CN106815320B - Investigation big data visual modeling method and system based on expanded three-dimensional histogram - Google Patents

Abstract

The invention discloses a research big data visualization modeling method and system based on an extended three-dimensional histogram. The method includes: initializing a three-dimensional visualization model; classifying and preprocessing the research data according to specific data types; reading the original research According to the requirements of the 3D visualization model and the results of classification preprocessing, the extended 3D histogram data is extracted and normalized to generate the extended 3D histogram data that conforms to the standard format of the 3D visualization model; the generated extended 3D histogram data Visual display is carried out according to the method of graphics. The invention is based on the expanded three-dimensional histogram, which can form a unified overall visual analysis result, and has wider applicability; through the expanded three-dimensional histogram, the visual chart is connected with the original research data, and the fidelity is high; it can be adopted according to different data types. Corresponding and effective data preprocessing methods are more effective. The invention can be widely used in the field of big data processing.

Description

Research big data visualization modeling method and system based on extended three-dimensional histogram

technical field

The invention relates to the field of big data processing, in particular to a research big data visualization modeling method and system based on an extended three-dimensional histogram.

Background technique

Educational equipment is a necessary condition for education modernization. Educational decision-making departments need to collect and count the research data on the application level of information technology, so as to master the equipment of the infrastructure in the process of education informatization, and to clarify the application of software and hardware facilities. Based on the survey data, comprehensively describe the progress of educational informatization, and generate various reports such as application analysis reports, problem diagnosis, planning consultation and development decision-making, so as to use the rich survey data to carry out the implementation status, application effect and development of educational informatization at all levels. Development level assessment, consultation and planning.

In recent years, with the huge investment in educational informatization and the huge gap between the application of educational informatization and the previous expected results, people have to pay attention to the deep problems of strategic decision-making and investment income of educational informatization. More and more people are beginning to pay attention to the performance of educational informatization, shifting the focus of educational informatization from investment, providing informatization solutions, platforms and systems to educational informatization integration, educational informatization value evaluation and sustainable development of educational informatization . The evaluation of educational informatization has gradually changed from the method of determining the level of educational informatization based on "investment" to the method of determining the level of educational informatization based on "performance", so as to promote the development of educational informatization through performance evaluation of educational informatization. application development. However, in the aspect of educational informatization performance evaluation, both foreign and domestic are in the stage of trial and exploration. The performance evaluation of educational informatization is a relatively difficult task: on the one hand, educational informatization is not only a dynamic development process, but also a problem of multiple inputs and multiple outputs, and its output cannot be easily measured by quantitative indicators; In this field, there is still no mature theoretical guidance and suitable measurement methods and measurement tools, and the evaluation of educational informatization itself is a very difficult task, because the benefits of educational informatization are diverse, not only depends on Its economic benefits are mainly based on social benefits, not only the current benefits are more manifested as long-term benefits, but only the inherent benefits are more manifested as derived benefits. Therefore, the performance evaluation of educational informatization has become a very important and urgent topic that everyone pays attention to.

The data types of educational informatization research data are relatively complex, and are dynamic data that will change according to the situation. The huge amount of data generated by large-scale surveys in provinces and cities urgently requires automated processing and visual analysis methods. Conventional visualization methods mainly carry out comparative statistics and visualization of simplified types of numerical values, which are no longer suitable for the needs of research and analysis due to the rapid increase in the amount and type of data.

The traditional cluster analysis calculation methods mainly include the following:

1. Partitioning methods

Given a data set with N tuples or records, the partition method will construct K groups, each group represents a cluster, K < N, and the K groups satisfy the following conditions: (1) Each group At least one data record is included; (2) each data record belongs to one and only one group (this requirement can be appropriately relaxed in some fuzzy clustering algorithms). For a given K, the algorithm first gives an initial grouping method, and then changes the grouping through repeated iterations, so that each improved grouping scheme is better than the previous one. A good measure is that records in the same group are as close as possible, and records in different groups are as far away as possible. Algorithms that use the basic idea of the division method are: K-MEANS algorithm, K-MEDOIDS algorithm and CLARANS algorithm.

Most partitioning methods are distance based. Given the number of partitions K to build, the partition method first creates an initial partition scheme; then, it employs an iterative relocation technique to partition by moving objects from one group to another. A good partitioning criterion in these partitioning methods is generally: objects in the same cluster are as close or related to each other as possible, while objects in different clusters are as far away or as different as possible. Traditional partitioning methods can only be extended to subspace clustering instead of searching the entire data space, and are suitable for data with many attributes and sparse data. In order to achieve the global optimum, the partition-based clustering method may need to exhaust all possible partitions, which is computationally intensive. In fact, most applications employ popular heuristics, such as k-means and k-center algorithms, to asymptotically improve clustering quality to approximate local optimal solutions. These heuristic clustering methods are well suited for finding spherical clusters in small and medium-sized databases. To find clusters with complex shapes and to cluster very large datasets, partition-based clustering methods need to be further extended.

2. Hierarchical methods

The hierarchical method decomposes a given data set hierarchically until a certain condition is satisfied, and it can be divided into two schemes: "bottom-up" and "top-down". Taking the "bottom-up" scheme as an example, each data record is initially formed into a separate group. In the next iteration, the hierarchical method combines those adjacent groups into a group until all records form a group. or until a certain condition is met. The representative algorithms of the hierarchical method are: BIRCH algorithm, CURE algorithm, CHAMELEON algorithm and so on.

Hierarchical clustering methods can be distance based or density or connectivity based. Some extensions of hierarchical clustering methods also consider subspace clustering content. The disadvantage of the hierarchical approach is that once a step (merge or split) is completed, it cannot be withdrawn (no consideration for the number of combinations of different options, low computational cost); however, this technique cannot correct bad decisions. Therefore, it is necessary to further improve the clustering quality of the hierarchical clustering method.

3. Model-based methods

Model-based methods assume a model for each cluster, and then search for a dataset that better satisfies this model. Such a model may be a function of the density distribution of data points in space or something else, one underlying assumption of which is that the target data set is determined by a series of probability distributions. Usually model-based methods have two directions of attempt: statistical schemes and neural network schemes.

To sum up, the current visualization modeling methods for informatization research big data have the following defects or deficiencies:

(1) Only simple local data visualization can be realized, and it is only suitable for intuitive comparison of the same data type, and cannot form a unified overall visualization analysis result, and its applicability is not wide;

(2) The visualization is irreversible to the data processing process, and the fidelity is low;

(3) The processing requirements of complex multi-type data cannot be met, and corresponding and effective data preprocessing methods cannot be adopted according to different data types.

SUMMARY OF THE INVENTION

In order to solve the above-mentioned technical problems, the purpose of the present invention is to provide a visualization modeling method of research big data based on extended three-dimensional histogram, which has wide applicability, high fidelity and effectiveness.

Another object of the present invention is to provide a research big data visualization modeling system based on extended three-dimensional histogram, which has wide applicability, fidelity and effectiveness.

The technical scheme adopted by the present invention is:

The research big data visualization modeling method based on extended three-dimensional histogram includes the following steps:

Initialize the 3D visualization model;

Preprocess the survey data into categories according to specific data types;

Read the original survey data, and extract and normalize the extended 3D histogram data according to the requirements of the 3D visualization model and the results of classification preprocessing, and generate the extended 3D histogram data that conforms to the standard format of the 3D visualization model. The horizontal dimension of the 3D histogram is composed of different reporting subjects with different levels, the vertical dimension includes a variety of complex data types, and the Z-dimension is composed of cell attributes, cell height and top texture and is related to the original survey data. In which, different reporting entities at different levels include but are not limited to provinces, cities, counties and schools, and a variety of complex data types include but are not limited to logical data, text data, numerical data and enumeration data. Data, the top texture uses different colors to represent the changing trend of the data;

The generated extended 3D histogram data is visualized according to the graphics method.

Further, the step of performing the initialization of the 3D visualization model includes:

Determine the absolute width, data interval, and the total number of minimum unit dimensions included in the horizontal and vertical directions of the extended three-dimensional histogram;

Determine the specific dimensional structure of the horizontal and vertical coordinates of the extended three-dimensional histogram;

Determine the total height in the z-axis direction of the extended three-dimensional histogram, the position of the ground plane, and the height representation method of different data types;

Sets the top texture parameter that extends the z-axis direction of the 3D histogram.

Further, the step of classifying and preprocessing the survey data according to specific data types includes:

Read in the original survey data;

According to the specific data type of the original research data, the corresponding classification processing is performed to obtain the processed data;

Archive the processed data.

Further, the step of performing corresponding classification processing according to the specific data type of the original survey data to obtain the processed data is specifically:

If the original survey data is numerical data, first determine the value range of the numerical data, then determine the average value of the numerical data, then determine the numerical mapping method of the numerical data, and finally identify the change trend of a single numerical data; If the original survey data is logical data, first enumerate the value range of each survey item of the logical data, then determine the reference value of the logical data, then determine the method of numerical mapping of the logical data, and finally identify a single logical data If the original research data is textual data, first enumerate the keywords of the textual data, then extract the abstract of the textual data, then determine the method of keyword mapping for the textual data, and finally identify a single textual data changing trend.

Further, the original research data is read, and according to the requirements of the 3D visualization model and the results of classification preprocessing, the extended 3D histogram data is extracted and normalized, and the extended 3D histogram data that conforms to the standard format of the 3D visualization model is generated. This step includes:

Read the original research data one by one, and perform in-depth analysis of the original research data layer by layer according to the data format of the predetermined composite structure research data, until the smallest data unit of the original research data is parsed;

According to the specific data type of the original research data, the requirements of the 3D visualization model and the results of classification preprocessing, extract the graphical representation data required by the original research data, and calculate the corresponding data change trend;

Normalize the 3D visualization model of the extracted graphical representation data and the calculated data change trend, and obtain data that conforms to the standard format of the 3D visualization model;

Write the data conforming to the standard format of the 3D visualization model into the extended 3D histogram dataset.

Further, the described step of normalizing the expanded three-dimensional histogram data includes:

Taking the ground plane as a reference plane, according to the characteristics of different data types, formulate a cubic height normalization pairing strategy;

The extended three-dimensional histogram data is normalized according to the cube height normalization strategy, and the height of the extended three-dimensional histogram in the Z-axis direction is higher than or lower than the ground level.

Another technical scheme adopted by the present invention is:

Research big data visualization modeling system based on extended 3D histogram, including:

The 3D visualization model initialization module is used to initialize the 3D visualization model;

The research data classification preprocessing module is used to preprocess the research data by classification according to specific data types;

The extended 3D histogram data generation module is used to read the original survey data, and according to the requirements of the 3D visualization model and the results of classification preprocessing, the extended 3D histogram data is extracted and normalized to generate a standard format that conforms to the 3D visualization model. The extended three-dimensional histogram data, the horizontal dimension of the extended three-dimensional histogram is composed of different reporting subjects with different levels, the vertical dimension includes a variety of complex data types, and the Z dimension is composed of cell attributes, cell height and A structure composed of top textures and linked to the original survey data, in which different reporting entities at different levels include but are not limited to provinces, cities, counties and schools, and various complex data types include but are not limited to logical data, text For class data, numerical data and enumeration data, the top texture uses different colors to represent the trend of data changes;

The expanded 3D histogram display module of the survey data is used to visualize the generated expanded 3D histogram data according to the graphics method.

Further, the survey data classification preprocessing module includes:

The data read-in unit is used to read in the original research data;

The classification processing unit is used to perform corresponding classification processing according to the specific data type of the original survey data to obtain the processed data;

Archiving unit for archiving processed data.

Further, the classification processing unit specifically performs the following operations:

If the original survey data is numerical data, first determine the value range of the numerical data, then determine the average value of the numerical data, then determine the numerical mapping method of the numerical data, and finally identify the change trend of a single numerical data; If the original survey data is logical data, first enumerate the value range of each survey item of the logical data, then determine the reference value of the logical data, then determine the method of numerical mapping of the logical data, and finally identify a single logical data If the original research data is textual data, first enumerate the keywords of the textual data, then extract the abstract of the textual data, then determine the method of keyword mapping for the textual data, and finally identify a single textual data changing trend.

Further, the expanded three-dimensional histogram data generation module includes:

The reading and parsing unit is used to read the original research data one by one, and perform in-depth analysis of the original research data layer by layer according to the data format of the predetermined composite structure research data, until the smallest data unit of the original research data is parsed;

The extraction and calculation unit is used to extract the graphical representation data required by the original research data according to the specific data type of the original research data, the requirements of the three-dimensional visualization model and the results of classification preprocessing, and calculate the corresponding data changes. trend;

The normalization unit is used to normalize the three-dimensional visualization model of the extracted graphical representation data and the calculated data change trend, so as to obtain data conforming to the standard format of the three-dimensional visualization model;

The writing unit is used to write the data conforming to the standard format of the 3D visualization model into the extended 3D histogram dataset.

The beneficial effects of the method of the present invention are: including the steps of initializing a three-dimensional visualization model, preprocessing the survey data according to specific data types, extracting and normalizing the expanded three-dimensional histogram data, and performing visual display, based on the expanded three-dimensional Histogram, through the extraction and normalization of extended 3D histogram data, generates extended 3D histogram data that conforms to the standard format of 3D visualization model and displays it visually, which can form a unified overall visual analysis result and has wider applicability; extended 3D histogram Z The directional dimension is a structure composed of cell attributes, cell heights and top textures and is related to the original research data. By expanding the three-dimensional histogram visualization chart to connect with the original research data, it overcomes the irreversibility of data processing by conventional visualization. Defects and high fidelity; the vertical dimension of the extended 3D histogram includes a variety of complex data types, the data types are expanded, and the preprocessing of the survey data according to specific data types is added, which satisfies the complex and complex data types. It is more effective to take corresponding and effective data preprocessing methods according to the processing requirements of different data types.

The beneficial effects of the system of the present invention are: including a three-dimensional visualization model initialization module, a research data classification preprocessing module, an expanded three-dimensional histogram data generation module, and a research data expanded three-dimensional histogram display module, based on the expanded three-dimensional histogram, in the expanded three-dimensional histogram The histogram data generation module and the survey data extension 3D histogram display module can generate the extended 3D histogram data conforming to the standard format of the 3D visualization model and visualize it by extracting and normalizing the extended 3D histogram data, which can form a unified overall visual analysis. As a result, the applicability is wider; the Z dimension of the extended three-dimensional histogram of the extended three-dimensional histogram data generation module is a structure composed of cell attributes, cell heights and top textures and is related to the original research data. 1. The visualization chart is connected with the original research data, which overcomes the irreversibility defect of conventional visualization for data processing, and has high fidelity; the extended three-dimensional histogram vertical dimension of the extended three-dimensional histogram data generation module includes a variety of complex data types. The data type has been expanded, and a research data classification preprocessing module has been added to preprocess the research data according to specific data types, which satisfies the processing requirements of complex multi-type data, and can take corresponding measures according to different data types. Effective data preprocessing methods are more effective.

Description of drawings

Fig. 1 is the step flow chart of the research big data visualization modeling method based on the expanded three-dimensional histogram of the present invention;

Fig. 2 is the flow chart that the present invention carries out corresponding classification processing steps according to the concrete data type of the original survey data;

FIG. 3 is a block diagram of the overall structure of the visualization modeling of the survey big data based on the extended three-dimensional histogram of the present invention.

Detailed ways

Referring to Fig. 1, the visualization modeling method of research big data based on extended three-dimensional histogram includes the following steps:

Initialize the 3D visualization model;

Preprocess the survey data into categories according to specific data types;

Read the original survey data, and extract and normalize the extended 3D histogram data according to the requirements of the 3D visualization model and the results of classification preprocessing, and generate the extended 3D histogram data that conforms to the standard format of the 3D visualization model. The horizontal dimension of the 3D histogram is composed of different reporting subjects with different levels, the vertical dimension includes a variety of complex data types, and the Z-dimension is composed of cell attributes, cell height and top texture and is related to the original survey data. In which, different reporting entities at different levels include but are not limited to provinces, cities, counties and schools, and a variety of complex data types include but are not limited to logical data, text data, numerical data and enumeration data. Data, the top texture uses different colors to represent the changing trend of the data;

The generated extended 3D histogram data is visualized according to the graphics method.

Further as a preferred embodiment, the step of carrying out the initialization of the 3D visualization model includes:

Determine the absolute width, data interval, and the total number of minimum unit dimensions included in the horizontal and vertical directions of the extended three-dimensional histogram;

Determine the specific dimensional structure of the horizontal and vertical coordinates of the extended three-dimensional histogram;

Determine the total height in the z-axis direction of the extended three-dimensional histogram, the position of the ground plane, and the height representation method of different data types;

Sets the top texture parameter that extends the z-axis direction of the 3D histogram.

Referring to Fig. 2, as a further preferred embodiment, the step of classifying and preprocessing the survey data according to specific data types includes:

Read in the original survey data;

According to the specific data type of the original research data, the corresponding classification processing is performed to obtain the processed data;

Archive the processed data.

Referring to Fig. 2, as a further preferred embodiment, the step of performing corresponding classification processing according to the specific data type of the original survey data to obtain the processed data is specifically:

If the original survey data is numerical data, first determine the value range of the numerical data, then determine the average value of the numerical data, then determine the numerical mapping method of the numerical data, and finally identify the change trend of a single numerical data; If the original survey data is logical data, first enumerate the value range of each survey item of the logical data, then determine the reference value of the logical data, then determine the method of numerical mapping of the logical data, and finally identify a single logical data If the original research data is textual data, first enumerate the keywords of the textual data, then extract the abstract of the textual data, then determine the method of keyword mapping for the textual data, and finally identify a single textual data changing trend.

Further as a preferred embodiment, the original research data is read, and according to the requirements of the three-dimensional visualization model and the results of classification preprocessing, the extended three-dimensional histogram data is extracted and normalized to generate a standard format that conforms to the three-dimensional visualization model. The step of expanding the 3D histogram data includes:

Read the original research data one by one, and perform in-depth analysis of the original research data layer by layer according to the data format of the predetermined composite structure research data, until the smallest data unit of the original research data is parsed;

According to the specific data type of the original research data, the requirements of the 3D visualization model and the results of classification preprocessing, extract the graphical representation data required by the original research data, and calculate the corresponding data change trend;

Normalize the 3D visualization model of the extracted graphical representation data and the calculated data change trend, and obtain data that conforms to the standard format of the 3D visualization model;

Write the data conforming to the standard format of the 3D visualization model into the extended 3D histogram dataset.

Further as a preferred embodiment, the described step of expanding the three-dimensional histogram data normalization includes:

Taking the ground plane as a reference plane, according to the characteristics of different data types, formulate a cubic height normalization pairing strategy;

The extended three-dimensional histogram data is normalized according to the cube height normalization strategy, and the height of the extended three-dimensional histogram in the Z-axis direction is higher than or lower than the ground level.

Referring to Figure 3, the research big data visualization modeling system based on the extended three-dimensional histogram includes:

The 3D visualization model initialization module is used to initialize the 3D visualization model;

The research data classification preprocessing module is used to preprocess the research data by classification according to specific data types;

The extended 3D histogram data generation module is used to read the original survey data, and according to the requirements of the 3D visualization model and the results of classification preprocessing, the extended 3D histogram data is extracted and normalized to generate a standard format that conforms to the 3D visualization model. The extended three-dimensional histogram data, the horizontal dimension of the extended three-dimensional histogram is composed of different reporting subjects with different levels, the vertical dimension includes a variety of complex data types, and the Z dimension is composed of cell attributes, cell height and A structure composed of top textures and linked to the original survey data, in which different reporting entities at different levels include but are not limited to provinces, cities, counties and schools, and various complex data types include but are not limited to logical data, text For class data, numerical data and enumeration data, the top texture uses different colors to represent the trend of data changes;

The expanded 3D histogram display module of the survey data is used to visualize the generated expanded 3D histogram data according to the graphics method.

Further as a preferred embodiment, the survey data classification preprocessing module includes:

The data read-in unit is used to read in the original research data;

The classification processing unit is used to perform corresponding classification processing according to the specific data type of the original survey data to obtain the processed data;

Archiving unit for archiving processed data.

As a further preferred embodiment, the classification processing unit specifically performs the following operations:

If the original survey data is numerical data, first determine the value range of the numerical data, then determine the average value of the numerical data, then determine the numerical mapping method of the numerical data, and finally identify the change trend of a single numerical data; If the original survey data is logical data, first enumerate the value range of each survey item of the logical data, then determine the reference value of the logical data, then determine the method of numerical mapping of the logical data, and finally identify a single logical data If the original research data is textual data, first enumerate the keywords of the textual data, then extract the abstract of the textual data, then determine the method of keyword mapping for the textual data, and finally identify a single textual data changing trend.

Further as a preferred embodiment, the expanded three-dimensional histogram data generation module includes:

The reading and parsing unit is used to read the original research data one by one, and perform in-depth analysis of the original research data layer by layer according to the data format of the predetermined composite structure research data, until the smallest data unit of the original research data is parsed;

The extraction and calculation unit is used to extract the graphical representation data required by the original research data according to the specific data type of the original research data, the requirements of the three-dimensional visualization model and the results of classification preprocessing, and calculate the corresponding data changes. trend;

The normalization unit is used to normalize the three-dimensional visualization model of the extracted graphical representation data and the calculated data change trend, so as to obtain data conforming to the standard format of the three-dimensional visualization model;

The writing unit is used to write the data conforming to the standard format of the 3D visualization model into the extended 3D histogram dataset.

The present invention will be further explained and illustrated below in conjunction with the accompanying drawings and specific embodiments of the description.

Example 1

Aiming at the problems of poor applicability, low fidelity and insufficient effectiveness of the prior art, the present invention proposes a new visualization modeling method and system for research big data based on extended three-dimensional histogram. The key point of the present invention is to upgrade the multi-level, multi-dimensional and multi-type investigation data processing process from the conventional intuitive comparison method of the same data type to a unified overall visual analysis model. The present invention is based on the expanded three-dimensional histogram, and the expanded three-dimensional histogram is expanded on the basis of the three-dimensional histogram: the horizontal dimension is composed of different parts of a simple same level and expanded to a province, a city, a county, and a school with different levels. It provides conditions for subsequent big data processing of different granularities; the vertical dimension is expanded from the usual pure numerical data type to include logical data, text data, numerical data, and enumeration data, etc. It is a collection of complex data types, which greatly expands the expressive ability and application scope of the visual 3D model; the Z dimension is expanded from the usual single height to the three connotations of cell attributes, cell height and top texture, and is consistent with the original. High-fidelity structures linked to survey data. Among them, the top texture uses different colors to represent the changing trend of data, which lays the foundation for the application of temporal database. In the process of visual modeling, when the present invention preprocesses newly introduced complex data types, a cube height normalization strategy is formulated according to the characteristics of different data types. For this reason, the present invention also specially introduces the ground plane as a reference plane, so that the expanded height can be expressed by being higher than or lower than the ground plane, which enhances the connotation and expressive ability of the cubic height.

As shown in FIG. 3 , the research big data visualization modeling system of the present invention includes four major categories: a research data classification preprocessing module, a three-dimensional visualization model initialization module, an expanded three-dimensional histogram data generation module, and a research data expanded three-dimensional histogram display module. part. Among them, the survey data preprocessing module is used for preprocessing different categories of survey data. The 3D visualization model initialization module is used to initialize the data that need to be initialized, such as the width, height and precision of the visualization 3D model. The expanded 3D histogram data generation module reads in the original survey data, combines and normalizes the data according to the requirements of the 3D visualization model, and finally obtains the standardized 3D visualization model standard format data. The research data expands the 3D histogram display module, and standardizes the data from the expanded 3D histogram data generation module according to the graphics method, row by column, according to the height and top texture to form the output data that can be directly visualized.

As shown in Figure 1, the research big data visualization modeling method of the present invention comprises the following steps:

(1) Initialize the 3D visualization model, including: determining the absolute width of the horizontal and vertical directions and the interval of the data; determining how many minimum unit dimensions there are in total; according to the needs of the research system, it can also be determined that the dimension of the horizontal and vertical coordinates is a How to include the structure; the z-axis direction should determine the total height, the position of the ground plane and the height representation method of different data types, so as to give the heights of pure numerical values in different plane positions different practical meanings related to the level of information technology applications . At the same time, a set of differentiated top textures should be developed to represent the changes in values during initialization.

(2) Pre-processing the survey data into categories. As shown in Figure 2, classification preprocessing mainly deals with three main types of data: numerical data, first determine the value range of numerical data, then determine the average value of numerical data, and then determine the numerical mapping of numerical data. method, and finally identify the change trend of a single numerical data; for logical data, first enumerate the value range of each survey item of the logical data, then determine the reference value of the logical data, and then determine the method of numerical mapping of the logical data, and finally Identify the change trend of a single logical data; for textual data, first enumerate the keywords of the textual data, then extract the abstract of the textual data, then determine the method of mapping the keywords of the textual data, and finally identify the changes of a single textual data trend.

(3) Expand the generation of 3D histogram data.

The specific process of generating the expanded 3D histogram data is as follows: First, read the original data of the survey one by one, and then go deeper into the original survey data layer by layer according to the data format of the predetermined composite structure survey data (that is, set the target format to be parsed). Analyze until the smallest data unit of the original survey data is parsed; then according to the data type of the original survey data, according to the processing method of the relevant type of data in step (2), to extract the required graphical representation data, and calculate The trend of corresponding data changes; then, the extracted and calculated data are normalized to the 3D visualization model, and finally written into the extended 3D histogram dataset.

(4) Expand the three-dimensional histogram display of the survey data.

This step is used to convert the extended three-dimensional histogram data with certain significance generated in the previous step (3) into a data set that is completely represented in the format required by graphics for visual display processing. This step can specify the length, width and height of the 3D visualization model, the interval and height between each row and column, and the specific display requirements for textures such as top textures. Basic conditions.

Embodiment 2

The evaluation of the application level of educational information technology is a complex system process, and the process of modeling by applying the visual modeling system of the first embodiment specifically includes the following steps:

(1) Establish an indicator system for the evaluation of information technology applications, including the main indicators of various planning, management, investment, application, training and other evaluations, and maintain relative stability in previous evaluations;

(2) Establish a network evaluation system, try to conduct cross-regional surveys through network means, and accumulate enough survey data;

(3) Establish the visual modeling system of the first embodiment, and improve the traditional data processing method in the direction of visual processing and analysis;

(4) According to the visual modeling system of the first embodiment, the visual analysis and presentation of the research data are carried out;

(5) On the basis of visual analysis, a long-term research mechanism is established based on the top texture, so as to transform from a final survey and evaluation to a continuous monitoring.

Compared with the prior art, the present invention has the following advantages:

a. Established a visualization model to improve the statistical analysis framework and transform the digital service form into a graphical service form;

b. Expand from simple local data visualization to system-wide data visualization;

c. By expanding the three-dimensional histogram as a visualization chart to connect with the original research data, it overcomes the irreversibility defect of conventional visualization for data processing, and the fidelity is higher;

d. The complex multi-type data is clustered and expanded on the basis of semantics. Different types of data are distinguished within the model and corresponding effective data preprocessing methods are adopted, which is more effective;

e. Make full use of the top texture, and add the fourth-dimensional information representing the changing trend to the traditional 3D visualization model, making it possible to transform the research system into a continuous monitoring system;

f. Expanded the connotation of 3D histogram, and constructed a structured basic visualization model with layers, analysis and interpretation.

The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments, and those skilled in the art can also make various equivalent deformations or replacements without departing from the spirit of the present invention, These equivalent modifications or substitutions are all included within the scope defined by the claims of the present application.

Claims (10)

1. The investigation big data visualization modeling method based on the expanded three-dimensional histogram is characterized in that: the method comprises the following steps:

initializing a three-dimensional visual model;

classifying and preprocessing the research data according to specific data types;

reading original investigation data, extracting and normalizing expanded three-dimensional histogram data according to the requirements of a three-dimensional visual model and classification preprocessing results, and generating expanded three-dimensional histogram data which accords with the standard format of the three-dimensional visual model, wherein the horizontal dimension of the expanded three-dimensional histogram is formed by different filling main bodies with different levels, the longitudinal dimension comprises multiple complex data types, and the Z-direction dimension is a structure which is formed by cell attributes, cell heights and top textures and is connected with the original investigation data, wherein the different filling main bodies with different levels comprise provinces, cities, counties and schools, the multiple complex data types comprise logic data, text data and numerical data, and the top textures adopt different colors to express the variation trend of the data;

and carrying out visual display on the generated expanded three-dimensional histogram data according to a graphics method.

2. The investigation big data visualization modeling method based on the extended three-dimensional histogram of claim 1, characterized in that: the step of initializing the three-dimensional visualization model includes:

determining the horizontal and longitudinal absolute widths, data intervals and the total number of the minimum unit dimensions contained in the expanded three-dimensional histogram;

determining a specific dimension structure for expanding the horizontal coordinate and the vertical coordinate of the three-dimensional histogram;

determining the total height of the expanded three-dimensional histogram in the z-axis direction, the position of a ground plane and height representation methods of different data types;

and setting a top texture parameter expanding the z-axis direction of the three-dimensional histogram.

3. The investigation big data visualization modeling method based on the extended three-dimensional histogram of claim 1, characterized in that: the step of classifying and preprocessing the research data according to specific data types comprises the following steps:

reading in original research data;

performing corresponding classification processing according to the specific data type of the original research data to obtain processed data;

and archiving the processed data.

4. The investigation big data visualization modeling method based on the extended three-dimensional histogram of claim 3, characterized in that: the step of performing corresponding classification processing according to the specific data type of the original research data to obtain processed data specifically includes:

if the original research data is numerical data, determining the value range of the numerical data, then determining the average value of the numerical data, then determining a numerical mapping method of the numerical data, and finally identifying the variation trend of the single numerical data; if the original investigation data is logic data, firstly enumerating the value range of each investigation item of the logic data, then determining the reference value of the logic data, then determining the logical data value mapping method, and finally identifying the variation trend of the single logic data; if the original research data is text data, firstly listing keywords of the text data, then extracting an abstract of the text data, then determining a method for mapping the keywords of the text data, and finally identifying the variation trend of the single text data.

5. The investigation big data visualization modeling method based on the extended three-dimensional histogram of claim 3, characterized in that: the step of reading original investigation data, extracting and normalizing expanded three-dimensional histogram data according to the requirements of the three-dimensional visual model and the classification preprocessing result, and generating expanded three-dimensional histogram data conforming to the standard format of the three-dimensional visual model comprises the following steps:

reading the original investigation data one by one, and carrying out deep analysis on the original investigation data layer by layer according to the data format of the predetermined composite structure investigation data until the minimum data unit of the original investigation data is analyzed;

extracting graphical representation data required by the original research data according to the specific data type of the original research data, the requirements of the three-dimensional visual model and the classification preprocessing result, and calculating the corresponding data change trend;

normalizing the three-dimensional visual model of the extracted graphical representation data and the calculated data change trend to obtain data conforming to the standard format of the three-dimensional visual model;

and writing the data which accords with the standard format of the three-dimensional visual model into an expanded three-dimensional histogram data set.

6. The method for visual modeling of research big data based on extended three-dimensional histograms according to any of claims 1-5, characterized in that: the step of performing extended three-dimensional histogram data normalization comprises:

a ground plane is used as a reference plane, and a cubic height normalization strategy is formulated according to the characteristics of different data types;

and normalizing the expanded three-dimensional histogram data according to the cubic height normalization strategy, wherein the height of the expanded three-dimensional histogram in the Z-axis direction is higher than the ground level or lower than the ground level.

7. An investigation big data visualization modeling system based on an expanded three-dimensional histogram is characterized in that: the method comprises the following steps:

the three-dimensional visualization model initialization module is used for initializing a three-dimensional visualization model;

the investigation data classification preprocessing module is used for classifying and preprocessing the investigation data according to specific data types;

an expanded three-dimensional histogram data generation module for reading the original investigation data, extracting and normalizing the expanded three-dimensional histogram data according to the requirements of the three-dimensional visualization model and the classification preprocessing result, generating expanded three-dimensional histogram data in accordance with the standard format of the three-dimensional visualization model, the horizontal dimension of the expanded three-dimensional histogram is formed by different filling main bodies with different levels, the longitudinal dimension comprises a plurality of complex data types, the Z-direction dimension is a structure which is formed by cell attributes, cell heights and top textures and is related to original research data, the different filling bodies of different levels comprise provinces, cities, counties and schools, the various complex data types comprise logic data, text data and numerical data, and the top texture adopts different colors to express the variation trend of the data;

and the investigation data expansion three-dimensional histogram display module is used for visually displaying the generated expansion three-dimensional histogram data according to a graphics method.

8. The system according to claim 7, wherein the developed three-dimensional histogram based research big data visualization modeling system comprises: the investigation data classification preprocessing module comprises:

the data reading unit is used for reading in original research data;

the classification processing unit is used for carrying out corresponding classification processing according to the specific data type of the original research data to obtain processed data;

and the archiving unit is used for archiving the processed data.

9. The system according to claim 8, wherein the developed three-dimensional histogram based research big data visualization modeling system comprises: the classification processing unit specifically executes the following operations:

if the original research data is numerical data, determining the value range of the numerical data, then determining the average value of the numerical data, then determining a numerical mapping method of the numerical data, and finally identifying the variation trend of the single numerical data; if the original investigation data is logic data, firstly enumerating the value range of each investigation item of the logic data, then determining the reference value of the logic data, then determining the logical data value mapping method, and finally identifying the variation trend of the single logic data; if the original research data is text data, firstly listing keywords of the text data, then extracting an abstract of the text data, then determining a method for mapping the keywords of the text data, and finally identifying the variation trend of the single text data.

10. The system according to claim 7, 8 or 9, wherein the histogram expansion based research big data visualization modeling system comprises: the expanded three-dimensional histogram data generation module comprises:

the reading and analyzing unit is used for reading the original investigation data one by one and deeply analyzing the original investigation data layer by layer according to the data format of the predetermined composite structure investigation data until the data unit with the minimum original investigation data is analyzed;

the extraction and calculation unit is used for extracting graphical representation data required by the original research data according to the specific data type of the original research data, the requirements of the three-dimensional visual model and the classification preprocessing result, and calculating the corresponding data change trend;

the normalization unit is used for normalizing the extracted graphical representation data and the calculated data change trend of the three-dimensional visualization model to obtain data conforming to the standard format of the three-dimensional visualization model;

and the writing unit is used for writing the data conforming to the standard format of the three-dimensional visual model into the expanded three-dimensional histogram data set.

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