management module 141 receives the input from the battery 142 and / or the charging management module 140 and supplies power to the processing module 110, the internal memory 121, the external memory, the display screen 194, the camera 193, the wireless communication module 160, etc. The power management module 141 can also be used to monitor parameters such as the battery capacity, the number of battery charge cycles, and the battery health status (leakage, impedance). In some other embodiments, the power management module 141 can also be provided in the processing module 110. In some other embodiments, the power management module 141 and the charging management module 140 can also be provided in the same device.

[0094] The wireless communication function of the mobile phone 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modulation and demodulation processor, and the baseband processor, etc.

[0095] Antenna 1 and Antenna 2 are used for transmitting and receiving electromagnetic wave signals. Each antenna in the mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be multiplexed to improve the utilization rate of the antennas. For example, Antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0096] The mobile communication module 150 can provide solutions for wireless communications such as 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 can include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves through Antenna 1, filter, amplify and process the received electromagnetic waves, and then transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor and convert it into electromagnetic waves through Antenna 1 for radiation. In some embodiments, at least some functional modules of the mobile communication module 150 can be disposed in the processing module 110. In some embodiments, at least some functional modules of the mobile communication module 150 and at least some modules of the processing module 110 can be disposed in the same device.

[0097] The modulation and demodulation processor can include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be transmitted into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. Subsequently, the demodulator transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After being processed by the baseband processor, the low-frequency baseband signal is transmitted to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, receiver 170B, etc.), or displays an image or video through the display screen 194. In some embodiments, the modulation and demodulation processor can be an independent device. In some other embodiments, the modulation and demodulation processor can be independent of the processing module 110 and be disposed in the same device as the mobile communication module 150 or other functional modules.

[0098] The wireless communication module 160 may provide wireless communication solutions applied to the mobile phone 100, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite systems (GNSS), frequency modulation (FM), near field communication (NFC), infrared technology (IR), etc. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, performs frequency modulation and filtering processing on the electromagnetic wave signals, and sends the processed signals to the processing module 110. The wireless communication module 160 may also receive the signals to be sent from the processing module 110, perform frequency modulation and amplification on them, and convert them into electromagnetic waves through the antenna 2 for radiation.

[0099] In some embodiments, antenna 1 of mobile phone 100 is coupled to the mobile communication module 150, and antenna 2 is coupled to the wireless communication module 160, enabling the mobile phone 100 to communicate with the network and other devices through wireless communication technologies. The wireless communication technologies may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc. The GNSS may include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0100] The mobile phone 100 implements the display function through the GPU, the display screen 194, and the application processor, etc. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processing module 110 may include one or more GPUs, which execute program instructions to generate or change the display information.

[0101] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a Miniled, a MicroLed, a Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include one or N display screens 194, where N is a positive integer greater than 1. In the embodiments of the present application, the display screen 194 can be used to display remote session windows, chronic non-healing wound images, wound detection reports, etc.

[0102] The mobile phone 100 can implement the shooting function through the ISP, the camera 193, the video codec, the GPU, the display screen 194, and the application processor, etc. In some embodiments, the mobile phone 100 can implement the video communication function through the ISP, the camera 193, the video codec, the GPU, and the application processor.

[0103] The ISP is used to process the data fed back by the camera 193. For example, when taking a photo, the shutter is opened, and the light passes through the lens and is transmitted to the camera sensor. The optical signal is converted into an electrical signal, and the camera sensor transmits the electrical signal to the ISP for processing and converts it into an image visible to the naked eye. The ISP can also optimize the noise, brightness, and skin color of the image through algorithms. The ISP can also optimize parameters such as the exposure and color temperature of the shooting scene. In some embodiments, the ISP can be set in the camera 193.

[0104] The camera 193 is used to capture static images or videos. An object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then transmits the electrical signal to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in standard formats such as RGB and YUV. In some embodiments, the mobile phone 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0105] The digital signal processor is used to process digital signals. In addition to processing digital image signals, it can also process other digital signals. For example, when the mobile phone 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy, etc.

[0106] The video codec is used to compress or decompress digital videos. The mobile phone 100 can support one or more video codecs. In this way, the mobile phone 100 can play or record videos in multiple encoding formats, such as: Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0107] The NPU is a neural-network (NN) computing processor. By learning from the biological neural network structure, such as learning from the transmission mode between human brain neurons, it can quickly process the input information and can also continuously self-learn. Through the NPU, applications such as intelligent cognition of the mobile phone 100 can be realized, such as: image recognition, face recognition, speech recognition, text understanding, etc.

[0108] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processing module 110 through the external memory interface 120 to achieve the data storage function. For example, files such as music and videos are saved in the external memory card.

[0109] The internal memory 121 can be used to store computer-executable program code, and the executable program code includes instructions. The processing module 110 executes various functional applications and data processing of the mobile phone 100 by running the instructions stored in the internal memory 121. The internal memory 121 can include a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.). The data storage area can store data created during the use of the mobile phone 100 (such as audio data, phone book, etc.). In addition, the internal memory 121 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0110] The mobile phone 100 can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the headphone jack 170D, and the application processor, etc. For example, music playback, recording, etc.

[0111] The audio module 170 is used to convert digital audio information into an analog audio signal for output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 can also be used for encoding and decoding audio signals. In some embodiments, the audio module 170 can be disposed in the processing module 110, or some functional modules of the audio module 170 can be disposed in the processing module 110.

[0112] The speaker 170A, also called a "loudspeaker", is used to convert an audio electrical signal into a sound signal. The mobile phone 100 can listen to music or hands-free calls through the speaker 170A.

[0113] The receiver 170B, also called a "handset", is used to convert an audio electrical signal into a sound signal. When the mobile phone 100 answers a call or a voice message, the voice can be received by bringing the receiver 170B close to the human ear.

[0114] The microphone 170C, also called a "microphone", a "transmitter", is used to convert a sound signal into an electrical signal. When making a call or sending a voice message, the user can speak close to the microphone 170C with the mouth to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In some other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals, but also implement a noise reduction function. In some other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to implement sound signal collection, noise reduction, and can also identify the sound source to implement a directional recording function, etc.

[0115] The headphone jack 170D is used to connect a wired headphone. The headphone jack 170D can be a USB jack 130, or a 3.5mm open mobile terminal platform (OMTP) standard jack, or a cellular telecommunications industry association of the USA (CTIA) standard jack.

[0116] The buttons 190 include a power-on button, volume buttons, etc. The buttons 190 can be mechanical buttons or touch buttons. The mobile phone 100 can receive button inputs and generate key signal inputs related to the user settings and function controls of the mobile phone 100.

[0117] The motor 191 can generate vibration prompts. The motor 191 can be used for incoming call vibration prompts or touch vibration feedback. For example, touch operations on different applications (such as taking pictures, audio playing, etc.) can correspond to different vibration feedback effects. Touch operations on different areas of the display screen 194 can also correspond to different vibration feedback effects for the motor 191. Different application scenarios (such as time reminder, receiving messages, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0118] The indicator 192 can be an indicator light and can be used to indicate the charging state, power change, or can also be used to indicate messages, missed calls, notifications, etc.

[0119] The SIM card interface 195 is used to connect a SIM card. The SIM card can be inserted into or removed from the SIM card interface 195 to achieve contact and separation from the mobile phone 100. The mobile phone 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to achieve functions such as calls and data communication. In some embodiments, the mobile phone 100 uses an eSIM, that is, an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

[0120] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation on this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification, so such modifications, improvements, and corrections still fall within the spirit and scope of the exemplary embodiments of this specification.

[0121] Meanwhile, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0122] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical letters, or the use of other names in this specification are not used to limit the order of the processes and methods of this specification. Although some currently considered useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details only serve the purpose of illustration. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0123] Similarly, it should be noted that, in order to simplify the expression of the disclosure of this specification and thus help the understanding of one or more embodiments of the invention, in the previous description of the embodiments of this specification, sometimes multiple features are merged into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0124] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification can be considered to be in line with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A data management method, characterized in that: include: Get historical operation data of multiple circuit boards; Determine a K value in a K-means clustering algorithm using an operation data processing model based on the historical operation data of the plurality of circuit boards; Clustering the historical operation data of the plurality of circuit boards based on a K-means clustering algorithm and a K value in the K-means clustering algorithm to obtain K clusters and a cluster center of each of the K clusters, wherein each of the K clusters includes the clustered historical operation data of the plurality of circuit boards; Constructing a circuit board graph structure, the circuit board graph structure includes K cluster nodes and multiple edges between the K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node includes historical operation data of multiple circuit boards in each cluster, and the edge between each cluster node represents the distance between the cluster centers of each cluster, and constructing the circuit board graph structure includes: Determine the stability of each circuit historical operation data in each cluster node based on the historical operation data of the plurality of circuit boards in each cluster node; Determining the weight of each cluster node based on the stability of historical operation data of each circuit in each cluster; Allocating the weight of each cluster node to each cluster node; Based on a graph convolutional network model, the circuit board diagram structure is processed to determine abnormal operation data of multiple circuit boards that need to be retained and normal operation data of multiple circuit boards that need to be deleted, wherein the input of the graph convolutional network model is the circuit board diagram structure, and the output of the graph convolutional network model is the abnormal operation data of multiple circuit boards that need to be retained and the normal operation data of multiple circuit boards that need to be deleted; Data management is performed based on the abnormal historical operation data of the plurality of circuit boards that need to be retained and the normal historical operation data of the plurality of circuit boards that need to be deleted.

2. The data management method according to claim 1, characterized in that: The historical operation data of the circuit board includes historical temperature sequence data, historical current sequence data, historical voltage sequence data, and historical operation logs.

3. The data management method according to claim 1, characterized in that: The operation data processing model is a long-short term neural network model, the input of the operation data processing model is the historical operation data of the multiple circuit boards, and the output of the operation data processing model is the K value in the K-means clustering algorithm.

4. A data management system, characterized in that: include: An acquisition module, used to acquire historical operation data of multiple circuit boards; A K value determination module, configured to determine a K value in a K-means clustering algorithm using an operation data processing model based on historical operation data of the plurality of circuit boards; A clustering module, used for clustering the historical operation data of the plurality of circuit boards based on a K-means clustering algorithm and a K value in the K-means clustering algorithm to obtain K clusters and a cluster center of each of the K clusters, wherein each of the K clusters includes the clustered historical operation data of the plurality of circuit boards; A construction module is used to construct a circuit board diagram structure, wherein the circuit board diagram structure includes K cluster nodes and multiple edges between the K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node includes historical operation data of multiple circuit boards in each cluster, and the edge between each cluster node represents the distance between the cluster centers of each cluster. The construction module is also used to: Determine the stability of each circuit historical operation data in each cluster node based on the historical operation data of the plurality of circuit boards in each cluster node; Determining the weight of each cluster node based on the stability of historical operation data of each circuit in each cluster; Allocating the weight of each cluster node to each cluster node; A graph convolution module, used for processing the circuit board graph structure based on a graph convolution network model to determine abnormal operation data of multiple circuit boards that need to be retained and normal operation data of multiple circuit boards that need to be deleted; A management module is used to perform data management based on the abnormal historical operation data of the multiple circuit boards that need to be retained and the normal historical operation data of the multiple circuit boards that need to be deleted, the input of the graph convolutional network model is the circuit board graph structure, and the output of the graph convolutional network model is the abnormal operation data of the multiple circuit boards that need to be retained and the normal operation data of the multiple circuit boards that need to be deleted.

5. The data management system according to claim 4, characterized in that: The historical operation data of the circuit board includes historical temperature sequence data, historical current sequence data, historical voltage sequence data, and historical operation logs.

6. An electronic device, characterized in that: include: processor; Memory; and a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the data management method according to any one of claims 1 to 3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the data management method according to any one of claims 1 to 3 is implemented.

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