A cross-domain data mapping method for new media project traffic monetization and financial collection

Abstract

The application discloses a new media project traffic monetization and financial collection cross-domain data mapping method, belongs to the technical field of business data processing, which comprises the following steps: instantiating business operation data and financial subject data, generating a value event atomic sequence and a financial impact factor set; performing time sequence causal tracking on target atoms, generating a dynamic correlation chain; combining a historical mapping knowledge graph for structured comparison, generating a mapping hypothesis set; generating a target mapping hypothesis through multi-stage verification; correlating and calculating the value event atomic sequence and the financial impact factor set, generating an industry and finance mapping relationship set; using external feedback signals for difference calibration, generating a calibrated industry and finance mapping relationship; and supplementing and logically correcting the historical mapping knowledge graph accordingly. The application adopts the technical means of constructing a business process causal chain and comparing and verifying with an evolvable knowledge graph, and can realize high-precision, self-adaptive and automatic mapping of new media business data and financial subjects.

Description

Technical Field

[0001] This invention relates to the field of commercial data processing technology, and in particular to a cross-domain data mapping method for monetizing traffic and collecting financial data for new media projects. Background Technology

[0002] In the new media industry, project monetization models are characterized by diversification, complexity, and rapid iteration, encompassing various forms such as advertising, paid content, e-commerce sales, and live-streaming rewards. These business activities generate massive amounts of heterogeneous operational data across various front-end and back-end systems. Financial aggregation, as a core aspect of enterprise management, requires the accurate and timely conversion of this scattered operational data into accounting vouchers that comply with accounting standards, thus truthfully reflecting the company's operating status. Achieving effective mapping between business data and financial data—that is, business-finance integration—is crucial for refined enterprise operations and digital management.

[0003] Among related technologies, Chinese invention patent application CN113222471A discloses an asset risk control method and device based on new media data. The method involves three steps: Step 1: Obtaining the company's financial data, new media public opinion data, and transaction data from a server; Step 2: Preprocessing the financial and transaction data, and summarizing the new media public opinion data according to its source and event subject, followed by further preprocessing; Step 3: Inputting the financial data, transaction data, and corresponding data from the new media traffic matrix of the monitored company, and using a trained model to predict whether its market value fluctuation will exceed a safe range within a fixed future time period. If the fluctuation exceeds the safe range, an early warning signal is issued.

[0004] Regarding the aforementioned technologies, the inventors believe they have technical deficiencies in practical applications. Their new media data processing technology primarily focuses on macro-level statistical correlation analysis, using financial data and new media sentiment data as aggregated features input to the model to predict overall market capitalization fluctuations. It lacks the ability to analyze the micro-level causal logic between front-end business operations and back-end financial accounting. This type of technology employs a static data snapshot processing model, failing to construct a time-series dynamic correlation chain that can represent the entire business process. Furthermore, it lacks a closed-loop mechanism for reverse calibration and difference correction of the model logic using external authoritative settlement data. This makes it difficult to achieve accurate cross-domain data mapping, automated business and financial aggregation, and interpretable logical tracing when facing high-frequency, fragmented new media monetization scenarios, thus failing to meet the needs of refined enterprise operation and management. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a cross-domain data mapping method for new media project traffic monetization and financial aggregation. It employs a technical approach of constructing a causal chain of business processes and comparing and verifying it with an evolvable knowledge graph, thereby achieving high-precision, adaptive, and automated mapping between new media business data and financial items.

[0006] The above objectives can be achieved through the following approach:

[0007] A cross-domain data mapping method for traffic monetization and financial aggregation in new media projects includes: standardizing and instantiating acquired business operation data and financial subject data to generate a sequence of value events and a set of financial influencing factors; performing temporal causal tracing on target atoms in the sequence of value events to generate dynamic association chains representing causal relationships in business processes; performing a structured comparison between the dynamic association chains and a historical mapping knowledge graph to generate a set of mapping hypotheses to explain the financial significance of the dynamic association chains; performing multi-stage backtracking and pilot verification on the set of mapping hypotheses to generate target mapping hypotheses; using the target mapping hypotheses to perform association calculations on the sequence of value events and the set of financial influencing factors to generate a set of business-finance mapping relationships; using received external feedback signals to perform difference calibration on the set of business-finance mapping relationships to generate calibrated business-finance mapping relationships; and using the calibrated business-finance mapping relationships and the corresponding dynamic association chains to supplement data and logically correct the historical mapping knowledge graph to generate an updated historical mapping knowledge graph.

[0008] Optionally, generating the atomic sequence of value events and the set of financial impact factors includes: extracting the operation type and context information from the business operation data, and instantiating the operation type and context information using standard business activity attribute definitions to generate the atomic sequence of value events; extracting the account code and amount attribute from the financial account data, and instantiating the account code and amount attribute using standard financial impact correspondence to generate the set of financial impact factors.

[0009] Optionally, generating a dynamic association chain representing the causal relationship of the business process includes: identifying the target atom in the value event atom sequence, determining a tracking strategy based on the atom type, the tracking strategy including tracking duration and data collection range; activating a temporary event probe according to the tracking strategy, capturing the user interaction event sequence and system transaction event triggered by the target atom within the tracking duration and data collection range; and linking the target atom, the user interaction event sequence, and the system transaction event according to causal logic and time order to generate a dynamic association chain that is connected end to end.

[0010] Optionally, generating a set of mapping hypotheses to explain the financial meaning of the dynamic association chain includes: extracting node type distribution and event sequence patterns from the dynamic association chain to generate chain structure features; using the chain structure features to search the historical mapping knowledge graph to obtain structurally similar historical association chains and corresponding verified mapping relationships; performing parameter adaptation on the verified mapping relationships for the dynamic association chain to generate initial mapping hypotheses; and combining all initial mapping hypotheses to generate a set of mapping hypotheses.

[0011] Optionally, generating the target mapping hypothesis includes: using each mapping hypothesis in the mapping hypothesis set to retrospectively extrapolate historical business scenarios and real financial result datasets, and calculating an extrapolation consistency index characterizing the accuracy of historical predictions; selecting candidate target hypotheses from the mapping hypothesis set based on the extrapolation consistency index, and using the candidate target hypotheses to conduct parallel pilot aggregation of the current business scenario to generate pending financial entries; comparing authoritative financial data for the current business scenario with the pending financial entries, and confirming the candidate target hypothesis as the target mapping hypothesis when the difference is within the tolerance threshold.

[0012] Optionally, generating the business-finance mapping relationship set includes: using the weight allocation parameters defined in the target mapping hypothesis to calculate the contribution weight of each atom in the value event atom sequence to the relevant factors in the financial impact factor set; using the contribution weight to proportionally allocate the amount in the financial impact factor set to generate a detailed contribution relationship between atoms and factors; and structurally integrating the detailed contribution relationship between atoms and factors and attaching the identification information of the target mapping hypothesis to generate the business-finance mapping relationship set.

[0013] Optionally, the method further includes: visually rendering the contribution relationship details of atoms and factors in the business-finance mapping relationship set to generate a business-finance mapping report; converting the format of the business-finance mapping relationship set to generate accounting voucher data that conforms to the interface specification of the target financial system, and sending it to the target financial system.

[0014] Optionally, generating the calibrated business-finance mapping relationship includes: obtaining manual adjustment instructions from the financial audit system or authoritative settlement data from an external settlement platform to obtain an external feedback signal; comparing the authoritative value in the external feedback signal with the corresponding entry in the business-finance mapping relationship set to identify discrepancies; and using the authoritative value to numerically correct the discrepancies to generate a calibrated business-finance mapping relationship containing confirmed and corrected entries.

[0015] Optionally, generating the updated historical mapping knowledge graph includes: adding the calibrated business-finance mapping relationship and the dynamic association chain as high-confidence samples to the historical mapping knowledge graph; performing logical analysis on the difference entries during the calibration process; and when a new business model or financial processing logic is identified, using the analysis results to update the knowledge base data in the historical mapping knowledge graph used to define value event atoms or financial impact factors, thereby generating the updated historical mapping knowledge graph.

[0016] Based on the same inventive concept, this invention also provides a cross-domain data mapping system for new media project traffic monetization and financial collection, including: a data atomization module, used to standardize and instantiate the acquired business operation data and financial subject data to generate a value event atomic sequence and a set of financial influencing factors; and a causal chain tracing module, used to perform time-series causal tracing of target atoms in the value event atomic sequence to generate a dynamic association chain representing the causal relationship of the business process.

[0017] The graph comparison module is used to perform a structured comparison between the dynamic association chain and the historical mapping knowledge graph to generate a set of mapping hypotheses to explain the financial meaning of the dynamic association chain; the multi-stage verification module is used to perform multi-stage backtracking and pilot verification on the mapping hypothesis set to generate the target mapping hypothesis.

[0018] The association calculation module is used to perform association calculations on the atomic sequence of value events and the set of financial influencing factors using the target mapping hypothesis, generating a business-finance mapping relationship set; the feedback calibration module is used to perform difference calibration on the business-finance mapping relationship set using received external feedback signals, generating calibrated business-finance mapping relationships; the graph evolution module is used to supplement and logically correct the historical mapping knowledge graph using the calibrated business-finance mapping relationships and the corresponding dynamic association chains, generating an updated historical mapping knowledge graph.

[0019] Compared with the prior art, the present invention has the following advantages:

[0020] This invention achieves a high degree of automation and intelligence in mapping business data to financial data by constructing dynamic relational chains and utilizing knowledge graphs for hypothesis verification and self-evolution. This method can automatically complete the entire process from raw data cleaning, business process restructuring, mapping rule generation to accounting voucher delivery, reducing reliance on manual configuration and reconciliation, improving the efficiency of integrated business and financial processing, and reducing errors caused by manual operation.

[0021] This invention provides unprecedented transparency and traceability of business and financial data. By generating dynamic relational chains reflecting the complete business logic through time-series causal tracing, and achieving refined allocation of financial results to individual value events, it ensures that every financial revenue or cost can be traced back to its specific business activity. This atomic-level penetrating analytical capability provides a chain of evidence for financial audits and data support for business departments to accurately evaluate the effectiveness of various operational activities.

[0022] This invention endows the system with powerful adaptive and self-optimization capabilities by introducing a closed-loop feedback calibration and knowledge graph evolution mechanism. This method can proactively absorb authoritative external data, such as manual adjustment instructions or channel settlement data, for self-correction, and embed new business models or financial logic identified during the correction process into the historical mapping knowledge graph. This enables the system to continuously learn and adapt to the rapidly changing business models of the new media industry, ensuring the long-term effectiveness and accuracy of the mapping rules.

[0023] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a cross-domain data mapping method for new media project traffic monetization and financial aggregation according to an embodiment of the present invention.

[0026] Figure 2 This is a schematic diagram of the historical backtesting and consistency analysis of the mapping hypothesis in an embodiment of the present invention.

[0027] Figure 3 This is a schematic diagram illustrating the generation of business-finance mapping relationship and amount allocation in an embodiment of the present invention.

[0028] Figure 4 This is a schematic diagram of the structure of a cross-domain data mapping system for monetizing traffic and collecting financial data in a new media project, according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] Reference Figure 1 One embodiment of the present invention proposes a cross-domain data mapping method for new media project traffic monetization and financial collection. It adopts the technical means of constructing a causal chain of business processes and comparing and verifying it with an evolvable knowledge graph, which can achieve high-precision, adaptive and automated mapping between new media business data and financial items.

[0031] The method described in this embodiment specifically includes:

[0032] S1. Standardize and instantiate the acquired business operation data and financial account data to generate a value event atomic sequence and a set of financial impact factors;

[0033] Optionally, the generation of the atomic sequence of value events and the set of financial influencing factors includes:

[0034] Extract the operation type and context information from the business operation data, and instantiate the operation type and context information using the standard business activity attribute definition to generate a value event atomic sequence;

[0035] Extract the account code and amount attribute from the financial account data, and instantiate the account code and amount attribute using the standard financial impact correspondence to generate a set of financial impact factors.

[0036] Specifically, in the business data processing branch, the acquired business operation data is parsed and instantiated. Based on preset data source parsing rules, such as JSONPath expressions for front-end event logs or regular expressions for back-end service logs, the original operation types and context information are extracted in batches, typically ranging from 10,000 to 50,000 records. Subsequently, the standard business activity attribute definition library is called. This library contains pre-defined event templates and validation logic sets used to instantiate the extracted raw information. This instantiation operation normalizes non-standard original operation types, such as "click-ad-banner-123", to the standard operation type "ad click", and populates the original context information, such as user identifier, device model, and IP address, into the standard data structure. Simultaneously, all timestamps are uniformly converted to ISO8601 format. Each instantiated business activity is encapsulated into a value event atom, and their sequence constitutes a value event atom sequence. A value event atom The structure can be represented as:

[0037] ,

[0038] in, It is the raw operation type extracted from business operation data; It is the extracted raw context information; function This represents an instantiation process that normalizes and structures the data based on standard business activity attribute definitions, and its output is... It is a standardized data object that includes standard operation types, a structured context information set, a uniform format timestamp, and a unique event ID.

[0039] In the financial data processing branch, a similar parsing and instantiation process is performed on the acquired financial account data. Key account codes and monetary attributes are extracted from the data exported from the financial software. Then, instantiation is performed using the standard financial impact correspondence. The standard financial impact correspondence is an internally maintained knowledge base that defines the financial nature of each account code. For example, account "6001" corresponds to "Main Business Revenue," with a positive increase in its impact direction, while account "6201" corresponds to "Selling Expenses," with a negative consumption in its impact direction. The instantiation process transforms the original account codes and monetary attributes into financial impact factors with clear business meanings and impact directions. All generated financial impact factors together constitute the financial impact factor set. The structure of a financial impact factor F can be represented as:

[0040] ,

[0041] in, These are the raw account codes extracted from financial account data; It is the extracted raw amount attribute; function This represents an instantiation process of interpreting and quantifying the corresponding relationships of standard financial impacts, and its output is... It is a standardized data object that includes standard subject code, absolute amount, direction of influence, and unique factor ID.

[0042] For example, in an integrated business and finance system for an e-commerce platform, the business data processing branch first receives a batch of 10,000 front-end event logs. The system uses the preset JSONPath expression $.data.action_type to parse the original operation type click_promo_2025 and extracts the original context information such as user_id:89757 and timestamp:1678886400000. Then, it calls the standard business activity attribute definition library to normalize the operation type to "promotional activity click", converts the timestamp to ISO 8601 format 2025-07-15T16:00:00Z, and uses a function to... Generates value event atoms, where For "promotional activity clicks", The output is structured user and device information. Includes a unique event ID evt_CN_001; in parallel, in the financial data processing branch, the system parses the CSV file exported by the financial software, extracts the account code 6001 and the amount 500.00, identifies 6001 as "main business revenue" and confirms its positive direction based on the standard financial impact correspondence database, and then uses a function... Generate financial influencing factors, among which It is 6001. The output is 500.00. The inclusion of the factor ID "fin_GL_001" and a clear value-added identifier completes the transformation of heterogeneous data into standardized units. Through parallel dual-branch processing and standardized instantiation functions, it can efficiently transform raw business and financial data with mixed sources and a mixture of structured and unstructured data into logically unified and formatted atomic objects, eliminating the semantic gap between data silos and providing quantifiable basic data units for subsequent business and financial data correlation and matching.

[0043] S2. Perform time-series causal tracing on the target atoms in the value event atomic sequence to generate a dynamic association chain representing the causal relationship of the business process;

[0044] Optionally, the generation of dynamic association chains representing causal relationships in business processes includes:

[0045] Identify the target atom in the value event atom sequence, and determine the tracking strategy based on the atom type. The tracking strategy includes the tracking duration and the data collection range.

[0046] According to the tracking strategy, a temporary event probe is activated to capture the sequence of user interaction events and system transaction events triggered by the target atom within the tracking duration and data collection range.

[0047] The target atom, the user interaction event sequence, and the system transaction event are linked according to causal logic and time order to generate a dynamic chain of interconnected relationships.

[0048] Specifically, based on a predefined set of rules, target atoms with high business triggering potential are identified within a sequence of value event atoms. This identification process is achieved by matching the atom type of the value event atoms; for example, atoms of type "ad click," "purchase initiation," or "registration completion" are marked as target atoms. Once a target atom is identified, the corresponding tracking strategy is immediately queried from the tracking strategy library and matched according to its atom type. A tracking strategy is a set of parameters that includes tracking duration and data collection scope. For example, for a target atom of type "ad click," its tracking duration might be set to 30 minutes, and the data collection scope would be limited to front-end behavior logs and back-end order system logs that share the same user identifier as the target atom; while for a "purchase initiation" atom, the tracking duration might be shortened to 5 minutes, and the data collection scope would focus on payment gateway callback logs and inventory management system logs.

[0049] Based on the activated tracking strategy, a temporary event probe is dynamically instantiated and activated. Essentially, this temporary event probe is a lightweight data listener that operates continuously for the tracking duration within a specified data collection range, using contextual information of the target atom, such as user ID or session ID, as a filter. This probe is responsible for capturing in real-time a series of subsequent user interaction events triggered by the target atom, such as "page browsing," "adding items to cart," and critical system transaction events, such as "payment success notification" and "order creation."

[0050] When the tracking period ends or a preset termination event such as "transaction completed" is captured, the temporary event probe automatically expires and returns the set of all captured events. Next, a linking operation is performed, taking the target atom (the starting point), the captured user interaction event sequence, and the system transaction events, and arranging them in ascending order based on a common causal logical association identifier such as the user ID, strictly according to the timestamps of the events. This interconnected, sequentially increasing set of events is ultimately constructed into a dynamic chain. The generation logic of this chain can be expressed as:

[0051] ,

[0052] in, Represents a dynamic association chain; It is the target atom that serves as the starting point; It is a collection of user interaction event sequences and system transaction events captured by temporary event probes; It is a Boolean function used to determine events. With target atom Whether the same causal context identifier is shared, such as the same user ID or session ID, the threshold confidence level for this determination is typically required to be higher than 95%. The function represents the operation of sorting and linking all events that meet the conditions according to their timestamps into a sequence, thereby ensuring the logical coherence and timing correctness of the chain.

[0053] For example, the system performs real-time scanning of the generated value event atomic sequence and identifies the atomic "Initiate Order" type. The ID "ord_009" was identified as the target atom. Subsequently, a temporary event probe lasting 15 minutes was activated based on the tracking strategy library. This probe, using "session_id:sess_999" as the filter, captured data in the data stream including "browsing product details" (…). , "Add to Cart" , "Payment successful" , The set of events including (points) All captured events are processed via a Boolean function. All verifications share the same session ID and have a confidence level greater than 95%; subsequently, the system executes the connection logic. The target atom is merged with the capture set and strictly sorted by timestamp, generating a dynamic association chain in the order of "browse -> add to cart -> initiate order -> payment successful". This allows for the reconstruction of complete causal business chains within a discrete sea of ​​data. Through dynamic probe mechanisms and time-series sorting algorithms, business process chains with causal logic can be automatically reconstructed from massive amounts of fragmented events. This not only enables complete tracking of long-cycle, cross-system business behaviors but also provides an accurate contextual structure for understanding the business drivers behind financial results, avoiding analysis based on fragmented information.

[0054] S3. Perform a structured comparison between the dynamic association chain and the historical mapping knowledge graph to generate a set of mapping hypotheses to explain the financial meaning of the dynamic association chain;

[0055] Optionally, generating the set of mapping hypotheses for explaining the financial meaning of the dynamic association chain includes:

[0056] Extract the node type distribution and event sequence patterns from the dynamic association chain to generate chain structure features;

[0057] By utilizing the chain-like structural features, a search is performed in the historical mapping knowledge graph to obtain structurally similar historical association chains and corresponding verified mapping relationships.

[0058] The verified mapping relationship is adapted to the parameters of the dynamic association chain to generate initial mapping hypotheses, and all initial mapping hypotheses are combined to generate a mapping hypothesis set.

[0059] Specifically, structured feature extraction is performed on the input dynamic association chain. The dynamic association chain is traversed, and the frequency of occurrence of different node types (i.e., the types of value event atoms) is statistically analyzed to form a node type distribution vector. Simultaneously, the types of adjacent event node pairs and the time intervals between them are recorded, constituting a temporal pattern sequence between events. These two pieces of information are integrated into the chain-like structural features of the dynamic association chain, which can be regarded as a digital fingerprint of the business process.

[0060] This chain-like structural feature is used to perform a retrieval task within a historical mapping knowledge graph. The historical mapping knowledge graph is a structured database storing a large number of historical dynamic association chains and their verified mapping relationships. The core of the retrieval is calculating the structural similarity between the current dynamic association chain and historical association chains in the knowledge graph. This similarity is obtained through a weighted algorithm, with a similarity threshold of 0.85 used to filter out historical association chains with highly similar structures. The similarity calculation formula can be expressed as:

[0061] ,

[0062] in, It is the total similarity score; and These are the node type distribution vectors for the current chain and the historical chain, respectively. This is a function used to calculate the similarity between two vectors, such as cosine similarity, specifically expressed as: ,in Indexes representing node types, and They represent the first and second digits of the vector. The values ​​of each component; and These are the time sequence patterns of events between the current chain and the historical chain, respectively. The function uses the normalized Levenstein distance calculation logic, and the specific expression is as follows: ,in, and Representing sequences respectively and The length of the two values ​​is given by the Max function, which takes the maximum of the two lengths. The specific values ​​are obtained by constructing a dynamic programming matrix. The calculation yields the following: Defined matrix The size is Matrix elements Represents a sequence The former Elements and sequences The former The minimum edit distance between elements is given by the state transition equation. ,in This represents the function that takes the minimum value when... The Each element and The When all elements are the same =0, otherwise The final calculated value is 1. That is The value; and These are preset weight coefficients, which sum to 1. They are used to adjust the importance of node distribution and temporal pattern in similarity calculation, for example, set to 0.4 and 0.6 respectively.

[0063] For each retrieved historical association chain with a similarity exceeding a threshold, its associated verified mapping relationship is extracted. This verified mapping relationship is a standardized rule template that defines how different types of event nodes should affect financial results. A parameter adaptation operation is performed, applying this general rule template to the current dynamic association chain, replacing the event type placeholders defined in the template with specific value event atomic instances in the current chain, thereby generating initial mapping hypotheses. Since the search results may contain multiple structurally similar historical association chains, each chain may correspond to different verified mapping relationships—for example, one pointing to full revenue recognition and another to net revenue recognition after deducting channel fees—multiple parallel initial mapping hypotheses are generated. All generated initial mapping hypotheses are then aggregated to form the final mapping hypothesis set.

[0064] For example, the system extracts features from the current dynamic association chain to obtain a node type distribution vector where type A appears twice and type B appears once. and time-series pattern sequences And retrieve the historical chain from the historical database, its vector ,sequence Set weights , First, calculate the cosine similarity. :molecular denominator Therefore Next, the sequence similarity is calculated. Lewinstein distance between sequences AAB and ABB A matrix needs to be constructed for calculation. The cost of changing A to A is 0, the cost of changing A to B is 1, the cost of changing B to B is 0, the minimum edit distance is 1, and the maximum length of the two sequences is [missing information]. Therefore Final total similarity Because the score exceeded the 0.70 threshold for this type of business scenario, the system determined a successful match and directly extracted the "prepaid accounts receivable converted into revenue" rule from the historical chain as the initial mapping hypothesis for the current chain, thus completing the automatic matching from business behavior patterns to financial accounting processing logic. By combining a hybrid similarity algorithm of vector space model and sequence edit distance, the system can accurately retrieve the most structurally similar business scenarios from historical experience and automatically reuse verified financial rules, reducing the complexity of manually configuring mapping rules and improving the system's automated interpretation capabilities when facing complex and ever-changing business scenarios.

[0065] S4. Perform multi-stage backtracking and pilot verification on the mapping hypothesis set to generate the target mapping hypothesis;

[0066] Optionally, the hypothesis for generating the target mapping includes:

[0067] By using each mapping hypothesis in the mapping hypothesis set, the historical business scenarios and the actual financial result dataset are retrospectively extrapolated, and the extrapolation consistency index, which characterizes the accuracy of historical predictions, is calculated.

[0068] Candidate target hypotheses are selected from the mapping hypothesis set based on the inference consistency index, and the candidate target hypotheses are used to conduct parallel pilot collection of the current business scenario to generate pending financial entries;

[0069] The authoritative financial data for the current business scenario is compared with the pending financial entries. When the difference is within the fault tolerance threshold, the candidate target hypothesis is confirmed as the target mapping hypothesis.

[0070] Specifically, for each mapping hypothesis in the mapping hypothesis set, a backtracking simulation is performed. A large-scale dataset of historical business scenarios and actual financial results is loaded, typically covering audited business and financial records from the past 3 to 6 months. Using the rules defined in the mapping hypothesis to be verified, the historical business scenario data in this dataset is processed to simulate and generate a set of predictive financial results. Subsequently, a simulation fit metric is calculated to quantify the consistency between the predicted results and the actual financial results in the dataset. This simulation fit metric... The calculation formula is:

[0071] ,

[0072] in, This represents the predicted amount for a specific financial item derived based on the current mapping hypothesis. This represents the actual amount corresponding to this subject in the historical dataset. The function sums the amounts of all relevant items within a financial cycle. The closer this indicator is to 1, the higher the historical accuracy of the hypothesis's predictions. For example... Figure 2 As shown in the figure, the historical backtesting results of the mapping hypothesis are presented, in which the predicted amount curve based on the hypothesis closely matches the historical actual financial amount curve over multiple business cycles.

[0073] Based on the calculated correlation coefficient, one or more hypotheses with the highest scores are selected from the mapping hypothesis set. For example, all hypotheses with a correlation coefficient higher than 0.98 are selected as candidate target hypotheses. A parallel pilot aggregation mode is initiated, using each candidate target hypothesis to process the currently generated real-time business operation data stream, generating multiple sets of parallel, non-interfering pending financial entries in memory. These entries are temporary and will not be written to the formal financial ledger. Within a preset short period, such as one hour, data synchronization is performed with authoritative financial data sources, such as interim reports manually confirmed by the finance department or final settlement statements from third-party payment platforms. Each set of pending financial entries is precisely compared with the authoritative financial data. When the difference between a set of entries and the authoritative data in core indicators is found to be lower than a preset fault tolerance threshold, such as less than 0.5%, the candidate target hypothesis on which this set of entries is based is generated, formally confirmed as the final target mapping hypothesis, and the pilot operation of other candidate hypotheses is terminated.

[0074] For example, after selecting the candidate mapping hypothesis "revenue recognition based on shipment," the system enters the historical backtesting verification stage, loading historical data from the past three months. The actual financial result A is 10 million yuan, and the predicted amount P derived using this hypothesis is 9.9 million yuan, according to the formula... This high score prompted the hypothesis to enter the second stage. In the parallel pilot verification, the system used the hypothesis to process the real-time stream for one hour, generating a temporary entry totaling 50,000 yuan. This was compared with the 50,000 yuan in the payment platform's real-time settlement statement. The difference was 0%, lower than the 0.5% fault tolerance threshold. Therefore, the system confirmed the hypothesis as the final target mapping hypothesis, completing the qualitative transformation from assumption to authoritative rule. This method employs a dual verification mechanism combining historical backtracking and real-time pilot testing, effectively avoiding the financial error risks that may arise from directly applying new rules, and ensuring that the final mapping rule has extremely high accuracy and reliability in both logic and numerical value.

[0075] S5. Using the target mapping hypothesis, perform correlation calculations on the atomic sequence of value events and the set of financial influencing factors to generate a business-finance mapping relationship set;

[0076] Optionally, the generated business-finance mapping relationship set includes:

[0077] Using the weight allocation parameters defined in the target mapping hypothesis, the contribution weight of each atom in the value event atom sequence to the relevant factors in the financial impact factor set is calculated;

[0078] The contribution weights are used to proportionally allocate the amounts in the set of financial impact factors, generating a detailed contribution relationship between atoms and factors;

[0079] The contribution relationship details of the atoms and factors are structurally integrated and the identification information of the target mapping hypothesis is added to generate a business-finance mapping relationship set.

[0080] Specifically, the process involves processing the financial influencing factor and its associated sequence of value events. First, according to the rules defined in the target mapping hypothesis, the contribution weight of each value event atom in the sequence to the financial influencing factor is calculated. These weighting parameters are typically preset based on business understanding. For example, in a typical advertising monetization scenario, the weight parameter for the "ad impression" atom might be 0.2, while the weight parameter for the "ad click conversion" atom might be 0.8, reflecting the latter's higher contribution to revenue.

[0081] Using the calculated contribution weights, the corresponding amounts in the set of financial impact factors are proportionally allocated. This allocation operation generates a detailed statement of the contribution relationship between atoms and factors, clearly recording the specific amount allocated to each value event atom. The formula for calculating the amount allocated to a single value event atom can be expressed as:

[0082] ,

[0083] in, It is allocated to the atoms of value events. The final amount; It is the financial influencing factor associated with this business process. The total amount, which is obtained directly from the set of financial impact factors; It was obtained from the target mapping hypothesis, targeting atoms. The original weight parameters of the type; It refers to the original weight parameters of all atoms participating in the allocation in the atomic sequence of this value event. The sum of these values ​​is used to normalize the weights. For example... Figure 3 As shown in the figure, this diagram intuitively illustrates the calculation process of business value allocation based on the mapping hypothesis.

[0084] All generated details of the contribution relationships between atoms and factors are structurally integrated. During the integration process, a unique identifier is attached to each detailed record. This identifier is directly derived from the target mapping hypothesis upon which the mapping relationship is based, such as a versioned UUID. This ensures that the computational logic of each attribution data point is traceable. After integration and the addition of identifiers, the resulting dataset constitutes the business-finance mapping relationship set.

[0085] For example, the system, based on a predetermined target mapping hypothesis, maps a total amount... The financial impact factor of "Membership Subscription" is processed. This business process comprises two value event atoms: one is "Clicking Ads," whose weight... Second is "payment subscription", which has a higher weight. The system first calculates the total weight. Then according to the formula Profit sharing: For the "click ad" atom, the profit sharing amount is... Yuan; for the "payment subscription" atom, the allocated amount The system encapsulates these two records into a set of business-finance mapping relationships with a unique UUID "uuid-v1-map-009", enabling precise attribution of macro-level financial revenue to micro-level user behavior. Through a weighted, refined allocation algorithm, it achieves atomic-level mapping from financial data to business actions, allowing enterprises to clearly quantify the financial contribution of each business operation, providing highly granular data support for calculating channel ROI and optimizing resource allocation.

[0086] Optionally, the method further includes:

[0087] The contribution relationship details of atoms and factors in the business-finance mapping relationship set are visualized and rendered to generate a business-finance mapping report;

[0088] The business-finance mapping relationship set is format-converted to generate accounting voucher data that conforms to the target financial system interface specification, and then sent to the target financial system.

[0089] Specifically, in the branch generating the business-finance mapping report, the contribution relationship details of atoms and factors in the business-finance mapping relationship set are visualized and rendered. A preset chart rendering engine is invoked, and an appropriate visualization template is selected based on the report's requirements. For example, a Sankey diagram can be used to show the complete path of revenue distribution from different traffic channels to specific user interactions, or a sunburst chart can be used to hierarchically display the cost composition across different promotional activities and materials. The rendering engine reads data such as the value event atom type, financial impact factor name, and allocated amount from the contribution relationship details, using these as attribute values ​​for the nodes and edges of the chart to dynamically generate an interactive business-finance mapping report. This report allows users to trace from the macro-level financial ledger to micro-level business operation events through drill-down, filtering, and other interactive operations, achieving in-depth linked analysis of business and financial data.

[0090] In the branch generating accounting voucher data, format conversion and data push are performed on the business-finance mapping relationship set. First, the target financial system interface specification library is loaded. This library defines the voucher data formats required by different financial software such as UFIDA U8 or Kingdee EAS, typically in the form of a specific XML structure or fixed-width text format. Based on the currently configured target financial system, the corresponding format conversion adapter is activated. This adapter traverses the business-finance mapping relationship set and summarizes the details of the contribution relationship between atoms and factors according to preset aggregation rules, such as by day or by business line, aggregating thousands of detailed records into debit and credit entries that conform to accounting standards. Subsequently, the aggregated data is mapped and populated into the target format template to generate accounting voucher data that conforms to the interface specification. Finally, the generated accounting voucher data is periodically sent to the target financial system via secure API calls or SFTP file transfer, completing the automated closed loop from business data to financial accounting.

[0091] For example, the system applies the generated business-finance mapping set in two directions: On the report generation side, it calls the ECharts rendering engine, reads the allocated details data, and generates a dynamic Sankey diagram to visually demonstrate how the "20 yuan" revenue flows from "clicking ads" to "total revenue," allowing analysts to drill down; on the voucher generation side, the system activates the "SAP adapter," summarizing all subscription revenue for the day and filling the aggregated amount into the XML template. <doc> <amt> 100.00< / amt> <code>6001< / code> < / doc> The data is then sent to the SAP financial system via the SFTP protocol, automatically generating and posting accounting vouchers, thus achieving a closed loop of data visualization and process automation. This method transforms complex business-finance mapping data into intuitive visualization charts and standard accounting vouchers, satisfying management's need for penetrating analysis of business operations while automating the financial accounting process, thereby improving the efficiency of financial work and the ease of use of data value.

[0092] S6. Use the received external feedback signal to perform difference calibration on the business-finance mapping relationship set, and generate calibrated business-finance mapping relationships;

[0093] Optionally, the generation of the calibrated business-finance mapping relationship includes:

[0094] Obtain manual adjustment instructions from the financial audit system or authoritative settlement data from external settlement platforms to get external feedback signals;

[0095] The authoritative value in the external feedback signal is compared with the corresponding entry in the business-finance mapping relationship set to identify the difference entries;

[0096] The authoritative value is used to numerically correct the discrepancy entries, generating a calibrated business-finance mapping relationship that includes both confirmed and corrected entries.

[0097] Specifically, data is periodically or event-drivenly retrieved from authoritative data sources through pre-defined data interfaces. These authoritative data sources include financial auditing systems, which obtain manual adjustment instructions issued by finance personnel via their APIs, specifying the accounts, amounts, and business cycles to be adjusted; or external settlement platforms, which obtain legally valid authoritative settlement data reports provided by channel partners or payment gateways via SFTP or API interfaces. These raw instructions or reports are parsed and standardized, and then uniformly packaged into external feedback signals.

[0098] The aggregated results of the business-finance mapping relationship set, such as daily revenue summaries by business line, are used as the data to be verified. Each record in the external feedback signals is traversed, and business identifiers within the record, such as settlement cycle, project number, or channel ID, are used to perform a matching search within the business-finance mapping relationship set to locate the corresponding entry. Then, the values ​​of these two values ​​in the same dimension are compared, i.e., the authoritative value and the system-calculated value. When the absolute value of the difference exceeds a preset minimum tolerance threshold, the entry is identified as a discrepancy entry. The criteria for identifying discrepancy entries can be expressed as:

[0099] ,

[0100] in, It is a Boolean value; a true value indicates that the entry is a difference entry. It is an authoritative value obtained from external feedback signals; These are the system-calculated values ​​corresponding to the business-finance mapping relationship set; This is the fault tolerance threshold, which is usually set to a very small value, such as 0.01 currency units, to ignore only minor differences caused by floating-point precision issues.

[0101] For each discrepancy entry, its original calculated value is directly updated to the authoritative value, and the correction action, original value, authoritative value, and difference are recorded in the audit log. Entries in the business-finance mapping set that are not identified as discrepancies are considered confirmed entries, and their values ​​remain unchanged. All confirmed entries are merged with the corrected entries to form the final calibrated business-finance mapping. This set, calibrated with authoritative data, represents the most accurate business-finance attribution result for the current period.

[0102] For example, at the end of the day, the system retrieves an authoritative bank statement from the bank interface, showing the actual receipt of a certain transaction. This amount is RMB, after deducting handling fees, and is the calculated value recorded in the system's business and financial mapping relationship central record. Yuan; the system performs comparison and judgment, and sets a threshold. ,calculate The entry was identified as a discrepancy; the system then performed a correction operation, updating the amount in the mapping set to 99.5 yuan, and recording "Automatic correction of -0.5 yuan due to bank fee differences" in the audit log. This ultimately generated a calibrated business and financial mapping relationship, ensuring complete consistency between the data and actual fund flows. This method, by introducing an external authoritative data source for automated comparison and calibration, establishes a final line of defense for data quality, enabling timely detection and correction of subtle deviations caused by calculation logic or external factors, thus guaranteeing a high degree of accuracy and compliance of business and financial data.

[0103] S7. Using the calibrated business-finance mapping relationship and the corresponding dynamic association chain, the historical mapping knowledge graph is supplemented with data and logically corrected to generate an updated historical mapping knowledge graph.

[0104] Optionally, generating the updated historical mapping knowledge graph includes:

[0105] The calibrated business-finance mapping relationship and the dynamic association chain are added to the historical mapping knowledge graph as high-confidence samples;

[0106] Logical analysis is performed on the discrepancies in the calibration process. When a new business model or financial processing logic is identified, the analysis results are used to update the knowledge base data in the historical mapping knowledge graph used to define value event atoms or financial impact factors, thereby generating an updated historical mapping knowledge graph.

[0107] Specifically, the calibrated business-finance mapping relationship and its corresponding dynamic association chain are treated as data pairs and marked as high-confidence samples. Their high confidence stems from the fact that the data pair has been verified and corrected by external authoritative signals, and its accuracy rating is typically set above 0.99. Subsequently, this verified dynamic association chain and its final financial attribution result are used as a complete, verified instance and directly added to the historical mapping knowledge graph.

[0108] A thorough logical analysis is conducted on discrepancies identified during the calibration process. This analysis goes beyond simple numerical substitution; it's a root cause analysis. Cluster analysis is performed on the structural characteristics, event sequence, and contextual parameters of the dynamic association chains that generate these discrepancies. When a discrepancy is identified as highly correlated with a specific, previously unseen chain-like structural feature or event combination, and its frequency exceeds a preset alarm threshold—for example, if the same type of deviation occurs for three consecutive financial reporting cycles in a specific business scenario—it's determined that a new business model or financial processing logic may have emerged. At this point, a knowledge base update process is triggered, using the new pattern characteristics derived from the analysis to update the underlying definitions of the historical mapping knowledge graph. For instance, if a new channel cooperation model is found to cause a systematic deviation in revenue recognition, the analysis results will be used to create new value event atomic definitions in the knowledge base or modify the attribute rules of existing financial influencing factors. In this way, not only is a single mapping result corrected, but the fundamental logic that generated that result is also corrected. These two levels of operations work together to generate a richer, more logically sound updated historical mapping knowledge graph.

[0109] For example, based on the frequent "0.5 yuan handling fee difference" discovered in the aforementioned calibration, the system identified that all difference chains contained the feature of "payment through a specific channel," triggering an alarm for three consecutive days. The system first added the accurate "99.5 yuan - payment through a specific channel" chain data pairs after calibration as high-confidence samples to the historical knowledge graph. Then, it triggered a logic correction, automatically updating the underlying mapping rules and adding a new processing logic: "If channel = specific, then deduct 0.5 yuan." The updated knowledge graph, when processing similar business the following day, directly generated an accurate 99.5 yuan prediction, eliminating the discrepancy and achieving system self-iteration and optimization. This method constructs a feedback-based adaptive learning mechanism that can use the difference data from the calibration process to reverse-optimize the core knowledge base and logic rules, enabling the system to automatically evolve with changes in business models and continuously maintain a high level of mapping accuracy and generalization ability.

[0110] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a cross-domain data mapping system for new media project traffic monetization and financial collection, including:

[0111] The data atomization module is used to standardize and instantiate the acquired business operation data and financial account data to generate a sequence of value events and a set of financial influencing factors.

[0112] The causal chain tracing module is used to perform temporal causal tracing of target atoms in the atomic sequence of the value events, and generate a dynamic association chain that represents the causal relationship of the business process;

[0113] The graph comparison module is used to perform a structured comparison between the dynamic association chain and the historical mapping knowledge graph, and generate a set of mapping hypotheses to explain the financial meaning of the dynamic association chain.

[0114] A multi-stage verification module is used to perform multi-stage backtracking and pilot verification on the mapping hypothesis set to generate a target mapping hypothesis.

[0115] The correlation calculation module is used to perform correlation calculations between the atomic sequence of value events and the set of financial influencing factors using the target mapping hypothesis, and generate a business-finance mapping relationship set.

[0116] The feedback calibration module is used to perform difference calibration on the business-finance mapping relationship set using the received external feedback signal, and generate calibrated business-finance mapping relationships;

[0117] The graph evolution module is used to supplement and logically correct the historical mapping knowledge graph by using the calibrated business-finance mapping relationship and the corresponding dynamic association chain, and generate an updated historical mapping knowledge graph.

[0118] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0119] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A cross-domain data mapping method for traffic monetization and financial collection in new media projects, characterized in that, The method includes: The acquired business operation data and financial account data are standardized and instantiated to generate a sequence of value events and a set of financial impact factors; The target atoms in the atomic sequence of the value events are subjected to time-series causal tracing to generate a dynamic association chain that represents the causal relationship of the business process; The dynamic association chains are structurally compared with the historical mapping knowledge graph to generate a set of mapping hypotheses to explain the financial meaning of the dynamic association chains. This includes: extracting node type distribution and event sequence patterns from the dynamic association chains to generate chain-like structural features; using the chain-like structural features to search the historical mapping knowledge graph to obtain structurally similar historical association chains and corresponding verified mapping relationships; adapting the parameters of the verified mapping relationships to the dynamic association chains to generate initial mapping hypotheses; and combining all initial mapping hypotheses to generate a set of mapping hypotheses. The mapping hypothesis set is subjected to multi-stage backtracking and pilot verification to generate a target mapping hypothesis. This includes: using each mapping hypothesis in the mapping hypothesis set to backtrack and extrapolate historical business scenarios and real financial result datasets, calculating an extrapolation consistency index that characterizes the accuracy of historical predictions; selecting candidate target hypotheses from the mapping hypothesis set based on the extrapolation consistency index, and using the candidate target hypotheses to conduct parallel pilot aggregation on the current business scenario to generate pending financial entries; comparing authoritative financial data for the current business scenario with the pending financial entries, and confirming the candidate target hypothesis as the target mapping hypothesis when the difference is within the tolerance threshold. Using the target mapping hypothesis, the correlation calculation between the atomic sequence of value events and the set of financial influencing factors is performed to generate a business-finance mapping relationship set; The received external feedback signal is used to perform difference calibration on the business-finance mapping relationship set to generate calibrated business-finance mapping relationships; The calibrated business-finance mapping relationship and the corresponding dynamic association chain are used to supplement the historical mapping knowledge graph with data and make logical corrections, thereby generating an updated historical mapping knowledge graph.

2. The cross-domain data mapping method for new media project traffic monetization and financial collection according to claim 1, characterized in that, The generated value event atomic sequence and financial impact factor set include: Extract the operation type and context information from the business operation data, and instantiate the operation type and context information using standard business activity attribute definitions to generate a sequence of value events. Extract the account code and amount attribute from the financial account data, and instantiate the account code and amount attribute using the standard financial impact correspondence to generate a set of financial impact factors.

3. The cross-domain data mapping method for new media project traffic monetization and financial collection according to claim 1, characterized in that, The dynamic association chain that generates the causal relationships in the business process includes: Identify the target atom in the value event atom sequence, and determine the tracking strategy based on the atom type. The tracking strategy includes the tracking duration and the data collection range. According to the tracking strategy, a temporary event probe is activated to capture the sequence of user interaction events and system transaction events triggered by the target atom within the tracking duration and data collection range. The target atom, the user interaction event sequence, and the system transaction event are linked according to causal logic and time order to generate a dynamic chain of interconnected relationships.

4. The cross-domain data mapping method for new media project traffic monetization and financial collection according to claim 1, characterized in that, The generated business-finance mapping relationship set includes: Using the weight allocation parameters defined in the target mapping hypothesis, the contribution weight of each atom in the value event atom sequence to the relevant factors in the financial impact factor set is calculated; The contribution weights are used to proportionally allocate the amounts in the set of financial impact factors, generating a detailed contribution relationship between atoms and factors; The contribution relationship details of the atoms and factors are structurally integrated and the identification information of the target mapping hypothesis is added to generate a business-finance mapping relationship set.

5. The cross-domain data mapping method for new media project traffic monetization and financial collection according to claim 1, characterized in that, The method further includes: The contribution relationship details of atoms and factors in the business-finance mapping relationship set are visualized and rendered to generate a business-finance mapping report; The business-finance mapping relationship set is format-converted to generate accounting voucher data that conforms to the target financial system interface specification, and then sent to the target financial system.

6. The cross-domain data mapping method for new media project traffic monetization and financial collection according to claim 1, characterized in that, The generated and calibrated business-finance mapping relationship includes: Obtain manual adjustment instructions from the financial audit system or authoritative settlement data from external settlement platforms to get external feedback signals; The authoritative value in the external feedback signal is compared with the corresponding entry in the business-finance mapping relationship set to identify the difference entries; The authoritative value is used to numerically correct the discrepancy entries, generating a calibrated business-finance mapping relationship that includes both confirmed and corrected entries.

7. The cross-domain data mapping method for new media project traffic monetization and financial collection according to claim 6, characterized in that, The generated updated historical mapping knowledge graph includes: The calibrated business-finance mapping relationship and the dynamic association chain are added to the historical mapping knowledge graph as high-confidence samples; Logical analysis is performed on the discrepancies in the calibration process. When a new business model or financial processing logic is identified, the analysis results are used to update the knowledge base data in the historical mapping knowledge graph used to define value event atoms or financial impact factors, thereby generating an updated historical mapping knowledge graph.

Images