CN110781378A - Data graphical processing method and device, computer equipment and storage medium - Google Patents
Data graphical processing method and device, computer equipment and storage medium Download PDFInfo
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9538—Presentation of query results
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/957—Browsing optimisation, e.g. caching or content distillation
Abstract
The invention discloses a data graphical processing method and device, computer equipment and a storage medium. The method comprises the following steps: receiving a graph browsing request sent by a client, and acquiring data screening conditions and graph keywords contained in the graph browsing request; acquiring behavior data meeting data screening conditions from a preset database; determining the graphic characteristics of the target graphic according to the graphic keywords; generating a target graph by using the graph characteristics and the behavior data; generating a target user portrait corresponding to the target graph according to the data screening condition and the behavior data; the target graphic and the target user representation are sent to the client. The technical scheme of the invention realizes the customization of the target graph, accurately matches the requirements of the client user, generates the target user portrait capable of reflecting the corresponding user characteristics and improves the flexibility of data graphical presentation.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a data graphical processing method and apparatus, a computer device, and a storage medium.
Background
When a large amount of data is analyzed daily, the data needs to be presented in a graphical mode in many cases.
In the conventional method, a preset fixed graph structure is usually adopted to graphically display data. However, different data browsing users have different browsing habits, even the same data browsing user may need different graphic structures for different data types, and the existing fixed graphic structure cannot accurately match the browsing requirements of the user, resulting in lower flexibility of data graphical presentation.
Disclosure of Invention
The embodiment of the invention provides a data graphical processing method and device, computer equipment and a storage medium, and aims to solve the problem that the data graphical presentation mode in the prior art cannot accurately match with the user requirements, so that the flexibility of data graphical presentation is low.
A data graphical processing method comprises the following steps:
receiving a graph browsing request sent by a client, and acquiring data screening conditions and graph keywords contained in the graph browsing request;
acquiring behavior data meeting the data screening conditions from a preset database;
determining the graphic characteristics of the target graphic according to the graphic keywords;
generating the target graph using the graph features and the behavior data;
generating a target user portrait corresponding to the target graph according to the data screening condition and the behavior data;
sending the target graphic and the target user representation to the client.
A data graphics processing apparatus, comprising:
the request receiving module is used for receiving a graphic browsing request sent by a client and acquiring a data screening condition and a graphic keyword contained in the graphic browsing request;
the data screening module is used for acquiring behavior data meeting the data screening conditions from a preset database;
the characteristic determining module is used for determining the graphic characteristics of the target graphic according to the graphic keywords;
a graph generation module for generating the target graph using the graph features and the behavior data;
the portrait generation module is used for generating a target user portrait corresponding to the target graph according to the data screening condition and the behavior data;
and the data sending module is used for sending the target graph and the target user portrait to the client.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the data graphical processing method when executing the computer program.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned data graphical processing method.
In the data graphical processing method, the data graphical processing device, the computer equipment and the storage medium, according to the data screening condition and the graphic keyword contained in the graphic browsing request sent by the client, behavior data meeting the data screening condition is obtained from a preset database, the graphic characteristic of the target graphic is determined according to the graphic keyword, then the graphic characteristic and the behavior data are used for generating the target graphic, a target user portrait corresponding to the target graphic is generated according to the data screening condition and the behavior data, then the target graphic and the target user portrait are sent to the client for presentation, the customization of the target graphic according to the data screening condition and the graphic keyword is realized, thereby the requirements of the client user are accurately matched, the flexibility of the graphical presentation of the data is improved, and simultaneously, the target user portrait capable of showing the corresponding user characteristic is generated according to the data screening condition and the behavior data, and the client is provided with visual display, so that the flexibility of data graphical presentation is further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of a data graphics processing method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a data graphics processing method according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S4 in the data graphics processing method according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S42 of the data graphics processing method according to an embodiment of the present invention;
FIG. 5 is a flowchart of step S5 in the data graphics processing method according to an embodiment of the present invention;
FIG. 6 is a flowchart of step S6 in the data graphics processing method according to an embodiment of the present invention;
FIG. 7 is a diagram of a data graphics processing apparatus according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The data graphical processing method provided by the application can be applied to an application environment shown in fig. 1, the application environment comprises a server and a client, the server and the client are connected through a network, the network can be a wired network or a wireless network, the client specifically comprises but is not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices, and the server can be specifically realized by an independent server or a server cluster formed by a plurality of servers. And the client sends the graphic browsing request to the server, and the server analyzes the graphic browsing request, and returns the target graphic and the target user portrait to the client.
In an embodiment, as shown in fig. 2, a data graphics processing method is provided, which is described by taking the application of the method to the server in fig. 1 as an example, and specifically includes steps S1 to S6, which are detailed as follows:
s1: and receiving a graph browsing request sent by a client, and acquiring a data screening condition and a graph keyword contained in the graph browsing request.
Specifically, a user may initiate a graphical browsing request for target data at a client, where the graphical browsing request includes a data filtering condition and a graphical keyword.
The data screening conditions include, but are not limited to, screening conditions for user categories, user attributes, behavior data types, and the like in the data to be screened. The user type is used for identifying the post information of the user, the user attribute is used for identifying the personal information of the user, and the behavior data type is used for identifying the data type of the target data needing to be graphically displayed. For example, the user type may be "salesperson" or "technical developer", etc., the user attribute may include a plurality of different sub-attributes such as "gender", "age", "region", "job position", etc., and the behavior data type may be "sales performance", "yield", etc.
In a specific embodiment, the data filtering condition may specifically be that the user type is "salesman", the user attribute is "sex man, age is more than 30 years old, region is Shenzhen, position is manager", and the behavior data type is "sales performance".
The graphic keywords are used for identifying graphic features such as colors, shapes, sizes and the like of the graphics and data display dimensions of the graphics. For example, the graphic keywords may include "color blue", "shape pie chart", and the like, which represent graphic features, and may also include "age" or "region", and the like, which represent dimensions of data presentation.
It should be noted that, the user may select the condition and the keyword through selectable condition attributes and selectable keywords displayed on a human-computer interaction interface provided by the client, for example, the user may select a desired user type from a plurality of preset selectable user types provided by the client, and may also select a desired color from a plurality of preset selectable colors provided by the client.
S2: and acquiring behavior data meeting data screening conditions from a preset database.
Specifically, the server side screens target data meeting the data screening conditions from the data to be screened in a preset database according to the received data screening conditions, wherein the target data is behavior data.
In a specific embodiment, if the data screening conditions include screening conditions of user types, user attributes and behavior data types, and each data record in a preset database includes fields of the user types, the user attributes, the behavior data types, the behavior data and the like, the server screens data records meeting the screening conditions of the user types, the user attributes and the behavior data types from the preset database, reads values of the behavior data fields from the screened data records, and accumulates the read values of each data field to obtain the behavior data.
S3: and determining the graphic characteristics of the target graphic according to the graphic keywords.
Specifically, the server extracts the graphic features of the target graphic from the graphic keywords according to the received graphic keywords.
The target graph is a graph showing the data screening condition and behavior data thereof, the graph features include attribute features of the target graph and data showing dimensions, the attribute features include but are not limited to colors, sizes, shapes and the like, the data showing dimensions are used for identifying the category of the showing data of the target graph, and the data showing dimensions can be a certain optional condition attribute in the data screening condition.
For example, if the data screening conditions are: the user type is "salesman", the user attributes are "sex male, age greater than 30 years old, the territory is Shenzhen, Beijing, Shanghai, the position is manager", and the behavior data type is "sales achievement", then the data presentation dimension can be the territory.
It should be noted that, there is no necessary sequential execution order between step S2 and step S3, and the steps may be executed in parallel, which is not limited herein.
S4: and generating the target graph by using the graph characteristics and the behavior data.
Specifically, the server performs graphical processing on the behavior data obtained in step S2 according to the graph feature obtained in step S3 to obtain the target graph.
And if the behavior data are multiple, the server side generates a corresponding target subgraph for each behavior data according to the graph characteristics, and combines the multiple target subgraphs to obtain the target graph.
S5: and generating a target user portrait corresponding to the target graph according to the data screening conditions and the behavior data.
Specifically, the server side extracts user features from data records matched with data screening conditions and behavior data in a preset database to serve as user labels, and generates target user portraits according to the extracted user labels.
The target user portrait is used for representing common characteristics of users matched with the data screening conditions and the behavior data.
S6: the target graphic and the target user representation are sent to the client.
Specifically, the server sends the target graphic obtained in step S4 and the target user representation obtained in step S5 to the client, and the client displays the target graphic and the target user representation in a preset display area of the display terminal, so as to facilitate the user to view the target graphic and the target user representation.
In the embodiment, according to the data screening condition and the graphic key word contained in the graphic browsing request sent by the client, the behavior data meeting the data screening condition is obtained from the preset database, the graphic characteristic of the target graphic is determined according to the graphic key word, then the target graphic is generated by using the graphic characteristic and the behavior data, the target user portrait corresponding to the target graphic is generated according to the data screening condition and the behavior data, then the target graphic and the target user portrait are sent to the client for presentation, the target graphic is customized according to the data screening condition and the graphic key word, thereby the requirements of the client user are accurately matched, the flexibility of the graphic presentation of the data is improved, meanwhile, the target user portrait capable of reflecting the corresponding user characteristic is generated according to the data screening condition and the behavior data and is provided for the client for visual presentation, the flexibility of data graphical presentation is further improved.
In one embodiment, the data filtering condition includes N specific conditional sub-items, where N is a positive integer.
Specifically, the data filtering condition may be divided into a plurality of specific condition sub-items, and each specific condition sub-item may be obtained according to a value combination selected by the user from selectable values of the selectable condition attribute of the client.
Further, the specific condition sub-items can be obtained by splitting the data screening conditions according to the data display dimensions, and each specific condition sub-item corresponds to one selectable value in the data display dimensions.
For example, if the data screening condition X is: the user type is sales personnel, the user attribute is gender male, the age is more than 30 years old, the region is Shenzhen, Beijing, Shanghai, the position is manager, and the behavior data type is sales achievement; and, the data presentation dimension is "region", the specific condition sub-item may include three, respectively:
specific condition sub-item a: the user type is sales personnel, the user attributes are sex male, the age is more than 30 years old, the region is Shenzhen, the position is manager, and the behavior data type is sales achievement;
specific condition sub-item B: the user type is "salesman", the user attributes are "sex male, age greater than 30 years old, area is Beijing, position is manager", and the behavior data type is "sales achievement";
specific condition sub-item C: the user type is "salesperson", the user attributes are "sex male, age greater than 30 years, region is shanghai, position is manager", and the behavior data type is "sales achievement".
Further, as shown in fig. 3, in step S4, using the graph feature and the behavior data, a target graph is generated, which specifically includes steps S41 to S43, which are detailed as follows:
s41: and screening out specific behavior data corresponding to each specific condition sub-item in the behavior data.
Specifically, according to each specific condition sub-item, the behavior data is decomposed to obtain specific behavior data satisfying each specific condition sub-item.
Continuing with the data filtering condition X as an example, if the behavior data corresponding to the data filtering condition X is "1500 ten thousand", then after the behavior data is decomposed according to the specific condition sub-item a, the specific condition sub-item B and the specific condition sub-item C, it can be obtained that the specific behavior data a satisfying the specific condition sub-item a is "500 ten thousand", the specific behavior data B satisfying the specific condition sub-item B is "600 ten thousand", and the specific behavior data C satisfying the specific condition sub-item C is "400 ten thousand".
S42: and generating a target subgraph corresponding to each specific behavior data by using the graphic characteristics and each specific behavior data to obtain N target subgraphs.
Specifically, attribute features of the target graph contained in the graph features are used, and each specific behavior data is converted into a corresponding target sub-graph according to requirements of the attribute features, so that N target sub-graphs are obtained.
S43: and combining the N target subgraphs according to a preset combination mode to obtain a target graph.
Specifically, the preset combination mode may specifically be a combination mode that is sorted from large to small according to the specific behavior data. The server-side sorts the specific behavior data of each specific condition sub-item from big to small, determines the position sequence of each target sub-graph according to the sorted specific behavior data, and combines the N target sub-graphs into a target graph according to the position sequence.
Continuing to take the data screening condition X in step S41 as an example, the specific behavior data a corresponds to the target sub-graph 1, the specific behavior data b corresponds to the target sub-graph 2, and the specific behavior data c corresponds to the target sub-graph 3, and the specific behavior data a, the specific behavior data b, and the specific behavior data c are obtained by sorting from large to small: the specific behavior data b > the specific behavior data a > the specific behavior data c, the position sequence of the target subgraph is: the server side arranges the position of each target subgraph in the target graph according to the position sequence, namely the target subgraph 2, the target subgraph 1 and the target subgraph 3.
It should be noted that the preset combination manner may also be a manner of combining according to the correlation between specific condition sub-items, for example, placing the target sub-images corresponding to the specific condition sub-items with higher correlation at adjacent positions, and spacing the target sub-images corresponding to the specific condition sub-items with lower correlation by a longer distance. The specific configuration may be set according to the requirements of practical applications, and is not limited herein.
In the embodiment, the data screening condition is divided into N specific condition sub-items, specific behavior data corresponding to each specific condition sub-item is determined, the graph feature and each specific behavior data are used for generating a target sub-graph corresponding to each specific behavior data, then the N target sub-graphs are combined according to a preset combination mode to obtain a target graph, the generation efficiency of the target graph can be improved by generating each target sub-graph in parallel and then combining the target sub-graphs, each specific behavior data can be clearly and intuitively embodied by the obtained target graph according to the preset combination mode, meanwhile, the differentiation among each specific behavior data can be embodied at a glance, and the data presentation performance is improved.
In one embodiment, the graphic features include graphic shapes, graphic colors, and reference dimensions.
The graphic shape may be a shape feature such as a circle, a column, a broken line, etc., the graphic color is a display color of the target graphic, such as a red light and a blue light, and the reference dimension is a unit dimension of the graphic shape, for example, the reference dimension of the column is 5mm by 5 mm.
Further, as shown in fig. 4, in step S42, using the graph feature and each specific behavior data to generate a target sub-graph corresponding to each specific behavior data, specifically including steps S421 to S424, which are detailed as follows:
s421: and acquiring the specific behavior data with the minimum value from the N specific behavior data as reference data, wherein other N-1 specific behavior data are all used as other data.
Specifically, according to the N specific behavior data obtained in step S41, the specific behavior data with the smallest value is used as the reference data, and the other specific behavior data is used as the other data.
S422: and calculating a reference ratio between the reference data and the preset value unit according to the preset value unit, and carrying out scaling processing on the reference size according to the reference ratio to obtain the graph size corresponding to the reference data.
In this embodiment, the preset value unit is a preset minimum data unit, different behavior data types may correspond to different preset value units, and the same behavior data type may also set different preset value units according to application requirements, for example, for a behavior data type of "sales performance", if the magnitude of the behavior data is in the ten thousand yuan level, the preset value unit may be set to "1 thousand", and if the magnitude of the behavior data is in the million yuan level, the preset value unit may be set to "1 ten thousand".
Specifically, a ratio between the reference data and the preset value unit is used as a reference ratio, for example, if the reference data is "10 ten thousand" and the preset value unit is "1 thousand", the calculated reference ratio is 100.
And the server performs equal-scale amplification or reduction processing on the reference size according to the reference ratio to obtain the graph size corresponding to the reference data. For example, if the pattern shape is a pillar shape and the reference size is 2mm × 2mm, the pattern size corresponding to the obtained reference data is 200mm × 200 mm.
S433: and generating a target subgraph corresponding to the reference data by using the graph shape, the graph color and the graph size.
Specifically, the server generates a target sub-graph corresponding to the reference data according to the graph shape and the graph color included in the graph feature and the graph size obtained in step S432.
For example, if the shape of the pattern is a column, the color of the pattern is blue, and the size of the pattern is 200mm by 200mm, the generated target sub-pattern is a blue column pattern of 200mm by 200 mm.
S434: and calculating a data ratio between each piece of other data and the reference data, and generating a target subgraph corresponding to each piece of other data according to the data ratio and the target subgraph corresponding to the reference data.
Specifically, for N-1 other data, a ratio between each other data and the reference data is calculated to obtain N-1 data ratios, and according to the data ratio of each other data, the target subgraph corresponding to the reference data is subjected to equal-scale amplification or reduction processing to obtain the target subgraph corresponding to the other data.
For example, if the reference data is "10 ten thousand", the target sub-graph corresponding to the reference data is a blue column graph of 200mm × 200mm, and the other data is "20 ten thousand", the data ratio is 2, and the server performs 2-fold amplification processing on the blue column graph of 200mm × 200mm according to the data ratio to obtain a blue column graph of 400mm × 400mm, which is used as the target sub-graph corresponding to the other data.
Further, in other embodiments, the graph area ratio of the target sub-graph corresponding to each specific behavior data may be determined according to the data ratio of each specific behavior data in the behavior data, the graph area of each target sub-graph is obtained according to the reference size, and then each target sub-graph is obtained by combining the graph shape and the graph color.
In this embodiment, the specific behavior data with the minimum value in the N specific behavior data is used as the reference data, the other N-1 specific behavior data is used as the other data, the reference size is scaled according to the reference ratio between the reference data and the preset value-taking unit to obtain the graph size corresponding to the reference data, the target subgraph corresponding to the reference data is generated by combining the graph shape and the graph color, then the data ratio between each other data and the reference data is calculated, and the target subgraph corresponding to each other data is generated according to the data ratio and the target subgraph corresponding to the reference data, such that the target subgraph corresponding to the minimum specific behavior data is generated first, and then the other target subgraphs can be generated quickly by scaling the target subgraph according to the ratio between the other specific behavior data and the minimum specific behavior data, the generation efficiency of each target sub-graph is improved.
In one embodiment, as shown in FIG. 5, in step S5, a target user representation corresponding to the target graphic is generated according to the data filtering condition and the behavior data, which specifically includes steps S51 to S53, as detailed below:
s51: and extracting information keywords from the user information corresponding to each specific condition sub-item by adopting a preset keyword extraction mode to obtain a user label corresponding to each specific condition sub-item.
Specifically, the server searches, according to the specific condition sub-item, user information included in the data record satisfying the specific condition sub-item in a preset database, where the user information may include user attribute information and user behavior information, where the user attribute information includes information for identifying user attributes such as name, gender, age, occupation, hobbies, academic calendar, and working hours, and the user behavior information includes data information related to behavior data.
The preset keyword extraction mode can be a TextRank keyword extraction algorithm, and is used for matching according to preset keywords from the user information corresponding to each specific condition sub-item, extracting information keywords matched with the preset keywords, and taking the extracted information keywords as user tags.
The number of the user tags corresponding to each specific condition sub-item may be one or more, and the user tags corresponding to different specific condition sub-items may be the same or different.
S52: and determining the label weight of each user label according to the specific behavior data corresponding to each specific condition subitem.
Specifically, according to a preset proportional correspondence between the value of the specific behavior data and the label weight, that is, the greater the value of the specific behavior data, the greater the label weight, and the smaller the value of the specific behavior data, the smaller the label weight, the label weight corresponding to the specific behavior data is obtained according to the specific behavior data corresponding to each specific condition sub-item, and the obtained label weight is used as the label weight of each user label corresponding to the specific condition sub-item.
It should be noted that, if different specific condition sub-items correspond to the same user tag, the tag weight of the same user tag in each specific condition sub-item may be calculated according to a preset calculation manner, so as to obtain the tag weight of the user tag.
The preset calculation mode may be to calculate an arithmetic average of the label weights, may also be to calculate a weighted average of the label weights, may also be to select a maximum value of the label weights, and the like, and may be specifically set according to the needs of the actual application, which is not limited herein.
S53: and inputting the user labels and the label weight of each user label into a preset word cloud picture generation tool to generate a word cloud picture, and taking the word cloud picture as a target user portrait.
Specifically, the user labels and label weights thereof are input into a preset word cloud picture generation tool to generate a word cloud picture, and the generated word cloud picture is used as a target user portrait.
The word cloud picture, also called character cloud, is a visual display mode for user labels, and the user labels are displayed by using different colors, shapes and sizes, so that the gist of texts can be visually embodied.
The preset word cloud picture generation tool can select the existing word cloud software such as Wordle, WordItOut and the like according to the requirements of practical application.
It should be noted that the user tags with higher tag weights are more obvious to be reflected in the word cloud graph. The embodying degree of the user label in the word cloud picture can be embodied by one or more of various characteristics of the size, the color or the thickness of the text of the user label in the word cloud picture. For example, the more heavily weighted a user label, the larger its text in the word cloud, the more vivid it is, or the thicker the text font lines.
In the embodiment, a preset keyword extraction mode is adopted, the user label is extracted from the user information corresponding to each specific condition subentry, the label weight of the user label is determined according to specific behavior data, then a preset word cloud picture generation tool is used, a word cloud picture is generated according to the user label and the label weight thereof and is used as a target user portrait, the more obvious the embodying degree of the user label with the larger label weight in the target user portrait is ensured, each user label can be accurately and fully embodied in the target user portrait, the visual display of the common characteristics of the users matched with the data screening conditions and the behavior data is achieved, and the flexibility of data graphical presentation is effectively improved.
In one embodiment, as shown in FIG. 6, in step S6, the target graphic and the target user representation are sent to the client, specifically including steps S61 through S64, as detailed below:
s61: and splitting the target graph into a preset number of sub-graph data.
Specifically, the server splits the target graph according to a preset splitting mode to obtain a preset number of sub-graph data.
The preset segmentation mode may be equal data size splitting, or splitting according to the data association degree, and may be specifically set according to the needs of practical applications, which is not limited here.
S62: and compressing each sub-graph data according to a preset compression mode to obtain a preset number of compressed graph data.
Specifically, the server performs compression processing on each sub-graphics data obtained in step S61 according to a preset compression mode, so as to obtain compressed graphics data corresponding to each sub-graphics data.
S63: sending a preset amount of compressed graphic data to the client so that the client decompresses the compressed graphic data and combines the decompressed graphic data to obtain a target graphic, and displaying the target graphic in a preset display area.
Specifically, the server side can send each piece of compressed graphic data to the client side in parallel, after the client side receives each piece of compressed graphic data, the client side decompresses each piece of compressed graphic data first, and after the decompression processing of a preset number of pieces of compressed graphic data is completed, the decompressed compressed graphic data are combined to complete the recovery processing of the target graph, and the target graph is displayed in a preset display area of the display interface.
S64: and if a user portrait viewing request sent by the client is received, sending the target user portrait to the client.
Specifically, when the user views the target graph at the client, a request for viewing the user portrait corresponding to the target graph may be initiated to the server. The server receives the user portrait viewing request from the client, and transmits the target user portrait created in step S5 to the client, and provides the user with the target user portrait for viewing.
In the embodiment, the target graph is divided into the sub-graph data with the preset number, each sub-graph data is compressed and then sent to the client, the data volume of network transmission between the server and the client can be reduced, the data transmission efficiency is improved, meanwhile, the target user portrait is sent to the client only when the user requests to check the user portrait, the single data transmission volume is reduced, and the graphical display is more flexible.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, a data graphic processing device is provided, and the data graphic processing device corresponds to the data graphic processing method in the above embodiments one to one. As shown in fig. 7, the data graphic processing apparatus includes: request receiving module 10, data filtering module 20, feature determination module 30, graphics generation module 40, representation generation module 50, and data sending module 60. The functional modules are explained in detail as follows:
a request receiving module 10, configured to receive a graphics browsing request sent by a client, and obtain a data screening condition and a graphics keyword included in the graphics browsing request;
the data screening module 20 is configured to obtain behavior data meeting data screening conditions from a preset database;
the characteristic determining module 30 is used for determining the graphic characteristics of the target graphic according to the graphic keywords;
a graph generation module 40 for generating a target graph using the graph features and the behavior data;
a portrait generation module 50, configured to generate a target user portrait corresponding to the target graphic according to the data filtering condition and the behavior data;
a data sending module 60 for sending the target graphic and the target user representation to the client.
Further, the data filtering condition includes N specific condition sub-items, where N is a positive integer, and the graph generating module 40 includes:
the specific data screening submodule 401 is configured to screen behavior data corresponding to each specific condition sub-item from the behavior data;
a target sub-graph generation sub-module 402, configured to generate a target sub-graph corresponding to each specific behavior data by using the graph features and each specific behavior data, so as to obtain N target sub-graphs;
and the graph combining submodule 403 is configured to combine the N target subgraphs according to a preset combining manner, so as to obtain a target graph.
Further, the graphic features include graphic shapes, graphic colors, and reference sizes, and the target subgraph generation submodule 402 includes:
the behavior data screening unit 4021 is configured to acquire the specific behavior data with the smallest value from the N specific behavior data as reference data, and use the other N-1 specific behavior data as other data;
the graph size determining unit 4022 is configured to calculate a reference ratio between the reference data and the preset value-taking unit according to the preset value-taking unit, and scale the reference size according to the reference ratio to obtain a graph size corresponding to the reference data;
a first sub-graph generating unit 4023, configured to generate a target sub-graph corresponding to the reference data by using the graph shape, the graph color, and the graph size;
the second sub-graph generating unit 4024 is configured to calculate a data ratio between each of the other data and the reference data, and generate a target sub-graph corresponding to each of the other data according to the data ratio and the target sub-graph corresponding to the reference data.
Further, the representation generation module 50 includes:
the tag extraction sub-module 501 is configured to extract an information keyword from the user information corresponding to each specific conditional sub-item by using a preset keyword extraction manner, so as to obtain a user tag corresponding to each specific conditional sub-item;
the weight determining submodule 502 is configured to determine a tag weight of each user tag according to the specific behavior data corresponding to each specific condition subitem;
the word cloud picture generation submodule 503 is configured to input the user tags and the tag weight of each user tag into a preset word cloud picture generation tool, generate a word cloud picture, and use the word cloud picture as a target user portrait.
Further, the data transmission module 60 includes:
the splitting sub-module 601 is configured to split the target graph into a preset number of sub-graph data;
the compression submodule 602 is configured to perform compression processing on each sub-graphics data according to a preset compression manner, so as to obtain a preset number of compressed graphics data;
the first sending submodule 603 is configured to send a preset number of compressed graphics data to the client, so that the client decompresses the compressed graphics data and combines the decompressed graphics data to obtain a target graphic, and displays the target graphic in a preset display area;
and a second sending sub-module 604, configured to send the target user representation to the client if a user representation viewing request sent by the client is received.
For specific limitations of the data graphic processing device, reference may be made to the above limitations of the data graphic processing method, which are not described herein again. The various modules in the data graphics processing apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a data graphical processing method.
In an embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the data graphical processing method in the above embodiments, such as steps S1 to S6 shown in fig. 2. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units of the data graphics processing apparatus in the above-described embodiments, such as the functions of the modules 10 to 60 shown in fig. 7. To avoid repetition, further description is omitted here.
In an embodiment, a computer readable storage medium is provided, on which a computer program is stored, and the computer program is executed by a processor to implement the data graphics processing method in the above method embodiment, or the computer program is executed by the processor to implement the functions of each module/unit in the data graphics processing apparatus in the above apparatus embodiment. To avoid repetition, further description is omitted here.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile 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 DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A data graphical processing method is characterized by comprising the following steps:
receiving a graph browsing request sent by a client, and acquiring data screening conditions and graph keywords contained in the graph browsing request;
acquiring behavior data meeting the data screening conditions from a preset database;
determining the graphic characteristics of the target graphic according to the graphic keywords;
generating the target graph using the graph features and the behavior data;
generating a target user portrait corresponding to the target graph according to the data screening condition and the behavior data;
sending the target graphic and the target user representation to the client.
2. The data graphical processing method of claim 1, wherein the data filtering condition includes N specific conditional sub-items, where N is a positive integer, and the generating the target graph using the graph feature and the behavior data includes:
screening out specific behavior data corresponding to each specific condition sub-item from the behavior data;
generating a target sub-graph corresponding to each specific behavior data by using the graphic features and each specific behavior data to obtain N target sub-graphs;
and combining the N target subgraphs according to a preset combination mode to obtain the target graph.
3. The data graphics processing method of claim 2, wherein said graphics features include graphics shapes, graphics colors, and reference dimensions, and said generating a target sub-graph corresponding to each of said specific behavior data using said graphics features and each of said specific behavior data comprises:
acquiring the specific behavior data with the minimum value from the N specific behavior data as reference data, and taking the other N-1 specific behavior data as other data;
calculating a reference ratio between the reference data and a preset value-taking unit according to a preset value-taking unit, and carrying out scaling processing on the reference size according to the reference ratio to obtain a graph size corresponding to the reference data;
generating the target subgraph corresponding to the reference data by using the graph shape, the graph color and the graph size;
and calculating a data ratio between each piece of other data and the reference data, and generating the target subgraph corresponding to each piece of other data according to the data ratio and the target subgraph corresponding to the reference data.
4. The method of claim 2, wherein generating a target user representation corresponding to the target graphic based on the data filtering criteria and the behavior data comprises:
extracting information keywords from the user information corresponding to each specific condition sub-item by adopting a preset keyword extraction mode to obtain a user label corresponding to each specific condition sub-item;
determining the label weight of each user label according to the specific behavior data corresponding to each specific condition subitem;
and inputting the user labels and the label weight of each user label into a preset word cloud picture generation tool to generate a word cloud picture, and taking the word cloud picture as the target user portrait.
5. A method of graphical processing of data as claimed in any one of claims 1 to 4, wherein said sending said target graphic and said target user representation to said client comprises:
splitting the target graph into sub-graph data with a preset number;
compressing each sub-graphic data according to a preset compression mode to obtain a preset number of compressed graphic data;
sending the preset amount of compressed graphic data to the client, so that the client decompresses the compressed graphic data and combines the decompressed graphic data to obtain the target graphic, and displaying the target graphic in a preset display area;
and if a user portrait viewing request sent by the client is received, sending the target user portrait to the client.
6. A data graphic processing apparatus, characterized in that the data graphic processing apparatus comprises:
the request receiving module is used for receiving a graphic browsing request sent by a client and acquiring a data screening condition and a graphic keyword contained in the graphic browsing request;
the data screening module is used for acquiring behavior data meeting the data screening conditions from a preset database;
the characteristic determining module is used for determining the graphic characteristics of the target graphic according to the graphic keywords;
a graph generation module for generating the target graph using the graph features and the behavior data;
the portrait generation module is used for generating a target user portrait corresponding to the target graph according to the data screening condition and the behavior data;
and the data sending module is used for sending the target graph and the target user portrait to the client.
7. The data pattern processing device according to claim 6, wherein the data screening condition includes N specific condition sub-items, where N is a positive integer, and the pattern generating module includes:
the specific data screening submodule is used for screening specific behavior data corresponding to each specific condition sub-item in the behavior data;
the target sub-graph generation sub-module is used for generating a target sub-graph corresponding to each specific behavior data by using the graph characteristics and each specific behavior data to obtain N target sub-graphs;
and the graph combination sub-module is used for combining the N target subgraphs according to a preset combination mode to obtain the target graph.
8. The data graphics processing apparatus of claim 7, wherein the graphics features include graphics shapes, graphics colors, and reference sizes, the target subgraph generation submodule comprising:
the behavior data screening unit is used for acquiring the specific behavior data with the minimum value from the N specific behavior data as reference data, and taking other N-1 specific behavior data as other data;
the graph size determining unit is used for calculating a reference ratio between the reference data and the preset value taking unit according to a preset value taking unit, and carrying out scaling processing on the reference size according to the reference ratio to obtain a graph size corresponding to the reference data;
a first sub-graph generating unit configured to generate the target sub-graph corresponding to the reference data using the graph shape, the graph color, and the graph size;
and the second subgraph generation unit is used for calculating a data ratio between each piece of other data and the reference data, and generating the target subgraph corresponding to each piece of other data according to the data ratio and the target subgraph corresponding to the reference data.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the data graphical processing method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements a data graphical processing method according to any one of claims 1 to 5.
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