CN114116961B - Information analysis method based on big data - Google Patents
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Abstract
The application relates to an information analysis method based on big data, which comprises the steps of obtaining loaded current data to be analyzed, and carrying out data analysis on the current data to be analyzed to generate current text data to be analyzed and current graphic data to be analyzed; acquiring a primary processing text to be analyzed, and simultaneously acquiring a primary processing graph to be analyzed; importing a primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing a primary processing graph to be analyzed into the big data information analysis model, and acquiring an output graph analysis data set; and acquiring the intersection of the data of the text analysis data set and the graphic analysis data set, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result. The application greatly improves the accuracy of information analysis and the speed of data analysis result generation.
Description
Technical Field
The application relates to the technical field of big data, in particular to an information analysis method based on big data.
Background
Big data, or huge amount of data, refers to information that the size of the data is so large that the data cannot be retrieved, managed, processed and consolidated in a reasonable time through the current mainstream software tools, and becomes a more positive goal for helping business operation decisions.
At present, information analysis methods based on big data are various, and a big data information analysis system based on cloud computing as disclosed in the patent of the invention with the application number of CN202011163338.0 belongs to the technical field of big data analysis, and comprises a big data acquisition module, wherein the output end of the big data acquisition module is connected with a big data input module, the output end of the big data input module is connected with a data preprocessing unit, the output end of the data preprocessing unit is connected with a data calling module, and the output end of the data calling module is connected with a data mining unit.
Although the technical scheme has a certain effect, the requirement of setting space of the server is reduced, meanwhile, data is defined when large data is input, the data processing difficulty is effectively reduced through data preprocessing, the processing efficiency is remarkably improved, the interaction requirement of large data analysis and cloud computing cooperation is met, the application of the method in information analysis still has the defect that the method cannot accurately and efficiently analyze information queried by a user when the user queries the information, and further the analysis result is obtained, so that the problems of low analysis speed and low analysis efficiency are often caused.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a big data-based information analysis method that can improve the accuracy of data analysis and the speed of data analysis result generation.
The technical scheme of the invention is as follows:
An information analysis method based on big data, the method comprising:
Step S10: acquiring loaded current data to be analyzed, carrying out data analysis on the current data to be analyzed, and generating current text data to be analyzed and current graphic data to be analyzed after the data analysis;
Step S20: preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing;
Step S30: importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set;
step S40: and acquiring the intersection of the data of the text analysis data set and the graph analysis data set, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result.
Specifically, the text processing rule is to perform text correction processing, text adding and deleting processing and text approximate semantic processing on the text to be analyzed;
The graphic processing rule is that graphic filtering processing, graphic brightness adjusting processing, graphic contour extracting processing and graphic layer extracting processing are carried out on the current graphic data to be analyzed;
Step S20: preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing; the method specifically comprises the following steps:
Step S210: performing text correction processing on the current text data to be analyzed, and generating corrected text;
Step S220: performing text adding and deleting processing on the current text data to be analyzed, and generating added and deleted text;
step S230: performing text approximate semantic processing on the current text data to be analyzed, generating an approximate voice text, and generating a primary processing text to be analyzed based on the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text;
Step S240: performing graphic filtering processing, graphic brightness adjustment processing, graphic contour extraction processing and graphic layer extraction processing on the current graphic data to be analyzed, and respectively generating filtered graphic data, graphic data after brightness adjustment, contour data to be analyzed and graphic layer data to be analyzed;
Step S250: and generating a primary processing graph to be analyzed according to the filtered graph data, the graph data after brightness adjustment, the contour data to be analyzed and the graph layer data to be analyzed.
Specifically, step S30: importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set; the method specifically comprises the following steps:
Step S310: respectively inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text in the primary processing text to be analyzed into a pre-established big data information analysis model, and respectively obtaining a text analysis result;
Step S320: removing text analysis results exceeding a preset first standard result range from each text analysis result, wherein the text analysis results not exceeding the first standard result range are matching analysis results;
step S330: generating a text analysis data set according to each matching analysis result;
Step S340: respectively inputting the filtered graphic data, the brightness-adjusted graphic data, the contour data and the layer data to be analyzed in the primary processing graphic to be analyzed into a pre-established big data information analysis model, and respectively obtaining a graphic analysis result;
Step S350: and generating a matched graph result according to the graph analysis result and a preset second standard result range, and generating a graph analysis data set according to each matched graph result.
Specifically, the method further comprises:
Step S510: acquiring main body head portrait image data of an information demand main body when the current data to be analyzed is loaded;
step S520: generating an information analysis library establishment query request according to the main body head portrait image data, wherein the information analysis library establishment query request is used for generating a compliant display interface, the compliant display interface is displayed with the query request, and when the information demand main body agrees to establish the information analysis library, the query request is replied;
Step S530: acquiring an information base agreement establishment response of the information demand main body agreeing to establish an information analysis base, and generating a basic information retrieval instruction based on the information base agreement establishment response;
Step S540: according to the basic information calling instruction, main body basic information of the information demand main body is called based on big data, wherein the main body basic information comprises main body basic information, historical information demand data and historical information satisfaction data;
Step S550: generating a main body characteristic tag according to the main body basic information, and generating supervised learning training data based on the historical information demand data and the historical information satisfaction data;
step S560: generating an information analysis using habit database according to the main body characteristic tag and the supervised learning training data;
Step S570: and performing supervised learning on the big data information analysis model by using the supervised learning training data in the habit database, and generating an updated information analysis model.
Specifically, step S40: the intersection of the data is taken from the text analysis data set and the graph analysis data set, an initial data analysis result is generated, an information analysis display interface is generated according to the initial data analysis result, and the information analysis display interface is used for displaying the initial data analysis result and then further comprises:
Step S410: obtaining a result data splitting trigger instruction of the initial data analysis result on the information analysis display interface;
Step S420: acquiring selected area data selected from the initial data analysis result according to the result data splitting trigger instruction;
Step S430: acquiring a selected data display area corresponding to the selected area data on the information analysis display interface, wherein the area except the selected data display area on the information analysis display interface is a solidification display area;
Step S440: generating data analysis audio according to the selected area data, and simultaneously generating a solidification area constant instruction, wherein the data analysis audio is analysis audio of the selected area data;
step S450: the cure display area is maintained unchanged based on the cure area constant instruction.
Specifically, an information analysis system based on big data, the system includes:
The data analysis module is used for acquiring the loaded current data to be analyzed, carrying out data analysis on the current data to be analyzed, and generating the current text data to be analyzed and the current graphic data to be analyzed after the data analysis;
the rule processing module is used for preprocessing the text to be analyzed on the current text data to be analyzed according to a preset text processing rule, acquiring a primary processed text to be analyzed after preprocessing, preprocessing the image to be analyzed on the current graphic data to be analyzed according to a preset graphic processing rule, and acquiring a primary processed graphic to be analyzed after preprocessing;
The text importing module is used for importing the text to be analyzed in the primary processing to a pre-built big data information analysis model, acquiring an output text analysis data set, importing the graph to be analyzed in the primary processing to the pre-built big data information analysis model, and acquiring an output graph analysis data set;
The result generation module is used for acquiring the intersection of the data of the text analysis data set and the graph analysis data set, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result.
Specifically, the rule processing module is further configured to:
Performing text correction processing on the current text data to be analyzed, and generating corrected text; performing text adding and deleting processing on the current text data to be analyzed, and generating added and deleted text; performing text approximate semantic processing on the current text data to be analyzed, generating an approximate voice text, and generating a primary processing text to be analyzed based on the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text; performing graphic filtering processing, graphic brightness adjustment processing, graphic contour extraction processing and graphic layer extraction processing on the current graphic data to be analyzed, and respectively generating filtered graphic data, graphic data after brightness adjustment, contour data to be analyzed and graphic layer data to be analyzed; generating a primary processing graph to be analyzed according to the filtered graph data, the brightness-adjusted graph data, the contour data to be analyzed and the graph layer data to be analyzed;
the text import module is further configured to:
Respectively inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text in the primary processing text to be analyzed into a pre-established big data information analysis model, and respectively obtaining a text analysis result; removing text analysis results exceeding a preset first standard result range from each text analysis result, wherein the text analysis results not exceeding the first standard result range are matching analysis results; generating a text analysis data set according to each matching analysis result; respectively inputting the filtered graphic data, the brightness-adjusted graphic data, the contour data and the layer data to be analyzed in the primary processing graphic to be analyzed into a pre-established big data information analysis model, and respectively obtaining a graphic analysis result; and generating a matched graph result according to the graph analysis result and a preset second standard result range, and generating a graph analysis data set according to each matched graph result.
Specifically, the system further comprises a data analysis module for:
Acquiring main body head portrait image data of an information demand main body when the current data to be analyzed is loaded; generating an information analysis library establishment query request according to the main body head portrait image data, wherein the information analysis library establishment query request is used for generating a compliant display interface, the compliant display interface is displayed with the query request, and when the information demand main body agrees to establish the information analysis library, the query request is replied; acquiring an information base agreement establishment response of the information demand main body agreeing to establish an information analysis base, and generating a basic information retrieval instruction based on the information base agreement establishment response; according to the basic information calling instruction, main body basic information of the information demand main body is called based on big data, wherein the main body basic information comprises main body basic information, historical information demand data and historical information satisfaction data; generating a main body characteristic tag according to the main body basic information, and generating supervised learning training data based on the historical information demand data and the historical information satisfaction data; generating an information analysis using habit database according to the main body characteristic tag and the supervised learning training data; performing supervised learning on the big data information analysis model by using supervised learning training data in a habit database, and generating an updated information analysis model; obtaining a result data splitting trigger instruction of the initial data analysis result on the information analysis display interface;
Acquiring selected area data selected from the initial data analysis result according to the result data splitting trigger instruction; acquiring a selected data display area corresponding to the selected area data on the information analysis display interface, wherein the area except the selected data display area on the information analysis display interface is a solidification display area; generating data analysis audio according to the selected area data, and simultaneously generating a solidification area constant instruction, wherein the data analysis audio is analysis audio of the selected area data; the cure display area is maintained unchanged based on the cure area constant instruction.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the big data based information analysis method described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the big data based information analysis method described above.
The invention has the following technical effects:
According to the big data-based information analysis method, the loaded current data to be analyzed are sequentially obtained, the data analysis is carried out on the current data to be analyzed, and the current text data to be analyzed and the current graphic data to be analyzed are generated after the data analysis; preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing; importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set; the invention acquires the intersection of the data of the text analysis data set and the graph analysis data set, generates an initial data analysis result, and generates an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result, and can be seen that when the current data to be analyzed is acquired, the data is firstly analyzed to acquire the analyzed current text data to be analyzed and the analyzed graph data to be analyzed, the data is analyzed to realize the refinement and the resolution of the data, the data after the refinement and the resolution are analyzed to improve the accuracy of analysis, and then the text to be analyzed and the graph to be analyzed to be primarily processed are respectively preprocessed by the text to be analyzed and the image to be analyzed to be primarily processed, and then, carrying out big data analysis on the primary processing text to be analyzed and the primary processing graph to be analyzed based on big data by utilizing a pre-constructed big data information analysis model, further obtaining a graph analysis dataset, then obtaining the intersection of the data of the text analysis dataset and the graph analysis dataset for realizing more accurate analysis of the data, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result, and meanwhile, realizing the visual display of the data through the information analysis display interface, so that the accuracy of information analysis is greatly improved, the generation speed of the data analysis result is greatly improved, and the comfort of information display is improved.
Drawings
FIG. 1 is a flow chart of a method of big data based information analysis in one embodiment;
FIG. 2 is a block diagram of the information analysis system based on big data in one embodiment;
FIG. 3 is an internal block diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In one embodiment, as shown in fig. 1, there is provided an information analysis method based on big data, the method comprising:
Step S10: acquiring loaded current data to be analyzed, carrying out data analysis on the current data to be analyzed, and generating current text data to be analyzed and current graphic data to be analyzed after the data analysis;
specifically, when the current data to be analyzed is obtained, the data is analyzed to obtain the analyzed text data and the analyzed graphic data, and the data is analyzed to realize the refinement and the resolution of the data, so that the data after the refinement and the resolution is analyzed, and the accuracy of the analysis is improved.
Step S20: preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing;
Specifically, the text to be analyzed and the image to be analyzed are preprocessed respectively to obtain the primarily processed text to be analyzed and the primarily processed image to be analyzed, and the preparation before the information analysis is realized through the preprocessing step, so that the normal operation of the subsequent information analysis is ensured, and the information analysis efficiency is improved.
Step S30: importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set;
Specifically, a pre-constructed big data information analysis model is utilized to realize big data analysis on the primary processing text to be analyzed and the primary processing graph to be analyzed based on big data, and then a graph analysis data set is obtained.
Step S40: and acquiring the intersection of the data of the text analysis data set and the graph analysis data set, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result.
Specifically, in order to realize more accurate analysis of data, further intersection of the data is acquired for the text analysis data set and the graphic analysis data set, an initial data analysis result is generated, and an information analysis display interface is generated according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result, and meanwhile, the visual display of the data is realized through the information analysis display interface, so that the accuracy of information analysis is greatly improved, the speed of data analysis result generation is greatly improved, and meanwhile, the comfort of information display is improved.
In one embodiment, the text processing rule is that text correction processing, text adding and deleting processing and text approximate semantic processing are performed on the text to be analyzed;
The graphic processing rule is that graphic filtering processing, graphic brightness adjusting processing, graphic contour extracting processing and graphic layer extracting processing are carried out on the current graphic data to be analyzed;
Step S20: preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing; the method specifically comprises the following steps:
Step S210: performing text correction processing on the current text data to be analyzed, and generating corrected text;
Step S220: performing text adding and deleting processing on the current text data to be analyzed, and generating added and deleted text;
step S230: performing text approximate semantic processing on the current text data to be analyzed, generating an approximate voice text, and generating a primary processing text to be analyzed based on the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text;
Specifically, after obtaining a text needing to be analyzed, the text correction processing, the text adding and deleting processing and the text approximation semantic processing are implemented, so that the accuracy of information analysis is improved, the number of the text is increased, and the compatibility of information analysis is improved.
That is, after the current text data to be analyzed is obtained, the corrected text, the added and deleted text and the similar voice text are respectively obtained after the text correction processing, the text adding and deleting processing and the text similar semantic processing, so that the initial processing text to be analyzed is generated, the expansion of information with the same or similar meaning of the data to be analyzed is realized, and the accuracy, the breadth and the compatibility of the subsequent information analysis are improved.
Step S240: performing graphic filtering processing, graphic brightness adjustment processing, graphic contour extraction processing and graphic layer extraction processing on the current graphic data to be analyzed, and respectively generating filtered graphic data, graphic data after brightness adjustment, contour data to be analyzed and graphic layer data to be analyzed;
Step S250: and generating a primary processing graph to be analyzed according to the filtered graph data, the graph data after brightness adjustment, the contour data to be analyzed and the graph layer data to be analyzed.
Similarly, similar to the processing process of the current text data to be analyzed, the processing of the current graphic data to be analyzed also includes generating filtered graphic data, brightness-adjusted graphic data, contour data to be analyzed and graphic layer data to be analyzed respectively by performing graphic filtering processing, graphic brightness-adjusted processing, graphic contour extraction processing and graphic layer extraction processing, wherein the graphic filtering processing is used for filtering interference factors, the graphic brightness-adjusted processing is used for filtering information analysis errors of graphics caused by brightness factors, the contour data to be analyzed is used for carrying out data analysis according to graphic contours, the graphic layer data to be analyzed is used for carrying out information analysis according to graphic layers, and the first processing of the graphic to be analyzed is generated according to the filtered graphic data, the brightness-adjusted graphic data, the contour data to be analyzed and the graphic layer data to be analyzed, so that compatibility of subsequent information analysis is realized on the basis of the further current graphic data to be analyzed.
Taking information analysis as a retrieval example, through the preprocessing step in the embodiment, the accuracy and the retrieval range of the subsequent retrieval result are greatly improved, and the retrieval efficiency is further improved.
In one embodiment, step S30: importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set; the method specifically comprises the following steps:
Step S310: respectively inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text in the primary processing text to be analyzed into a pre-established big data information analysis model, and respectively obtaining a text analysis result;
And after one text analysis result is a data in the primary processing text to be analyzed is input into the big data information analysis model, outputting a result value, namely, inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text into a pre-established big data information analysis model respectively, and respectively obtaining one text analysis result.
The big data information analysis model is built in advance and is built based on big data technology.
Step S320: removing text analysis results exceeding a preset first standard result range from each text analysis result, wherein the text analysis results not exceeding the first standard result range are matching analysis results;
step S330: generating a text analysis data set according to each matching analysis result;
specifically, the first standard result range is preset, the number limit of the analysis results and the similarity threshold are included in the first standard result range, and when the number limit and the similarity threshold are met at the same time, the screening of the matching analysis results is achieved by setting the first standard result range so that the accuracy of the final result is improved.
Step S340: respectively inputting the filtered graphic data, the brightness-adjusted graphic data, the contour data and the layer data to be analyzed in the primary processing graphic to be analyzed into a pre-established big data information analysis model, and respectively obtaining a graphic analysis result;
Step S350: and generating a matched graph result according to the graph analysis result and a preset second standard result range, and generating a graph analysis data set according to each matched graph result.
Similarly, in order to achieve stability of a final result, the filtered graphic data, the brightness-adjusted graphic data, the outline data to be analyzed and the layer data to be analyzed in the primary processing graphic to be analyzed are input into a pre-established big data information analysis model respectively, a graphic analysis result is obtained respectively, then a matched graphic result is generated according to the graphic analysis result and a preset second standard result range, and a graphic analysis data set is generated according to each matched graphic result.
In one embodiment, the method further comprises:
Step S510: acquiring main body head portrait image data of an information demand main body when the current data to be analyzed is loaded;
Specifically, the main body head portrait information is feature information of the user, such as face information or gesture password information.
Step S520: generating an information analysis library establishment query request according to the main body head portrait image data, wherein the information analysis library establishment query request is used for generating a compliant display interface, the compliant display interface is displayed with the query request, and when the information demand main body agrees to establish the information analysis library, the query request is replied;
Step S530: acquiring an information base agreement establishment response of the information demand main body agreeing to establish an information analysis base, and generating a basic information retrieval instruction based on the information base agreement establishment response;
Specifically, the obtaining the information requirement subject agrees to build an information base agrees to build a reply to the information analysis base, the reply including but not limited to a voice reply, a text reply, fingerprint verification, password verification, and the like.
Step S540: according to the basic information calling instruction, main body basic information of the information demand main body is called based on big data, wherein the main body basic information comprises main body basic information, historical information demand data and historical information satisfaction data;
the main body basic information is basic identity information, such as working information, identity information, position information and the like, which is recorded on each information analysis platform by an information demand main body based on big data, the history demand data is information when history information analysis is carried out in the past, and the history information satisfaction data is satisfaction result information after information analysis is carried out in the past.
Step S550: generating a main body characteristic tag according to the main body basic information, and generating supervised learning training data based on the historical information demand data and the historical information satisfaction data;
And the supervision learning training data is used for facilitating the subsequent reinforcement of the big data information analysis model.
Step S560: generating an information analysis using habit database according to the main body characteristic tag and the supervised learning training data;
Step S570: and performing supervised learning on the big data information analysis model by using the supervised learning training data in the habit database, and generating an updated information analysis model.
Specifically, in order to realize more accurate analysis of the subsequent data, the big data information analysis model needs to be subjected to supervised learning and training, and the data sources during specific training are the historical information demand data and the historical information satisfaction data, so that the big data information analysis model is subjected to supervised learning by generating an information analysis using habit database according to the main feature tag and the supervised learning training data, and an updated information analysis model is generated, and is used for the subsequent data analysis, and then the big data information analysis model can be enhanced after each information analysis.
In one embodiment, step S40: the intersection of the data is taken from the text analysis data set and the graph analysis data set, an initial data analysis result is generated, an information analysis display interface is generated according to the initial data analysis result, and the information analysis display interface is used for displaying the initial data analysis result and then further comprises:
Step S410: obtaining a result data splitting trigger instruction of the initial data analysis result on the information analysis display interface;
Step S420: acquiring selected area data selected from the initial data analysis result according to the result data splitting trigger instruction;
Step S430: acquiring a selected data display area corresponding to the selected area data on the information analysis display interface, wherein the area except the selected data display area on the information analysis display interface is a solidification display area;
Step S440: generating data analysis audio according to the selected area data, and simultaneously generating a solidification area constant instruction, wherein the data analysis audio is analysis audio of the selected area data;
step S450: the cure display area is maintained unchanged based on the cure area constant instruction.
In this step, firstly, a result data splitting trigger instruction for the initial data analysis result on the information analysis display interface is obtained, then, selected area data selected from the initial data analysis result is obtained according to the result data splitting trigger instruction, then, a selected data display area corresponding to the selected area data on the information analysis display interface is obtained, the area except the selected data display area on the information analysis display interface is a solidification display area, and a solidification area constant instruction is generated, wherein the data analysis audio is analysis audio of the selected area data, on the one hand, audio analysis of the selected area data is realized through the data analysis audio, on the other hand, the solidification display area is kept unchanged based on the solidification area constant instruction, and user experience is improved.
In summary, when the current data to be analyzed is obtained, the data is analyzed to obtain the analyzed current text data to be analyzed and the analyzed graphic data to be analyzed, the data is analyzed to refine and split the data to analyze the data after refinement and split, the accuracy of analysis is improved, the text preprocessing to be analyzed and the image preprocessing to be analyzed are respectively carried out to obtain the text to be analyzed and the graphic to be analyzed to be processed, and then the pre-built big data information analysis model is utilized to realize big data analysis on the text to be analyzed and the graphic to be analyzed to obtain the graphic analysis data set, then in order to realize more accurate analysis of the data, the intersection of the text analysis data set and the graphic analysis data set is obtained to generate an initial data analysis result, and an information analysis display interface is generated according to the initial data analysis result, and is used for displaying the initial data analysis result, and meanwhile, the information analysis display interface is used for realizing visual display of the data, so that the accuracy of information is greatly improved, and the accuracy of data analysis is displayed is improved.
In one embodiment, as shown in FIG. 2, a big data based information analysis system, the system comprising:
The data analysis module is used for acquiring the loaded current data to be analyzed, carrying out data analysis on the current data to be analyzed, and generating the current text data to be analyzed and the current graphic data to be analyzed after the data analysis;
the rule processing module is used for preprocessing the text to be analyzed on the current text data to be analyzed according to a preset text processing rule, acquiring a primary processed text to be analyzed after preprocessing, preprocessing the image to be analyzed on the current graphic data to be analyzed according to a preset graphic processing rule, and acquiring a primary processed graphic to be analyzed after preprocessing;
The text importing module is used for importing the text to be analyzed in the primary processing to a pre-built big data information analysis model, acquiring an output text analysis data set, importing the graph to be analyzed in the primary processing to the pre-built big data information analysis model, and acquiring an output graph analysis data set;
The result generation module is used for acquiring the intersection of the data of the text analysis data set and the graph analysis data set, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result.
In one embodiment, the rule processing module is further configured to:
Performing text correction processing on the current text data to be analyzed, and generating corrected text; performing text adding and deleting processing on the current text data to be analyzed, and generating added and deleted text; performing text approximate semantic processing on the current text data to be analyzed, generating an approximate voice text, and generating a primary processing text to be analyzed based on the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text; performing graphic filtering processing, graphic brightness adjustment processing, graphic contour extraction processing and graphic layer extraction processing on the current graphic data to be analyzed, and respectively generating filtered graphic data, graphic data after brightness adjustment, contour data to be analyzed and graphic layer data to be analyzed; generating a primary processing graph to be analyzed according to the filtered graph data, the brightness-adjusted graph data, the contour data to be analyzed and the graph layer data to be analyzed;
the text import module is further configured to:
Respectively inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text in the primary processing text to be analyzed into a pre-established big data information analysis model, and respectively obtaining a text analysis result; removing text analysis results exceeding a preset first standard result range from each text analysis result, wherein the text analysis results not exceeding the first standard result range are matching analysis results; generating a text analysis data set according to each matching analysis result; respectively inputting the filtered graphic data, the brightness-adjusted graphic data, the contour data and the layer data to be analyzed in the primary processing graphic to be analyzed into a pre-established big data information analysis model, and respectively obtaining a graphic analysis result; and generating a matched graph result according to the graph analysis result and a preset second standard result range, and generating a graph analysis data set according to each matched graph result.
In one embodiment, the system further comprises a data analysis module for:
Acquiring main body head portrait image data of an information demand main body when the current data to be analyzed is loaded; generating an information analysis library establishment query request according to the main body head portrait image data, wherein the information analysis library establishment query request is used for generating a compliant display interface, the compliant display interface is displayed with the query request, and when the information demand main body agrees to establish the information analysis library, the query request is replied; acquiring an information base agreement establishment response of the information demand main body agreeing to establish an information analysis base, and generating a basic information retrieval instruction based on the information base agreement establishment response; according to the basic information calling instruction, main body basic information of the information demand main body is called based on big data, wherein the main body basic information comprises main body basic information, historical information demand data and historical information satisfaction data; generating a main body characteristic tag according to the main body basic information, and generating supervised learning training data based on the historical information demand data and the historical information satisfaction data; generating an information analysis using habit database according to the main body characteristic tag and the supervised learning training data; performing supervised learning on the big data information analysis model by using supervised learning training data in a habit database, and generating an updated information analysis model; obtaining a result data splitting trigger instruction of the initial data analysis result on the information analysis display interface;
Acquiring selected area data selected from the initial data analysis result according to the result data splitting trigger instruction; acquiring a selected data display area corresponding to the selected area data on the information analysis display interface, wherein the area except the selected data display area on the information analysis display interface is a solidification display area; generating data analysis audio according to the selected area data, and simultaneously generating a solidification area constant instruction, wherein the data analysis audio is analysis audio of the selected area data; the cure display area is maintained unchanged based on the cure area constant instruction.
In one embodiment, as shown in fig. 3, a computer device includes a memory storing a computer program and a processor implementing the steps of the big data based information analysis method described above when the processor executes the computer program.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the big data based information analysis method described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (8)
1. An information analysis method based on big data, the method comprising:
Step S10: acquiring loaded current data to be analyzed, carrying out data analysis on the current data to be analyzed, and generating current text data to be analyzed and current graphic data to be analyzed after the data analysis;
Step S20: preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing;
Step S30: importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set;
step S40: acquiring an intersection of data from the text analysis data set and the graphic analysis data set, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result;
The text processing rule is used for carrying out text correction processing, text adding and deleting processing and text approximate semantic processing on the text to be analyzed;
The graphic processing rule is that graphic filtering processing, graphic brightness adjusting processing, graphic contour extracting processing and graphic layer extracting processing are carried out on the current graphic data to be analyzed;
Step S20: preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing; the method specifically comprises the following steps:
step S210: performing text correction processing on the current text data to be analyzed, and generating corrected text;
step S220: performing text adding and deleting processing on the current text data to be analyzed, and generating added and deleted text;
step S230: performing text approximate semantic processing on the current text data to be analyzed, generating an approximate voice text, and generating a primary processing text to be analyzed based on the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text;
Step S240: performing graphic filtering processing, graphic brightness adjustment processing, graphic contour extraction processing and graphic layer extraction processing on the current graphic data to be analyzed, and respectively generating filtered graphic data, graphic data after brightness adjustment, contour data to be analyzed and graphic layer data to be analyzed;
Step S250: generating a primary processing graph to be analyzed according to the filtered graph data, the brightness-adjusted graph data, the contour data to be analyzed and the graph layer data to be analyzed;
Step S30: importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set; the method specifically comprises the following steps:
Step S310: respectively inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text in the primary processing text to be analyzed into a pre-established big data information analysis model, and respectively obtaining a text analysis result;
Step S320: removing text analysis results exceeding a preset first standard result range from each text analysis result, wherein the text analysis results not exceeding the first standard result range are matching analysis results;
step S330: generating a text analysis data set according to each matching analysis result;
Step S340: respectively inputting the filtered graphic data, the brightness-adjusted graphic data, the contour data and the layer data to be analyzed in the primary processing graphic to be analyzed into a pre-established big data information analysis model, and respectively obtaining a graphic analysis result;
Step S350: and generating a matched graph result according to the graph analysis result and a preset second standard result range, and generating a graph analysis data set according to each matched graph result.
2. The big data based information analysis method of claim 1, wherein the method further comprises:
Step S510: acquiring main body head portrait image data of an information demand main body when the current data to be analyzed is loaded;
step S520: generating an information analysis library establishment query request according to the main body head portrait image data, wherein the information analysis library establishment query request is used for generating a compliant display interface, the compliant display interface is displayed with the query request, and when the information demand main body agrees to establish the information analysis library, the query request is replied;
Step S530: acquiring an information base agreement establishment response of the information demand main body agreeing to establish an information analysis base, and generating a basic information retrieval instruction based on the information base agreement establishment response;
Step S540: according to the basic information calling instruction, main body basic information of the information demand main body is called based on big data, wherein the main body basic information comprises main body basic information, historical information demand data and historical information satisfaction data;
Step S550: generating a main body characteristic tag according to the main body basic information, and generating supervised learning training data based on the historical information demand data and the historical information satisfaction data;
step S560: generating an information analysis using habit database according to the main body characteristic tag and the supervised learning training data;
Step S570: and performing supervised learning on the big data information analysis model by using the supervised learning training data in the habit database, and generating an updated information analysis model.
3. The big data based information analysis method according to claim 1, wherein step S40: the intersection of the data is taken from the text analysis data set and the graph analysis data set, an initial data analysis result is generated, an information analysis display interface is generated according to the initial data analysis result, and the information analysis display interface is used for displaying the initial data analysis result and then further comprises:
Step S410: obtaining a result data splitting trigger instruction of the initial data analysis result on the information analysis display interface;
Step S420: acquiring selected area data selected from the initial data analysis result according to the result data splitting trigger instruction;
Step S430: acquiring a selected data display area corresponding to the selected area data on the information analysis display interface, wherein the area except the selected data display area on the information analysis display interface is a solidification display area;
Step S440: generating data analysis audio according to the selected area data, and simultaneously generating a solidification area constant instruction, wherein the data analysis audio is analysis audio of the selected area data;
step S450: the cure display area is maintained unchanged based on the cure area constant instruction.
4. An information analysis system based on big data, the system comprising:
The data analysis module is used for acquiring the loaded current data to be analyzed, carrying out data analysis on the current data to be analyzed, and generating the current text data to be analyzed and the current graphic data to be analyzed after the data analysis;
the rule processing module is used for preprocessing the text to be analyzed on the current text data to be analyzed according to a preset text processing rule, acquiring a primary processed text to be analyzed after preprocessing, preprocessing the image to be analyzed on the current graphic data to be analyzed according to a preset graphic processing rule, and acquiring a primary processed graphic to be analyzed after preprocessing;
The text importing module is used for importing the text to be analyzed in the primary processing to a pre-built big data information analysis model, acquiring an output text analysis data set, importing the graph to be analyzed in the primary processing to the pre-built big data information analysis model, and acquiring an output graph analysis data set;
The result generation module is used for acquiring the intersection of the data of the text analysis data set and the graph analysis data set, generating an initial data analysis result, and generating an information analysis display interface according to the initial data analysis result, wherein the information analysis display interface is used for displaying the initial data analysis result;
The text processing rule is used for carrying out text correction processing, text adding and deleting processing and text approximate semantic processing on the text to be analyzed;
The graphic processing rule is that graphic filtering processing, graphic brightness adjusting processing, graphic contour extracting processing and graphic layer extracting processing are carried out on the current graphic data to be analyzed;
Preprocessing the text to be analyzed according to preset text processing rules, acquiring a primarily processed text to be analyzed after preprocessing, preprocessing the image to be analyzed according to preset graphic processing rules, and acquiring a primarily processed graphic to be analyzed after preprocessing; the method specifically comprises the following steps:
step S210: performing text correction processing on the current text data to be analyzed, and generating corrected text;
step S220: performing text adding and deleting processing on the current text data to be analyzed, and generating added and deleted text;
step S230: performing text approximate semantic processing on the current text data to be analyzed, generating an approximate voice text, and generating a primary processing text to be analyzed based on the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text;
Step S240: performing graphic filtering processing, graphic brightness adjustment processing, graphic contour extraction processing and graphic layer extraction processing on the current graphic data to be analyzed, and respectively generating filtered graphic data, graphic data after brightness adjustment, contour data to be analyzed and graphic layer data to be analyzed;
Step S250: generating a primary processing graph to be analyzed according to the filtered graph data, the brightness-adjusted graph data, the contour data to be analyzed and the graph layer data to be analyzed;
Importing the primary processing text to be analyzed into a pre-constructed big data information analysis model, acquiring an output text analysis data set, importing the primary processing graph to be analyzed into the pre-constructed big data information analysis model, and acquiring an output graph analysis data set; the method specifically comprises the following steps:
Step S310: respectively inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text in the primary processing text to be analyzed into a pre-established big data information analysis model, and respectively obtaining a text analysis result;
Step S320: removing text analysis results exceeding a preset first standard result range from each text analysis result, wherein the text analysis results not exceeding the first standard result range are matching analysis results;
step S330: generating a text analysis data set according to each matching analysis result;
Step S340: respectively inputting the filtered graphic data, the brightness-adjusted graphic data, the contour data and the layer data to be analyzed in the primary processing graphic to be analyzed into a pre-established big data information analysis model, and respectively obtaining a graphic analysis result;
Step S350: and generating a matched graph result according to the graph analysis result and a preset second standard result range, and generating a graph analysis data set according to each matched graph result.
5. The big data based information analysis system of claim 4, wherein the rule processing module is further configured to:
Performing text correction processing on the current text data to be analyzed, and generating corrected text; performing text adding and deleting processing on the current text data to be analyzed, and generating added and deleted text; performing text approximate semantic processing on the current text data to be analyzed, generating an approximate voice text, and generating a primary processing text to be analyzed based on the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text; performing graphic filtering processing, graphic brightness adjustment processing, graphic contour extraction processing and graphic layer extraction processing on the current graphic data to be analyzed, and respectively generating filtered graphic data, graphic data after brightness adjustment, contour data to be analyzed and graphic layer data to be analyzed; generating a primary processing graph to be analyzed according to the filtered graph data, the brightness-adjusted graph data, the contour data to be analyzed and the graph layer data to be analyzed;
the text import module is further configured to:
Respectively inputting the current text to be analyzed, the corrected text, the added and deleted text and the approximate voice text in the primary processing text to be analyzed into a pre-established big data information analysis model, and respectively obtaining a text analysis result; removing text analysis results exceeding a preset first standard result range from each text analysis result, wherein the text analysis results not exceeding the first standard result range are matching analysis results; generating a text analysis data set according to each matching analysis result; respectively inputting the filtered graphic data, the brightness-adjusted graphic data, the contour data and the layer data to be analyzed in the primary processing graphic to be analyzed into a pre-established big data information analysis model, and respectively obtaining a graphic analysis result; and generating a matched graph result according to the graph analysis result and a preset second standard result range, and generating a graph analysis data set according to each matched graph result.
6. The big data based information analysis system of claim 5, further comprising a data analysis module for:
Acquiring main body head portrait image data of an information demand main body when the current data to be analyzed is loaded; generating an information analysis library establishment query request according to the main body head portrait image data, wherein the information analysis library establishment query request is used for generating a compliant display interface, the compliant display interface is displayed with the query request, and when the information demand main body agrees to establish the information analysis library, the query request is replied; acquiring an information base agreement establishment response of the information demand main body agreeing to establish an information analysis base, and generating a basic information retrieval instruction based on the information base agreement establishment response; according to the basic information calling instruction, main body basic information of the information demand main body is called based on big data, wherein the main body basic information comprises main body basic information, historical information demand data and historical information satisfaction data; generating a main body characteristic tag according to the main body basic information, and generating supervised learning training data based on the historical information demand data and the historical information satisfaction data; generating an information analysis using habit database according to the main body characteristic tag and the supervised learning training data; performing supervised learning on the big data information analysis model by using supervised learning training data in a habit database, and generating an updated information analysis model; obtaining a result data splitting trigger instruction of the initial data analysis result on the information analysis display interface;
Acquiring selected area data selected from the initial data analysis result according to the result data splitting trigger instruction; acquiring a selected data display area corresponding to the selected area data on the information analysis display interface, wherein the area except the selected data display area on the information analysis display interface is a solidification display area; generating data analysis audio according to the selected area data, and simultaneously generating a solidification area constant instruction, wherein the data analysis audio is analysis audio of the selected area data; the cure display area is maintained unchanged based on the cure area constant instruction.
7. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 3 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 3.
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