Intelligent analysis method for improving financial decision-making efficiency

Abstract

The invention relates to the technical field of data analysis, and particularly discloses an intelligent analysis method for improving financial decision-making efficiency, comprising the following steps: S1, extracting a target index in financial information, and classifying the financial information based on the target index to obtain an information class; s2, preprocessing the target index to obtain a standard index, generating standard points based on the standard index, and determining a minimum enclosing rectangle of a region formed by the standard points; s3, performing grid division on the minimum enclosing rectangle, clustering the standard points by taking grid intersection points and the standard points as clustering centers to obtain a clustering cluster, determining the density of the standard points in the clustering cluster, screening a standard cluster based on the density of the standard points, and executing a decomposition step to update the standard cluster to obtain a target cluster; and determining the circle center of the minimum circumcircle of the target cluster, and extracting financial data corresponding to the circle center to be displayed to the user. The financial decision-making efficiency is improved by extracting core data from massive financial data.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an intelligent analysis method for improving financial decision-making efficiency. Background Art

[0002] Financial decision-making refers to the process in which decision-makers (individuals, enterprises or governments) make choices involving capital investment, risk-taking and expected returns based on the use, allocation and management of funds under limited resources and constraints. It is the core link in financial activities, and its purpose is to achieve the optimal allocation of funds and maximize value.

[0003] Financial markets change rapidly and information is updated frequently, which includes not only traditional market data such as stock prices, trading volume and volatility, but also information on macroeconomic indicators, industry trends and global political events, resulting in a huge amount of data used for financial decision-making. Due to the large and complex amount of data, it takes a lot of time and resources to analyze and process this information, which may lead to delayed decisions and missed market opportunities. At the same time, too much information may lead to information overload, making it difficult for decision makers to quickly and accurately extract key data, thus affecting the efficiency of financial decision-making. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent analysis method for improving the efficiency of financial decision-making and solve the following technical problems:

[0005] Due to the large amount and complexity of data, analyzing and processing this information requires a lot of time and resources, which may lead to delayed decision-making and missed market opportunities. At the same time, too much information may lead to information overload, making it difficult for decision makers to extract key data quickly and accurately, thus affecting the efficiency of financial decision-making.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An intelligent analysis method for improving financial decision-making efficiency includes the following steps:

[0008] S1: marking the indicators that affect financial decisions as target indicators, extracting the target indicators from the financial information, and classifying the financial information based on the target indicators to obtain information classes, wherein the financial information of a single information class contains the same number and type of target indicators;

[0009] S2: Marking a single financial information in the information class as target information, preprocessing the target indicator corresponding to the target information to obtain a standard indicator, generating standard points (C1, C2, ..., Cm) based on the standard indicator, Cm represents the mth standard indicator, and determining the minimum circumscribed rectangle D of the area composed of the standard points;

[0010] S3: Divide the minimum circumscribed rectangle D into grids, the length and width of each grid are preset values, set the cluster radius, cluster the standard points with the grid intersection and the standard points as cluster centers to obtain cluster clusters, determine the density of the standard points in the cluster clusters, select the standard clusters from the cluster clusters based on the standard point density, and execute the preset decomposition steps to update the standard clusters to obtain the target clusters;

[0011] The center Pyx of the minimum circumscribed circle P of the target cluster is determined, and the financial data corresponding to the center Pyx is extracted and displayed to the user to assist the user in making financial decisions.

[0012] As a further solution of the present invention: in the step S3, the process of determining the standard point density in the cluster specifically includes:

[0013] Calculate the standard point density F = f / (πr 2 ), f represents the number of target points in the cluster, and r represents the radius of the minimum circumscribed circle of the area composed of the standard points.

[0014] As a further solution of the present invention: in step S3, the process of determining the target cluster specifically includes:

[0015] The standard cluster containing the same standard point is taken as the undetermined cluster, the undetermined cluster with the largest standard point density is taken as the receiving cluster, and the standard point is merged into the receiving cluster until the standard cluster does not contain the same standard point;

[0016] Determine the standard point density of the standard cluster, when the standard point density is less than a preset density threshold Fys, take the corresponding standard cluster as a decomposition cluster, and merge the standard points in the decomposition cluster into the nearest standard cluster;

[0017] When the standard cluster includes a new standard point, the standard point density is calculated. When the standard point density is greater than or equal to the density threshold Fys, the standard point is retained. Otherwise, the standard point is removed as noise.

[0018] All standard clusters are taken as target clusters.

[0019] As a further solution of the present invention: in step S2, the process of determining the standard point composition area specifically includes:

[0020] The plane where the standard point is located is used as a reference plane, and the standard point is used as the origin to establish a plane rectangular coordinate system in the reference plane;

[0021] Determine the target quadrant according to a preset rule, determine a standard point A in the target quadrant that is closest to the origin, and connect point A and the origin with a straight line;

[0022] Repeat the above steps until all standard points are connected by straight lines to obtain a connection diagram;

[0023] The area of ​​the minimum circumscribed rectangle of the connection graph is determined, the connection graph corresponding to the minimum area is used as the target graph, and the target graph is used as the area composed of the standard points.

[0024] As a further solution of the present invention: the process of determining the target quadrant is specifically as follows:

[0025] Let the priorities of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant be 1, 2, 3, and 4 from high to low respectively;

[0026] According to the priority order, when there is a standard point in the first quadrant of the plane rectangular coordinate system, the first quadrant is the target quadrant; when there is no standard point in the first quadrant of the plane rectangular coordinate system, it is determined whether the second quadrant is the target quadrant;

[0027] Repeat the above steps until the target quadrant is determined.

[0028] As a further solution of the present invention: in step S3, the process of screening the standard cluster specifically includes:

[0029] The clusters are sorted in descending order according to the size of the standard point density to obtain a cluster sorting. Starting from the last cluster in the cluster sorting, the clusters are removed in sequence until the number of standard points not included in the cluster after removing a certain cluster is greater than or equal to a preset number, and the cluster at this time is used as the standard cluster.

[0030] As a further solution of the present invention: when the standard densities of two or more clusters are the same, the closer the cluster center of the cluster is to the center of the minimum circumscribed rectangle D, the higher the ranking of the corresponding cluster.

[0031] As a further solution of the present invention: in step S2, the process of determining the standard index specifically includes:

[0032] Encode non-numerical target indicators;

[0033] Standardize numerical target indicators to eliminate dimensions.

[0034] Beneficial effects of the present invention: In this solution, by extracting key indicators (target indicators) that have an important impact on financial decision-making, redundant information is initially filtered, and then the target indicators are classified, and information containing the same number and type of target indicators is classified into one category, simplifying the complexity of data processing, thereby reducing the amount of data and standardizing the data structure, providing a basis for subsequent steps, avoiding processing irrelevant or redundant information, saving time and resources; then, the target information is preprocessed (including encoding and standardization) to eliminate the differences between different target indicators to make the data comparable, and the preprocessed target indicators are mapped to standard points, and the distribution area of ​​these standard points in the plane space (minimum circumscribed rectangle D) is determined, so that complex data is visualized and structured, which is convenient for clustering and screening operations in subsequent steps, laying the foundation for the final extraction of core data; then, by gridding the minimum circumscribed rectangle, the search range of the clustering operation is limited to improve Clustering efficiency, and clustering the standard points based on density and distance characteristics, screening out standard clusters with high relevance and representativeness, further stratifying the data through clustering, highlighting key information, reducing information overload caused by excessive data volume, and screening and optimizing the standard clusters to ensure that only high-value data is retained, providing more accurate data support for subsequent decision-making; then, updating the preliminarily screened standard clusters, removing noise data or low-density clusters, and further optimizing the important clusters, and finally obtaining the target cluster as the most valuable core data set for decision-making, further filtering noise and invalid information, so that decision makers pay attention to high-density, high-value target clusters; finally, extracting the center of the minimum circumscribed circle of the target cluster as the refinement of the core data of the target cluster, and displaying the core data to the user, intuitively providing decision-making basis, realizing the simplification of complex information into core points, quickly assisting users in understanding and judging, avoiding information overload, improving decision-making efficiency, and helping users grasp market opportunities. The present invention finally realizes the fast and accurate display of key data to users through layer-by-layer filtering and extraction, thereby improving the efficiency of financial decision-making and reducing the negative impact of information overload and decision delay on efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 It is a flow chart of an intelligent analysis method for improving financial decision-making efficiency according to the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] See also Figure 1 As shown, the present invention is an intelligent analysis method for improving financial decision-making efficiency, comprising the following steps:

[0039] S1: marking the indicators that affect financial decisions as target indicators, extracting the target indicators from the financial information, and classifying the financial information based on the target indicators to obtain information classes, wherein the financial information of a single information class contains the same number and type of target indicators;

[0040] S2: Marking a single financial information in the information class as target information, preprocessing the target indicator corresponding to the target information to obtain a standard indicator, generating standard points (C1, C2, ..., Cm) based on the standard indicator, Cm represents the mth standard indicator, and determining the minimum circumscribed rectangle D of the area composed of the standard points;

[0041] S3: Divide the minimum circumscribed rectangle D into grids, the length and width of each grid are preset values, set the cluster radius, cluster the standard points with the grid intersection and the standard points as cluster centers to obtain cluster clusters, determine the density of the standard points in the cluster clusters, select the standard clusters from the cluster clusters based on the standard point density, and execute the preset decomposition steps to update the standard clusters to obtain the target clusters;

[0042] The center Pyx of the minimum circumscribed circle P of the target cluster is determined, and the financial data corresponding to the center Pyx is extracted and displayed to the user to assist the user in making financial decisions.

[0043] It should be noted that by extracting key indicators (target indicators) that have a significant impact on financial decision-making, redundant information is initially filtered, and then the target indicators are classified, and information containing the same number and type of target indicators is classified into one category, simplifying the complexity of data processing, thereby reducing the amount of data and standardizing the data structure, providing a basis for subsequent steps, avoiding processing irrelevant or redundant information, saving time and resources; then, the target information is preprocessed (including encoding and standardization) to eliminate the differences between different target indicators and make the data comparable, and the preprocessed target indicators are mapped to standard points, and the distribution area of ​​these standard points in the plane space is determined (the minimum enclosing rectangle D), so that the complex data is visualized and structured, which is convenient for clustering and screening operations in subsequent steps, laying the foundation for the final extraction of core data; then, by gridding the minimum enclosing rectangle, the search range of the clustering operation is limited to improve the clustering efficiency, and the standard points are clustered based on the density and distance characteristics to screen out those with high The standard clusters of relevance and representativeness are further stratified by clustering to highlight key information and reduce information overload caused by excessive data volume. The screening and optimization of standard clusters ensure that only high-value data are retained, providing more accurate data support for subsequent decision-making. Then, the initially screened standard clusters are updated to remove noise data or low-density clusters, and the important clusters are further optimized to finally obtain the target cluster as the core data set most valuable for decision-making. Noise and invalid information are further filtered out to make decision makers focus on high-density, high-value target clusters. Finally, the center of the minimum circumscribed circle of the target cluster is extracted as the refinement of the core data of the target cluster, and the core data is displayed to the user, providing a basis for decision-making intuitively, simplifying complex information into core points, quickly assisting users in understanding and judging, avoiding information overload, improving decision-making efficiency, and helping users seize market opportunities. Among them, when the coordinates of the center of the circle contain a value that is not an integer, the standard point G closest to the circle is determined, and the financial data on the standard point G is extracted and displayed to the user.

[0044] In another preferred embodiment of the present invention, in step S3, the process of determining the standard point density in the cluster specifically includes:

[0045] Calculate the standard point density F = f / (πr 2 ), f represents the number of target points in the cluster, and r represents the radius of the minimum circumscribed circle of the area composed of the standard points.

[0046] In another preferred embodiment of the present invention, in step S3, the process of determining the target cluster specifically includes:

[0047] The standard cluster containing the same standard point is taken as the undetermined cluster, the undetermined cluster with the largest standard point density is taken as the receiving cluster, and the standard point is merged into the receiving cluster until the standard cluster does not contain the same standard point;

[0048] Determine the standard point density of the standard cluster, when the standard point density is less than a preset density threshold Fys, take the corresponding standard cluster as a decomposition cluster, and merge the standard points in the decomposition cluster into the nearest standard cluster;

[0049] When the standard cluster includes a new standard point, the standard point density is calculated. When the standard point density is greater than or equal to the density threshold Fys, the standard point is retained. Otherwise, the standard point is removed as noise.

[0050] All standard clusters are taken as target clusters.

[0051] It can be understood that the standard clusters containing the same standard point are classified as pending clusters to avoid repeated processing of the same data point. By selecting the pending cluster with the largest density as the receiving cluster, the most representative cluster structure can be retained first, the data integration efficiency can be improved, and the final target cluster can be ensured to have a high degree of information concentration and availability; when the density of the standard cluster is less than the preset density threshold, it is decomposed into decomposition clusters, and its points are merged into the nearest standard cluster, which can avoid the low-density cluster may contain noise points or invalid data, thereby reducing the quality of the target cluster, and dynamically adjusting the cluster structure can effectively eliminate the interference of low-density clusters, and assign important points to high-correlation clusters, further enhancing the target cluster's ability to describe key information; all standard clusters that have undergone the above processing are used as target clusters to avoid the characteristic differences between different information being masked and the loss of the description of data diversity, among which the standard point density of the standard cluster can refer to the clustering cluster, which will not be repeated here.

[0052] In another preferred embodiment of the present invention, in step S2, the process of determining the standard point composition area specifically includes:

[0053] The plane where the standard point is located is used as a reference plane, and the standard point is used as the origin to establish a plane rectangular coordinate system in the reference plane;

[0054] Determine the target quadrant according to a preset rule, determine a standard point A in the target quadrant that is closest to the origin, and connect point A and the origin with a straight line;

[0055] Repeat the above steps until all standard points are connected by straight lines to obtain a connection diagram;

[0056] The area of ​​the minimum circumscribed rectangle of the connection graph is determined, the connection graph corresponding to the minimum area is used as the target graph, and the target graph is used as the area composed of the standard points.

[0057] In another preferred embodiment of the present invention, the process of determining the target quadrant is specifically as follows:

[0058] Let the priorities of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant be 1, 2, 3, and 4 from high to low respectively;

[0059] According to the priority order, when there is a standard point in the first quadrant of the plane rectangular coordinate system, the first quadrant is the target quadrant; when there is no standard point in the first quadrant of the plane rectangular coordinate system, it is determined whether the second quadrant is the target quadrant;

[0060] Repeat the above steps until the target quadrant is determined.

[0061] In another preferred embodiment of the present invention, in step S3, the process of screening the standard cluster specifically includes:

[0062] The clusters are sorted in descending order according to the size of the standard point density to obtain a cluster sorting. Starting from the last cluster in the cluster sorting, the clusters are removed in sequence until the number of standard points not included in the cluster after removing a certain cluster is greater than or equal to a preset number, and the cluster at this time is used as the standard cluster.

[0063] It is understandable that a large number of clusters may be generated during the clustering process, some of which have low density or contain low information value and do not have sufficient analytical significance. Retaining all clusters will lead to redundancy or increased noise in the target cluster, affecting subsequent data processing and analysis. Density is an important indicator for measuring the quality of clusters. The higher the density, the more concentrated the distribution of points in the cluster, indicating that the correlation between these points is stronger. By sorting in descending order, we can quickly focus on high-density clusters and gradually eliminate them starting from low-density clusters to avoid interference with high-value clusters from the beginning. The preset number can be set according to actual conditions or by professionals, such as 0, so that all standard points are retained.

[0064] In another preferred embodiment of the present invention, when the standard densities of two or more clusters are the same, the closer the cluster center of the cluster is to the center of the minimum circumscribed rectangle D, the higher the ranking of the corresponding cluster.

[0065] In another preferred embodiment of the present invention, in step S2, the process of determining the standard index specifically includes:

[0066] Encode non-numerical target indicators;

[0067] Standardize numerical target indicators to eliminate dimensions.

[0068] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent analysis method for improving financial decision-making efficiency, characterized in that: The following steps are involved: S1: marking the indicators that affect financial decisions as target indicators, extracting the target indicators from the financial information, and classifying the financial information based on the target indicators to obtain information classes, wherein the financial information of a single information class contains the same number and type of target indicators; S2: Marking a single financial information in the information class as target information, preprocessing the target indicator corresponding to the target information to obtain a standard indicator, generating standard points (C1, C2, ..., Cm) based on the standard indicator, Cm represents the mth standard indicator, and determining the minimum circumscribed rectangle D of the area composed of the standard points; S3: Divide the minimum circumscribed rectangle D into grids, the length and width of each grid are preset values, set the cluster radius, cluster the standard points with the grid intersection and the standard points as cluster centers to obtain cluster clusters, determine the density of the standard points in the cluster clusters, select the standard clusters from the cluster clusters based on the standard point density, and execute the preset decomposition steps to update the standard clusters to obtain the target clusters; The center Pyx of the minimum circumscribed circle P of the target cluster is determined, and the financial data corresponding to the center Pyx is extracted and displayed to the user to assist the user in making financial decisions.

2. The intelligent analysis method for improving financial decision-making efficiency according to claim 1, characterized in that: In step S3, the process of determining the standard point density in the cluster specifically includes: Calculate the standard point density F = f / (πr 2 ), f represents the number of target points in the cluster, and r represents the radius of the minimum circumscribed circle of the standard point composition area.

3. The intelligent analysis method for improving financial decision-making efficiency according to claim 2, characterized in that: In step S3, the process of determining the target cluster specifically includes: The standard cluster containing the same standard point is taken as the undetermined cluster, the undetermined cluster with the largest standard point density is taken as the receiving cluster, and the standard point is merged into the receiving cluster until the standard cluster does not contain the same standard point; Determine the standard point density of the standard cluster, when the standard point density is less than a preset density threshold Fys, take the corresponding standard cluster as a decomposition cluster, and merge the standard points in the decomposition cluster into the nearest standard cluster; When the standard cluster includes a new standard point, the standard point density is calculated. When the standard point density is greater than or equal to the density threshold Fys, the standard point is retained. Otherwise, the standard point is removed as noise. All standard clusters are taken as target clusters.

4. The intelligent analysis method for improving financial decision-making efficiency according to claim 1, characterized in that: In step S2, the process of determining the standard point composition area specifically includes: The plane where the standard point is located is used as a reference plane, and the standard point is used as the origin to establish a plane rectangular coordinate system in the reference plane; Determine the target quadrant according to a preset rule, determine a standard point A in the target quadrant that is closest to the origin, and connect point A and the origin with a straight line; Repeat the above steps until all standard points are connected by straight lines to obtain a connection diagram; The area of ​​the minimum circumscribed rectangle of the connection graph is determined, the connection graph corresponding to the minimum area is used as the target graph, and the target graph is used as the area composed of the standard points.

5. The intelligent analysis method for improving financial decision-making efficiency according to claim 4, characterized in that: The process of determining the target quadrant is as follows: Let the priorities of the first quadrant, the second quadrant, the third quadrant, and the fourth quadrant be 1, 2, 3, and 4 from high to low respectively; According to the priority order, when there is a standard point in the first quadrant of the plane rectangular coordinate system, the first quadrant is the target quadrant; when there is no standard point in the first quadrant of the plane rectangular coordinate system, it is determined whether the second quadrant is the target quadrant; Repeat the above steps until the target quadrant is determined.

6. The intelligent analysis method for improving financial decision-making efficiency according to claim 1, characterized in that: In step S3, the process of screening the standard cluster specifically includes: The clusters are sorted in descending order according to the size of the standard point density to obtain a cluster sorting. Starting from the last cluster in the cluster sorting, the clusters are removed in sequence until the number of standard points not included in the cluster after removing a certain cluster is greater than or equal to a preset number, and the cluster at this time is used as the standard cluster.

7. The intelligent analysis method for improving financial decision-making efficiency according to claim 1, characterized in that: When the standard densities of two or more clusters are the same, the closer the cluster center is to the center of the minimum circumscribed rectangle D, the higher the corresponding cluster ranking is.

8. The intelligent analysis method for improving financial decision-making efficiency according to claim 1, characterized in that: In step S2, the process of determining the standard index specifically includes: Encode non-numerical target indicators; Standardize numerical target indicators to eliminate dimensions.

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