Financial voucher processing method and system based on ultra-lightweight feature compression

Through the combination of ultra-lightweight feature compression technology and financial voucher processing model, the problem of excessive resource consumption in financial data processing is solved, efficient and fast financial voucher data processing is achieved, and the intelligence and efficiency of medical financial management is improved.

CN119251850BActive Publication Date: 2025-03-25SHANDONG RES INST OF TUMOUR PREVENTION TREATMENT
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Patent Information

Application Number
CN202411783343.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-12-06
Publication Date
2025-03-25
Estimated Expiration
2044-12-06

AI Technical Summary

Technical Problem

The prior art has problems of data redundancy and excessive resource consumption when processing massive financial voucher data, which is difficult to effectively apply in resource-constrained environments.

Method used

Using ultra-lightweight feature compression technology, the simplified compression algorithm greatly reduces the data dimension without significantly losing data feature information, and builds a financial voucher processing model including a global lightweight feature extractor, a local lightweight feature extractor, a compression senser, an attention module, a classifier and a text extractor.

Benefits of technology

It realizes efficient processing of financial voucher data under limited computing resources, improves data processing speed and resource utilization, supports real-time analysis and decision-making, and improves hospital operation efficiency.

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Abstract

The present invention relates to a financial voucher processing method and system based on ultra-lightweight feature compression, which belongs to the field of financial artificial intelligence. It includes the following steps: collecting financial voucher images and text information, constructing a Mini-Fin data set, and dividing it into a training set and a test set; using the HSV brightness enhancement method to perform data preprocessing on the image data of the training set to obtain financial image data; constructing a financial voucher processing model based on ultra-lightweight feature compression, the model includes a global lightweight feature extractor, a local lightweight feature extractor, a compressed perceptron, an attention module, a classifier, and a text extractor; inputting the financial image data into the model for training; optimizing the model through a classification loss function and a text loss function to obtain a trained model; the financial voucher image to be detected in the test set is input into the trained model to obtain a financial voucher processing result. The present invention can improve the efficiency of processing large-scale financial data.
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