Cross-platform accounting automatic transfer system and method thereof

Abstract

The invention discloses a cross-platform accounting automatic carry-over system which comprises a front-end application module, a data synchronization engine module, an intelligent carry-over module and a data analysis and safety guarantee module. The front-end application module is in real-time two-way communication with the rear end through WebSocket, monitors account operation of a user, and packages the account operation into a JSON data packet containing an operation type, a timestamp and a data fingerprint; the data synchronization engine module receives the operation data, stores the operation data into a database after standardization conversion and integrity verification, and pushes the operation data to a target platform in real time; a neural network model based on historical data and artificial sample training is built in the intelligent carry-over module, data needing to be carried over are automatically screened, and compliance subject conversion is executed; the data analysis and security assurance module generates a multi-dimensional financial statement, and data security is ensured by adopting encryption, redundant storage and abnormal monitoring; through the automatic processing flow, the efficiency of account transfer is improved, and manual intervention and errors are avoided.

Description

Technical Field

[0001] The present invention relates to a cross-platform automatic account transfer system, and in particular to a cross-platform automatic account transfer system and method thereof. Background Art

[0002] The financial processing process refers to the series of activities carried out by the finance department to achieve financial accounting objectives. It encompasses data collection, processing, storage, and output, serving as a bridge between business and management processes. The financial processing process is a crucial component of a company's operations, and its rationality, standardization, and efficiency directly impact the company's economic performance. A scientific and efficient financial processing process ensures the accuracy, completeness, and timeliness of financial data, providing strong support for corporate decision-making.

[0003] In existing financial processes, account transfers are often complex and time-consuming, especially when multiple platforms or systems are involved. Traditional account transfer methods often require manual intervention, which is inefficient and prone to errors. Furthermore, data formats and standards often differ between platforms, further increasing the difficulty and complexity of account transfers.

[0004] Therefore, there is an urgent need for a cross-platform account automatic transfer system and method to solve the technical problems existing in the above-mentioned prior art. Summary of the Invention

[0005] The present invention overcomes the deficiencies of the prior art and provides a cross-platform account automatic carry-over system and method.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a cross-platform account automatic transfer system, including: a front-end application module, a data synchronization engine module, an intelligent transfer module, and a data analysis and security assurance module; the system adopts a modular architecture, and each module has a clear division of labor. The front-end is responsible for interaction, the engine processes data flow, the intelligent module implements business logic, and the security module ensures data reliability, forming a complete automated financial processing closed loop.

[0007] The front-end application module establishes a real-time two-way communication channel with the back-end server through the WebSocket protocol, monitors user account operation events and transmits operation event data in real time, and encapsulates the operation event data into a standardized JSON data packet containing the operation type, timestamp, and data fingerprint; the WebSocket protocol implements full-duplex communication and is more suitable for high-frequency operation scenarios than HTTP. JSON standardized encapsulation ensures the uniformity and integrity of the data format.

[0008] The data synchronization engine module is used to receive the operation event data, perform data format standardization conversion and integrity verification, and then store it in the database, and push the processed data to at least one target platform in real time; the engine acts as a data relay station, ensuring data consistency between heterogeneous systems through the ETL process, and the real-time push mechanism supports multi-platform synchronous updates, thereby improving business collaboration efficiency.

[0009] The intelligent carry-forward module includes a neural network model trained based on historical account data and manual operation samples. The neural network model automatically screens the data to be carried forward according to preset rules and performs account conversion operations in compliance with accounting standards; a decision model is established through supervised learning, and preset rules ensure compliance, achieving a balance between automation and risk control.

[0010] The data analysis and security assurance module realizes the generation of multi-dimensional financial statements and uses data encryption, redundant storage and anomaly monitoring mechanisms to ensure data security.

[0011] In a preferred embodiment of the present invention, the process of establishing the real-time two-way communication channel includes:

[0012] S1. Define an accounting operation listener at the front-end application layer to capture add, modify, and delete operation events; the event-driven architecture reduces polling overhead, accurately captures business operations, and provides a trigger mechanism for subsequent automated processing.

[0013] S2. Encapsulate the operation event data in JSON format and maintain the stability of the data connection through a heartbeat mechanism; the heartbeat mechanism can detect the activity between data transmission connections and ensure the reliability of data transmission through automatic reconnection.

[0014] S3 and backend servers deploy a load-balanced WebSocket server cluster to support horizontal expansion. Clustered deployment eliminates single points of failure and achieves load balancing, thereby improving concurrent processing capabilities and meeting enterprise-level high availability requirements.

[0015] In a preferred embodiment of the present invention, in step S2, the process of encapsulating the operation event data in JSON format includes:

[0016] S201. Define four state machines: pending, processing, submitted, and synchronized. Implement fault recovery through Redis storage state snapshots. The state machine is used to track the data life cycle, and Redis memory storage implements fast state recovery to ensure system fault tolerance.

[0017] S202. Use binlog to capture database changes in real time and build a data change stream through CDC technology. CDC technology based on database logs enables zero-intrusive data collection, which is more efficient and real-time than API polling.

[0018] S203. Detect concurrent operation conflicts based on the vector clock algorithm and provide user-configurable merge rules. By selecting vector clocks, the problem of eventual consistency in distributed systems is resolved, thus balancing business flexibility and data consistency.

[0019] In a preferred embodiment of the present invention, the data synchronization engine module is used to convert heterogeneous data into a unified accounting element coding format and establish a validation rule library that stores validation rules, including data types, value ranges, and associated account constraints. The unified coding enables semantic interoperability across systems, and the validation rule library uses a whitelist mechanism to prevent the influx of dirty data, thereby improving data quality.

[0020] In a preferred embodiment of the present invention, the intelligent carry-forward module constructs a sample feature set encompassing revenue / cost / tax type, custom amount ranges, and quarter-end time characteristics. It then employs a deep neural network to build a multi-classification model, outputting carry-forward recommendations for accounts receivable / accounts payable / taxes payable. A manual review and feedback mechanism is implemented, using the review results as incremental data to iteratively optimize the multi-classification model. By integrating business scenario knowledge with the LSTM time series model through feature engineering, the module can capture the cyclical patterns of the data, and through a closed-loop feedback mechanism, the model can be continuously optimized.

[0021] In a preferred embodiment of the present invention, the data analysis and security assurance module includes: a visual report generator, a hybrid cloud storage architecture, a three-copy redundant storage strategy, and a Prometheus-based system monitoring component; wherein, the visual report generator is used to support user-defined drag-and-drop selection of financial indicators and display dimensions; the hybrid cloud storage architecture uses the AES-256 encryption algorithm to perform field-level encryption on sensitive fields; the three-copy redundant storage strategy uses a scheduled task scheduling mechanism that combines incremental backup and full backup; the Prometheus-based system monitoring component integrates threshold alarm and automatic failover functions.

[0022] In a preferred embodiment of the present invention, based on the above-mentioned cross-platform account automatic transfer system, a cross-platform account automatic transfer method is proposed, which is applied to the above-mentioned cross-platform account automatic transfer system and includes the following steps:

[0023] A1. Obtain a historical accounting dataset and the corresponding manual carry-forward operation logs, and extract high-dimensional feature vectors that include accounting periods, account types, and amount distributions. Normalize the data to address the impact of different dimensional features on model training.

[0024] A2. Build a neural network model consisting of an input layer, an LSTM memory unit layer, and a fully connected output layer.

[0025] A3. Dynamically calculate carry-over priority based on external contextual data, and ensure operational compliance through pre-processing integrity verification and post-processing balance verification.

[0026] A4. Using manual review results as incremental training data, we continuously optimize the neural network model containing LSTM units and output confidence-based instruction conversion recommendations. The purpose of incremental learning is to avoid full retraining, while the confidence mechanism provides decision makers with a basis for decision-making, thus achieving AI-assisted rather than complete replacement of manual work.

[0027] In a preferred embodiment of the present invention, in step A2, an isolation forest algorithm is introduced and adopted to detect and correct outliers, and a feature enhancement expression mechanism based on an association graph is constructed.

[0028] In a preferred embodiment of the present invention, in step S3, the TLS1.3 protocol is used to establish an encrypted tunnel in the neural network model, a data block verification mechanism is set, and the transmission integrity is verified by the SHA-3 hash algorithm.

[0029] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0030] (1) Real-time data collection is achieved through WebSocket plus CDC, and automatic transfer is completed by combining neural network models. A hybrid cloud architecture is used to ensure data security, forming a complete link from operation capture, intelligent processing to secure storage, so as to be applicable to complex financial scenarios across platforms and multiple accounting standards, thereby improving work efficiency.

[0031] (2) Through the automated processing flow, the front-end application module can capture the user's account operation events in real time and realize the real-time flow and synchronization of data through the data synchronization engine module. The intelligent carry-forward module can automatically screen the data to be carried forward and perform account conversion operations according to the preset rules and neural network model, thereby improving the efficiency of account carry-forward and avoiding manual intervention and errors.

[0032] (3) The data synchronization engine module realizes the format conversion and integrity verification between heterogeneous data, ensuring the consistency of data across different platforms. At the same time, the data analysis and security assurance module adopts data encryption, redundant storage and anomaly monitoring mechanisms to effectively ensure the security of data.

[0033] (4) The present invention can flexibly adapt to the needs of different platforms and accounting standards. Through the neural network model and manual review feedback mechanism of the intelligent carry-forward module, it can continuously optimize and improve the carry-forward rules to meet the financial processing needs of different enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts.

[0035] Figure 1 It is a system flow chart of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0038] like Figure 1 As shown, the cross-platform automatic account transfer system includes: front-end application module, data synchronization engine module, intelligent transfer module and data analysis and security assurance module.

[0039] The front-end application module uses a publish-subscribe model through a WebSocket bidirectional channel to monitor user account operation events in real time and generate a standardized JSON data packet containing the operation type, timestamp, and data fingerprint;

[0040] The data stream processing dimension further includes: defining a four-state lifecycle management state machine: "pending", "processing", "submitted", and "synchronized", and using Redis to store state snapshots to support fault recovery;

[0041] Changes are captured in real time through binlog, a CDC data change stream is constructed, and concurrency conflicts are detected and handled in conjunction with the vector clock algorithm. The Airflow orchestration tool is used to define data pipelines from local to cloud, supporting OLAP analysis driven by natural language queries. Manual review results are used as streaming label data, and neural network model parameters are continuously optimized through incremental learning.

[0042] The WebSocket protocol enables full-duplex communication, making it more suitable for high-frequency operations than HTTP, improving the real-time and efficiency of data transmission. JSON standardized encapsulation simplifies data parsing and processing, reducing system complexity.

[0043] The data synchronization engine module integrates the Kafka message queue to ensure the sequentiality of operation data and load balancing, performs dynamic format verification through the Schema Registry, and configures the CDC engine to capture database change streams in real time.

[0044] It realizes format conversion and integrity verification between heterogeneous data, ensures the consistency of data between different platforms, and the real-time push mechanism supports simultaneous updates on multiple platforms, improving business collaboration efficiency.

[0045] The intelligent carry-forward module includes: composite event processor, context-aware model, bidirectional verification mechanism and cross-platform data router;

[0046] Among them, the composite event processor is used to subscribe to data change streams and rule libraries, triggering composite events such as amount threshold breakthroughs and accounting period arrivals; the context-aware model is used to integrate external data such as corporate credit ratings and industry indices to dynamically adjust carry-forward priorities; the two-way verification mechanism is used to verify data integrity in the preprocessing stage and perform account balance reconciliation in the post-processing stage; the cross-platform data router is used to dynamically convert data protocols according to the characteristics of the target platform, supports Protobuf binary transmission to optimize mobile network scenarios, and ensures the eventual consistency of multiple databases through a distributed transaction manager.

[0047] In the intelligent carry-forward module, a high-dimensional feature vector including accounting period, account type, amount distribution, and seasonal factors is constructed, and the isolation forest algorithm is introduced to correct outliers; a decision map including feature contribution is generated to support audit tracing and model debugging; users are allowed to configure conflict resolution strategies with amount priority and timestamp priority, and automatic adaptation of accounting standards versions is supported.

[0048] By establishing a decision-making model through supervised learning, a balance is achieved between automation and risk control. Preset rules ensure compliance, while supporting a manual review and feedback mechanism to achieve continuous optimization of the model.

[0049] The data analysis and security assurance module is used to integrate a stream-batch integrated analysis engine, an end-to-end encrypted transport layer, zero-trust access control, and a self-healing assurance mechanism.

[0050] Specifically, the AES-GCM algorithm is used to implement field-level encryption, combined with HSM to manage keys, and a transport layer tunnel is established through the TLS1.3 protocol; column-level permission management is implemented based on ABE attribute encryption, and OAuth2.0 is integrated to issue short-term tokens; SPC statistical process control is used to detect data distribution deviations and automatically trigger the model retraining process; an intelligent retry queue (exponential backoff + jitter algorithm) is configured to establish a multi-dimensional health scoring model that includes integrity, timeliness, and consistency.

[0051] It provides rich data visualization functions to facilitate users to quickly obtain and analyze financial information, strengthens data security measures, and effectively prevents data leakage and tampering.

[0052] In a preferred embodiment, the system introduces the spatiotemporal attention mechanism of the Transformer architecture based on LSTM, while capturing the temporal dependencies and cross-subject spatial correlations of accounting data (such as the linkage between accounts receivable and cash flow), thereby improving the accuracy of anomaly detection.

[0053] For cross-enterprise accounting collaboration scenarios, a federated learning framework is used to implement model training, ensuring that parameter updates can be completed without sensitive data leaving the local machine. Differential privacy technologies (such as DP-SGD) are combined to further strengthen data privacy.

[0054] Build a decision model based on causal graphs to identify causal paths between accounting events (such as the impact of changes in relevant regulations on costs), output explainable decision recommendations, and support attribution analysis in audit scenarios.

[0055] Build an accounting standards library based on knowledge graphs, automatically parse new regulations and update the rule library, dynamically adjust the carry-forward logic through reinforcement learning, and support global multi-standard parallel processing.

[0056] In a preferred embodiment, a cross-platform automatic account transfer method is proposed, which is applied to the above-mentioned cross-platform automatic account transfer system, including the following steps:

[0057] A1. Obtain a historical accounting dataset and the corresponding manual carry-forward operation logs, and extract high-dimensional feature vectors that include accounting periods, account types, and amount distributions. Normalize the data to address the impact of different dimensional features on model training.

[0058] A2. Build a neural network model consisting of an input layer, an LSTM memory unit layer, and a fully connected output layer.

[0059] Furthermore, in step A2, the isolation forest algorithm is introduced and adopted to detect and correct outliers, and a feature enhancement expression mechanism based on the association graph is constructed.

[0060] A3. Dynamically calculate carry-over priority based on external contextual data, and ensure operational compliance through pre-processing integrity verification and post-processing balance verification.

[0061] Furthermore, in step S3, the TLS1.3 protocol is used to establish an encrypted tunnel in the neural network model, a data block verification mechanism is set, and the transmission integrity is verified by the SHA-3 hash algorithm.

[0062] A4. Using manual review results as incremental training data, we continuously optimize the neural network model containing LSTM units and output confidence-based instruction conversion recommendations. The purpose of incremental learning is to avoid full retraining, while the confidence mechanism provides decision makers with a basis for decision-making, thus achieving AI-assisted rather than complete replacement of manual work.

[0063] The above-mentioned cross-platform automatic account transfer method has brought the following beneficial effects:

[0064] (1) Combining LSTM time series modeling with isolation forest anomaly detection, it takes into account both data pattern mining and noise filtering. Through TLS1.3 encryption and ABE attribute encryption, it achieves full-link data security from "transport layer to storage layer to access layer".

[0065] (2) Dynamic priority calculation and compliance verification reduce financial risks and improve the accuracy and efficiency of accounting processing. Incremental learning and confidence output support collaborative decision-making between AI and humans, balancing automation and controllability.

[0066] (3) The design of high-dimensional feature vectors and association graphs not only facilitates the expansion of new features, but also realizes a series of processes such as feature extraction, model training, and compliance verification through a modular architecture, thereby enabling rapid adaptation to different industries or accounting standards.

[0067] Example

[0068] The goal of this embodiment is to verify the synchronization efficiency improvement of the quantitative fusion architecture over the traditional independent architecture, and to verify the actual effect of the computing-storage collaborative optimization and heterogeneous resource dynamic scheduling mechanism proposed in the technical solution.

[0069] The implementation steps include: configuring a 4-node cluster with 2.4GHz 32-core CPUs, 256GB of memory, and 2 4TB x 4 NVMe SSDs. The network conditions were 100Gbps RDMA with end-to-end latency ≤ 2μs. The dataset used a 10TB mixed workload, consisting of 70% structured data and 30% unstructured data. A real-time synchronization scenario was simulated, with a write rate of 5GB / s and 100 concurrent tasks.

[0070] Comparison: Traditional computing layer and storage layer separation, independent architecture without resource pooling. All other variables remain unchanged. The experimental results are shown in Table 1.

[0071] Table 1

[0072] index Traditional architecture Converged Architecture Percentage increase End-to-end synchronization delay 82ms 45ms 45.1% ↓ Throughput (GB / s) 4.8 6.7 39.6% ↑ CPU utilization 68% 82% 20.6% ↑ Storage IOPS 1.2M 1.8M 50.0% ↑ Task completion time (10TB) 34min 21min 38.2% ↓

[0073] As shown in Table 1, the converged architecture reduces synchronization latency by 45.1% through Memory Direct technology and RDMA zero-copy transmission, meeting real-time requirements.

[0074] Heterogeneous resource pooling improves computing and storage parallelism, with a throughput of 6.7GB / s (close to 83% of the theoretical bandwidth), a 39.6% improvement over traditional architecture.

[0075] CPU utilization increased from 68% to 82%, and storage IOPS increased by 50%, demonstrating that hardware resources were fully activated and validating the effectiveness of the dynamic scheduling algorithm. The synchronization time for 10TB of data was shortened to 21 minutes, meeting the end-of-day batch processing window requirements for financial services.

[0076] The performance improvement attribution analysis is shown in Table 2.

[0077] Table 2

[0078] Optimization points Contribution illustrate Decoupling computing and storage logic 25% Avoid lock contention and improve concurrency Heterogeneous resource pooling scheduling 30% GPU / NPU accelerates unstructured data processing, while CPU focuses on metadata operations RDMA+memory pass-through technology 20% Eliminate data handling overhead and reduce serialization costs Intelligent prefetching and batch submission 15% Predict hot data and reduce random I / O Pipeline parallel optimization 10% Data compression, encryption, and transmission pipeline

[0079] The scenario performance comparison is shown in Table 3.

[0080] Table 3

[0081] Scenario Traditional architecture Converged Architecture Improvement High-frequency small file synchronization 1.2kops / s 2.1kops / s 75% ↑ Large object sequential writes 4.5GB / s 5.8GB / s 28.9% ↑ Mixed load 3.8GB / s 5.3GB / s 39.5% ↑

[0082] Summary: Through experimental verification of the embodiment and the comparative solution, the converged architecture can achieve the following while keeping the hardware cost unchanged:

[0083] (1) The overall synchronization efficiency is improved by 35%~40%.

[0084] (2) Resource utilization rate increased by more than 20%.

[0085] (3) The performance compliance rate of key business scenarios increased from 78% to 95%.

[0086] In summary, the cross-platform automatic account transfer system provided by the present invention has the following advantages:

[0087] Real-time data collection and change capture are achieved through the WebSocket protocol and CDC technology, significantly improving the real-time and efficiency of accounting processing.

[0088] The intelligent carry-forward module uses a neural network model to achieve automated carry-forwarding, avoiding manual intervention and errors and reducing financial processing costs.

[0089] The data analysis and security assurance module adopts a variety of security measures, including data encryption, redundant storage and anomaly monitoring, to effectively ensure the security of data.

[0090] The system adopts a modular architecture with clear division of labor among modules, facilitating system expansion and maintenance. Furthermore, the intelligent carry-forward module supports the requirements of different platforms and accounting standards, flexibly adapting to the financial processing needs of different enterprises.

[0091] The data synchronization engine module enables format conversion and integrity verification between heterogeneous data, ensuring data consistency across different platforms. Furthermore, the real-time push mechanism supports simultaneous updates across multiple platforms, improving business collaboration efficiency.

[0092] The above description is based on the ideal embodiment of the present invention. Based on the above description, relevant personnel can make various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the content of the specification and must be determined according to the scope of the claims.

Claims

1. The cross-platform account automatic transfer system is characterized by: include: Front-end application module, data synchronization engine module, intelligent carry-over module, and data analysis and security assurance module; The front-end application module establishes a real-time two-way communication channel with the back-end server through the WebSocket protocol, monitors user account operation events and transmits operation event data in real time, and encapsulates the operation event data into a standardized JSON data packet containing the operation type, timestamp, and data fingerprint; The data synchronization engine module is used to receive the operation event data, perform data format standardization conversion and integrity verification, and then store it in the database, and push the processed data to at least one target platform in real time; The intelligent carry-forward module includes a neural network model trained based on historical account data and manual operation samples. The neural network model automatically screens the data to be carried forward according to preset rules and performs account conversion operations that comply with accounting standards. The data analysis and security assurance module realizes the generation of multi-dimensional financial statements and uses data encryption, redundant storage and anomaly monitoring mechanisms to ensure data security.

2. The cross-platform automatic account transfer system according to claim 1 is characterized by: The process of establishing the real-time two-way communication channel includes: S1. Define an accounting operation listener in the front-end application layer to capture add, modify, and delete operation events; S2. Encapsulate the operation event data in JSON format and maintain the stability of the data connection through a heartbeat mechanism; S3 and backend servers deploy a load-balanced WebSocket server cluster to support horizontal expansion.

3. The cross-platform automatic account transfer system according to claim 2 is characterized by: In step S2, the process of encapsulating the operation event data in JSON format includes: S201. Define four state machines: pending, processing, submitted, and synchronized, and implement fault recovery through Redis storage state snapshots. S202. Use binlog to capture database changes in real time and build a data change stream using CDC technology. S203. Detect concurrent operation conflicts based on a vector clock algorithm and provide user-configurable merging rules.

4. The cross-platform automatic account transfer system according to claim 1 is characterized by: The data synchronization engine module is used to convert heterogeneous data into a unified accounting element coding format and establish a verification rule library to store verification rules including data types, value ranges, and associated subject constraints.

5. The cross-platform automatic account transfer system according to claim 1 is characterized by: The intelligent carry-forward module constructs a sample feature set including revenue / cost / tax types, custom amount ranges, and quarter-end time features, uses a deep neural network to build a multi-classification model, outputs carry-forward suggestions including accounts receivable / accounts payable / taxes payable, and sets up a manual review and feedback mechanism, using the audit results as incremental data to iteratively optimize the multi-classification model.

6. The cross-platform automatic account transfer system according to claim 1 is characterized by: The data analysis and security assurance module includes: a visual report generator, a hybrid cloud storage architecture, a three-copy redundant storage strategy, and a Prometheus-based system monitoring component; wherein, the visual report generator is used to support user-defined drag-and-drop selection of financial indicators and display dimensions; the hybrid cloud storage architecture uses the AES-256 encryption algorithm to perform field-level encryption on sensitive fields; the three-copy redundant storage strategy adopts a scheduled task scheduling mechanism that combines incremental backup and full backup; the Prometheus-based system monitoring component integrates threshold alarms and automatic failover functions.

7. A cross-platform account automatic transfer method, applied to the cross-platform account automatic transfer system according to any one of claims 1 to 6, characterized in that: The following steps are involved: A1. Obtain a historical accounting dataset and the corresponding manual carry-forward operation logs, and extract a high-dimensional feature vector containing the accounting period, account type, and amount distribution. A2. Build a neural network model consisting of an input layer, an LSTM memory unit layer, and a fully connected output layer. A3. Dynamically calculate carry-over priority based on external contextual data, and ensure operational compliance through pre-processing integrity verification and post-processing balance verification. A4. Use manual review results as incremental training data to continuously optimize the neural network model containing LSTM units and output instruction conversion suggestions with confidence.

8. The cross-platform automatic account transfer method according to claim 7, characterized in that: In step A2, the isolation forest algorithm is introduced and used to detect and correct outliers, and a feature enhancement expression mechanism based on the association graph is constructed.

9. The cross-platform automatic account transfer method according to claim 7, characterized in that: In step S3, the TLS1.3 protocol is used to establish an encrypted tunnel in the neural network model, a data block verification mechanism is set, and the integrity of data transmission is verified by the SHA-3 hash algorithm.

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