Financial data resource distribution management method and system based on credit label

Abstract

The invention discloses a financial data resource distribution management method and system based on a credit label, and relates to the technical field of financial data management, and the method comprises the steps: carrying out the financial data calling of an online financial service, analyzing a periodic development influence index, and outputting the periodic main financial data of the online financial service; determining an associated online financial service of each online financial service, and constructing an associated period main type financial data matrix of each online financial service; performing development association index analysis on the periodic main financial data associated with the online financial service according to the associated periodic main financial data matrix, and determining a periodic main financial data association chain; calling and analyzing the association conduction index of the main financial data association chain of each period, evaluating the association credit of the main financial data association chain of each period based on the analysis data, and making a resource distribution decision based on the evaluation data; according to the method and the device, accurate resource distribution of financial data combination among multi-source financial data is realized.

Description

Technical Field

[0001] This invention relates to the field of financial data management technology, specifically to a method and system for the distribution and management of financial data resources based on credit tags. Background Technology

[0002] In the current wave of digital economy, the financial industry has entered the era of data elements. Data circulation has become a key driving force for improving the efficiency of financial resource allocation and empowering the real economy. At the same time, financial data has the characteristics of extensive interconnectivity, dynamic frequency and other features. Different entities have different credit levels, which requires refined management. The financial data market is expanding rapidly, with the application scope of digital financial services becoming increasingly broad. The probability of cross-application of financial services in enterprises and related units is gradually increasing. In this context, the types and quantities of financial data involved in enterprise financial services are numerous and diverse. Therefore, the allocation of financial data resources is particularly important in the cross-application of different financial services. However, the traditional distribution of financial data resources lacks in-depth mining of the correlation between multi-source data, making it difficult to achieve comprehensive and accurate resource allocation of financial data. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system technical solution for the distribution and management of financial data resources based on credit tags, so as to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: A method for distributing and managing financial data resources based on credit tags, comprising the following steps: Retrieve online financial transactions from the financial service platform and extract financial data from these transactions. Analyze the cyclical development impact index of the financial data retrieved from online financial services, and determine the main cyclical financial data of online financial services based on the analysis data; Identify the associated online financial businesses for each online financial business and construct a matrix of associated cyclical main financial data for each online financial business; conduct development correlation index analysis on the cyclical main financial data of the associated online financial businesses based on the matrix of associated cyclical main financial data to determine the correlation chain of cyclical main financial data; The association transmission index of the main financial data association chain in each cycle is retrieved and analyzed. Based on the analysis data, the association credit of the main financial data association chain in each cycle is assessed, and resource allocation decisions are made based on the assessment data.

[0005] Furthermore, based on management permissions, the system retrieves financial transactions stored in the financial business platform, identifies online financial transactions based on their real-time status, and retrieves the corresponding financial data for those online financial transactions. The real-time status of the financial business refers to the current operational status of the financial business, including whether the business is completed, online, or not yet started; the financial data of the online financial business refers to the various types of financial data generated during the operation of the online business.

[0006] Furthermore, the financial data of each online financial business is divided into periods T, and the latest period financial data is used as the evaluation period financial data for the corresponding online financial business; where period T is the evaluation period. Based on the financial data corresponding to the evaluation period of each online financial business, the development curves of various types of financial data within the evaluation period of each online financial business are obtained through coordinate system mapping. Based on the development curves of each type of financial data, the changes in financial data between adjacent time points on the curves within the evaluation period are determined, and the cyclical development impact index Pdp(n,i) of the corresponding type of financial data is analyzed. The analysis is as follows: ; Wherein, Pdp(n,i) is the cyclical development impact index of the i-th type of financial data corresponding to the n-th online financial business; A(J n,i,t ≠J n,i,t+1 J represents the number of adjacent time points corresponding to the unequal curve values ​​of the i-th type of financial data for online financial business number n within the evaluation period; n,i,t and J n,i,t+1 For the online financial business with serial number n, the values ​​of the i-th type of financial data on the curve at time t and time t+1 within the evaluation period; A(T) is the number of time points included in the evaluation period T; T is the evaluation period; n is the serial number of the online financial business; i is the type serial number of the financial data. It should be noted that the coordinate system mapping process involves labeling the data at each time point within the period in the coordinate system and then outputting the corresponding periodic development curve of the data through curve fitting. Based on the cyclical development impact index of various types of financial data corresponding to each online financial business, a classification threshold is set to compare and classify the various types of financial data corresponding to each online financial business. Among them, financial data whose cyclical development impact index is greater than or equal to the classification threshold are classified as cyclical main category financial data; Financial data whose cyclical development impact index is less than the classification threshold are classified as cyclical auxiliary financial data.

[0007] It should be noted that the index analysis of the cyclical development impact of financial data corresponding to online financial business is specifically determined by superimposing the changes in the curve values ​​within the cycle and then performing a proportion analysis with the total value of the curve; the changes in the curve are obtained by calculating the difference between the curve values ​​at adjacent time points on the curve. Furthermore, retrieve the data interaction records between various online financial services, identify the remaining online financial services with which each online financial service has data interaction, and record them as related online financial services; coordinate the periodic main category financial data of each online financial service and related online financial services, and construct a related periodic main category financial data matrix corresponding to each online financial service. The steps for constructing the associated periodic main category financial data matrix are as follows: T1. Construct a corresponding periodic main category financial dataset for each online financial business by coordinating the periodic main category financial data of each online financial business, and sort the periodic main category financial data in descending order. T2. Take any online financial business as the target online financial business, then make the period main class financial dataset corresponding to the target online financial business the target period main class financial dataset; traverse the related online financial businesses of the target online financial business, then make the period main class financial dataset corresponding to the related online financial businesses the related period main class financial dataset. T3. Determine the degree of correlation between each related online financial business and the target online financial business, and assign a priority for matrix synthesis of each related period's main financial dataset based on the degree of correlation. T4. Based on the matrix synthesis priority allocation results of each related period main category financial dataset, perform vertical same-position matrix merging processing on each related period main category financial dataset according to the target period main category financial dataset, and output the related period main category financial data matrix of the target online financial business. It should be noted that the vertical same-position matrix merging refers to merging the main financial datasets of the target period as the first row of the matrix, and then merging the main financial datasets of each related period in parallel according to the vertical same-position of each element in the set; during the matrix merging process of the main financial datasets of related periods, the matrix synthesis order is based on the matrix synthesis priority; after the matrix merging is completed, any empty elements in the matrix are set to 0; the 0-position bits are only used for matrix construction and do not participate in subsequent calculations; Furthermore, based on the related cyclical main financial data matrix, a development correlation index analysis is performed on the cyclical main financial data of each online financial business and related online financial businesses, and the correlation chain of cyclical main financial data is output; the analysis steps are as follows: S1. Divide the data into hierarchical levels according to the number of rows contained in the main financial data matrix of the related period, and determine the hierarchical priority distribution according to the distribution of the number of rows in the matrix; S2. Based on the hierarchical priority distribution, for adjacent levels, select any period of main financial data at the high-priority level and perform development correlation index Dec[(k,i):(k+1,u)] analysis on the main financial data of each period at the low-priority level; its calculation is as follows: ; Where Dec[(k,i):(k+1,u)] is the development correlation index between the main financial data of the period corresponding to index q at level k and the main financial data of the period corresponding to index u at level k+1; Pdp(k,q) and Pdp(k+1,u) are the cyclical development impact indices of the main financial data of the period corresponding to index q and index u at levels k and k+1, respectively. For the curve vector of the main category of financial data in the period corresponding to the index q at level k; For the curve vector of the main category financial data of the period corresponding to the index u at level k+1; α[ , ] is the angle between the curve vector of the main financial data of the period corresponding to index q at level k and the curve vector of the main financial data of the period corresponding to index u at level k+1; Among them, the curve vector of the main category of periodic financial data is constructed by taking the origin of the coordinate system as the base point and the curve points at each time point on the development curve of the main category of periodic financial data as the base point, and then performing vector synthesis processing on the sub-vectors of adjacent time points one by one until all sub-vectors on the curve are synthesized and the curve vector of the main category of periodic financial data is output. S3. Introduce a comparison threshold Dec(X) to perform comparative analysis on the development correlation index Dec[(k,i):(k+1,u)] between the main financial data of the cycle at adjacent levels; If Dec[(k,i):(k+1,u)]≥Dec(X), then the main financial data of the period corresponding to (k,i) and (k+1,u) are recorded as strongly correlated data. If Dec[(k,i):(k+1,u)]<Dec(X), then the main financial data of the period corresponding to (k,i) and (k+1,u) are recorded as weakly correlated data. S4. In the related periodic main category financial data matrix, perform concatenation processing on strongly related data at adjacent levels, and output the concatenation path as the periodic main category financial data association chain. Among them, the single-cycle main category financial data association chain only involves a single cycle of main category financial data at any single level; Furthermore, the correlation chains of the main financial data categories within the relevant periodic financial data matrix are comprehensively identified; and the correlation transmission index Acd is retrieved from each main financial data category correlation chain for each period. m The analysis is as follows: ; Among them, Acd mPdp(m,k,e) is the correlation transmission index of the main category financial data association chain of period m; Pdp(m,k,e) is the cycle development influence index of the main category financial data of period m with index e in level k in the main category financial data association chain; Pdp(m,k+1,w) is the cycle development influence index of the main category financial data of period m with index w in level k+1 in the main category financial data association chain; Pdp(m) is the sum of the main category financial data of each period in the main category financial data association chain of period m; Dec[(m,k,e):(m,k+1,w)] is the development correlation index between the main category financial data of period e in level k and the main category financial data of period w in level k+1 in the main category financial data association chain of period m. It should be noted that when calculating the correlation transmission index, since k represents the number of levels in the matrix of main financial data of the related cycles, and when analyzing the development correlation index between adjacent levels of main financial data of the cycles, the calculation of all adjacent levels in the matrix is ​​completed when the number of levels reaches k-1; therefore, ∑ k-1 This indicates that only k-1 summations are needed in the process of summing the correlation transmission index; Furthermore, a matrix vector is constructed for the main financial data of each period between adjacent levels on the association chain of main financial data of each period; the angle between any two matrix vectors on the association chain of main financial data of the same period is obtained by vector translation, and the maximum angle is selected as the angle for association credit assessment. Among them, matrix vector construction refers to: following the concatenation chain of periodic main financial data between adjacent levels, and pointing the high-priority periodic main financial data to the low-priority periodic main financial data to complete the vector construction; Vector translation refers to shifting two vectors from their starting points until they coincide, in order to obtain the angle between the two vectors. The correlation credit level Rcs is determined based on the correlation transmission index and the angle between correlation credit assessment and the correlation main category financial data correlation chain. m An analysis was conducted; the analysis was as follows: ; Among them, Rcs m β represents the degree of association credit of the main category financial data association chain in period m; β represents the maximum angle between any two matrix vectors in the main category financial data association chain in period m. Furthermore, based on the assessment results of the correlation credit degree of the main financial data correlation chain in each cycle, a descending order comparison analysis is performed; resource distribution priority is allocated according to the comparison analysis results, and resource distribution decision is placed at the top based on the resource distribution priority.

[0008] Credit Tag-Based Financial Data Resource Distribution Management System: The system includes a business financial data monitoring unit, a financial data cycle impact assessment unit, a correlation matrix construction unit, a correlation chain construction unit, a correlation chain data transmission and analysis unit, a correlation chain credit assessment unit, and a resource distribution decision-making unit; The business financial data monitoring unit is used to identify online financial transactions and retrieve the corresponding financial data for those transactions. The financial data cycle impact assessment unit determines the financial data corresponding to the assessment cycle of online financial business based on cycle division, and analyzes the cycle development impact index of each type of financial data by fitting development curves; based on the cycle development impact index of each type of financial data corresponding to each online financial business, a classification threshold is set to compare and classify each type of financial data corresponding to each online financial business; among which, the comparison and classification of each type of financial data includes cycle main category financial data and cycle auxiliary category financial data. The association matrix construction unit retrieves the data interaction records between various online financial businesses, identifies the remaining online financial businesses that have data interactions with each online financial business, and records them as associated online financial businesses; it coordinates the periodic main category financial data of each online financial business and associated online financial businesses, and constructs an associated periodic main category financial data matrix corresponding to each online financial business. The association chain construction unit, based on the associated periodic main category financial data matrix, performs development correlation index analysis on the periodic main category financial data of each online financial business and the related online financial businesses, and outputs the periodic main category financial data association chain. The data transmission analysis unit of the related chain determines the related chains of the main financial data of the main financial data of the related cycles in the related cycle matrix; and retrieves the related chains of the main financial data of each cycle for related transmission index analysis. The associated chain credit assessment unit constructs matrix vectors for adjacent levels of the main financial data in each cycle's associated chain; obtains the angle between any two matrix vectors in the same cycle's associated chain by vector translation, selects the maximum angle as the associated credit assessment angle; and analyzes the degree of associated credit based on the associated transmission index and the associated credit assessment angle of the main financial data associated chain in each cycle. The resource distribution decision unit assesses the creditworthiness of the main financial data association chains for each cycle and performs a descending order comparison analysis; it allocates resource distribution priorities based on the comparison analysis results and prioritizes resource distribution decisions based on these priorities.

[0009] Compared with the prior art, the beneficial effects of the present invention are: This invention integrates a financial business platform to monitor the status of financial business and retrieve financial data; it then analyzes the impact of the retrieved financial data on the business cycle development to classify and filter the financial data; finally, it performs matrix correlation processing on the filtered financial data to assess the developmental correlation between multi-source financial data and construct a correlation chain between the financial data; by analyzing the correlation chain transmission index and the credit level of the correlation chain, it enables decision-making on resource allocation; this application explores the deep correlation between multi-source financial data of different financial businesses to construct a data chain for credit assessment, achieving precise resource allocation through the combination of financial data among multi-source financial data. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the financial data resource distribution and management method based on credit tags according to the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Example 1: As Figure 1 As shown, the present invention provides a technical solution: A method for distributing and managing financial data resources based on credit tags, comprising the following steps: Retrieve online financial transactions from the financial service platform and extract financial data from these transactions. Analyze the cyclical development impact index of the financial data retrieved from online financial services, and determine the main cyclical financial data of online financial services based on the analysis data; Identify the associated online financial businesses for each online financial business and construct a matrix of associated cyclical main financial data for each online financial business; conduct development correlation index analysis on the cyclical main financial data of the associated online financial businesses based on the matrix of associated cyclical main financial data to determine the correlation chain of cyclical main financial data; The association transmission index of the main financial data association chain in each cycle is retrieved and analyzed. Based on the analysis data, the association credit of the main financial data association chain in each cycle is assessed, and resource allocation decisions are made based on the assessment data.

[0013] Furthermore, based on management permissions, the system retrieves financial transactions stored in the financial business platform, identifies online financial transactions based on their real-time status, and retrieves the corresponding financial data for those online financial transactions. Among them, the real-time status of financial business refers to the current operating status of financial business, including business completed, business online, and business not started; the financial data of online financial business refers to the various types of financial data generated during the operation of online business. It should be noted that, in this embodiment, the principle followed for searching online services is to filter out financial services that are in the completed or unstarted state, and output financial services that are in the online state. The types of financial data in online financial services include, but are not limited to, transaction data, fund flow data, and product parameters.

[0014] Furthermore, the financial data of each online financial business is divided into periods T, and the latest period financial data is used as the evaluation period financial data for the corresponding online financial business; where period T is the evaluation period. In this embodiment, it should be noted that the evaluation period T is manually set and can be adjusted according to the timeliness of financial data, so as to conduct timely observation and analysis of data within the period. Since financial data has a certain timeliness, when a certain time interval is exceeded, the observation or reference value of financial data will decrease. Therefore, when analyzing financial business, the time period factor needs to be considered. Based on the financial data corresponding to the evaluation period of each online financial business, the development curves of various types of financial data within the evaluation period of each online financial business are obtained through coordinate system mapping. Based on the development curves of each type of financial data, the changes in financial data between adjacent time points on the curves within the evaluation period are determined, and the cyclical development impact index Pdp(n,i) of the corresponding type of financial data is analyzed. The analysis is as follows: ; Wherein, Pdp(n,i) is the cyclical development impact index of the i-th type of financial data corresponding to the n-th online financial business; A(J n,i,t ≠J n,i,t+1 J represents the number of adjacent time points corresponding to the unequal curve values ​​of the i-th type of financial data for online financial business number n within the evaluation period; n,i,t and J n,i,t+1 For the online financial business with serial number n, the values ​​of the i-th type of financial data on the curve at time t and time t+1 within the evaluation period; A(T) is the number of time points included in the evaluation period T; T is the evaluation period; n is the serial number of the online financial business; i is the type serial number of the financial data. It should be noted that coordinate system mapping processing involves labeling the data at each time point within the period in a coordinate system and then outputting the corresponding periodic development curve of the data through curve fitting. Based on the cyclical development impact index of various types of financial data corresponding to each online financial business, a classification threshold is set to compare and classify the various types of financial data corresponding to each online financial business. Among them, financial data whose cyclical development impact index is greater than or equal to the classification threshold are classified as cyclical main category financial data; Financial data whose cyclical development impact index is less than the classification threshold are classified as cyclical auxiliary financial data.

[0015] It should be noted that the index analysis of the cyclical development impact of financial data corresponding to online financial business is specifically determined by superimposing the changes in the curve values ​​within the cycle and then performing a proportion analysis with the total value of the curve; the changes in the curve are obtained by calculating the difference between the curve values ​​at adjacent time points on the curve. In this embodiment, the financial data cycle development impact index for online business assesses the cumulative change of current financial data within a cycle to determine the impact of financial data on business development within the cycle. Furthermore, comparing financial data based on the financial data cycle development impact index to classify financial data reflects the impact of financial data on business development by judging the cumulative change of corresponding financial data within a cycle. Specifically, financial data with positive fluctuations within a cycle better reflects the impact on real-time business development. Furthermore, retrieve the data interaction records between various online financial services, identify the remaining online financial services with which each online financial service has data interaction, and record them as related online financial services; coordinate the periodic main category financial data of each online financial service and related online financial services, and construct a related periodic main category financial data matrix corresponding to each online financial service. The steps for constructing the main category of financial data matrix related to the cycle are as follows: T1. Construct a corresponding cyclical main category financial dataset for each online financial business by coordinating the cyclical main category financial data of each online financial business, and sort the cyclical main category financial data in descending order; wherein, in this embodiment, the cyclical main category financial data is sorted in descending order based on the cyclical development influence index of each cyclical main category financial data to achieve sorting from largest to smallest. T2. Take any online financial business as the target online financial business, then make the period main class financial dataset corresponding to the target online financial business the target period main class financial dataset; traverse the related online financial businesses of the target online financial business, then make the period main class financial dataset corresponding to the related online financial businesses the related period main class financial dataset. T3. Determine the degree of correlation between each related online financial business and the target online financial business, and assign a priority for matrix synthesis of each related period's main financial dataset based on the degree of correlation. In this embodiment, the degree of correlation is determined by the degree of data interaction between each related financial business and the target financial business, and it is calculated as follows: ; Wherein, P(c,b) represents the degree of association between the target online financial business at sequence number c and the associated online financial business at sequence number b; N(c,b) represents the number of data interactions between the target online financial business at sequence number c and the associated online financial business at sequence number b; t(c,b) represents the duration of data interactions between the target online financial business at sequence number c and the associated online financial business at sequence number b; N(c) represents the total number of data interactions between the target online financial business at sequence number c and each associated financial business; and t(c) represents the total duration of data interactions between the target online financial business at sequence number c and each associated financial business. It should be noted that when performing matrix synthesis priority allocation on the main financial datasets of each related period, the principle of giving high priority to the main financial datasets of the related period corresponding to the one with the greater correlation between the related online financial business and the target online financial business. T4. Based on the matrix synthesis priority allocation results of each related period main category financial dataset, perform vertical same-position matrix merging processing on each related period main category financial dataset according to the target period main category financial dataset, and output the related period main category financial data matrix of the target online financial business. It should be noted that vertical same-position matrix merging refers to merging the main financial dataset of the target period as the first row of the matrix, and then merging the main financial datasets of each related period in parallel according to the elements in the set, vertically and in the same position. During the matrix merging process of the main financial datasets of related periods, the matrix synthesis order is based on the matrix synthesis priority. After the matrix merging is completed, any empty elements in the matrix are set to 0. The 0-position bits are only used for matrix construction and do not participate in subsequent calculations. In this embodiment, if there are three periodic main financial datasets H1=[h1,h2,h3], H2=[h4,h5,h6,h7], and H3=[h8,h9,h10], with H1 as the target periodic main financial dataset and H2 having a higher matrix composition priority than H3 based on correlation calculations, then during the merging of related periodic main financial data matrices, H1 is used as the first row of the matrix, and H2 is merged first. After merging H3, we get Then the merging of the main category financial data matrix of the related cycle is completed; Furthermore, based on the related cyclical main financial data matrix, a development correlation index analysis is performed on the cyclical main financial data of each online financial business and related online financial businesses, and the correlation chain of cyclical main financial data is output; the analysis steps are as follows: S1. Divide the data into hierarchical levels according to the number of rows contained in the main financial data matrix of the related period, and determine the hierarchical priority distribution according to the distribution of the number of rows in the matrix; In this embodiment, if the associated period main category financial data matrix contains 5 rows, and the number of rows is distributed from top to bottom as 1-5 rows, then the corresponding level is divided into 5 levels, and the level priority is from top to bottom as level 1-5; where level 1 has higher priority than level 2, and so on to determine the priority comparison between the 5 levels. S2. Based on the hierarchical priority distribution, for adjacent levels, select any period of main financial data at the high-priority level and perform development correlation index Dec[(k,i):(k+1,u)] analysis on the main financial data of each period at the low-priority level; its calculation is as follows: ; Where Dec[(k,i):(k+1,u)] is the development correlation index between the main financial data of the period corresponding to index q at level k and the main financial data of the period corresponding to index u at level k+1; Pdp(k,q) and Pdp(k+1,u) are the cyclical development impact indices of the main financial data of the period corresponding to index q and index u at levels k and k+1, respectively. For the curve vector of the main category of financial data in the period corresponding to the index q at level k; For the curve vector of the main category financial data of the period corresponding to the index u at level k+1; α[ , ] is the angle between the curve vector of the main financial data of the period corresponding to index q at level k and the curve vector of the main financial data of the period corresponding to index u at level k+1; Among them, the curve vector of the main category of periodic financial data is constructed by taking the origin of the coordinate system as the base point and the curve points at each time point on the development curve of the main category of periodic financial data as the base point, and then performing vector synthesis processing on the sub-vectors of adjacent time points one by one until all sub-vectors on the curve are synthesized and the curve vector of the main category of periodic financial data is output. In this embodiment, it should be noted that level k and level k+1 are adjacent levels, and level k has a higher priority than level k+1; and (k,i) and (k+1,u) are the position coordinates of the corresponding period main category financial data in the associated period main category financial data matrix, respectively. α[ , The value is obtained by determining the curve vectors separately. The angle α between the coordinate axis and the x-axis , curve vector The angle α between the coordinate axis and the x-axis Then calculate α and α The absolute value of the difference between them is output as α[ , ]; S3. Introduce a comparison threshold Dec(X) to perform comparative analysis on the development correlation index Dec[(k,i):(k+1,u)] between the main financial data of the cycle at adjacent levels; If Dec[(k,i):(k+1,u)]≥Dec(X), then the main financial data of the period corresponding to (k,i) and (k+1,u) are recorded as strongly correlated data. If Dec[(k,i):(k+1,u)]<Dec(X), then the main financial data of the period corresponding to (k,i) and (k+1,u) are recorded as weakly correlated data. S4. In the related periodic main category financial data matrix, perform concatenation processing on strongly related data at adjacent levels, and output the concatenation path as the periodic main category financial data association chain. Among them, the single-cycle main category financial data association chain only involves a single cycle of main category financial data at any single level; In this embodiment, if the associated period's main category financial data matrix is If, based on the development correlation index analysis results of the main financial data of the cycle between adjacent levels, h1 and h4 are strongly correlated, and h4 and h8 are strongly correlated, then they are connected in series and the correlation chain of the main financial data of the cycle is output as h1-h4-h8. If h2 and h5, h6 are strongly correlated, h5 and h9 are strongly correlated, and h6 and h10 are strongly correlated, then the output cycle-based main category financial data correlation chains include: h2-h5-h9 and h2-h6-h10; other cases can be deduced by analogy based on the above generation process; it follows that this correlation chain only involves a single cycle-based main category financial data at a single level; Furthermore, the correlation chains of the main financial data categories within the relevant periodic financial data matrix are comprehensively identified; and the correlation transmission index Acd is retrieved from each main financial data category correlation chain for each period. m The analysis is as follows: ; Among them, Acd mPdp(m,k,e) is the correlation transmission index of the main category financial data association chain of period m; Pdp(m,k,e) is the cycle development influence index of the main category financial data of period m with index e in level k in the main category financial data association chain; Pdp(m,k+1,w) is the cycle development influence index of the main category financial data of period m with index w in level k+1 in the main category financial data association chain; Pdp(m) is the sum of the main category financial data of each period in the main category financial data association chain of period m; Dec[(m,k,e):(m,k+1,w)] is the development correlation index between the main category financial data of period e in level k and the main category financial data of period w in level k+1 in the main category financial data association chain of period m. It should be noted that when calculating the correlation transmission index, since k represents the number of levels in the matrix of main financial data of the related cycles, and when analyzing the development correlation index between adjacent levels of main financial data of the cycles, the calculation of all adjacent levels in the matrix is ​​completed when the number of levels reaches k-1; therefore, ∑ k-1 This indicates that only k-1 summations are needed in the process of summing the correlation transmission index; Furthermore, a matrix vector is constructed for the main financial data of each period between adjacent levels on the association chain of main financial data of each period; the angle between any two matrix vectors on the association chain of main financial data of the same period is obtained by vector translation, and the maximum angle is selected as the angle for association credit assessment. Among them, matrix vector construction refers to: following the concatenation chain of periodic main financial data between adjacent levels, and pointing the high-priority periodic main financial data to the low-priority periodic main financial data to complete the vector construction; Vector translation refers to shifting two vectors from their starting points until they coincide, in order to obtain the angle between the two vectors. The correlation credit level Rcs is determined based on the correlation transmission index and the angle between correlation credit assessment and the correlation main category financial data correlation chain. m An analysis was conducted; the analysis was as follows: ; Among them, Rcs m β represents the degree of association credit of the main category financial data association chain in period m; β represents the maximum angle between any two matrix vectors in the main category financial data association chain in period m. Furthermore, based on the assessment results of the correlation credit degree of the main financial data correlation chain in each cycle, a descending order comparison analysis is performed; resource distribution priority is allocated according to the comparison analysis results, and resource distribution decision is placed at the top based on the resource distribution priority.

[0016] In this embodiment, based on the descending comparison results of the association credit degree of the main financial data association chains of each cycle, the main financial data association chains of the cycle with a high association credit degree are given high priority in resource allocation; Example 2: The present invention provides another technical solution: Credit Tag-Based Financial Data Resource Distribution Management System: The system includes a business financial data monitoring unit, a financial data cycle impact assessment unit, a correlation matrix construction unit, a correlation chain construction unit, a correlation chain data transmission and analysis unit, a correlation chain credit assessment unit, and a resource distribution decision-making unit; Among them, the business financial data monitoring unit is used to identify online financial business and retrieve the corresponding financial data of online financial business; The financial data cycle impact assessment unit determines the financial data corresponding to the assessment cycle of online financial business based on cycle division, and analyzes the cycle development impact index of each type of financial data by fitting development curves; based on the cycle development impact index of each type of financial data corresponding to each online financial business, a classification threshold is set to compare and classify each type of financial data corresponding to each online financial business; among which, the comparison and classification of each type of financial data includes cycle main category financial data and cycle auxiliary category financial data. The association matrix construction unit retrieves the data interaction records between various online financial businesses, identifies the remaining online financial businesses that have data interactions with each online financial business, and records them as related online financial businesses; it coordinates the periodic main category financial data of each online financial business and related online financial businesses, and constructs the associated periodic main category financial data matrix corresponding to each online financial business. The association chain construction unit, based on the associated periodic main category financial data matrix, performs development correlation index analysis on the periodic main category financial data of each online financial business and related online financial businesses, and outputs the periodic main category financial data association chain; The data transmission analysis unit comprehensively identifies the correlation chains of the main financial data of each cycle in the matrix of main financial data of each cycle; and retrieves the correlation chains of the main financial data of each cycle for correlation transmission index analysis. The related chain credit assessment unit constructs matrix vectors for adjacent levels of the main financial data in each cycle's related chain; it obtains the angle between any two matrix vectors in the same cycle's related chain by vector translation, selects the maximum angle as the related credit assessment angle; and analyzes the degree of related credit based on the related transmission index and the related credit assessment angle of the main financial data in the cycle's related chain. The resource distribution decision unit assesses the creditworthiness of the main financial data linkage chains for each cycle and performs a descending order comparison analysis; it then allocates resource distribution priorities based on the comparison analysis results and prioritizes resource distribution decisions based on these priorities.

[0017] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for distributing and managing financial data resources based on credit tags, characterized in that: Retrieve online financial transactions from the financial service platform and extract financial data from these transactions. Analyze the cyclical development impact index of the financial data retrieved from online financial services, and determine the main cyclical financial data of online financial services based on the analysis data; Identify the associated online financial businesses for each online financial business and construct a matrix of associated cyclical main financial data for each online financial business; conduct development correlation index analysis on the cyclical main financial data of the associated online financial businesses based on the matrix of associated cyclical main financial data to determine the correlation chain of cyclical main financial data; The association transmission index of the main financial data association chain in each cycle is retrieved and analyzed. Based on the analysis data, the association credit of the main financial data association chain in each cycle is assessed, and resource allocation decisions are made based on the assessment data.

2. The financial data resource distribution and management method based on credit tags according to claim 1, characterized in that: Retrieve data interaction records between various online financial services, identify other online financial services that have data interactions with each online financial service, and record them as related online financial services; coordinate the periodic master category financial data of various online financial services and related online financial services, and construct a related periodic master category financial data matrix corresponding to each online financial service. The steps for constructing the associated periodic main category financial data matrix are as follows: T1. Construct a corresponding periodic main category financial dataset for each online financial business by coordinating the periodic main category financial data of each online financial business, and sort the periodic main category financial data in descending order. T2. Take any online financial business as the target online financial business, and then let the period main class financial dataset corresponding to the target online financial business be the target period main class financial dataset. If we iterate through the associated online financial businesses of the target online financial business, then we make the period main class financial dataset corresponding to the associated online financial business the associated period main class financial dataset. T3. Determine the degree of correlation between each related online financial business and the target online financial business, and assign a priority for matrix synthesis of each related period's main financial dataset based on the degree of correlation. T4. Based on the matrix synthesis priority allocation results of each related period main category financial dataset, perform vertical same-position matrix merging processing on each related period main category financial dataset according to the target period main category financial dataset, and output the related period main category financial data matrix of the target online financial business.

3. The financial data resource distribution and management method based on credit tags according to claim 1, characterized in that: Based on the associated cyclical main financial data matrix, we conduct development correlation index analysis on the cyclical main financial data of each online financial business and related online financial businesses, and output the cyclical main financial data correlation chain. The analysis steps are as follows: S1. Divide the data into hierarchical levels according to the number of rows contained in the main financial data matrix of the related period, and determine the hierarchical priority distribution according to the distribution of the number of rows in the matrix; S2. Based on the hierarchical priority distribution, for adjacent levels, select any period of main financial data at the high-priority level and perform development correlation index Dec[(k,i):(k+1,u)] analysis on the main financial data of each period at the low-priority level; its calculation is as follows: ； Where Dec[(k,i):(k+1,u)] is the development correlation index between the main financial data of the period corresponding to index q at level k and the main financial data of the period corresponding to index u at level k+1; Pdp(k,q) and Pdp(k+1,u) are the cyclical development impact indices of the main financial data of the period corresponding to index q and index u at levels k and k+1, respectively. For the curve vector of the main category of financial data in the period corresponding to the index q at level k; For the curve vector of the main category financial data of the period corresponding to the index u at level k+1; α[ , ] is the angle between the curve vector of the main financial data of the period corresponding to index q at level k and the curve vector of the main financial data of the period corresponding to index u at level k+1; S3. Introduce a comparison threshold Dec(X) to perform comparative analysis on the development correlation index Dec[(k,i):(k+1,u)] between the main financial data of the cycle at adjacent levels; If Dec[(k,i):(k+1,u)]≥Dec(X), then the main financial data of the period corresponding to (k,i) and (k+1,u) are recorded as strongly correlated data. If Dec[(k,i):(k+1,u)]<Dec(X), then the main financial data of the period corresponding to (k,i) and (k+1,u) are recorded as weakly correlated data. S4. In the related periodic main category financial data matrix, perform concatenation processing on strongly related data at adjacent levels, and output the concatenation path as the periodic main category financial data association chain.

4. The financial data resource distribution and management method based on credit tags according to claim 1, characterized in that: The correlation chains of major financial data categories within the relevant periodic financial data matrix are determined holistically; and the correlation transmission index Acd is calculated by retrieving the correlation chains of major financial data categories for each period. m The analysis is as follows: ； Among them, Acd m Pdp(m,k,e) is the correlation transmission index of the main category financial data association chain of period m; Pdp(m,k,e) is the cycle development influence index of the main category financial data of period m with index e in level k in the main category financial data association chain; Pdp(m,k+1,w) is the cycle development influence index of the main category financial data of period m with index w in level k+1 in the main category financial data association chain; Pdp(m) is the sum of the main category financial data of each period in the main category financial data association chain of period m; Dec[(m,k,e):(m,k+1,w)] is the development correlation index between the main category financial data of period e in level k and the main category financial data of period w in level k+1 in the main category financial data association chain of period m.

5. The financial data resource distribution and management method based on credit tags according to claim 4, characterized in that: Matrix vectors are constructed for the main financial data of each period between adjacent levels in the association chain of main financial data of each period; the angle between any two matrix vectors in the association chain of main financial data of the same period is obtained by vector translation, and the maximum angle is selected as the angle for association credit assessment. The correlation credit level Rcs is determined based on the correlation transmission index and the angle between correlation credit assessment and the correlation main category financial data correlation chain. m An analysis was conducted; the analysis was as follows: ； Among them, Rcs m β represents the degree of association credit of the main category financial data association chain in period m; β represents the maximum angle between any two matrix vectors in the main category financial data association chain in period m.

6. The financial data resource distribution and management method based on credit tags according to claim 1, characterized in that: Based on the assessment results of the correlation credit degree of the main financial data correlation chain in each cycle, a descending order comparison analysis is performed; resource distribution priority is allocated according to the comparison analysis results, and resource distribution decision is placed at the top based on the resource distribution priority.

7. The financial data resource distribution and management method based on credit tags according to claim 1, characterized in that: Based on management permissions, the system retrieves financial transactions stored on the financial business platform, identifies online financial transactions based on their real-time status, and retrieves the corresponding financial data for those online financial transactions.

8. The financial data resource distribution and management method based on credit tags according to claim 1, characterized in that: The financial data of each online financial business is divided into periods T, and the latest period financial data is used as the evaluation period financial data for the corresponding online financial business. Based on the financial data corresponding to the evaluation period of each online financial business, the development curves of various types of financial data within the evaluation period of each online financial business are obtained through coordinate system mapping. Based on the development curves of each type of financial data, the changes in financial data between adjacent time points on the curves within the evaluation period are determined, and the cyclical development impact index Pdp(n,i) of the corresponding type of financial data is analyzed. The analysis is as follows: ； Wherein, Pdp(n,i) is the cyclical development impact index of the i-th type of financial data corresponding to the n-th online financial business; A(J n,i,t ≠J n,i,t+1 J represents the number of adjacent time points corresponding to the unequal curve values ​​of the i-th type of financial data for online financial business number n within the evaluation period; n,i,t and J n,i,t+1 A(T) represents the values ​​of the i-th type of financial data corresponding to the online financial business with serial number n at time t and time t+1 on the curve within the evaluation period; A(T) represents the number of time points included in the evaluation period T; T is the evaluation period; n is the serial number of the online financial business; and i is the type serial number of the financial data.

9. A system for executing any one of the credit tag-based financial data resource distribution and management methods according to claims 1-8, characterized in that: The system includes a business financial data monitoring unit, a financial data cycle impact assessment unit, a correlation matrix construction unit, a correlation chain construction unit, a correlation chain data transmission and analysis unit, a correlation chain credit assessment unit, and a resource distribution decision-making unit; The business financial data monitoring unit is used to identify online financial transactions and retrieve the corresponding financial data for those transactions. The financial data cycle impact assessment unit determines the financial data corresponding to the assessment cycle of online financial business based on cycle division, and analyzes the cycle development impact index of each type of financial data by fitting development curves; based on the cycle development impact index of each type of financial data corresponding to each online financial business, a classification threshold is set to compare and classify each type of financial data corresponding to each online financial business; among which, the comparison and classification of each type of financial data includes cycle main category financial data and cycle auxiliary category financial data. The association matrix construction unit retrieves data interaction records between various online financial services, identifies other online financial services with which each online financial service has data interaction, and records them as associated online financial services; it coordinates the periodic main category financial data of each online financial service and associated online financial services, and constructs an associated periodic main category financial data matrix corresponding to each online financial service.

10. The system according to claim 9, characterized in that: The association chain construction unit, based on the associated periodic main category financial data matrix, performs development correlation index analysis on the periodic main category financial data of each online financial business and the related online financial businesses, and outputs the periodic main category financial data association chain. The data transmission analysis unit of the related chain determines the related chains of the main financial data of the main financial data of the related cycles in the related cycle matrix; and retrieves the related chains of the main financial data of each cycle for related transmission index analysis. The associated chain credit assessment unit constructs matrix vectors for adjacent levels of the main financial data in each cycle's associated chain; obtains the angle between any two matrix vectors in the same cycle's associated chain by vector translation, selects the maximum angle as the associated credit assessment angle; and analyzes the degree of associated credit based on the associated transmission index and the associated credit assessment angle of the main financial data associated chain in each cycle. The resource distribution decision unit assesses the creditworthiness of the main financial data association chains for each cycle and performs a descending order comparison analysis; it allocates resource distribution priorities based on the comparison analysis results and prioritizes resource distribution decisions based on these priorities.

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