Data management method and system

Abstract

The present invention provides a data management method and system, which relate to the field of data management technology. The method includes using an operation data processing model to determine the K value in a K-means clustering algorithm based on historical operation data of multiple circuit boards; clustering the historical operation data of 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 cluster centers of each cluster in the K clusters; processing the circuit board graph structure based on a graph convolutional 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; and performing data management based on the abnormal historical operation data of multiple circuit boards that need to be retained and the normal historical operation data of multiple circuit boards that need to be deleted. The method can quickly and accurately screen and manage the abnormal data and normal data of the circuit boards.

Description

Technical Field

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

[0002] As core components of modern electronic devices, circuit boards generate a vast amount of historical operating data during their operation, including temperature, current, and voltage series data, as well as operation logs. However, the volume of data generated by circuit boards during long-term operation is extremely large. Most of this data is normal, representing the board's normal operating conditions. However, it is the abnormal data that truly deserves attention. These data may be early warning signs of circuit board failure or indicators of performance degradation. Identifying and analyzing abnormal data is crucial for preventing failures and optimizing circuit board performance. In contrast, while normal data constitutes the majority, it often eludes attention. Excessive storage and analysis of normal data not only consumes significant resources but can also obscure truly important abnormal information. Traditional data management methods, such as rule-based screening or simple statistical analysis, struggle to effectively distinguish normal from abnormal data within this massive data volume. This is primarily because abnormal data is often low-frequency and uncertain, and its patterns can vary over time or be driven by external factors.

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

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

[0005] According to a first aspect, the present invention provides a data management method, comprising: acquiring historical operation data of multiple circuit boards; determining the K value in a 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 K value in the K-means clustering algorithm to obtain K clusters and cluster centers of each cluster in the K clusters, each cluster in the K clusters including 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 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 nodes representing the distance between the cluster centers of each cluster; processing the circuit board graph structure based on a graph convolutional 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; and 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.

[0006] 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.

[0007] Furthermore, it is 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.

[0008] Furthermore, the step of constructing the circuit board diagram structure further includes:

[0009] The stability of the historical operation data of each circuit in each cluster node is determined based on the historical operation data of multiple circuit boards in each cluster node; the weight of each cluster node is determined based on the stability of the historical operation data of each circuit in each cluster; and the weight of each cluster node is allocated to each cluster node.

[0010] Furthermore, 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 multiple circuit boards that need to be retained and normal operation data of multiple circuit boards that need to be deleted.

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

[0012] An acquisition module is used to obtain historical operation data of multiple circuit boards;

[0013] 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;

[0014] a clustering module, configured to cluster 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;

[0015] A construction module is used to construct a circuit board graph structure, wherein 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;

[0016] A graph convolution module is used to process 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;

[0017] The 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.

[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 building block is further configured to:

[0020] determining the stability of historical operation data of each circuit in each cluster node based on historical operation data of a plurality of circuit boards in each cluster node;

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

[0022] The weight of each cluster node is assigned to each cluster node.

[0023] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: 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, the method comprising: obtaining historical operating data of a plurality of circuit boards; determining the K value in the K-means clustering algorithm based on the historical operating data of the plurality of circuit boards using an operating data processing model; clustering the historical operating 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 cluster centers of each of the K clusters, wherein each of the K clusters includes a plurality of clustered Historical operation data of a circuit board; 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 multiple circuit boards in each cluster, and the edge between each cluster node representing the distance between the cluster 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 that need to be retained and the normal operation data of multiple circuit boards that need to be deleted; 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 having a computer program stored thereon, which, when executed by a processor, implements the data management method provided above, the method comprising: obtaining historical operating data of a plurality of circuit boards; determining the K value in the K-means clustering algorithm based on the historical operating data of the plurality of circuit boards using an operating data processing model; clustering the historical operating 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 cluster centers of each of the K clusters, each of the K clusters including the historical operating data of the plurality of circuit boards after clustering; constructing a K-means cluster; and A circuit board graph structure is established, wherein 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 edges between each cluster node represent the distance between the cluster centers of each cluster; the circuit board graph structure is processed based on a graph convolutional 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; data management is performed 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] The present invention provides a data management method and system, which include acquiring historical operation data of multiple circuit boards; determining a K value in a 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 K value in the K-means clustering algorithm to obtain K clusters and cluster centers of each of the K clusters, each of the K clusters including the clustered historical operation data of the multiple circuit boards; 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, a node feature of each cluster node including the historical operation data of the multiple circuit boards in each cluster, and an edge between each cluster node representing the distance between the cluster centers of each cluster; processing the circuit board graph structure based on a graph convolutional network model to determine abnormal operation data of the multiple circuit boards that need to be retained and normal operation data of the multiple circuit boards that need to be deleted; and 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. The method can quickly and accurately filter and manage abnormal data and normal data of the circuit boards. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0027] Figure 2 A schematic diagram of a process for determining the weight of each cluster node provided by an embodiment of the present invention;

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

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

[0030] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described in detail below by means of specific embodiments in conjunction with the accompanying drawings. Similar elements in different embodiments are numbered with associated similar elements. In the following embodiments, many detailed descriptions are provided to enable the present invention to be better understood. However, those skilled in the art will readily appreciate that some of the features may be omitted under different circumstances, or may 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. This is to avoid the core of the present invention being overwhelmed by excessive descriptions, and for those skilled in the art, it is not necessary to describe these related operations in detail. They can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0032] In addition, the features, operations, or characteristics described in the specification may be combined in any appropriate manner to form various embodiments. Furthermore, the steps or actions in the method description may be reordered or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various sequences in the specification and drawings are provided solely for the purpose of clearly describing a particular embodiment and are not intended to be mandatory, unless otherwise specified.

[0033] Component numbers used herein, such as "first" and "second," are used solely to distinguish the components being described and do not convey any sequential or technical meaning. References to "connection" and "coupling" herein, unless otherwise specified, include both direct and indirect connections (couplings).

[0034] In an embodiment of the present invention, there is provided Figure 1 A data management method is shown, the data management method comprising steps S1 to S6:

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

[0036] A printed circuit board (PCB) is a substrate used to carry and connect electronic components in electronic devices. It is typically made of insulating material and has copper foil traces and pads on it for electrical connection and support of electronic components. Examples include computer motherboards, network interface boards in routers, and main control boards in mobile phones.

[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] Historical temperature series data refers to the temperature change data recorded during the operation of the circuit board, which is usually stored in the form of a time series. For example, the temperature of the circuit board is recorded every 1 minute, forming a series of temperature data points.

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

[0040] Historical voltage series data shows how the voltage at each point on a circuit board changes over time. For example, it records the power supply voltage and signal line voltage on the circuit board, taking voltage readings 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 firmware was upgraded, 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 operating data processing model is a long-short-term neural network model. Its input is the historical operating data of the multiple circuit boards, and its output is the K value in the K-means clustering algorithm. The long-short-term neural network model includes a long short-term memory (LSTM) neural network. The LSTM neural network model can process sequential data, capture information in the sequence, and output results based on the correlation between previous and next data in the sequence. When analyzing the temperature, current, and voltage series data of the circuit boards, the LSTM neural network can capture the trends and patterns of these data changes over time. The historical operating data of the circuit boards is time series data, containing information such as temperature, current, and voltage that changes over time. This data is not only complex but also dynamic. The LSTM neural network model can effectively process this type of data, capturing the correlation between previous and next data in the sequence, thereby better understanding the inherent structure and patterns of the data. The choice of K value directly affects the clustering effect. If the K value is not chosen properly, the clustering may be too detailed or too coarse, 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 used to partition a dataset into K clusters, where the data points within each cluster are similar, while the data points between clusters are highly dissimilar. The algorithm's goal is to find K cluster centers such that the sum of the squared distances from each data point to its cluster center is minimized. The choice of K is crucial for clustering effectiveness.

[0045] In some embodiments, the operation 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, and 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 remaining circuit board. 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 remaining circuit board, and the output of the overall difference determination layer is the degree of difference of the operation data of the entire circuit board. The input of the K value determination layer is the degree of difference of the operation data of the entire circuit board, and the output of the K value determination layer is the K value in the K-means clustering algorithm.

[0046] The overall difference determination layer is based on the individual difference analysis. This layer focuses on the macro level and calculates the degree of difference of the overall circuit board operation data by aggregating all individual difference information.

[0047] The K value determination layer is based on the overall variability of the PCB operational data. This layer's task is to determine the optimal K value for the K-means clustering algorithm. The choice of K value is crucial for the clustering results: too small a K value can lead to information loss, while too large a K value can cause overfitting. By analyzing the overall variability, the model can more accurately estimate the natural clustering structure of the data, thereby selecting a K value that captures data diversity while maintaining cluster simplicity.

[0048] Step S3, 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 cluster center of each cluster in the K clusters, each cluster in the K clusters includes the historical operation data of the multiple circuit boards after clustering.

[0049] In some embodiments, K clusters and the cluster centers of each of the K clusters can be obtained by the following steps. 1. Initialize cluster centers: randomly select K data points as 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 a preliminary cluster. 3. Update cluster centers: for each cluster, calculate the average value of all its data points as the new cluster center. 4. Repeat iterations: repeat steps 2 and 3 until the cluster center no longer changes significantly, or the preset number of iterations is reached. 5. Output results: obtain K clusters and the cluster center of each cluster, as well as the historical operation data of the circuit boards contained in each cluster. Through clustering, complex historical operation data can be simplified into several representative clusters for further analysis and management.

[0050] Step S4, constructing a circuit board diagram structure, which includes K cluster nodes and multiple edges between K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node includes the 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.

[0051] A PCB graph is a data structure based on graph theory that represents the organization and relationships of PCB operational data. In this graph, nodes represent clusters of PCB operational data, and edges represent the distances between cluster centers within each cluster.

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

[0053] Each node in the graph structure represents a cluster—a collection of circuit board data with similar operating characteristics. For example, if a clustering algorithm divides the circuit board data into five different clusters, there will be five cluster nodes in the circuit board graph structure. Node characteristics include historical operating data for multiple circuit boards in each cluster, such as historical temperature series data, historical current series data, historical voltage series data, and historical operation logs.

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

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

[0056] Step S21 : determining the stability of the historical operation data of each circuit 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 the historical operating data of each circuit in each cluster node can be determined using a deep neural network model. The input of the deep neural network model is the historical operating data of multiple circuit boards in each cluster node, and the output of the deep neural network model is the stability of the historical operating data of each circuit in each cluster node.

[0058] Step S22 : determining the weight of each cluster node based on the stability of the historical operation data of each circuit 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 the historical operating data of each circuit in each cluster and the weight of each cluster node. The preset relationship can be manually set in advance. Cluster nodes with high weight values ​​mean that the circuit boards in the cluster are operating more stably, which may represent the normal operating 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, cluster nodes with low weight values ​​may have more operating problems on their circuit boards, and the model will give a higher anomaly sensitivity during detection to more carefully identify potential abnormal patterns.

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

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

[0062] Step S5: Process the circuit board graph structure based on the graph convolutional 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.

[0063] The graph convolutional network model includes the 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 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.

[0065] The abnormal operating data of multiple circuit boards that need to be retained are in the circuit board diagram structure. Through graph convolutional network model analysis, it is possible to identify which circuit boards' operating data deviate from the normal mode, which may indicate a failure or performance degradation. These data need to be retained for in-depth analysis and troubleshooting.

[0066] The normal operating data for the multiple circuit boards that need to be deleted is the opposite of abnormal data, which refers to data that does not show any abnormal patterns. In big data management, to save storage space and computing resources, normal data may not need to be retained for a long time, especially after it has been fully analyzed and confirmed to be normal.

[0067] The graph convolutional network model can process circuit board graph structures to identify abnormal and normal operating data because it effectively utilizes the adjacency information in the graph structure and learns the complex patterns in the dataset, thereby accurately identifying and classifying the circuit board's operating status. This capability has significant advantages in circuit board fault prediction, performance monitoring, and resource optimization.

[0068] In a circuit board diagram, the graph convolutional network model can learn to distinguish between normal and abnormal data. Because abnormal data often exhibits distinctive characteristics in the diagram, such as connection patterns with other nodes and abnormal values ​​of node features, the graph convolutional network model can capture these abnormal characteristics and distinguish abnormal data from normal data through the model output.

[0069] Step S6: performing data management 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.

[0070] In some embodiments, the operation data of the circuit board is divided into abnormal data and normal data. The abnormal data should be marked as needing to be retained, while the 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 in an embodiment of the present invention, the data management system comprising:

[0072] An acquisition module 31 is used to acquire historical operation data of multiple circuit boards;

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

[0074] a clustering module 33 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;

[0075] A construction module 34 is configured to construct a circuit board map structure, wherein the circuit board map 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 operating data of multiple circuit boards in each cluster, and the edges between each cluster node represent the distance between cluster centers of each cluster;

[0076] A graph convolution module 35 is configured to process 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;

[0077] The management module 36 is configured to perform data management 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.

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

[0079] The invention comprises: a processor 41; a memory 42; and a computer program; wherein the computer program is stored in the memory 42 and is configured to be executed by the processor 41 to implement the data management method provided above, the method comprising: obtaining historical operation data of a plurality of circuit boards; determining the 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 cluster centers of each of the K clusters, wherein each of the K clusters includes the historical operation data of the plurality of circuit boards after clustering. Historical operation data of multiple circuit boards; 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 multiple circuit boards in each cluster, and the edge between each cluster node representing the distance between the cluster 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 that need to be retained and the normal operation data of multiple circuit boards that need to be deleted; 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.

[0080] Based on the same inventive concept, this embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by the processor 41, the data management method provided above is implemented. The method includes: Acquire historical operation data of multiple circuit boards; 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; 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 cluster centers of each cluster in the K clusters, each cluster in the K clusters includes the historical operation data of the multiple circuit boards after clustering; construct 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 multiple circuit boards in each cluster, and the edges between each cluster nodes represent the distance between the cluster centers of each cluster; process the circuit board graph structure based on a graph convolutional network model to determine 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; 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.

[0081] The data management method provided in the embodiments of the present 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), augmented reality (AR) and virtual reality (VR) devices, smart home devices, and in-vehicle computers. The embodiments of the present application do not impose any restrictions on this.

[0082] Taking the mobile phone 100 as an example of the electronic device, Figure 4 FIG. 1 shows a schematic structural diagram of the mobile phone 100 .

[0083] like Figure 4As shown, 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, an earphone 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] The sensor module 180 may include a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor, and other sensors.

[0085] It should 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 shown, or may combine or separate certain components, or arrange the components differently. The components shown in the illustrations 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). The different processing units may be independent devices or integrated into one or more processors.

[0087] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that directs the various components of the mobile phone 100 to coordinate operations according to instructions. The controller can generate operation control signals based on instruction opcodes and timing signals to complete the control of instruction fetching and execution.

[0088] The application processor can be installed with the mobile phone 100's operating system, which is used to manage the phone's hardware and software resources. For example, it manages and configures memory, determines the priority of system resource supply and demand, manages the file system, and manages drivers. The operating system also provides an interface for user interaction with the system. Various software can be installed within the operating system, such as drivers and application programs (apps). For example, the operating system of the mobile phone 100 can be Android, Linux, etc.

[0089] Processing module 110 may also include a memory for storing instructions and data. In some embodiments, the memory in processing module 110 is a cache memory. This memory can store instructions or data that have just been used or are being recycled by processing module 110. If processing module 110 needs to use the same instruction or data again, it can directly access the memory. This avoids duplicate accesses, reduces the waiting time of processing module 110, and thus improves system efficiency.

[0090] In some embodiments, the processing module 110 may include one or more interfaces. The interfaces may include an inter-integrated circuit (I2C) interface, an inter-integrated circuits 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.

[0091] The processing module 110 can be used to: obtain historical operation data of multiple circuit boards; 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; 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 cluster centers of each cluster in the K clusters, each cluster in the K clusters includes the historical operation data of the multiple circuit boards after clustering; construct a circuit board graph structure, the circuit board graph structure includes K cluster nodes and multiple edges between K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node includes the historical operation data of multiple circuit boards in each cluster, and the edges between each cluster nodes represent the distance between the cluster centers of each cluster; process the circuit board graph structure based on a graph convolutional network model to determine 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; and 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.

[0092] The charging management module 140 is configured to receive charging input from a charger. The charger can be either a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 can receive charging input from the wired charger via the USB interface 130. In some wireless charging embodiments, the charging management module 140 can receive wireless charging input via the wireless charging coil of the mobile phone 100. While charging the battery 142, the charging management module 140 can also provide 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 input from the battery 142 and / or the charging management module 140, and provides power to the processing module 110, the internal memory 121, the external memory, the display 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle count, and battery health status (leakage, impedance). In some other embodiments, the power management module 141 can also be set in the processing module 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set 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 modem processor and the baseband processor.

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

[0096] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and 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 for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be set in the same device as at least some of the modules of the processing module 110.

[0097] The modem processor may include a modulator and a demodulator. 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. The demodulator then 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 passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.) or displays an image or video through the display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processing module 110 and be set in the same device as the mobile communication module 150 or other functional modules.

[0098] The wireless communication module 160 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the mobile phone 100. The wireless communication module 160 can be one or more devices that integrate at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, frequency modulate it, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0099] In some embodiments, the antenna 1 of the mobile phone 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the mobile phone 100 can 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 technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0100] Mobile phone 100 implements display functionality through a GPU, display screen 194, and an application processor. The GPU is a microprocessor for image processing that connects display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. Processing module 110 may include one or more GPUs that execute program instructions to generate or modify 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 or an active matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include 1 or N display screens 194, where N is a positive integer greater than 1. In an embodiment of the present application, the display screen 194 can be used to display a remote conversation window, an image of a chronic, difficult-to-heal wound, and a wound detection report.

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

[0103] The ISP processes data fed back by camera 193. For example, when taking a photo, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, which is then passed to the ISP for processing and transformed into a visible image. The ISP can also perform algorithmic optimization for image noise, brightness, and skin tone. It can also optimize parameters such as exposure and color temperature of the captured scene. In some embodiments, the ISP can be located within camera 193.

[0104] The camera 193 is used to capture still images or videos. The 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 light signal into an electrical signal, and then passes the electrical signal to the ISP for conversion 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 a standard RGB, YUV or other format. In some embodiments, the mobile phone 100 may include 1 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.

[0106] Video codecs are used to compress or decompress digital video. Mobile phone 100 may support one or more video codecs. This allows mobile phone 100 to play or record videos in various encoding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, and MPEG4.

[0107] The NPU is a neural network (NN) computing processor. Drawing on the structure of biological neural networks, such as the transmission patterns between neurons in the human brain, it rapidly processes input information and can continuously self-learn. The NPU enables intelligent cognitive applications in the phone 100, such as image recognition, face recognition, voice recognition, and text comprehension.

[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 via the external memory interface 120 to implement data storage functions. For example, files such as music and videos can be stored on the external memory card.

[0109] The internal memory 121 can be used to store computer executable program codes, which include 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 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the mobile phone 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

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

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

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

[0113] The receiver 170B, also called the "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or a voice message, the voice can be heard by placing the receiver 170B close to the ear.

[0114] Microphone 170C, also known as "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to realize sound signal collection, noise reduction, and identification of sound sources, and realize directional recording function, etc.

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

[0116] Keys 190 include a power button, a volume button, etc. Keys 190 may be mechanical keys or touch keys. Mobile phone 100 may receive key inputs and generate key signal inputs related to user settings and function control of mobile phone 100.

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

[0118] The indicator 192 may be an indicator light, which may be used to indicate the charging status, power level changes, messages, missed calls, notifications, etc.

[0119] The SIM card interface 195 is used to connect a SIM card. The SIM card can be connected to and disconnected from the mobile phone 100 by inserting it into or removing it from the SIM card interface 195. 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, and the like. 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 implement functions such as calls and data communications. In some embodiments, the mobile phone 100 uses an eSIM, i.e., 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] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0121] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0122] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and 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 are consistent with the spirit 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 by software solutions, such as installing the described system on an existing server or mobile device.

[0123] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0124] Finally, it should be understood that the embodiments described in this specification are intended only 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 may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A data management method, characterized in that: include: Get historical operation data of multiple circuit boards; 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, wherein the operation data processing model is a long-short-term neural network model, an input of the operation data processing model is the historical operation data of the plurality of circuit boards, and an output of the operation data processing model is the K value in the K-means clustering algorithm; 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 including K cluster nodes and multiple edges between the K cluster nodes, each cluster node representing a cluster, a node feature of each cluster node including historical operating data of multiple circuit boards in each cluster, and an edge between each cluster node representing a distance between cluster centers of each cluster; Processing the circuit board graph structure based on a graph convolutional 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; 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, wherein: 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, wherein: The circuit board diagram structure construction further includes: determining the stability of historical operation data of each circuit in each cluster node based on historical operation data of a plurality of circuit boards in each cluster node; determining a weight of each cluster node based on the stability of historical operation data of each circuit in each cluster; The weight of each cluster node is assigned to each cluster node.

4. The data management method according to claim 1, wherein: 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 that need to be retained and the normal operation data of multiple circuit boards that need to be deleted.

5. A data management system, characterized in that: include: An acquisition module is used to obtain historical operation data of multiple circuit boards; A K value determination module, configured to determine the K value in the K-means clustering algorithm using an operation data processing model based on the historical operation data of the plurality of circuit boards, wherein 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; a clustering module, configured to cluster 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 graph structure, wherein 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; A graph convolution module is used to process 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; The 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.

6. The data management system according to claim 5, wherein: 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.

7. The data management system according to claim 5, wherein: The building blocks are also used to: determining the stability of historical operation data of each circuit in each cluster node based on historical operation data of a plurality of circuit boards in each cluster node; determining a weight of each cluster node based on the stability of historical operation data of each circuit in each cluster; The weight of each cluster node is assigned to each cluster node.

8. 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 4.

9. 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 4 is implemented.

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