882 results about "Invoice" patented technology

The invention provides a reimbursement data management system and method based on AI artificial intelligence, and the system comprises a multi-modal data collection and fusion module which is used for providing an access reimbursement application entrance; the auditing and compliance judgment module is used for invoice authenticity identification, reason rationality identification, reimbursement item classification and automatic auditing; the approval process optimization module is used for learning and optimizing a reimbursement approval process and automatically simplifying or increasing approval levels according to reimbursement application features; the analysis and prediction module is used for performing multi-dimensional analysis on the reimbursement data based on a big data processing framework and a data mining algorithm and generating a visual report and result; and the data storage and block chain module is used for constructing a distributed and non-tampering data storage account book by adopting a block chain technology, and executing a reimbursement process automation rule by utilizing a block chain intelligent contract. The efficiency, quality and safety of reimbursement data management are comprehensively improved, and enterprise financial management is promoted to develop towards the intelligent and refined direction.

The embodiment of the invention discloses a method and system for identifying transaction risks, and the method comprises the following steps: obtaining multi-source heterogeneous data, and initializing a dynamic transaction graph according to a multi-source heterogeneous data flow; the multi-source heterogeneous data comprises invoice data, fund flow data, an enterprise stock equity relationship and geographic data; receiving a multi-source event stream through a message queue, and updating the dynamic transaction graph in real time based on the multi-source event stream; for enterprise nodes affected by updating in the dynamic transaction graph, feature aggregation is executed, topological risk features are extracted, and risk scores of the enterprise nodes are output; verifying the consistency of the invoice hash and the fund flow hash through a relay chain protocol, and obtaining a cross-chain verification result; and according to the risk score and the cross-chain verification result, identifying a transaction risk. According to the embodiment of the invention, systematic risk identification is realized through data integration, graph construction, risk mining and cross-chain verification, and the accuracy of invoice risk identification can be improved.

The invention relates to the technical field of enterprise finance and taxation management, and discloses an enterprise finance and taxation intelligent management system based on data analysis. According to the method, the problems of data islands, response delay and business disjunction in the prior art are solved by constructing a multi-source data real-time access and dynamic knowledge graph and stream calculation analysis engine collaborative architecture. Specifically, deep fusion and real-time perception of enterprise finance and taxation total data are realized, invoice risks and compliance deviations in a business flow can be actively identified, and the conversion from post-event recheck to in-event intervention is completed; through combination of a dynamic knowledge graph and an AI model, an analysis decision deeply accords with a business scene, a tax preferential policy is matched, and the finance and tax planning efficiency is intelligently improved; meanwhile, the system has self-learning and self-adaptive capabilities and can be continuously optimized along with policy and regulation and business changes, the risk control level and compliance capability of an enterprise are enhanced, and the real-time performance, accuracy and intelligent degree of finance and tax management are improved.

The invention discloses a whole-process collaborative production logistics management optimization system and method. The core of the system is intelligent inquiry and blockchain cost management. The inquiry module fuses a dynamic weight algorithm and a machine learning model, intelligently screens goods opportunities, pushes an AES encrypted inquiry sheet, and optimizes a quotation scheme in a multi-objective manner. And the delivery module is in butt joint with the ERP through the OData API, automatically analyzes the order, generates a delivery order, integrates weighing data for fee calculation, and carries out early warning on an abnormal order. And the customs declaration module identifies invoice information through OCR, automatically extracts customs declaration fields, verifies compliance through API, and generates a standardized customs declaration. The in-transit tracking module integrates a multi-platform API, pulls a logistics state in real time, pushes the logistics state to a client through WebSocket, and transparentizes an order state. The cost management module constructs a distributed account book based on a PBFT consensus mechanism block chain, ensures that cost information is safe and credible, and supports multi-currency settlement and dispute arbitration. The system comprehensively optimizes the production logistics link, realizes full-process collaboration, improves the efficiency, reduces the cost, and enhances the enterprise competitiveness.

Orchestration solutions for agentic automation for artificial intelligence (AI) agents, robotic process automation (RPA) robots, agentic orchestration processes (AOPs), and humans-in-the-loop with self-healing and capabilities to increase autonomy. Agentic orchestration, via a conductor application, enables the automation, modeling, and monitoring of complex business processes from start to finish. A user or an initiating software process requests that a given AOP be run. The AOP defines a business process (e.g., in Business Process Model and Notation (BPMN)), and may utilize one or more RPA robots, one or more AI agents, and / or one or more humans to assist with accomplishing this process, such as resolving invoice discrepancies, onboarding an employee, performing compliance checks, synchronizing order data among multiple backend systems, etc.

The embodiment of the invention provides a bill collaborative management method and system based on multi-source heterogeneous data fusion, and belongs to the technical field of data processing. The method comprises the following steps: performing hash calculation according to standardized bill data to obtain a unique identifier; and obtaining a map node corresponding to the standardized bill data to obtain a target map node. The path from the map root node to the target map node is a target structure path. And obtaining a complete behavior sequence of the standardized bill data according to the unique identifier and the behavior log. And constructing a behavior structure joint sequence according to the target structure path and the complete behavior sequence. And inputting the behavior structure joint sequence into the structure perception embedding model to obtain a sequence embedding vector. And obtaining a structure behavior inconsistency score and an abnormal type label according to the structure behavior consistency evaluation function, the behavior structure joint sequence and the sequence embedding vector. And generating a suggestion text according to the structure behavior inconsistency score and the abnormal type label, and displaying the suggestion text. Accurate identification of abnormal bills is realized.

A system for real-time creditworthiness assessment of small and medium-sized enterprises (SMEs) based on agent-based artificial intelligence (AI), the system includes: a large number of autonomous agents connected to each other via secure communication protocols; a data ingestion processing unit configured to capture and preprocess heterogeneous data streams from structured and unstructured sources, including APIs for financial transactions, tax databases, e-invoicing registers, inventory management systems, social sentiment feeds, and IoT-enabled devices, with preprocessing including schema harmonization, anomaly detection, and encryption-based integrity preservation; an ensemble of scoring control units configured to generate dynamic credit scores, wherein the ensemble includes machine learning models, including gradient-enhanced decision trees, recurrent neural networks, and graph neural networks, the graph neural networks being configured to assess relational dependencies between SMEs, suppliers, and customer nodes in a business ecosystem; an audit controller configured to generate regulatory-compliant justification paths for each credit score; a feedback processing unit configured to update the evaluation control unit ensemble using reinforcement learning signals derived from observed repayment behavior, payment default trends, and fraud detection markers; and a user interface configured to display timestamped credit histories, confidence intervals and explanatory justifications, with the entire system running on a containerized cloud-native infrastructure with blockchain-based ledgers for version control of each point update.

The invention discloses a reimbursement approval method and device, equipment and a storage medium. In the scheme, based on the tax identifier of the target enterprise, the electronic invoice data associated with the tax identifier is acquired through the third-party electronic invoice platform interface. And extracting target text information from the electronic invoice data according to a predefined field rule, and generating structured reimbursement data based on the target text information. And generating a reimbursement application form according to the structured reimbursement data, and performing compliance verification on the reimbursement application form to obtain a compliance verification result. And if the compliance verification result is that the compliance verification is passed, performing risk assessment on the reimbursement application form based on the intelligent risk control model, and generating an approval suggestion. And executing a corresponding reimbursement approval process operation according to the approval suggestion and a preset approval rule. According to the technical scheme, the efficiency and accuracy of reimbursement approval are remarkably improved while the financial risk of an enterprise is reduced.

The invention discloses an RPA-based full-automatic all-electricity invoice issuing method and system. The method comprises the following steps: constructing an RPA system: creating an RPA user, creating a business process, and defining a process rule; data acquisition and preprocessing: capturing basic transaction data, and performing data preprocessing on the captured data; invoicing and intelligent inspection: logging in a full-electricity invoice service platform, filling and submitting data information in an invoicing interface, and verifying various contents of an invoice; invoice circulation and signing determination: sending the issued full-electricity invoice to the user, and monitoring the invoice signing condition; and storage and filing management: uploading the issued all-electricity invoice to a management system. According to the invention, by introducing the RPA technology, the full-automatic processing of the full-electricity invoices from data acquisition and issuing to circulation and archiving is realized, the time consumption of manual operation is greatly reduced, and the invoice issuing efficiency is improved compared with the traditional manual or semi-automatic process.

The invention belongs to the crossing field of computer technology and business management, and discloses a purchase return risk early warning and management method and system based on multi-mode intelligent auditing, electronic equipment and a computer readable storage medium. The method comprises the following steps: segmenting a PDF invoice image through an improved Apache PDFBox analyzer; a CLIP model and an OCR technology are combined, a cross-modal attention layer is used for correcting an OCR text, a multi-modal consistency difference value is generated, and invoice authenticity is judged; when a false invoice is detected, generating a standardized risk event, and extracting supplier, purchaser and transaction edge data from the graph database to construct a time sequence sub-graph; and identifying an abnormal mode based on the graph neural network, verifying the risk by using a time sequence risk scoring model, and executing a treatment strategy. According to the method, through multi-modal fusion, dynamic graph analysis and time sequence modeling, the problems of low false invoice detection precision, long abnormal fund tracing time consumption, risk prediction lag and the like in traditional auditing are solved.

The invention relates to a tax refund management method and system based on a first-in first-out principle, and the method comprises the following steps: employing a warehousing management robot to generate timestamps for accepted and warehoused goods according to the warehousing order of an acceptance bill, verifying the consistency of the warehoused goods and the acceptance bill, and generating a fund payment list; during delivery, according to the timestamp sequence and a first-in first-out principle, related information of delivered goods is generated; calculating the total amount of the delivered goods according to the related information of the delivered goods, automatically generating a delivery batch number and invoicing information, and sending the invoicing information to a supplier; after the invoice returned by the supplier is received, the invoice is matched through the purchase order and the delivery batch number, and the consistency of the cargo details, the amount and the quantity is verified; and verifying the consistency of the invoice and the customs declaration data through an intelligent matching algorithm, and generating a corresponding tax refund batch number and a corresponding tax refund amount.

Methods, systems, and computer-readable storage media for providing a set of column pairs, each column pair including a column of a bank statement table, and a column of a super invoice table, each column pair corresponding to a modality, the super invoice table including at least one row including data associated with multiple invoices, for each column pair, determining a feature descriptor based on an operator, a feature vector being provided based on feature descriptors of the set of column pairs, inputting the feature vector to a ML model that processes the feature vector to determine a probability of a match between the bank statement, and a super invoice represented by the super invoice table, and outputting a binary output representing one of a match and no match between the bank statement, and the super invoice based on the probability.

The invention discloses an invoice automatic auditing and intelligent settlement system for automobile road rescue, and relates to the technical field of intelligent settlement systems, a correction module integrates a machine learning model to analyze historical bills, automatically corrects unreasonable settlement amount and generates a correction suggestion report, a prompt module builds an intelligent priority matrix, and the intelligent priority matrix is sent to a server. The reminding frequency is dynamically adjusted according to parameters such as the historical settlement scale and the service life of a supplier, the settlement module recognizes an abnormal invoicing terminal by combining an invoicing equipment fingerprint recognition technology, a payment process is automatically triggered after verification is passed, a supplier credit portrait system is established, and a dynamic settlement scheme is supported. The settlement system establishes an intelligent priority matrix to realize important supplier high-frequency reminding and low-risk project low-frequency intervention, so that the whole process timeliness is guaranteed, the defects of a traditional rule engine in coping with complex scenes are effectively overcome, and the response capability and the autonomous decision-making capability of the system to abnormal behaviors are improved.

The present invention discloses a blockchain-integrated financial reputation scoring system designed to assess financial credibility within decentralized ecosystems. The system generates a dynamic accountability score based on blockchain transactions, including escrow payments, invoice completions, smart contract interactions, DeFi transactions, NFT trading, and cross-chain financial activities. A risk-adaptive score weighting system dynamically adjusts the impact of financial behaviors based on transaction risk, counterparty trustworthiness, and historical financial patterns. The score is leveraged for tiered access to financial products, lending, and collateral adjustments. AI-enhanced anomaly detection prevents fraudulent score inflation, while privacy-preserving verification ensures secure reputation authentication. The system further extends into insurance underwriting, compliance verification (AML / KYC), and financial access expansion, allowing traditional institutions to assess blockchain-based creditworthiness. Quantum-resistant cryptographic techniques secure reputation data, ensuring trust and adaptability in both decentralized and traditional financial ecosystems.

The invention relates to the technical field of big data, and discloses a big-data-based financial account closing method, device, equipment and medium, and the method comprises the steps: collecting various financial related data from a financial system, a business system and an external data source in an enterprise, and carrying out the cleaning and unification processing of the various financial related data, obtaining standard financial data; for the standard financial data, invoice repeated reimbursement checking, tax risk checking, expense overbudget checking and supplier risk checking are carried out in sequence, and if at least one checking result is abnormal, a first-level early warning signal is prompted; and if all the checking results are not abnormal, key feature extraction is carried out on the standard financial data, the key feature extraction is input into a financial checking model, the checking result of the standard financial data is output, and if the checking result is not compliant, a second-level early warning signal is prompted. According to the invention, the problem of low efficiency of account closing review can be solved.

The invention discloses an analysis method and system for internet enterprise credit risk assessment, and belongs to the technical field of internet finance, and the method comprises the steps: obtaining multi-source enterprise operation data of a target enterprise and an associated party, the multi-source enterprise operation data comprising payment flow data and tax invoice data; based on the multi-source enterprise operation data, identifying an enterprise association relationship between a target enterprise and an associated party, and constructing a dynamic enterprise relationship graph by using the enterprise association relationship; based on the multi-source enterprise operation data, extracting a time sequence behavior index of the target enterprise; fusing the dynamic enterprise relation graph and the time sequence behavior indexes to generate coupling risk features; and generating a credit risk assessment result according to the coupling risk features. According to the method, a multi-source data fusion mechanism and a dynamic enterprise relation graph construction technology are adopted, coupling risk features can be generated, a risk infection path can be simulated, and panoramic risk portraits and prospective early warning are realized.

The invention belongs to the cross technical field of block chain technology and supply chain finance, and provides a supply chain dynamic risk control method and system based on a smart contract, and the technical scheme is as follows: obtaining node data of each participant in a supply chain; dynamically evaluating the risk of the supply chain in combination with the obtained node data of each participant and the constructed smart contract; wherein in the basic contract, the logistics data and the invoice data are verified respectively, and a verification result is sent to the business contract after verification is passed; in a business contract, according to received enterprise data, introducing a timestamp mechanism, recording creditor's right generation time in a block chain, and realizing multi-level creditor's right splitting and merging through a Merkle tree; in the risk control contract, calculating a risk assessment index based on the on-chain data, the off-chain data and the risk assessment model; and adjusting the credit granting strategy in real time according to the change of the risk assessment index to obtain an adjusted credit granting strategy. Risk control is embedded into contract execution logic, so that the risk loss of the supply chain is reduced.

There is a system for admitting automobiles to a venue comprising: at least one camera for recognizing an automobile, and a license plate. A server is configured for receiving a picture of the automobile and a picture of the license plate of the automobile. Furthermore, there is at least one database for storing, matching and identifying the picture of the automobile and the license plate. In addition, there is at least one screen for allowing a user to identify their automobile taken from a list of automobiles. There is also a process for tracking an automobile within an area which comprises the steps of identifying a pre-defined area and identifying an automobile for screening into the area. Next the auto and its license plate are photographed. This information is stored in a database and then used to generate invoices and track the location of these automobiles. Customer can access the database, enter the license plate of his vehicle, the database recognizes his vehicle, the customer makes payment for his vehicle, and the app places the customer information onto the PAID category list on the database.

The invention relates to the technical field of data processing, in particular to an invoicing, accounting and tax declaration method and system for a supply chain, and the method comprises the following steps: obtaining original data, including purchase order, customs declaration and invoice information, generated in each business link of the supply chain, and synchronizing the original data to a financial data center through a system interface for standardization processing; classifying the standardized data based on a preset rule, generating a to-be-processed receivable bill and a to-be-processed payable bill, and associating corresponding business records; comparing the purchase order, the acceptance record and the invoice information, generating an account checking difference report, and pushing the account checking difference report to a related service terminal; updating the account period state according to the account checking result; extracting tax refund elements in the customs declaration, generating a tax refund application, and tracking an auditing progress until the tax refund application arrives at an account and is filed; the method automatically synchronizes data of links such as customs affair and storage through a system interface and performs standardization processing, solves the problem of lagging of manual data collection, and improves the data synchronization efficiency.

The present invention relates to the field of invoice data management, and discloses an invoice entry classification identification method and identification system, including: a data initial processing module piles up the invoice data to be classified, obtains multiple invoice data piles, and classifies them according to classification patterns, the classification patterns include invoice classification identification based on invoice content and invoice classification identification based on invoice amount, identifies invoice classification identification features, and completes invoice classification identification. The present invention significantly improves the processing speed of invoice classification through data pile-up and multi-container parallel processing mechanism, and is particularly suitable for processing large-scale invoice data. The dynamic classification feature extraction and merging strategy is adopted to effectively cope with the diversity and complexity of invoice data and improve the accuracy of classification.

The invention relates to the technical field of invoice risk control management, and discloses an invoice risk control management method and system based on multi-source data linkage. According to the method, multi-source data of the whole invoice business process is collected in real time through a multi-source data fusion gateway. And performing streaming ETL processing and encryption integration on the multi-source data to generate an encrypted unified data view. And based on the view, constructing an association hypergraph by utilizing a dynamic graph association technology, analyzing an entity relationship by virtue of a graph neural network, and identifying false invoice making gang and repeated financing risks. And calculating a dynamic risk score by adopting a risk quantification and traceability technology, and outputting a risk level, a responsibility node identifier and a capital flow thermodynamic diagram. When the risk score exceeds a threshold value, risk gradient information is safely shared among the group headquarters, the molecular companies and the tax-bank institutions, and linkage processing actions such as invoice freezing, red character notification or credit adjustment are triggered. According to a manual reexamination result, the risk model is finely adjusted through a knowledge self-updating technology, and the rule base is hot-updated.

The invention provides a safe short message link invoice generation system and method based on a bank intranet, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining an available invoicing amount interval based on the change condition of a centralized verification risk time period in an effective time interval of an invoicing short message link at a current time period under different invoicing amounts, and when the matching invoice amount is not in the available invoice amount interval, according to the change condition of the matching invoice in different concentrated verification risk time periods and the invoice amount influence condition of the different concentrated verification risk time periods on the future time period, the matching invoice amount is calculated according to the change condition of the matching invoice in the different concentrated verification risk time periods. According to the generation processing method for determining the effective time interval of the short message link invoice of the server of the bank intranet in the current time period, the congestion condition of the server for checking and downloading the invoice is avoided.

The invention discloses a business hall invoice intelligent printing system, which comprises a multi-source data acquisition module used for acquiring equipment state data, user queue data and business attribute data of each printing terminal in a business hall in real time; the dynamic priority evaluation module is used for calculating an invoice issuing emergency degree quantized value E based on the business attribute data; the self-adaptive scheduling engine is used for generating a printing task distribution instruction according to the equipment state data, the user queue data and the emergency degree quantized value E; the predictive maintenance module is used for constructing a consumable consumption prediction model according to the consumable allowance historical data; the execution control module is used for driving the target printing terminal to execute the printing task; wherein the equipment state data comprises the busy and idle states of the printing terminal, the printing rate and the consumable allowance. According to the method, tasks can be intelligently scheduled, the efficiency is improved, the stability is guaranteed, the experience is optimized, and the cost is reduced.

The invention discloses an invoice information automatic filling method and system based on a finance and taxation big language model. The method comprises the steps of obtaining a related finance and taxation data set; pre-marking, checking and correcting the finance and taxation data set to complete pre-training and fine tuning of a finance and taxation big language model; common voice information input by a user is converted into text information through voice recognition; and on the basis of the finance and taxation large language model, according to the prompt word text of the finance and taxation large language model, combining the text information with background information and invoicing information of a user to complete invoice information automatic filling. The invoice information is automatically extracted, so that the requirement of manual input is reduced, the labor cost is reduced, and meanwhile, the extra cost caused by errors can be saved.

The invention provides a semantic anchor point definition method based on a guide word and a topological structure. The semantic anchor point definition method is used for solving the problem that key fields in structured bills with the same semantic content but large format difference are difficult to position. The method comprises the following steps: constructing a semantic field and guide word dictionary; carrying out OCR identification on the bill image; identifying a matched guide word block in the text block; establishing a spatial adjacency relationship between the guide words; and finally generating an anchor point structure triple containing the semantic field (S), the adjacent structure (N) and the semantic content extraction direction (D). According to the method, the structured representation for expressing the semantic layout of the bills can be constructed, good cross-format adaptability is achieved, stable anchor point support is provided for follow-up format matching and field content extraction, and the method is suitable for processing scenes of multi-type heterogeneous bills such as customs production place certificates, customs declarations and invoices.

The invention provides a pre-training model-based commodity name intelligent code mapping method and system, and the method comprises the following steps: S1, inputting a commodity name by a user, carrying out the commodity tax classification code matching through a caching mechanism, directly returning a matching result after the matching succeeds, and entering a step S2 if the matching succeeds; s2, matching is carried out through the model service of the pre-training model, if matching succeeds, the pre-training model and historical invoice data are subjected to weighted rearrangement through a weighted rearrangement mechanism, finally, a model reasoning result is returned to a user, and meanwhile, the model reasoning result is written into a cache mechanism; if the matching is inaccurate, entering the step S3; and S3, the user submits a result of the accuracy rate through a feedback mechanism, and after the salesman passes auditing, the data enters a service database to be used for upgrading iteration of the pre-training model next time. According to the commodity name intelligent code mapping method and system based on the pre-training model, the accuracy of commodity name tax classification code mapping is effectively improved through a specific data preprocessing method based on the pre-training model and weighted rearrangement of a cache mechanism and model reasoning, and commodity code mapping is efficiently and accurately achieved.

Commodity category values can be determined automatically for suppliers in an e-procurement system using a computer-implemented process that is supplier-focused and uses successive heuristics, supplemented with machine learning models that predict category and subcategory values based on supplier names and invoice descriptions. Embodiments can support community intelligence applications to enable buyer computers to query and obtain lists of suppliers corresponding to categories and to generate graphs or charts that aggregate historic invoice data based on canonical category values that have been determined for suppliers.

Systems and methods are described for executing tenant artificial intelligence pipelines by dynamically selecting service providers based on resource consumption. A server can poll a group of service providers and receive resource information that indicates compute, network, storage, and token requirements to perform an action. When the tenant AI pipeline executes, a pipeline engine can select a service provider to execute the action based on stored resource information. The service providers available in the group can also dynamically change based on terms of service.

The invention relates to the technical field of relation extraction, in particular to an invoice relation extraction method based on natural language analysis, and the method comprises the following steps: analyzing multiple features such as an image seal, a two-dimensional code and a handwritten annotation, matching text and image space information, checking the consistency of the two-dimensional code and a tax-control code, and recognizing the field combination and the abnormality of the amount and a payer. According to the method, detail features such as seals, two-dimensional codes and handwritten marks in invoice images are analyzed through multi-level fusion, space joint judgment of image-text content is achieved, the credibility of information verification is improved through character safety comparison and coding consistency, deep relation mining between payment behaviors and entities is enhanced by means of a context semantic dependency structure, and the payment efficiency is improved. According to the method, elements such as the amount and the payer are dynamically classified and aggregated, automatic identification of a complex transaction relationship is realized, intelligent judgment and real-time label generation of abnormal risk behaviors are further realized through linkage clustering and difference tracking, and the active risk control capability of financial management is improved.

The embodiment of the invention discloses a business receipt and invoice data matching method and system, and the method comprises the following steps: accessing original business receipt data and invoice data from a plurality of heterogeneous data sources through a uniform interface, and carrying out the data cleaning according to preset mapping, merging, splitting and conversion rules, generating a standardized matching flow order; adopting streaming data loading and a Worker parallel computing mechanism to carry out multi-dimensional matching on business receipt and invoice data in the standardized flow bill, and generating a relation matrix representing a corresponding relation between the receipt and the invoice based on a matching result; and automatically checking the relation matrix by calling a rule engine and a machine learning model, marking the matching result of the business document and the invoice which are checked to be abnormal, pushing the matching result to manual auditing, and feeding back the manual auditing result to a knowledge base to optimize the subsequent matching and checking rule. According to the embodiment of the invention, the matching efficiency of the business document and the invoice data and the accuracy of the matching result can be improved.