A fast analysis method based on cross-time-zone data

Abstract

The present application relates to the technical field of transaction data processing, and especially relates to a fast analysis method based on cross-time zone data, which comprises collecting a current time zone where a transaction data center is located and a first time zone corresponding to a time stamp of transaction data, determining an analysis strategy for transaction data according to a time offset between the current time zone and the first time zone, preliminarily determining the eligibility of the transaction data according to an analysis result of the analysis strategy, determining an abnormal rate of the transaction data according to the analysis result of the analysis strategy, and determining time zone conversion of the transaction data according to the abnormal rate. The fast analysis method overcomes the problem that the prior art only considers the consistency of time zone conversion of cross-time zone data and the need for time stamp calibration of all transaction data in the conversion process, resulting in low data analysis accuracy and slow speed in the analysis of cross-time zone data, and low analysis efficiency of cross-time zone data.

Description

Technical Field

[0001] This invention relates to the field of transaction data processing technology, and in particular to a rapid analysis method based on cross-time zone data. Background Technology

[0002] Asset and liability management is a crucial aspect for financial institutions to enhance their business decision-making, investment planning, and risk management capabilities. It enables financial institutions to implement comprehensive and detailed management of credit users. Among these, dynamic cash flow analysis is a key focus for financial institutions in managing credit users and implementing decisions. It combines adjustments to user behavior and forecasts of future new business to reveal the future cash flow situation of existing business, thereby analyzing future cash flow from a dynamic perspective.

[0003] Businesses distributed across time zones is a common phenomenon in the financial sector. To achieve comprehensive and detailed management of individual businesses distributed across time zones, financial institutions need to conduct cross-time zone analysis. However, due to the time zone difference between the business's location and the financial data center, as well as the different network providers, cross-time zone businesses face difficulties in data synchronization and analysis, resulting in low analysis efficiency.

[0004] Chinese Patent Publication No. CN115455068A discloses a cross-time zone data processing method, apparatus, device, and storage medium. The method includes: receiving a data retrieval request from a user and determining the first time zone and query time period of the source data to be retrieved; determining whether a data collection table with the first time zone as a timestamp exists in a cloud database; if not, determining the first time zone of the data collection table in the cloud database, and determining the time period information of the source data in the first time zone based on the first time zone, the first time zone, and the query time period; and extracting the corresponding source data from the data collection table based on the time period information. The technical solution proposed in this application unifies the time zone of source data storage by converting the time zone of the source data uploaded by the acquisition device, and converts the time zone and time period in the data retrieval request submitted by the user. This greatly reduces the configuration of data acquisition rules and ensures that customers in different time zones at home and abroad can obtain the most accurate equipment operation statistics. It can be seen that the existing technology only considers the consistency of time zone conversion for cross-time zone data, and the conversion process requires timestamp calibration of all transaction data, resulting in low accuracy and slow speed in the analysis of cross-time zone data, thus causing low efficiency in the analysis of cross-time zone data. Summary of the Invention

[0005] To address this, the present invention provides a rapid analysis method based on cross-time zone data, which overcomes the problems of low accuracy and slow speed in the analysis of cross-time zone data in existing technologies, resulting in low efficiency in the analysis of cross-time zone data.

[0006] To achieve the above objectives, the present invention provides a rapid analysis method based on cross-time zone data, comprising:

[0007] Collect the current time zone of the transaction data center and the first time zone corresponding to the timestamp of the transaction data;

[0008] The analysis strategy for transaction data is determined based on the time offset between the current time zone and the first time zone;

[0009] The eligibility of the transaction data is preliminarily determined based on the analysis results of the aforementioned analysis strategy.

[0010] The anomaly rate of the transaction data is determined based on the analysis results of the aforementioned analysis strategy;

[0011] The time zone conversion of the transaction data is determined based on the anomaly rate;

[0012] Specifically, when the time zone conversion mode is determined to be completed, the parameters of the time zone conversion are adjusted based on the transaction stability.

[0013] Furthermore, determining the analysis strategy for the transaction data based on the time offset includes determining the analysis of the transaction data based on the comparison result between the time offset and the standard time offset.

[0014] Furthermore, the analysis of the feature data of the transaction data includes comparing the feature data with the standard transaction data of the transaction data center, and determining that the transaction data is abnormal when the feature data is inconsistent with the features of the standard transaction data.

[0015] Furthermore, the analysis of the characteristic data of the transaction data includes determining the time node of the first time zone that generated the transaction data when the time offset is less than or greater than the standard time offset, and determining the transaction data to be abnormal when the time node does not meet the preset conditions.

[0016] The preset condition is that the generated transaction data contains a number of time nodes corresponding to the number of transactions in the transaction data.

[0017] Furthermore, when the transaction data is abnormal, the abnormality rate of the transaction data is calculated, and the time zone conversion of the transaction data is determined based on the comparison result of the abnormality rate and the preset abnormality rate threshold.

[0018] Furthermore, the time zone conversion of the transaction data includes shifting the timestamp of the transaction data by the standard time offset when the timestamp meets a preset rule.

[0019] Furthermore, the time zone conversion of the transaction data includes shifting the transaction data by the standard time offset when the similarity between the feature data and the standard transaction data features is less than or equal to a preset feature similarity.

[0020] Furthermore, when determining the analysis strategy for the current data based on the anomaly rate, the method also includes collecting historical transaction data from the transaction data center and determining transaction stability evaluation values ​​for several historical transaction data. When the transaction stability evaluation value is less than or equal to a preset transaction stability evaluation value, the parameters for the time zone conversion are adjusted.

[0021] Furthermore, the transaction stability evaluation value is calculated according to the following formula, and is set as follows:

[0022]

[0023] Where W is the transaction stability evaluation value, Ri is the proportion of abnormal data in the i-th historical transaction data, n is the number of historical transactions, and Rz is the total amount of data in the historical transaction data.

[0024] Furthermore, when it is determined that the parameters of the time zone conversion should be adjusted, the difference between the transaction stability evaluation value and the preset transaction stability evaluation value is calculated, so as to determine the adjustment of the preset abnormality rate threshold in the time zone conversion based on the difference.

[0025] Compared with the prior art, the beneficial effects of the present invention are that it determines the time offset between the current time zone and the first time zone corresponding to any timestamp of the transaction data by analyzing the current time zone where the transaction data center is located and the first time zone corresponding to any timestamp of the transaction data. This allows for the preliminary determination of the analysis scheme for the transaction data based on the time offset, thereby enabling the preliminary determination of the time zone where the cross-time zone data is located. By conducting the analysis scheme for the transaction data based on the determined time zone, the accuracy and speed of the analysis process for cross-time zone data are improved, thus improving the efficiency of the analysis of cross-time zone data.

[0026] Furthermore, this invention determines the time offset of cross-time zone data, thereby determining the analysis method for cross-time zone data based on the determined time offset. When the time offset equals the standard time offset, feature analysis is performed on the transaction data to determine the accuracy of the transaction data based on its characteristics, thus improving the data analysis speed of cross-time zone transaction data. Alternatively, when the time offset does not equal the standard time offset, time node analysis of the transaction data in the first time zone is performed to determine the accuracy of the time zone of the transaction site, thereby improving the accuracy of cross-time zone transaction data analysis and further increasing the analysis speed.

[0027] Furthermore, when analyzing the characteristic data of transaction data, this invention compares the transaction data with the standard transaction data in the transaction data center to determine data consistency. Based on the data consistency, it determines whether there are any anomalies in the transaction data transmitted to the transaction data center, thereby further improving the accuracy and speed of data analysis in the process of analyzing cross-time zone data, and thus further improving the efficiency of cross-time zone data analysis.

[0028] Furthermore, when analyzing the characteristic data of transaction data, this invention determines whether the transaction data is abnormal by identifying the time node of the first time zone of the transaction site corresponding to the transaction data, thereby determining the accuracy of the cross-time zone data analysis process and further improving the analysis efficiency of cross-time zone data.

[0029] Furthermore, this invention determines the time zone conversion mode of transaction data based on the anomaly rate when transaction data is abnormal, thereby determining the time zone conversion process of transaction data analysis, improving the accuracy and speed of cross-time zone data analysis, and further improving the efficiency of cross-time zone data analysis.

[0030] Furthermore, the present invention determines whether the timestamp of the transaction data meets a preset rule in the first conversion mode, so that the transaction data is time-offset with a standard time offset when the timestamp meets the preset rule, or the transaction data is backed up when the timestamp does not meet the preset rule, thereby further improving the accuracy of transaction data analysis and improving the efficiency of cross-time zone data analysis. Attached Figure Description

[0031] Figure 1 This is a flowchart of a rapid analysis method based on cross-time zone data according to an embodiment of the present invention;

[0032] Figure 2 This is a flowchart illustrating the determination of the analysis strategy in a rapid analysis method based on cross-time zone data according to an embodiment of the present invention.

[0033] Figure 3 This is a flowchart illustrating the conversion mode determination process of a rapid analysis method based on cross-time zone data according to an embodiment of the present invention.

[0034] Figure 4 This is a flowchart illustrating the time zone conversion adjustment process of the fast analysis method based on cross-time zone data in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0036] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0037] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0038] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0039] Please see Figure 1 As shown, Figure 1 This is a flowchart of a rapid analysis method based on cross-time zone data according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the determination of the analysis strategy in a rapid analysis method based on cross-time zone data according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating the conversion mode determination process of a rapid analysis method based on cross-time zone data according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating the time zone conversion adjustment process of the fast analysis method based on cross-time zone data in an embodiment of the present invention.

[0040] The present invention provides a rapid analysis method based on cross-time zone data, comprising:

[0041] Step S1: Collect the current time zone of the transaction data center and the first time zone corresponding to the timestamp of the transaction data;

[0042] Step S2: Determine the analysis strategy for the transaction data based on the time offset between the current time zone and the first time zone;

[0043] Step S3: Based on the analysis results of the analysis strategy, preliminarily determine the eligibility of the transaction data;

[0044] Step S4: Determine the anomaly rate of the transaction data based on the analysis results of the analysis strategy;

[0045] Step S5: Determine the time zone conversion of the transaction data based on the anomaly rate.

[0046] Specifically, this invention analyzes the current time zone where the transaction data center is located and the first time zone corresponding to any timestamp of the transaction data to determine the time offset between the current time zone and the first time zone. This allows for the preliminary determination of the analysis scheme for the transaction data based on the time offset, thereby enabling the preliminary determination of the time zone where the cross-time zone data is located. By using the determined time zone to formulate the analysis scheme for the transaction data, the accuracy and speed of the cross-time zone data analysis process are improved, thus enhancing the efficiency of cross-time zone data analysis.

[0047] Specifically, determining the analysis strategy for the transaction data based on the time offset includes comparing the time offset with a standard time offset to determine the analysis method for the transaction data based on the comparison result;

[0048] If the time offset is equal to the standard time offset, then it is determined that the transaction data will be analyzed using the first analysis method.

[0049] If the time offset is less than or greater than the standard time offset, it is determined that the transaction data will be analyzed using the second analysis method.

[0050] The first analysis method is used to analyze the characteristic data of the transaction data; the second analysis method is used to analyze the time node of the first time zone of the transaction site that generated the transaction data.

[0051] In this embodiment of the invention, the transaction data includes user information, transaction type, transaction information, and associated documents. The user information specifically includes the user's identity information, the transaction information includes the transaction contract and transaction account information, and the associated documents include import / export licenses and tax documents. The feature data includes the amount of standard data required to be filled in the corresponding user information, transaction type, transaction information, and associated documents in the transaction information.

[0052] Specifically, this invention determines the time offset of cross-time zone data, thereby determining the analysis method for cross-time zone data based on the determined time offset. When the time offset is equal to the standard time offset, feature analysis is performed on the transaction data to determine the accuracy of the transaction data based on the characteristics, thereby improving the data analysis speed of cross-time zone transaction data. Alternatively, when the time offset is not equal to the standard time offset, time node analysis of the transaction data in the first time zone is performed to determine the accuracy of the time zone of the transaction site, thereby improving the accuracy of cross-time zone transaction data analysis and further improving the analysis speed of transaction data.

[0053] Specifically, the analysis of the feature data of the transaction data includes comparing the feature data with the standard transaction data of the transaction data center to determine the feature consistency of the transaction data;

[0054] If the characteristic data is consistent with the characteristics of standard transaction data, then the transaction data is deemed qualified.

[0055] When the characteristic data is inconsistent with the characteristics of standard transaction data, the transaction data is determined to be abnormal.

[0056] Specifically, when analyzing the characteristic data of transaction data, this invention compares the transaction data with the standard transaction data in the transaction data center to determine data consistency. Based on the data consistency, it determines whether there are any anomalies in the transaction data transmitted to the transaction data center, thereby further improving the accuracy and speed of data analysis in the process of analyzing cross-time zone data, and thus further improving the efficiency of cross-time zone data analysis.

[0057] Specifically, the analysis of the time node of the first time zone that generates the transaction data includes determining whether the time node meets a preset condition. If the preset condition is met, the transaction data is deemed qualified; if the preset condition is not met, the transaction data is deemed abnormal. The preset condition is the number of time nodes in the generated transaction data that correspond to the number of transactions in the transaction data.

[0058] Specifically, when analyzing the characteristic data of transaction data, this invention determines whether the transaction data is abnormal by determining the time node of the first time zone of the transaction site corresponding to the transaction data, thereby determining the accuracy of the cross-time zone data analysis process and further improving the analysis efficiency of cross-time zone data.

[0059] Specifically, when the transaction data is abnormal, the abnormality rate of the transaction data is calculated, and the time zone conversion of the transaction data is determined based on the comparison result of the abnormality rate and the preset abnormality rate threshold.

[0060] If the anomaly rate is less than or equal to a preset anomaly rate threshold, it is determined that the transaction data will be converted to a time zone using the first conversion mode.

[0061] If the anomaly rate is greater than a preset anomaly rate threshold, it is determined that the transaction data will be converted to a different time zone using the second conversion mode.

[0062] In this embodiment of the invention, the preset abnormality rate threshold is set to 0.25, but this value is not limited to this, and those skilled in the art can adjust it according to the actual situation.

[0063] Specifically, this invention determines the time zone conversion mode of transaction data based on the anomaly rate when transaction data is abnormal, thereby determining the time zone conversion process of transaction data analysis, improving the accuracy and speed of cross-time zone data analysis, and further improving the efficiency of cross-time zone data analysis.

[0064] Specifically, when performing time zone conversion on the transaction data using the first conversion mode, it is determined whether the timestamp of the transaction data meets a preset rule;

[0065] When the timestamp meets a preset rule, it is determined that the timestamp of the transaction data will be shifted by the standard time offset.

[0066] If the timestamp does not meet the preset pattern, it is determined that the transaction data will be returned to the site where the transaction data is located.

[0067] The preset rule is that several timestamps in the transaction data correspond to the order of the transaction data with time increments.

[0068] Specifically, the present invention determines whether the timestamp of the transaction data meets a preset rule in a first conversion mode, so that the transaction data is time-offset with a standard time offset when the timestamp meets the preset rule, or the transaction data is backed up when the timestamp does not meet the preset rule, thereby further improving the accuracy of transaction data analysis and improving the efficiency of cross-time zone data analysis.

[0069] Specifically, when performing time zone conversion on the transaction data using the second conversion mode, the similarity between the feature data and the features of the standard transaction data is compared with a preset feature similarity, so as to determine the processing of the transaction data based on the comparison result;

[0070] When the similarity is greater than or equal to the preset feature similarity, it is determined that the transaction data will be timestamped using the standard time offset.

[0071] When the similarity is less than the preset feature similarity, it is determined that the transaction data will be returned to the site where the transaction data is located.

[0072] In this embodiment of the invention, the preset feature similarity value is 0.95, but this value is not limited to this, and those skilled in the art can adjust it according to the actual situation.

[0073] Specifically, the present invention compares the feature data of transaction data with the feature data of standard transaction data in the second conversion mode to determine the similarity, so as to determine the time zone conversion of the transaction data based on the similarity, thereby ensuring the accuracy of the transaction data undergoing time zone conversion, thereby improving the analysis speed of transaction data, and further improving the analysis efficiency of cross-time zone data.

[0074] Specifically, when determining the analysis strategy for the current data based on the anomaly rate, the method further includes collecting historical transaction data from the transaction data center, determining transaction stability evaluation values ​​for several historical transaction data, and determining whether to adjust the parameters of the time zone conversion based on the comparison results between the transaction stability evaluation values ​​and the preset transaction stability evaluation values.

[0075] When the transaction stability evaluation value is less than or equal to the preset transaction stability evaluation value, it is determined that the parameters of the time zone conversion should be adjusted.

[0076] When the transaction stability evaluation value is greater than the preset transaction stability evaluation value, it is determined that the time zone conversion parameters will not be adjusted.

[0077] In this embodiment of the invention, the preset transaction stability evaluation value is determined by the total amount of historical transaction data, and the preset transaction stability evaluation value is 0.1% of the total amount of data.

[0078] Specifically, this invention determines the stability evaluation value of historical transaction data, and then adjusts the parameters for time zone conversion of the transaction data based on the stability of historical transactions. This improves the accuracy and speed of cross-time zone transaction data analysis, thereby further improving the efficiency of cross-time zone data analysis.

[0079] Specifically, the transaction stability evaluation value is calculated according to the following formula, and is set as follows:

[0080]

[0081] Where W is the transaction stability evaluation value, Ri is the proportion of abnormal data in the i-th historical transaction data, n is the number of historical transactions, and Rz is the total amount of data in the historical transaction data.

[0082] Specifically, when it is determined that the analysis strategy should be adjusted, the difference between the transaction stability evaluation value and the preset transaction stability evaluation value is calculated, and the adjustment coefficient of the preset abnormality rate threshold in the time zone conversion is determined based on the comparison result of the difference and the preset difference.

[0083] When the difference is less than or equal to a preset difference, it is determined that the preset abnormality rate threshold is adjusted by a first adjustment coefficient;

[0084] When the difference is greater than a preset difference, it is determined that the preset abnormality rate threshold is adjusted by a second adjustment coefficient.

[0085] In this embodiment of the invention, the preset difference is 0.05% of the total data volume, 0.9 > first adjustment coefficient > 0.8 > second adjustment coefficient > 0.7, preferably, the first adjustment coefficient is 0.85 and the second adjustment coefficient is 0.75.

[0086] In this embodiment of the invention, the adjusted preset anomaly rate threshold is set to 0.75Y0 or 0.85Y0, where Y0 represents the preset anomaly rate threshold.

[0087] Specifically, this invention calculates the difference between the transaction stability evaluation value and the preset transaction stability evaluation value, thereby adjusting the specific parameters of the transaction data analysis strategy according to different transaction stability conditions. Different adjustment coefficients are set for different conditions to enable precise control of the adjustment process, thereby further improving the accuracy and speed of data analysis in the process of analyzing cross-time zone data, and thus further improving the efficiency of cross-time zone data analysis.

[0088] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0089] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A rapid analysis method based on cross-time zone data, characterized in that, include: Collect the current time zone of the transaction data center and the first time zone corresponding to the timestamp of the transaction data; The analysis strategy for transaction data is determined based on the time offset between the current time zone and the first time zone; The eligibility of the transaction data is preliminarily determined based on the analysis results of the aforementioned analysis strategy. The preliminary determination of the qualification of the transaction data based on the analysis results of the analysis strategy includes comparing the feature data of the transaction data with the standard transaction data of the transaction data center, and determining that the transaction data is abnormal when the feature data is inconsistent with the features of the standard transaction data. The anomaly rate of the transaction data is determined based on the analysis results of the aforementioned analysis strategy; The time zone conversion of the transaction data is determined based on the anomaly rate; When the transaction data is abnormal, the abnormality rate of the transaction data is calculated, and the time zone conversion of the transaction data is determined based on the comparison result of the abnormality rate and the preset abnormality rate threshold. Specifically, when the time zone conversion mode is determined to be completed, the parameters of the time zone conversion are adjusted based on transaction stability. When determining the analysis strategy for the current data based on the anomaly rate, the method further includes collecting historical transaction data from the transaction data center and determining transaction stability evaluation values ​​for several historical transaction data. When the transaction stability evaluation value is less than or equal to a preset transaction stability evaluation value, the parameters for the time zone conversion are adjusted. The transaction stability evaluation value is calculated according to the following formula, and is set as follows: ， Where W is the transaction stability evaluation value, Ri is the proportion of abnormal data in the i-th historical transaction data, n is the number of historical transactions, and Rz is the total amount of data in the historical transaction data; When it is determined that the parameters of the time zone conversion need to be adjusted, the difference between the transaction stability evaluation value and the preset transaction stability evaluation value is calculated, so as to determine the adjustment of the preset abnormality rate threshold in the time zone conversion based on the difference.

2. The rapid analysis method based on cross-time zone data according to claim 1, characterized in that, The step of determining the analysis strategy for the transaction data based on the time offset includes determining the analysis of the transaction data based on the comparison result between the time offset and the standard time offset.

3. The rapid analysis method based on cross-time zone data according to claim 2, characterized in that, The preliminary determination of the qualification of the transaction data based on the analysis results of the analysis strategy includes determining the time node of the first time zone that generated the transaction data when the time offset is less than or greater than the standard time offset, and determining the transaction data to be abnormal when the time node does not meet the preset conditions. The preset condition is that the generated transaction data contains a number of time nodes corresponding to the number of transactions in the transaction data.

4. The rapid analysis method based on cross-time zone data according to claim 3, characterized in that, The time zone conversion of the transaction data includes shifting the timestamp of the transaction data by the standard time offset when the timestamp meets a preset rule.

5. The rapid analysis method based on cross-time zone data according to claim 4, characterized in that, The time zone conversion of the transaction data includes shifting the transaction data by the standard time offset when the similarity between the feature data and the standard transaction data features is greater than or equal to a preset feature similarity.

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