CN118093981A - Cloud data visual analysis system and method based on artificial intelligence - Google Patents
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Abstract
The invention discloses a cloud data visual analysis system and method based on artificial intelligence, and relates to the technical field of cloud data processing. In order to solve the problems that the data management and analysis are low in efficiency and accuracy, the user interface is not friendly, the user interaction function cannot be realized, and the use experience and satisfaction of the user are affected; the cloud data visual analysis system based on artificial intelligence comprises a data crawling unit, a data processing unit, a data comparison unit, an intelligent analysis unit and a visual unit; by combining cloud computing, artificial intelligence and data visualization technologies, a high-efficiency and accurate data analysis and management solution is provided for users, and by automatically crawling, processing, comparing and analyzing cloud data and automatically identifying and extracting useful information and modes in the data, comprehensive insights and high-efficiency utilization of the data are realized, and meanwhile, analysis results are easier to understand and spread through visual visualization display.
Description
Technical Field
The invention relates to the technical field of cloud data processing, in particular to an artificial intelligence-based cloud data visual analysis system and method.
Background
With the development of cloud computing technology, more and more data is stored in the cloud. However, how to effectively manage and access these cloud data and obtain valuable information therefrom becomes an important issue. Regarding visual analysis of cloud data, the publication number is: the China patent CN116821200A discloses an artificial intelligence cloud data visual analysis system and an analysis method thereof, wherein the system comprises an artificial comparison module, a data processing module, a data labeling module, a data model generation module, a visual generation module, a cloud data storage module, a data analysis module and a data crawler module, wherein the existing data model and intercepted data are compared through the artificial intelligence comparison module, the closest data model is selected, and then the data and the similar data model are transmitted into the data model generation module to generate a new data model, and the new data model is modified on the similar data model, so that the time from intercepting the data to producing the data model is greatly reduced, the speed of data visual analysis is faster, and a large amount of calculation force is saved.
Although the cloud data can be stored and accessed, the problems of low efficiency and low accuracy still exist in the aspects of data management and analysis, the user interface is not friendly, the user interaction function cannot be realized, and the use experience and satisfaction of a user are affected.
Disclosure of Invention
The invention aims to provide an artificial intelligence based cloud data visual analysis system and method, which realize comprehensive insight and efficient utilization of data by automatically crawling, processing, comparing and analyzing cloud data, and simultaneously enable analysis results to be easier to understand and spread by visual display, so that efficient management and access of the data are realized, and the problems in the background technology are solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an artificial intelligence based cloud data visualization analysis system comprising:
the data crawling unit is used for determining a target range of cloud data to be crawled, determining a data crawling rule based on the target range of the crawled cloud data, and crawling the target data from the cloud database based on the crawling rule by using a web crawler technology;
the data processing unit is used for carrying out data preprocessing on the captured target data, extracting key features from the preprocessed target data, classifying the target data based on the extracted key features, and storing the target data into the local cache database according to a classification result;
The data comparison unit is used for extracting the existing data from the local cache database as reference data, comparing the target data with the reference data based on a comparison model, determining the difference and the change of the data based on the comparison result, and generating a comparison report;
the intelligent analysis unit is used for importing the comparison report into the statistical analysis model, carrying out deep analysis on the comparison data and determining an analysis result;
and the visualization unit is used for matching the corresponding visualization type according to the type and the characteristics of the analysis result, converting the analysis result into an intuitive graph, and displaying the generated graph to a user so as to facilitate the user to intuitively understand and analyze the data.
Further, the data crawling unit includes:
the target data identification module is used for analyzing the data source and determining a target range to be crawled based on an analysis result;
The data grabbing module is used for matching corresponding crawling rules based on the structure, the format and the storage mode of the data, and grabbing target data in a target range based on the crawling rules;
and the data monitoring module is used for acquiring log information in the data crawling process and simultaneously monitoring the state and performance of the data crawling in real time.
Further, the data grabbing module grabs data based on a crawling rule, specifically:
Extracting standard network flow of a local cache data node from a cloud database, and determining initial data cache characteristics of the local cache data node according to the standard network flow;
obtaining a crawling rule of a local cache database, and generating a data transmission network protocol between the cloud database and the local cache database according to the crawling rule and the initial data cache characteristic of a local cache data node;
invoking a data sample in the local cache database, and operating the data transmission network protocol based on the data sample to obtain an operation result;
According to the operation result, acquiring an interaction record between a local cache database and a local cache data node, and according to the interaction record, acquiring the data transmission characteristics of a data transmission network protocol;
and establishing a data sharing mechanism of the cloud database and the local cache database based on the data transmission characteristics, and capturing target data based on the data sharing mechanism.
Further, establishing a data sharing mechanism of the cloud database and the local cache database based on the data transmission characteristics, capturing target data based on the data sharing mechanism, including:
Invoking a data transmission requirement between a cloud database and a local cache database, wherein the data transmission requirement comprises a maximum transmission data amount of single data transmission and a maximum transmission frequency of data in unit time;
And calling the data quantity generated in the unit time of the cloud database and the local cache database, and acquiring a data quantity parameter coefficient by utilizing the data quantity generated in the unit time of the cloud database and the local cache database, wherein the data quantity parameter coefficient is acquired by the following formula:
；
wherein c represents a data amount parameter coefficient; n represents the total number of unit time which the cloud database and the local cache database run; c 01i and C 02i respectively represent the data quantity to be shared generated by the cloud database and the local cache database in the ith unit time; c z01i and C z02i represent total data amounts generated by the cloud database and the local cache database at the ith unit time, respectively; c 01 and c 02 represent a first data amount fluctuation coefficient and a second data amount fluctuation coefficient, respectively;
Combining the data quantity parameter coefficients according to the data transmission requirements between the cloud database and the local cache database, and acquiring data quantity constraint conditions corresponding to the grabbing target data of a data sharing mechanism between the cloud database and the local cache database;
Setting a data export interface in a cloud database, wherein the data export interface of the cloud database is used for exporting data according to a preset format and protocol;
Setting a data import interface in a local cache database, wherein the data export interface of the local cache database is used for receiving data exported by a cloud database and updating the local cache;
setting the data volume of target data to be grabbed for each data sharing according to the data volume constraint condition, and carrying out data grabbing according to the data volume of the target data.
Further, according to the data transmission requirement between the cloud database and the local cache database, combining the data volume parameter coefficients, obtaining a data volume constraint condition corresponding to the capturing target data by a data sharing mechanism between the cloud database and the local cache database includes:
The maximum transmission data quantity of the single data transmission contained in the data transmission requirement is called, and a first constraint condition parameter is obtained according to the maximum transmission data quantity of the single data transmission; the first constraint condition parameters are obtained through the following formula:
；
Wherein λ 01 represents a first constraint parameter; δ 01 represents the first adjustment coefficient; c max denotes the maximum transmission data amount of a single data transmission;
the maximum data transmission frequency in the unit time contained in the data transmission requirement is called, and a second constraint condition parameter is obtained according to the maximum data transmission frequency in the unit time; the second constraint condition parameters are obtained through the following formula:
；
Wherein λ 02 represents a second constraint parameter; δ 02 represents the second adjustment coefficient; n c represents the data transmission times corresponding to the maximum data transmission frequency in unit time; n 01i and N 02i respectively represent the data generation times of the data to be shared, which are generated by the cloud database and the local cache database in the ith unit time;
and acquiring a data volume constraint condition corresponding to the target data by a data sharing mechanism between the cloud database and the local cache database according to the first constraint condition parameter and the second constraint condition parameter and combining the data volume parameter coefficient.
Further, acquiring the data volume constraint condition corresponding to the target data by a data sharing mechanism between the cloud database and the local cache database according to the first constraint condition parameter and the second constraint condition parameter and combining the data volume parameter coefficient, including:
Calling the first constraint condition parameter and the second constraint condition parameter;
Calling a data quantity parameter coefficient;
Acquiring a data volume lower limit value corresponding to the data volume constraint condition by combining the first constraint condition parameter with a data volume parameter coefficient; the lower limit value of the data quantity is obtained through the following formula:
；
Wherein C down represents a data amount lower limit value; c min01 and C min02 represent the minimum data amount to be shared generated by the cloud database and the local cache database in a unit time;
acquiring a data volume upper limit value corresponding to the data volume constraint condition by combining the second constraint condition parameters with the data volume parameter coefficients; wherein, the data volume upper limit value is obtained by the following formula:
；
Wherein C up denotes an upper limit value of the data amount.
Further, the data processing unit includes:
the data reading module is used for reading the target data captured from the cloud database, verifying the integrity and the accuracy of the target data and ensuring that the data is not damaged or lost;
the data preprocessing module is used for cleaning, converting and missing value processing of target data;
The feature extraction module is used for extracting key features according to the characteristics and analysis requirements of the target data and converting the extracted key features;
the data classification module is used for classifying the preprocessed target data through a trained classification model according to the key characteristics and classification requirements of the target data.
Further, the data comparison unit includes:
The data extraction module is used for selecting a data set which needs to be used as a comparison standard from the local cache database and extracting the selected standard data to the data comparison module;
The data comparison module is used for inputting the acquired target data and the extracted reference data into the comparison module for comparison, determining the difference and change of the target data and the reference data and determining the comparison result;
and the comparison report generation module is used for filling the report content according to the comparison result and outputting the generated comparison report to the intelligent analysis unit.
Further, the intelligent analysis unit includes:
The statistical analysis module is used for:
Dividing target data into different clusters based on the distribution characteristics and data characteristics of the target data, and respectively calculating the distance between each word vector in any cluster and a standard word vector corresponding to any cluster;
Screening out keywords corresponding to the word vector with the minimum distance as target keywords in any cluster, and determining the structure in the target data based on the target keywords;
Trend prediction module for:
extracting time sequence data of target data, performing data stability test based on the time sequence data, and judging whether the time sequence data shows periodic change along with time change;
based on the data characteristics of the target data, matching a prediction model, and based on the prediction model, predicting future time series data corresponding to the target data;
The abnormality monitoring module is used for:
Detecting abnormal values which are significantly different from most data points based on the data type and the abnormal type and matching an abnormal detection algorithm in an abnormal detection database, and extracting detection abnormal data points;
and processing the detected abnormal value, integrating the detected abnormal data points to generate an abnormal data set, and evaluating the accuracy of an abnormal detection result.
Further, the visualization unit includes:
The visual determining module is used for determining a visual category according to the type of the analysis result, mapping analysis data into a graph corresponding to the visual category, determining visual elements in the graph, and matching corresponding color coding schemes based on different visual elements;
The graphic generation module is used for generating a visual graphic of analysis data according to a color coding scheme by using a graphic library;
the chart display module is used for determining the position and the size of the visual graph on the display interface and the distribution of each element in the visual graph, and rendering the generated visual graph in the user interface;
and the user interaction module is used for receiving the interaction input instruction of the user and providing feedback according to the interaction input instruction of the user.
The invention provides another technical scheme, namely an artificial intelligence-based cloud data visualization analysis method, which comprises the following steps:
Step one: determining analysis targets and requirements: determining the data type and the data range which need to support the analysis target according to the data type and the purpose which need to be analyzed;
Step two: data crawling: determining a source of data based on data requirements, crawling target data from a cloud database based on a crawler program, and storing the target data in a local cache database;
step three: and (3) data processing: performing operations of cleaning, preprocessing and feature extraction on the crawled data, and extracting features valuable for analysis from the data;
step four: data comparison: comparing the existing data with the newly acquired target data by utilizing a data comparison model, and finding out the difference and the similarity between the data;
step five: intelligent analysis: deep analysis is carried out on the compared data, and valuable information is extracted;
Step six: visual display: the analysis result is displayed in the form of visual graphics, so that the user can understand and analyze the analysis result conveniently, and the interactive function is provided.
Compared with the prior art, the invention has the beneficial effects that:
By combining cloud computing, artificial intelligence and data visualization technologies, a high-efficiency and accurate data analysis and management solution is provided for users, useful information and modes in data are automatically identified and extracted through automatically crawling, processing, comparing and analyzing cloud data, comprehensive insights and high-efficiency utilization of the data are realized, and meanwhile analysis results are easier to understand and spread through visual visualization display, so that high-efficiency management and access of the data are realized.
Drawings
FIG. 1 is a block diagram of an artificial intelligence based cloud data visualization analysis system of the present invention;
Fig. 2 is a flow chart of a cloud data visualization analysis method based on artificial intelligence.
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.
In order to solve the technical problems of low efficiency and low accuracy in data management and analysis, please refer to fig. 1, the present embodiment provides the following technical scheme:
an artificial intelligence based cloud data visualization analysis system comprising:
the data crawling unit is used for determining a target range of cloud data to be crawled, determining a data crawling rule based on the target range of the crawled cloud data, and crawling the target data from the cloud database based on the crawling rule by using a web crawler technology;
the data processing unit is used for carrying out data preprocessing on the captured target data, extracting key features from the preprocessed target data, classifying the target data based on the extracted key features, and storing the target data into the local cache database according to a classification result;
In this embodiment, the data processing unit includes:
the data reading module is used for reading the target data captured from the cloud database, verifying the integrity and the accuracy of the target data and ensuring that the data is not damaged or lost;
the data preprocessing module is used for cleaning, converting and missing value processing of target data;
The feature extraction module is used for extracting key features according to the characteristics and analysis requirements of the target data and converting the extracted key features;
The data classification module is used for selecting a proper classification algorithm, such as a decision tree, a support vector machine, a neural network and the like, according to key characteristics and classification requirements of the target data, and classifying the preprocessed target data through a trained classification model.
In this embodiment, the original features are converted into a form more suitable for processing by a classification algorithm through the manners of feature scaling, feature encoding, feature construction and the like, for example, for text data, the text data can be converted into word vectors or TF-IDF vectors; for image data, characteristics such as texture, color, shape and the like of the image data can be extracted, irrelevant and redundant characteristics are eliminated, and the most relevant characteristics of a target task are reserved, so that the accuracy of a classification algorithm is improved, and the operation efficiency of the algorithm is improved;
The data comparison unit is used for extracting the existing data from the local cache database as reference data, comparing the target data with the reference data based on a comparison model, determining the difference and the change of the data based on the comparison result, and generating a comparison report comprising information such as data difference, similarity and the like;
A data comparison unit comprising:
The data extraction module is used for selecting a data set which needs to be used as a comparison standard from the local cache database and extracting the selected standard data to the data comparison module;
The data comparison module is used for inputting the acquired target data and the extracted reference data into the comparison module for comparison, determining the difference and change of the target data and the reference data and determining the comparison result;
The comparison report generation module is used for filling report contents according to comparison results, displaying data differences and similarities in the forms of charts, tables and the like, and outputting the generated comparison report to the intelligent analysis unit for viewing and analysis by a user;
In the embodiment, through deep comparison and analysis of the data, a user can find out the mode and trend hidden in the data, powerful support is provided for decision making, the automatic data comparison and analysis flow can greatly reduce manual intervention, improve the efficiency of data processing and analysis, discover data change in time and provide powerful support for data management;
The intelligent analysis unit is used for importing the comparison report into the statistical analysis model, carrying out deep analysis on the comparison data, and determining analysis results, including trend prediction, pattern recognition, anomaly detection and the like;
The visualization unit is used for matching the corresponding visualization types, such as a line graph, a histogram, a scatter graph, a thermodynamic diagram and the like, according to the types and the characteristics of the analysis results, converting the analysis results into visual graphs, and displaying the generated graphs to a user so as to facilitate the user to intuitively understand and analyze the data.
In this embodiment, the existing data and the newly acquired data are precisely compared by using the data comparison model, the difference and the similarity between the data are identified, the data are deeply mined and analyzed by using methods such as statistical analysis, trend prediction and association analysis, valuable information is extracted, and the analysis result is displayed in the form of an intuitive graph, so that a user can more easily understand and analyze the data, an interactive function is provided, the user is allowed to perform operations such as screening, sorting, amplifying and shrinking of the data, the readability and the comprehensibility of the data are further improved, and intelligent decision support is provided for the user.
In this embodiment, the data crawling unit includes:
The target data identification module is used for analyzing the data source, determining the structure, format and storage mode of the data, determining the specific field and content of the data to be crawled, and determining the target range to be crawled based on the analysis result;
the data grabbing module is used for matching corresponding crawling rules based on the structure, the format and the storage mode of the data, and grabbing target data in a target range based on the crawling rules, wherein the crawling rules are used for grabbing the data specifically:
Extracting standard network flow of a local cache data node from a cloud database, and determining initial data cache characteristics of the local cache data node according to the standard network flow;
obtaining a crawling rule of a local cache database, and generating a data transmission network protocol between the cloud database and the local cache database according to the crawling rule and the initial data cache characteristic of a local cache data node;
invoking a data sample in the local cache database, and operating the data transmission network protocol based on the data sample to obtain an operation result;
According to the operation result, acquiring an interaction record between a local cache database and a local cache data node, and according to the interaction record, acquiring the data transmission characteristics of a data transmission network protocol;
establishing a data sharing mechanism of a cloud database and a local cache database based on data transmission characteristics, and capturing target data based on the data sharing mechanism;
the data monitoring module is used for acquiring log information in the data crawling process, including crawling time, data quantity, abnormal conditions and the like, so that subsequent analysis and optimization can be facilitated, and meanwhile, the data crawling state and performance can be monitored in real time, and the stability and efficiency of the data crawling process can be ensured.
Specifically, a data sharing mechanism of a cloud database and a local cache database is established based on data transmission characteristics, and target data is captured based on the data sharing mechanism, which comprises the following steps:
Invoking a data transmission requirement between a cloud database and a local cache database, wherein the data transmission requirement comprises a maximum transmission data amount of single data transmission and a maximum transmission frequency of data in unit time;
And calling the data quantity generated in the unit time of the cloud database and the local cache database, and acquiring a data quantity parameter coefficient by utilizing the data quantity generated in the unit time of the cloud database and the local cache database, wherein the data quantity parameter coefficient is acquired by the following formula:
；
wherein c represents a data amount parameter coefficient; n represents the total number of unit time which the cloud database and the local cache database run; c 01i and C 02i respectively represent the data quantity to be shared generated by the cloud database and the local cache database in the ith unit time; c z01i and C z02i represent total data amounts generated by the cloud database and the local cache database at the ith unit time, respectively; c 01 and c 02 represent a first data amount fluctuation coefficient and a second data amount fluctuation coefficient, respectively;
Combining the data quantity parameter coefficients according to the data transmission requirements between the cloud database and the local cache database, and acquiring data quantity constraint conditions corresponding to the grabbing target data of a data sharing mechanism between the cloud database and the local cache database;
Setting a data export interface in a cloud database, wherein the data export interface of the cloud database is used for exporting data according to a preset format and protocol;
Setting a data import interface in a local cache database, wherein the data export interface of the local cache database is used for receiving data exported by a cloud database and updating the local cache;
setting the data volume of target data to be grabbed for each data sharing according to the data volume constraint condition, and carrying out data grabbing according to the data volume of the target data.
The technical effects of the technical scheme are as follows: by calling the data transmission requirements between the cloud database and the local cache database, the data transmission efficiency is ensured, wherein the requirements comprise the maximum transmission data quantity of single data transmission and the maximum transmission frequency of data in unit time. Meanwhile, the data sharing mechanism can be dynamically adjusted by utilizing the data quantity parameter coefficient so as to adapt to the change of the data quantity in different time periods, thereby avoiding the congestion and delay of data transmission. The data quantity constraint condition corresponding to the target data can be acquired by the data sharing mechanism by calculating the data quantity parameter coefficient and combining the data transmission requirement. The data grabbing process is more accurate, and data loss or redundancy caused by overlarge or overlarge data size is avoided, so that the accuracy of data synchronization is improved. According to the technical scheme, the data export interface is arranged in the cloud database, and the data import interface is arranged in the local cache database, so that data can flow in two directions between the cloud database and the local cache database. The technical scheme not only enhances the flexibility of data sharing, but also facilitates the backup and recovery of data. By setting data export and import interfaces and combining data transmission requirements and data volume constraints, the integrity and security of data in the data sharing process can be ensured. Meanwhile, due to the fact that the cloud database and the local cache database are combined, data in the local cache database can provide certain data guarantee even if a network is unstable or the cloud database fails.
In summary, according to the technical scheme, effective data sharing between the cloud database and the local cache database is realized by optimizing data transmission efficiency, improving data synchronization accuracy, enhancing data sharing flexibility and guaranteeing data security, and powerful technical support is provided for practical application.
Specifically, according to the data transmission requirement between the cloud database and the local cache database in combination with the data volume parameter coefficient, obtaining the data volume constraint condition corresponding to the capturing target data by the data sharing mechanism between the cloud database and the local cache database includes:
The maximum transmission data quantity of the single data transmission contained in the data transmission requirement is called, and a first constraint condition parameter is obtained according to the maximum transmission data quantity of the single data transmission; the first constraint condition parameters are obtained through the following formula:
；
Wherein λ 01 represents a first constraint parameter; δ 01 represents the first adjustment coefficient; c max denotes the maximum transmission data amount of a single data transmission;
the maximum data transmission frequency in the unit time contained in the data transmission requirement is called, and a second constraint condition parameter is obtained according to the maximum data transmission frequency in the unit time; the second constraint condition parameters are obtained through the following formula:
；
Wherein λ 02 represents a second constraint parameter; δ 02 represents the second adjustment coefficient; n c represents the data transmission times corresponding to the maximum data transmission frequency in unit time; n 01i and N 02i respectively represent the data generation times of the data to be shared, which are generated by the cloud database and the local cache database in the ith unit time;
and acquiring a data volume constraint condition corresponding to the target data by a data sharing mechanism between the cloud database and the local cache database according to the first constraint condition parameter and the second constraint condition parameter and combining the data volume parameter coefficient.
The technical effects of the technical scheme are as follows: by retrieving the maximum transmission data amount of a single data transmission and calculating the first constraint condition parameter, it is ensured that the data amount does not exceed the transmission capacity limit between the cloud database and the local cache database during each data transmission. This helps to avoid congestion and delay in the data transmission process, and improves the efficiency and stability of data transmission. And calculating a second constraint condition parameter according to the maximum data transmission frequency in unit time, so that the accurate control of the data transmission frequency can be realized. By limiting the data transmission times, the frequent transmission of data in a short time can be avoided, the load pressure of the system is lightened, and the instantaneity and the effectiveness of the data are ensured. The data quantity constraint condition corresponding to the grabbing target data of the data sharing mechanism can be obtained by combining the first constraint condition parameter, the second constraint condition parameter and the data quantity parameter coefficient. The condition can adaptively adjust the data amount of data grabbing according to the data transmission requirement and the dynamic change of the data amount parameter coefficient, so that the data sharing mechanism can meet the requirement of practical application. By precisely controlling the data quantity and the data transmission frequency, invalid data transmission can be reduced, and the efficiency of data sharing can be improved. Meanwhile, by optimizing a data transmission mechanism, the resource consumption in the data transmission process can be reduced, and the system cost is reduced.
In summary, according to the technical scheme, the data transmission quantity and the transmission frequency are precisely controlled, and the data quantity constraint condition corresponding to the grabbing of the target data by the data sharing mechanism is obtained by combining the data quantity parameter coefficients. The method is beneficial to optimizing the data transmission efficiency, improving the accuracy and the flexibility of data sharing, and providing a more efficient and stable data sharing scheme for practical application.
Specifically, acquiring the data volume constraint condition corresponding to the target data by a data sharing mechanism between the cloud database and the local cache database according to the first constraint condition parameter and the second constraint condition parameter and combining the data volume parameter coefficient, including:
Calling the first constraint condition parameter and the second constraint condition parameter;
Calling a data quantity parameter coefficient;
Acquiring a data volume lower limit value corresponding to the data volume constraint condition by combining the first constraint condition parameter with a data volume parameter coefficient; the lower limit value of the data quantity is obtained through the following formula:
；
Wherein C down represents a data amount lower limit value; c min01 and C min02 represent the minimum data amount to be shared generated by the cloud database and the local cache database in a unit time;
acquiring a data volume upper limit value corresponding to the data volume constraint condition by combining the second constraint condition parameters with the data volume parameter coefficients; wherein, the data volume upper limit value is obtained by the following formula:
；
Wherein C up denotes an upper limit value of the data amount.
The technical effects of the technical scheme are as follows: and calculating a lower limit value and an upper limit value of the data quantity corresponding to the data quantity constraint condition by combining the first constraint condition parameter, the second constraint condition parameter and the data quantity parameter coefficient, so that the range of capturing target data in a data sharing mechanism is defined. The method is beneficial to avoiding the situation that the burden of a system is increased due to excessive data grabbing or the situation that the data is incomplete due to insufficient data grabbing, and improves the accuracy and efficiency of data sharing. By setting the lower limit value and the upper limit value of the data volume, the data transmission can be ensured to be effective every time, and the transmission of invalid data is avoided. This helps to reduce the waste of resources in the data transmission process and improve the efficiency of data transmission. The calculation mode of the lower limit value and the upper limit value of the data volume considers the actual running conditions of the cloud database and the local cache database, and comprises factors such as data transmission requirements, the data volume generated in unit time, data volume parameter coefficients and the like. This enables the data sharing mechanism to adaptively adjust according to different operating environments and requirements, improving the flexibility of the mechanism. By setting reasonable data volume constraint conditions, excessive sensitive information can be prevented from being leaked in the data sharing process, so that the safety of data is guaranteed. Meanwhile, the setting of the data quantity constraint condition is also beneficial to preventing the data from being tampered or destroyed maliciously, and the safety of the data is further enhanced.
In summary, according to the technical scheme, the first constraint condition parameter, the second constraint condition parameter and the data quantity parameter coefficient are combined to calculate the lower limit value and the upper limit value of the data quantity corresponding to the data quantity constraint condition, so that the data transmission efficiency is optimized, the accuracy and the flexibility of data sharing are improved, and the safety of data is guaranteed. This provides a more reliable and efficient data sharing solution for practical applications.
In this embodiment, by generating a data transmission network protocol between the cloud database and the local cache database, the module can accurately acquire target data, so as to ensure accuracy and reliability of data used in an analysis process, and a data sharing mechanism established based on data transmission features not only improves data crawling efficiency, but also reduces unnecessary data transmission, thereby reducing network load and cost, acquiring log information in the data crawling process in real time, finding and solving problems in the data crawling process in time, detecting potential safety risks, and enhancing security of the whole data crawling and analysis process.
In this embodiment, the intelligent analysis unit includes:
The statistical analysis module is used for:
Based on the distribution characteristics and data characteristics of the target data, judging the characteristics of the overall data through reference data, such as hypothesis test, confidence interval and the like, researching the relation among variables, predicting the change trend of dependent variables, dividing the target data into different clusters, and respectively calculating the distance between each word vector in any cluster and the standard word vector corresponding to any cluster;
Screening out keywords corresponding to the word vector with the minimum distance as target keywords in any cluster, and determining the structure in the target data based on the target keywords;
Trend prediction module for:
Extracting time sequence data of target data, performing data stability test based on the time sequence data, and judging whether the time sequence data shows periodic change along with time change, such as a support vector machine, a neural network, a random forest and the like, for predicting and classifying tasks;
based on the data characteristics of the target data, matching a prediction model, and based on the prediction model, predicting future time series data corresponding to the target data;
The abnormality monitoring module is used for:
based on the data type and the anomaly type, an anomaly detection algorithm is matched in an anomaly detection database, such as Z-score, IQR, isolated forest, one-Class SVM, DBSCAN and the like, anomaly values which are obviously different from most data points are detected, and detected anomaly data points are extracted;
Processing the detected abnormal value, such as deleting, replacing or further analyzing, integrating the detected abnormal data points to generate an abnormal data set, evaluating the accuracy of the abnormal detection result, such as indexes of accuracy, recall rate, F1 score and the like, helping users to better understand data, find potential rules and make intelligent decisions;
in this embodiment, the visualization unit includes:
The visual determining module is used for determining a visual category, such as a line graph, a histogram, a scatter diagram, a thermodynamic diagram and the like, for example, the line graph is suitable for showing trend, the histogram is suitable for comparing quantity, the scatter diagram is suitable for revealing relation and the like, mapping analysis data into a graph corresponding to the visual category, determining visual elements in the graph, such as color, size, shape, position and the like, and matching corresponding color coding schemes based on different visual elements;
The graphic generation module is used for generating a visual graphic of analysis data according to a color coding scheme by using a graphic library, and adding interactive functions such as zooming, translation, hovering prompt and the like to the graphic, so that the user experience is improved, the graphic is attractive, and the high interactivity is realized;
The chart display module is used for determining the position and the size of the visual graph on the display interface and the distribution of each element in the visual graph, and rendering the generated visual graph in the user interface to ensure that the chart is clear and attractive;
And the user interaction module is used for receiving interaction input instructions of a user, such as clicking, dragging, zooming and the like, and providing feedback according to the interaction input instructions of the user.
In the embodiment, a proper visual category is selected according to the type of the analysis result, and the best graph type and visual element are automatically matched, so that accurate and visual expression of data is ensured, efficient management and access of the data are ensured, visual analysis results which are visual and easy to understand are provided, a user is ensured to intuitively see the analysis results, the efficiency and accuracy of visual analysis of the data are greatly improved, the participation degree and understanding depth of the user are greatly enhanced by the interaction function, the user can search and analyze the data more deeply, and the data analysis experience of the user is improved.
Referring to fig. 2, the invention provides a cloud data visualization analysis method based on artificial intelligence, which comprises the following steps:
Determining analysis targets and requirements: the data type and the purpose which need to be analyzed are determined, and the data type and the data range which need to support the analysis target are determined, wherein the data type and the data range comprise historical data, real-time data, third party data and the like;
Data crawling: determining a source of data based on data requirements, such as an API (application program interface), a database, a third-party website and the like, crawling target data from a cloud database based on a crawler program, and storing the target data in a local cache database;
And (3) data processing: the operation of cleaning, preprocessing and extracting the characteristics of the crawled data ensures the quality and accuracy of the data, and extracts the characteristics which are valuable for analysis, such as statistics, time sequences, text keywords and the like from the data;
Data comparison: comparing the existing data with the newly acquired target data by utilizing a data comparison model, and finding out the difference and the similarity between the data;
Intelligent analysis: deep analysis is carried out on the compared data, including statistical analysis, trend prediction, association analysis and the like, and valuable information is extracted;
Visual display: the analysis result is displayed in the form of an intuitive graph, so that the user can understand and analyze conveniently, an interactive function is provided, the user is allowed to perform operations such as screening, sorting, amplifying and shrinking of data, and the readability and the understandability of the data are improved.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (11)
1. Cloud data visual analysis system based on artificial intelligence, its characterized in that: comprising the following steps:
the data crawling unit is used for determining a target range of cloud data to be crawled, determining a data crawling rule based on the target range of the crawled cloud data, and crawling the target data from the cloud database based on the crawling rule by using a web crawler technology;
the data processing unit is used for carrying out data preprocessing on the captured target data, extracting key features from the preprocessed target data, classifying the target data based on the extracted key features, and storing the target data into the local cache database according to a classification result;
The data comparison unit is used for extracting the existing data from the local cache database as reference data, comparing the target data with the reference data based on a comparison model, determining the difference and the change of the data based on the comparison result, and generating a comparison report;
the intelligent analysis unit is used for importing the comparison report into the statistical analysis model, carrying out deep analysis on the comparison data and determining an analysis result;
and the visualization unit is used for matching the corresponding visualization type according to the type and the characteristics of the analysis result, converting the analysis result into an intuitive graph, and displaying the generated graph to a user so as to facilitate the user to intuitively understand and analyze the data.
2. The artificial intelligence based cloud data visualization analysis system of claim 1, wherein: a data crawling unit comprising:
the target data identification module is used for analyzing the data source and determining a target range to be crawled based on an analysis result;
The data grabbing module is used for matching corresponding crawling rules based on the structure, the format and the storage mode of the data, and grabbing target data in a target range based on the crawling rules;
and the data monitoring module is used for acquiring log information in the data crawling process and simultaneously monitoring the state and performance of the data crawling in real time.
3. The artificial intelligence based cloud data visualization analysis system of claim 2, wherein: the data grabbing module grabs data based on a crawling rule, and specifically comprises the following steps:
Extracting standard network flow of a local cache data node from a cloud database, and determining initial data cache characteristics of the local cache data node according to the standard network flow;
obtaining a crawling rule of a local cache database, and generating a data transmission network protocol between the cloud database and the local cache database according to the crawling rule and the initial data cache characteristic of a local cache data node;
invoking a data sample in the local cache database, and operating the data transmission network protocol based on the data sample to obtain an operation result;
According to the operation result, acquiring an interaction record between a local cache database and a local cache data node, and according to the interaction record, acquiring the data transmission characteristics of a data transmission network protocol;
and establishing a data sharing mechanism of the cloud database and the local cache database based on the data transmission characteristics, and capturing target data based on the data sharing mechanism.
4. The artificial intelligence based cloud data visualization analysis system of claim 3, wherein: establishing a data sharing mechanism of a cloud database and a local cache database based on data transmission characteristics, capturing target data based on the data sharing mechanism, and comprising the following steps:
Invoking a data transmission requirement between a cloud database and a local cache database, wherein the data transmission requirement comprises a maximum transmission data amount of single data transmission and a maximum transmission frequency of data in unit time;
And calling the data quantity generated in the unit time of the cloud database and the local cache database, and acquiring a data quantity parameter coefficient by utilizing the data quantity generated in the unit time of the cloud database and the local cache database, wherein the data quantity parameter coefficient is acquired by the following formula:
；
wherein c represents a data amount parameter coefficient; n represents the total number of unit time which the cloud database and the local cache database run; c 01i and C 02i respectively represent the data quantity to be shared generated by the cloud database and the local cache database in the ith unit time; c z01i and C z02i represent total data amounts generated by the cloud database and the local cache database at the ith unit time, respectively; c 01 and c 02 represent a first data amount fluctuation coefficient and a second data amount fluctuation coefficient, respectively;
Combining the data quantity parameter coefficients according to the data transmission requirements between the cloud database and the local cache database, and acquiring data quantity constraint conditions corresponding to the grabbing target data of a data sharing mechanism between the cloud database and the local cache database;
Setting a data export interface in a cloud database, wherein the data export interface of the cloud database is used for exporting data according to a preset format and protocol;
Setting a data import interface in a local cache database, wherein the data export interface of the local cache database is used for receiving data exported by a cloud database and updating the local cache;
setting the data volume of target data to be grabbed for each data sharing according to the data volume constraint condition, and carrying out data grabbing according to the data volume of the target data.
5. The artificial intelligence based cloud data visualization analysis system of claim 4, wherein: according to the data transmission requirement between the cloud database and the local cache database and combining the data volume parameter coefficients, acquiring the data volume constraint condition corresponding to the grabbing target data by a data sharing mechanism between the cloud database and the local cache database comprises the following steps:
The maximum transmission data quantity of the single data transmission contained in the data transmission requirement is called, and a first constraint condition parameter is obtained according to the maximum transmission data quantity of the single data transmission; the first constraint condition parameters are obtained through the following formula:
；
Wherein λ 01 represents a first constraint parameter; δ 01 represents the first adjustment coefficient; c max denotes the maximum transmission data amount of a single data transmission;
the maximum data transmission frequency in the unit time contained in the data transmission requirement is called, and a second constraint condition parameter is obtained according to the maximum data transmission frequency in the unit time; the second constraint condition parameters are obtained through the following formula:
；
Wherein λ 02 represents a second constraint parameter; δ 02 represents the second adjustment coefficient; n c represents the data transmission times corresponding to the maximum data transmission frequency in unit time; n 01i and N 02i respectively represent the data generation times of the data to be shared, which are generated by the cloud database and the local cache database in the ith unit time;
and acquiring a data volume constraint condition corresponding to the target data by a data sharing mechanism between the cloud database and the local cache database according to the first constraint condition parameter and the second constraint condition parameter and combining the data volume parameter coefficient.
6. The artificial intelligence based cloud data visualization analysis system of claim 5, wherein: acquiring the data volume constraint condition corresponding to the target data by a data sharing mechanism between the cloud database and the local cache database according to the first constraint condition parameter and the second constraint condition parameter and combining the data volume parameter coefficient, wherein the data volume constraint condition comprises the following steps:
Calling the first constraint condition parameter and the second constraint condition parameter;
Calling a data quantity parameter coefficient;
Acquiring a data volume lower limit value corresponding to the data volume constraint condition by combining the first constraint condition parameter with a data volume parameter coefficient; the lower limit value of the data quantity is obtained through the following formula:
；
Wherein C down represents a data amount lower limit value; c min01 and C min02 represent the minimum data amount to be shared generated by the cloud database and the local cache database in a unit time;
acquiring a data volume upper limit value corresponding to the data volume constraint condition by combining the second constraint condition parameters with the data volume parameter coefficients; wherein, the data volume upper limit value is obtained by the following formula:
；
Wherein C up denotes an upper limit value of the data amount.
7. The artificial intelligence based cloud data visualization analysis system of claim 3, wherein: a data processing unit comprising:
the data reading module is used for reading the target data captured from the cloud database, verifying the integrity and the accuracy of the target data and ensuring that the data is not damaged or lost;
the data preprocessing module is used for cleaning, converting and missing value processing of target data;
The feature extraction module is used for extracting key features according to the characteristics and analysis requirements of the target data and converting the extracted key features;
the data classification module is used for classifying the preprocessed target data through a trained classification model according to the key characteristics and classification requirements of the target data.
8. The artificial intelligence based cloud data visualization analysis system of claim 7, wherein: a data comparison unit comprising:
The data extraction module is used for selecting a data set which needs to be used as a comparison standard from the local cache database and extracting the selected standard data to the data comparison module;
The data comparison module is used for inputting the acquired target data and the extracted reference data into the comparison module for comparison, determining the difference and change of the target data and the reference data and determining the comparison result;
and the comparison report generation module is used for filling the report content according to the comparison result and outputting the generated comparison report to the intelligent analysis unit.
9. The artificial intelligence based cloud data visualization analysis system of claim 8, wherein: an intelligent analysis unit comprising:
The statistical analysis module is used for:
Dividing target data into different clusters based on the distribution characteristics and data characteristics of the target data, and respectively calculating the distance between each word vector in any cluster and a standard word vector corresponding to any cluster;
Screening out keywords corresponding to the word vector with the minimum distance as target keywords in any cluster, and determining the structure in the target data based on the target keywords;
Trend prediction module for:
extracting time sequence data of target data, performing data stability test based on the time sequence data, and judging whether the time sequence data shows periodic change along with time change;
based on the data characteristics of the target data, matching a prediction model, and based on the prediction model, predicting future time series data corresponding to the target data;
The abnormality monitoring module is used for:
Detecting abnormal values which are significantly different from most data points based on the data type and the abnormal type and matching an abnormal detection algorithm in an abnormal detection database, and extracting detection abnormal data points;
and processing the detected abnormal value, integrating the detected abnormal data points to generate an abnormal data set, and evaluating the accuracy of an abnormal detection result.
10. The artificial intelligence based cloud data visualization analysis system of claim 9, wherein: a visualization unit comprising:
The visual determining module is used for determining a visual category according to the type of the analysis result, mapping analysis data into a graph corresponding to the visual category, determining visual elements in the graph, and matching corresponding color coding schemes based on different visual elements;
The graphic generation module is used for generating a visual graphic of analysis data according to a color coding scheme by using a graphic library;
the chart display module is used for determining the position and the size of the visual graph on the display interface and the distribution of each element in the visual graph, and rendering the generated visual graph in the user interface;
and the user interaction module is used for receiving the interaction input instruction of the user and providing feedback according to the interaction input instruction of the user.
11. The artificial intelligence-based cloud data visual analysis method is realized based on the artificial intelligence-based cloud data visual analysis system as claimed in claim 7, and is characterized in that: the method comprises the following steps:
Step one: determining analysis targets and requirements: determining the data type and the data range which need to support the analysis target according to the data type and the purpose which need to be analyzed;
Step two: data crawling: determining a source of data based on data requirements, crawling target data from a cloud database based on a crawler program, and storing the target data in a local cache database;
step three: and (3) data processing: performing operations of cleaning, preprocessing and feature extraction on the crawled data, and extracting features valuable for analysis from the data;
step four: data comparison: comparing the existing data with the newly acquired target data by utilizing a data comparison model, and finding out the difference and the similarity between the data;
step five: intelligent analysis: deep analysis is carried out on the compared data, and valuable information is extracted;
Step six: visual display: the analysis result is displayed in the form of visual graphics, so that the user can understand and analyze the analysis result conveniently, and the interactive function is provided.
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