A data analysis visualization method and related device for comprehensive school conditions

Abstract

The present invention discloses a data analysis visualization method for comprehensive school conditions and related devices, relating to the field of data processing technology. The method comprises: extracting target data from a data warehouse using semantic embedding; performing student learning situation analysis using the target data based on a decision tree, and performing subject comprehensive development analysis based on the target data; performing comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data; determining corresponding visualization areas based on the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data using object relationships; performing rendering layout analysis on each visualization area; and visually displaying the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data in the corresponding visualization area based on the rendering layout analysis data. The present invention enables relevant personnel to intuitively and clearly understand the analysis data of the comprehensive school situation.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a data analysis visualization method and related devices for comprehensive school conditions. Background Art

[0002] Comprehensive school situation analysis is a necessary step to understand the current status of the school and the learning situation of students. Visual processing of analysis data can enable relevant personnel to directly and clearly understand the overall situation of the school. Among them, student learning situation analysis and subject development analysis are important links in comprehensive school situation analysis. At present, it is mainly through managers to screen and analyze data to issue analysis reports. However, this analysis method is inefficient and prone to errors. Moreover, when the number of personnel to be analyzed is too large, it cannot cover all personnel, resulting in the analyzed comprehensive school situation information being unable to fully and accurately reflect the overall situation of the school. After obtaining the analysis data, it is currently usually used to perform visual configuration layout through the template tool library. However, using the template tool library for visual layout will result in the size mismatch between components and the visual interface, and unreasonable visual layout, resulting in the final data visualization layout not being beautiful enough, and it is difficult for relevant personnel to clearly observe the analysis data, thus affecting their understanding of the comprehensive school situation. Summary of the Invention

[0003] The purpose of the present invention is to overcome the shortcomings of the existing technology. The present invention provides a data analysis visualization method and related devices for comprehensive school conditions, which improves the rationality and aesthetics of the visualization arrangement and enables relevant personnel to understand the analysis data of the comprehensive school conditions intuitively and clearly.

[0004] In order to solve the above technical problems, the present invention provides a data analysis and visualization method for comprehensive school information, which is applied to a data center. The data center includes a public dimension model layer and an application data layer. The method includes:

[0005] The common dimensional model layer uses semantic embedding to extract target data in the data warehouse;

[0006] The application data layer uses the target data to perform student learning situation analysis based on the decision tree to obtain student learning situation analysis data, and performs subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data;

[0007] Performing a comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data;

[0008] Determine corresponding visualization areas using object relationships based on the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data;

[0009] Perform rendering layout analysis on the visualization areas corresponding to the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data to obtain corresponding rendering layout analysis data;

[0010] Based on the rendering layout analysis data, the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data are visualized in the corresponding visualization area.

[0011] Optionally, extracting target data from a data warehouse using semantic embedding includes:

[0012] Parse the query statement in the query request to obtain query task information;

[0013] Perform semantic embedding based on the query task information to obtain a first semantic embedding vector, and match the first semantic embedding vector with a plurality of second semantic embedding vectors in the index database to obtain a matching result;

[0014] The corresponding operation engine is determined based on the operator in the query statement, and the operation engine is used to extract target data from the data warehouse based on the matching result.

[0015] Optionally, performing a student learning situation analysis based on the decision tree and utilizing the target data to obtain student learning situation analysis data, and performing a subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data, includes:

[0016] Extracting student data from the target data, and dividing the student data into objective evaluation data and subjective evaluation data;

[0017] Performing a student learning situation analysis using the objective evaluation data and the subjective evaluation data based on a random forest model composed of a plurality of decision trees to obtain student learning situation analysis data;

[0018] Based on the target data, analyze the school conditions, faculty team development, talent cultivation, scientific research and academic degree status to obtain data on school conditions, faculty team development, talent cultivation, scientific research and academic degree status;

[0019] Comprehensive discipline development analysis data is generated based on school conditions data, faculty team construction data, talent training data, scientific research data and discipline degree data.

[0020] Optionally, performing a comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data includes:

[0021] Determining assessment dimensions and evaluation indicators, and performing school profile analysis using the student learning situation analysis data and the subject comprehensive development analysis data based on the assessment dimensions and evaluation indicators to obtain school profile information;

[0022] Classify the student learning situation analysis data based on support vector machine and obtain classification results;

[0023] Generate comprehensive school situation analysis data based on classification results and school profile information.

[0024] Optionally, determining a corresponding visualization area based on the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data by using object relationships includes:

[0025] Match corresponding visualization components to student learning analysis data, comprehensive subject development analysis data, and comprehensive school situation analysis data;

[0026] Analyze the object information of student learning analysis data, comprehensive subject development analysis data, and comprehensive school situation analysis data;

[0027] Perform display resolution analysis based on the data volume of student learning situation analysis data, subject comprehensive development analysis data, and comprehensive school situation analysis data to obtain the corresponding display resolution;

[0028] Determine the horizontal spacing and the vertical spacing between the visual components using the object information based on the width and height of the view area and the width and height of each visual component;

[0029] The three-dimensional spatial position is determined based on the visualization components and display resolution corresponding to the comprehensive discipline development analysis data, and the corresponding regional coordinate position is determined based on the three-dimensional spatial position and the horizontal and vertical spacing between each visualization component, and the corresponding visualization area is determined based on the regional coordinate position.

[0030] Optionally, performing rendering layout analysis on the visualization areas corresponding to the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data to obtain corresponding rendering layout analysis data includes:

[0031] Determine the rendering information of each visualization component in the corresponding visualization area for student learning situation analysis data, subject comprehensive development analysis data, and comprehensive school situation analysis data;

[0032] Determining a layout mode of the view area based on rendering information of each visualization component, and performing a visualization preview based on the rendering information and the layout mode to obtain a visualization preview effect;

[0033] Performing color distribution analysis based on the visual preview effect to obtain color distribution information, and performing a color mixing test based on the color distribution information to obtain a color mixing test result;

[0034] The rendering information of each visualization component is adjusted based on the color mixing test result to obtain the adjusted rendering information of each visualization component, and corresponding rendering layout analysis data is determined based on the adjusted rendering information and the layout mode of each visualization component.

[0035] Optionally, visually displaying the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data in corresponding visualization areas based on the rendering layout analysis data includes:

[0036] Based on the rendering layout analysis data, the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data are visually drawn in the corresponding visualization area, and a monitoring component is deployed in the corresponding visualization area.

[0037] In addition, the present invention also provides a data analysis and visualization device for comprehensive school conditions, which is applied to a data center platform, wherein the data center platform includes a public dimension model layer and an application data layer; the device includes:

[0038] Data extraction module: used in the public dimension model layer to extract target data from the data warehouse using semantic embedding;

[0039] The learning situation and subject analysis module is used to apply the data layer to analyze the student learning situation using the target data based on the decision tree to obtain student learning situation analysis data, and to perform subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data;

[0040] School situation analysis module: used to perform comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data;

[0041] Region determination module: used to determine the corresponding visualization region using object relationships based on the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data;

[0042] Layout analysis module: used to perform rendering layout analysis on the visualization areas corresponding to student learning situation analysis data, subject comprehensive development analysis data, and comprehensive school situation analysis data, and obtain corresponding rendering layout analysis data;

[0043] Visualization display module: used to visualize the student learning situation analysis data, subject comprehensive development analysis data and comprehensive school situation analysis data in the corresponding visualization area based on the rendering layout analysis data.

[0044] In addition, the present invention also provides an electronic device, which includes a processor and a memory, the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the above-mentioned comprehensive school situation data analysis and visualization method.

[0045] In addition, the present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the above-mentioned comprehensive school situation data analysis and visualization method.

[0046] In an embodiment of the present invention, the public dimension model layer of the data center uses semantic embedding to extract target data in the data warehouse, and can quickly and accurately extract target data from large data sets. Using target data based on the decision tree to analyze student learning conditions, and based on the target data to analyze comprehensive subject development, can improve the efficiency and accuracy of student learning conditions analysis and comprehensive subject development analysis, so that the comprehensive school situation analysis data obtained can more comprehensively and accurately reflect the overall situation of the school. Based on the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, the corresponding visualization area is determined using object relationships, and each visualization area is rendered and layout analyzed to visualize the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, so as to avoid the mismatch between the visualization components and the interface size, improve the rationality and aesthetics of the visualization layout, and enable relevant personnel to intuitively and clearly understand the comprehensive school situation analysis data. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0048] Figure 1 1 is a flow chart of a method for visualizing data analysis of comprehensive school conditions in an embodiment of the present invention;

[0049] Figure 2 is a flow chart of a method for visualizing comprehensive school information data analysis in another embodiment of the present invention;

[0050] Figure 3 Schematic diagram of the structure of the device for analyzing and visualizing comprehensive school information in an embodiment of the present invention;

[0051] Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] Example 1

[0054] See also Figure 1 , Figure 1 This is a flow chart of a method for visualizing data analysis of comprehensive school conditions in an embodiment of the present invention. The method is applied to a data center, which includes a public dimension model layer and an application data layer. The method includes:

[0055] S11: The common dimensional model layer uses semantic embedding to extract target data in the data warehouse;

[0056] In the specific implementation process of the present invention, the use of semantic embedding to extract target data in the data warehouse includes: parsing the query statement in the query request to obtain query task information; performing semantic embedding based on the query task information to obtain a first semantic embedding vector, and matching the first semantic embedding vector with several second semantic embedding vectors in the index database to obtain a matching result; determining the corresponding operation engine based on the operator in the query statement, and using the operation engine to extract the target data in the data warehouse based on the matching result.

[0057] Specifically, the data middle platform serves as a support platform for data collection, data asset storage, management, and data development. Data collection includes heterogeneous networks, heterogeneous data sources, offline access, real-time access, and visual configuration. Data asset management includes data assets in various subject domains shared by various data layers and school affairs. Data asset management includes metadata management, data standard management, data model management, and data quality management. Data development includes offline development, real-time development, algorithm development, and label development. Among them, the data middle platform includes a public dimension model layer and an application data layer. The public dimension model layer summarizes relevant data tables, such as student management information, school information, academic information, and faculty information, and can extract corresponding data according to needs. The application data layer is used to perform relevant analysis based on the data extracted from the public dimension model layer.

[0058] Receive the query request sent by the client, parse the query statement in the query request, and match the syntax parser of the query statement in the query request. Different query statements have different syntax parsers, such as PostgreSQL syntax parser and SQL syntax parser. According to the syntax parser, the query statement is parsed into an abstract syntax tree. The abstract syntax tree is a tree representation method used to describe the syntax structure of program code. Each node of the syntax tree represents a syntax structure in the program code, which better avoids the restrictions on the database language. The nested relationship in the query statement is analyzed, and the hierarchical relationship between several branches related to the query statement in the abstract syntax tree is determined based on the nested relationship. The hierarchical relationship of the statement block is determined based on the syntax nodes in each branch and the hierarchical relationship between them. The statement type, execution intention and goal are determined based on the hierarchical relationship of the statement block and the nested relationship of the query statement, that is, the query task information is obtained. Semantic embedding is performed based on query task information, a query task statement is generated based on the query task information, and an embedding model is used to semantically embed the query task statement, that is, embed the query task statement with demand words to obtain a number of corresponding demand word embedding vectors, that is, obtain a first semantic embedding vector, and match the first semantic embedding vector with a number of second semantic embedding vectors in the index database. The first semantic embedding vector and the second semantic embedding vector can be matched for similarity. The higher the similarity, the stronger the semantic relationship between the vectors, and a matching result is obtained. Based on the operator in the query statement, a corresponding operation engine is determined, and based on the matching result, the operation engine is used to extract target data from the data warehouse. The required second semantic embedding vector is determined based on the matching result. Each second semantic embedding vector corresponds to the index of the data. Based on the index of the data, the operation engine is used to extract the target data from the data warehouse. The target data includes teacher information, student information, scientific research information, subject and degree point information, etc.

[0059] S12: The application data layer uses the target data to perform student learning situation analysis based on the decision tree to obtain student learning situation analysis data, and performs subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data;

[0060] In the specific implementation process of the present invention, the decision tree-based method uses the target data to perform student learning situation analysis to obtain student learning situation analysis data, and the target data is used to perform discipline comprehensive development analysis to obtain discipline comprehensive development analysis data, including: extracting student data from the target data, and dividing the student data into objective evaluation data and subjective evaluation data; based on a random forest model composed of several decision trees, using the objective evaluation data and subjective evaluation data to perform student learning situation analysis to obtain student learning situation analysis data; based on the target data, performing school conditions analysis, faculty team construction analysis, talent training analysis, scientific research situation analysis and discipline degree position situation analysis to obtain school conditions data, faculty team construction data, talent training data, scientific research situation data and discipline degree position situation data; generating discipline comprehensive development analysis data based on school conditions data, faculty team construction data, talent training data, scientific research situation data and discipline degree position situation data.

[0061] Specifically, student data is extracted from the target data. The student data includes each student's grades in various subjects, self-evaluation and teacher evaluation, etc., and the student data is divided into objective evaluation data and subjective evaluation data. Objective evaluation data is information that is not affected by the subjective wishes of relevant personnel, such as grades in various subjects. Subjective evaluation data is information that is affected by the subjective wishes of relevant personnel, such as teacher evaluation. Based on a random forest model composed of several decision trees, the objective evaluation data and subjective evaluation data are used to analyze the student learning situation. During training, each decision tree is trained in a cycle, and the training samples of each decision tree are obtained by Bootstrap sampling from the original training set. The features used in training each node of the decision tree are also randomly sampled from the new feature space. Each decision tree is recursively split according to the judgment criteria. The recursive splitting step includes forming the root node of the extracted sample set, splitting the sample set into the first sample set and the second sample set according to the preset splitting criteria, recursively establishing the left subtree of the first sample set, and establishing the right subtree of the second sample set. When no further splitting is possible, the node is marked as a leaf node. After the training of each decision tree is completed, the trained decision trees are used to form a random forest model, and the objective evaluation data and subjective evaluation data are input into the random forest model to analyze the student learning situation, so as to obtain the student's learning effect of each subject and the overall learning effect, that is, to obtain the student's learning situation analysis data. Based on the target data, we conduct an analysis of school conditions, faculty development, talent cultivation, scientific research, and academic degree status. The school conditions analysis includes analysis of school operating indicators, campus area and layout, and student and academic discipline distribution. The faculty development analysis includes an overall overview of faculty in each discipline. The talent cultivation analysis includes analysis of the number and categories of students in each discipline and analysis of student majors. The scientific research analysis includes analysis of scientific research funding, scientific research projects, and scientific research institutions in each discipline. The academic degree status analysis includes discipline coverage, degree program development, and discipline evaluation and ranking. Data on school conditions, faculty development, talent cultivation, scientific research, and academic degree status are obtained. Based on the data on school conditions, faculty development, talent cultivation, scientific research, and academic degree status, comprehensive discipline development analysis data is generated. The above data constitutes the comprehensive discipline development analysis data.

[0062] S13: Performing a comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data;

[0063] In the specific implementation process of the present invention, the comprehensive school situation analysis is performed based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data, including: determining evaluation dimensions and evaluation indicators, and performing school situation profile analysis based on the evaluation dimensions and evaluation indicators using the student learning situation analysis data and the subject comprehensive development analysis data to obtain school situation profile information; classifying the student learning situation analysis data based on a support vector machine to obtain classification results; and generating comprehensive school situation analysis data based on the classification results and the school situation profile information.

[0064] Specifically, the evaluation dimensions and evaluation indicators are determined, such as academic quality, the effect of scientific research investment, etc., and the evaluation indicators are such as the evaluation standards for academic quality, the evaluation standards for scientific research investment, etc., and based on the evaluation dimensions and evaluation indicators, the student learning situation analysis data and the subject comprehensive development analysis data are used to perform school situation overview analysis, that is, analyze the overview of each data contained in the student learning situation analysis data and the subject comprehensive development analysis data, such as analyzing the proportion and number of each academic quality level and subject development level based on the student learning situation analysis data and the subject comprehensive development analysis data, such as the proportion and number of students whose overall academic quality reaches the excellent level, and obtain school situation overview information. The student learning situation analysis data is classified based on the support vector machine, that is, the student learning situation analysis data is classified into different grades and different subjects, the support vector machine is trained by the sequence minimum optimization algorithm, and the student learning situation analysis data is input into the trained support vector machine to obtain student learning situation analysis data of different grades and different subjects, that is, obtain the classification result. Based on the classification results and school profile information, comprehensive school situation analysis data is generated. According to the student learning situation analysis data of different grades and subjects, the specific student subject learning situation and the overall learning situation of different grades are analyzed, and comprehensive school situation analysis data is generated in combination with the school profile information.

[0065] S14: Determine a corresponding visualization area using object relationships based on the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data;

[0066] In the specific implementation process of the present invention, the corresponding visualization area is determined based on the object relationship of the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data, including: matching the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data with corresponding visualization components; analyzing the object information of the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data; performing display resolution analysis based on the data volume of the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data to obtain the corresponding display resolution; determining the horizontal spacing and vertical spacing between each visualization component based on the width and height of the view area and the width and height of each visualization component using the object information; determining the three-dimensional spatial position based on the visualization component and display resolution corresponding to the comprehensive subject development analysis data, and determining the corresponding regional coordinate position based on the three-dimensional spatial position and the horizontal spacing and vertical spacing between each visualization component, and determining the corresponding visualization area based on the regional coordinate position.

[0067] Specifically, student learning analysis data, comprehensive subject development analysis data, and comprehensive school situation analysis data are matched with corresponding visualization components. Visualization components are visual graphics displayed in the application software interface, such as charts, edit boxes, and dialog boxes. Visualization components also include interface controls with user interface functions and static components without interactive functions. Each analysis data is matched with a corresponding visualization component in the component database. The object information of the student learning analysis data, comprehensive subject development analysis data, and comprehensive school situation analysis data is analyzed, that is, the analysis objects corresponding to the student learning analysis data, comprehensive subject development analysis data, and comprehensive school situation analysis data are analyzed. For example, the analysis object corresponding to the student learning analysis data is students, and the analysis objects corresponding to each data in the comprehensive subject development analysis data include teachers, scientific research institutions, and students. The analysis objects corresponding to the comprehensive school situation analysis data include students, scientific research institutions, etc. The display resolution analysis is performed based on the data volume of the student learning analysis data, comprehensive subject development analysis data, and comprehensive school situation analysis data. The larger the data volume, the greater the required display resolution. From this, the display resolution of the visualization component corresponding to each analysis data can be determined. Based on the width and height of the view area and the width and height of each visualization component, the object information is used to determine the horizontal and vertical spacing between each visualization component. The view area is the display area of ​​the display canvas. The width and height of each visualization component are matched by the corresponding display resolution. Among the data contained in the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data, the visualization components corresponding to the data with the same analysis object should be arranged in adjacent positions and have a shorter spacing to avoid users spending too much time viewing the analysis data of the same analysis object. The preliminary horizontal and vertical spacing between the components are determined by the width and height of the view area and the width and height of each visualization component, as well as the number of visualization components. Then, the preliminary horizontal and vertical spacing are adjusted according to the analysis object of each data, that is, the spacing between the visualization components with the same analysis object is shortened, so as to obtain the final horizontal and vertical spacing.The three-dimensional spatial position is determined based on the visualization components and display resolution corresponding to the comprehensive discipline development analysis data. Since the comprehensive discipline development analysis data contains three-dimensional data such as campus layout and scientific research institution layout, its corresponding visualization component is a three-dimensional visualization graphic. In order to make the visualization layout more reasonable and beautiful, it is necessary to first determine the spatial position of the three-dimensional data, and use the preset typesetting rules according to its visualization components and display resolution to determine its spatial position loaded in the viewing area, that is, the three-dimensional spatial position, and determine the corresponding regional coordinate position based on the three-dimensional spatial position and the horizontal and vertical spacing between each visualization component, and determine the coordinate position of each visualization component in the viewing area based on the three-dimensional spatial position and the horizontal and vertical spacing between each visualization component, and determine the corresponding visualization area based on the regional coordinate position, that is, determine the visualization area position of each visualization component in the display canvas according to the coordinate position of each visualization component in the viewing area and its width and height.

[0068] S15: performing rendering layout analysis on the visualization areas corresponding to the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data to obtain corresponding rendering layout analysis data;

[0069] In the specific implementation process of the present invention, the rendering layout analysis is performed on the visualization area corresponding to the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data to obtain the corresponding rendering layout analysis data, including: determining the rendering information of each visualization component of the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data in the corresponding visualization area; determining the layout mode of the view area based on the rendering information of each visualization component, and performing a visualization preview based on the rendering information and the layout mode to obtain a visualization preview effect; performing color distribution analysis based on the visualization preview effect to obtain color distribution information, and performing a color mixing test based on the color distribution information to obtain a color mixing test result; adjusting the rendering information of each visualization component based on the color mixing test result to obtain adjusted rendering information of each visualization component, and determining the corresponding rendering layout analysis data based on the adjusted rendering information and the layout mode of each visualization component.

[0070] Specifically, the rendering information of each visualization component in the corresponding visualization area for the student learning analysis data, the comprehensive subject development analysis data, and the comprehensive school situation analysis data is determined. The corresponding rendering information is matched to each visualization component of each analysis data. Different visualization components correspond to different rendering information. The rendering information includes color, shadow, and background. The layout mode of the viewing area is determined based on the rendering information of each visualization component. The overall layout of the viewing area, namely the layout mode, is determined based on the rendering information of each visualization component. The layout mode includes the background color configuration and font color configuration of the viewing area. A visual preview is performed based on the rendering information and layout mode. That is, the student learning analysis data, the comprehensive subject development analysis data, and the comprehensive school situation analysis data are previewed in the corresponding visualization area based on the rendering information and layout mode to obtain a visual preview effect. Based on the visual preview effect, color distribution analysis is performed to obtain color distribution information. The color distribution information includes the distribution of different colors in the viewing area. A color mixing test is performed based on the color distribution information. The contrast between adjacent colors is analyzed based on the color distribution information. The light and dark contrast difference between two colors is analyzed. Each contrast is projected onto a preset human eye model to perform a color mixing test. The color combination is tested in the human eye to obtain a color mixing test result. The rendering information of each visualization component is adjusted based on the color mixing test result. If the color contrast is too low, the human eye cannot distinguish different colors. If the color contrast is too high, it will cause dizziness to the user. Therefore, the rendering information of the visualization component needs to be adjusted according to the manifestation of the color combination in the human eye, so that the rendering of the visualization component is more in line with the user's visual experience, and the adjusted rendering information of each visualization component is obtained. The corresponding rendering layout analysis data is determined based on the adjusted rendering information and layout mode of each visualization component, and the overall layout color of the view area is adjusted according to the adjusted rendering information. The rendering layout analysis data is generated by the adjusted rendering information of each visualization component and the adjusted layout mode.

[0071] S16: Visually displaying the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data in corresponding visualization areas based on the rendering layout analysis data.

[0072] During the specific implementation of the present invention, the student learning situation analysis data, subject comprehensive development analysis data and comprehensive school situation analysis data are visualized in the corresponding visualization area based on the rendering layout analysis data, including: drawing the student learning situation analysis data, subject comprehensive development analysis data and comprehensive school situation analysis data in the corresponding visualization area based on the rendering layout analysis data, and deploying a monitoring component in the corresponding visualization area.

[0073] Specifically, based on the rendered layout analysis data, the student learning analysis data, the comprehensive subject development analysis data, and the comprehensive school situation analysis data are rendered in the corresponding visualization area, and the rendering results are displayed on the display canvas. A monitoring component is deployed in the corresponding visualization area to monitor changes in the display canvas size. When the display canvas size is adjusted, the size of each visualization component is also adjusted accordingly, achieving adaptive component layout.

[0074] In an embodiment of the present invention, the public dimension model layer of the data center uses semantic embedding to extract target data in the data warehouse, and can quickly and accurately extract target data from large data sets. Using target data based on the decision tree to analyze student learning conditions, and based on the target data to analyze comprehensive subject development, can improve the efficiency and accuracy of student learning conditions analysis and comprehensive subject development analysis, so that the comprehensive school situation analysis data obtained can more comprehensively and accurately reflect the overall situation of the school. Based on the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, the corresponding visualization area is determined using object relationships, and each visualization area is rendered and layout analyzed to visualize the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, so as to avoid the mismatch between the visualization components and the interface size, improve the rationality and aesthetics of the visualization layout, and enable relevant personnel to intuitively and clearly understand the comprehensive school situation analysis data.

[0075] Example 2

[0076] See also Figure 2 , Figure 2 : This is a flow chart of a method for visualizing data analysis of comprehensive school conditions in another embodiment of the present invention. The method is applied to a data center, which includes a public dimension model layer and an application data layer. The method includes:

[0077] S201: The public dimension model layer uses semantic embedding to extract target data in the data warehouse;

[0078] S202: The application data layer uses the target data to perform student learning situation analysis based on the decision tree to obtain student learning situation analysis data, and performs subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data;

[0079] S203: Performing a comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data;

[0080] S204: Determine a corresponding visualization area using object relationships based on the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data;

[0081] S205: Determine rendering information of each visualization component of the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data in the corresponding visualization area;

[0082] S206: Determine a layout mode of the view area based on the rendering information of each visualization component, and perform a visualization preview based on the rendering information and the layout mode to obtain a visualization preview effect;

[0083] S207: performing color distribution analysis based on the visual preview effect to obtain color distribution information, and performing a color mixing test based on the color distribution information to obtain a color mixing test result;

[0084] S208: Adjusting the rendering information of each visualization component based on the color mixing test result to obtain adjusted rendering information of each visualization component, and determining corresponding rendering layout analysis data based on the adjusted rendering information and layout mode of each visualization component;

[0085] S209: Visually displaying the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data in corresponding visualization areas based on the rendering layout analysis data.

[0086] In an embodiment of the present invention, the public dimension model layer of the data center uses semantic embedding to extract target data in the data warehouse, and can quickly and accurately extract target data from large data sets. Using target data based on the decision tree to analyze student learning conditions, and based on the target data to analyze comprehensive subject development, can improve the efficiency and accuracy of student learning conditions analysis and comprehensive subject development analysis, so that the comprehensive school situation analysis data obtained can more comprehensively and accurately reflect the overall situation of the school. Based on the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, the corresponding visualization area is determined using object relationships, and each visualization area is rendered and layout analyzed to visualize the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, so as to avoid the mismatch between the visualization components and the interface size, improve the rationality and aesthetics of the visualization layout, and enable relevant personnel to intuitively and clearly understand the comprehensive school situation analysis data.

[0087] Example 3

[0088] See also Figure 3 , Figure 3 : This is a schematic diagram of the structure of a comprehensive school situation data analysis and visualization device in an embodiment of the present invention. The device is applied to a data center, which includes a public dimension model layer and an application data layer. The device includes:

[0089] Data extraction module 31: used for extracting target data from the data warehouse by using semantic embedding in the common dimension model layer;

[0090] The learning situation and subject analysis module 32 is used to apply the data layer to perform student learning situation analysis using the target data based on the decision tree to obtain student learning situation analysis data, and to perform subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data;

[0091] School situation analysis module 33: used to perform comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data;

[0092] Region determination module 34: used to determine corresponding visualization regions using object relationships based on the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data;

[0093] Layout analysis module 35: used to perform rendering layout analysis on the visualization areas corresponding to the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data, and obtain corresponding rendering layout analysis data;

[0094] Visualization display module 36: used to visualize the student learning situation analysis data, subject comprehensive development analysis data and comprehensive school situation analysis data in the corresponding visualization area based on the rendering layout analysis data.

[0095] In the specific implementation process of the present invention, the specific implementation method of the device item can refer to the implementation method of the above-mentioned method item, which will not be repeated here.

[0096] In an embodiment of the present invention, the public dimension model layer of the data center uses semantic embedding to extract target data in the data warehouse, and can quickly and accurately extract target data from large data sets. Using target data based on the decision tree to analyze student learning conditions, and based on the target data to analyze comprehensive subject development, can improve the efficiency and accuracy of student learning conditions analysis and comprehensive subject development analysis, so that the comprehensive school situation analysis data obtained can more comprehensively and accurately reflect the overall situation of the school. Based on the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, the corresponding visualization area is determined using object relationships, and each visualization area is rendered and layout analyzed to visualize the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, so as to avoid the mismatch between the visualization components and the interface size, improve the rationality and aesthetics of the visualization layout, and enable relevant personnel to intuitively and clearly understand the comprehensive school situation analysis data.

[0097] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the program implements the comprehensive school information data analysis and visualization method described in any of the above-described embodiments. The computer-readable storage medium includes, but is not limited to, any type of disk (including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks), ROM (Read-Only Memory), RAM (Random Access Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory, magnetic cards, or optical cards. In other words, a storage device includes any medium that can store or transmit information in a readable form by a device (e.g., a computer or mobile phone), and can be a read-only memory, a magnetic disk, or an optical disk.

[0098] Example 4

[0099] See also Figure 4 , Figure 4 It is a schematic diagram of the structure of an electronic device in an embodiment of the present invention.

[0100] The embodiment of the present invention further provides an electronic device, such as Figure 4 As shown, the electronic device includes a memory 41, a processor 43, and a computer program 42 stored in the memory 41 and executable on the processor 43. It will be understood by those skilled in the art that Figure 3The electronic devices shown do not constitute a limitation on all devices and may include more or fewer components than shown, or combinations of certain components. The memory 41 can be used to store the computer program 42 and various functional modules, and the processor 43 runs the computer program 42 stored in the memory 41, thereby executing various functional applications and data processing of the device. The memory can be internal memory or external memory, or include both internal memory and external memory. The internal memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, or random access memory. The external memory can include a hard disk, floppy disk, ZIP disk, USB flash drive, magnetic tape, etc. The processor 43 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, a single-chip microcomputer, or processor 43, or any conventional processor. The processor and memory disclosed in the present invention include but are not limited to these types of processors and memories. The processor and memory disclosed in the present invention are only examples and not limitations.

[0101] As an embodiment, the electronic device includes: one or more processors 43, a memory 41, and one or more computer programs 42, wherein the one or more computer programs 42 are stored in the memory 41 and are configured to be executed by the one or more processors 43, and the one or more computer programs 42 are configured to execute the comprehensive school situation data analysis visualization method in any of the above-mentioned embodiments. For the specific implementation process, please refer to the above-mentioned embodiments and will not be repeated here.

[0102] In an embodiment of the present invention, the public dimension model layer of the data center uses semantic embedding to extract target data in the data warehouse, and can quickly and accurately extract target data from large data sets. Using target data based on the decision tree to analyze student learning conditions, and based on the target data to analyze comprehensive subject development, can improve the efficiency and accuracy of student learning conditions analysis and comprehensive subject development analysis, so that the comprehensive school situation analysis data obtained can more comprehensively and accurately reflect the overall situation of the school. Based on the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, the corresponding visualization area is determined using object relationships, and each visualization area is rendered and layout analyzed to visualize the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, so as to avoid the mismatch between the visualization components and the interface size, improve the rationality and aesthetics of the visualization layout, and enable relevant personnel to intuitively and clearly understand the comprehensive school situation analysis data.

[0103] In addition, the above is a detailed introduction to a comprehensive school situation data analysis visualization method and related devices provided by an embodiment of the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A data analysis and visualization method for comprehensive school conditions, characterized by: Applied to a data middle platform, the data middle platform includes a public dimension model layer and an application data layer; the method includes: The common dimensional model layer uses semantic embedding to extract target data in the data warehouse; The application data layer uses the target data to perform student learning situation analysis based on the random forest model to obtain student learning situation analysis data, and performs subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data; Performing a comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data; Match the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data with corresponding visualization components; analyze the object information of the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data; perform display resolution analysis based on the data volume of the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data to obtain the corresponding display resolution; determine the horizontal and vertical spacing between each visualization component based on the width and height of the view area and the width and height of each visualization component using the object information, and in the data contained in the student learning situation analysis data, the comprehensive subject development analysis data and the comprehensive school situation analysis data, the visualization components corresponding to the data with consistent object information are arranged in adjacent positions with shorter spacing; determine the three-dimensional spatial position based on the visualization component corresponding to the comprehensive subject development analysis data and the display resolution, and determine the corresponding regional coordinate position based on the three-dimensional spatial position and the horizontal and vertical spacing between each visualization component, and determine the corresponding visualization area based on the regional coordinate position; Determine the rendering information of each visualization component in the corresponding visualization area for the student learning situation analysis data, the comprehensive subject development analysis data, and the comprehensive school situation analysis data; determine the layout mode of the view area based on the rendering information of each visualization component, and perform a visualization preview based on the rendering information and the layout mode to obtain a visualization preview effect; perform color distribution analysis based on the visualization preview effect to obtain color distribution information, and analyze the contrast between adjacent colors based on the color distribution information, analyze the light and dark contrast difference between two colors, project each contrast onto a preset human eye model to perform a color mixing test, and obtain a color mixing test result; adjust the rendering information of each visualization component based on the color mixing test result to obtain adjusted rendering information of each visualization component, and determine corresponding rendering layout analysis data based on the adjusted rendering information and layout mode of each visualization component; Based on the rendering layout analysis data, the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data are visualized in the corresponding visualization area.

2. The data analysis and visualization method for comprehensive school conditions according to claim 1 is characterized in that: The method of extracting target data from a data warehouse by using semantic embedding includes: Parse the query statement in the query request to obtain query task information; Perform semantic embedding based on the query task information to obtain a first semantic embedding vector, and match the first semantic embedding vector with a plurality of second semantic embedding vectors in the index database to obtain a matching result; The corresponding operation engine is determined based on the operator in the query statement, and the operation engine is used to extract target data from the data warehouse based on the matching result.

3. The data analysis and visualization method for comprehensive school conditions according to claim 1 is characterized in that: The random forest model is used to perform student learning situation analysis using the target data to obtain student learning situation analysis data, and the target data is used to perform comprehensive subject development analysis to obtain comprehensive subject development analysis data, including: extracting student data from the target data, and dividing the student data into objective evaluation data and subjective evaluation data; Performing a student learning situation analysis using the objective evaluation data and the subjective evaluation data based on a random forest model composed of a plurality of decision trees to obtain student learning situation analysis data; Based on the target data, analyze the school conditions, faculty team development, talent cultivation, scientific research and academic degree status to obtain data on school conditions, faculty team development, talent cultivation, scientific research and academic degree status; Comprehensive discipline development analysis data is generated based on school conditions data, faculty team construction data, talent training data, scientific research data and discipline degree data.

4. The data analysis and visualization method for comprehensive school conditions according to claim 1 is characterized in that: The comprehensive school situation analysis is performed based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data, including: Determining assessment dimensions and evaluation indicators, and performing school profile analysis using the student learning situation analysis data and the subject comprehensive development analysis data based on the assessment dimensions and evaluation indicators to obtain school profile information; Classify the student learning situation analysis data based on support vector machine and obtain classification results; Generate comprehensive school situation analysis data based on classification results and school profile information.

5. The data analysis and visualization method of comprehensive school conditions according to claim 1 is characterized in that: The rendering layout analysis data is used to visualize the student learning situation analysis data, the subject comprehensive development analysis data, and the comprehensive school situation analysis data in corresponding visualization areas, including: Based on the rendering layout analysis data, the student learning situation analysis data, the subject comprehensive development analysis data and the comprehensive school situation analysis data are visually drawn in the corresponding visualization area, and a monitoring component is deployed in the corresponding visualization area.

6. A data analysis and visualization device for comprehensive school conditions, characterized in that: Applied to a data middle platform, the data middle platform includes a public dimension model layer and an application data layer; the device includes: Data extraction module: used in the public dimension model layer to extract target data from the data warehouse using semantic embedding; The learning situation and subject analysis module is used to apply the data layer to analyze the student learning situation using the target data based on the random forest model to obtain student learning situation analysis data, and to perform subject comprehensive development analysis based on the target data to obtain subject comprehensive development analysis data; School situation analysis module: used to perform comprehensive school situation analysis based on the student learning situation analysis data and the subject comprehensive development analysis data to obtain comprehensive school situation analysis data; Region determination module: used to match corresponding visualization components to student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data; analyze object information of student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data; perform display resolution analysis based on the data volume of student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data to obtain corresponding display resolution; determine the horizontal spacing and vertical spacing between each visualization component based on the width and height of the view area and the width and height of each visualization component using the object information, and in the data contained in the student learning situation analysis data, comprehensive subject development analysis data and comprehensive school situation analysis data, the visualization components corresponding to the data with consistent object information are arranged in adjacent positions with shorter spacing; determine the three-dimensional spatial position based on the visualization component corresponding to the comprehensive subject development analysis data and the display resolution, and determine the corresponding regional coordinate position based on the three-dimensional spatial position and the horizontal spacing and vertical spacing between each visualization component, and determine the corresponding visualization area based on the regional coordinate position; Layout analysis module: used to determine the rendering information of each visualization component in the corresponding visualization area of ​​student learning situation analysis data, subject comprehensive development analysis data and comprehensive school situation analysis data; determine the layout mode of the view area based on the rendering information of each visualization component, and perform visualization preview based on the rendering information and layout mode to obtain the visualization preview effect; perform color distribution analysis based on the visualization preview effect to obtain color distribution information, and analyze the contrast between adjacent colors based on the color distribution information, analyze the light and dark contrast difference between two colors, project each contrast onto a preset human eye model to perform a color mixing test, and obtain the color mixing test result; adjust the rendering information of each visualization component based on the color mixing test result to obtain the adjusted rendering information of each visualization component, and determine the corresponding rendering layout analysis data based on the adjusted rendering information and layout mode of each visualization component; Visualization display module: used to visualize the student learning situation analysis data, subject comprehensive development analysis data and comprehensive school situation analysis data in the corresponding visualization area based on the rendering layout analysis data.

7. An electronic device comprising a processor and a memory, characterized in that: The memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the data analysis and visualization method for comprehensive school conditions as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and when the computer instructions are executed on an electronic device, the electronic device executes the data analysis and visualization method for comprehensive school conditions according to any one of claims 1 to 5.

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