Field device topology generation method and device with self-adaptive attribute configuration and medium

Abstract

The invention discloses an adaptive attribute configuration field equipment topology generation method, equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: executing field equipment attribute data acquisition, and establishing an attribute data set; obtaining node self-defined configuration; reading the selected nodes, calling the intelligent analysis network to carry out association analysis based on the attribute data set, establishing association nodes and carrying out dynamic sorting; creating a basic topological graph by using the selected nodes, the associated nodes and the sorting result; obtaining screen data, and performing layout optimization to generate a result; and utilizing a layout optimization result and node custom configuration graph presentation, and dynamically calculating node associated information. The technical problems that in the prior art, topological graph generation is tedious, and the dynamic display data association capacity is insufficient are solved, and the technical effects of improving topology generation efficiency and enhancing the dynamic data display capacity are achieved through self-defined node attribute configuration, association analysis of an intelligent analysis network and dynamic layout optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, device and medium for generating field device topology with adaptive attribute configuration. Background Art

[0002] With the rapid development of modern industry and energy industry, the types and quantity of field equipment are increasing, such as switchgear, metering equipment and other types of equipment in power systems. These devices usually have complex hierarchical relationships and diverse attribute data in actual use. In order to achieve efficient management and real-time monitoring of equipment, a topology diagram that can intuitively display the status and connection relationship of the equipment is required. However, the existing technology still has many problems in device topology generation and attribute configuration. Traditional methods require complex secondary data calculations on the server side and only generate temporary data points for graphical display, which not only increases the workload of system development and maintenance, but also easily leads to configuration errors, thereby reducing the efficiency of topology generation. In addition, the existing topology diagram can usually only associate directly collected data points, cannot reflect the dynamic relationship between devices, and is difficult to meet the application scenarios that require real-time adjustment of the hierarchical relationship between devices. At the same time, the existing methods also have obvious deficiencies in the adaptability of topological layout, and cannot be optimized according to the resolution and screen ratio of different display devices, resulting in poor display effect and poor user experience. In addition, most systems currently cannot support users to customize node attributes and display methods according to their needs, such as node appearance, data point display position, etc. This fixed display form seriously limits the flexibility and practicality of the system. Summary of the invention

[0003] The present application provides a method, device and medium for generating field equipment topology with adaptive attribute configuration, which solves the technical problems of complicated topology graphics generation and insufficient dynamic data association capability in the prior art. By customizing node attribute configuration, intelligent network association analysis and dynamic layout optimization, the technical effect of improving topology generation efficiency and enhancing dynamic data display capability is achieved.

[0004] In view of the above problems, the present application provides a method, device and medium for generating field device topology with adaptive attribute configuration.

[0005] The first aspect of the present application provides a method for generating a field device topology with an adaptive attribute configuration, the method comprising: performing basic attribute data collection of field devices to establish a basic attribute data set, the basic attribute data set comprising device type, operating status, and connection relationship; obtaining a user's node custom configuration, the node custom configuration comprising the node's appearance attributes and data display method; reading a user's selected node, taking the selected node as a central node, calling an intelligent analysis network to perform an association analysis based on the basic attribute data set, establishing an associated node, and dynamically sorting the associated nodes by priority to establish a dynamic priority sorting result; creating a basic topology map using the selected nodes, the associated nodes, and the dynamic priority sorting result; obtaining display screen data, performing layout optimization of the basic topology map based on the display screen data, and establishing a layout optimization result; performing graphical presentation using the layout optimization result and the node custom configuration, and dynamically calculating information for setting node associations within the graph.

[0006] According to a second aspect of the present application, there is provided an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute the method for generating a field device topology with adaptive attribute configuration provided by the present application.

[0007] According to a third aspect of the present application, a computer-readable storage medium is provided, which stores a computer program, and the computer program is used to execute the method for generating a field device topology with adaptive attribute configuration provided by the present application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: First, the basic attribute data of the field equipment is collected to establish a basic attribute data set, which includes the device type, operating status, and connection relationship. Subsequently, the user's node custom configuration is obtained, and the node custom configuration includes the node's appearance attributes and data display mode. Then, the user's selected node is read, and the selected node is used as the central node. The intelligent analysis network is called to perform association analysis based on the basic attribute data set, establish associated nodes, and dynamically sort the associated nodes to establish the priority dynamic sorting result. Further, the selected node, the associated node, and the priority dynamic sorting result are used to create a basic topology map. After that, the display screen data is obtained, and the layout optimization of the basic topology map is performed according to the display screen data to establish the layout optimization result. Finally, the layout optimization result and the node custom configuration are used for graphic presentation, and the node association in the graphic is set to dynamically calculate information. The technical problems of cumbersome topology graphics generation and insufficient dynamic display data association capability in the prior art are solved. Through the customized node attribute configuration, the association analysis of the intelligent analysis network, and the dynamic layout optimization, the technical effect of improving the topology generation efficiency and enhancing the dynamic data display capability is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a method for generating a field device topology with adaptive attribute configuration provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of a flow chart of establishing a layout optimization result of a method for generating a field device topology with adaptive attribute configuration provided in an embodiment of the present application.

[0012] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0013] Description of the reference numerals: processor 11 , memory 12 , input device 13 , output device 14 . DETAILED DESCRIPTION

[0014] This application solves the technical problems of complicated topology graphics generation and insufficient dynamic data association display capability in the prior art by providing a field equipment topology generation method, equipment and medium with adaptive attribute configuration. Through customized node attribute configuration, intelligent analysis of network association analysis and dynamic layout optimization, the application achieves the technical effect of improving topology generation efficiency and enhancing dynamic data display capability.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0017] Embodiment 1, as Figure 1 As shown, the present application provides a method for generating a field device topology with adaptive attribute configuration, wherein the method comprises: Perform basic attribute data collection of field equipment and establish a basic attribute data set, wherein the basic attribute data set includes equipment type, operating status, and connection relationship.

[0018] In one embodiment, basic information of each device is collected through the monitoring system of the field equipment. This information includes the type of equipment (such as switches, electricity meters, water meters, gas flow meters, transformers, relays, generators, capacitors, etc.), operating status (such as normal operating data, fault data, standby data, etc.), and connection relationships between devices (such as the connection method and location between upper-level devices, lower-level devices, and devices at the same level). These data will be organized and stored in a data set to form a comprehensive basic attribute data set. This data set is the basis for the entire analysis and topology generation, and can provide necessary data support for subsequent node association analysis and graphic display.

[0019] Obtain the user's node custom configuration, which includes the node's appearance attributes and data display method.

[0020] In one embodiment, the user can personalize each node according to actual needs, establish a connection with the user end, obtain user feedback data, and thus obtain the user's node custom configuration. This node custom configuration includes the appearance attributes of the node and the data display method. Among them, the appearance attributes of the node refer to the user being able to adjust the display method of the node in the graph, such as the shape of the node (such as circle, square, rectangle, etc.), color (such as green for normal, red for fault, etc.), size (such as adjusting the size of the node according to the importance or capacity of the device), etc. These appearance settings help users more intuitively identify different types of devices and the working status of the device. The data display method refers to how to display the specific data of the device on the node, such as displaying the name of the device, the current status, the real-time data value (such as current, voltage, etc.), etc. In addition, the user can also set the format of data display, for example, choose to present data in the form of digital form, bar chart, dashboard, etc., or control whether the data is displayed, etc., so that the display of the node can be more in line with the needs of the user and improve the availability and visualization of the node. Through this custom configuration, the user can flexibly optimize the display method of the device topology map according to different on-site needs, improve operational efficiency and user experience.

[0021] Furthermore, obtaining the user's node custom configuration includes: The basic attribute data set is used as matching data to establish an industrial node standard template; the user account of the user is read, and a user general database is established according to the user account; the user general database and the industrial node standard module are integrated and sent to the user, the user's feedback data is received, and the node custom configuration is obtained according to the feedback data.

[0022] Preferably, by collecting basic attribute data sets of field equipment (including equipment type, operating status, connection relationship, etc.), these data are used as standardized matching data to construct an industrial node standard template, which contains all equipment attributes that may need to be displayed, and stipulates basic mapping rules between equipment type and its appearance and data display method. For example, an electric meter node can be defined as a circle to display the power value, and a switch device node can be defined as a switch symbol to display status information (such as on or off); then, by reading the user's account information, accessing the historical operation records of the user associated with the account, counting the number of times the appearance and data display method used by each device, and taking the appearance and data display method with the largest number of times as the common mode of the device, and then constructing a user general database based on the common mode of each device counted. The data in this user general database are all node forms that are most frequently used by users in various scenarios, and have a certain degree of universality; then, the user general database and the industrial node standard template are integrated to form a configuration package that meets user needs. This configuration package contains a standard template and the user's personalized settings, and is sent to the user. Users can view and modify these configurations to better meet the needs of field device management. After receiving the configuration package, the user will select the appearance attributes and data display methods of the nodes corresponding to each device when the field device topology is generated according to the appearance attributes and data display methods recorded in the configuration package, or further adjust the appearance attributes and data display methods of the nodes according to actual needs. After the user completes the selection or modification, the selected or modified data will be fed back. After receiving these feedback data, the node custom configuration is updated according to the user's feedback content. For example, the user may adjust the display method of certain nodes, change the format of data display or add new dynamic display conditions. According to these feedback data, the user's node custom configuration is finally obtained. Through the above process, not only can the preliminary node configuration be generated based on the standard template, but it can also be adjusted and optimized according to the user's personalized needs, thereby providing users with a flexible and customized device topology display solution.

[0023] The user's selected node is read, the selected node is used as the central node, the intelligent analysis network is called to perform association analysis based on the basic attribute data set, an associated node is established, and the associated nodes are dynamically prioritized to establish a dynamic priority ranking result.

[0024] In one embodiment, a selected node selected by a user is read from a user terminal, and the selected node is processed as a central node. The selected node represents the equipment that the user needs to pay attention to, which may be an important switch device, metering device, power device or other type of equipment. Subsequently, based on the previously collected basic attribute data set (such as device type, operating status and connection relationship, etc.), a pre-built intelligent analysis network is used to conduct an in-depth analysis of the association between nodes. The intelligent analysis network can calculate the association values ​​of other nodes and the central node through the learned mapping relationship through the basic attribute data set, and filter out the associated nodes according to these association values. These association values ​​will be output together with the corresponding nodes as associated nodes. Subsequently, priority dynamic sorting is performed based on the association value corresponding to each associated node, that is, the associated nodes are arranged in descending order according to the size of the association value, and a priority dynamic sorting result is established. This result provides a basis for the generation of a topology map, ensuring that nodes important to the central node can be displayed first, thereby improving the efficiency and operability of device management.

[0025] For intelligent analysis networks, they can be constructed based on multi-layer perceptrons, decision trees, graph neural networks, etc. Taking multi-layer perceptrons as an example, firstly, sample data for training will be collected, including sample basic attribute data, sample center nodes, and sample association nodes, where the sample association nodes are marked with sample association values; a deep neural network structure is designed based on the multi-layer perceptron, including input layer, hidden layer, and output layer, and then the sample basic attribute data, sample center nodes, and sample association nodes are input into the deep neural network for forward propagation, and are transmitted layer by layer through the input layer, hidden layer, output layer, etc., to calculate the prediction results including the association value; then, the mean square error loss function is used to calculate the loss value between the prediction result and the sample association value, and the gradient of the loss to the weight of each layer is calculated layer by layer through back propagation. Then, the Adam optimizer is used to optimize the network parameters and adjust the weights to minimize the value of the loss function. Repeat the above process until the maximum number of iterations is reached; after the training is completed, use sample data that has not been used for training to test the network performance and evaluate the accuracy of the network in the association value prediction task. If the accuracy meets the expected expectations, the current deep neural network is output as the final intelligent analysis network. Otherwise, adjust the hyperparameters such as the learning rate and the number of training batches to further improve the prediction effect of the intelligent analysis network; finally, the associated nodes can be screened out through the association threshold of the output layer (determined based on business needs and expert decisions).

[0026] A basic topology graph is created using the selected nodes, the associated nodes, and the priority dynamic sorting results.

[0027] In one embodiment, in the process of creating a basic topology map, the user's selected node is first used as the starting point, and the nodes associated with it (i.e., associated nodes) are arranged around the central node in the order of the priority dynamic sorting result; then, according to the size of the association value in the priority dynamic sorting result, the distance between the central node and the associated node is adjusted. The larger the association value, the smaller the distance between the associated node and the central node. Conversely, the larger the distance between the associated node and the central node. After all the associated nodes are arranged, a basic topology map can be constructed. This basic topology map displays the central node, associated nodes and their connection relationship in a graphical form, and dynamically reflects the priority of the node. The user can intuitively understand the hierarchical relationship, connection status and importance between devices through the topology map. The topology map can further support interactive functions, such as zooming in, zooming out, dragging, etc., to facilitate user viewing and operation. In this way, the basic topology map can comprehensively and intuitively reflect the relationship between the selected node and its associated nodes, and provide users with effective equipment management and decision support tools.

[0028] Display screen data is obtained, and a layout optimization of a basic topology graph is performed according to the display screen data to establish a layout optimization result.

[0029] In one embodiment, after the basic topology map is generated, in order to ensure the best display effect on different screen devices, it is necessary to perform layout optimization according to the data of the display screen; first, relevant information of the display screen is collected, including resolution (such as 1920x1080), screen ratio (such as 16:9 or 4:3), etc., which will serve as the basis for layout optimization to ensure that the topology map can be adapted to different types of screens (such as computers, mobile phones, and tablets); then, according to the resolution and ratio of the screen, a valid area suitable for displaying the topology map is defined, which can be the entire screen or a specified part of the display area (such as in a multi-window display). The size and shape of the effective display area directly affect the arrangement of nodes and edges. After that, the layout of nodes in the topology map is adjusted according to the size and shape of the effective display area, and the pre-built layout optimization channel is used to distribute the nodes in the display area, while avoiding excessive density between nodes or boundary overflow. The topology map after layout optimization will generate the final layout optimization result, which includes the optimized position of the nodes, the optimized path of the connecting edges, and the spatial distribution of the overall topology map. The final topology map will be rendered based on this optimization result to ensure that it can be presented clearly and intuitively on the current screen. Through this process, the layout optimization result can adapt to the display requirements of different screens, improve the visualization effect and user experience of the topology map, and achieve the best display effect whether it is a large display or a small mobile phone screen.

[0030] Furthermore, if Figure 2As shown, the obtaining of display screen data, performing layout optimization of a basic topology graph according to the display screen data, and establishing a layout optimization result include: After placing the selected nodes at the center point, hierarchical arrangement is performed according to the basic topology diagram to establish a basic layout; the display resolution and screen ratio are obtained based on the display screen data, and an effective display area is created based on the display resolution and the screen ratio; layout optimization is performed based on the effective display area and the basic layout according to the layout optimization channel, and a layout optimization result is established.

[0031] Preferably, the node selected by the user is used as the center point of the topology map, which means that all devices directly or indirectly associated with the node will be arranged around the node; based on the arrangement method in the basic topology map, the associated nodes with different side lengths are arranged in layers, and the nodes with high priority are placed in the layer closest to the central node, and the nodes with lower priority are placed in the more peripheral layers, so as to intuitively reflect the relationship between the central node and the associated nodes, and highlight the high-priority nodes. At this stage, a basic layout is generated, including the location distribution (hierarchy) and connection relationship of the nodes; subsequently, in order to ensure that the topology map can be optimally displayed on the screen To achieve the display effect, the effective display area is calculated according to the display screen data. By collecting the screen resolution and ratio information (for example, 1920×1080 resolution, 16:9 ratio), combined with the set border margin, the graphics display is prevented from being too close to the edge of the screen. The border margin can be calculated as 5% of the screen width and height. For example, the horizontal margin is 5% of the screen width, and the vertical margin is 5% of the screen height. When calculating the effective display area, the border margins on both sides need to be subtracted from the total width and height of the screen. The effective width is equal to the total screen width minus twice the horizontal margin, and the effective height is equal to the total screen height minus Double the vertical margin. The calculated effective area ensures that the topology map can be displayed in the center of the screen. In order to adapt the effective area to the screen ratio (such as 16:9), the size of the effective display area can be adjusted. For example, when the target ratio is 16:9, the corresponding target height can be calculated by the effective width, or the target width can be calculated by the effective height. If the calculated target height is greater than the effective height, the width is recalculated based on the height. If the target width is greater than the effective width, the height is recalculated based on the width. The obtained effective display area includes the adjusted width and height. If the topology map needs to be embedded in other boundaries, For surfaces (such as multi-window applications), the effective display area will be further adjusted to fit the size of the specified window; afterwards, the basic layout is adjusted through the pre-built layout optimization channel combined with the effective display area to ensure that the nodes are reasonably distributed in the display area and the connection relationship is clear. The layout optimization channel optimizes the basic layout in the effective display area through the internal force-directed sub-channel, hierarchical layout sub-channel and dynamic edge weight sub-channel, respectively, to minimize the intersection of edges and ensure the overall clarity of the topology map, thereby generating the layout optimization results, including the final position coordinates of each node, the optimized path of each edge (starting point, end point and curve information), etc. Through the above steps, the subsequently generated topology map can not only fully reflect the connection relationship of the device, but also be displayed on the screen in an intuitive and clear form, providing users with the best operation and observation experience.

[0032] Furthermore, performing layout optimization based on the effective display area and the basic layout according to the layout optimization channel to establish a layout optimization result includes: A first optimization result is established by analyzing the balance of attraction and repulsion of the basic layout in the effective display area through a force-directed sub-channel; a second optimization result is established by analyzing the uniform distribution of layer nodes of the basic layout in the effective display area through a hierarchical layout sub-channel; a third optimization result is established by optimizing the connection of the basic layout through a dynamic edge weight sub-channel, and a layout optimization result is established using the first optimization result, the second optimization result and the third optimization result, wherein the force-directed sub-channel, the hierarchical layout sub-channel and the dynamic edge weight sub-channel are sub-processing channels of the layout optimization channel.

[0033] Optionally, the layout optimization channel is an integrated channel, which includes a force-directed sub-channel, a hierarchical layout sub-channel, and a dynamic edge weight sub-channel. By transferring the basic layout and the effective display area to the force-directed sub-channel, the force-directed sub-channel can balance the gravitational and repulsive relationships of each node in the basic layout, that is, the distance relationship between each node and the surrounding nodes, thereby generating a first optimization result. This first optimization result can ensure that the nodes are evenly distributed and non-overlapping in the effective display area; the force-directed sub-channel can be constructed based on PPO, DQN, etc. in the reinforcement learning model. Taking PPO as an example, in the PPO model in deep reinforcement learning, When constructing a layout optimization channel, we first need to define the current layout state as the state space, and the node adjustment action as the action space. The effect of layout optimization is measured by the reward function. The goal is to continuously optimize the position of the node through the policy network to maximize the reward value of the overall layout. Specifically, the input state includes the boundary conditions of the basic layout and the effective display area. The policy network receives this state information and outputs the moving direction and moving distance of each node. The execution of each action will update the current layout state. The reward value is calculated based on the new state. The reward value is evaluated by three core indicators. The first is the uniformity of node distribution, which is measured by detecting whether the minimum distance between nodes exceeds the set threshold. The second is the degree of optimization of the connection relationship, whether the node with high priority is closer to the central node, and the third is the reduction in the number of edge crossings. The fewer the edge crossings, the higher the reward. The reward function obtains the optimization score of the current layout by weighted synthesis of these factors. The core of the model includes the policy network and the value network. The policy network is used to predict the node's movement strategy and output the probability distribution of the moving direction and distance. The value network is used to estimate the overall score of the current layout state and guide the strategy improvement. During the training process, the PPO clipping strategy optimization method is used to avoid excessive strategy updates and ensure To ensure the stability of model convergence, at the beginning of training, the positions of all nodes are initialized to the default coordinates of the basic layout. Through the policy network sampling action, each node moves according to the predicted direction and distance to generate a new layout state; then, the new layout score is calculated and compared with the score of the previous state to evaluate the effectiveness of the strategy. After each action is completed, the reward value will be fed back to the model to update the parameters of the policy network and the value network. This process continues to iterate until the model converges and finally generates a force-directed sub-channel that meets the expectations. The data used in the training process includes the sample basic layout, the sample effective display area, and the sample optimized layout.

[0034] By passing the basic layout and the effective display area to the hierarchical layout sub-channel, the hierarchical layout sub-channel evenly distributes the nodes in different levels in the effective display area according to the hierarchical relationship of the nodes, thereby generating a second optimization result. This second optimization result can ensure that the nodes are evenly distributed inside and outside the hierarchy while highlighting the hierarchical structure. The hierarchical layout sub-channel can be constructed based on the multi-layer perceptron, graph convolutional network, etc. in the neural network. When using the multi-layer perceptron for construction, the specific process is the same as the aforementioned intelligent analysis network.

[0035] By passing the basic layout and the effective display area to the dynamic edge weight sub-channel, the dynamic edge weight sub-channel optimizes the connection relationship between the nodes, making the edge display path as clear and concise as possible, while highlighting the important connection relationship, thereby generating a third optimization result. This third optimization result can ensure that the edge path layout is concise and clear, while highlighting the key connection relationship; the dynamic edge weight sub-channel can be constructed based on PPO, DQN, etc. in the reinforcement learning model. When using PPO for construction, the specific process is the same as the aforementioned force-directed sub-channel.

[0036] Finally, the node balance distribution of the force-directed sub-channel (the first optimization result), the hierarchical structure optimization of the hierarchical layout sub-channel (the second optimization result), and the connection relationship optimization of the dynamic edge weight sub-channel (the third optimization result) are integrated to generate a complete layout optimization result. This result includes node positions (the final node coordinates after comprehensive consideration of force balance and hierarchical relationships), connecting edge paths (edge ​​path information optimized according to dynamic edge weights), visual properties of nodes and edges (hierarchical positions, curve shapes of edges), etc. The layout optimization results will be used to generate the final topology map to ensure that the topology map presents clear node distribution, hierarchical structure and connection relationships within the effective display area, while having good readability and aesthetics.

[0037] The layout optimization result and the node custom configuration are used to perform graphic presentation, and the nodes in the graphic are associated with dynamic calculation information.

[0038] In one embodiment, according to the layout optimization result, the position and connection relationship of the nodes and edges are determined, which includes the arrangement of the nodes in the effective display area, the connection path between the nodes, and the display mode of the nodes after priority sorting. The layout optimization result ensures that the distance between the nodes is reasonable, the connection relationship is clear, and the overall graphics are beautiful; then, combined with the settings made by the user in the node custom configuration, each node is personalized for display. These custom settings include the appearance attributes of the node (such as shape, size, color) and the data display mode (such as display content, format, display mode, etc.). For example, the user can set the color of the central node to green, the color of the high priority node to red, and the color of the low priority node to blue, or set some nodes to be displayed in a flashing manner to emphasize their importance; then, each node in the topological map is further processed and displayed according to the dynamic calculation information of the node. When the user touches or clicks a node, the detailed information of the node will be displayed, and further dynamic calculation and display will be performed based on the node; through these steps, a topological map that is both beautiful and rich in information can be generated according to the layout optimization result and the user's personalized configuration, ensuring that the user can intuitively and clearly see the relationship between devices and obtain dynamic information of key nodes at any time.

[0039] Furthermore, the step of associating the nodes in the graph with the dynamic calculation information includes: Perform node association evaluation on each node in the graph and establish a node backtracking chain; obtain a database for each node in the graph, the database including a real-time database, a time series database, and a relational database; associate the node backtracking chain and the database with the nodes in the graph as dynamic computing information, and generate an information display of the dynamic computing information through the node custom configuration; when a user touches a node in the graph, display the dynamic computing information based on the information display.

[0040] Preferably, after the final graph is constructed, the real-time data of each node in the graph will be evaluated for node association through an intelligent analysis network to calculate the association value of the node with other nodes. For each node, based on the calculated association value, the association relationship and changes between the node and other nodes will be tracked and recorded. For example, when a node establishes an association with other nodes for the first time, the event will be recorded in the backtracking chain, including the associated node, timestamp, association strength and other information. If the state or connection relationship of the node changes, resulting in an increase or decrease in the association with other nodes, this change will also be recorded in the backtracking chain, and the relevant The reasons for the connection change (such as changes in device status, changes in functional dependencies, etc.) are recorded. If the node is disconnected from other nodes, the time of disconnection, the reason for disconnection, and the node status after disconnection are recorded. By summarizing these associations and changes, a node backtracking chain is established for each node. This backtracking chain records the historical relationship, interaction, and change trajectory of the node with other nodes, which is convenient for subsequent dynamic analysis and calculation; then, the database information of each node is obtained. The database of each node contains different types of data sources related to the node, including real-time databases, time series databases, and relational databases. Among them, real-time databases The database stores the real-time data of the current node, such as the current status of the device, operating parameters, load conditions, etc. The time series database records the historical data of the node, especially the dynamic data that changes over time, such as the operation history of the device, fault records, performance changes, etc. The relational database stores structured data related to the node, such as the association between devices, operation logs, configuration settings, etc. The data in these databases provides basic support for subsequent dynamic calculations and can help obtain the status and historical information of the node in real time; then, the node backtracking chain and the database information of the node are combined as dynamic calculation information and associated with each node in the graph. Based on this information, information display will be generated through node custom configuration, that is, the display format is customized for each node, for example, the load data of a node in a certain period of time is displayed in the form of a curve graph; when the user touches a node in the graph, the relevant calculation information will be dynamically calculated and displayed according to the touch event, which means that when the user selects a node, the relevant data and calculation results of the node will be displayed in real time according to the user's interaction. This dynamic display method based on real-time data and historical information can help users deeply understand the working status and change process of each node, so as to make more accurate decisions and improve user experience.

[0041] Furthermore, when the user touches a node in the graph, the dynamically calculated information display is performed based on the information display, including: When the user touches a node in the graph, the touched node is used as a key central node; the association chain is reconstructed with the key central node, and the display weight of the association chain is configured; the display node is reconstructed according to the display weight and the association chain, and is highlighted, and the remaining nodes in the graph are set as background nodes to complete the dynamic calculation information display.

[0042] Optionally, when a user touches a node in the graph, the node will be used as the key center node. This node will be the focus node of the current interaction. In the graph, this key node will be highlighted, and the user can see more information and content related to it. Subsequently, the association chain is reconstructed according to the selected key center node, that is, the currently displayed node backtracking chain and database are reconstructed to display data related to the key center node. The reconstructed association chain represents the historical association and dynamic interaction between the node and other nodes. The specific steps of reconstructing the association chain are to recalculate the association value based on the characteristics, status and relationship with other nodes of the key center node, and obtain the nodes associated with the key center node. The relationship between these nodes may be adjusted according to real-time data. The association relationships and changes of these nodes related to the key center node are connected in a certain order to form a new node backtracking chain, and then the key center node is connected to the node. The real-time database, time series database, and relational database corresponding to the key center node are obtained to complete the reconstruction of the association chain. This chain can reflect the role of the key node in the current graph and its relationship with other nodes. Once the association chain is reconstructed, display weights will be assigned to them according to the association value of each association node. The higher the association value, the higher the display weight of the node. For high-weight nodes in the association chain, these nodes will be used as display nodes and highlighted in the graph, usually through the color, size or border of the node defined in the node custom configuration. In this way, users can quickly identify nodes that are highly related to the key center node. For nodes with a low degree of association with the key center node, they will be set as background nodes. These nodes will become less prominent in the graph and will be displayed through the color, brightness, size, etc. defined in the node custom configuration to help users focus on the key nodes. Finally, through the above operations, the important nodes in the graph are highlighted, the secondary nodes are set as the background, and the dynamic calculation information associated with each node is displayed. Users can view detailed information by clicking or touching the nodes, thereby enhancing the user's experience of interacting with the graph and ensuring that users can focus on the key information related to the current key nodes.

[0043] Furthermore, the method further comprises: A dynamic response layer is established, and the dynamic response layer is used to perform real-time monitoring of field equipment and establish real-time monitoring feedback; dynamic topology optimization is established according to the real-time monitoring feedback; and the graphics are reconstructed through the dynamic topology optimization.

[0044] Preferably, in order to make the graphics conform to the real-time changes of the field equipment, a dynamic response layer will be established. This dynamic response layer needs to be connected to the real-time data source of the field equipment. As an interaction layer between external devices, the dynamic response layer is responsible for receiving the real-time information of the field equipment and processing this information for further analysis. The dynamic response layer obtains the status information of the field equipment in real time, including the working status, performance indicators, fault alarms, load conditions, communication conditions between devices, etc. of the equipment. These data may come from different types of sensors, device interfaces or real-time databases. By real-time tracking of the status and performance changes of each device, it monitors whether there are any equipment failures, load abnormalities, damage, whether there are new devices added, etc. This information reflects the immediate performance of the equipment. Based on this information, the dynamic response layer can establish real-time monitoring feedback to feedback on devices that may be abnormal (such as a device being removed due to a fault) or newly added devices, laying the foundation for subsequent image update optimization; after receiving the real-time monitoring feedback, dynamic topology optimization will be performed based on these feedback information. The goal of topology optimization is to adjust the node layout, connection relationship and display priority in the topology diagram according to the real-time status and feedback of the field equipment to reconstruct a new graph to help users discover and respond to device changes in a timely manner.

[0045] Furthermore, the method further comprises: A node linkage warning is established according to the basic attribute data set; a warning signal is configured with the node linkage warning, and the warning signal is displayed through the graphic mark.

[0046] Preferably, the basic attribute data set is input into the node linkage warning mechanism, which determines the potential linkage relationship between nodes by analyzing the basic attributes of each node, including device type, operating status and connection relationship with other devices. When a node fails, other devices that depend on the node may also be affected. Therefore, the node linkage warning mechanism can screen out the nodes that may be affected, and establish node linkage warning based on these nodes that may be affected; the node linkage warning mechanism is a linkage warning function implemented based on the connected node linkage warning channel. This channel can be based on a deep neural network, through the sample basic attribute data set (including normal The basic attribute data set and the basic attribute data set with faults) and the sample radiation node set are trained, and the specific training method is the same as mentioned above; then, the established node linkage warning is combined with the node custom configuration to configure the warning signal, including the color, icon, form (such as single node box selection, range box selection), etc. that describe the fault. For example, each faulty node may be represented by black, or a light red area may be used to box out all the faulty nodes; then, the warning signal is displayed through a graph, and the nodes in the graph will change dynamically according to the warning signal. Users can quickly identify which nodes have problems and which are normal through the visual effects such as the color, size, and shape of the graph.

[0047] In summary, the embodiments of the present application have at least the following technical effects: First, the basic attribute data of the field equipment is collected to establish a basic attribute data set, which includes the device type, operating status, and connection relationship. Subsequently, the user's node custom configuration is obtained, and the node custom configuration includes the node's appearance attributes and data display mode. Then, the user's selected node is read, and the selected node is used as the central node. The intelligent analysis network is called to perform association analysis based on the basic attribute data set, establish associated nodes, and dynamically sort the associated nodes to establish the priority dynamic sorting result. Further, the selected node, the associated node, and the priority dynamic sorting result are used to create a basic topology map. After that, the display screen data is obtained, and the layout optimization of the basic topology map is performed according to the display screen data to establish the layout optimization result. Finally, the layout optimization result and the node custom configuration are used for graphic presentation, and the node association in the graphic is set to dynamically calculate information. The technical problems of cumbersome topology graphics generation and insufficient dynamic display data association capability in the prior art are solved. Through the customized node attribute configuration, the association analysis of the intelligent analysis network, and the dynamic layout optimization, the technical effect of improving the topology generation efficiency and enhancing the dynamic data display capability is achieved.

[0048] Embodiment 2, based on the same inventive concept as the method for generating a field device topology with adaptive attribute configuration in the above embodiment, this embodiment provides an electronic device, Figure 3 The schematic structural diagram of the electronic device provided in the second embodiment of the present invention shows a block diagram of an exemplary electronic device suitable for implementing the implementation mode of the present invention. Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 3 As shown, the electronic device includes a processor 11, a memory 12, an input device 13 and an output device 14; the number of processors 11 in the electronic device can be one or more. Figure 3 Taking a processor 11 as an example, the processor 11, memory 12, input device 13 and output device 14 in the electronic device can be connected through a bus or other means. Figure 3 The example of connecting through bus is taken in the following.

[0049] Embodiment 3, based on the same inventive concept as the method for generating field device topology with adaptive attribute configuration in the aforementioned embodiment, this embodiment provides a computer-readable storage medium, which can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the method for generating field device topology with adaptive attribute configuration in the embodiment of the present application. The processor executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory, that is, realizing the generation of field device topology with adaptive attribute configuration.

[0050] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0052] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for generating field device topology with adaptive attribute configuration, characterized in that: The method comprises: Perform basic attribute data collection of field equipment and establish a basic attribute data set, wherein the basic attribute data set includes equipment type, operating status, and connection relationship; Obtain the user's node custom configuration, which includes the node's appearance attributes and data display method; Read the user's selected node, use the selected node as the central node, call the intelligent analysis network to perform association analysis based on the basic attribute data set, establish associated nodes, and dynamically sort the associated nodes by priority to establish a dynamic priority sorting result; Creating a basic topology map using the selected nodes, the associated nodes, and the priority dynamic sorting results; Acquire display screen data, perform layout optimization of a basic topology graph according to the display screen data, and establish a layout optimization result; The layout optimization result and the node custom configuration are used to perform graphic presentation, and the nodes in the graphic are associated with dynamic calculation information.

2. The method for generating field device topology with adaptive attribute configuration according to claim 1, characterized in that: The obtaining of display screen data, performing layout optimization of a basic topology graph according to the display screen data, and establishing a layout optimization result includes: After placing the selected nodes at the center point, hierarchical arrangement is performed according to the basic topology diagram to establish a basic layout; Acquire a display resolution and a screen ratio based on the display screen data, and create an effective display area based on the display resolution and the screen ratio; Performing layout optimization based on the effective display area and the basic layout according to the layout optimization channel, and establishing a layout optimization result.

3. The method for generating field device topology with adaptive attribute configuration according to claim 2, characterized in that: The performing layout optimization based on the effective display area and the basic layout according to the layout optimization channel to establish a layout optimization result includes: Performing an attractive force and a repulsive force balance analysis of the basic layout in the effective display area through a force-directed subchannel to establish a first optimization result; Performing uniform distribution analysis of layer nodes of the basic layout within the effective display area through a hierarchical layout sub-channel to establish a second optimization result; The connection optimization of the basic layout is performed through the dynamic edge weight sub-channel to establish a third optimization result, and the layout optimization result is established using the first optimization result, the second optimization result, and the third optimization result, wherein the force-directed sub-channel, the hierarchical layout sub-channel, and the dynamic edge weight sub-channel are sub-processing channels of the layout optimization channel.

4. The method for generating field device topology with adaptive attribute configuration according to claim 1, characterized in that: The step of associating the nodes in the graph with dynamic calculation information includes: Performing node association evaluation on each node in the graph and establishing a node backtracking chain; Obtaining a database of each node in the graph, the database comprising a real-time database, a time series database, and a relational database; Associating the node backtracking chain and the database as dynamic computing information with the nodes in the graph, and generating information display of the dynamic computing information through the node custom configuration; When a user touches a node within the graph, the dynamically calculated information display is performed based on the information display.

5. The method for generating field device topology with adaptive attribute configuration according to claim 4, characterized in that: When the user touches a node in the graph, the dynamically calculated information is displayed based on the information display, including: When the user touches a node in the graph, the touched node is used as a key central node; Reconstructing the association chain with the key central node, and configuring the display weight of the association chain; The display nodes are reconstructed according to the display weights and the association chains, and are highlighted, and the remaining nodes in the graph are set as background nodes to complete the display of the dynamic calculation information.

6. The method for generating field device topology with adaptive attribute configuration according to claim 1, characterized in that: The step of obtaining the user's node custom configuration includes: Using the basic attribute data set as matching data, establishing an industrial node standard template; Reading a user account of a user, and establishing a user general database according to the user account; The user general database and the industrial node standard module are integrated and sent to the user, user feedback data is received, and the node custom configuration is obtained according to the feedback data.

7. The method for generating field device topology with adaptive attribute configuration according to claim 1, characterized in that: The method further comprises: Establishing a dynamic response layer, utilizing the dynamic response layer to perform real-time monitoring of on-site equipment, and establishing real-time monitoring feedback; Establishing dynamic topology optimization based on the real-time monitoring feedback; The graph is reconstructed through the dynamic topology optimization.

8. The method for generating field device topology with adaptive attribute configuration according to claim 1, characterized in that: The method further comprises: Establishing node linkage warning according to the basic attribute data set; The node linkage warning is used to configure a warning signal, and the warning signal is displayed through the graphic mark.

9. An electronic device, characterized in that: The electronic device comprises: a processor; and a memory for storing instructions executable by the processor; wherein the processor is used to execute the method for generating a field device topology with adaptive attribute configuration according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, so that the computer program is used to execute the method for generating a field device topology with adaptive attribute configuration according to any one of claims 1 to 8.

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