Data comparison method and device, equipment, storage medium and product

Abstract

The invention discloses a data comparison method and device, equipment, a storage medium and a product, and relates to the technical field of data comparison, and the method comprises the steps: obtaining fund report data; identifying accounting detail data of the fund report data, the accounting detail data at least comprising one of subjects, accounts, indexes and valuation items, and splitting an overall comparison task for the fund report data into a plurality of sub comparison tasks based on the accounting detail data; and based on the plurality of sub-comparison tasks, comparing first fund report data and second fund report data in the fund report data to obtain a data comparison result. According to the invention, the data comparison efficiency can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to data comparison methods, apparatus, equipment, storage media and products. Background Technology

[0002] In the field of fund asset management, establishing a rigorous internal and external verification mechanism is crucial to ensure the safety of fund assets and the accuracy of accounting. Fund managers (our side) need to regularly compare their own accounting data (such as data generated by portfolio management systems and valuation systems) with data from external independent institutions. This external data typically comes from custody reports and account statements provided by fund custodians (such as banks), or from transaction confirmation data provided by securities firms or counterparties. However, traditional data comparison methods heavily rely on manual operation, resulting in inefficiency.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a data comparison method, apparatus, device, storage medium and product, which aims to solve the technical problem of low efficiency caused by the heavy reliance on manual operation in traditional data comparison methods.

[0005] To achieve the above objectives, this application proposes a data comparison method, which includes: Obtain fund report data; Identify the accounting details of the fund's financial statements, wherein the accounting details include at least one of the following: accounts, indicators, and valuation items; Based on the aforementioned accounting details, the overall comparison task for the fund statement data is broken down into multiple sub-comparison tasks; Based on the multiple sub-comparison tasks, the first fund report data and the second fund report data in the fund report data are compared to obtain the data comparison results.

[0006] In one embodiment, the step of comparing the first fund report data and the second fund report data in the fund report data based on the plurality of sub-comparison tasks to obtain the data comparison results includes: When the accounting details data corresponding to the self-comparison task are accounts or subjects, the first fund statement data and the second fund statement data are matched and retrieved based on a preset matching strategy to obtain the subject comparison result or the account comparison result. When the accounting details data corresponding to the self-comparison task are indicators, the corresponding indicator values ​​in the first fund report data and the second fund report data are compared based on the preset indicator identification system to obtain the indicator comparison results. When the accounting details data corresponding to the self-comparison task are estimated values, the corresponding valuation items in the first fund report data and the second fund report data are compared to obtain the valuation item comparison results. Based on the subject comparison results, the account comparison results, the identifier comparison results, and the valuation item comparison results, the data comparison results are determined.

[0007] In one embodiment, the step of matching and retrieving the first fund report data and the second fund report data based on a preset matching strategy to obtain the subject comparison result or the account comparison result includes: Obtain the first subject account tree index structure of the first fund report data, and obtain the second subject account tree index structure of the second fund report data; When the accounting details data is a subject, the search starts from the root node of the first subject account tree index structure and the second subject account tree index structure, and moves downwards to locate the corresponding target subject node. Then, branch pruning is performed on the non-target sibling branches of the target subject node to form a first optimized subtree structure and a second optimized subtree structure rooted at the target subject node. The first optimized subtree structure and the second optimized subtree structure are then matched and searched to obtain the subject comparison results. When the accounting details data is an account, the search starts from the root node of the first account tree index structure and the second account tree index structure, and moves downwards to locate the corresponding target account leaf node. The target account leaf node is then pruned to preserve the complete path from the root node to the target account leaf node, forming a first single path optimized structure and a second single path optimized structure. The first single path optimized structure and the second single path optimized structure are then matched and searched to obtain the account comparison results.

[0008] In one embodiment, the step of obtaining the first subject account tree index structure of the first fund report data includes: Obtain the standard system document for fund subjects, wherein the standard system document includes standard subject codes and subject hierarchical relationships; Using the standard subject codes as nodes, a subject tree structure is constructed according to the subject hierarchy. Extract the mapping relationship between accounts and subjects from the fund report data, and attach the accounts as leaf nodes to the subject nodes corresponding to the subject tree structure according to the mapping relationship, forming the first subject account tree index structure of the first fund report data.

[0009] In one embodiment, the step of comparing the corresponding indicator values ​​in the first fund report data and the second fund report data based on a preset indicator identification system to obtain the indicator comparison result includes: According to the preset indicator identification system, the corresponding indicator definition metadata is extracted from the first fund report data and the second fund report data, wherein the indicator definition metadata includes the indicator calculation formula, data source and business constraints. Based on the metadata defining the indicators, the basic data for calculating the indicators in the first fund report data and the second fund report data are traced and verified to obtain the verification results. Under the premise that the verification results confirm that the basic data for calculating the indicators are consistent, the indicator values ​​in the first fund report data and the second fund report data are compared according to the preset business tolerance threshold to obtain the indicator comparison results.

[0010] In one embodiment, the step of comparing the corresponding valuation items in the first fund report data and the second fund report data to obtain the valuation item comparison result includes: Extract the input parameter set, calculation model identifier, and calculation result of the valuation item from the first fund report data to form the first valuation data group; Extract the input parameter set, calculation model identifier, and calculation result of the corresponding valuation item from the second fund report data to form the second valuation data group; The first valuation data group and the second valuation data group are compared in multiple dimensions to obtain the multi-dimensional comparison results. The multi-dimensional comparison includes input parameter consistency comparison, calculation model identifier consistency comparison and calculation result numerical comparison. Based on the multi-dimensional comparison results, an evaluation item comparison result is generated.

[0011] Furthermore, to achieve the above objectives, this application also proposes a data comparison device, which includes: The acquisition module is used to acquire fund report data; The identification module is used to identify the accounting details of the fund statement data, wherein the accounting details include at least one of the following: account, indicator, and valuation item. The splitting module is used to split the overall comparison task for the fund statement data into multiple sub-comparison tasks based on the accounting details data; The comparison module is used to compare the first fund report data and the second fund report data in the fund report data based on the multiple sub-comparison tasks, and obtain the data comparison results.

[0012] In addition, to achieve the above objectives, this application also proposes a data comparison device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data comparison method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the data comparison method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the data comparison method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: The data comparison method, apparatus, equipment, storage medium, and product proposed in this application acquire fund statement data; identify the accounting details of the fund statement data, wherein the accounting details include at least one of subjects, accounts, indicators, and valuation items; based on the accounting details, the overall comparison task for the fund statement data is divided into multiple sub-comparison tasks; based on the multiple sub-comparison tasks, the first fund statement data and the second fund statement data in the fund statement data are compared to obtain data comparison results. This invention addresses the technical problem of inefficiency caused by the heavy reliance on manual operation in traditional data comparison methods. Compared with existing technologies, this application achieves intelligent decomposition of comparison tasks based on structured identification of multi-dimensional accounting details, including subjects, accounts, indicators, and valuation items. It also achieves precise location of the root causes of discrepancies through fine-grained task splitting, reducing the problem location time from several hours of manual tracing in traditional methods to a system-automated identification with a response time of seconds. In addition, by constructing a multi-dimensional verification system covering valuation items and business indicators, it builds a more comprehensive risk control capability than traditional single-amount verification, effectively identifying potential risks such as abnormal valuation parameters and indicator deviations that traditional methods cannot detect. Attached Figure Description

[0016] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the data comparison method of this application in Embodiment 1. Figure 2 This is a schematic diagram of data comparison provided in Embodiment 1 of the data comparison method of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the data comparison method of this application; Figure 4 This is a schematic diagram of the account matching process provided in Embodiment 2 of the data comparison method of this application; Figure 5 This is a schematic diagram of the dedicated tree structure provided in Embodiment 2 of the data comparison method of this application; Figure 6 This is a schematic diagram of the module structure of the data comparison device in an embodiment of this application; Figure 7 This is a schematic diagram of the device structure of the hardware operating environment involved in the data comparison method in the embodiments of this application.

[0019] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0020] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0021] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0022] The main solution of this application embodiment is: to obtain fund statement data; to identify the accounting details of the fund statement data, wherein the accounting details include at least one of subjects, accounts, indicators, and valuation items; based on the accounting details, to break down the overall comparison task for the fund statement data into multiple sub-comparison tasks; and based on the multiple sub-comparison tasks, to compare the first fund statement data and the second fund statement data in the fund statement data to obtain data comparison results.

[0023] As can be seen from the above embodiments, this application obtains fund statement data; identifies the accounting details of the fund statement data, wherein the accounting details include at least one of subjects, accounts, indicators, and valuation items. Based on the accounting details, the overall comparison task for the fund statement data is divided into multiple sub-comparison tasks; based on the multiple sub-comparison tasks, the first fund statement data and the second fund statement data in the fund statement data are compared to obtain data comparison results. This solves the technical problem of low efficiency caused by the heavy reliance on manual operation in traditional data comparison methods. Compared with the prior art, this application identifies multi-dimensional accounting details including subjects, accounts, indicators, and valuation items through structured identification and realizes intelligent decomposition of comparison tasks based on multi-dimensional features. It also achieves accurate positioning of the root causes of differences through fine-grained task decomposition, improving the problem positioning time from several hours of manual tracing in the traditional method to a second-level response of automatic system identification. In addition, by constructing a multi-dimensional verification system covering valuation items and business indicators, a more comprehensive risk control capability than traditional single-amount verification is built, effectively identifying potential risks such as abnormal valuation parameters and indicator deviations that cannot be detected by traditional methods.

[0024] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or data comparison device capable of performing the above functions. The following description uses a data comparison device as an example to illustrate this embodiment and the subsequent embodiments.

[0025] Based on this, embodiments of this application provide a data comparison method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the data comparison method of this application.

[0026] In this embodiment, the data comparison method includes steps S10 to S40: Step S10: Obtain fund statement data; It should be noted that fund statement data refers to any formatted data that reflects a fund's financial condition, investment portfolio, and operating results. Fund statement data includes primary fund statement data and secondary fund statement data. Primary fund statement data refers to fund accounting and holding data (i.e., internal data) generated, recorded, and maintained by our system (usually the fund manager, asset manager, or investment advisor). Secondary fund statement data refers to data provided by external partners, the core source of which is the custody report or account statement generated independently by the fund custodian (such as a bank), and may also include transaction confirmations and account information (i.e., external data) provided by securities firms, counterparties, etc.

[0027] Step S20: Identify the accounting details of the fund statement data, wherein the accounting details include at least one of the following: account, indicator, and valuation item; It's important to note that an account is a specific classification of accounting elements; each detailed data point must belong to a specific accounting account. Examples include: bank deposits, trading financial assets, management fees payable, and operating revenue. Each data entry will have a "account code" or "account name" field. An account, on the other hand, refers to a detailed account opened under a specific account for a specific accounting object. It is a refinement and extension of the account (used to track changes in each specific asset, liability, or equity). For example, under the "trading financial assets" account, detailed accounts can be set up for holding company A and holding company B; under the "interest payable" account, detailed accounts can be set up for bond A and bond B. Indicators typically refer to quantitative data used to measure and analyze fund performance or risk. These may not appear directly in the traditional three financial statements, but they are crucial data for fund management and investment decisions. Examples include: return indicators: seven-day annualized return, cumulative net asset value growth rate; risk indicators: volatility, Sharpe ratio, maximum drawdown; and holding indicators: equity position ratio, concentration of the top ten holdings. Valuation Items: This is a core concept in fund accounting, specifically referring to the various parameters and data used to calculate the fair value of financial assets; for example: for stocks: closing price, adjusted closing price, price-to-earnings ratio; for bonds: full price, net price, accrued interest, yield to maturity; for derivatives: fair value, Greek letter; function: the calculation of a fund's net asset value (NAV) depends heavily on the accuracy and timeliness of these valuation items, and identifying these data is a prerequisite for asset valuation and NAV calculation.

[0028] In specific implementations, such as Figure 2 As shown, for the four types of accounting detail data—subjects, accounts, indicators, and valuation items—different types of accounting detail data cannot be matched with each other. Therefore, each reconciliation comparison can be split into multiple comparisons of different types of accounting details to improve logic reusability, reduce logic coupling, and minimize expected time consumption.

[0029] Step S30: Based on the accounting details data, the overall comparison task for the fund statement data is divided into multiple sub-comparison tasks. It should be noted that during the comparison process, corresponding types of accounting detail data are matched and compared. Different types of accounting detail data cannot be matched with each other. Therefore, each reconciliation comparison can be split into multiple comparisons of different types of accounting details to improve logic reusability, reduce logic coupling, and reduce expected time consumption.

[0030] Understandably, the overall reconciliation process is broken down into multiple independent sub-reconciliation modules, each specifically handling a particular report type (such as balance sheet, valuation statement, income statement, and balance sheet) and its corresponding accounting detail data types (such as indicator accounting details, accounts, and valuation items). This breakdown allows for customized optimization for different data types, rather than using a single reconciliation logic.

[0031] Step S40: Based on the multiple sub-comparison tasks, compare the first fund report data and the second fund report data in the fund report data to obtain the data comparison results.

[0032] Understandably, breaking down a massive report (potentially containing tens of thousands of records) into multiple smaller tasks based on logic (such as by fund product, accounting subject, or asset type) makes each task easier to manage and process. These sub-tasks can be processed in parallel, meaning they can be executed simultaneously on different CPU cores or servers, thereby significantly reducing the overall comparison time.

[0033] In the specific implementation, all the account entries to be compared can be extracted from the first fund report data and the second fund report data respectively. Each entry contains at least two key fields: standardized account code (e.g., 1001) and the corresponding account amount (e.g., 5,000,000.00). Then, using the standardized account code as the unique key, a temporary mapping table or hash table is created in memory or a database. This stage aims to quickly associate accounts with the same code in the two reports. For example, a mapping relationship is established between code 1001 in the first report and code 1001 in the second report. Finally, item-by-item comparison and result marking: For each account code that has been successfully mapped, numerical comparison logic is executed. This is usually not just a matter of judging "equal" or "unequal," but is based on preset business rules: Exact match: When the two amounts are completely identical, it is marked as "match successful." Tolerance match: Considering issues such as rounding in calculations, a reasonable tolerance range can be preset (e.g., the absolute value of the difference is less than 1 yuan, or the difference percentage is less than 0.01%). If the amount difference is within this range, it can still be marked as "match passed." Match Failure: If the amount difference exceeds the tolerance range, it is marked as "Amount Difference". Missing Exception: If an account code that exists in another report cannot be found in one report (e.g., the first report has code 1501, but the second report does not), it is marked as "Account Missing", which is a more serious type of difference.

[0034] This embodiment acquires fund statement data; identifies the accounting details of the fund statement data, wherein the accounting details include at least one of the following: accounts, indicators, and valuation items. Based on the accounting details, the overall comparison task for the fund statement data is broken down into multiple sub-comparison tasks. Based on the multiple sub-comparison tasks, the first fund statement data and the second fund statement data in the fund statement data are compared to obtain the data comparison results. This solves the technical problem of low efficiency caused by the heavy reliance on manual operation in traditional data comparison methods. Compared with the prior art, this application identifies multi-dimensional accounting details including accounts, accounts, indicators, and valuation items through structured identification and realizes intelligent decomposition of comparison tasks based on multi-dimensional features. It also achieves accurate positioning of the root causes of differences through fine-grained task decomposition, improving the problem positioning time from several hours of manual tracing in the traditional method to a system-automated identification with a response time of seconds. In addition, by constructing a multi-dimensional verification system covering valuation items and business indicators, a more comprehensive risk control capability than traditional single-amount verification is built, effectively identifying potential risks such as abnormal valuation parameters and indicator deviations that cannot be detected by traditional methods.

[0035] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S30 also includes steps S301 to S304: Step S301: When the accounting detail data corresponding to the self-comparison task is a subject or an account, based on the preset matching strategy, the first fund statement data and the second fund statement data are matched and retrieved to obtain the subject comparison result or the account comparison result. In the context of reconciliation, an account refers to a specific type of asset, liability, income, or expense. It categorizes and summarizes managed assets according to their core nature and may have multiple levels of accounts. An account, on the other hand, is a more specific and detailed recording unit set up under an account. It is used to track each specific asset, liability, or transaction under that account; therefore, an account is sometimes called a detailed account. The two are hierarchical. Account data is the most frequent and largest in terms of volume in the reconciliation function, requiring priority for algorithm optimization. For account data comparison, the matching and search algorithm can be optimized by constructing a tree and pruning based on the account system data.

[0036] In specific implementations, such as Figure 3As shown, obtaining the self-account data, counterparty data, and effective reconciliation settings list based on accounting details can be achieved by constructing an account-account tree based on a standardized account system. In this tree, accounts serve as branch nodes, and accounts serve as leaf nodes attached to specific accounts, forming a hierarchical and clearly defined tree-like index structure. Subsequently, a bidirectional matching retrieval stage based on the tree structure is entered: details are retrieved one by one from the reconciliation settings and used as search criteria. The matching process is not a simple loop traversal but is deeply optimized according to data type: for account-type data, a highly efficient "fast statistical lookup + further search" strategy is executed using the pre-built tree structure. This process can be understood as: first, quickly locating the target account node (such as the "cash" account) in the tree; this operation benefits from the tree index and does not require a full table scan; then, accurately matching all account leaf nodes (i.e., their subtrees) under that account node. This "first locate the trunk, then search the branches and leaves" approach greatly narrows the scope of each search, achieving a significant performance improvement. For other non-account data, due to the lack of a clear hierarchical tree structure, a traditional iterative search method is used for matching. After obtaining the matching datasets through tree retrieval, a refined verification is performed based on business rules. This stage not only distinguishes between "complete matches" and "partial matches," but also handles special business scenarios such as risk pairs and opposite numbers, demonstrating the algorithm's adaptability to complex business logic. Finally, the system performs anomaly identification and result aggregation.

[0037] This embodiment transforms the traditional flat data matching problem into an efficient retrieval problem based on an optimized tree structure by introducing a subject-account tree index structure. Especially for subject and account data, the strategy of "quickly locating subject nodes + fine-grained searching of account subtrees" achieves a qualitative leap in retrieval efficiency.

[0038] In one feasible implementation, the step of matching and retrieving the first fund statement data and the second fund statement data based on a preset matching strategy to obtain the subject comparison result or account comparison result includes: obtaining the first subject account tree index structure of the first fund statement data, and obtaining the second subject account tree index structure of the second fund statement data; when the accounting detail data is a subject, starting from the root node of the first subject account tree index structure and the second subject account tree index structure, searching downwards to locate the corresponding target subject node, and performing branch pruning on the non-target sibling branches of the target subject node to form a first optimal category rooted at the target subject node. The system first optimizes the subtree structure and the second optimizes the subtree structure, and performs matching retrieval on the first optimizes the subtree structure and the second optimizes the subtree structure to obtain the account comparison results. When the accounting details data is an account, the system starts from the root node of the first account tree index structure and the second account tree index structure and searches downwards to locate the corresponding target account leaf node. The system then performs node pruning on the target account leaf node, retaining the complete path from the root node to the target account leaf node to form the first single path optimized structure and the second single path optimized structure. The system then performs matching retrieval on the first single path optimized structure and the second single path optimized structure to obtain the account comparison results.

[0039] In specific implementations, such as Figure 5 The diagram shows the optimized subtree structure. Use case: Reconciliation settings: 100101, matching ends at 100101; Account data: 10010101601, 10010101602, 10010102601.

[0040] Understandably, since accounts belong to subjects and there are multiple levels of subjects, subject definition data is naturally suitable for classifying and quickly retrieving accounts; and the matching relationship of account data has subject-level settings, which allows matching of all accounts under a specific subject. Using tree-structured optimization for retrieval can greatly reduce retrieval time.

[0041] In the specific implementation, the reconciliation details data can be constructed into a dedicated tree structure using the subject definition data, with the last layer being the account details. Then, based on our settings in the matching relationship, the search starts from the root node of the dedicated tree and proceeds downwards. If it is a subject setting, the corresponding matching subject is located and the branch is pruned; if it is an account setting, the leaf node is located and the node is pruned.

[0042] It's important to note that when performing "account matching" (e.g., comparing the total amount under the "Cash" account): the target account node (e.g., "Cash") is located in the global tree, and then all non-target sibling branches (e.g., sibling accounts such as "Accounts Receivable" and "Inventory") are pruned. This results in an "optimized subtree" where the root is the "Cash" account, and the subtree contains all leaf nodes of accounts associated with the "Cash" account. This focuses on all accounts under the target account, eliminating interference from irrelevant accounts and achieving precise narrowing of the search scope. When performing "account matching" (e.g., precisely comparing the account "XX Bank - RMB Account"): the target account leaf node is precisely located in the global tree, and then even more intensive node pruning is performed, retaining only all nodes on the unique path from the root to the leaf node. This results in a "single-path optimized structure" that clearly shows the complete hierarchical relationship of the specific account within the entire account system (e.g., Assets -> Current Assets -> Cash -> Cash -> XX Bank - RMB Account).

[0043] Step S302: When the accounting detail data corresponding to the self-comparison task is an indicator, the corresponding indicator values ​​in the first fund report data and the second fund report data are compared based on the preset indicator identification system to obtain the indicator comparison result. It's important to note that indicators typically refer to data that reflects a fund's specific performance or status, calculated or summarized, such as "net asset value per unit," "cumulative net asset value," "seven-day annualized return," and "Sharpe ratio." The core of this system lies in matching based on a pre-defined indicator identification system. This means that each indicator has a globally unique, standardized key (e.g., NAV represents net asset value per unit, and TOTAL_NAV represents cumulative net asset value). This key acts as a logical bridge connecting the same indicator in two reports. The actual implementation is far more complex than a simple numerical equality comparison: First, using the indicator identification, the two indicator values ​​to be compared are precisely located in the first and second reports. Then, the comparison logic invokes specific rules pre-defined for that indicator. These rules may include allowable absolute tolerances (e.g., the difference in net asset value per unit must be less than 0.001 yuan) or relative tolerances (e.g., the difference in return must be less than 0.01%) to determine if it falls within a reasonable fluctuation range. For some complex indicators (e.g., "portfolio turnover"), the system may even need to retrieve the original data for a new consistency check. The final output of the "indicator comparison results" will not only mark the status as "consistent", "inconsistent" or "one side missing", but will also record in detail the comparison standards, actual differences and whether they exceed the tolerance threshold. This provides risk control and operations personnel with authoritative and in-depth insights into the consistency of the fund's core performance data, which ensures that the comparison work can cover a deeper level of financial accounting.

[0044] Step S303: When the accounting detail data corresponding to the self-comparison task is an estimated value, the corresponding valuation items in the first fund statement data and the second fund statement data are compared to obtain the valuation item comparison result. It is important to note the specialized processing required for the unique nature of "valuation items." Unlike structured data such as accounts and subjects, which have clear codes and static relationships, estimated values ​​(such as fair value and net asset value (NAV)) typically originate from complex financial models, market parameter assumptions, and calculation processes. Their comparison cannot simply rely on exact matching; instead, it requires multi-dimensional "correlational data comparison." The core of this process lies in verifying the consistency of the valuation logic, not just the numerical results.

[0045] Understandably, for the "valuation item," the final valuation result will not be directly compared. Instead, the underlying input parameters upon which the valuation depends will be traced and compared. This includes: 1. Market data: such as risk-free interest rate, exchange rate, credit spread, etc. The system will check whether the parameter sources (such as data vendors and time points) and values ​​used by both parties are consistent. 2. Key model assumptions: such as discount rate, volatility, expected default rate, etc. The system will compare whether the values ​​of these key assumptions set by both parties in the model are the same. 3. Underlying asset data: such as the underlying asset's cash flow forecast, strike price, maturity date, etc. 4. Verification of the calculation process: Under the premise that the input parameters are consistent, the calculation logic itself will be further verified. This does not require the system to reimplement all complex models, but is done in the following ways: 1. Calculation pipeline verification: Compare the formulas, model version numbers, or calculation component hash values ​​used by both parties in the valuation calculation to ensure that the same set of calculation logic is used; 2. Intermediate value verification: For known and standardized calculation steps, key intermediate calculation results can be extracted and compared to locate the specific points where differences occur. 3. Tolerance Analysis of Output Results: Even if the input and logic are completely identical, slight differences may exist in the final values ​​due to non-core factors such as calculation precision and rounding methods. Therefore, the system does not require absolute equality for the final valuation items, but rather bases the comparison on a preset business tolerance threshold (e.g., a difference of less than 0.01% or 100 yuan). This ensures that the rigor of the comparison is combined with business practicality.

[0046] In one feasible implementation, the step of comparing corresponding indicator values ​​in the first fund report data and the second fund report data based on a preset indicator identification system to obtain indicator comparison results includes: extracting corresponding indicator definition metadata from the first fund report data and the second fund report data according to the preset indicator identification system, wherein the indicator definition metadata includes indicator calculation formula, data source, and business constraints; performing source verification on the indicator calculation basis data in the first fund report data and the second fund report data based on the indicator definition metadata to obtain verification results; and, under the premise that the verification results confirm that the indicator calculation basis data is consistent, performing numerical comparison on the indicator values ​​in the first fund report data and the second fund report data according to a preset business tolerance threshold to obtain indicator comparison results.

[0047] It should be noted that the unique "indicator identifier" can accurately locate the same indicator that needs to be compared in two reports, avoiding matching errors caused by inconsistent indicator names, aliases, or codes.

[0048] The indicator calculation formula defines how the value is calculated and is fundamental to verifying the consistency of the calculation logic. The data source indicates which system, table, or interface the original data used to calculate the indicator originates from, serving as the starting point for traceability verification. Business constraints define the business rules for the indicator, such as the calculation period (daily, monthly, quarterly), currency, and whether it is annualized, ensuring that the comparison is conducted within the same business context. The traceability verification mechanism includes four key stages: data lineage tracing, calculation logic consistency verification, basic data consistency verification, and business rule environment verification. Data lineage tracing constructs a complete traceability path from the indicator value to the specific database table and field of the source system by parsing the indicator calculation formula; calculation logic consistency verification parses the calculation formula into a standard calculation tree, ensuring strict consistency of the operation expression and order through node comparison; basic data consistency verification compares each item of basic data involved in the calculation, verifying key attributes such as the time of value acquisition, data version, and update frequency; and business rule environment verification ensures that both indicators are calculated under the same business framework, including accounting policies, valuation methods, and exchange rate conversion rules. Through this complete traceability and verification mechanism, the system not only confirms the surface consistency of the "numerical results", but also achieves full-link verification from the data source, calculation process to business environment, thereby completing in-depth verification of the authenticity and reliability of the indicator data and effectively preventing misjudgments of "coincidental similarities in results" caused by different calculation foundations.

[0049] Understandably, the traceability verification mechanism, by tracing the underlying data and computational logic upon which the indicators are calculated, can effectively identify indicator deviations caused by deeper reasons such as inconsistent data sources and differences in calculation rules, fundamentally avoiding the false consistency phenomenon of "same numerical values ​​but different underlying data." The numerical comparison based on this, combined with intelligent judgment using business tolerance thresholds, ensures both the accuracy of the comparison and the flexibility of practical business applications. This dual verification mechanism significantly improves the depth and reliability of indicator comparison, upgrading traditional numerical comparison into a comprehensive verification system covering data traceability, computational logic, and result verification, providing a more robust technical guarantee for the accuracy and consistency of fund statement data.

[0050] In one feasible implementation, the step of performing a correlational data comparison on the corresponding valuation items in the first fund report data and the second fund report data to obtain the valuation item comparison result includes: extracting the input parameter set, calculation model identifier, and calculation result of the valuation item in the first fund report data to form a first valuation data group; extracting the input parameter set, calculation model identifier, and calculation result of the corresponding valuation item in the second fund report data to form a second valuation data group; performing a multi-dimensional comparison on the first valuation data group and the second valuation data group to obtain a multi-dimensional comparison result, wherein the multi-dimensional comparison includes input parameter consistency comparison, calculation model identifier consistency comparison, and calculation result numerical comparison; and generating a valuation item comparison result based on the multi-dimensional comparison result.

[0051] It's important to note that by breaking down valuation items into three key dimensions—input parameters, calculation models, and calculation results—the system can accurately pinpoint the root causes of discrepancies. Input parameter consistency comparison ensures the uniformity of the market data and underlying assumptions upon which the valuation relies, eliminating deviations caused by differences in data sources or timing. Calculation model identifier consistency comparison verifies whether both parties have adopted identical valuation logic and calculation rules by checking model versions, algorithm hashes, and other identifiers, avoiding systemic problems caused by model iterations or configuration differences. Finally, calculation result numerical comparison, assuming consistency in the above two dimensions, performs a reasonableness check on the final output value based on business tolerance. This multi-dimensional correlation comparison mechanism not only efficiently identifies surface numerical differences but also deeply diagnoses logical and procedural errors hidden in the valuation process. This elevates data comparison from simple "numerical correctness" checks to verification of "procedural reasonableness and logical consistency," significantly enhancing the depth, accuracy, and reliability of fund statement valuation data verification, providing crucial technical support for risk management and audit tracking.

[0052] Understandably, this multi-layered comparison of correlated data allows for the precise identification of the root causes of valuation discrepancies—whether they stem from inconsistencies in input data, differences in model logic, or acceptable minor errors. This significantly enhances the depth and efficiency of valuation verification, elevating traditional manual reconciliation from "result verification" to an intelligent level of "process cause verification," providing unprecedented transparency and insight for risk management, compliance auditing, and post-investment management.

[0053] Step S305: Based on the subject comparison results, the account comparison results, the identifier comparison results, and the valuation item comparison results, determine the data comparison results.

[0054] This embodiment, when the accounting detail data corresponding to the self-comparison task is a subject or account, performs matching retrieval on the first fund statement data and the second fund statement data based on a preset matching strategy to obtain subject comparison results or account comparison results; when the accounting detail data corresponding to the self-comparison task is an indicator, compares the corresponding indicator values ​​in the first fund statement data and the second fund statement data based on a preset indicator identification system to obtain indicator comparison results; when the accounting detail data corresponding to the self-comparison task is an estimated value, performs correlation data comparison on the corresponding valuation items in the first fund statement data and the second fund statement data to obtain valuation item comparison results; based on the subject comparison results, the account comparison results, the identification comparison results, and the valuation item comparison results, the data comparison results are determined. Through the above methods, a multi-level, automated data comparison engine is constructed. Through standardized coding direct matching, tree-structured index precise retrieval, identifier association, and valuation item correlation comparison, it achieves accurate and efficient verification of all types of data, including subjects, accounts, indicators, and estimated values, significantly improving the automation level and reliability of fund statement data self-comparison.

[0055] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the data comparison method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0056] This application also provides a data comparison device, please refer to... Figure 6 The data comparison device includes: Module 10 is used to acquire fund report data; The identification module 20 is used to identify the accounting details of the fund statement data, wherein the accounting details include at least one of the following: subject, account, indicator, and valuation item; The splitting module 30 is used to split the overall comparison task for the fund statement data into multiple sub-comparison tasks based on the accounting details data. The comparison module 40 is used to compare the first fund report data and the second fund report data in the fund report data based on the multiple sub-comparison tasks, and obtain the data comparison results.

[0057] The data comparison device provided in this application, employing the data comparison method described in the above embodiments, can solve the technical problem of low efficiency caused by the heavy reliance on manual operation in traditional data comparison methods. Compared with the prior art, the beneficial effects of the data comparison device provided in this application are the same as those of the data comparison method provided in the above embodiments, and other technical features in the data comparison device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0058] This application provides a data comparison device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the data comparison method in Embodiment 1 above.

[0059] The following is for reference. Figure 7 The diagram illustrates a structural schematic of a data comparison device suitable for implementing embodiments of this application. The data comparison device in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The data comparison device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0060] like Figure 7As shown, the data comparison device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the data comparison device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the data comparison device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show data comparison devices with various systems, it should be understood that implementing or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0061] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0062] The data comparison device provided in this application, employing the data comparison method described in the above embodiments, can solve the technical problem of low efficiency caused by the heavy reliance on manual operation in traditional data comparison methods. Compared with the prior art, the beneficial effects of the data comparison device provided in this application are the same as those of the data comparison method provided in the above embodiments, and other technical features of this data comparison device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0063] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0064] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0065] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the data comparison method described in the above embodiments.

[0066] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0067] The aforementioned computer-readable storage medium may be included in the data comparison device; or it may exist independently and not assembled into the data comparison device.

[0068] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the data comparison device, the data comparison device causes the following actions: to acquire fund statement data; to identify the accounting details of the fund statement data, wherein the accounting details include at least one of subjects, accounts, indicators, and valuation items; to break down the overall comparison task for the fund statement data into multiple sub-comparison tasks based on the accounting details; and to compare the first fund statement data and the second fund statement data in the fund statement data based on the multiple sub-comparison tasks to obtain a data comparison result.

[0069] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0071] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0072] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described data comparison method. This solves the technical problem of low efficiency caused by the heavy reliance on manual operation in traditional data comparison methods. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the data comparison method provided in the above embodiments, and will not be repeated here.

[0073] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data comparison method described above.

[0074] The computer program product provided in this application can solve the technical problem of low efficiency caused by the heavy reliance on manual operation in traditional data comparison methods. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the data comparison method provided in the above embodiments, and will not be repeated here.

[0075] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A data comparison method, characterized in that, The data comparison method includes: Obtain fund report data; Identify the accounting details of the fund's financial statements, wherein the accounting details include at least one of the following: accounts, indicators, and valuation items; Based on the aforementioned accounting details, the overall comparison task for the fund statement data is broken down into multiple sub-comparison tasks; Based on the multiple sub-comparison tasks, the first fund report data and the second fund report data in the fund report data are compared to obtain the data comparison results.

2. The data comparison method as described in claim 1, characterized in that, The step of comparing the first fund report data and the second fund report data in the fund report data based on the multiple sub-comparison tasks to obtain the data comparison results includes: When the accounting details data corresponding to the self-comparison task are accounts or subjects, the first fund statement data and the second fund statement data are matched and retrieved based on a preset matching strategy to obtain the subject comparison result or the account comparison result. When the accounting details data corresponding to the self-comparison task are indicators, the corresponding indicator values ​​in the first fund report data and the second fund report data are compared based on the preset indicator identification system to obtain the indicator comparison results. When the accounting details data corresponding to the self-comparison task are estimated values, the corresponding valuation items in the first fund report data and the second fund report data are compared to obtain the valuation item comparison results. Based on the subject comparison results, the account comparison results, the identifier comparison results, and the valuation item comparison results, the data comparison results are determined.

3. The data comparison method as described in claim 2, characterized in that, The step of matching and retrieving the first fund report data and the second fund report data based on a preset matching strategy to obtain the subject comparison result or the account comparison result includes: Obtain the first subject account tree index structure of the first fund report data, and obtain the second subject account tree index structure of the second fund report data; When the accounting details data is a subject, the search starts from the root node of the first subject account tree index structure and the second subject account tree index structure, and moves downwards to locate the corresponding target subject node. Then, branch pruning is performed on the non-target sibling branches of the target subject node to form a first optimized subtree structure and a second optimized subtree structure rooted at the target subject node. The first optimized subtree structure and the second optimized subtree structure are then matched and searched to obtain the subject comparison results. When the accounting details data is an account, the search starts from the root node of the first account tree index structure and the second account tree index structure, and moves downwards to locate the corresponding target account leaf node. The target account leaf node is then pruned to preserve the complete path from the root node to the target account leaf node, forming a first single path optimized structure and a second single path optimized structure. The first single path optimized structure and the second single path optimized structure are then matched and searched to obtain the account comparison results.

4. The data comparison method as described in claim 3, characterized in that, The step of obtaining the first subject account tree index structure of the first fund report data includes: Obtain the standard system document for fund subjects, wherein the standard system document includes standard subject codes and subject hierarchical relationships; Using the standard subject codes as nodes, a subject tree structure is constructed according to the subject hierarchy. Extract the mapping relationship between accounts and subjects from the fund report data, and attach the accounts as leaf nodes to the subject nodes corresponding to the subject tree structure according to the mapping relationship, forming the first subject account tree index structure of the first fund report data.

5. The data comparison method as described in claim 2, characterized in that, The step of comparing the corresponding indicator values ​​in the first fund report data and the second fund report data based on the preset indicator identification system to obtain the indicator comparison result includes: According to the preset indicator identification system, the corresponding indicator definition metadata is extracted from the first fund report data and the second fund report data, wherein the indicator definition metadata includes the indicator calculation formula, data source and business constraints. Based on the metadata defining the indicators, the basic data for calculating the indicators in the first fund report data and the second fund report data are traced and verified to obtain the verification results. Under the premise that the verification results confirm that the basic data for calculating the indicators are consistent, the indicator values ​​in the first fund report data and the second fund report data are compared according to the preset business tolerance threshold to obtain the indicator comparison results.

6. The data comparison method as described in claim 2, characterized in that, The step of comparing the corresponding valuation items in the first fund report data and the second fund report data to obtain the valuation item comparison results includes: Extract the input parameter set, calculation model identifier, and calculation result of the valuation item from the first fund report data to form the first valuation data group; Extract the input parameter set, calculation model identifier, and calculation result of the corresponding valuation item from the second fund report data to form the second valuation data group; The first valuation data group and the second valuation data group are compared in multiple dimensions to obtain the multi-dimensional comparison results. The multi-dimensional comparison includes input parameter consistency comparison, calculation model identifier consistency comparison and calculation result numerical comparison. Based on the multi-dimensional comparison results, an evaluation item comparison result is generated.

7. An apparatus, characterized in that, The data comparison device includes: The acquisition module is used to acquire fund report data; The identification module is used to identify the accounting details of the fund statement data, wherein the accounting details include at least one of the following: account, indicator, and valuation item. The splitting module is used to split the overall comparison task for the fund statement data into multiple sub-comparison tasks based on the accounting details data; The comparison module is used to compare the first fund report data and the second fund report data in the fund report data based on the multiple sub-comparison tasks, and obtain the data comparison results.

8. A data comparison device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data comparison method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the data comparison method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the data comparison method as described in any one of claims 1 to 6.

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