CN117851503A - Data visualization method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application discloses a data visualization method, a device, electronic equipment and a storage medium, and relates to the field of data processing, wherein the method comprises the following steps: acquiring a target data set in a network general data format; the target data set comprises network universal data format data of multiple types of scientific elements; constructing a three-dimensional category structure body corresponding to a plurality of data types according to the target data set; transforming each three-dimensional class structure into a corresponding vector model; each vector model is visualized. According to the method and the device, the three-dimensional type structural body of each data type is transformed into the corresponding vector model and visualized, the change process of each scientific element in the network general data format data and corresponding information can be displayed, and even if a user without professional knowledge can clearly and intuitively know the data characteristics of each scientific element through the visualized vector model, the method and the device can improve the readability of the network general data format data.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a data visualization method, a data visualization device, an electronic device, and a storage medium.
Background
The network general data format (Network Common Data Form, abbreviated as NetCDF or NC) is a file format for storing scientific data, but the current visualization mode for the network general data format data is based on direct rendering of data points, and a two-dimensional section is formed by using a contour line or gradual change method, wherein the included scientific phenomenon or characteristic is not disclosed, and only scientific researchers can read the data by using professional knowledge or experience.
Disclosure of Invention
The embodiment of the application mainly aims to provide a data visualization method, a data visualization device, electronic equipment and a storage medium, so as to improve the readability of network general data format data.
To achieve the above object, an aspect of an embodiment of the present application proposes a data visualization method, including:
acquiring a target data set in a network general data format; the target data set comprises network universal data format data of a plurality of types of scientific elements;
constructing a three-dimensional category structure body corresponding to a plurality of data types according to the target data set;
transforming each three-dimensional category structure into a corresponding vector model;
and visualizing each vector model.
In some embodiments, the obtaining the target data set in the network generic data format includes:
acquiring a space-time data set in a network general data format;
acquiring an attribute data set in a network general data format;
performing space-time transformation according to the space-time data set and the attribute data set to obtain a space-time variation data set;
and combining the spatiotemporal dataset, the attribute dataset and the spatiotemporal variation dataset into the target dataset.
In some embodiments, the performing a space-time transformation according to the space-time dataset and the attribute dataset to obtain a space-time variation dataset includes:
selecting data to be changed of each scientific element;
and carrying out interpolation calculation on the corresponding data to be changed according to the space-time relationship of each scientific element to obtain the space-time change data set of each scientific element.
In some embodiments, the constructing a three-dimensional category structure corresponding to a plurality of data types according to the target data set includes:
classifying the characteristics of various scientific elements in the target data set by using a first intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set;
Or clustering the characteristics of various scientific elements in the target data set by using a second intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; and constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set.
In some embodiments, said transforming each of said three-dimensional class structures into a corresponding vector model comprises:
determining a center data point, an inner data point and a boundary data point of each three-dimensional category structure according to the space position of each data point in each three-dimensional category structure;
determining interfaces to which the boundary data points belong according to the position relation between the boundary data points and the corresponding center data points;
fitting each internal data point and each boundary data point in each interface to a curved surface to obtain a curved surface function;
determining a curve formed by intersecting each curved surface as an intersecting line curve function;
and constructing a corresponding vector model according to the central data point of each three-dimensional category structure body, each curved surface function and each intersecting line curve function.
In some embodiments, said visualizing each of said vector models comprises at least one of the following steps:
visualizing different types of data in each vector model;
or scaling or rotating each vector model and then visualizing.
In some embodiments, the visualizing the different types of data in each of the vector models includes at least one of the following steps:
respectively visualizing scalar data of a single type in each vector model;
or, performing superposition visualization on scalar data of different types in each vector model;
or, performing superposition visualization on at least one scalar data and at least one vector data in each vector model.
To achieve the above object, another aspect of the embodiments of the present application proposes a data visualization apparatus, including:
a data set acquisition unit for acquiring a target data set in a network general data format; the target data set comprises network universal data format data of a plurality of types of scientific elements;
a structure body construction unit, configured to construct a three-dimensional category structure body corresponding to a plurality of data types according to the target data set;
A structure vectorization unit, configured to transform each three-dimensional class structure into a corresponding vector model;
and the model visualization unit is used for visualizing each vector model.
To achieve the above object, another aspect of the embodiments of the present application proposes an electronic device including a memory storing a computer program and a processor implementing the above method when executing the computer program.
To achieve the above object, another aspect of the embodiments of the present application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above method.
The embodiment of the application at least comprises the following beneficial effects:
the method comprises the steps of obtaining a target data set in a network general data format; the target data set comprises network universal data format data of multiple types of scientific elements; constructing a three-dimensional category structure body corresponding to a plurality of data types according to the target data set; transforming each three-dimensional class structure into a corresponding vector model; each vector model is visualized. According to the method and the device, the three-dimensional type structural body of each data type is transformed into the corresponding vector model and visualized, the change process of each scientific element in the network general data format data and corresponding information can be displayed, and even if a user without professional knowledge can clearly and intuitively know the data characteristics of each scientific element through the visualized vector model, the method and the device can improve the readability of the network general data format data.
Drawings
Fig. 1 is a flow chart of a data visualization method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of S100 in fig. 1 according to an embodiment of the present application;
FIG. 3 is an exemplary diagram of a three-dimensional class structure provided in an embodiment of the present application;
fig. 4 is a schematic flowchart of S120 in fig. 1 according to an embodiment of the present application;
FIG. 5 is an exemplary flowchart of a data visualization method provided in an embodiment of the present application;
fig. 6 is a schematic structural diagram of a data visualization device according to an embodiment of the present application;
fig. 7 is a schematic hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the embodiments of the application, but are merely examples of apparatuses and methods consistent with some aspects of the embodiments of the application as detailed in the accompanying claims.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various concepts, but are not limited by these terms unless otherwise specified. These terms are only used to distinguish one concept from another. For example, the first information may also be referred to as second information, and similarly, the second information may also be referred to as first information, without departing from the scope of embodiments of the present application. The words "if", as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination", depending on the context.
The terms "at least one," "a plurality," "each," "any" and the like as used herein, wherein at least one includes one, two or more, and a plurality includes two or more, each referring to each of a corresponding plurality, and any one referring to any one of the plurality.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
Before describing embodiments of the present application in detail, a part of related art related to the embodiments of the present application will be described below:
the network general data format (Network Common Data Form, netCDF or NC for short) data is widely applied to data storage and analysis in the fields of climate model, earth science, atmospheric science, oceanography and the like. NC data can provide a convenient, flexible and reliable way to process and exchange scientific data, and the network generic data format is a self-describing, scalable, platform-independent format, NC data can be used as a large dataset for storing marine, meteorological, climatic etc. fields. NC data consists of variables, dimensions, and attributes. Variables are actual data and may be scalar, vector or multidimensional arrays. The dimension defines the size of the variable, which may be temporal, spatial, or other dimensions. Attributes are metadata about data sets and variables that describe the characteristics and meaning of the data.
At present, a visualization mode of network general data format data is based on direct rendering of data points, a two-dimensional section is formed by using a contour line or gradual change method and the like, scientific phenomena or characteristics contained in the two-dimensional section are not revealed, and only scientific researchers can read the two-dimensional section by using professional knowledge or experience.
In view of this, embodiments of the present application provide a data visualization method, apparatus, electronic device, and storage medium. The scheme is that a target data set in a network general data format is obtained; the target data set comprises network universal data format data of multiple types of scientific elements; constructing a three-dimensional category structure body corresponding to a plurality of data types according to the target data set; transforming each three-dimensional class structure into a corresponding vector model; each vector model is visualized. According to the method and the device, the three-dimensional type structural body of each data type is transformed into the corresponding vector model and visualized, the change process of each scientific element in the network general data format data and corresponding information can be displayed, and even if a user without professional knowledge can clearly and intuitively know the data characteristics of each scientific element through the visualized vector model, the method and the device can improve the readability of the network general data format data.
The embodiment of the application provides a data visualization method, and relates to the technical field of data processing. The data visualization method provided by the embodiment of the application can be applied to the terminal, can be applied to the server, and can also be software running in the terminal or the server. In some embodiments, the terminal may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a vehicle-mounted terminal, and the like; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can be configured as a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligence platforms, and the server can also be a node server in a blockchain network; the software may be an application or the like that implements the data visualization method, but is not limited to the above form.
The subject application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Referring to fig. 1, an embodiment of the present application provides a data visualization method, which may include, but is not limited to, S100 to S130, specifically as follows:
S100: acquiring a target data set in a network general data format; the target data set includes network generic data format data for a plurality of types of scientific elements.
Specifically, the target data set may include network general data format data (NC data) of a plurality of types of scientific elements, wherein the types of the scientific elements may include geographical data such as temperature, salinity or ocean current, climate data, and the like. It should be noted that, in the embodiment of the present application, specific types of the scientific elements are not strictly limited, but NC data corresponding to the scientific elements may be acquired according to different visualization requirements.
Further, referring to fig. 2, S100 may include S101 to S104:
s101: a spatiotemporal dataset in a network generic data format is obtained.
S102: an attribute dataset in a network generic data format is obtained.
S103: and performing space-time transformation according to the space-time data set and the attribute data set to obtain a space-time variation data set.
Specifically, NC data may store data of each scientific element (meteorology, oceanography, geology, etc.), NC data toThe multi-dimensional form stores, each data point having multiple dimensions. For example O i =（SK，Sx）={lat i ，lon i ，dep i ，tim i }，{tem i ，sal i ，u i ，v i ，up i Ph. Wherein, each parameter is described as follows: o is original NC data, SK represents a space-time data set, sx represents an attribute data set, lat is latitude, lon is longitude, dep is depth, tim is time, tem is temperature, sal is salinity, u is east-west flow velocity component, v is south flow velocity, up is rising flow velocity, ph is pH value, and subscript i is sequence.
Further, S103 may include:
selecting data to be changed of each scientific element; and carrying out interpolation calculation on the corresponding data to be changed according to the space-time relationship of each scientific element to obtain the space-time change data set of each scientific element.
Specifically, the data to be changed of the scientific element (such as temperature, salinity or ocean current) is selected from the stored NC data, and the corresponding data to be changed is interpolated according to the spatiotemporal relationship to obtain a spatiotemporal change data set of the corresponding scientific element, for example, a spatiotemporal change data set wj (tem) corresponding to the temperature, wj (tem) =tem，/>=tem（lat i ）-tem（lat i -1）|lat i -lat i -1. Wherein the superscript-represents the average value of each parameter, wj is the spatio-temporal variation dataset.
The above-mentioned space-time relationship may refer to a temporal or spatial relationship as an independent variable, each scientific element as an independent variable, and the temporal or spatial relationship varies, and the specific numerical value of each scientific element also varies. The NC data may include a spatiotemporal relationship of each scientific element.
S104: and combining the spatiotemporal dataset, the attribute dataset and the spatiotemporal variation dataset into the target dataset.
Specifically, the calculated spatiotemporal variation dataset is added to the NC dataset, i.e. new O i =（Sk，Sx，wj），wj={......}。
Specifically, the present embodiment may record stored NC data as NC o （NC o Including a spatiotemporal dataset and an attribute dataset) and then performing a spatiotemporal transformation in combination with the spatiotemporal dataset and the attribute dataset to form a spatiotemporal variation dataset, and adding the spatiotemporal variation dataset to the NC o In the process, a new space-time set change data set, namely a target data set, is formed and is marked as NC n 。
The expression of the space-time transform can be expressed as: NC (numerical control) o (SK，Sx)→NC n (SK, sx, wj), where SK represents a spatiotemporal dataset, which may include time, latitude and longitude, and depth; sx represents an attribute dataset, which may include temperature, salinity, ocean currents, etc., wj represents a spatiotemporal variation dataset.
S110: and constructing a three-dimensional category structure body corresponding to the data types according to the target data set.
Specifically, in this embodiment, the target data set may be divided according to a scientific basis (expert knowledge or scientific classification) to form three-dimensional class structures, each of which is a data point set, and the three-dimensional class structures is denoted as Li.
According to the embodiment, the data points in the target data set can be divided according to the value domain and the spatial position relation by using an intelligent algorithm, so that a three-dimensional category structure body is formed: the three-dimensional class structure refers to a classification of ocean, weather or geography according to a formed scientific structure or composition, and may include cold vortices in the ocean, cyclones in the weather or strata in the geography, etc., and is a set of data points aggregated in a target data set with similar features and spatially connected internal regions. Taking marine data as an example, as shown in fig. 3, the present embodiment may divide the target data set oi of the marine space by using an intelligent algorithm (the intelligent algorithm may include a clustering algorithm or a classification algorithm), so as to obtain each classification system of the marine space, where each classification system may include a plurality of three-dimensional class structures. Fig. 3 shows two alternative classification schemes, for example, the three-dimensional class structure of classification scheme 1 may include: ocean layering, surface layers, mixed layers, middle layers and deep layers; the three-dimensional class structure of classification system 2 may include: mesoscale phenomena, ocean currents, eddies, water clusters, internal waves, and the like, as well as other classification systems are not listed.
Further, S110 may include S111 or S112:
s111: classifying the characteristics of various scientific elements in the target data set by using a first intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; and constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set.
Specifically, the intelligent algorithm in the present embodiment may include a neural network algorithm, a support vector machine algorithm, an annealing algorithm, an ant colony algorithm, a random forest or a fuzzy mathematical algorithm, and the like.
Optionally, the first intelligent algorithm of the embodiment may be implemented through a neural network model, that is, the neural network model may be used to classify the features of various scientific elements in the target data set, and constructing and training the neural network model may specifically include the following steps:
1. and (3) constructing a model: parameters of the neural network model are determined, including the number of network layers, the number of neurons per layer of network, activation functions, and the like.
2. Parameter initialization: the parameters of the model are initialized, alternatively, the embodiment may use a random initialization or pre-training mode to initialize the parameters.
3. Forward propagation: and inputting training data into a neural network model, and obtaining a final output result by continuously performing matrix calculation and function activation processing.
4. Calculating a loss function: and comparing the output result of the neural network model with the real label, and calculating the value of the loss function. The loss function of the present embodiment includes a cross entropy loss function, a mean square error, and the like.
5. Back propagation: and calculating the parameter derivative of the loss function on the neural network model, and continuously adjusting the parameters of the neural network model by utilizing optimization algorithms such as gradient descent and the like, so that the loss function is gradually reduced.
6. Parameter updating: and updating parameters of the neural network model according to rules of an optimization algorithm, so that the performance of the neural network model is gradually improved.
7. Repeating training: and repeating the steps iteratively until the neural network model converges or reaches the preset training round number.
8. Model evaluation: and evaluating the trained neural network model by using the test set, calculating indexes such as accuracy, precision, recall rate and the like of the neural network model, and judging whether the performance of the neural network model meets the standard.
S112: clustering the characteristics of various scientific elements in the target data set by using a second intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; and constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set.
Specifically, the second intelligent algorithm of the embodiment may be implemented through a clustering algorithm model, that is, the features of various scientific elements in the target data set may be clustered by using the clustering algorithm model, and taking the clustering algorithm model as an example, the construction and training of the clustering algorithm model may specifically include the following steps:
1. data preparation: a target dataset for clustering is acquired.
2. Feature selection: and selecting proper characteristics from the target data set according to actual requirements.
3. Data normalization: the feature is normalized to ensure that all features have the same dimensions.
4. Support vector machine model construction: the use of support vector machine algorithms to construct a classification model may include, in particular, selecting appropriate kernel functions (e.g., linear kernel functions, polynomial kernel functions, gaussian kernel functions, etc.) and other parameters.
5. Training a model: the support vector machine model is trained using the labeled dataset.
6. And (3) predicting: and predicting the target data set by using the trained support vector machine model.
7. Clustering: and dividing the data points into different clusters according to the prediction result.
8. Clustering evaluation: and evaluating the quality of the clusters according to indexes such as contour coefficients or Calinski-Harabasz indexes. The Calinski-Harabasz index, also referred to as the CH index, is an index used to evaluate the effectiveness of a clustering algorithm. It calculates the quality of a cluster based on the degree of dispersion of the clustering results and the degree of separation between clusters.
S120: and transforming each three-dimensional category structure into a corresponding vector model.
Specifically, the present embodiment may construct a corresponding vector model from each data point in each three-dimensional class structure.
Further, referring to fig. 4, S120 may include S121 to S125:
s121: determining a center data point, an inner data point and a boundary data point of each three-dimensional category structure according to the space position of each data point in each three-dimensional category structure;
s122: determining interfaces to which the boundary data points belong according to the position relation between the boundary data points and the corresponding center data points;
s123: fitting each internal data point and each boundary data point in each interface to a curved surface to obtain a curved surface function;
s124: determining a curve formed by intersecting each curved surface as an intersecting line curve function;
s125: and constructing a corresponding vector model according to the central data point of each three-dimensional category structure body, each curved surface function and each intersecting line curve function.
Specifically, vectorizing a three-dimensional class structure body to establish a vector model, which comprises the following steps:
(1) A central data point P is determined.
Selecting a center point from all data points of each three-dimensional category structure according to the space position, wherein the expression is as follows:
P=（lat（lat min +lat max /2），lon（lon min +lon max /2），dep（dep min +dep max /2). Wherein the subscript min represents the minimum value of each parameter and the subscript max represents the maximum value of each parameter.
(2) Boundary data points and their positional relationship to the center data point are determined.
Define internal data points: the category of the internal data points is the same as the category of the surrounding data points.
Defining boundary data points: the category of the boundary data point is one or more points different from the category of the surrounding data points.
Determining the positional relationship of the boundary data point and the center data point:
and establishing a coordinate system by taking the central data point as an origin, setting x, y and z axes, dividing quadrants and determining directions.
Dividing each interface according to the relation between the boundary data point and the origin, including: front 1, back 2, left 3, right 4, up 5, down 6.
And determining the interface to which the boundary data point belongs according to the dividing direction of the position relation between the boundary data point and the central data point.
(3) After boundary data points are divided into planes, all data points in each interface are fitted into a curved surface, and a curved surface function S is constructed.
(4) And drawing intersecting line curves of the interfaces.
And determining a curve formed by intersecting each curved surface as an intersecting line curve function, and constructing an intersecting line curve function L.
S130: and visualizing each vector model.
Specifically, the embodiment can be visually displayed by rendering or marking colors on the vector model.
Further, S130 may include S131 or S132:
s131: and visualizing different types of data in each vector model.
Specifically, each vector model may include multiple types of data, and the embodiment may visually display one or more types of data therein.
Further, S131 may include at least one of S1311 to S1313:
s1311: and respectively visualizing scalar data of a single type in each vector model.
In particular, the present embodiment may visualize scalar data of a single type, such as temperature or time alone, and time-varying visualizations may specifically include the following steps, for example:
at some point t1 the vector model is visualized: drawing a central data point; drawing each curved surface fit by the interface, alternatively, the present embodiment may display each curved surface fit by the interface using a certain color having transparency; and drawing each intersecting line curve.
Drawing a vector model at the next moment ti; and displaying the vector models at all the moments according to the time sequence, and drawing the central data points into track curves.
S1312: and performing superposition visualization on scalar data of different types in each vector model.
Specifically, taking the example of simultaneous visualization of two scalar quantities (e.g., temperature or salinity), the relationship of the two is displayed: visualizing one of the scalars, including a central data point, a curved surface fitted by an interface, and an intersecting line curve; visualizing another scalar, including a center data point, a curved surface approximated by an interface, and an intersecting line curve; and overlapping the two visualized images.
Alternatively, this embodiment may calculate each intersecting line of the interface between the two, where the intersecting line forms an intersection of the two, and the intersection is represented by another color, and the other portion is represented by the original color.
S1313: and carrying out superposition visualization on at least one scalar data and at least one vector data in each vector model.
In particular, at least one scalar is visualized, including a center data point, a curved surface approximated by an interface, and an intersecting line curve; and further display vector data on a curved surface fitted by the interface.
S132: and scaling or rotating each vector model and then visualizing.
Specifically, the present embodiment may visualize the inside of the model with appropriate amount, for example, zoom in or zoom out, and the specific implementation steps may include: firstly, visualizing all vector models; the target vector model is determined, and then the target vector model can be displayed independently and rotated to display any angle; the target vector model is amplified to display an internal structure, and as the internal knowledge values of the same vector model have tiny differences, the characteristics are similar, so that the embodiment can display the internal characteristics after amplification to change in light micro color, when a certain data point in the vector model is selected, the value of the data point can be displayed, wherein if the data point is an NC data point, the value of the data point is directly displayed, and if the data point is not the NC data point, the actual value of the data point is solved according to a neighborhood set and then displayed.
The embodiment of the application can utilize an intelligent algorithm to dig out scientific phenomena in NC data, construct a three-dimensional category structure body, convert the three-dimensional category structure body into a vector model, realize visualization on scientific elements in time and space (from outside to inside) and perform superposition visualization on two elements (two scalar quantities or scalar quantity and vector quantity). The three-dimensional vector model is visualized, scientific phenomena or characteristics contained in the target data set can be revealed, professional knowledge and experience of scientific researchers are fused into the intelligent algorithm model through the intelligent algorithm, the scientific phenomena are directly read, and the NC data mining, utilization, popularization and application are facilitated.
The following describes the solution of the embodiment of the present application with reference to specific application examples:
referring to fig. 5, the present embodiment provides an example flowchart of a data visualization method.
Specifically, the present embodiment may include the steps of:
s1: acquiring an NC data set and performing space-time transformation on the NC data set to obtain a new NC data set;
s2: classifying or clustering the data in the new NC data set according to scientific elements by utilizing an intelligent algorithm to obtain each three-dimensional category structure;
s3: transforming each three-dimensional class structure (data point set) into a vector model of a central data point-interface-boundary line;
s4: each vector model is visualized in a manner including time variation, spatial variation from outside to inside, scalar superposition or vector superposition.
According to the embodiment, an intelligent algorithm (such as a neural network algorithm, a support vector machine algorithm, an annealing algorithm, an ant colony algorithm, a random forest or fuzzy mathematics and the like) can be utilized to conduct data mining on NC data, classification or clustering is conducted according to scientific elements to obtain a three-dimensional type structure, then the three-dimensional type structure is subjected to three-dimensional vectorization to obtain a vector model, and finally the three-dimensional vector model is subjected to visual rendering, so that scientific phenomena contained in the NC data can be revealed, and non-professionals can intuitively read scientific contents and scientific phenomena displayed by the NC data.
Referring to fig. 6, an embodiment of the present application further provides a data visualization device, which may implement the data visualization method, where the device includes:
a data set acquisition unit for acquiring a target data set in a network general data format; the target data set comprises network universal data format data of a plurality of types of scientific elements;
a structure body construction unit, configured to construct a three-dimensional category structure body corresponding to a plurality of data types according to the target data set;
a structure vectorization unit, configured to transform each three-dimensional class structure into a corresponding vector model;
and the model visualization unit is used for visualizing each vector model.
Optionally, the data set acquisition unit includes:
the space-time data acquisition unit is used for acquiring a space-time data set in a network general data format;
the attribute data acquisition unit is used for acquiring an attribute data set in a network general data format;
the space-time transformation unit is used for performing space-time transformation according to the space-time data set and the attribute data set to obtain a space-time variation data set;
and the data set acquisition subunit is used for combining the space-time data set, the attribute data set and the space-time change data set into the target data set.
Optionally, the space-time transform unit comprises:
the data selecting unit is used for selecting the data to be changed of each scientific element;
and the interpolation calculation unit is used for carrying out interpolation calculation on the corresponding data to be changed according to the space-time relationship of each scientific element to obtain the space-time change data set of each scientific element.
Optionally, the structure building unit includes:
the first structural body constructing subunit is used for classifying the characteristics of various scientific elements in the target data set by utilizing a first intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set;
the second structural body constructing subunit is used for clustering the characteristics of various scientific elements in the target data set by utilizing a second intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; and constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set.
Optionally, the structure vectorization unit includes:
a data point defining unit, configured to determine a center data point, an inner data point and a boundary data point of each three-dimensional category structure according to a spatial position of each data point in each three-dimensional category structure;
The interface dividing unit is used for determining the interface of each boundary data point according to the position relation between each boundary data point and the corresponding center data point;
the curved surface fitting unit is used for fitting each internal data point and each boundary data point in each interface into a curved surface to obtain a curved surface function;
the curve function determining unit is used for determining curves formed by intersecting the curved surfaces as intersecting line curve functions;
and the vector model construction unit is used for constructing the corresponding vector model according to the central data point of each three-dimensional category structure body, each curved surface function and each intersecting line curve function.
Optionally, the model visualization unit includes:
the first model visualization subunit is used for visualizing different types of data in each vector model;
and the second model visualization subunit is used for carrying out scaling or rotation on each vector model and then carrying out visualization.
Optionally, the first model visualization subunit includes:
a scalar visualization unit, configured to visualize scalar data of a single type in each vector model;
The vector visualization unit is used for carrying out superposition visualization on scalar data of different types in each vector model;
and the scalar vector superposition visualization unit is used for carrying out superposition visualization on at least one scalar data and at least one vector data in each vector model.
It can be understood that the content in the above method embodiment is applicable to the embodiment of the present device, and the specific functions implemented by the embodiment of the present device are the same as those of the embodiment of the above method, and the achieved beneficial effects are the same as those of the embodiment of the above method.
The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the data visualization method when executing the computer program. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
It can be understood that the content in the above method embodiment is applicable to the embodiment of the present apparatus, and the specific functions implemented by the embodiment of the present apparatus are the same as those of the embodiment of the above method, and the achieved beneficial effects are the same as those of the embodiment of the above method.
Referring to fig. 7, fig. 7 illustrates a hardware structure of an electronic device according to another embodiment, the electronic device includes:
the processor 701 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present application;
the memory 702 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM). The memory 702 may store an operating system and other application programs, and when the technical solutions provided in the embodiments of the present disclosure are implemented by software or firmware, relevant program codes are stored in the memory 702, and the processor 701 invokes a data visualization method for executing the embodiments of the present disclosure;
an input/output interface 703 for implementing information input and output;
the communication interface 704 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g. USB, network cable, etc.), or may implement communication in a wireless manner (e.g. mobile network, WIFI, bluetooth, etc.);
A bus 705 for transferring information between various components of the device (e.g., the processor 701, memory 702, input/output interfaces 703, and communication interfaces 704);
wherein the processor 701, the memory 702, the input/output interface 703 and the communication interface 704 are in communication connection with each other inside the device via a bus 705.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the data visualization method when being executed by a processor.
It can be understood that the content of the above method embodiment is applicable to the present storage medium embodiment, and the functions of the present storage medium embodiment are the same as those of the above method embodiment, and the achieved beneficial effects are the same as those of the above method embodiment.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and as those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by those skilled in the art that the technical solutions shown in the figures do not constitute limitations of the embodiments of the present application, and may include more or fewer steps than shown, or may combine certain steps, or different steps.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.
Preferred embodiments of the present application are described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.
Claims (10)
1. A method of visualizing data, the method comprising:
acquiring a target data set in a network general data format; the target data set comprises network universal data format data of a plurality of types of scientific elements;
constructing a three-dimensional category structure body corresponding to a plurality of data types according to the target data set;
transforming each three-dimensional category structure into a corresponding vector model;
and visualizing each vector model.
2. A method of visualizing data as in claim 1, wherein said obtaining a target data set in a network generic data format comprises:
acquiring a space-time data set in a network general data format;
acquiring an attribute data set in a network general data format;
performing space-time transformation according to the space-time data set and the attribute data set to obtain a space-time variation data set;
and combining the spatiotemporal dataset, the attribute dataset and the spatiotemporal variation dataset into the target dataset.
3. A data visualization method as recited in claim 2, wherein said performing a spatio-temporal transformation based on said spatio-temporal dataset and said attribute dataset to obtain a spatio-temporal variation dataset comprises:
Selecting data to be changed of each scientific element;
and carrying out interpolation calculation on the corresponding data to be changed according to the space-time relationship of each scientific element to obtain the space-time change data set of each scientific element.
4. A data visualization method as recited in claim 1, wherein constructing a three-dimensional class structure corresponding to a plurality of data types from the target data set comprises:
classifying the characteristics of various scientific elements in the target data set by using a first intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set;
or clustering the characteristics of various scientific elements in the target data set by using a second intelligent algorithm to obtain three-dimensional class data sets corresponding to the various scientific elements; and constructing the three-dimensional category structure corresponding to the scientific element according to each three-dimensional category data set.
5. A method of visualizing data in accordance with claim 1, wherein said transforming each of said three-dimensional class structures into a corresponding vector model comprises:
Determining a center data point, an inner data point and a boundary data point of each three-dimensional category structure according to the space position of each data point in each three-dimensional category structure;
determining interfaces to which the boundary data points belong according to the position relation between the boundary data points and the corresponding center data points;
fitting each internal data point and each boundary data point in each interface to a curved surface to obtain a curved surface function;
determining a curve formed by intersecting each curved surface as an intersecting line curve function;
and constructing a corresponding vector model according to the central data point of each three-dimensional category structure body, each curved surface function and each intersecting line curve function.
6. A data visualization method as recited in claim 1, wherein said visualizing each of said vector models comprises at least one of:
visualizing different types of data in each vector model;
or scaling or rotating each vector model and then visualizing.
7. A method of visualizing data according to claim 6, wherein said visualizing different types of data in each of said vector models comprises at least one of the following steps:
Respectively visualizing scalar data of a single type in each vector model;
or, performing superposition visualization on scalar data of different types in each vector model;
or, performing superposition visualization on at least one scalar data and at least one vector data in each vector model.
8. A data visualization apparatus, the apparatus comprising:
a data set acquisition unit for acquiring a target data set in a network general data format; the target data set comprises network universal data format data of a plurality of types of scientific elements;
a structure body construction unit, configured to construct a three-dimensional category structure body corresponding to a plurality of data types according to the target data set;
a structure vectorization unit, configured to transform each three-dimensional class structure into a corresponding vector model;
and the model visualization unit is used for visualizing each vector model.
9. An electronic device comprising a memory storing a computer program and a processor implementing the method of any of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 7.
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