Dynamic analysis method for integrated processing of business data and financial data

Abstract

The present invention provides a dynamic analysis method for the integrated processing of business data and financial data. It includes: determining the target data source through the internal data structure and business needs of the enterprise, and obtaining a preliminary screening data source set; using a coverage check algorithm to select a data source that meets the needs from the preliminary screening data source set as the target data source; according to the attributes of the preliminary data set, labeling the data set as a target business and financial data set, and importing it into the analysis database for data fusion; performing time-based dynamic analysis, and performing static analysis, comparative analysis, and periodic dynamic analysis and display on the target data set. The present invention effectively improves the comprehensive analysis capabilities of business data and financial data through multi-level data processing and fusion methods, especially in the correlation analysis between business and finance, providing enterprises with efficient data analysis tools, which helps support enterprise decision-making and management.

Description

Technical Field

[0001] The present invention relates to the field of edge detection, and in particular to a method and system for processing high-speed rail original images based on edge filtering under low visibility. Background Art

[0002] As the scale of enterprises expands and the complexity of their business increases, the integrated processing of business data and financial data has become an important issue in enterprise management and decision-making. Traditional data management models usually process business data and financial data separately. Business data covers operational activities such as production, sales, and procurement, while financial data includes financial statements such as costs, revenues, and profits. However, this decentralized management model leads to weak correlation between data, making it difficult to reflect the overall operation of the enterprise, affecting the real-time and accuracy of decision-making.

[0003] At present, data-driven management models have gradually been applied to all walks of life. Enterprises need to integrate business data and financial data through dynamic analysis to achieve multi-dimensional data correlation analysis. By integrating business and financial data, it is possible to more comprehensively evaluate the direct impact of business activities on finance, such as real-time tracking of the impact of sales orders on revenue and costs, and analyzing the impact of inventory turnover on cash flow. This integrated dynamic analysis can not only enhance the ability of financial forecasting and cost control, but also support enterprises to respond quickly to market changes.

[0004] In the process of realizing the integrated processing of business data and financial data, the existing technology mainly adopts the following technical routes:

[0005] 1. Data integration based on data warehouse: By building a data warehouse, data from business systems and financial systems are regularly aggregated, cleaned, and converted to form a unified data storage. The advantage of data warehouse technology is that it can effectively store historical data, which is convenient for subsequent analysis and decision-making.

[0006] 2. Data synchronization based on ETL (extraction, transformation, loading): ETL technology is widely used in integrated processing, synchronizing data from various data sources to a unified analysis platform in real time or regularly. This method can achieve basic data flow and consistency between different systems through automated data processing processes.

[0007] 3. Unified data service based on data middle platform: In recent years, data middle platform technology has attracted attention, aiming to achieve flexible call and deep association of business and financial data by building a unified data service layer. The data middle platform integrates data governance, data sharing and data analysis on the same platform to provide support for integrated data processing.

[0008] 4. Analysis solution based on big data and real-time stream processing: This technical route uses big data technology and stream processing technology to realize the analysis of real-time data, integrates business and financial data on the same analysis platform, and realizes dynamic analysis of business and finance through real-time calculation of data streams. It can effectively process high-concurrency data and meet real-time analysis needs.

[0009] The problems existing in the above prior art mainly include:

[0010] 1. Lack of real-time performance: Most technical routes are based on batch data processing, and the response to dynamic data is not timely enough, making it difficult to meet the company's needs for real-time monitoring and rapid decision-making.

[0011] 2. Data consistency and accuracy issues: During the multi-source data synchronization process, data may be distorted due to system asynchrony. Especially under the batch processing mechanism of ETL or data warehouse, data consistency is difficult to guarantee.

[0012] 3. High implementation cost: Data middle platform and real-time stream processing solutions are technically complex, require high investment, and have a long construction period. Especially for small and medium-sized enterprises, it is difficult to bear the high costs and maintenance requirements.

[0013] 4. Complex data structure and lack of standardization: The data structure and granularity of business and financial data are different. A large amount of data conversion and cleaning is required during the integration process, which can easily lead to problems such as data loss or inconsistent formats.

[0014] In summary, existing technologies have many deficiencies in the dynamic analysis of integrated processing of business and financial data, and new technical solutions are urgently needed to improve the real-time, accuracy and flexibility of data integration. Summary of the invention

[0015] In view of the above problems, the present invention aims to propose a dynamic analysis method for integrated processing of business data and financial data, the method comprising the following steps:

[0016] S1, data source determination step: determining at least one data source from within the enterprise as a target data source;

[0017] S2, data extraction step: pulling a data source list from the target data source to obtain a preliminary data set;

[0018] S3, data indexing step: according to the attributes of the preliminary data set, indexing the preliminary data set into the corresponding target business and financial data set;

[0019] S4, data fusion step: import the target business and financial data set into the analysis database for data set fusion;

[0020] S5, dynamic analysis step: dynamic analysis of the target business and financial data set;

[0021] S6, result output step: output the dynamic analysis result obtained in step S6 to a terminal for displaying data analysis results.

[0022] Furthermore, the “determining at least one data source as a target data source” in step S1 specifically includes:

[0023] S1.1. According to the internal data structure and business needs of the enterprise, determine the preliminary screening criteria for the target data source and obtain a preliminary screening data source set; the screening criteria meet the following requirements:

[0024]

[0025] in, It is the initial screening data source set; subscript is the data source serial number, For all candidate data sources data sources, is the set of all candidate data sources, For data source 's initial relevance score; Indicates The number of calls to a data source. The total number of calls for all data sources; For the Weighted scores for each data source; is the predetermined initial screening threshold;

[0026] S1.2. Select a data source that meets the final requirements from the initial screening data source set using a coverage check algorithm as the target data source. The coverage check algorithm satisfies:

[0027] in, Indicates that for Coverage checking algorithm for each data source; It represents a truth test, and returns a true value when the test object meets the preset conditions; Indicates the reference Data sources The volume of business; Represents a collection of all businesses; is the total business volume, is the preset coverage; Indicates the reference Data sources The number of elements of the intersection of the business volume and all business sets;

[0028] The above formula indicates that when the Data sources When the ratio of the number of elements in the intersection of the business volume of the business volume and all business sets to the total business volume exceeds the preset coverage threshold, the first Data sources Through coverage checking, it is determined to be the target data source.

[0029] Furthermore, step S3 is specifically as follows:

[0030] According to the attributes of the preliminary data set, the preliminary data set is indexed as the corresponding target business and financial data set , the target business and financial data set satisfy:

[0031]

[0032] Among them, the superscript is the data source serial number, is the total number of data sources; is the target business data packet number, The total number of target business and financial data packets; is the business data sequence number, The total number of business data; is the financial data serial number, is the total number of financial data; Indicates that from The sequence number of the data source is Target business and financial data package; and They are from The sequence number of the data source is The target business data packet has a sequence number of The business data and serial number are financial data;

[0033] Furthermore, step S4 specifically includes:

[0034] The target business and financial data set Import into a blank data table to obtain a fused data form. The data import process is expressed as:

[0035]

[0036] in, Indicates that for sequence numbers 1 to The fused data form is obtained by fusing the target business and financial data set of the data source; Indicates that the target business and financial data sets of all data sources are unioned.

[0037] Furthermore, step S4 also includes: merging the data form satisfy:

[0038]

[0039] in, It is composed in the form of a double set.

[0040] Furthermore, step S4 also includes: Perform de-indexing operation to meet the following requirements:

[0041]

[0042] in, represents the deindexed business and financial data set. Represents the sequence of all business data after removing the index, To remove the serial number of the business data, the total number is ; represents the sequence of all financial data after removing indexes, To remove the serial number of financial data, the total number is .

[0043] Furthermore, in step S5, the dynamic analysis is a time-based horizontal comparative analysis.

[0044] Furthermore, time-based horizontal comparative analysis specifically refers to: obtaining the comparative relationship between business data and financial data in the de-indexed business and financial data set within the previous time period, calculating the business and financial correlation sequence, and satisfying:

[0045]

[0046] in, Indicates the business-finance correlation sequence; For the current moment, is the forward time period; Indicates that the initial state of the business-finance correlation sequence is an empty sequence; , and Respectively indicate the serial number The current business volume, previous business volume and business increment of the de-indexed business data; , and Respectively indicate the serial number The current business volume, previous business volume and business increment of the de-indexed financial data;

[0047] It is the maximum traversal algorithm, which means that the maximum value on the right side is filled into the business-finance correlation sequence in sequence , the traversal index is the sequence number and serial number .

[0048] Beneficial Effects

[0049] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0050] 1. Improve data analysis efficiency: Through the integrated processing of enterprise business data and financial data, the present invention can achieve effective integration of multiple data sources, reduce redundancy and duplication of work in traditional data analysis, and improve data processing efficiency.

[0051] 2. Enhance data correlation analysis capabilities: This method can deeply explore the intrinsic relationship between business data and financial data and provide more accurate decision support by introducing time-based dynamic analysis, horizontal comparative analysis and correlation calculation of business and financial data.

[0052] 3. Improve decision-making quality: By checking the coverage of target data sources and accurately matching business needs, the accuracy and timeliness of analysis results are ensured, providing enterprises with a scientific and reliable analysis basis and helping them optimize resource allocation and management decisions.

[0053] 4. Flexible scalability: This method can adapt to the various data requirements and structures of different enterprises, has good versatility and scalability, and is suitable for wide application in data analysis systems of various enterprises.

[0054] In summary, the present invention not only improves data utilization efficiency through innovative data processing and analysis methods, but also provides enterprises with more powerful data insights and decision-making support capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0056] In order to deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with examples. The examples are only used to explain the present invention and do not constitute a limitation on the protection scope of the present invention.

[0057] Embodiment 1

[0058] according to Figure 1 As shown, this embodiment provides a dynamic analysis method for integrated processing of business data and financial data, the method comprising the following steps:

[0059] S1, data source determination step: determining at least one data source from within the enterprise as a target data source;

[0060] S2, data extraction step: pulling a data source list from the target data source to obtain a preliminary data set;

[0061] S3, data indexing step: according to the attributes of the preliminary data set, indexing the preliminary data set into the corresponding target business and financial data set;

[0062] S4, data fusion step: import the target business and financial data set into the analysis database for data set fusion;

[0063] S5, dynamic analysis step: dynamic analysis of the target business and financial data set;

[0064] S6, result output step: output the dynamic analysis result obtained in step S6 to a terminal for displaying data analysis results.

[0065] Furthermore, the “determining at least one data source as a target data source” in step S1 specifically includes:

[0066] S1.1. According to the internal data structure and business needs of the enterprise, determine the preliminary screening criteria for the target data source and obtain a preliminary screening data source set; the screening criteria meet the following requirements:

[0067]

[0068] in, It is the initial screening data source set; subscript is the data source serial number, For all candidate data sources data sources, is the set of all candidate data sources, For data source 's initial relevance score; Indicates The number of calls to a data source. The total number of calls for all data sources; For the Weighted scores for each data source; is the predetermined initial screening threshold;

[0069] S1.2. Select a data source that meets the final requirements from the initial screening data source set using a coverage check algorithm as the target data source. The coverage check algorithm satisfies:

[0070] in, Indicates that for Coverage checking algorithm for each data source; It represents a truth test, and returns a true value when the test object meets the preset conditions; Indicates the reference Data sources The volume of business; Represents a collection of all businesses; is the total business volume, is the preset coverage; Indicates the reference Data sources The number of elements of the intersection of the business volume and all business sets;

[0071] The above formula indicates that when the Data sources When the ratio of the number of elements in the intersection of the business volume of the business volume and all business sets to the total business volume exceeds the preset coverage threshold, the first Data sources Through coverage checking, it is determined to be the target data source.

[0072] Furthermore, step S3 is specifically as follows:

[0073] According to the attributes of the preliminary data set, the preliminary data set is indexed as the corresponding target business and financial data set , the target business and financial data set satisfy:

[0074]

[0075] Among them, the superscript is the data source serial number, is the total number of data sources; is the target business data packet number, The total number of target business and financial data packets; is the business data sequence number, The total number of business data; is the financial data serial number, is the total number of financial data; Indicates that from The sequence number of the data source is Target business and financial data package; and They are from The sequence number of the data source is The target business data packet has a sequence number of The business data and serial number are financial data;

[0076] Furthermore, step S4 specifically includes:

[0077] The target business and financial data set Import into a blank data table to obtain a fused data form. The data import process is expressed as: ;in, Indicates that for sequence numbers 1 to The fused data form is obtained by fusing the target business and financial data set of the data source; Indicates that the target business and financial data sets of all data sources are unioned.

[0078] Furthermore, step S4 also includes: merging the data form satisfy:

[0079] in, It is composed in the form of a double set.

[0080] Furthermore, step S4 also includes: Perform de-indexing operation to meet the following requirements:

[0081]

[0082] in, represents the deindexed business and financial data set. Represents the sequence of all business data after removing the index, To remove the serial number of the business data, the total number is ; represents the sequence of all financial data after removing indexes, To remove the serial number of financial data, the total number is .

[0083] Furthermore, in step S5, the dynamic analysis is a time-based horizontal comparative analysis.

[0084] Furthermore, time-based horizontal comparative analysis specifically refers to: obtaining the comparative relationship between business data and financial data in the de-indexed business and financial data set within the previous time period, calculating the business and financial correlation sequence, and satisfying:

[0085]

[0086] in, Indicates the business-finance correlation sequence; For the current moment, is the forward time period; Indicates that the initial state of the business-finance correlation sequence is an empty sequence; , and Respectively indicate the serial number The current business volume, previous business volume and business increment of the de-indexed business data; , and Respectively indicate the serial number The current business volume, previous business volume and business increment of the de-indexed financial data;

[0087] It is the maximum traversal algorithm, which means that the maximum value on the right side is filled into the business-finance correlation sequence in sequence , the traversal index is the sequence number and serial number .

[0088] Embodiment 2

[0089] A large retail enterprise hopes to improve the accuracy of its financial forecasts and optimize inventory management and sales strategies through a dynamic analysis method that integrates business data and financial data. The enterprise has multiple different data sources, including sales systems, financial management systems, and inventory management systems. The data types and structures in each system are different, so how to effectively integrate these heterogeneous data sources has become a key issue facing the enterprise.

[0090] Data source determination: The enterprise first selects the target data source from multiple systems based on its business needs and management goals. In this embodiment, the sales system (including sales data), financial management system (including financial statements and budget data) and inventory management system (providing inventory data and supply chain information) are selected as target data sources. Through preliminary analysis, the importance of these data sources to the current business analysis is determined. The enterprise performs a preliminary relevance score on each data source and selects the appropriate target data source based on its historical usage frequency, data quality and business relevance.

[0091] Example: Assume that a store's sales data (d1) has 20 historical calls, financial statement data (d2) has 15 historical calls, and inventory management data (d3) has 10 historical calls. The enterprise scores these calls, assuming V1(d1) = 0.8, V2(d2) = 0.6, V3(d3) = 0.4. The enterprise calculates the initial relevance score and sets a threshold (such as α1 = 0.5) to filter out the most valuable target data sources.

[0092] Data extraction and indexing: By analyzing the specific attributes of the data source, the enterprise extracts data from the selected target data source and indexes the data. For example, sales data is indexed as business data, and financial statements are indexed as financial data. Sales data includes sales, profits, return rates, etc. of each store; financial data includes balance sheets, income statements, etc. For each data item, the enterprise classifies it according to its nature and business meaning, and cleans and standardizes the data to ensure that all data conforms to a unified format.

[0093] Example: For sales data d1, assume that the monthly sales data of the store (such as sales and refunds) has been classified and standardized, and the following data set is formed after processing:

[0094] B1(j): Sales = 50000, Refund = 2000

[0095] The financial data d_2 is processed to form the following data set:

[0096] F1 (k): Profit = 10000, Cost = 3000

[0097] Data fusion: Import the indexed target data set into the company's analytical database to form an integrated database system. In this step, the system associates data from different sources through common identifiers. For example, sales data and financial data are matched by time, region, and product category. The fused data set contains the business data and financial data of each sales store, which is convenient for subsequent dynamic analysis. The results of data fusion show that there is a strong correlation between sales and financial expenditures of some stores, while the financial expenditures of other stores have little to do with sales, providing important reference information for the finance department.

[0098] Example: Assume that the data set after the fusion of the sales data and financial data of store M1 is as follows:

[0099] Tn= {[B1(j): Sales = 50000, Refund = 2000], [F1(k): Profit = 10000, Cost = 3000]}

[0100] The system will perform similar processing on the data sets of multiple stores, and merge them according to dimensions such as time and region to generate a data set containing all stores for subsequent analysis.

[0101] Dynamic analysis: Based on data fusion, enterprises conduct periodic dynamic analysis. For example, the financial team regularly analyzes the relationship between sales revenue, profit, and cost based on historical data and business needs. By comparing data from different months or quarters, the system can calculate the changing trends of sales revenue of each store and how these changes affect the overall profit and financial situation.

[0102] Example: When performing dynamic analysis, suppose that a company wants to analyze the relationship between sales data and financial data for the past three months. Through calculation, the sales revenue of store M1 has shown an upward trend in the past three months (for example, an increase of 10%), and the financial profit has also increased accordingly (for example, an increase of 15%). By comparing the changes in these two indicators, the system finds that sales growth has a positive impact on profits.

[0103] Result output: Based on dynamic analysis, the company outputs the analysis results through visualization tools and presents the result report to the decision-makers. For example, at the monthly financial report meeting, the company displays a chart showing the correlation between sales and finance, which helps management to intuitively see which promotions have successfully increased sales and which have failed to achieve the expected results. In addition, the report also shows the financial health of each store, helping the finance team make budget adjustments.

[0104] Example: Through dynamic analysis, the company found that the correlation between sales growth and profit of store M1 was 0.8, while the correlation of M2 was lower, only 0.3. The system displays these data through visual charts to help management quickly identify which stores are performing well and which stores need to adjust their business strategies.

[0105] Business optimization: Through continuous dynamic analysis, the company discovered problems with the business model of some stores. For example, the sales growth of some stores was relatively slow, but their financial expenditure was at a high level. The analysis found that excess inventory in some stores led to a waste of funds, and some sales strategies did not match the inventory level. Based on this analysis result, the company decided to adjust the inventory management strategy, reduce unnecessary inventory, optimize promotional activities, and avoid over-reliance on discount sales.

[0106] Example: Assume that store M2 has excess inventory, which results in wasted capital, low sales growth, and no significant increase in profits. Based on the analysis results, the company decides to reduce the inventory of M2 and shift funds to high-demand goods, thereby optimizing inventory and increasing sales.

[0107] Ultimately, by implementing the method of the present invention, the enterprise significantly improved the efficiency and accuracy of financial and business data analysis, optimized resource allocation, and achieved synergy in multiple business areas (such as sales, inventory, and finance). Through data fusion and dynamic analysis, the enterprise can promptly identify problems in operations and respond quickly, thereby improving overall operational efficiency.

[0108] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A dynamic analysis method for integrated processing of business data and financial data, characterized in that: The method comprises the following steps: S1. Data source determination step: Determine at least one data source from within the enterprise as the target data source, specifically including: S1.

1. According to the internal data structure and business needs of the enterprise, determine the preliminary screening criteria for the target data source and obtain a preliminary screening data source set; the screening criteria meet the following requirements: ； in, It is the initial screening data source set; subscript is the data source serial number, For all candidate data sources data sources, is the set of all candidate data sources, For data source 's initial relevance score; Indicates The number of calls to a data source. The total number of calls for all data sources; For the Weighted scores for each data source; is the predetermined initial screening threshold; S1.

2. Select a data source that meets the final requirements from the initial screening data source set using a coverage check algorithm as the target data source. The coverage check algorithm satisfies: ； in, Indicates that for Coverage checking algorithm for each data source; It represents a truth test, and returns a true value when the test object meets the preset conditions; Indicates the reference Data sources The volume of business; Represents a collection of all businesses; is the total business volume, is the preset coverage; Indicates the reference Data sources The number of elements of the intersection of the business volume and all business sets; The above formula indicates that when the Data sources When the ratio of the number of elements in the intersection of the business volume of the business volume and all business sets to the total business volume exceeds the preset coverage threshold, the first Data sources Through coverage check, it is determined as the target data source; S2, data extraction step: pulling a data source list from the target data source to obtain a preliminary data set; S3, data indexing step: according to the attributes of the preliminary data set, indexing the preliminary data set into the corresponding target business and financial data set; S4, data fusion step: import the target business and financial data set into the analysis database for data set fusion; S5, dynamic analysis step: dynamic analysis is performed on the target business and financial data set; the dynamic analysis is a time-based horizontal comparative analysis; specifically, it means: obtaining the comparative relationship between business data and financial data in the de-indexed business and financial data set within the forward time period, calculating the business and financial correlation sequence, and satisfying: ； in, Indicates the business-finance correlation sequence; For the current moment, is the forward time period; Indicates that the initial state of the business-finance correlation sequence is an empty sequence; , and Respectively indicate the serial number The current business volume, previous business volume and business increment of the de-indexed business data; , and Respectively indicate the serial number The current business volume, previous business volume and business increment of the de-indexed financial data; It is the maximum traversal algorithm, which means that the maximum value on the right side is filled into the business-finance correlation sequence in sequence , the traversal index is the sequence number and serial number ; S6, result output step: output the dynamic analysis result obtained in step S6 to a terminal for displaying data analysis results.

2. A dynamic analysis method for integrated processing of business data and financial data according to claim 1, characterized in that: Step S3 is specifically as follows: According to the attributes of the preliminary data set, the preliminary data set is indexed as the corresponding target business and financial data set , the target business and financial data set satisfy: ； Among them, the superscript is the data source serial number, is the total number of data sources; is the target business data packet number, The total number of target business and financial data packets; is the business data sequence number, The total number of business data; is the financial data serial number, is the total number of financial data; Indicates that from The sequence number of the data source is Target business and financial data package; and They are from The sequence number of the data source is The target business data packet has a sequence number of The business data and serial number are financial data.

3. A dynamic analysis method for integrated processing of business data and financial data according to claim 2, characterized in that: Step S4 specifically includes: The target business and financial data set Import into a blank data table to obtain a fused data form. The data import process is expressed as: ； in, Indicates that for sequence numbers 1 to The fused data form is obtained by fusing the target business and financial data set of the data source; Indicates that the target business and financial data sets of all data sources are unioned.

4. A dynamic analysis method for integrated processing of business data and financial data according to claim 3, characterized in that: Step S4 also includes: merging the data form satisfy: ;in, It is composed in the form of a double set.

5. A dynamic analysis method for integrated processing of business data and financial data according to claim 4, characterized in that: Step S4 also includes: Perform de-indexing operation to meet the following requirements: ； in, represents the deindexed business and financial data set. Represents the sequence of all business data after removing the index, To remove the serial number of the business data, the total number is ; represents the sequence of all financial data after removing indexes, To remove the serial number of financial data, the total number is .

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