A Big Data-Based Enterprise Financial Sharing Assessment Method and System

Abstract

This invention relates to the field of enterprise management technology, specifically to a big data-based enterprise financial shared assessment method and system, comprising the following steps: acquiring and sorting procurement and acceptance data for cumulative comparison; determining settlement quantities based on amount and unit price to screen discrepancy nodes; locating acceptance intervals and associating them with cost centers and suppliers; combining multiple time periods to divide segments and merge data; and associating business segments with cost categories to calculate indicators. In this invention, by constructing a continuous sequence around acceptance time and quantity and performing cumulative verification, introducing settlement amount and unit price to derive quantity identifiers and identify discrepancy nodes, extracting acceptance segments by procurement batch and time interval and fusing associated identifiers, and dividing business segments according to multiple time nodes for integration and mapping, business events are aligned and linked within a unified time framework, strengthening data continuity and node traceability capabilities, improving anomaly identification capabilities and segment attribution consistency, and enhancing the stability and analytical consistency of assessment results.

Description

Technical Field

[0001] This invention relates to the field of enterprise management technology, and in particular to a method and system for enterprise financial sharing assessment based on big data. Background Technology

[0002] Enterprise management technology involves the organization of enterprise operational data, the setting of financial accounting processes, the collection of business data, the integration of cross-system data, and the formulation of evaluation rules. Specifically, this includes obtaining raw data from business processes such as procurement, sales, and expense reimbursement; classifying and processing data according to accounting subjects; summarizing and calculating revenue, cost, and expense data; forming indicator data based on unified standards; and completing the data entry, storage, and retrieval process through information systems to support management analysis. Traditional big data-based enterprise financial shared service evaluation methods refer to methods that centrally process and evaluate data from multiple sources in an enterprise financial shared service scenario. This typically involves extracting raw record data from financial systems, business systems, and expense reimbursement systems; performing field mapping and format conversion on the extracted data; grouping and summarizing revenue, cost, and expense data according to time and business type; calculating indicators such as profit margin, expense ratio, and turnover rate according to fixed formulas; weighting multiple indicators according to set weights; and combining historical data for ranking and comparison to form the evaluation result.

[0003] Data processing relies on predetermined aggregation paths and single time division methods. Various business records lack continuous trajectory correlation before entering the statistical stage. In the face of scattered acceptance rhythms or delayed settlement nodes, it is difficult to form a complete link. This results in the same batch of business being processed in different time segments, which in turn leads to ambiguity in the generation of indicators. For example, when some costs and corresponding revenues are recorded separately, aggregation by period can easily lead to structural bias. At the same time, the processing lacks node-level identification and difference verification mechanisms, making it difficult to effectively locate the source of anomalies. This results in highly volatile evaluation conclusions that are difficult to support in-depth analysis. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a big data-based enterprise financial sharing assessment method; To achieve the above objectives, the present invention adopts the following technical solution: a big data-based enterprise financial shared assessment method, comprising the following steps: S1: Obtain the purchase batch number, warehousing time and quantity, and acceptance time and quantity; group them and sort them in ascending order by acceptance time; accumulate the accepted quantities and compare them to obtain the acceptance time sequence record chain. S2: Based on the acceptance time sequence record chain, obtain the settlement amount and unit price to determine the settlement quantity identifier, compare the cumulative acceptance quantity to filter the difference nodes, extract the acceptance time and purchase batch number and merge them to obtain the settlement location record set; S3: Based on the settlement location record set, call the purchase batch number and the acceptance time sequence record chain to locate the acceptance interval, read the acceptance data and associate it with the cost center code and the supplier code to obtain the settlement purchase acceptance association set; S4: Based on the aforementioned settlement, procurement, and acceptance association set, obtain the revenue verification time, cost accounting time, and expense completion time, identify the time interval and divide it into intervals, merge the interval data and associate the procurement batch number with the cost center code to obtain the business event segment set; S5: Based on the set of business event segments, call the segment data to associate the business segment code and cost category code, group them and perform proportional calculation and mapping to obtain the set of financial shared evaluation index values.

[0005] As a further embodiment of the present invention, the acceptance time sequence record chain includes an acceptance batch identifier, a cumulative acceptance node, a time series marker, a quantity increment interval, and a sequence consistency identifier; the settlement positioning record set includes a difference quantity node, a corresponding acceptance time point, a procurement batch index, a settlement matching identifier, and a deviation positioning label; the settlement procurement acceptance association set includes an acceptance interval identifier, a continuous time segment, a batch association key, a settlement amount mapping item, a cost center identifier, and a supplier identifier; the business event segment set includes a time segment label, an event type identifier, a segment merging unit, a batch association identifier, a cost center mapping item, and an event sequence index; and the financial shared evaluation indicator value set includes business segment indicators, cost category indicator values, a proportional allocation coefficient, an indicator sequence code, and an evaluation result identifier.

[0006] As a further aspect of the present invention, the sorting by acceptance time in ascending order refers to sorting the acceptance records of the same procurement batch from earliest to latest according to the acceptance time, thereby constructing a continuous data chain in chronological order. The settlement quantity identifier refers to the quantity value calculated based on the settlement amount and settlement unit price, and is used as identifier data for comparison with the cumulative acceptance quantity.

[0007] As a further aspect of the present invention, the difference node refers to the quantity node position corresponding to the inconsistency between the settlement quantity identifier and the cumulative acceptance quantity, thereby locating the data deviation point; The acceptance interval refers to a continuous set of acceptance data within a time range determined based on the difference nodes in the acceptance time series.

[0008] As a further aspect of the present invention, the specific steps of S1 are as follows: S101: Obtain the purchase batch number, entry time, and entry quantity from the purchase entry document; collect the acceptance time and acceptance quantity from the acceptance document; match and verify the two types of data item by item according to the purchase batch number; associate and map data with the same number to obtain batch association verification collection. S102: Based on the batch association verification collection, group each purchase batch number, sort them in ascending order according to the acceptance time, continuously accumulate the acceptance quantity after sorting, and map the cumulative quantity to the corresponding acceptance time to obtain the cumulative time mapping sequence. S103: Based on the cumulative time mapping sequence, compare the progressive relationship of adjacent cumulative quantities, verify the consistency between the increasing trend of quantities and the time sequence direction, remove data with inconsistent sequence direction, and connect the remaining data in a chain according to the time sequence to obtain the acceptance time sequence record chain.

[0009] As a further aspect of the present invention, the specific steps of S2 are as follows: S201: Based on the acceptance time sequence record chain, obtain the settlement amount and settlement unit price in the supplier's settlement sheet, deduce the quantity value according to the ratio of amount to unit price, set the deduced quantity value as an identifier and mark it to obtain the settlement quantity identifier; S202: Call the cumulative quantity node in the acceptance time sequence record chain, compare the settlement quantity identifier with the cumulative quantity node item by item, identify the position of inconsistent values ​​and extract the corresponding node sequence information to obtain the difference node sequence; S203: Based on the difference node sequence, extract the corresponding acceptance time and purchase batch number, and merge the acceptance time and purchase batch number to obtain the settlement location record set.

[0010] As a further aspect of the present invention, the specific steps of S3 are as follows: S301: Based on the settlement location record centralized procurement batch number and the acceptance time sequence record chain, locate the node in the acceptance time sequence record chain according to the procurement batch number, extract the node sequence within the corresponding start and end acceptance time range, extract the acceptance quantity, acceptance time and procurement batch number corresponding to the node and associate them accordingly to obtain the acceptance interval data sequence. S302: Based on the acceptance interval data sequence, collect the cost center code and supplier code from the enterprise financial shared service center ledger, match and verify according to the purchase batch number, and associate the cost center code and supplier code with the corresponding node in the acceptance interval data sequence to obtain the interval code association sequence; S303: Based on the interval coding association sequence, determine the time interval according to the adjacent acceptance time, identify continuous time nodes and retain the corresponding node set, and merge the purchase batch number, settlement amount, acceptance time range, cost center code, and supplier code to obtain the settlement purchase acceptance association set.

[0011] As a further aspect of the present invention, the specific steps of S4 are as follows: S401: Based on the settlement, procurement and acceptance association set, obtain the verification time in the revenue verification record, the accounting time in the cost accounting record, and the completion time in the expense approval record. Call the acceptance time range in the settlement, procurement and acceptance association set, align the three types of time data according to the time axis and map them to the corresponding acceptance time range interval to obtain a multi-source time mapping sequence. S402: Based on the multi-source time mapping sequence, determine the continuity of time intervals between time points, divide time intervals according to the chronological order, and aggregate time points in the same interval to obtain a time interval aggregation sequence; S403: Based on the time interval aggregation sequence, associate the time data within the interval with the corresponding purchase batch number and cost center code, and centrally merge them according to the interval range to obtain a set of business event segments.

[0012] As a further aspect of the present invention, the specific steps of S5 are as follows: S501: Based on the segment data, purchase batch number, and cost center code in the business event segment set, collect the business segment code and cost category code, verify the corresponding matching of the purchase batch number and cost center code, and associate the business segment code and cost category code with the corresponding item in the segment data to obtain the segment code association sequence. S502: Based on the segment code association sequence, group the same business segment code and cost category code, perform proportional calculation on the segment data within the group, derive the proportional value based on the relationship between the segment data value and the total amount within the group, and obtain the group proportional sequence; S503: Based on the grouping ratio sequence, the ratio values ​​are sequentially mapped according to the business segment code and cost category code, and the corresponding code and ratio values ​​are matched to obtain the set of financial shared evaluation index values.

[0013] A big data-based enterprise financial shared services assessment system includes: The data chain construction module obtains the purchase batch number, warehousing time and quantity, and acceptance time and quantity, groups them and sorts them in ascending order by acceptance time, accumulates and compares the acceptance quantity, filters data with the same order, and obtains the acceptance time sequence record chain. The settlement positioning module, based on the acceptance time sequence record chain, obtains the settlement amount and unit price to determine the settlement quantity identifier, compares the cumulative quantity nodes to filter the difference nodes, extracts the acceptance time and purchase batch number and merges them to obtain the settlement positioning record set; The acceptance association module, based on the settlement location record set, calls the purchase batch number and acceptance time sequence record chain to locate the acceptance interval, reads the acceptance data and associates it with the cost center code and supplier code, filters continuous time data, and obtains the settlement purchase acceptance association set. The segmentation module, based on the settlement, procurement and acceptance association set, obtains the revenue verification time, cost accounting time and expense completion time, identifies the time interval and divides the interval, merges the interval data and associates the procurement batch number with the cost center code to obtain the business event segment set. The indicator mapping module, based on the set of business event segments, calls the segment data to associate business segment codes and cost category codes, groups them, performs proportional calculations and mappings, and obtains a set of financial shared evaluation indicator values.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, a continuous sequence is constructed around the acceptance time and quantity, and cumulative verification is performed. The settlement amount and unit price are introduced to derive quantity identifiers and identify discrepancy nodes. Acceptance segments are extracted according to procurement batches and time intervals and associated identifiers are integrated. Business segments are divided according to multiple time nodes and integrated and mapped. This enables business events to be aligned and linked within a unified time framework, strengthens data continuity and node traceability, improves anomaly identification and segment attribution consistency, and enhances the stability and parsing consistency of evaluation results. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is a detailed schematic diagram of S1 of the present invention; Figure 3 This is a detailed schematic diagram of S2 of the present invention; Figure 4 This is a detailed schematic diagram of S3 of the present invention; Figure 5 This is a detailed schematic diagram of S4 of the present invention; Figure 6 This is a detailed schematic diagram of S5 of the present invention; Figure 7 This is a system module diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] Please see Figure 1 This invention provides a big data-based enterprise financial sharing assessment method, including the following steps: S1: Obtain the purchase batch number, entry time, and entry quantity from the purchase entry document; collect the acceptance time and acceptance quantity from the acceptance document; group the acceptance records according to the purchase batch number and sort them in ascending order according to the acceptance time; accumulate the acceptance quantity item by item and associate it with the corresponding acceptance time; compare adjacent accumulated quantities and filter data with consistent time order to obtain the acceptance time sequence record chain. S2: Based on the acceptance time sequence record chain, obtain the settlement amount and settlement unit price in the supplier's settlement sheet, perform corresponding calculations on the settlement amount and settlement unit price to obtain the settlement quantity identifier, call the cumulative quantity node in the acceptance time sequence record chain, compare the settlement quantity identifier with the cumulative quantity node and select the difference node, extract the corresponding acceptance time and purchase batch number and centrally merge them to obtain the settlement location record set; S3: Based on the settlement location record centralized procurement batch number and acceptance time sequence record chain, locate the acceptance record interval, read the acceptance quantity, acceptance time and procurement batch number within the interval, associate with the cost center code and supplier code in the enterprise financial shared service center ledger, analyze the interval of acceptance time within the interval and filter continuous time data, and associate with the corresponding procurement batch number, settlement amount, acceptance time range, cost center code and supplier code to obtain the settlement procurement acceptance association set; S4: Based on the settlement, procurement and acceptance association set, obtain the verification time in the revenue verification record, the accounting time in the cost accounting record, and the completion time in the expense approval record. Call the acceptance time range in the settlement, procurement and acceptance association set, identify the time interval and divide the interval according to the time order, merge the data in the same interval and associate the procurement batch number with the cost center code to obtain the business event segment set. S5: Based on the segment data in the business event segment set and the purchase batch number and cost center code, associate the business segment code and cost category code, group the segment data and perform proportional calculations on the data within the group, and map the proportional results according to the business segment code and cost category code to obtain the set of financial shared evaluation index values.

[0020] The acceptance time sequence record chain includes acceptance batch identifier, cumulative acceptance node, time series marker, quantity increment interval, and sequence consistency identifier. The settlement positioning record set includes difference quantity node, corresponding acceptance time point, procurement batch index, settlement matching identifier, and deviation positioning label. The settlement procurement acceptance association set includes acceptance interval identifier, continuous time segment, batch association key, settlement amount mapping item, cost center identifier, and supplier identifier. The business event segment set includes time segment label, event type identifier, segment merging unit, batch association identifier, cost center mapping item, and event sequence index. The financial shared evaluation indicator value set includes business segment indicator, cost category indicator value, proportional allocation coefficient, indicator sequence code, and evaluation result identifier.

[0021] Please see Figure 2 The specific steps of S1 are as follows: S101: Obtain the purchase batch number, entry time, and entry quantity from the purchase entry document; collect the acceptance time and acceptance quantity from the acceptance document; match and verify the two types of data item by item according to the purchase batch number; associate and map data with the same number to obtain batch association verification collection. First, purchase receipt documents are retrieved from the Enterprise Resource Planning (ERP) procurement management interface. Database-level query commands are used to extract the purchase batch number, receipt time, and receipt quantity recorded in the documents. Given the massive volume of business data, a distributed data acquisition architecture is implemented. Interceptors are used to perform preliminary filtering and structuring of the heterogeneous documents flowing in in real time, addressing the input / output pressure of the ultra-large dataset. The purchase batch number uses a unique eight-digit identifier, the receipt time is accurate to the second, and the receipt quantity is derived from sensor measurement records during the physical receipt process. Simultaneously, corresponding acceptance documents are collected from the quality inspection platform's acceptance records, extracting the acceptance time and quantity confirmed by the quality inspection department. The process aligns the formats of both types of raw data, uniformly retaining the measurement precision of the receipt and acceptance quantities to two decimal places. Then, using the purchase batch number as the core index key, a hash mapping table is established in the in-memory processing unit. For each specific purchase batch number, the complete matching receipt and acceptance record items are retrieved and extracted. By executing field-level matching logic, inbound data with consistent numbers is mapped to acceptance data, ensuring that each physical inbound transaction is supported by a corresponding quality acceptance result. For example, the inbound document with purchase batch number 04010001 is retrieved, recording the inbound time as April 1st 09:30:00 and the inbound quantity as 1000.00 pieces; simultaneously, the corresponding acceptance document with number 04010001 is collected, showing the acceptance time as April 1st 14:20:00 and the acceptance quantity as 998.00 pieces. The execution process logically binds these two records using number 04010001 and automatically injects the cleaned and standardized data into the distributed file system, providing a highly reliable basic data source for subsequent large-scale parallel computing, ultimately resulting in batch-related verification data.

[0022] S102: Based on batch association verification collection, group each purchase batch number, sort them in ascending order according to the acceptance time, continuously accumulate the accepted quantity after sorting, and map the cumulative quantity to the corresponding acceptance time to obtain the cumulative time mapping sequence. First, the entire dataset is logically grouped using the purchase batch number as the primary key. To optimize computational efficiency, a parallel processing framework is employed in this stage, dividing the entire dataset into multiple data shards and distributing them to different computing nodes in the cluster for synchronous processing. All acceptance records belonging to the same batch are grouped into the same computational sequence. Within each group, a time-dimensional sorting operation is performed, obtaining the acceptance timestamp of each record and sorting them in ascending order according to time sequence. After sorting, a continuous accumulation operation is performed, setting the acceptance quantity at the top of the sorted list as the initial accumulation value, and then summing the acceptance quantity at the second-highest sorted list with the initial accumulation value to obtain the cumulative quantity for the first stage. After each accumulation operation, a coordinate mapping relationship is immediately established between the current cumulative quantity value and the corresponding acceptance time that generated the increment. This mapping logic dynamically depicts the evolution curve of the total arrival volume of the batch of materials over time. Taking batch number 04010002 as an example, this batch contains three acceptance records. The first acceptance time is 08:00:00, with a quantity of 200.00 pieces; the second acceptance time is 10:00:00, with a quantity of 300.00 pieces; and the third acceptance time is 15:00:00, with a quantity of 400.00 pieces. The execution process first sorts by time, with an initial cumulative quantity of 200.00 pieces; then, the second record of 300.00 pieces is added to obtain a cumulative quantity of 500.00 pieces; finally, the third record of 400.00 pieces is added to obtain a final cumulative quantity of 900.00 pieces. The execution process utilizes an in-memory computing engine to persistently cache the above sequence, improving the response speed of frequently triggered iterative queries, and ultimately obtaining the cumulative time mapping sequence.

[0023] S103: Based on the cumulative time mapping sequence, compare the progressive relationship of adjacent cumulative quantities, verify the consistency between the increasing trend of quantities and the time sequence direction, remove data with inconsistent sequence direction, and connect the remaining data in time sequence to obtain the acceptance time sequence record chain. First, by extracting the cumulative quantity of two adjacent nodes in the sequence, the difference between the cumulative quantity of the current node and the cumulative quantity of the previous node is calculated. It is then determined whether this difference is greater than or equal to 0 to verify whether the increasing trend of the quantity conforms to physical logic. Simultaneously, the timestamps of the corresponding nodes are extracted, and the time interval between the time of the next node and the time of the previous node is calculated. This interval is then checked to see if it is greater than 0. If the difference is less than 0 or the time interval is less than or equal to 0, the process classifies such data as logically conflicting entries and removes them. After cleaning up invalid nodes, the retained compliant data is connected in a chain structure according to the chronological order of the timeline, ensuring that each node accurately points to its direct successor node in the business flow. In the specific calculation logic, the current node's cumulative quantity is 500.00 items, the previous node's is 200.00 items, and the increment is 300.00 items, conforming to an increasing trend. Furthermore, the current node's time is 10:00:00, the previous node's is 08:00:00, and the time interval is 2 hours, conforming to the chronological order. Furthermore, the data governance engine simultaneously monitors abnormal disturbances in real time to ensure the quality of underlying data in the big data environment, ultimately resulting in an acceptance time sequence record chain.

[0024] Please see Figure 3 The specific steps of S2 are as follows: S201: Based on the acceptance time sequence record chain, obtain the settlement amount and settlement unit price in the supplier's settlement statement, deduce the quantity value according to the ratio of amount to unit price, set the deduced quantity value as an identifier and mark it to obtain the settlement quantity identifier; First, supplier settlement statements are extracted from the financial settlement management module, obtaining the settlement amount and settlement unit price. The settlement amount is used as the divisor, and the settlement unit price as the divisor; the corresponding derived quantity value is calculated through division. After calculation, this derived quantity value is defined as a settlement identifier and assigned a globally unique digital label. This labeling process achieves quantitative alignment between the financial accounting dimension and the physical acceptance dimension, providing a core anchor point for subsequent cross-dimensional comparisons. For example, in a settlement with a settlement amount of 99,800.00 yuan and a settlement unit price of 100.00 yuan, the process calculates the corresponding business quantity as 998.00 pieces. 998.00 is set as the identifier and labeled as settlement identifier 001, ensuring the derived quantity has a strict financial source. Furthermore, the extraction process of this financial data is encapsulated as an independent data service interface, supporting seamless integration with the upper-level analysis platform to ultimately obtain the settlement quantity identifier.

[0025] S202: Call the cumulative quantity node in the acceptance time sequence record chain, compare the settlement quantity identifier with the cumulative quantity node item by item, identify the position of inconsistent values ​​and extract the corresponding node sequence information to obtain the sequence of difference nodes; First, all cumulative quantity nodes in the acceptance time sequence record chain are retrieved and used as the benchmark reference for physical acceptance. A step-by-step comparison logic is executed, verifying the consistency between the settlement quantity identifier and each cumulative quantity node in the record chain. A very small numerical fluctuation tolerance of 0.01 is set during the comparison process; if the absolute difference between the settlement quantity identifier and the node's cumulative quantity is less than this tolerance, a successful match is determined. If the settlement quantity lies between two adjacent cumulative node values, the node positions with inconsistent values ​​are immediately identified and marked. Then, the node sequence information corresponding to these discrepancies is extracted, and these discrete sequences are sorted in ascending order of numerical value. Taking a settlement quantity identifier of 998.00 pieces as an example, the retrieved cumulative quantities of the record chain nodes are 200.00 pieces, 500.00 pieces, 900.00 pieces, and 1200.00 pieces respectively. Comparing 998.00 with each node value, it is found to be between the third node 900.00 and the fourth node 1200.00, ultimately yielding the sequence of discrepancies.

[0026] S203: Based on the differential node sequence, extract the corresponding acceptance time and purchase batch number, and merge the acceptance time and purchase batch number to obtain the settlement location record set; First, based on each sequence number, the process redirects to the corresponding node in the acceptance time sequence record chain, extracting the acceptance timestamp and associated purchase batch number carried by that node. The execution process uses an internal index query to transform the sequence number information into time and batch parameters with clear business meaning. Then, the extracted acceptance times and purchase batch numbers are aggregated, grouping multiple time points belonging to the same settlement difference cycle and mapping them to their corresponding numbers. This merging process aims to transform discrete outliers into a structured record set, providing location coordinates for subsequent cost traceability. Taking sequence numbers 3 and 4 as examples, a backtracking of the record chain reveals that the acceptance time corresponding to sequence number 3 is April 1st, 15:00:00, and the acceptance time corresponding to sequence number 4 is April 2nd, 10:00:00, both belonging to batch 04010002. Merging these times and numbers yields the final settlement location record set.

[0027] Please see Figure 4 The specific steps of S3 are as follows: S301: Based on the settlement location record, the centralized procurement batch number and the acceptance time sequence record chain are used to locate the node in the acceptance time sequence record chain according to the procurement batch number, extract the node sequence within the corresponding start and end acceptance time range, extract the acceptance quantity, acceptance time and procurement batch number corresponding to the node and associate them accordingly to obtain the acceptance interval data sequence. First, based on the purchase batch numbers in the settlement location record set, a location retrieval is performed in the acceptance time sequence record chain. By matching the batch numbers, all nodes associated with that batch in the record chain are located. Then, according to the time limits in the location record set, a range truncation operation is performed to extract all consecutive node sequences within the range of the start and end acceptance times from the record chain. During the truncation process, deep synchronization of node attributes is performed, sequentially extracting the acceptance quantity, acceptance time, and the corresponding purchase batch number for each node, and establishing a three-in-one association structure. Through this on-demand truncation method, the lengthy original record chain is simplified into a local sequence closely related to the current settlement business. In actual execution, based on the range from 15:00:00 on April 1st to 10:00:00 on April 2nd, all nodes including these two time points and those in between are truncated. The cumulative quantity of Node 1 is extracted as 900.00 pieces, and the cumulative quantity of Node 2 is extracted as 1200.00 pieces, ultimately obtaining the acceptance range data sequence.

[0028] S302: Based on the acceptance interval data sequence, collect the cost center code and supplier code from the enterprise's financial shared service center ledger, match and verify according to the purchase batch number, and associate the cost center code and supplier code with the corresponding node in the acceptance interval data sequence to obtain the interval code association sequence; First, after obtaining the acceptance interval data sequence, it is connected to the enterprise's financial shared service center ledger. Cost center codes and supplier codes related to procurement are collected from this ledger to clarify the responsible department and transaction partner for expenditures. The execution process uses the procurement batch number as a common field for association mapping, performing matching and verification logic between the acceptance interval data sequence and the financial ledger. For each data node in the sequence, the corresponding responsible cost center is queried in the ledger, and the supplier's unique identification code is obtained simultaneously. Subsequently, the obtained cost center code and supplier code are added as new attribute fields and injected into the corresponding node attributes of the acceptance interval data sequence. Taking cost center code 1001 and supplier code SUP001 as an example, these two sets of codes are sequentially written into the attributes of the sequence nodes. Furthermore, this step utilizes an extraction, transformation, and loading tool to achieve data flow between heterogeneous databases, effectively solving the cross-domain integration problem of isolated financial and business data, ultimately resulting in an interval code association sequence.

[0029] S303: Based on the interval coding association sequence, determine the time interval according to the adjacent acceptance time, identify continuous time nodes and retain the corresponding node set, and merge the corresponding purchase batch number, settlement amount, acceptance time range, cost center code, and supplier code to obtain the settlement purchase acceptance association set. First, the acceptance time between two adjacent nodes is obtained and the time interval is calculated. This time interval is then compared to a preset 24-hour continuity threshold. If the time interval between two adjacent nodes is less than or equal to this threshold, the two nodes are determined to belong to the same continuous business cycle. Next, a multi-dimensional merging operation is performed to centrally encapsulate the purchase batch number, settlement amount, acceptance time range, cost center code, and supplier code within the set. Through this spatiotemporal clustering operation, scattered interval nodes are integrated into settlement units with management significance. In the operation logic, the difference between node one and node two is 19 hours, meeting the continuity requirement, and merging is performed. The settlement amount of this set is extracted as 99,800.00 yuan, and the complete acceptance time range is recorded, ultimately obtaining the settlement purchase acceptance associated set.

[0030] Please see Figure 5 The specific steps of S4 are as follows: S401: Based on the settlement, procurement and acceptance association set, obtain the verification time in the revenue verification record, the accounting time in the cost accounting record, and the completion time in the expense approval record. Call the acceptance time range in the settlement, procurement and acceptance association set, align the three types of time data according to the time axis and map them to the corresponding acceptance time range interval to obtain a multi-source time mapping sequence. First, a cross-domain timeline alignment process is initiated. Revenue verification time, cost accounting time, and expense approval completion time are obtained through a data integration interface. During execution, the acceptance time range from the associated set is used as the baseline timeline, mapping the three types of heterogeneous time data to the overlapping intervals. The judgment rule is that if the verification time, accounting time, or completion time falls between the start and end times of the acceptance process, a logical association is established. Taking the acceptance time range of April 1st 10:00:00 to April 2nd 10:00:00 as an example, a comparison shows that both the revenue verification time and cost accounting time fall within the range. This association is retained, and based on big data time series analysis technology, time lag items in cross-departmental processes are automatically identified and intelligently associated and attributed, ultimately resulting in a multi-source time mapping sequence.

[0031] S402: Based on the multi-source time mapping sequence, determine the continuity of time intervals between time points, divide time intervals according to the chronological order, and aggregate time points in the same interval to obtain a time interval aggregation sequence; First, a 12-hour time abrupt change threshold is set as the criterion, and all business event points are divided into intervals according to their chronological order. The time span distance between adjacent points is calculated; if this distance remains within the threshold, they are considered to be in the same active period. All time points within the same interval are aggregated, including markers from multiple dimensions such as acceptance, verification, and accounting. In the calculation logic, the difference between the verification time and the accounting time is 16 hours. Since this is greater than the threshold, the execution process is logically cut off at this point. The two are divided into different segments, ultimately resulting in the time interval aggregated sequence.

[0032] S403: Based on the time interval aggregation sequence, associate the time data within the interval with the corresponding purchase batch number and cost center code, and centrally merge them according to the interval range to obtain the business event segment set; First, the time data within each aggregation interval is re-associated with its original purchase batch number and cost center code. Then, business transaction data, financial transaction data, and corresponding organizational codes within the same segment are centrally merged and processed according to the interval range constraint. These are then encapsulated according to a preset data structure to form structured business segment data units. Each business segment data unit includes at least the following fields: segment start and end time, purchase batch number, cost center code, quantity received, settlement amount, revenue verification time, cost accounting time, and expense completion time. These fields characterize the business execution process and financial flow within the segment. Taking a specific time segment as an example, purchase batch 04010002 is associated with cost center 1001. Acceptance records and revenue verification records within this segment are merged, and relationships between data items are established based on chronological order. The execution order and correspondence between business nodes are clarified. Furthermore, a set of association mapping relationships between data items is constructed based on the chronological order and business attribute identifiers of various data types within the segment. This achieves structured association and integration between business processes such as acceptance, settlement, accounting, and expense processing, resulting in a set of business event segments.

[0033] Please see Figure 6 The specific steps of S5 are as follows: S501: Based on the segment data, purchase batch number, and cost center code in the business event segment set, collect the business segment code and cost category code, verify the corresponding match between the purchase batch number and the cost center code, and associate the business segment code and cost category code with the corresponding item in the segment data to obtain the segment code association sequence. First, based on the segment data in the business event segment set, the enterprise's business segment codes and cost category codes are further collected. Using the purchase batch number and cost center code as the association intermediary, alignment verification is performed in the enterprise's resource catalog. The business segment code identifies the segment to which the enterprise belongs, while the cost category code distinguishes between materials, labor, or manufacturing costs. During the execution process, the retrieved business segment codes and cost category codes are precisely associated with each record in the segment data, constructing a multi-dimensional coding index system. For example, using business segment code B01 representing intelligent manufacturing and cost category code C05 representing material costs, these codes are marked on the business event segments, ultimately resulting in a segment code association sequence.

[0034] S502: Based on the segment code association sequence, group the code of the same business segment and the cost category code, perform proportional calculation on the segment data within the group, derive the proportional value based on the relationship between the segment data value and the total amount within the group, and obtain the group proportional sequence; First, data is nested and grouped according to business segment codes and cost category codes, aggregating segment data with the same segment and cost nature. Within each group, a proportional derivation calculation is performed, dividing the business value of a single segment by the sum of the business values ​​of all segments within that group. In the calculation logic, the current acceptance quantity of material costs under the intelligent manufacturing segment is 998.00 pieces, and the total acceptance quantity in the same group is 10000.00 pieces. Dividing the acceptance quantity yields a proportional value of 0.0998. This calculation is based on big data mining technology, revealing the cost distribution characteristics between different segments through association rule analysis, ultimately obtaining the grouped proportional series.

[0035] S503: Based on the grouped proportion sequence, the proportion values ​​are sequentially mapped according to the business segment code and the cost category code, and the corresponding code and proportion values ​​are matched to obtain the set of financial shared evaluation index values. First, the calculated proportion values ​​are serialized and mapped according to the hierarchical structure of business segment codes and cost category codes. During execution, a corresponding proportion value is assigned to each code combination in the evaluation matrix, achieving the alignment of codes and values. Taking the material cost of the intelligent manufacturing segment as an example, the mapping result shows an indicator value of 0.0998. Comparing this value with a preset healthy range indicates the cost concentration and business activity of this segment within a specific period. This indicator set directly connects to the company's operational analysis, providing a quantitative basis for resource allocation. Furthermore, by constructing a big data early warning model, real-time anomaly detection is performed on fluctuations in indicator values, realizing a transformation from post-audit to pre-prevention financial management, ultimately yielding a set of financial shared service evaluation indicator values.

[0036] Please see Figure 7 A big data-based enterprise financial shared services assessment system includes: The data chain construction module obtains the purchase batch number, warehousing time and quantity, and acceptance time and quantity, groups them and sorts them in ascending order by acceptance time, accumulates and compares the acceptance quantity, filters data with the same order, and obtains the acceptance time sequence record chain. The settlement location module, based on the sequential record chain of acceptance time, obtains the settlement amount and unit price to determine the settlement quantity identifier, compares the cumulative quantity nodes to filter out the difference nodes, extracts the acceptance time and purchase batch number and merges them to obtain the settlement location record set; The acceptance association module, based on the settlement location record set, calls the purchase batch number and acceptance time sequence record chain to locate the acceptance interval, reads the acceptance data and associates it with the cost center code and supplier code, filters the continuous time data, and obtains the settlement purchase acceptance association set; The segmentation module, based on the settlement, procurement and acceptance association set, obtains the revenue verification time, cost accounting time and expense completion time, identifies the time interval and divides the interval, merges the interval data and associates the procurement batch number with the cost center code to obtain the business event segment set. The indicator mapping module, based on the set of business event segments, calls the segment data to associate business segment codes and cost category codes, groups them, performs proportional calculations and mappings, and obtains a set of financial shared evaluation indicator values.

[0037] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A big data-based enterprise financial shared services assessment method, characterized in that: Includes the following steps: S1: Obtain the purchase batch number, warehousing time and quantity, and acceptance time and quantity; group them and sort them in ascending order by acceptance time; accumulate the accepted quantities and compare them to obtain the acceptance time sequence record chain. S2: Based on the acceptance time sequence record chain, obtain the settlement amount and unit price to determine the settlement quantity identifier, compare the cumulative acceptance quantity to filter the difference nodes, extract the acceptance time and purchase batch number and merge them to obtain the settlement location record set; S3: Based on the settlement location record set, call the purchase batch number and the acceptance time sequence record chain to locate the acceptance interval, read the acceptance data and associate it with the cost center code and the supplier code to obtain the settlement purchase acceptance association set; S4: Based on the aforementioned settlement, procurement, and acceptance association set, obtain the revenue verification time, cost accounting time, and expense completion time, identify the time interval and divide it into intervals, merge the interval data and associate the procurement batch number with the cost center code to obtain the business event segment set; S5: Based on the set of business event segments, call the segment data to associate the business segment code and cost category code, group them and perform proportional calculation and mapping to obtain the set of financial shared evaluation index values.

2. The enterprise financial shared services assessment method based on big data according to claim 1, characterized in that: The acceptance time sequence record chain includes acceptance batch identifier, cumulative acceptance node, time series marker, quantity increment interval, and sequence consistency identifier. The settlement positioning record set includes difference quantity node, corresponding acceptance time point, procurement batch index, settlement matching identifier, and deviation positioning label. The settlement procurement acceptance association set includes acceptance interval identifier, continuous time segment, batch association key, settlement amount mapping item, cost center identifier, and supplier identifier. The business event segment set includes time segment label, event type identifier, segment merging unit, batch association identifier, cost center mapping item, and event sequence index. The financial shared evaluation indicator value set includes business segment indicator, cost category indicator value, proportional allocation coefficient, indicator sequence code, and evaluation result identifier.

3. The enterprise financial shared services assessment method based on big data according to claim 1, characterized in that: The sorting by acceptance time in ascending order refers to sorting the acceptance records of the same procurement batch from earliest to latest according to the acceptance time, thus constructing a continuous data chain in chronological order. The settlement quantity identifier refers to the quantity value calculated based on the settlement amount and settlement unit price, and is used as identifier data for comparison with the cumulative acceptance quantity.

4. The enterprise financial sharing assessment method based on big data according to claim 1, characterized in that: The difference node refers to the quantity node position corresponding to the inconsistency between the settlement quantity identifier and the cumulative acceptance quantity, thus locating the data deviation point; The acceptance interval refers to a continuous set of acceptance data within a time range determined based on the difference nodes in the acceptance time series.

5. The enterprise financial sharing assessment method based on big data according to claim 1, characterized in that, The specific steps of S1 are as follows: S101: Obtain the purchase batch number, entry time, and entry quantity from the purchase entry document; collect the acceptance time and acceptance quantity from the acceptance document; match and verify the two types of data item by item according to the purchase batch number; associate and map data with the same number to obtain batch association verification collection. S102: Based on the batch association verification collection, group each purchase batch number, sort them in ascending order according to the acceptance time, continuously accumulate the acceptance quantity after sorting, and map the cumulative quantity to the corresponding acceptance time to obtain the cumulative time mapping sequence. S103: Based on the cumulative time mapping sequence, compare the progressive relationship of adjacent cumulative quantities, verify the consistency between the increasing trend of quantities and the time sequence direction, remove data with inconsistent sequence direction, and connect the remaining data in a chain according to the time sequence to obtain the acceptance time sequence record chain.

6. The enterprise financial sharing assessment method based on big data according to claim 1, characterized in that, The specific steps of S2 are as follows: S201: Based on the acceptance time sequence record chain, obtain the settlement amount and settlement unit price in the supplier's settlement sheet, deduce the quantity value according to the ratio of amount to unit price, set the deduced quantity value as an identifier and mark it to obtain the settlement quantity identifier; S202: Call the cumulative quantity node in the acceptance time sequence record chain, compare the settlement quantity identifier with the cumulative quantity node item by item, identify the position of inconsistent values ​​and extract the corresponding node sequence information to obtain the difference node sequence; S203: Based on the difference node sequence, extract the corresponding acceptance time and purchase batch number, and merge the acceptance time and purchase batch number to obtain the settlement location record set.

7. The enterprise financial sharing assessment method based on big data according to claim 1, characterized in that, The specific steps for S3 are as follows: S301: Based on the settlement location record centralized procurement batch number and the acceptance time sequence record chain, locate the node in the acceptance time sequence record chain according to the procurement batch number, extract the node sequence within the corresponding start and end acceptance time range, extract the acceptance quantity, acceptance time and procurement batch number corresponding to the node and associate them accordingly to obtain the acceptance interval data sequence. S302: Based on the acceptance interval data sequence, collect the cost center code and supplier code from the enterprise financial shared service center ledger, match and verify according to the purchase batch number, and associate the cost center code and supplier code with the corresponding node in the acceptance interval data sequence to obtain the interval code association sequence; S303: Based on the interval coding association sequence, determine the time interval according to the adjacent acceptance time, identify continuous time nodes and retain the corresponding node set, and merge the purchase batch number, settlement amount, acceptance time range, cost center code, and supplier code to obtain the settlement purchase acceptance association set.

8. The enterprise financial shared services assessment method based on big data according to claim 1, characterized in that, The specific steps of S4 are as follows: S401: Based on the settlement, procurement and acceptance association set, obtain the verification time in the revenue verification record, the accounting time in the cost accounting record, and the completion time in the expense approval record. Call the acceptance time range in the settlement, procurement and acceptance association set, align the three types of time data according to the time axis and map them to the corresponding acceptance time range interval to obtain a multi-source time mapping sequence. S402: Based on the multi-source time mapping sequence, determine the continuity of time intervals between time points, divide time intervals according to the chronological order, and aggregate time points in the same interval to obtain a time interval aggregation sequence; S403: Based on the time interval aggregation sequence, associate the time data within the interval with the corresponding purchase batch number and cost center code, and centrally merge them according to the interval range to obtain a set of business event segments.

9. The enterprise financial sharing assessment method based on big data according to claim 1, characterized in that, The specific steps of S5 are as follows: S501: Based on the segment data, purchase batch number, and cost center code in the business event segment set, collect the business segment code and cost category code, verify the corresponding matching of the purchase batch number and cost center code, and associate the business segment code and cost category code with the corresponding item in the segment data to obtain the segment code association sequence. S502: Based on the segment code association sequence, group the same business segment code and cost category code, perform proportional calculation on the segment data within the group, derive the proportional value based on the relationship between the segment data value and the total amount within the group, and obtain the group proportional sequence; S503: Based on the grouping ratio sequence, the ratio values ​​are sequentially mapped according to the business segment code and cost category code, and the corresponding code and ratio values ​​are matched to obtain the set of financial shared evaluation index values.

10. A big data-based enterprise financial shared services evaluation system, characterized in that, The system is used to implement the enterprise financial sharing assessment method based on big data as described in any one of claims 1-9, and the system comprises: The data chain construction module obtains the purchase batch number, warehousing time and quantity, and acceptance time and quantity, groups them and sorts them in ascending order by acceptance time, accumulates and compares the acceptance quantity, filters data with the same order, and obtains the acceptance time sequence record chain. The settlement positioning module, based on the acceptance time sequence record chain, obtains the settlement amount and unit price to determine the settlement quantity identifier, compares the cumulative quantity nodes to filter the difference nodes, extracts the acceptance time and purchase batch number and merges them to obtain the settlement positioning record set; The acceptance association module, based on the settlement location record set, calls the purchase batch number and acceptance time sequence record chain to locate the acceptance interval, reads the acceptance data and associates it with the cost center code and supplier code, filters continuous time data, and obtains the settlement purchase acceptance association set. The segmentation module, based on the settlement, procurement and acceptance association set, obtains the revenue verification time, cost accounting time and expense completion time, identifies the time interval and divides the interval, merges the interval data and associates the procurement batch number with the cost center code to obtain the business event segment set. The indicator mapping module, based on the set of business event segments, calls the segment data to associate business segment codes and cost category codes, groups them, performs proportional calculations and mappings, and obtains a set of financial shared evaluation indicator values.

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