CN118606397A - Visual automatic layout method and system for model calculation analysis - Google Patents
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Abstract
The invention discloses a model calculation analysis visualization automatic layout method and a system, which relate to the field of graphic visualization and comprise the following steps: dynamically loading data from various data sources according to a user request, determining the type of the data and preprocessing the data; classifying the data to form data nodes and drawing a preliminary topological graph; identifying and optimizing repeated data nodes and redundant edges; and analyzing the optimized topological graph, and selecting a proper framework graph type for layout adjustment. The invention utilizes the betweenness centrality, the degree centrality and the feature vector centrality to identify and process the repeated data nodes, thereby reducing the redundancy of the graphic layout and avoiding the information redundancy; the connection of the edges is optimized through the calculation redundancy, so that the simplicity and the attractiveness of the graphic layout are improved. The layout classification and optimization method can select an optimal layout algorithm according to different data structures, and high efficiency and intuitiveness of graphic layout are ensured. Finally, the graphical layout is further optimized by preventing data nodes from overlapping, optimizing intersecting edges, and aligning data nodes.
Description
Technical Field
The invention relates to the technical field of graphic visualization, in particular to a model calculation analysis visualization automatic layout method and a system.
Background
With the rapid development of big data technology and computing power, data visualization has become an indispensable part of the data analysis and mining process. By graphically presenting the data, the data visualization not only improves the efficiency of data analysis, but also enhances the intuitiveness and convenience of data understanding. Automatic layout techniques are particularly important in the field of data visualization. Automatic layout techniques aim to automatically generate an optimal graphical layout based on the structure and relationship of the data, so that the user can more intuitively and efficiently understand and analyze the data. This technique is widely used for the generation and optimization of various graphic structures such as network diagrams, flowcharts, hierarchical diagrams and the like. However, as data size and complexity increases, conventional automatic layout techniques face a number of challenges in handling massive and complex data.
Currently, the development of automatic layout technology is mainly focused on several classical layout algorithms, such as tree layout, hierarchical layout, grid layout, etc. These algorithms each have advantages and are widely applied to different types of data visualization scenarios. For example, tree-type layouts are suitable for data presentation with hierarchical structures, ring-type layouts are commonly used for data presentation with cyclic or periodic structures, and force directed layouts are widely used for social network analysis and biological network analysis. However, existing automatic layout techniques often exhibit certain limitations in the face of dynamic data, real-time data updates, and integration of multiple data sources. Specifically, the prior art has shortcomings in several aspects: first, the data loading and preprocessing stages lack flexibility and intelligence, making it difficult to efficiently process multiple data sources and update them in real time. Secondly, the recognition and processing mechanism of the repeated data nodes and the redundant edges is not perfect enough, and redundancy and information redundancy of the graphic layout are easy to cause. In addition, when the existing layout algorithm processes a complex network structure, optimization and aesthetic property of the layout cannot be guaranteed, so that users are influenced in data understanding and analysis.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: how to provide an intelligent automatic layout method under the condition of facing multisource data and dynamic data updating, and ensure the high efficiency and the attractive appearance of the data in the process of visualization.
In order to solve the technical problems, the invention provides the following technical scheme: a visual automatic layout method for model calculation and analysis comprises the following steps,
Dynamically loading data from various data sources according to a user request, determining the type of the required data, and preprocessing;
classifying the preprocessed data by using a classification algorithm to form data nodes corresponding to the types of data, and drawing a preliminary topological graph based on the relationship of the data nodes;
identifying repeated data nodes and redundant edges, and optimizing a preliminary topological graph;
analyzing the optimized topological graph, automatically selecting a proper framework graph type, and carrying out layout adjustment;
The dynamic loading data is to analyze a user request through a natural language processing technology according to the specific request of the user, determine the major category, format and priority of data sources, dynamically select a proper data source to capture data according to real-time response and data quality evaluation of the data source by utilizing an intelligent selection algorithm in a data loading stage, and enter preprocessing after the data is transmitted through a network, remove repeated and irrelevant data, correct errors of data input, format all data and perform data verification to ensure the integrity and logic consistency of the data;
the intelligent selection algorithm is used for carrying out real-time evaluation based on performance indexes provided by each data source, calculating the comprehensive score of each data source through a preset standard, and dynamically determining that a data grabbing task is allocated to the data source with the highest score according to the obtained score.
As a preferred embodiment of the model calculation analysis visualization automatic layout method of the present invention, the method further comprises: the method comprises the steps of classifying preprocessed data, namely extracting key features from the preprocessed data, classifying the data into a plurality of specific demand types according to the features, wherein each specific demand type represents a research direction, creating corresponding data nodes for the data of each specific demand type, distributing unique identifiers for each data node, recording the type attributes of each data node, and automatically drawing a preliminary topological graph by using a graphic algorithm according to predefined relation rules among the data nodes;
The topological graph has the expression:
，
Wherein, For a dataset, including all pre-processed and categorized data points,Establishing a specific relation rule for the edge set;
if each pair of data nodes In the presence of a relationship according to a predefined rule, thenAndAdding an edge between them；
The expression of the edge set is:
。
As a preferred embodiment of the model calculation analysis visualization automatic layout method of the present invention, the method further comprises: the step of identifying the repeated data nodes and the redundant edges comprises the steps of identifying and processing the repeated data nodes;
Identifying potential repeated data nodes through data node attributes, comprehensively evaluating the number of connecting edges of each repeated data node and roles in a graph, judging whether the current repeated data node is reserved according to betweenness centrality, degree centrality and feature vector centrality, selecting representative data nodes for the data nodes needing to be combined, updating relevant edges, deleting redundant data nodes, and maintaining connection relations and unique attributes for the data nodes needing to be reserved;
the betweenness centrality, degree centrality and feature vector centrality are expressed as follows:
，
，
，
Wherein, Is a data nodeIs characterized by the medium number centrality of (2),Is a data nodeAndThe number of all shortest paths in between,Is through the data nodeIs used to determine the number of shortest paths,Is a data nodeIs characterized by a degree of centrality,Is a data nodeIs connected toIs provided with a number of sides of the pattern,Is a data nodeIs characterized by the feature vector centrality of (1),Is a constant value of the characteristic value,Is a data nodeIs a set of neighboring data nodes of (a),Representing data nodesAndWhether an edge exists between the two adjacent layers,Feature vector centrality for neighbor data nodes;
And evaluating the importance of the data node by using the betweenness centrality, the degree centrality and the feature vector centrality, setting a first comprehensive threshold, judging that the current data node has a key effect in the way if any index of the betweenness centrality, the degree centrality and the feature vector centrality is larger than the first comprehensive threshold, retaining the current repeated data node, and otherwise deleting the current repeated data node.
As a preferred embodiment of the model calculation analysis visualization automatic layout method of the present invention, the method further comprises: the step of identifying the repeated data nodes and the redundant edges further comprises identifying the redundant edges;
Calculating the shortest paths among all data node pairs by using a Dijkstra algorithm, identifying a critical path, judging that a path is a critical path if the occurrence frequency of all edges in a certain path is greater than a set second comprehensive threshold value, and reserving the current edge for the edges on the critical path; for edges on non-critical paths, it is further determined whether they are redundant edges,
And if the redundancy of a certain side is higher than a set first redundancy threshold value by calculating the redundancy, deleting the redundant side, wherein the expression is as follows:
，
Wherein, Is a sideIs used for the redundancy of the (c) in the (c),For the set of all paths to be present,To indicate the function, if the edgeOn the pathIf it appears, thenOtherwise, the device can be used to determine whether the current, ；
And outputting the optimized topological graph by deleting the repeated data nodes and the repeated edges.
As a preferred embodiment of the model calculation analysis visualization automatic layout method of the present invention, the method further comprises: the topology graph after the optimization is analyzed, and the proper type of the frame graph is automatically selected according to the data node attribute and the data node position and the edge in the relation dynamic calculation graph;
the frame graph type comprises tree layout, hierarchical layout and grid layout;
If the preliminary topological graph is a closed-loop-free connected graph, no isolated subgraph or independent data nodes exist, n data nodes correspond to n-1 edges, and only one connecting edge exists between any two data nodes, the proportion of the data nodes with high centrality of the preliminary topological graph is smaller than a first high centrality threshold value, and the clustering coefficient of the preliminary topological graph is smaller than a first clustering threshold value, the tree-type layout is classified;
The high-centrality data node proportion is smaller than a first high-centrality threshold value, wherein the calculation of the degree of each data node, the average degree of the preliminary topological graph and the standard deviation of the average degree comprises the following expressions:
，
，
，
Wherein, Is a data nodeIs used for the degree of (3),For the average degree of the preliminary topological graph,Standard deviation of average degree;
Statistical degree higher than in preliminary topological graph Is set for the number of data nodes of the (a), the expression is:
，
，
Wherein, Is an exponential function, has a value of 1 when the condition is satisfied, and has a value of 0 otherwise,The proportion of the data nodes with high centrality is high;
When (when) If the clustering coefficient of the preliminary topological graph is smaller than the first high centrality threshold value, further judging whether the clustering coefficient of the preliminary topological graph is smaller than the first clustering threshold value, wherein the expression is as follows:
，
，
Wherein, Is a data nodeIs used for the clustering of the coefficients of (a),Is a data nodeThe number of edges actually present between neighboring data nodes,Is a data nodeIs provided with a number of neighbor data nodes,Average clustering coefficients of the preliminary topological graph;
If the preliminary topological graph is an undirected closed-loop graph or a directed closed-loop graph, the entering degree of the data nodes is 0, only edges pointing to the lower-layer data nodes from the upper-layer data nodes are arranged between any two data nodes, the number of the data nodes on each layer is uniformly distributed, the variance of the number of the data nodes on each layer is smaller than a first variance threshold value, and no reverse edges and no cross-layer edges are arranged, and the data nodes are classified into the layer layout;
the ingress of 0 is that the number of edges entering the current data node is 0;
the hierarchy is a set of root data nodes or source data nodes with all incidence degrees of 0 in the preliminary topological graph, and the hierarchy of each data node is calculated by using breadth-first search, wherein the hierarchy is the shortest path length from the root data node to the data node;
the variance of the number of nodes of each level of data is smaller than a first variance threshold, and the expression is:
，
，
Wherein, For the variance of the number of nodes of the data of each level,For the number of data nodes on each level,For the number of the maximum number of layers,Average the number of data nodes per layer;
If the connecting edge of any one data node in the preliminary topological graph is not more than 4 and no isolated data node exists, judging that the grid layout exists;
The first comprehensive threshold, the second comprehensive threshold, the first redundancy threshold, the first high centrality threshold, the first clustering threshold and the first variance threshold are set according to a history record of a user request.
As a preferred embodiment of the model calculation analysis visualization automatic layout method of the present invention, the method further comprises: the layout adjustment is to perform adjustment for preventing data node overlapping, edge optimization and data node alignment according to the frame diagram after classification;
The data node overlapping prevention is to detect the data node position, calculate the distance of each pair of adjacent data nodes, if the distance is smaller than the preset minimum distance, apply repulsive force to make the data nodes separate and adjust the position, and repeat the detection and adjustment until the distance between all the data nodes is greater than or equal to the minimum distance;
the optimization of the edges is to find out the crossed edges by using an edge crossing detection algorithm, adjust the positions of data nodes or re-plan the paths of the edges, and reduce the crossing points of the edges;
The data node alignment is to determine an alignment rule, calculate a target position of each data node according to the alignment rule, and adjust the alignment of the data node position and the target position;
and outputting and rendering the result after the layout adjustment to a user interface, and rendering by using a Canvas technology to complete the visual automatic layout.
Another object of the present invention is to provide a model calculation analysis visualization automatic layout system, which can dynamically load data through an intelligent selection algorithm, comprehensively use betweenness centrality, degree centrality and feature vector centrality to identify and process repeated data nodes, calculate connection of redundancy optimization edges, and select an optimal layout according to different data structures, so as to solve the problems of the existing automatic layout technology in terms of flexibility, information redundancy and layout aesthetic property.
In order to solve the technical problems, the invention provides the following technical scheme: a model calculation analysis visualization automatic layout system, comprising: the system comprises a data loading module, a data preprocessing module, an optimizing module, a layout classifying module and a visual rendering module;
the data loading module dynamically loads data from various data sources according to user requests, analyzes the user requests, determines the types, formats and priorities of the data sources, carries out real-time evaluation based on performance indexes provided by each data source, and dynamically selects proper data sources for data capture;
The data preprocessing module is used for carrying out de-duplication, correction, formatting and verification on the loaded data, extracting key features from the preprocessed data, classifying the data into a plurality of types according to the features, creating corresponding data nodes, and automatically drawing a preliminary topological graph by using a graph algorithm according to a predefined relation rule among the data nodes;
the optimization module identifies potential repeated data nodes through data node attributes, comprehensively evaluates the number of connecting edges of each repeated data node, judges whether the current repeated data node is reserved or not by using betweenness centrality, degree centrality and feature vector centrality, identifies and deletes redundant edges through calculating redundancy, and reduces or eliminates crossed edges through rearranging data node positions or reconnecting edges;
The layout classification module is used for analyzing the optimized topological graph, automatically selecting proper frame graph types, calculating the positions and edges of the data nodes in the graph according to the data node attributes and the relation dynamics, and carrying out layout adjustment according to classification rules;
The visual rendering module is used for detecting the positions of the data nodes, calculating the distance between each pair of adjacent data nodes, if the distance is smaller than the preset minimum distance, applying repulsive force to enable the data nodes to adjust the positions separately, using an edge crossing detection algorithm to find crossed edges, adjusting the positions of the data nodes or re-planning paths of the edges, reducing crossing points of the edges, determining an alignment rule, calculating the target position of each data node according to the alignment rule, adjusting the alignment of the positions of the data nodes and the target positions, outputting a final layout result, rendering the final layout result on a user interface, and using a Canvas technology to conduct rendering to complete visual automatic layout.
A computer device comprising a memory storing a computer program and a processor implementing the steps of a model calculation analysis visualization automatic layout method as described above when the computer program is executed.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a model calculation analysis visualization automatic layout method as described above.
The invention has the beneficial effects that: the invention dynamically loads data through the intelligent selection algorithm, ensures the efficient selection and real-time updating of the data source, and solves the problems of insufficient flexibility and intelligence in the data loading and preprocessing stages in the prior art. And the repeated data nodes are identified and processed by comprehensively using the betweenness centrality, the degree centrality and the feature vector centrality, so that the redundancy of the graphic layout is effectively reduced, and the information redundancy is avoided. By optimizing the connection of the edges through the calculation redundancy, the simplicity and the attractiveness of the graphic layout are greatly improved, and the clarity and the legibility of the graphic are ensured. The layout classification and optimization method can select the optimal layout algorithm according to different data structures, and ensures the high efficiency and intuitiveness of graphic layout. Through steps of preventing data nodes from overlapping, optimizing crossed edges, aligning the data nodes and the like, the display effect of the graphic layout is further optimized, and the final visual result is tidier and more standard. The invention not only solves the defects of the existing automatic layout technology in terms of flexibility, information redundancy and layout attractiveness, but also provides a high-efficiency and intelligent automatic layout method for users, and remarkably improves the efficiency and user experience of big data analysis and display.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a general flow chart of a visual automatic layout method for model calculation and analysis according to a first embodiment of the present invention;
FIG. 2 is a block diagram of a model calculation analysis visualization automatic layout system according to a second embodiment of the present invention;
FIG. 3 is a schematic diagram of a hierarchical layout diagram of a visual automatic layout method for model calculation and analysis according to a third embodiment of the present invention;
FIG. 4 is a hierarchical layout diagram of a visual automatic layout method for model calculation and analysis according to a third embodiment of the present invention;
FIG. 5 is a preliminary topological graph corresponding to a grid layout graph of a visual automatic layout method for model calculation and analysis according to a third embodiment of the present invention;
FIG. 6 is a grid layout diagram of a visual automatic layout method for model calculation and analysis according to a third embodiment of the present invention;
FIG. 7 is a preliminary topology diagram corresponding to a tree-type layout diagram of a visual automatic layout method for model calculation and analysis according to a third embodiment of the present invention;
fig. 8 is a tree layout diagram of a visual automatic layout method for model calculation and analysis according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Embodiment 1, referring to fig. 1, provides a model calculation analysis visualization automatic layout method according to an embodiment of the present invention, including:
And dynamically loading data from various data sources according to the user request, determining the type of the required data, and preprocessing.
And classifying the preprocessed data by using a classification algorithm to form data nodes corresponding to the types of data, and drawing a preliminary topological graph based on the relationship of the data nodes.
And identifying repeated data nodes and redundant edges, and optimizing the primary topological graph.
And analyzing the optimized topological graph, automatically selecting a proper framework graph type, and carrying out layout adjustment.
The dynamic loading of data is to analyze a user request through a natural language processing technology according to the specific request of the user, determine the major category, format and priority of data sources, dynamically select a proper data source to capture data according to real-time response and data quality evaluation of the data source by utilizing an intelligent selection algorithm in a data loading stage, and enter preprocessing after the data is transmitted through a network, remove repeated and irrelevant data, correct errors of data input, format all data, and perform data verification to ensure the integrity and logic consistency of the data.
The intelligent selection algorithm is used for carrying out real-time evaluation based on performance indexes provided by each data source, calculating the comprehensive score of each data source through a preset standard, and dynamically determining that the data capture task is distributed to the data source with the highest score according to the obtained score.
Further to be explained are:
The intelligent selection algorithm evaluates the data sources in real time according to predefined criteria to determine the applicability of each data source. These criteria include, but are not limited to, response time of the data source, data integrity, update frequency, accessibility, and historical reliability. The algorithm converts these criteria into a quantified scoring index:
The average time of the data source responding to the query request is shorter, the response time score is higher, the ratio of the data integrity provided by the data source is higher, and the data source score with high integrity is higher.
The more frequently a data source updates data, the higher the score, the higher the availability ratio of the data source at a given time, the higher the score for high accessibility, and the higher the historically stable data source score based on past performance evaluations.
The metrics are combined into a composite score by a weighting algorithm, the weights being preset by a system administrator based on business requirements and characteristics of the data sources. The intelligent selection algorithm uses the comprehensive scores to sort the data sources, and dynamically selects the data source with the highest score for data capture. If the highest scoring data source fails to be grabbed, the algorithm will automatically select the next higher scoring data source attempt until the data is successfully grabbed or all options attempt to fail.
The method comprises the steps of classifying preprocessed data, namely extracting key features from the preprocessed data, classifying the data into a plurality of specific demand types according to the features, wherein each specific demand type represents a research direction, creating corresponding data nodes for the data of each specific demand type, distributing unique identifiers for each data node, recording the type attributes of each data node, and automatically drawing a preliminary topological graph by using a graphic algorithm according to predefined relation rules among the data nodes.
The topological graph has the expression:
，
Wherein, For a dataset, including all pre-processed and categorized data points,Establishing a specific relation rule for the edge set;
if each pair of data nodes In the presence of a relationship according to a predefined rule, thenAndAdding an edge between them；
The expression of the edge set is:
。
further to be explained are:
The user request is analyzed through natural language processing technology, and the categories of the data such as wind power plants, hydropower plants, new energy stations, nuclear power plants and the like are determined.
Taking a new energy station as an example, the requirements of the users are further clarified, such as cost benefits, environmental influence, operation efficiency of operation equipment and the like.
The corresponding data nodes are created by taking cost benefits as an example, such as equipment investment, maintenance cost, power generation benefits and the like.
Specific data: equipment investment cost, maintenance cost, generation income and the like.
Identifying duplicate data nodes and superfluous edges includes identifying and processing duplicate data nodes;
And identifying potential repeated data nodes through the data node attributes, comprehensively evaluating the number of connecting edges of each repeated data node and roles in the graph, judging whether the current repeated data node is reserved according to the betweenness centrality, the degree centrality and the feature vector centrality, selecting representative data nodes for the data nodes needing to be combined, updating relevant edges, deleting redundant data nodes, and maintaining the connection relation and the unique attribute for the data nodes needing to be reserved.
The betweenness centrality, degree centrality and feature vector centrality are expressed as:
，
，
，
Wherein, Is a data nodeIs characterized by the medium number centrality of (2),Is a data nodeAndThe number of all shortest paths in between,Is through the data nodeIs used to determine the number of shortest paths,Is a data nodeIs characterized by a degree of centrality,Is a data nodeIs connected toIs provided with a number of sides of the pattern,Is a data nodeIs characterized by the feature vector centrality of (1),Is a constant value of the characteristic value,Is a data nodeIs a set of neighboring data nodes of (a),Representing data nodesAndWhether an edge exists between the two adjacent layers,Feature vector centrality for neighboring data nodes.
It is further noted that a is an adjacency matrix representing the connection relationship between nodes in the graph.
Assuming that the graph has 4 nodes, the adjacency matrix can be expressed as:
,
first row and first column:
A11 Node 1 and no edge per se, a12=1 with an edge between node 1 and node 2, a13=0 with no edge between node 1 and node 3, a14=0 with no edge between node 1 and node 4.
Second row and second column:
a21 There is an edge between node 2 and node 1, a22=0, node 2 and no edge per se, a23=1, node 2 and node 3, a24=1, node 2 and node 4.
Third row and third column:
A31 No edge between node 3 and node 1, a32=1, an edge between node 3 and node 2, a33=0, node 3 and no edge itself, a34=1, an edge between node 3 and node 4.
Fourth row and fourth column:
A41 There is no edge between node 4 and node 1, a42=1 there is an edge between node 4 and node 2, a43=1 there is an edge between node 4 and node 3, a44=0 there is no edge between node 4 and itself.
And evaluating the importance of the data node by using the betweenness centrality, the degree centrality and the feature vector centrality, setting a first comprehensive threshold, judging that the current data node has a key effect in the way if any index of the betweenness centrality, the degree centrality and the feature vector centrality is larger than the first comprehensive threshold, retaining the current repeated data node, and otherwise deleting the current repeated data node. According to the data node attribute and the data node position and the edge in the relation dynamic calculation graph;
The category of the frame map includes tree layout, hierarchical layout, and grid layout.
If the preliminary topological graph is a closed-loop-free connected graph, no isolated subgraph or independent data nodes exist, n data nodes correspond to n-1 edges, and only one connecting edge exists between any two data nodes, the proportion of the data nodes with high centrality of the preliminary topological graph is smaller than a first high centrality threshold value, and the clustering coefficient of the preliminary topological graph is smaller than a first clustering threshold value, the tree-type layout is classified.
The high centrality data node proportion is smaller than a first high centrality threshold value, wherein the calculation of the degree of each data node, the average degree of the preliminary topological graph and the standard deviation of the average degree comprises the following expressions:
，
，
，
Wherein, Is a data nodeIs used for the degree of (3),For the average degree of the preliminary topological graph,Standard deviation of average degree;
Statistical degree higher than in preliminary topological graph Is set for the number of data nodes of the (a), the expression is:
，
，
Wherein, Is an exponential function, has a value of 1 when the condition is satisfied, and has a value of 0 otherwise,The proportion of the data nodes with high centrality is high;
When (when) If the clustering coefficient of the preliminary topological graph is smaller than the first high centrality threshold value, further judging whether the clustering coefficient of the preliminary topological graph is smaller than the first clustering threshold value, wherein the expression is as follows:
，
，
Wherein, Is a data nodeIs used for the clustering of the coefficients of (a),Is a data nodeThe number of edges actually present between neighboring data nodes,Is a data nodeIs provided with a number of neighbor data nodes,Is the average clustering coefficient of the preliminary topological graph.
If the preliminary topological graph is an undirected closed-loop graph or a directed closed-loop graph, the entering degree of the data nodes is 0, only edges pointing to the lower-layer data nodes from the upper-layer data nodes are arranged between any two data nodes, the number of the data nodes on each layer is uniformly distributed, the variance of the number of the data nodes on each layer is smaller than a first variance threshold value, and no reverse edges and no cross-layer edges exist, and the data nodes are classified into the layer layout.
An ingress of 0 is a number of edges into the current data node of 0.
The hierarchy is a set of root data nodes or source data nodes with all the incomings of 0 in the preliminary topological graph, and the hierarchy of each data node is calculated by using breadth-first search, wherein the hierarchy is the shortest path length from the root data node to the data node.
The variance of the number of nodes of each level of data is smaller than a first variance threshold, and the expression is:
，
，
Wherein, For the variance of the number of nodes of the data of each level,For the number of data nodes on each level,For the number of the maximum number of layers,To average the number of data nodes per layer.
And if the connecting edge of any one data node in the preliminary topological graph is not more than 4 and no isolated data node exists, judging that the grid layout exists.
The first comprehensive threshold, the second comprehensive threshold, the first redundancy threshold, the first high centrality threshold, the first clustering threshold and the first variance threshold are set according to the history record of the user request.
Performing layout adjustment includes preventing data node overlap, edge optimization, and data node alignment.
Preventing data node overlap is to detect the data node position, calculate the distance of each pair of adjacent data nodes, if the distance is smaller than the preset minimum distance, apply repulsive force to make the data nodes separate and adjust the position, repeat the detection and adjustment until the distance between all data nodes is greater than or equal to the minimum distance.
The optimization of the edges is to find out the crossed edges by using an edge crossing detection algorithm, adjust the positions of data nodes or re-plan the paths of the edges, and reduce the crossing points of the edges.
The data node alignment is to determine an alignment rule, calculate a target position of each data node according to the alignment rule, and adjust the alignment of the data node position and the target position.
And outputting and rendering the final layout result to a user interface.
Embodiment 2 referring to fig. 2, for an embodiment of the present invention, a system for a model calculation analysis visualization automatic layout method is provided, including: the system comprises a data loading module, a data preprocessing module, an optimizing module, a layout classifying module and a visual rendering module.
The data loading module dynamically loads data from various data sources according to user requests, analyzes the user requests, determines the types and formats of the data and the priority of the data sources, evaluates the data in real time based on performance indexes provided by each data source, and dynamically selects a proper data source for data capture.
The data preprocessing module is used for carrying out de-duplication, correction, formatting and verification on the loaded data, extracting key features from the preprocessed data, classifying the data into a plurality of types according to the features, creating corresponding data nodes, and automatically drawing a preliminary topological graph by using a graphic algorithm according to a predefined relation rule among the data nodes.
The optimization module is used for identifying potential repeated data nodes through data node attributes, comprehensively evaluating the number of connecting edges of each repeated data node, judging whether the current repeated data node is reserved or not by using betweenness centrality, degree centrality and feature vector centrality, identifying and deleting redundant edges through calculating redundancy, and reducing or eliminating crossed edges through rearranging node positions or reconnecting edges.
The layout classification module is used for analyzing the optimized topological graph, automatically selecting proper frame graph types, dynamically calculating node positions and edges in the graph according to node attributes and relations, and carrying out layout adjustment according to classification rules.
The visual rendering module is used for detecting the positions of nodes, calculating the distance between each pair of adjacent nodes, if the distance is smaller than the preset minimum distance, applying repulsive force to separate the nodes to adjust the positions, using an edge crossing detection algorithm to find out crossed edges, adjusting the positions of the nodes or re-planning the paths of the edges, reducing the crossing points of the edges, determining an alignment rule, calculating the target position of each node according to the alignment rule, adjusting the alignment of the positions of the nodes and the target positions, outputting and rendering the final layout result on a user interface, and using a Canvas technology to perform rendering to complete visual automatic layout.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In embodiment 3, referring to fig. 3 to 8, in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments. The present embodiment has been conducted by the conventional method and the method of the present embodiment.
Along with the change of the electric power market, most of power plants currently develop the work of blending coal, burning, wind and smoke system transformation and the like, so that the margin of the speed per hour of a fan of the power plant is insufficient. Once the operation is improper or the adjustment amplitude is too large, the stall probability of the axial flow fan is greatly increased.
Fan stall is essentially the fan operating point falling into the stall zone. Due to irregularities in the stall area, the characteristic curve deviates from the field reality, which makes it difficult for an operator to judge the occurrence of an accident in advance by the field parameters.
The corresponding accident handling measures are generally adopted on site through the parameter change after the fan stalls, but the accident handling measures are basically post-processing, and the difference is only in the morning and evening with the discovery of the accident.
Referring to fig. 3, according to a specific request of a user, the user request is analyzed through a natural language processing technology, the type and specific requirement of data are determined, corresponding data nodes are created, and a preliminary topological diagram is output.
Referring to fig. 4, a hierarchical layout is constructed by optimizing and classifying a preliminary topology by the present invention.
In power plants, the heat rate of a turbine is one of the key indicators for its performance. The heat rate reflects the ability of the turbine to efficiently convert thermal energy to electrical energy. A lower heat rate indicates an efficient conversion process with less energy loss, while a higher heat rate indicates a lower energy conversion efficiency with greater energy loss. This not only directly results in an increase in the cost of power generation and a decrease in economic efficiency, but may also cause additional wear and risk of failure of the equipment. Sustained high heat rates may be indicative of equipment aging, improper maintenance, or non-optimal operating parameters. Therefore, the method for monitoring the heat rate of the steam turbine in real time and giving early warning in time when the steam turbine abnormally rises is an important measure for guaranteeing the operation efficiency of the power plant and the safety of equipment. Through effective control and early warning mechanism, can take corresponding adjustment and maintenance measure when the problem is at the beginning, prevent that little problem from developing into big trouble to ensure the high-efficient, the steady operation of power plant.
Referring to fig. 5 and 6, according to a specific request of a user, the user request is analyzed through a natural language processing technology, the type and specific requirement of data are determined, corresponding data nodes are created, and a preliminary topological graph and a constructed grid layout graph are output.
Further to be explained are:
Obtaining a real-time value of the heat rate of the steam turbine: and (3) node: real-time value of heat rate of the steam turbine.
Explanation: and acquiring the current heat rate value of the steam turbine from the monitoring system in real time.
Obtaining a heat consumption rate threshold value of the steam turbine: and (3) node: the heat rate of the steam turbine is found to be a good value.
Explanation: the ideal threshold (reference value) for the turbine heat rate is obtained from historical data or design parameters.
The difference value calculation comprises the following steps of: a subtracter.
Explanation: and calculating the difference between the real-time heat rate and the threshold value to obtain the heat rate deviation.
And (3) deviation judgment, namely, nodes: and a comparator.
Explanation: the deviation of the heat rate is compared with a preset value (e.g., 200) to determine whether the deviation exceeds the set range.
Signal hold, node: the signal is continuous.
Explanation: if the heat rate deviation exceeds the set point, this signal is maintained to ensure that the system is able to detect this abnormal condition.
The unit load acquisition comprises the following steps of: unit load.
Explanation: load data of the current turbine are obtained from the monitoring system.
Load stability judgment, namely nodes: and judging the stability of the unit load.
Explanation: and judging whether the current load is stable or not. If the load fluctuation is large, accuracy of the heat rate may be affected, and thus it is necessary to confirm that the load is stable.
Condition merging, node: AND (d).
Explanation: and combining the two conditions of heat rate deviation and load stabilization. If both are satisfied (i.e. the heat rate deviation is large and the load is stable), the early warning is triggered.
High early warning of the heat consumption rate of the steam turbine: and (3) node: the heat consumption rate of the steam turbine is high and early-warned.
Explanation: when the conditions are met, the system generates a high heat rate early warning signal to remind operators to take corresponding measures, such as checking equipment, adjusting operation parameters and the like.
In modern power production processes, steam turbines and coal mills are important equipment in power plants, directly affecting the operating efficiency and stability of the power plants. The steam turbine converts the heat energy of steam into mechanical energy and then into electric energy, and the running state of the steam turbine is directly related to the power generation efficiency and the safety. The coal mill is responsible for grinding coal blocks into coal dust, is one of core equipment of the coal-fired power plant, and the performance and the running state of the coal mill directly influence the combustion efficiency and the environmental emission of the boiler. Therefore, the operation states of the steam turbine and the coal mill are monitored in real time and analyzed in data, the overall operation efficiency of the power plant can be effectively improved, the equipment failure rate is reduced, and the maintenance cost is reduced.
Referring to fig. 7 and 8, according to a specific request of a user, the user request is analyzed through a natural language processing technology, the type and specific requirement of data are determined, corresponding data nodes are created, an output preliminary topological graph is generated, and a corresponding tree-shaped layout graph is constructed.
The time sequence data defines: and defining time sequence data.
Explanation: a time series data format and structure collected from various sensors and devices is defined. For example: temperature, pressure, vibration, etc.
Data acquisition, namely, nodes: turbine data/coal mill data.
Explanation: data acquisition is carried out from actual equipment through sensors such as temperature and pressure.
Data cleaning, namely cleaning the value range: and (3) node: and (5) cleaning the value range.
Explanation: unreasonable values in the data, such as data with temperature sensor readings outside of normal ranges, are filtered out.
Null value cleaning: and (3) node: and (5) cleaning a null value.
Explanation: null values in the data are processed, for example filling in missing data or deleting records containing null values.
Data output and visualization, node: data report, rainbow plot, data view, histogram set, data trend plot.
Explanation: various types of reports and charts are generated from the cleaned data to aid in analysis and decision making.
Data reporting: a detailed statistical report is generated.
Rainbow diagram: and showing the distribution situation of the multidimensional data.
Data view: the processed data is presented in tabular form.
Histogram group: showing the frequency distribution of the data.
Data trend graph: and showing the trend of the data over time.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (9)
1. A model calculation analysis visualization automatic layout method, comprising:
Dynamically loading data from various data sources according to a user request, determining the type of the required data, and preprocessing;
classifying the preprocessed data by using a classification algorithm to form data nodes corresponding to the types of data, and drawing a preliminary topological graph based on the relationship of the data nodes;
identifying repeated data nodes and redundant edges, and optimizing a preliminary topological graph;
analyzing the optimized topological graph, automatically selecting a proper framework graph type, and carrying out layout adjustment;
The dynamic loading data is to analyze a user request through a natural language processing technology according to the specific request of the user, determine the major category, format and priority of data sources, dynamically select a proper data source to capture data according to real-time response and data quality evaluation of the data source by utilizing an intelligent selection algorithm in a data loading stage, and enter preprocessing after the data is transmitted through a network, remove repeated and irrelevant data, correct errors of data input, format all data and perform data verification to ensure the integrity and logic consistency of the data;
the intelligent selection algorithm is used for carrying out real-time evaluation based on performance indexes provided by each data source, calculating the comprehensive score of each data source through a preset standard, and dynamically determining that a data grabbing task is allocated to the data source with the highest score according to the obtained score.
2. The model calculation analysis visualization automatic layout method as claimed in claim 1, wherein: the method comprises the steps of classifying preprocessed data, namely extracting key features from the preprocessed data, classifying the data into a plurality of specific demand types according to the features, wherein each specific demand type represents a research direction, creating corresponding data nodes for the data of each specific demand type, distributing unique identifiers for each data node, recording the type attributes of each data node, and automatically drawing a preliminary topological graph by using a graphic algorithm according to predefined relation rules among the data nodes;
The topological graph has the expression:
，
Wherein, For a dataset, including all pre-processed and categorized data points,Establishing a specific relation rule for the edge set;
if each pair of data nodes In the presence of a relationship according to a predefined rule, thenAndAdding an edge between them；
The expression of the edge set is:
。
3. A model calculation analysis visualization automatic layout method as claimed in claim 2, wherein: the step of identifying the repeated data nodes and the redundant edges comprises the steps of identifying and processing the repeated data nodes;
Identifying potential repeated data nodes through data node attributes, comprehensively evaluating the number of connecting edges of each repeated data node and roles in a graph, judging whether the current repeated data node is reserved according to betweenness centrality, degree centrality and feature vector centrality, selecting representative data nodes for the data nodes needing to be combined, updating relevant edges, deleting redundant data nodes, and maintaining connection relations and unique attributes for the data nodes needing to be reserved;
the betweenness centrality, degree centrality and feature vector centrality are expressed as follows:
，
，
，
Wherein, Is a data nodeIs characterized by the medium number centrality of (2),Is a data nodeAndThe number of all shortest paths in between,Is through the data nodeIs used to determine the number of shortest paths,Is a data nodeIs characterized by a degree of centrality,Is a data nodeIs connected toIs provided with a number of sides of the pattern,Is a data nodeIs characterized by the feature vector centrality of (1),Is a constant value of the characteristic value,Is a data nodeIs a set of neighboring data nodes of (a),Representing data nodesAndWhether an edge exists between the two adjacent layers,Feature vector centrality for neighbor data nodes;
And evaluating the importance of the data node by using the betweenness centrality, the degree centrality and the feature vector centrality, setting a first comprehensive threshold, judging that the current data node has a key effect in the way if any index of the betweenness centrality, the degree centrality and the feature vector centrality is larger than the first comprehensive threshold, retaining the current repeated data node, and otherwise deleting the current repeated data node.
4. A model calculation analysis visualization automatic layout method as in claim 3, wherein: the step of identifying the repeated data nodes and the redundant edges further comprises identifying the redundant edges;
Calculating the shortest paths among all data node pairs by using a Dijkstra algorithm, identifying a critical path, judging that a path is a critical path if the occurrence frequency of all edges in a certain path is greater than a set second comprehensive threshold value, and reserving the current edge for the edges on the critical path; for edges on non-critical paths, it is further determined whether they are redundant edges,
And if the redundancy of a certain side is higher than a set first redundancy threshold value by calculating the redundancy, deleting the redundant side, wherein the expression is as follows:
，
Wherein, Is a sideIs used for the redundancy of the (c) in the (c),For the set of all paths to be present,To indicate the function, if the edgeOn the pathIf it appears, thenOtherwise, the device can be used to determine whether the current, ；
And outputting the optimized topological graph by deleting the repeated data nodes and the repeated edges.
5. The model calculation analysis visualization automatic layout method according to claim 4, wherein: the topology graph after the optimization is analyzed, and the proper type of the frame graph is automatically selected according to the data node attribute and the data node position and the edge in the relation dynamic calculation graph;
the frame graph type comprises tree layout, hierarchical layout and grid layout;
If the preliminary topological graph is a closed-loop-free connected graph, no isolated subgraph or independent data nodes exist, n data nodes correspond to n-1 edges, and only one connecting edge exists between any two data nodes, the proportion of the data nodes with high centrality of the preliminary topological graph is smaller than a first high centrality threshold value, and the clustering coefficient of the preliminary topological graph is smaller than a first clustering threshold value, the tree-type layout is classified;
The high-centrality data node proportion is smaller than a first high-centrality threshold value, wherein the calculation of the degree of each data node, the average degree of the preliminary topological graph and the standard deviation of the average degree comprises the following expressions:
，
，
，
Wherein, Is a data nodeIs used for the degree of (3),For the average degree of the preliminary topological graph,Standard deviation of average degree;
Statistical degree higher than in preliminary topological graph Is set for the number of data nodes of the (a), the expression is:
，
，
Wherein, Is an exponential function, has a value of 1 when the condition is satisfied, and has a value of 0 otherwise,The proportion of the data nodes with high centrality is high;
When (when) If the clustering coefficient of the preliminary topological graph is smaller than the first high centrality threshold value, further judging whether the clustering coefficient of the preliminary topological graph is smaller than the first clustering threshold value, wherein the expression is as follows:
，
，
Wherein, Is a data nodeIs used for the clustering of the coefficients of (a),Is a data nodeThe number of edges actually present between neighboring data nodes,Is a data nodeIs provided with a number of neighbor data nodes,Average clustering coefficients of the preliminary topological graph;
If the preliminary topological graph is an undirected closed-loop graph or a directed closed-loop graph, the entering degree of the data nodes is 0, only edges pointing to the lower-layer data nodes from the upper-layer data nodes are arranged between any two data nodes, the number of the data nodes on each layer is uniformly distributed, the variance of the number of the data nodes on each layer is smaller than a first variance threshold value, and no reverse edges and no cross-layer edges are arranged, and the data nodes are classified into the layer layout;
the ingress of 0 is that the number of edges entering the current data node is 0;
the hierarchy is a set of root data nodes or source data nodes with all incidence degrees of 0 in the preliminary topological graph, and the hierarchy of each data node is calculated by using breadth-first search, wherein the hierarchy is the shortest path length from the root data node to the data node;
the variance of the number of nodes of each level of data is smaller than a first variance threshold, and the expression is:
，
，
Wherein, For the variance of the number of nodes of the data of each level,For the number of data nodes on each level,For the number of the maximum number of layers,Average the number of data nodes per layer;
If the connecting edge of any one data node in the preliminary topological graph is not more than 4 and no isolated data node exists, judging that the grid layout exists;
The first comprehensive threshold, the second comprehensive threshold, the first redundancy threshold, the first high centrality threshold, the first clustering threshold and the first variance threshold are set according to a history record of a user request.
6. The model calculation analysis visualization automatic layout method according to claim 5, wherein: the layout adjustment is to perform adjustment for preventing data node overlapping, edge optimization and data node alignment according to the frame diagram after classification;
The data node overlapping prevention is to detect the data node position, calculate the distance of each pair of adjacent data nodes, if the distance is smaller than the preset minimum distance, apply repulsive force to make the data nodes separate and adjust the position, and repeat the detection and adjustment until the distance between all the data nodes is greater than or equal to the minimum distance;
the optimization of the edges is to find out the crossed edges by using an edge crossing detection algorithm, adjust the positions of data nodes or re-plan the paths of the edges, and reduce the crossing points of the edges;
The data node alignment is to determine an alignment rule, calculate a target position of each data node according to the alignment rule, and adjust the alignment of the data node position and the target position;
and outputting and rendering the result after the layout adjustment to a user interface, and rendering by using a Canvas technology to complete the visual automatic layout.
7. A system employing the model calculation analysis visualization automatic layout method of any of claims 1-6, wherein: the system comprises a data loading module, a data preprocessing module, an optimizing module, a layout classifying module and a visual rendering module;
the data loading module dynamically loads data from various data sources according to user requests, analyzes the user requests, determines the types, formats and priorities of the data sources, carries out real-time evaluation based on performance indexes provided by each data source, and dynamically selects proper data sources for data capture;
The data preprocessing module is used for carrying out de-duplication, correction, formatting and verification on the loaded data, extracting key features from the preprocessed data, classifying the data into a plurality of types according to the features, creating corresponding data nodes, and automatically drawing a preliminary topological graph by using a graph algorithm according to a predefined relation rule among the data nodes;
the optimization module identifies potential repeated data nodes through data node attributes, comprehensively evaluates the number of connecting edges of each repeated data node, judges whether the current repeated data node is reserved or not by using betweenness centrality, degree centrality and feature vector centrality, identifies and deletes redundant edges through calculating redundancy, and reduces or eliminates crossed edges through rearranging data node positions or reconnecting edges;
The layout classification module is used for analyzing the optimized topological graph, automatically selecting proper frame graph types, calculating the positions and edges of the data nodes in the graph according to the data node attributes and the relation dynamics, and carrying out layout adjustment according to classification rules;
The visual rendering module is used for detecting the positions of the data nodes, calculating the distance between each pair of adjacent data nodes, if the distance is smaller than the preset minimum distance, applying repulsive force to enable the data nodes to adjust the positions separately, using an edge crossing detection algorithm to find crossed edges, adjusting the positions of the data nodes or re-planning paths of the edges, reducing crossing points of the edges, determining an alignment rule, calculating the target position of each data node according to the alignment rule, adjusting the alignment of the positions of the data nodes and the target positions, outputting a final layout result, rendering the final layout result on a user interface, and using a Canvas technology to conduct rendering to complete visual automatic layout.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of a model calculation analysis visualization automatic layout method according to any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a model calculation analysis visualization automatic layout method according to any of claims 1 to 6.
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