CN117149892A - Enterprise dimension information display method based on big data display screen - Google Patents
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Abstract
The invention relates to the technical field of enterprise dimension information display, and discloses an enterprise dimension information display method based on a big data display screen.
Description
Technical Field
The invention relates to the technical field of ultra-high temperature materials, in particular to an enterprise dimension information display method based on a big data display screen.
Background
The large data intelligent application has become an important trend of enterprise informatization, in the aspect of enterprise large data visualization, the traditional tables and charts can only provide simpler data presentation and analysis, and the requirements of enterprises on data display and decision analysis are difficult to meet, so that innovation of an enterprise dimension information display method based on a large data display screen occurs.
However, the existing enterprise dimension information display method can only provide a limited solution in terms of data presentation, and cannot meet the requirements of users in terms of data processing, interaction analysis and the like, and in addition, the existing enterprise dimension information display method has the following defects and shortcomings:
1. the data presentation form is single, multi-dimensional data is difficult to display, the traditional chart can only present data with two dimensions, and more complex data association is difficult to display.
2. The data interactivity is low, users are difficult to deeply mine information behind the data, and the existing interaction analysis tools are difficult to meet the requirements of the users, such as searching specific information or establishing association relation on the data;
3. the existing display screen panel lacks an intelligent decision assistance function, does not consider the operation habit and decision flow of a user, and cannot provide intelligent decision assistance support for the user.
The invention aims to overcome the defects and shortcomings in the aspects of data processing, interaction analysis and decision assistance of the conventional big data display screen panel, and provides more convenient, efficient and intelligent support for enterprise decision analysis by optimizing the display effect and improving the data interactivity.
Disclosure of Invention
The invention aims to provide an enterprise dimension information display method based on a big data display screen, so as to solve the problems of single data presentation form, low data interactivity and lack of an intelligent decision-making auxiliary function of the existing enterprise dimension information display method.
In order to achieve the above purpose, the present invention provides the following technical solutions: the enterprise dimension information display method based on the big data display screen comprises a data acquisition module, a data cleaning and processing module, a data visualization module, a data analysis module, a real-time updating module, a security management module and a user interaction module;
the data acquisition module is used for acquiring relevant data of an enterprise by connecting a database, an ERP system and a CRM system in the enterprise or connecting an external data platform;
the data cleaning and processing module is used for preprocessing and cleaning the original data to improve the quality and accuracy of the data, and has the main tasks of removing noise, abnormal values, repeated data and missing data which are not required, and converting, merging and formatting the data so as to facilitate the subsequent data analysis and mining;
The data visualization module designs proper chart types, colors and fonts according to data characteristics and service requirements, and displays the designed visual charts on an interface, wherein the data visualization module comprises visual design and visual display;
the data analysis module processes and analyzes the data by using various algorithms and techniques to find association, trend, anomaly and rule information in the data;
the real-time updating module is used for timely updating data, recalculating a model and a result in the data processing and analyzing process, feeding back the result to a user or a system in real time, and keeping the accuracy and the instantaneity of the data under the scene that the data change is quick or quick response is needed so as to improve the accuracy and the efficiency of decision making, reestablishing the model according to the data updated in real time in the working process, optimizing and evaluating the model, and recalculating the parameters and the result of the model;
the safety management module is used for enterprises to take various measures in daily operation and protecting the safety, integrity and reliability of enterprise core data and information systems;
the user interaction module is used for providing various interaction modes, helping a user to quickly locate required information, and providing intelligent searching and recommending functions so as to improve user experience and satisfaction.
Further, the main operations of the data acquisition module include the following aspects:
selection of a data source: selecting a proper data source, and selecting a database, an ERP system and a CRM system in an enterprise or selecting a third party data provider and a public data platform according to reliability, accuracy and integrity factors of display requirements and the data source;
and (3) extracting data: selecting a data extraction mode according to requirements, extracting data by a data interface, a data capturing or file importing mode, and keeping the extracted data quantity and the extracted frequency within a reasonable range so as to avoid overlarge burden on a data source;
data cleaning and processing: the collected data needs to be cleaned and processed, including data deduplication, data format conversion and abnormal data processing;
transmission and storage of data: the collected data needs to be transmitted and stored, so that the reliability and the safety of the data are ensured, and the data are transmitted and stored in a data transmission protocol or data encryption mode;
updating and maintaining data: the data acquisition module needs to update and maintain the data, and ensures the real-time performance and the integrity of the data.
Further, the specific steps and methods of the data cleaning and processing module are as follows:
data collection and acquisition: firstly, data are required to be collected and acquired from different data sources, wherein the data comprise a database and a file HE API interface, the format, the structure and the effectiveness of the data are noted in the data collection process, and the integrity and the accuracy of the data are ensured;
data preprocessing and cleaning: preprocessing and cleaning the collected data, including data de-duplication, filling, conversion and formatting, specifically including the steps of:
s1, data deduplication: the repeated data is removed by comparing the unique identification or the key words of the data so as to ensure the consistency and the accuracy of the data;
s2, data filling: filling the missing data by using an average value, a median and a mode method;
s3, data conversion: converting the data, including converting numerical data, text data and date data, so as to facilitate subsequent data analysis and mining;
s4, data formatting: the data is subjected to unified formatting, including unification of case, date format and unit, so that the data can be compared and analyzed conveniently;
data quality inspection and repair: the data is subjected to quality inspection and repair, including abnormal value detection and missing value detection HE data accuracy detection, and the specific steps include:
S1, abnormal value detection: detecting abnormal values and noise points in the data by using statistical analysis and visual analysis methods, and repairing or deleting;
s2, detecting a missing value: detecting a missing value in the data through statistical analysis HE visual analysis, and filling or deleting;
s3, detecting data accuracy: the data is compared and verified, the accuracy and the consistency of the data are detected, and repair or deletion is carried out;
data integration and processing: integrating and processing data with different sources, different formats and different structures to form a unified data set, wherein the method comprises the following specific steps:
s1, data integration: integrating data from different sources, and performing data matching and fusion by using a data mining HE machine learning DE method;
s2, data processing: processing the integrated data, including data conversion and standardized HE normalization operation, so as to facilitate subsequent data analysis and mining;
data visualization and reporting: the cleaned and processed data are visualized and reported so as to facilitate the data analysis and decision making of the user, and the specific steps comprise:
s1, data visualization: the data are visualized in the form of charts and tables, so that a user can conveniently analyze and mine the data;
S2, data report: reporting the cleaned and processed data, including data analysis reports, data mining reports, and data quality reports, to facilitate decision making and planning by the user.
Further, the visual design refers to a process of selecting proper chart types, colors and font elements according to data characteristics and service requirements and presenting the data, and the following aspects need to be considered in the process:
data characteristics: different types of data are suitable for different chart types;
business requirements: selecting proper chart types and elements according to business requirements;
design style: the proper color and font elements are selected according to brands and user requirements, so that the chart is more attractive and easy to understand.
Further, the visual presentation refers to a process of presenting a designed visual chart on an interface, and a user can acquire more information and insight through interactive operation, and the following aspects need to be considered in the process:
interactivity: through interactive operation, the user can acquire more information and insight;
data granularity: selecting proper data granularity display data according to the user demand;
data depth: and selecting the depth of the display data according to the requirements of the user.
Further, the main steps of the data analysis module for data modeling and analysis include:
s1, data preprocessing: cleaning, de-duplication, filling in missing values and converting data types of the original data so as to facilitate subsequent analysis and modeling;
s2, feature selection: selecting useful features from all the features to improve modeling accuracy and efficiency;
s3, data modeling: modeling the data using a decision tree algorithm;
s4, model evaluation: evaluating the established model, wherein the model comprises accuracy, precision, recall rate and F1 value index;
s5, model optimization: optimizing the model according to the evaluation result to improve the quality and performance of the model;
s6, prediction and application: new data is predicted and inferred using the built model to solve the actual problem and make decisions.
Further, the real-time update module needs to pay attention to the following points:
data quality: in the process of updating in real time, quality control and monitoring are required to be carried out on the data so as to ensure the accuracy and the integrity of the data;
model stability: in the process of updating in real time, the stability and reliability of the model need to be ensured, and the problems of over fitting and under fitting are avoided;
Response speed: in the process of updating in real time, the response speed and efficiency of the system need to be ensured to support rapid decision making and coping.
Further, the main functions of the security management module include:
security policy management: making and implementing security policies and specifications of enterprises, including data classification, authority control and security audit, so as to ensure the information security of the enterprises;
security event monitoring: monitoring security events and abnormal behaviors in an enterprise information system, including invasion, attack, viruses and user behavior abnormality, and timely discovering and coping with security problems;
security hole scanning: performing vulnerability scanning and evaluation on the enterprise information system, and finding and repairing security vulnerabilities to avoid security risks and threats;
security event response: timely responding and processing the security event, including isolation, recovery and tracking, so as to reduce the influence of the security event on the enterprise information system;
safety report analysis: and analyzing and counting the security events and abnormal behaviors of the enterprise information system to find the commonality and trend of the security problems, and providing corresponding security suggestions and measures.
Further, the main functions of the user interaction module include:
And an interaction mode module: the user interaction module provides various interaction modes, and realizes dragging, clicking and sliding operations on the big data display screen so as to facilitate a user to quickly position required information;
and a data visualization module: the user interaction module adopts a data visualization technology to display complex data and information on a big data display screen in the form of a chart, a map and an instrument panel for a user so as to improve the readability and the understandability of the data;
and an intelligent searching module: the user interaction module provides an intelligent search function, and searches and matches data and documents in the enterprise informatization system through a search engine technology so as to meet the information requirement of a user;
and a recommendation service module: the user interaction module provides personalized recommendation services, and related products and services are recommended to the user according to the historical behaviors and preferences of the user, so that user experience and satisfaction are improved.
Further, in the user interaction module, the following points need to be noted:
user experience: the design and implementation of the user interaction module should be centered on the user experience, providing a concise, easy-to-use and friendly interface and function;
data security: the user interaction module ensures the safety and privacy of the user data and avoids the leakage and abuse of the user data;
System performance: the user interaction module should ensure the performance and reliability of the system, and avoid influencing the user experience and satisfaction degree due to system faults.
Compared with the prior art, the invention has the following beneficial effects:
according to the enterprise dimension information display method based on the big data display screen, through graphical display modes such as the digital instrument panel, the color histogram and the line graph, information of the big data of an enterprise is displayed more intuitively and visually, more information is displayed in a limited screen space as much as possible, and a user can quickly position required information through interaction settings of operations such as dragging, clicking and sliding.
Drawings
FIG. 1 is a schematic diagram of an enterprise dimension information display method based on a big data display screen;
FIG. 2 is a schematic diagram of a method for displaying dimension information of enterprises based on a big data display screen;
FIG. 3 is a schematic diagram of a data cleaning and processing module based on a big data display screen;
FIG. 4 is a schematic diagram of a visual design classification structure based on a big data display screen;
FIG. 5 is a schematic diagram of a data analysis module based on a big data display screen;
FIG. 6 is a schematic diagram of a security management module based on a big data display screen according to the present invention;
fig. 7 is a schematic diagram of a user interaction module structure based on a big data display screen.
Reference numerals in the drawings: 1. a data acquisition module; 101. selecting a data source; 102. extracting data; 103. cleaning and processing data; 104. Transmission and storage of data; 105. Updating and maintaining data; 2. a data cleaning and processing module; 201. Data collection and acquisition; 202. Preprocessing and cleaning data; 203. checking and repairing data quality; 204. Data integration and processing; 205. Data visualization and reporting; 3. a data visualization module; 301. visual design; 302. visual display; 4. a data analysis module; 5. a real-time updating module; 6. a security management module; 601. security policy management; 602. monitoring a security event; 603. scanning security holes; 604. a security event response; 607. safety report analysis; 7. a user interaction module; 701. an interaction mode module; 702. a data visualization module; 703. an intelligent searching module; 704. and recommending a service module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 and 2, the method for displaying enterprise dimension information based on a big data display screen includes a data acquisition module 1, a data cleaning and processing module 2, a data visualization module 3, a data analysis module 4, a real-time update module 5, a security management module 6 and a user interaction module 7.
The data collection module 1 is a first step of an enterprise dimension information display method, and collects relevant data of an enterprise by connecting a database, an ERP system, a CRM system and the like in the enterprise or connecting an external data platform. The main operation of the data acquisition module 1 comprises the following aspects:
1. selection of data sources 101: and selecting a proper data source according to factors such as reliability, accuracy, completeness and the like of display requirements and the data source. Databases, ERP systems, CRM systems, etc. within an enterprise are common data sources, and related data may also be obtained from external data platforms, such as third party data providers, public data platforms, etc.
2. Extraction of data 102: the data extraction 102 mode is selected according to the requirement, and the data extraction 102 can be performed by a data interface, a data capture mode, a file import mode and the like. Attention is paid to the amount of data extracted and the frequency of extraction, avoiding excessive burden on the data source.
3. Data cleaning and processing 103: the collected data needs to be cleaned and processed, including data deduplication, data format conversion, abnormal data processing and the like. The purpose of data cleaning and processing is to ensure the accuracy and timeliness of data and avoid problems in the subsequent data analysis and visualization process.
4. Transmission and storage of data 104: the collected data needs to be transmitted and stored, so that the reliability and the safety of the data are ensured. The data transmission and storage can be performed by means of data transmission protocols, data encryption and the like.
5. Updating and maintaining 105 of data: the data acquisition module needs to update and maintain 105 the data, and ensures the real-time performance and the integrity of the data. The data is required to be updated and maintained regularly according to actual conditions, so that the data is prevented from being outdated or lost.
The data cleaning and processing module 2 is used for preprocessing and cleaning the original data to improve the quality and accuracy of the data, and has the main tasks of removing noise, abnormal values, repeated data, missing data and other unsatisfactory parts in the data, and simultaneously performing operations such as conversion, merging, formatting and the like on the data so as to facilitate subsequent data analysis and mining. The specific steps and methods of the data cleaning and processing module 2 are as follows:
1. Data collection and acquisition 201: firstly, data needs to be collected and acquired from different data sources, including databases, files, API interfaces and the like, and the format, structure and effectiveness of the data need to be paid attention to in the data collection process, so that the integrity and accuracy of the data are ensured.
2. Data preprocessing and cleaning 202: preprocessing and cleaning the collected data, including operations such as data de-duplication, filling, conversion, formatting and the like, and specifically comprises the following steps:
data deduplication: and comparing the unique identification or the key words of the data to remove the repeated data so as to ensure the consistency and the accuracy of the data.
And (3) filling data: the missing data may be filled in by means of average, median, mode, or the like.
Data conversion: the data is converted, including conversion of numeric data, text data, date data, etc., to facilitate subsequent data analysis and mining.
Formatting data: the data is uniformly formatted, including the unification of cases, date formats, units and the like, so as to facilitate the comparison and analysis of the data.
3. Data quality inspection and repair 203: the quality inspection and repair of the data comprise abnormal value detection, missing value detection, data accuracy detection and the like, and the specific steps comprise:
Abnormal value detection: and detecting abnormal values and noise points in the data by methods such as statistical analysis, visual analysis and the like, and repairing or deleting.
And (3) missing value detection: and detecting the missing value in the data by methods such as statistical analysis, visual analysis and the like, and filling or deleting.
And (3) detecting data accuracy: and comparing and verifying the data, detecting the accuracy and consistency of the data, and repairing or deleting the data.
4. Data integration and processing 204: integrating and processing data with different sources, different formats and different structures to form a unified data set, wherein the method comprises the following specific steps:
data integration: the data from different sources are integrated, and data matching and fusion can be performed by using methods such as data mining, machine learning and the like.
And (3) data processing: and processing the integrated data, including data conversion, standardization, normalization and other operations, so as to facilitate subsequent data analysis and mining.
5. Data visualization and reporting 205: the cleaned and processed data are visualized and reported so as to facilitate the data analysis and decision making of the user, and the specific steps comprise:
data visualization: the data is visualized in the form of charts, tables, etc. to facilitate data analysis and mining by the user.
Data reporting: reporting the cleaned and processed data, including data analysis reports, data mining reports, data quality reports, etc., to facilitate decision making and planning by the user.
In summary, the data cleaning and processing module is an important step of data analysis and mining, and quality and accuracy of the data cleaning and processing module have important influence on subsequent data analysis and mining results, so that a scientific and effective method is needed to perform data cleaning and processing to improve quality and accuracy of data.
The data visualization module 3 is a process of designing elements such as proper chart types, colors, fonts and the like according to data characteristics and service requirements. Visual presentation is a process of presenting a designed visual chart to an interface, and a user can acquire more information and insight through interactive operation. The data visualization module 3 comprises a visualization design 301 and a visualization presentation 302. The visual design 301 refers to a process of selecting appropriate elements such as chart type, color, font and the like according to the characteristics of data and service requirements, and presenting the data, wherein the following aspects need to be considered in the process:
1. data characteristics: different types of data are suitable for different chart types, e.g. classification data are suitable for use of pie charts, quantity data are suitable for use of bar charts, trend data are suitable for use of line charts etc.
2. Business requirements: the selection of the appropriate chart type and elements according to business needs, such as when displaying sales data, requires the selection of the appropriate colors to distinguish sales of different products or regions.
3. Design style: and proper color, font and other elements are selected according to brands and user requirements, so that the chart is more attractive and easy to understand.
Visual presentation 302 refers to the process of presenting a designed visual chart to an interface, where a user may obtain more information and insight through interactive operations. This process requires consideration of several aspects:
1. interactivity: through interactive operation, a user can acquire more information and insight, such as mouse hovering or clicking on elements on a chart, and can view specific data information.
2. Data granularity: when selecting the appropriate data granularity presentation data, such as sales data, according to user needs, monthly, quarterly, or yearly presentation may be selected to better understand the trend of the data by the user.
3. Data depth: the depth of the display data is selected according to the requirements of the user, for example, when the sales data is displayed, the total sales can be selected to be displayed, and sales of different products or areas can be selected to be displayed, so that the user can better understand the data.
In summary, the visual design 301 and visual presentation 302 are closely related processes that require flexible adjustment and optimization according to data characteristics and business requirements in order for the user to better understand the data and make better decisions.
The data analysis module 4 processes and analyzes the data using various algorithms and techniques to discover information in the data such as associations, trends, anomalies, and laws. Data modeling and analysis can help us better understand data, predict future trends, find problems, and solve problems. The main steps of the data analysis module 4 for data modeling and analysis include:
data preprocessing: the original data is subjected to operations such as cleaning, de-duplication, filling in missing values, converting data types and the like so as to facilitate subsequent analysis and modeling. Data processing can be performed using the Pandas library in Python.
Missing value processing:
deletion of missing values:xi, jXi, j represents the data of the ith row and the jth column.
Interpolation method: the missing values are filled in using interpolation methods, such as linear interpolation, polynomial interpolation, etc.
Outlier processing:
probability distribution based method: the outliers are identified and processed by analyzing and modeling the data, assuming that the data obeys some probability distribution.
Distance-based method: outliers are identified and processed by calculating the distance between the data points.
2. Feature selection: useful features are selected from all features to improve modeling accuracy and efficiency. Feature selection may be performed using a feature selector in the Scikit-learn library in Python.
3. Modeling data: the data is modeled using a decision tree algorithm. The decision tree algorithm is a classification algorithm based on a tree structure, which classifies data into different categories. Modeling can be performed using a decision tree classifier in the Scikit-learn library in Python.
4. Model evaluation: and evaluating the established model, wherein the model comprises indexes such as accuracy, precision, recall rate, F1 value and the like.
Accuracy analysis:
accuracy refers to the proportion of the number of correctly classified samples to the total number of samples, and the formula is as follows:
where TP represents the number of real cases, TN represents the number of true cases, FP represents the number of false cases, and FN represents the number of false cases.
And (3) precision analysis:
precision refers to the proportion of the number of samples for which the model is correctly predicted to be positive to the number of samples for which all predictions are positive, and the formula is as follows:
recall rate analysis:
The recall is the ratio of the number of samples for which the model is correctly predicted to be positive to the number of samples for which the model is actually positive, and the formula is as follows:
f1 value analysis:
the F1 value is a harmonic mean of precision and recall, used to comprehensively evaluate the performance of the model, and its formula is as follows:
different indexes can be selected for evaluation according to specific application scenes and task requirements. In general, accuracy is an important index for evaluating overall performance of a model, and accuracy and recall are focused on evaluating classification performance of positive and negative samples. The F1 value integrates the advantages and disadvantages of precision and recall rate, and considers the conditions of correct classification and incorrect classification, thereby being a comprehensive evaluation index.
5. Model optimization: and optimizing the model according to the evaluation result to improve the quality and performance of the model. Optimization may be attempted using different parameters, features, algorithms, etc.
6. Prediction and application: new data is predicted and inferred using the built model to solve the actual problem and make decisions. The prediction can be performed using a prediction function in the Scikit-learn library in Python.
In the decision tree algorithm, we need to select the proper features, decision nodes, pruning strategies, etc. to build the decision tree model. Meanwhile, the problems of over-fitting and under-fitting of the decision tree are also required to be considered, and the optimization is performed by using a proper strategy.
The real-time updating module 5 is used for updating data, recalculating a model and a result in time in the data processing and analyzing process, and feeding back the result to a user or a module of the system in real time. The method has the effect that under the scene that the data change is faster or quick response is required, the accuracy and the instantaneity of the data can be kept, so that the accuracy and the efficiency of decision making are improved. During the working process, the model is rebuilt according to the data updated in real time, optimization and evaluation are carried out, and then the model parameters and results are recalculated. Modeling analysis and model updating can be performed using Scikit-learn library in Python, etc. The real-time update module 5 needs to pay attention to the following points:
1. data quality: in the process of updating in real time, quality control and monitoring are required to be carried out on the data so as to ensure the accuracy and the integrity of the data.
2. Model stability: in the process of updating in real time, the stability and reliability of the model need to be ensured, and the problems of over fitting, under fitting and the like are avoided.
3. Response speed: in the process of updating in real time, the response speed and efficiency of the system need to be ensured to support rapid decision making and coping.
The security management module 6 is used for enterprises to take various measures in daily operations, and protecting the security, integrity and reliability of enterprise core data and information systems. The security management module is an important component of enterprise information security protection, and is mainly used for monitoring, analyzing and managing security events and abnormal behaviors in an enterprise information system so as to protect the core data of an enterprise and the security of the information system. The main functions of the security management module 6 include:
1. Security policy management 601: and (3) formulating and implementing security policies and specifications of enterprises, including aspects of data classification, authority control, security audit and the like, so as to ensure the information security of the enterprises.
2. Security event monitoring 602: security events and abnormal behaviors in an enterprise information system are monitored, including security events such as invasion, attack and viruses, user behavior abnormality and the like, and security problems are found and dealt with in time.
3. Security hole scan 603: and performing vulnerability scanning and evaluation on the enterprise information system, and finding and repairing security vulnerabilities to avoid security risks and threats.
4. Security event response 604: timely response and processing are carried out on the security events, including isolation, recovery, tracking and other aspects, so as to reduce the influence of the security events on the enterprise information system.
5. Security report analysis 605: and analyzing and counting the security events and abnormal behaviors of the enterprise information system to find the commonality and trend of the security problems, and providing corresponding security suggestions and measures.
In the security management module, the following points need to be noted:
1. and (3) making a security policy: the enterprise should make corresponding security policy and specification according to its own business characteristics and security requirements to ensure the enterprise information security.
2. The safety technology is applied: enterprises should employ advanced security technologies and tools, such as firewalls, intrusion detection systems, vulnerability scanners, etc., to improve the security and reliability of enterprise information systems.
3. Safety consciousness training: enterprises should strengthen the security awareness and training of staff, improve the knowledge and understanding of staff on information security, so as to reduce security problems caused by human factors.
4. The application of the security management module includes enterprises and institutions in various industries such as finance, telecom, medical and government. By using the safety management module, the enterprise can monitor and respond to the safety problem in time, and the safety and stability of the enterprise information system are ensured.
The user interaction module 7 is used for providing various interaction modes, helping a user to quickly locate required information, and providing intelligent searching and recommending functions so as to improve user experience and satisfaction. The main functions of the user interaction module 7 include:
1. interaction mode module 701: the user interaction module provides various interaction modes, such as dragging, clicking, sliding and the like, so that a user can conveniently and quickly position required information.
2. The data visualization module 702: the user interaction module adopts a data visualization technology to present complex data and information to a user in the forms of charts, maps, dashboards and the like so as to improve the readability and the understandability of the data.
3. Intelligent search module 703: the user interaction module provides an intelligent search function, and searches and matches data and documents in the enterprise informatization system through a search engine technology so as to meet the information requirement of a user.
4. Recommendation service module 704: the user interaction module provides personalized recommendation services, and related products and services are recommended to the user according to the historical behaviors and preferences of the user, so that user experience and satisfaction are improved.
In the user interaction module 7, the following points need to be noted:
1. user experience: the design and implementation of the user interaction module should be centered on the user experience, providing a compact, easy-to-use, friendly interface and function.
2. Data security: the user interaction module should ensure the safety and privacy of the user data, and avoid the disclosure and abuse of the user data.
3. System performance: the user interaction module should ensure the performance and reliability of the system, and avoid influencing the user experience and satisfaction degree due to system faults.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The enterprise dimension information display method based on the big data display screen is characterized by comprising the following steps of: the method comprises a data acquisition module (1), a data cleaning and processing module (2), a data visualization module (3), a data analysis module (4), a real-time updating module (5), a security management module (6) and a user interaction module (7);
the data acquisition module (1) is used for acquiring relevant data of an enterprise by connecting a database, an ERP system and a CRM system in the enterprise or connecting an external data platform;
the data cleaning and processing module (2) is used for preprocessing and cleaning the original data to improve the quality and accuracy of the data, and has the main tasks of removing noise, abnormal values, repeated data and missing data which are unsatisfactory parts in the data, and simultaneously converting, merging and formatting the data so as to facilitate the subsequent data analysis and mining;
The data visualization module (3) designs proper chart types, colors and fonts according to data characteristics and service requirements, and displays the designed visual charts on an interface, and the data visualization module (3) comprises a visual design (301) and a visual display (302);
the data analysis module (4) processes and analyzes the data by using various algorithms and techniques to find out association, trend, abnormality and rule information in the data;
the real-time updating module (5) is used for timely updating data, recalculating a model and a result in the data processing and analyzing process, feeding back the result to a user or a system in real time, and keeping the accuracy and the instantaneity of the data under the scene that the data change is faster or quick response is needed so as to improve the accuracy and the efficiency of decision making, and reestablishing the model according to the data updated in real time in the working process, optimizing and evaluating the model and recalculating the model parameters and the result;
the safety management module (6) is used for enterprises to take various measures in daily operation and protecting the safety, integrity and reliability of enterprise core data and information systems;
The user interaction module (7) is used for providing various interaction modes, helping a user to quickly locate required information, and providing intelligent searching and recommending functions so as to improve user experience and satisfaction.
2. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the main operation of the data acquisition module (1) comprises the following aspects:
selection of data sources (101): selecting a proper data source, and selecting a database, an ERP system and a CRM system in an enterprise or selecting a third party data provider and a public data platform according to reliability, accuracy and integrity factors of display requirements and the data source;
extraction of data (102): selecting a data extraction (102) mode according to requirements, extracting the data (102) through a data interface, a data capture or a file import mode, and keeping the extracted data quantity and the extracted frequency within a reasonable range so as to avoid overlarge burden on a data source;
data cleaning and processing (103): the collected data needs to be cleaned and processed, including data deduplication, data format conversion and abnormal data processing;
Transmission and storage of data (104): the collected data needs to be transmitted and stored, so that the reliability and the safety of the data are ensured, and the data are transmitted and stored in a data transmission protocol or data encryption mode;
update and maintenance of data (105): the data acquisition module needs to update and maintain (105) the data, and ensures the real-time performance and the integrity of the data.
3. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the specific steps and methods of the data cleaning and processing module (2) are as follows:
data collection and acquisition (201): firstly, data are required to be collected and acquired from different data sources, wherein the data comprise a database and a file HE API interface, the format, the structure and the effectiveness of the data are noted in the data collection process, and the integrity and the accuracy of the data are ensured;
data preprocessing and cleaning (202): preprocessing and cleaning the collected data, including data de-duplication, filling, conversion and formatting, specifically including the steps of:
s1, data deduplication: the repeated data is removed by comparing the unique identification or the key words of the data so as to ensure the consistency and the accuracy of the data;
S2, data filling: filling the missing data by using an average value, a median and a mode method;
s3, data conversion: converting the data, including converting numerical data, text data and date data, so as to facilitate subsequent data analysis and mining;
s4, data formatting: the data is subjected to unified formatting, including unification of case, date format and unit, so that the data can be compared and analyzed conveniently;
data quality inspection and repair (203): the data is subjected to quality inspection and repair, including abnormal value detection and missing value detection HE data accuracy detection, and the specific steps include:
s1, abnormal value detection: detecting abnormal values and noise points in the data by using statistical analysis and visual analysis methods, and repairing or deleting;
s2, detecting a missing value: detecting a missing value in the data through statistical analysis HE visual analysis, and filling or deleting;
s3, detecting data accuracy: the data is compared and verified, the accuracy and the consistency of the data are detected, and repair or deletion is carried out;
data integration and processing (204): integrating and processing data with different sources, different formats and different structures to form a unified data set, wherein the method comprises the following specific steps:
S1, data integration: integrating data from different sources, and performing data matching and fusion by using a data mining HE machine learning DE method;
s2, data processing: processing the integrated data, including data conversion and standardized HE normalization operation, so as to facilitate subsequent data analysis and mining;
data visualization and reporting (205): the cleaned and processed data are visualized and reported so as to facilitate the data analysis and decision making of the user, and the specific steps comprise:
s1, data visualization: the data are visualized in the form of charts and tables, so that a user can conveniently analyze and mine the data;
s2, data report: reporting the cleaned and processed data, including data analysis reports, data mining reports, and data quality reports, to facilitate decision making and planning by the user.
4. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the visual design (301) refers to a process of selecting proper chart types, colors and font elements according to data characteristics and service requirements and presenting the data, wherein the following aspects need to be considered:
Data characteristics: different types of data are suitable for different chart types;
business requirements: selecting proper chart types and elements according to business requirements;
design style: the proper color and font elements are selected according to brands and user requirements, so that the chart is more attractive and easy to understand.
5. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the visual presentation (302) refers to a process of presenting a designed visual chart on an interface, and a user can acquire more information and insight through interactive operation, wherein the following aspects need to be considered:
interactivity: through interactive operation, the user can acquire more information and insight;
data granularity: selecting proper data granularity display data according to the user demand;
data depth: and selecting the depth of the display data according to the requirements of the user.
6. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the main steps of the data analysis module (4) for data modeling and analysis include:
s1, data preprocessing: cleaning, de-duplication, filling in missing values and converting data types of the original data so as to facilitate subsequent analysis and modeling;
S2, feature selection: selecting useful features from all the features to improve modeling accuracy and efficiency;
s3, data modeling: modeling the data using a decision tree algorithm;
s4, model evaluation: evaluating the established model, wherein the model comprises accuracy, precision, recall rate and F1 value index;
s5, model optimization: optimizing the model according to the evaluation result to improve the quality and performance of the model;
s6, prediction and application: new data is predicted and inferred using the built model to solve the actual problem and make decisions.
7. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the real-time update module (5) needs to pay attention to the following points:
data quality: in the process of updating in real time, quality control and monitoring are required to be carried out on the data so as to ensure the accuracy and the integrity of the data;
model stability: in the process of updating in real time, the stability and reliability of the model need to be ensured, and the problems of over fitting and under fitting are avoided;
response speed: in the process of updating in real time, the response speed and efficiency of the system need to be ensured to support rapid decision making and coping.
8. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the main functions of the security management module (6) include:
security policy management (601): making and implementing security policies and specifications of enterprises, including data classification, authority control and security audit, so as to ensure the information security of the enterprises;
security event monitoring (602): monitoring security events and abnormal behaviors in an enterprise information system, including invasion, attack, viruses and user behavior abnormality, and timely discovering and coping with security problems;
security breach scan (603): performing vulnerability scanning and evaluation on the enterprise information system, and finding and repairing security vulnerabilities to avoid security risks and threats;
security event response (604): timely responding and processing the security event, including isolation, recovery and tracking, so as to reduce the influence of the security event on the enterprise information system;
security report analysis (605): and analyzing and counting the security events and abnormal behaviors of the enterprise information system to find the commonality and trend of the security problems, and providing corresponding security suggestions and measures.
9. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: the main functions of the user interaction module (7) include:
Interaction mode module (701): the user interaction module provides various interaction modes, and realizes dragging, clicking and sliding operations on the big data display screen so as to facilitate a user to quickly position required information;
data visualization module (702): the user interaction module adopts a data visualization technology to display complex data and information on a big data display screen in the form of a chart, a map and an instrument panel for a user so as to improve the readability and the understandability of the data;
intelligent search module (703): the user interaction module provides an intelligent search function, and searches and matches data and documents in the enterprise informatization system by using a search engine technology through the big data display screen so as to meet the information requirement of a user;
recommendation service module (704): the user interaction module provides personalized recommendation services, and related products and services are recommended to the user according to the historical behaviors and preferences of the user, so that user experience and satisfaction are improved.
10. The enterprise dimension information presentation method based on the big data display screen as claimed in claim 1, wherein: in the user interaction module (7), the following points need to be noted:
user experience: the design and implementation of the user interaction module should be centered on the user experience, providing a concise, easy-to-use and friendly interface and function;
Data security: the user interaction module ensures the safety and privacy of the user data and avoids the leakage and abuse of the user data;
system performance: the user interaction module should ensure the performance and reliability of the system, and avoid influencing the user experience and satisfaction degree due to system faults.
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