CN117710111A - Method and system for visually presenting information processing results - Google Patents

Abstract

The invention provides a method and a system for visually presenting information processing results, wherein the method comprises the following steps: obtaining data of a plurality of different dimensions related to the market from one or more data sources; calculating a score of the acquired data of each dimension according to a predefined scoring rule corresponding to each dimension; accumulating the scores of the data of each dimension to obtain a comprehensive score serving as the information processing result; and visually presenting the information processing result.

Description

Method and system for visually presenting information processing results

Technical Field

The present invention relates to the field of financial technology, and more particularly, to a method and system for visually presenting information processing results.

Background

The rising and falling of the financial market reflect the result of the multi-space two-party force game. For market participants, due to factors such as incompleteness, untimely information collection, subjective bias and the like, a large number of people consider information to be positive, and a blank party considers information to be negative. Thus, there is a divergence in the views that will not contribute to the transaction and investment. From a long-term perspective, if the directional signal behind the mass information of the market can be accurately mastered, more victory can be achieved on the investment and financial road, and excess benefits can be obtained accordingly. Thus, in the financial arts, investment capability is, to some extent, the ability to collect information and process information.

However, for most common users, there is a lack of ability to judge information, and at the same time, there is a lack of means to collect information comprehensively, as compared to professional investors. For example, an average user will typically judge the overall emotion of the market at present based on the real-time quotation of some indexes, as this is the most readily available and intuitive type of information. However, it is clearly not comprehensive to rely solely on the index to judge market emotion. On the one hand, the comprehensive index or the industry index is not comprehensive, on the other hand, the index only provides visual perception of data dimension, but the numerical value of the index is influenced by factors, most users cannot analyze the index, and if the users do investment based on the index, the situation of rising, killing and falling is likely to occur. In addition, even if some users can pay attention to other information and have certain capability of helping to judge market emotion by means of the information, the information is complicated in source, and the users are also complicated to collect, summarize, sort and analyze the information.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

In order to solve the difficulties and drawbacks faced in the prior art, the present invention is directed to a more scientific, intelligent, and objective method and system for information collection and information processing. According to the invention, through obtaining a plurality of data with different dimensionalities related to the market, under the condition of comprehensively considering factors influencing the financial investment market, the information collection and information processing of the financial investment market are efficiently realized by utilizing technologies such as artificial intelligence, big data and the like. Meanwhile, the result of information processing is visually presented to the user, so that the user can intuitively acquire multi-dimensional information and the processing result of the information.

According to one aspect of the present invention, there is provided a method for visually presenting information processing results, comprising: obtaining data of a plurality of different dimensions related to the market from one or more data sources; calculating a score of the acquired data of each dimension according to a predefined scoring rule corresponding to each dimension; accumulating the scores of the data of each dimension to obtain a comprehensive score serving as the information processing result; and visually presenting the information processing result.

According to a further embodiment of the invention, the method further comprises: preprocessing the acquired data, the preprocessing including one or more of: analyzing text information; data cleaning; the corresponding dimensions of the data are transformed.

According to a further embodiment of the present invention, calculating the score of the acquired data for each dimension according to the predefined scoring rule corresponding to each dimension further comprises: determining whether the data of each dimension belongs to a interest message or a sky message; and calculating a scoring value of each dimension according to the degree of the market emotion reflected by the data of each dimension.

According to a further embodiment of the invention, the method further comprises: the scoring rules are obtained through machine learning based on the corresponding relation between the historical data and the historical market trend.

According to a further embodiment of the invention, the method further comprises: the plurality of different dimensions is divided into a plurality of levels.

According to a further embodiment of the present invention, accumulating the scores of the data for each dimension to obtain a composite score further comprises: assigning a weight coefficient to each node in the plurality of tiers; and calculating a weighted score as the composite score based on the score of the data for each dimension and the weight coefficient for each node.

According to a further embodiment of the invention, the method further comprises: the distribution mode of the weight coefficient is obtained through machine learning based on the corresponding relation between the sum scores calculated according to the historical data in different weight coefficient distribution modes and the historical market trend.

According to a further embodiment of the invention, the method further comprises: the plurality of tiers includes at least a first tier, a second tier, and a third tier, wherein nodes of the first tier include a message plane, a base plane, a funding plane, an emotion plane, and a technology plane.

According to a further embodiment of the present invention, visually presenting the information processing result further includes: the score of the data for each dimension with a score other than zero is visually presented in the same interface while the composite score is presented.

According to a further embodiment of the invention, visually presenting the score of the data for each dimension with a score other than zero further comprises: at the same time as the score is presented, a textual description is presented that relates to the data for that dimension.

According to a further embodiment of the invention, the textual description further includes: a description about the dimension; and an overview description of the data.

According to a further embodiment of the invention, the method further comprises: in response to a user selecting a score and a textual description of a dimension in an interface, one or more of: a detailed description of the data, a link to a source of the data, a link to an explanatory description of the data.

According to a further embodiment of the present invention, visually presenting the information processing result further includes: and visually presenting the information processing results of interest and sky in different interfaces respectively.

According to a further embodiment of the present invention, visually presenting the information processing results of interest and emptiness in different interfaces, respectively, further includes: displaying spheres of different colors indicating interest and sky; on one side of the sphere, scores of multiple dimensions are distributed in a circular arc shape; the multiple-dimensional scores may be displayed by a touch-slide scroll cycle.

According to a further embodiment of the present invention, visually presenting the information processing results of interest and emptiness in different interfaces, respectively, further includes: displaying a sphere indicating another interface in the current interface in an area smaller than the sphere of the current interface; and in response to the user clicking on the sphere of the other interface, jumping to the other interface.

According to still another aspect of the present invention, there is provided a system for visually presenting information processing results, including: a data acquisition module configured to acquire data of a plurality of different dimensions related to the marketplace from one or more data sources; an information processing module configured to: calculating a score of the acquired data of each dimension according to a predefined scoring rule corresponding to each dimension; accumulating the scores of the data of each dimension to obtain a comprehensive score as the information processing result; and a visualization module configured to visually present the information processing results.

The above describes a method of visually presenting information processing results according to the invention, which has at least the following advantages over the prior art:

1. by utilizing the technologies of artificial intelligence, big data and the like, the information collection and information processing of a plurality of different dimensionalities related to the financial investment market are efficiently realized;

2. the result of the information processing is visually presented to the user so that the user can intuitively acquire the multidimensional information and the processing result of the information.

These and other features and advantages will become apparent upon reading the following detailed description and upon reference to the associated drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed.

Drawings

So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only certain typical aspects of this invention and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects.

FIG. 1 is an example flow chart of a method for visually presenting information processing results in accordance with an aspect of the present invention.

FIG. 2 is a diagram of an example of a hierarchy in accordance with one embodiment of the present invention.

FIG. 3 is a diagram of an example of calculating a score for data for each dimension according to one embodiment of the invention.

Fig. 4 is an exemplary architecture diagram of a system for visually presenting information processing results in accordance with an aspect of the present invention.

Detailed Description

The features of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the described exemplary embodiments. It will be apparent, however, to one skilled in the art, that the described embodiments may be practiced without some or all of these specific details. In other exemplary embodiments, well-known structures or processing steps have not been described in detail in order to avoid unnecessarily obscuring the concepts of the present disclosure.

In the present specification, unless otherwise indicated, the term "a or B" as used throughout the present specification refers to "a and B" and "a or B" and is not meant to exclude a and B.

FIG. 1 is an example flow chart of a method for visually presenting information processing results in accordance with an aspect of the present invention. As shown in FIG. 1, the method 100 begins at step 102 with obtaining data of a plurality of different dimensions associated with a marketplace from one or more data sources. As mentioned previously, the data associated with the market (here, a financial market is taken as an example) is of a wide variety, such as macro-economic data, industry data, market index, industry index, asset price, enterprise data, media news, research reports, and the like.

Different data may be obtained from different data sources, for example china, which may include, but are not limited to:

1. financial regulatory institutions: such as a central office (China people's bank), a license, a national financial administration, a national resource commission, a national foreign exchange management, etc.;

2. financial institutions: such as banks, insurance companies, securities companies, trust companies, fund companies, etc.;

3. media: such as the internet, television, radio, newspaper, etc.;

4. investigation and consultation companies;

5. third party data service provider: such as ten thousand (Wind), peng Bo (bloom), thomson road penetration (Thomson Reuters), and the like.

These data sources may publish data publicly or commercially, such as on their official websites, or to subscribing users via dedicated clients. Accordingly, according to one embodiment of the present invention, corresponding data acquisition means may be employed for different data distribution modes. For example, data of interest may be obtained through a dedicated client or dedicated data interface, or related data may be obtained by crawling an official website.

Each type of data acquired may be referred to as a data dimension. Meanwhile, different data dimensions may be divided into a plurality of different aspects according to their properties. As non-limiting examples, data can be divided into the following five aspects: the message plane, the base plane, the fund plane, the emotion plane, and the technical plane, for example, the data belonging to the message plane may be a message, a significant politics, an economic news, etc. which are considered to affect the change of the market with respect to the national policy, the data belonging to the fund plane may be the data of the latest generalized monetary supply (M2), and so on. It will be appreciated that data may also be provided in more or less dimensions as desired, for example, to facilitate reading and understanding by most users of the information processing results presentation system of the present invention.

According to an alternative embodiment of the present invention, the acquired data of multiple dimensions may be preprocessed. As mentioned previously, the data may come from a number of different data sources and may therefore have different data formats, e.g., the data may be text-like data (e.g., news, research reports, etc.), and may be numeric-like data (e.g., index, real-time price, etc.). Some of these data need to be pre-processed to be able to be further analyzed, processed and presented. As non-limiting examples, preprocessing may include, but is not limited to: data cleaning, text information analysis, corresponding dimension conversion of data and the like. Data cleansing may include filtering, removing information unrelated to the content of interest or duplicate information from the acquired most current data. Text parsing may include parsing text-like data, such as news published on the day, using natural language processing techniques to understand its semantics. When some numeric class data is present in the text message, for example, there is up-to-date economic statistics in the latest reports on macroscopic economy issued by the national statistics office, the data can be extracted therefrom by text parsing, i.e. the message class data is converted into basic plane data.

In step 104, the score of the acquired data for each dimension is calculated according to predefined scoring rules corresponding to each dimension. A predefined scoring rule is a method of scoring or rating determined in advance for each dimension according to certain criteria and algorithms. Scoring is a quantitative assessment of an object according to specific rules and criteria. The specific scoring rules may vary according to different application scenarios and requirements.

As a non-limiting example of the invention, the scoring rules of the invention may indicate qualitative and quantitative analysis results for data for each dimension. More specifically, the data may first be qualitative, i.e. it may be determined whether the data for each dimension belongs to a interest message or a emptiness message, depending on the nature of the data, in what case the data is considered to be advantageous, and in what case the data is considered to be emptier. And secondly, quantitatively scoring the data, and calculating the scoring value of each dimension according to the degree of reflecting the emotion of the market by the data of each dimension. For example, for the data dimension "poor liability", the specific data is the latest 3.28% poor return on liability, and this data is first judged as a good message, and then the score value "10.7" of this data is obtained according to the degree to which it reflects the market emotion. In this example, a 3.28% difference in the share bond return indicates that the average return on the investment equity market is higher than the average return on the investment bond market, so this is generally considered to be good information for the equity market, so this data is firstly qualitative as good, and therefore scored positive (+), and secondly a "10.5" score indicates how good the share bond return is by "3.28%" such as relative to the maximum share bond return difference in history. It can be understood that the data with different types and dimensions can be provided with the respective suitable scoring rules according to the needs, so long as the scoring rules are favorable for helping users to better understand the meaning of the data, qualitative and quantitative analysis results of the data can be indicated, and reference is provided for investment decisions of the users.

It should be noted that, the score of each data dimension displayed in the interface is the final score of the data dimension, but in the scoring process, an initial score may be calculated for each data dimension according to the scoring rule of each data dimension, and then a weighted score may be calculated as the final score according to the weight value of each data dimension. This will be described in further detail hereinafter. The initial score may be used to reflect the extent to which the data value affects in the single data dimension, e.g., a maximum value and a minimum value may be determined from the data distribution for each data dimension, to determine a data range and corresponding scoring criteria, e.g., a maximum value of a full score (e.g., 100 points), a minimum value of 0 points, and then calculate a score based on the location of the current data value in the data range. Similarly, the score may also be based on the percentile of the current value in the data value, and so on.

According to an alternative embodiment of the present invention, the scoring rules may be derived by computer-implemented means such as big data, machine learning, etc., based on the correspondence of historical data to historical market trends. This involves feature extraction and modeling of historical data, and predicting and interpreting market trends using machine learning algorithms (e.g., logistic regression, decision trees, neural networks, etc.), thereby generating a set of scoring criteria.

In step 106, the scores of the data in each dimension are accumulated to obtain a comprehensive score as an information processing result. For example, some of the data dimensions for the day and their scores are: the "banks" of the message face (the small banks in the last half year show bright eyes) score (+1.7), similarly the "vacation" score of the base face (+1.4), the "poor liability" score of the base face (+10.7), the "reverse purchase" score of the fund face (+9.9), the "period of the technical face" score (+8.6), all of which are good messages, while the other, open, data dimensions and their scores, including "a strand" (-2.5), "beauty strand" (-1.4), "north funds" (-6.3), "a strand" (-4.9), "hand-off" (-4.5). The scores of all the data dimensions on the same day are accumulated together to finally obtain a comprehensive score of "-4".

Finally, at step 108, the information processing results are visually presented. The composite score "-4" is displayed significantly in the interface as a result of the information processing, indicating that the emotional index of the market as a whole is slightly off-air or negative.

According to a further embodiment of the present invention, the score of the data for each dimension with a score other than zero may be displayed on the same interface while the display composite score is presented. It will be appreciated that the data dimension that can be collected is extremely numerous, many of which are not updated every day. For example, "reverse purchase" will only have data if this action is made in the central row. Thus, in the absence of new data, the data dimension "reverse purchase" scores 0. In this case, it is not necessary to display the data dimension with a score of 0 in the interface as well, and to match the score of 0. This will help save limited screen space and focus attention on the valuable data dimension.

According to a further embodiment of the invention, a textual description relating to the data of the relevant dimension may be presented when the score is presented. For example, while presenting a score of 10.7 for "share liability difference," a textual description of "share liability return difference 3.28%" may be displayed below to represent the specific content of the data dimension.

According to a further embodiment of the invention, the relevant literal description may also include a description about the dimension and an overview description of the data. For example, "Shanghai deep 300 valuation futures rising 0.08%" is a description of the specific content of the data dimension "period finger", which applies generally to numeric data dimensions; while "small banks show a bright eye in the last half year" is an overview description of the data dimension "banks", which is generally applicable to text-like data.

According to a still further embodiment of the invention, the detailed description of the data, the links to the source of the data, or the links to the explanatory description of the data may be further displayed in the interface in response to the user selecting the score and the textual description of a dimension in the interface. For example, the literal description of the data dimension "poor in liability" has a label of "i" with a circle on the side of "poor in liability return 3.28%" indicating that the description is with additional information. In response to a user selecting the score or the textual description, a detailed description associated with "poor liability return" may be further displayed in a pop-up interface, such as: "the difference in the return of the liability on the day of the last transaction was 3.28%, the former value was 3.24%. Poor return on the strand bond was calculated by subtracting the national bond profitability in the decade from the reciprocal of the profitability of all a-strand fingers. In general, the larger the differential in the return on the bonds, the more attractive the representative of the stock investment and the display of another differential link in the return on the bonds. The "this detailed description is actually accompanied by an explanatory description of the" share-debt ", which is advantageous for the non-professional investor user to understand the meaning of the share-debt more accurately. Optionally, considering that the user still may not accurately understand the concept of poor return of the liability, a link to the hundred-degree page of "poor return of the liability" may be further provided in the pop-up interface, so as to facilitate the user to jump to the page to get a more detailed explanation by clicking the link when the user is interested. The manner in which the links are provided is also well suited for links to sources of numeric data as well as news-type data (e.g., original web pages on which the data was published).

According to an alternative embodiment of the invention, the information processing results of interest and empty can be visually presented in different interfaces respectively. For example, data dimensions that score positive are summarized and presented in the interface and have a favorable overall score 63. And additionally aggregate and present data dimensions with a negative score, and a total share of interest by summing these data dimensions-67. It will be appreciated that the aforementioned composite score "-4" is the sum of the benefit total score "63" and the benefit air total score "-67".

According to further embodiments of the present invention, in order to better present the emptiness and the good data and scores, a graphical user interface may be designed that facilitates user reading and interaction. As a non-limiting example, spheres of different colors may be displayed in the interest and hollow interfaces, respectively, and for ease of description, spheres may be described as being good spheres and hollow spheres, respectively, with red and green as the primary color. On one side of the sphere, the scores of multiple dimensions may be distributed in a circular arc shape, and may be displayed by a touch scroll cycle. For example, on one side of the hollow sphere, the A strands (index), the American strands, the north funds, the A strands (stock quantity of rising and falling) and the hands are distributed in a circular arc shape. The user can scroll through the various items of data on the day by touching. This facilitates user interaction and browsing of data content, especially when each interface has a greater number of data dimensions than can be accommodated in the current interface.

According to a further alternative embodiment of the invention, a sphere indicating another interface is displayed in the current interface in an area smaller than the sphere of the current interface, enabling a jump to the other interface in response to a user clicking on the sphere of the other interface. For example, the advantage ball is displayed in a larger area, so that the arrangement and display of the advantage data are facilitated, and meanwhile, the advantage ball is displayed in a smaller area, and when a user clicks the advantage ball, the user can jump to the interface of the advantage ball directly. This allows for both the contrast of the two balls and the detail of the currently displayed balls.

As mentioned previously, the composite score and the profit and empty total scores are a scoring accumulation of the data for each dimension, and preferably this accumulation may be a weighted accumulation, i.e., each data dimension may be assigned a weight coefficient such that the data dimension that is relatively more visually reflective of market emotion is assigned a greater weight coefficient. As described previously, the initial score for each data dimension may be a score that is 100 minutes full, with the final scores being obtained by multiplying the scores by the weight coefficients, such as 1.7, 1.4, 10.7, 9.9, 8.6, etc. The total score of the good ball is then the sum of all positive individual scores, i.e. the weighted sum.

In addition to giving different weights to different data dimensions, different aspects to which the data belongs may also be given different weights as needed, e.g. for 5 example aspects of message, basic, fund, emotional, and technical aspects, it may be desirable to see the influence of multiple emotions on market emotion, then data dimensions belonging to emotional aspects may be given greater weight relative to data dimensions of other aspects. To this end, according to further embodiments of the present invention, the plurality of different dimensions may be divided into a plurality of levels. As an example, as shown in fig. 2, the data dimension may be divided into three levels: the first level, the second level, and the third level, each level may include a plurality of nodes. For example, a first hierarchy may include 5 branches, i.e., 5 nodes, each corresponding to a different aspect of what is reflected by the data, such as a message plane, a base plane, a funding plane, an emotion plane, and a technology plane in embodiments of the present invention; the second level may correspond to an overview of commonalities reflected by some data under each aspect, e.g., two nodes "big-disc rise and fall" and "individual-strand rise and fall distribution" of the second level may be included under the "emotional surface" node of the first level; the third level corresponds to specific data dimensions, such as shown in fig. 2, and two nodes of the third level may be further included under the "large disk expansion and fall" node of the second level, each third level node corresponding to a specific data dimension: "windwhole A-day (%)" (from ten thousand whole A strands of daily fluctuation amplitude) "and" windwhole A-5 days (5%) "(from ten thousand whole A strands of daily fluctuation amplitude). Similarly, the data dimensions "bank", "vacation", "stock bond difference", "reverse purchase", "period finger" all belong to the third level node. In other words, in the final displayed interface, only the names of the third level nodes (i.e., specific data dimensions) and their corresponding scores need to be displayed, while the node settings of the first and second levels are used only for the underlying score calculation, as will be described in detail below.

According to one embodiment of the invention, each node in the plurality of levels of the dimension is assigned a weight coefficient, and a weighted score may be calculated based on the score of the data for each dimension and the weight coefficient for each node. More specifically, in the example of fig. 3, 5 nodes in the first hierarchy may be first assigned weight coefficients, and the sum of the weight coefficients of these 5 nodes is 1. Similarly, each node in the second hierarchy may be assigned a weight coefficient, where the sum of the weight coefficients of the nodes of the second hierarchy under the same first hierarchy is 1. Similarly, each node in the third level may be assigned a weight coefficient, where the sum of the weight coefficients of the third level nodes under the same second level is 1. After the weight coefficients of the nodes are allocated in this way, the score of each specific third level node (i.e., data dimension) is the product of its initial score and the weight coefficient of each level node to which it belongs. As an example, the Score (X) for data dimension X is:

Score(X)＝InitialScore(X)*Weight(X)；

Weight(X)＝Weight(X,L1)*Weight(X,L2)*Weight(X,L3)

where InitialScore (X) is the initial score for data dimension X, weight (X) is the composite Weight coefficient for data dimension X, weight (X, L1), weight (X, L2), and Weight (X, L3) are the Weight coefficients for each node on the hierarchical branch to which data dimension X belongs, respectively.

Based on the score of each data dimension, the profit total score and the profit-space total score may be the sum (i.e., weighted sum) of all the profit or profit-space data dimensions, respectively, and the overall score may be the sum of the profit total score and the profit-space total score or the weighted sum of all the data dimensions.

According to a further embodiment of the present invention, the distribution manner of the weight coefficients is obtained by machine learning based on the correspondence between the sum scores calculated according to the historical data in different weight coefficient distribution manners and the historical market trend.

FIG. 4 is an example architectural diagram of a system 400 for visually presenting information processing results in accordance with an aspect of the present invention. As shown in fig. 4, the system 400 may include a data acquisition module 402, an information processing module 404, and a visualization module 406.

The data acquisition module 402 may be configured to acquire data of a plurality of different dimensions related to the marketplace from one or more data sources. In one embodiment, the data acquisition module 402 may be further configured to pre-process the acquired data, the pre-processing including one or more of: text information analysis, data cleaning and corresponding dimension conversion of data.

The information processing module 404 may be configured to calculate a score for the acquired data for each dimension according to predefined scoring rules corresponding to each dimension; and accumulating the scores of the data of each dimension to obtain a comprehensive score as the information processing result. In one embodiment, the information processing module 404 may be further configured to determine whether the data for each dimension pertains to a interest message or a sky message, and calculate a scoring value for each dimension based on how well the data for each dimension reflects the emotion of the market. In another embodiment, the information processing module 404 may be further configured to divide the plurality of different dimensions into a plurality of levels, assign a weight coefficient to each level and each dimension, and calculate a weighted score as a composite score based on the score of the data for each dimension and the weight coefficient for each level and each dimension.

The visualization module 406 may be configured to visually present the information processing results. In one embodiment, the visualization module 406 may be further configured to visually present the scores of the data for each dimension with a score other than zero at the same interface while presenting the composite score. In another embodiment, the visualization module 406 may be further configured to present the textual description related to the data for the dimension while presenting the score. In another embodiment, the visualization module 406 may be further configured to display one or more of the following in the interface in response to the user selecting a score and textual description of a dimension in the interface: a detailed description of the data, a link to a source of the data, a link to an explanatory description of the data. In another embodiment, the visualization module 406 may be further configured to visually display the results of the information processing of interest and interest at different interfaces, respectively, with spheres of different colors being used for the display of interest and interest, scores of multiple dimensions being distributed in a circular arc shape on one side of the sphere, and the scores of multiple dimensions being displayable through a touch sliding scrolling cycle. In yet another embodiment, the visualization module 406 may be further configured to display a sphere in the current interface indicating another interface in an area smaller than the sphere of the current interface; and in response to the user clicking on the sphere of the other interface, jumping to the other interface.

According to one embodiment of the invention, the system 400 may be implemented as a server-client architecture, where the data acquisition module 402, the information processing module 404 may be deployed on the server side for centralized collection and processing of data, while the visualization module 406 may be deployed on the client side for locally generating user interaction elements and content presented in a graphical user interface on the client (e.g., mobile application) based on the processed data received from the server, and supporting user interaction in the local client.

What has been described above includes examples of aspects of the claimed subject matter. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the claimed subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations of the claimed subject matter are possible. Accordingly, the disclosed subject matter is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims.

Claims (16)

1. A method for visually presenting information processing results, comprising:

obtaining data of a plurality of different dimensions related to the market from one or more data sources;

calculating a score of the acquired data of each dimension according to a predefined scoring rule corresponding to each dimension;

accumulating the scores of the data of each dimension to obtain a comprehensive score serving as the information processing result; and

the information processing results are visually presented.

2. The method of claim 1, wherein the method further comprises: preprocessing the acquired data, the preprocessing including one or more of:

analyzing text information;

data cleaning;

the corresponding dimensions of the data are transformed.

3. The method of claim 1, wherein calculating the score for the acquired data for each dimension according to the predefined scoring rules corresponding to each dimension further comprises:

determining whether the data of each dimension belongs to a interest message or a sky message; and

the scoring value of each dimension is calculated according to the degree to which the data of each dimension reflects the emotion of the market.

4. The method of claim 3, wherein the scoring rules are derived by machine learning based on historical data versus historical market patterns.

5. The method of claim 1, wherein the plurality of different dimensions are divided into a plurality of hierarchies.

6. The method of claim 5, wherein accumulating the scores for the data for each dimension to obtain a composite score further comprises:

assigning a weight coefficient to each node in the plurality of tiers; and

a weighted score is calculated as the composite score based on the score of the data for each dimension and the weight coefficient for each node.

7. The method of claim 6, wherein the weighting factor assignment is based on a correspondence of a sum score calculated from historical data with different weighting factor assignments to historical market trends through machine learning.

8. The method of claim 5, wherein the plurality of tiers includes at least a first tier, a second tier, and a third tier, wherein nodes of the first tier include a message plane, a base plane, a funding plane, an emotion plane, and a technology plane.

9. The method of claim 1, wherein visually presenting the information processing results further comprises:

the score of the data for each dimension with a score other than zero is visually presented in the same interface while the composite score is presented.

10. The method of claim 9, wherein visually presenting the score of the data for each dimension having a score other than zero further comprises:

at the same time as the score is presented, a textual description is presented that relates to the data for the dimension.

11. The method of claim 10, wherein the textual description further comprises:

a description about the dimension; and

an overview description of the data.

12. The method of claim 9, wherein the method further comprises:

in response to a user selecting a score and a textual description of a dimension in an interface, one or more of: a detailed description of the data, a link to a source of the data, a link to an explanatory description of the data.

13. The method of claim 1, wherein visually presenting the information processing results further comprises:

and visually presenting the information processing results of interest and sky in different interfaces respectively.

14. The method of claim 13, wherein visually presenting results of the information processing of interest and empty, respectively, in different interfaces further comprises:

displaying spheres of different colors indicating interest and sky;

on one side of the sphere, scores of multiple dimensions are distributed in a circular arc shape;

the scores for the multiple dimensions may be displayed by a touch slide scroll cycle.

15. The method of claim 13, wherein visually presenting results of the information processing of interest and empty, respectively, in different interfaces further comprises:

displaying a sphere indicating another interface in the current interface in an area smaller than the sphere of the current interface; and

and responding to the spherical shape of the other interface clicked by the user, and jumping to the other interface.

16. A system for visually presenting information processing results, comprising:

a data acquisition module configured to acquire data of a plurality of different dimensions related to a market from one or more data sources;

an information processing module configured to:

calculating a score of the acquired data of each dimension according to a predefined scoring rule corresponding to each dimension; and

accumulating the scores of the data of each dimension to obtain a comprehensive score serving as the information processing result; and

and a visualization module configured to visually present the information processing results.