Data management method and system

Abstract

The invention provides a data management method and system, and relates to the technical field of data management, and the method comprises the steps: determining a K value in a K-means clustering algorithm through employing an operation data processing model based on the historical operation data of a 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 clustering center of each cluster in the K clusters; processing the circuit board graph structure based on the graph convolutional network model to determine abnormal operation data of a plurality of circuit boards needing to be reserved and normal operation data of a plurality of circuit boards needing to be deleted; data management is performed based on the abnormal historical operation data of the plurality of circuit boards needing to be reserved and the normal historical operation data of the plurality of circuit boards needing to be deleted. The method can quickly and accurately perform screening management on the abnormal data and the 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 a core component of modern electronic devices, circuit boards generate a large amount of historical operation data during their operation, including temperature, current, voltage sequence data, and operation logs. However, the amount of data generated by circuit boards during long-term operation is extremely large. Among the circuit board operation data, most of the data is normal, representing the state of the circuit board under normal conditions. However, what really deserves attention is the abnormal data, which may be an early warning sign of circuit board failure or a sign of performance degradation. The identification and analysis of abnormal data is crucial to prevent failures and optimize circuit board performance. In contrast, although normal data accounts for the majority, they will not attract attention in most cases. Excessive normal data storage and analysis not only consumes a lot of resources, but may also cover up the 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 from massive data. This is mainly because abnormal data often has low frequency and uncertainty, and its pattern may change over time or be caused by external factors.

[0003] Therefore, how to quickly and accurately screen and manage abnormal data and normal data of circuit boards is a problem that needs to be solved urgently. 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 of the K clusters, each of the K clusters including the clustered historical operation data of the multiple circuit boards; constructing 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 edges between each cluster nodes representing the distance between the cluster centers of each cluster; processing the circuit board diagram 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; 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 construction of the circuit board diagram structure also includes: Determine 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; determine the weight of each cluster node based on the stability of the historical operation data of each circuit in each cluster; and assign the weight of each cluster node to each cluster node.

[0009] Furthermore, the input of the graph convolutional network model is the circuit board diagram 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.

[0010] According to a second aspect, the present invention provides a data management system, comprising: 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 based on the historical operation data of the plurality of circuit boards using an operation data processing model; 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; 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; 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.

[0011] 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.

[0012] Furthermore, the building block is also used for: 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; The weight of each cluster node is assigned to each cluster node.

[0013] 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 is configured to be executed by the processor to implement the method as described above, the method comprising: obtaining historical operation data of multiple circuit boards; 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; 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 multiple clusters after clustering The invention relates to a method for processing a circuit board diagram, comprising: collecting historical operation data of a circuit board; constructing a circuit board diagram structure, wherein the circuit board diagram structure comprises K cluster nodes and multiple edges between the K cluster nodes, each cluster node represents a cluster, the node feature of each cluster node comprises 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; processing the circuit board diagram 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.

[0014] 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 operation data of a plurality of circuit boards; determining the 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; 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, each of the K clusters including the historical operation data of the plurality of circuit boards after clustering; constructing A circuit board diagram structure is established, 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; 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; 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.

[0015] The present invention provides a data management method and system, the method comprising 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 historical operation data of the multiple circuit boards after clustering; constructing a circuit board diagram structure, the circuit board diagram structure comprising K cluster nodes and multiple edges between the K cluster nodes, each cluster node representing a cluster, the node feature of each cluster node comprising the historical operation data of the multiple circuit boards in each cluster, and the edges between each cluster node representing the distance between the cluster centers of each cluster; processing the circuit board diagram 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; 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 screen and manage the abnormal data and normal data of the circuit boards. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a data management method provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process for determining the weight of each cluster node provided by an embodiment of the present invention; Figure 3 A schematic diagram of a data management system provided by an embodiment of the present invention; Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention; Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present invention better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, 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 part of the present invention being overwhelmed by too much description, and 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 according to the description in the specification and the general technical knowledge in the art.

[0018] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0019] The serial numbers assigned to the components herein, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in the present invention include direct and indirect connections (couplings) unless otherwise specified.

[0020] 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: Step S1, obtaining historical operation data of multiple circuit boards.

[0021] A circuit board is a substrate used to carry and connect electronic components in electronic devices. It is usually made of insulating material with copper foil lines and pads on it 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.

[0022] 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.

[0023] 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 to form a series of temperature data points.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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, the output of the individual difference determination layer is the difference degree between each circuit board operation data and each remaining circuit board operation data, the input of the overall difference determination layer is the difference degree between each circuit board operation data and each remaining circuit board operation data, the output of the overall difference determination layer is the difference degree of the overall circuit board operation data, the input of the K value determination layer is the difference degree of the overall circuit board operation data, and the output of the K value determination layer is the K value in the K-means clustering algorithm.

[0031] 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.

[0032] The K value determination layer is based on the degree of difference in the overall circuit board operation data. The task of this layer is to determine the best K value in the K-means clustering algorithm. The selection of the K value is crucial to the clustering results. A too small K value may lead to information loss, while a too large K value may lead to overfitting. By analyzing the overall differences, the model can more accurately estimate the natural clustering structure of the data, thereby selecting a K value that can capture the diversity of the data while maintaining the simplicity of clustering.

[0033] 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 clustered historical operation data of the multiple circuit boards.

[0034] In some embodiments, K clusters and the cluster center of each of the K clusters can be obtained through 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.

[0035] Step S4, constructing 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 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.

[0036] The circuit board graph structure is a data structure based on graph theory, which is 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.

[0037] 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 operation 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 operation 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.

[0038] Each node in the graph structure represents a cluster, that is, a set of circuit board data with similar operating characteristics. For example, assuming that the circuit board data is divided into five different clusters through the clustering algorithm, there will be five cluster nodes in the circuit board graph structure. The node characteristics include the historical operating data of multiple circuit boards in each cluster, such as historical temperature series data, historical current series data, historical voltage series data, and historical operation logs.

[0039] In some embodiments, a weight of each cluster node may also be determined and assigned to each cluster node.

[0040] 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 the process for determining the weight of each cluster node comprises steps S21 to S23: 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.

[0041] In some embodiments, the stability of the historical operation data of each circuit 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 the historical operation data of each circuit in each cluster node.

[0042] Step S22: determining the weight of each cluster node based on the stability of the historical operation data of each circuit in each cluster.

[0043] In some embodiments, the weight of each cluster node can be determined based on the preset relationship between the stability of the historical operation data of each circuit in each cluster and the weight of each cluster node. The preset relationship can be set artificially in advance. Cluster nodes with high weight values ​​mean that the circuit boards in the cluster are running relatively 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 abnormal sensitivity to avoid misjudging normal fluctuations as abnormalities. On the contrary, cluster nodes with low weight values ​​may have more operating problems in their circuit boards, and the model will give higher abnormal sensitivity during detection to more carefully identify potential abnormal patterns.

[0044] Step S23: assigning the weight of each cluster node to each cluster node.

[0045] The weight value calculated in step S22 is assigned to the corresponding cluster node as an additional attribute of the node.

[0046] Step S5, processing the circuit board diagram structure based on the 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] The normal operation data of multiple circuit boards that need to be deleted are those that do not show any abnormal patterns, as opposed to abnormal data. 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 to be normal.

[0051] The graph convolutional network model can process the circuit board graph structure to determine abnormal and normal operation data because it can effectively utilize the adjacency information in the graph structure and learn the complex patterns in the data set, thereby achieving accurate recognition and classification of the circuit board's operating status. This capability has significant advantages in circuit board fault prediction, performance monitoring, and resource optimization.

[0052] In the circuit board diagram structure, the graph convolutional network model can learn the difference between normal operation data and abnormal operation data. Since abnormal operation data usually shows distinctive features in the graph, such as connection patterns with other nodes, abnormal values ​​of node features, etc., the graph convolutional network model can capture these abnormal features and distinguish abnormal data from normal data through model output.

[0053] 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.

[0054] 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.

[0055] 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: An acquisition module 31 is used to acquire historical operation data of multiple circuit boards; A K value determination module 32, 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; A clustering module 33, 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 34 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; A graph convolution module 35, 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; The management module 36 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.

[0056] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 4 As shown, including: 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 multiple circuit boards; 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; 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 historical operation data of the multiple circuit boards after clustering; Historical operation data of multiple circuit boards; constructing a circuit board diagram structure, 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 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; processing the circuit board diagram 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.

[0057] 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 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 of the K clusters, each of the K clusters including the historical operation data of the 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 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.

[0058] The data management method provided in the embodiments of the present application can be applied to 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) and virtual reality (VR) devices, smart home devices, car computers and other electronic devices, and the embodiments of the present application do not impose any restrictions on this.

[0059] Taking the mobile phone 100 as an example of the electronic device, Figure 4 A schematic structural diagram of the mobile phone 100 is shown.

[0060] 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.

[0061] 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.

[0062] It is to 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 illustrated, or combine certain components, or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0063] 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 processor (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. Different processing units may be independent devices or integrated into one or more processors.

[0064] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that commands the various components of the mobile phone 100 to work in coordination according to the instructions. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0065] The application processor may be installed with an operating system of the mobile phone 100, which is used to manage the hardware and software resources of the mobile phone 100. For example, it may manage and configure memory, determine the priority of system resource supply and demand, manage file systems, manage drivers, etc. The operating system may also be used to provide an operating interface for users to interact with the system. Various types of software may be installed in the operating system, such as drivers, applications (applications, apps), etc. Exemplarily, the operating system of the mobile phone 100 may be an Android system, a Linux system, etc.

[0066] A memory may also be provided in the processing module 110 for storing instructions and data. In some embodiments, the memory in the processing module 110 is a cache memory. The memory may store instructions or data that have just been used or are cyclically used by the processing module 110. If the processing module 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processing module 110, and thus improves the efficiency of the system.

[0067] In some embodiments, the processing module 110 may include one or more interfaces. The interface 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, etc.

[0068] 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 of the K clusters, each of the K clusters including the historical operation data of the multiple circuit boards after clustering; construct 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 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 diagram 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; 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.

[0069] The charging management module 140 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger through the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive wireless charging input through a wireless charging coil of the mobile phone 100. While the charging management module 140 is charging the battery 142, it may also power the electronic device through the power management module 141.

[0070] 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 supplies power to the processing module 110, the internal memory 121, the external memory, the display screen 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 number, battery health status (leakage, impedance), etc. 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.

[0071] 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.

[0072] 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 the utilization of antennas. For example, antenna 1 can be reused 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.

[0073] 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 arranged in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the same device as at least some of the modules of the processing module 110.

[0074] The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent 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 the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a 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.

[0075] 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 integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the frequency of the electromagnetic wave signal and performs filtering, and sends the processed signal to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, modulate the frequency of the signal, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0076] 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 technology. The wireless communication technology 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).

[0077] The mobile phone 100 implements the display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, which is 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 display information.

[0078] 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 the embodiment of the present application, the display screen 194 can be used to display a remote session window, an image of a chronic, difficult-to-heal wound, and a wound detection report.

[0079] The mobile phone 100 can realize the shooting function through the ISP, the camera 193, the video codec, the GPU, the display screen 194 and the application processor. In some embodiments, the mobile phone 100 can realize the video communication function through the ISP, the camera 193, the video codec, the GPU and the application processor.

[0080] ISP is used to process the data fed back by camera 193. For example, when taking a photo, the shutter is opened, and the light is transmitted to the camera photosensitive element through the lens. The light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to ISP for processing and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on the noise, brightness, and skin color of the image. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, ISP can be set in camera 193.

[0081] 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 optical signal into an electrical signal, and then passes 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 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.

[0082] The digital signal processor is used to process digital signals, and can process not only digital image signals but also 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.

[0083] Video codecs are used to compress or decompress digital videos. Mobile phone 100 may support one or more video codecs. Thus, mobile phone 100 may play or record videos in various coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0084] NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, the intelligent cognition of the mobile phone 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

[0085] 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 implement a data storage function, such as storing music, video and other files in the external memory card.

[0086] 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.

[0087] The mobile phone 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor.

[0088] 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 arranged in the processing module 110, or some functional modules of the audio module 170 can be arranged in the processing module 110.

[0089] The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The mobile phone 100 can listen to music or listen to a hands-free call through the speaker 170A.

[0090] The receiver 170B, also called a "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or voice message, the voice can be received by placing the receiver 170B close to the ear.

[0091] Microphone 170C, also called "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 collect sound signals, reduce noise, identify the sound source, realize directional recording function, etc.

[0092] The earphone interface 170D is used to connect a wired earphone and can be a USB interface 130 or a 3.5 mm open mobile terminal platform (OMTP) standard interface or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0093] The key 190 includes a power key, a volume key, etc. The key 190 can be a mechanical key or a touch key. The mobile phone 100 can receive key input and generate key signal input related to the user settings and function control of the mobile phone 100.

[0094] 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.

[0095] Indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0096] The SIM card interface 195 is used to connect the SIM card. The SIM card can be connected to and separated from the mobile phone 100 by inserting it into the SIM card interface 195 or pulling it out 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, 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 realize 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.

[0097] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of 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 belong to the spirit and scope of the exemplary embodiments of this specification.

[0098] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in 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.

[0099] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached 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 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 by software solutions, such as installing the described system on an existing server or mobile device.

[0100] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0101] 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, as an example and not a 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 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 the K value in the K-means clustering algorithm using an operation data processing model based on the historical operation data of the multiple 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 multiple circuit boards, and the 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 comprising K cluster nodes and a plurality of edges between the K cluster nodes, each cluster node representing a cluster, a node feature of each cluster node comprising historical operation data of a plurality of 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, 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 circuit board diagram structure construction further 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; The weight of each cluster node is assigned to each cluster node.

4. The data management method according to claim 1, characterized in that: 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.

5. 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, used 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 multiple 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 multiple circuit boards, and the output of the operation data processing model is the K value in the K-means clustering algorithm; 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; 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; 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, 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.

7. The data management system according to claim 5, characterized in that: The building blocks are 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; 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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