Building industry finance and tax compliance digital evidence chain integrated platform

Abstract

The invention provides a building industry finance and tax compliance digital evidence chain integrated platform which is deployed based on a cloud computing architecture and comprises a distributed server cluster, a network communication module and a database storage system. By constructing the integrated digital platform, fundamental efficiency improvement and risk management and control reinforcement of finance and taxation management in the building industry are realized, the platform enables a data acquisition process to be highly automatic, manual repeated entry work is greatly eliminated, financial personnel are liberated from tedious account checking, and the financial personnel are greatly improved. The compliance check module based on a rule engine and artificial intelligence can perform real-time and comprehensive automatic analysis on all transaction data, significantly improves the identification accuracy and timeliness of potential compliance risks, realizes instant alarm, converts passive risk response into active risk response, and improves the risk handling efficiency. Compared with a traditional manual mode, the data processing speed is improved in order of magnitude, the monthly checkout and report generation time is greatly shortened, the enterprise decision cycle is remarkably accelerated, and the overall operation efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of construction management, and more particularly to an integrated digital evidence chain platform for financial and tax compliance in the construction industry. Background Technology

[0002] In the construction industry, traditional financial and tax management models heavily rely on manual operations and paper documents, facing severe compliance challenges and efficiency bottlenecks. Firstly, business data is fragmented and isolated, with information silos formed between project management systems, financial software, tax invoicing platforms, and on-site documents. Data requires repetitive manual entry and verification, which is not only inefficient but also prone to errors. Secondly, financial and tax compliance audits primarily rely on the personal experience of financial personnel. Faced with massive transactions and frequently updated complex tax policies (such as specific regulations on value-added tax and corporate income tax), manual review is insufficient for comprehensive coverage, leading to a high risk of omissions and misjudgments, making companies vulnerable to tax audits. The risks and hefty fines are significant. Furthermore, building a complete and credible chain of audit evidence is extremely difficult. Paper vouchers are easily damaged or lost, and electronic data is easily tampered with and it is difficult to prove its originality and integrity. In the event of a dispute or audit, companies are often in a passive position because they cannot provide effective evidence. Some existing financial software or tax management systems have limited functions and can only solve local problems. They are not suitable for integrated solutions that deeply integrate with the business scenarios of the construction industry. They cannot achieve automatic association and credible evidence storage for the entire process from business occurrence, invoice circulation, financial processing to tax declaration. Data traceability is costly and its authenticity is difficult to guarantee.

[0003] Therefore, in order to address the above issues, we are now developing an integrated digital evidence chain platform for financial and tax compliance in the construction industry. Summary of the Invention

[0004] In order to overcome the shortcomings of existing technologies in practical application, this invention provides an integrated digital evidence chain platform for financial and tax compliance in the construction industry.

[0005] The technical solution of this invention is: an integrated digital evidence chain platform for financial and tax compliance in the construction industry. The platform is deployed based on a cloud computing architecture and includes a distributed server cluster, a network communication module, and a database storage system. The platform further includes: The data acquisition module is used to collect financial and tax-related data of the construction industry in real time from multiple heterogeneous data sources, including enterprise resource planning systems, electronic invoice platforms, tax declaration systems, engineering project management software, IoT sensor devices, and manual input interfaces. The data acquisition module supports multiple data protocols, including HTTP / HTTPS, FTP, MQTT, and API interfaces, and has data encryption transmission function, using TLS / SSL protocol to ensure data transmission security. The data processing module is used to clean, deduplicate, standardize the format, and perform semantic parsing on the collected raw data. The cleaning process includes outlier detection, missing value imputation, and logical consistency verification. Format standardization converts the data into a unified format. Semantic parsing uses natural language processing technology to extract key fields, such as invoice amount, tax rate, project number, and supplier information. The compliance inspection module is used to automatically analyze the processed data based on a pre-set financial and tax regulations knowledge base. The knowledge base stores national and local financial and tax regulations, policy documents and historical cases of the construction industry, and integrates a rule engine and a machine learning model. The rule engine executes based on compliance rule matching, and the machine learning model is trained on historical compliance data using deep learning algorithms to identify potential violation patterns, such as false invoices, inflated costs or abnormal tax deductions. The evidence chain generation module is used to generate an immutable digital evidence chain. This module calculates the hash value of the data, adds timestamps and digital signatures, and stores the evidence information in the blockchain network to ensure the integrity, traceability and non-repudiation of the data. The blockchain adopts a consortium blockchain structure, and the participating nodes include construction companies, tax authorities and third-party auditing institutions. The user interface module provides a graphical user interface that supports role-based access control, including roles such as administrator, finance personnel, and auditor. Interface functions include data visualization dashboards, compliance report generation, evidence retrieval, and alarm notifications.

[0006] As a preferred embodiment of the present invention, the data acquisition module further includes: a multi-source adapter submodule, used to dynamically adapt to different data source formats. The adapter automatically parses data based on template configuration, supporting structured data such as database tables and unstructured data such as scanned documents. The document processing integrates optical character recognition technology to convert paper invoices and contracts into digital text; a real-time streaming data processing submodule, using a streaming processing framework to achieve high-throughput data and setting up a data buffer to cope with peak traffic; a data verification submodule, used to perform preliminary verification during the acquisition phase, including format checking, range verification, and business logic verification, such as verifying the legality of invoice codes and the existence of project numbers; the data acquisition module also includes a data lineage tracing function, recording data sources, acquisition time, and operation logs to ensure data traceability transparency, and reducing network load through an incremental acquisition strategy, only synchronously changing data.

[0007] As a preferred embodiment of the present invention, the data processing module further includes: a data cleaning engine, which uses statistical methods and clustering algorithms to identify abnormal data and performs manual review in conjunction with business rules. During the cleaning, deduplication, format standardization, and semantic parsing processes, the data cleaning engine synchronously records and associates the cleaning history and data source information of each data item. The data source information includes the original system identifier, data collection timestamp, and operation log to ensure the traceability of the cleaning process; a data conversion unit, used to map heterogeneous data to a standard data model, which defines unified fields such as project ID, cost category, and tax identifier. The conversion process includes data type conversion, unit unification, and encoding mapping; a data enrichment submodule, which supplements data by calling external APIs, such as obtaining real-time tax rate information from the tax system or querying supplier credit scores from a credit database; and a data quality monitoring submodule, which calculates data quality indicators in real time, such as completeness, accuracy, and consistency scores, and triggers an automatic repair process.

[0008] As a preferred embodiment of the present invention, the compliance inspection module further includes: a rule base management submodule, used for dynamically updating financial and tax regulations and rules, supporting a graphical rule editing interface, and allowing users to customize rule priorities and effective times; a rule execution engine, based on Drools or a similar engine, to implement parallel rule evaluation, generate compliance scores and detailed reports, including descriptions of violations, risk levels, and recommended measures; wherein the rule base management submodule has a built-in special rule set for verifying the integration of the four flows, namely, the matching and cross-validation of business data of invoice flow, fund flow, contract flow, and goods flow, and the rule execution engine compares different business data... Key identifiers in the workflow, such as invoice numbers, contract numbers, project numbers, and time logic, are automatically detected to identify inconsistencies, breakpoints, or logical conflicts between flows, and a unified compliance analysis report is generated. The machine learning analysis submodule uses supervised learning models, such as random forests or gradient boosting trees, trained on historical audit data to predict high-risk transactions. The model is periodically retrained to adapt to policy changes. The scenario simulation unit is used to build virtual compliance scenarios to test the compliance impact under different policies. The real-time alert submodule sends immediate alerts via SMS, email, or mobile push when serious violations are detected, and integrates with the workflow engine to automatically allocate processing tasks.

[0009] As a preferred embodiment of the present invention, the evidence chain generation module further includes: a hash calculation unit, which generates data hashes using SHA-256 or a similar algorithm to ensure uniqueness and tamper-proofness; a timestamp service submodule, which adds trusted timestamps by calling an authoritative timestamp agency API; a digital signature submodule, which signs evidence using enterprise digital certificates based on public key infrastructure technology; a blockchain storage interface, used to package evidence data into blocks and automatically execute storage logic through smart contracts, wherein the smart contracts are encoded with evidence verification rules, such as hash comparison and signature verification; an evidence chain visualization submodule, which generates a graphical evidence chain diagram to display the data flow path and key event points; and an evidence query engine, which supports fast retrieval based on keywords, time range, or blockchain transaction IDs.

[0010] As a preferred embodiment of the present invention, the user interface module further includes: a role-customized interface, providing a system configuration panel for administrators, data entry and report viewing functions for financial personnel, and comprehensive audit trajectory query for auditors; a data dashboard module, using ECharts or a similar library to implement interactive charts, displaying compliance rates, risk trends, and project financial and tax overviews; a report generator, supporting custom report templates and outputting detailed compliance reports in PDF or Excel format; a mobile adaptation submodule, based on responsive design, providing mobile APP access and supporting offline data collection and synchronization; collaboration tool integration, such as integrating instant messaging software to facilitate team discussions on compliance issues; and a user experience optimization unit, continuously improving the interface design through A / B testing and integrating a voice assistant to support voice command operations.

[0011] As a preferred embodiment of the present invention, the blockchain network adopts a permissioned consortium blockchain, with nodes composed of construction companies, tax authorities, banks, and certification bodies. It uses a practical Byzantine fault-tolerant consensus algorithm to ensure high throughput and low latency. The smart contract code includes automatic compliance check logic, such as automatically triggering tax declarations or evidence archiving. A cross-chain interoperability submodule allows interaction with other blockchain systems, such as supply chain finance chains, to achieve data sharing. A blockchain explorer is integrated to provide a publicly accessible evidence browsing interface.

[0012] As a preferred embodiment of the present invention, the core artificial intelligence further includes: a predictive analysis submodule, which uses a time series analysis model to predict the financial and tax risks of construction projects, such as cash flow risks or the impact of policy changes; a natural language processing engine, which parses regulatory texts, automatically extracts rules, and updates the knowledge base; an anomaly detection algorithm, which identifies abnormal transaction patterns based on isolated forests or autoencoders; an optimization recommendation submodule, which provides financial and tax planning suggestions to enterprises through reinforcement learning; and an AI model management unit, which supports model version control, A / B testing, and automated deployment.

[0013] As a preferred embodiment of the present invention, the platform further includes a report generation module for automatically generating comprehensive financial and tax compliance reports. The report content includes a data summary, compliance analysis, evidence chain summary, and recommended measures. The report template is customizable and supports multilingual output. The report generation module integrates an automatic evidence verification unit for assessing the authenticity, legality, and relevance of key evidence cited in the report: authenticity assessment is based on blockchain hash verification and digital signature verification results; legality assessment is based on the authority of the data source, such as the matching results of rules between direct data from the tax system and the compliance inspection module; relevance assessment is based on data lineage tracing technology, analyzing the logical correlation strength between evidence and specific business scenarios, projects, and financial conclusions. The assessment results are presented in the report in the form of a visual score. A report distribution submodule automatically sends the report to relevant parties via email or message queues. A report archiving unit stores the report in a distributed file system and links it to the evidence chain for auditing purposes.

[0014] As a preferred embodiment of the present invention, the platform further includes a security protection module, which relates to data encryption, access control, and intrusion detection; data encryption uses the AES-256 algorithm to encrypt static and transmitted data; access control is based on a role-based permission model to achieve fine-grained permission management, such as attribute-based access control; the intrusion detection system uses machine learning to monitor security events in real time; a security audit submodule records all operation logs and generates security reports periodically; and a disaster recovery mechanism ensures business continuity through data backup and off-site disaster recovery.

[0015] By adopting the above technical solution, the present invention has the following advantages: 1. This invention, by constructing an integrated digital platform, fundamentally improves the efficiency and strengthens risk control in the financial and tax management of the construction industry. The platform highly automates the data collection process, greatly eliminating repetitive manual data entry and freeing financial personnel from tedious reconciliation. Based on a rule engine and artificial intelligence-based compliance inspection module, it can perform real-time and comprehensive automated analysis of all transaction data, significantly improving the accuracy and timeliness of identifying potential compliance risks and enabling immediate alerts. This transforms risk response from passive to proactive. The data processing speed is orders of magnitude faster than traditional manual methods, significantly shortening monthly closing and report generation time, significantly accelerating the enterprise decision-making cycle, and improving overall operational efficiency.

[0016] 2. Based on blockchain and digital signature technology, this invention ensures that every key financial and tax data is fixed and legally valid from its inception. Any tampering will be detected immediately, providing enterprises with strong and credible evidence to deal with audits and disputes. Auditors can quickly and conveniently trace the complete business context and process documents behind any financial result through the platform, greatly reducing the time and complexity of audit data preparation, significantly shortening the audit cycle, comprehensively enhancing the transparency and credibility of corporate finance and taxation, and greatly reducing compliance risks and reputational losses caused by insufficient or distorted evidence. This builds a solid foundation of credit for enterprises in the digital age. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the first structure of the present invention.

[0018] Figure 2 This is a schematic diagram of the second structure of the present invention. Detailed Implementation

[0019] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0020] A digital evidence chain platform integrating financial and tax compliance in the construction industry, such as Figure 1 As shown, the platform is deployed based on a cloud computing architecture, including a distributed server cluster, a network communication module, and a database storage system. The platform further includes, for example... Figure 2 As shown: The data acquisition module is used to collect financial and tax-related data from multiple heterogeneous data sources in real time. These data sources include enterprise resource planning systems, electronic invoice platforms, tax declaration systems, engineering project management software, IoT sensor devices, and manual input interfaces. The data acquisition module supports multiple data protocols, including HTTP / HTTPS, FTP, MQTT, and API interfaces, and has data encryption transmission capabilities, using TLS / SSL protocols to ensure data transmission security. The data processing module is used to clean, deduplicate, standardize the format, and perform semantic parsing on the collected raw data. The cleaning process includes outlier detection, missing value imputation, and logical consistency verification. Format standardization converts the data into a unified format. Semantic parsing uses natural language processing technology to extract key fields, such as invoice amount, tax rate, project number, and supplier information. The compliance inspection module is used to automatically analyze the processed data based on a pre-set financial and tax regulations knowledge base. The knowledge base stores national and local financial and tax regulations, policy documents and historical cases of the construction industry, and integrates a rule engine and a machine learning model. The rule engine executes based on compliance rule matching, and the machine learning model is trained on historical compliance data using deep learning algorithms to identify potential violation patterns, such as false invoices, inflated costs or abnormal tax deductions. The evidence chain generation module is used to generate an immutable digital evidence chain. This module calculates the hash value of the data, adds timestamps and digital signatures, and stores the evidence information in the blockchain network to ensure the integrity, traceability and non-repudiation of the data. The blockchain adopts a consortium blockchain structure, and the participating nodes include construction companies, tax authorities and third-party auditing institutions. The user interface module provides a graphical user interface that supports role-based access control, including roles such as administrator, finance personnel, and auditor. Interface functions include data visualization dashboards, compliance report generation, evidence retrieval, and alarm notifications.

[0021] Specifically, the data acquisition module further includes: a multi-source adapter submodule, used to dynamically adapt to different data source formats. The adapter automatically parses data based on template configuration, supporting structured data such as database tables and unstructured data such as scanned documents. The document processing integrates optical character recognition technology to convert paper invoices and contracts into digital text; a real-time streaming data processing submodule, using a streaming processing framework to achieve high-throughput data and setting up a data buffer to handle peak traffic; a data verification submodule, used to perform preliminary verification during the acquisition phase, including format checking, range verification, and business logic verification, such as verifying the legality of invoice codes and the existence of project numbers; the data acquisition module also includes a data lineage tracking function, recording data sources, acquisition time, and operation logs to ensure data traceability transparency and reduce network load through an incremental acquisition strategy, only synchronizing changed data; The data processing module further includes: a data cleaning engine, which uses statistical methods and clustering algorithms to identify abnormal data and performs manual review in conjunction with business rules; a data transformation unit, which maps heterogeneous data to a standard data model, which defines unified fields such as project ID, cost category, and tax identifier, and the transformation process includes data type conversion, unit unification, and code mapping; a data enrichment submodule, which supplements data by calling external APIs, such as obtaining real-time tax rate information from the tax system or querying supplier credit scores from a credit database; and a data quality monitoring submodule, which calculates data quality indicators in real time, such as completeness, accuracy, and consistency scores, and triggers automatic repair processes. The compliance inspection module further includes: a rule base management submodule, used to dynamically update financial and tax regulations and rules, supporting a graphical rule editing interface, allowing users to customize rule priorities and effective times; a rule execution engine, based on Drools or a similar engine, to achieve parallel rule evaluation, generate compliance scores and detailed reports, including descriptions of violations, risk levels, and recommended measures; a machine learning analysis submodule, using supervised learning models, such as random forests or gradient boosting trees, trained on historical audit data to predict high-risk transactions, with the model periodically retrained to adapt to policy changes; a scenario simulation unit, used to build virtual compliance scenarios to test the compliance impact under different policies; and a real-time alert submodule, which issues immediate alerts via SMS, email, or mobile push when serious violations are detected, and integrates a workflow engine to automatically allocate processing tasks. The evidence chain generation module further includes: a hash calculation unit that uses SHA-256 or similar algorithms to generate data hashes, ensuring uniqueness and tamper-proofing; a timestamp service submodule that adds trusted timestamps by calling an authoritative timestamp agency's API; a digital signature submodule that uses enterprise digital certificates to sign evidence based on public key infrastructure technology; a blockchain storage interface that packages evidence data into blocks and automatically executes storage logic through smart contracts, which encode evidence verification rules such as hash comparison and signature verification; an evidence chain visualization submodule that generates a graphical evidence chain diagram, displaying the data flow path and key event points; and an evidence query engine that supports fast retrieval based on keywords, time ranges, or blockchain transaction IDs. The user interface module further includes: a role-customization interface, providing a system configuration panel for administrators, data entry and report viewing functions for finance personnel, and comprehensive audit trajectory queries for auditors; a data dashboard module, using ECharts or similar libraries to implement interactive charts, displaying compliance rates, risk trends, and project financial and tax overviews; a report generator, supporting custom report templates and outputting detailed compliance reports in PDF or Excel format; a mobile adaptation submodule, based on responsive design, providing mobile app access and supporting offline data collection and synchronization; collaboration tool integration, such as integrating instant messaging software to facilitate team discussions on compliance issues; and a user experience optimization unit, continuously improving the interface design through A / B testing and integrating a voice assistant to support voice command operations.

[0022] The blockchain network adopts a permissioned consortium blockchain, with nodes composed of construction companies, tax authorities, banks, and certification bodies. It uses a practical Byzantine fault-tolerant consensus algorithm to ensure high throughput and low latency. The smart contract code includes automatic compliance check logic, such as automatically triggering tax declarations or evidence archiving. A cross-chain interoperability submodule allows interaction with other blockchain systems, such as supply chain finance chains, to achieve data sharing. A blockchain explorer is integrated to provide a publicly verifiable evidence browsing interface.

[0023] The core of artificial intelligence further includes: a predictive analytics submodule, which uses time series analysis models to predict financial and tax risks of construction projects, such as cash flow risks or the impact of policy changes; a natural language processing engine, which parses regulatory texts, automatically extracts rules, and updates the knowledge base; an anomaly detection algorithm, which identifies abnormal transaction patterns based on isolated forests or autoencoders; an optimization recommendation submodule, which provides financial and tax planning suggestions for enterprises through reinforcement learning; and an AI model management unit, which supports model version control, A / B testing, and automated deployment.

[0024] The platform also includes a report generation module for automatically generating comprehensive financial and tax compliance reports. These reports cover data summaries, compliance analysis, evidence chain summaries, and recommended actions. Report templates are customizable and support multilingual output. A report distribution submodule automatically sends reports to relevant parties via email or message queues. A report archiving unit stores reports in a distributed file system and links them to the evidence chain for auditing purposes. The platform also includes a security protection module covering data encryption, access control, and intrusion detection. Data encryption uses the AES-256 algorithm to encrypt both static and transmitted data. Access control is based on a role-based permission model, enabling fine-grained permission management, such as attribute-based access control. The intrusion detection system uses machine learning to monitor security events in real time. A security audit submodule records all operation logs and generates security reports periodically. A disaster recovery mechanism ensures business continuity through data backup and off-site disaster recovery.

[0025] It should be noted that, through its built-in multi-source adapter submodule, it proactively establishes secure connections with various heterogeneous data sources. For enterprise internal ERP systems and project management software, the adapter uses predefined API interfaces to periodically or in real-time retrieve structured data such as project contract information, cost details, subcontractor payment applications, and material purchase orders by polling or subscribing to events. For external data sources, such as the VAT invoice management platform of the tax bureau, the platform uses the standard interface provided by the tax department to automatically download the full data of input and output electronic invoices related to the enterprise, and ensures encryption of the transmission process through the HTTPS protocol. For unstructured data generated in the physical world, such as scanned copies of paper invoices provided by suppliers, on-site signed contract attachments, and images or PDF files of project acceptance forms, the platform immediately triggers the integrated optical character recognition service after uploading through the manual input interface. This service converts the text information in the image into text data that can be processed by a computer and automatically extracts key fields such as invoice code, amount, and invoice date. IoT sensor data is transmitted in real time through lightweight IoT protocols such as MQTT, which may be related to cost collection and amortization, such as concrete pouring temperature and equipment operating hours. All collection operations are recorded in the data lineage tracing subsystem, forming the initial data traceability log. The collected raw data stream first enters an Apache-based... Kafka's high-throughput real-time streaming data processing submodule acts as a buffer and distribution center, capable of handling data surges that may occur during specific phases of a construction project (such as monthly settlements and year-end final accounts), ensuring system stability. Before flowing into the core processing flow, data undergoes preliminary screening by the data verification submodule, performing format checks (such as whether invoice numbers conform to rules), range checks (such as whether amounts are non-negative), and simple business logic verifications (such as checking whether the same invoice has been uploaded repeatedly). This intercepts obviously invalid or duplicate data at the system edge, reducing the pressure on the backend processing. The collected and preliminarily verified data then enters the data processing module, the core of which is the data cleaning engine. This engine uses statistical methods (such as calculating Z-scores) and clustering algorithms. Methods (such as K-means) are used to detect outliers in numerical data (such as material unit prices and labor hours), identifying abnormal records that significantly deviate from historical averages or project norms. These outliers are marked for subsequent manual review or automatic correction according to preset rules. For missing data, the engine adopts different strategies based on field importance. Missing critical fields (such as invoice amounts) trigger alarms and pause the process until data is added. Non-critical fields may be handled using mean filling, regression prediction, or direct ignore. Next, the data transformation unit maps the cleaned, differently formatted raw data onto a predefined standard data model within the platform. This standard model defines entities and attributes commonly used in the construction industry's financial and tax fields, such as projects, cost centers, accounting subjects, and invoice line items.Each entity has standardized field names, data types, and constraints. For example, project numbers from different project management software may have different formats. The conversion unit will map them uniformly to fields in the standard model and ensure they conform to coding standards. For issues with inconsistent units (such as material usage in different units like "tons" or "cubic meters"), the conversion unit will refer to the standard unit library for conversion. At this stage, the data enrichment submodule will proactively call the API of external trusted data sources to inject more valuable information into the existing data. For example, based on the supplier's name and taxpayer identification number, it will query the enterprise credit information platform for the supplier's credit rating and operating status, or query the tax system for the latest applicable tax rate policy, ensuring that subsequent calculations are based on the latest regulations. The entire processing is tracked in real time by the data quality monitoring submodule, which calculates and displays data such as data integrity on the dashboard. Indicators such as integrity (whether there are missing data), accuracy (comparison with authoritative sources), and consistency (whether logical relationships are correct) can trigger an automatic repair workflow if these indicators fall below a threshold. To efficiently process the massive amounts of historical and real-time streaming data in the construction industry, this module employs Spark-based in-memory computing technology, significantly accelerating batch data processing and supporting a hybrid mode of batch and real-time streaming processing to adapt to the data processing needs at different stages of the project lifecycle. High-quality data that has been cleaned and standardized is then sent to the compliance inspection module. The rule base management submodule maintains a dynamically updated knowledge base of financial and tax regulations. This knowledge base not only stores the text of current legal provisions but, more importantly, transforms them into business rules that computers can understand and execute. These rules typically exist in the form of "if-then," such as if (invoice type == VAT invoice) and (amount > ...). (10,000 yuan) Then (must have a real goods or service transaction background) and (must be verified through the invoice verification platform). The rule base supports graphical editing, allowing corporate tax experts to easily add, modify, or deactivate rules according to local policies or corporate internal control requirements, and set the priority and effective time of the rules. The rule execution engine is responsible for loading these rules and performing parallel evaluation on each incoming tax data fact. For example, when processing a material purchase invoice, the engine will simultaneously trigger dozens or even hundreds of rules related to VAT deduction, cost confirmation, supplier qualifications, project budget overrun checks, etc., generating a detailed compliance report, pointing out the approved items, warning items, and violations, and explaining the reasons, risk level (high, medium, low), and suggested corrective measures for each violation. At the same time, the machine learning analysis submodule uses classification models (such as random forest, gradient boosting tree GBDT) trained on historically accumulated compliance audit data (containing a large number of labeled normal and non-compliant transactions) to automatically learn complex violation patterns hidden deep in the data. These models can identify more hidden potential risks that may bypass simple rule checks, for example,By analyzing supplier relationships, transaction frequency, and amount distribution, the model identifies abnormal transaction patterns suspected of bid rigging, inflated costs, or money laundering. It calculates a risk probability score for each transaction, which is corroborated by the rule engine's results to form a more comprehensive risk assessment. The scenario simulation unit allows users to test the compliance impact of new policies or specific business scenarios in a sandbox environment, aiding decision-making. Once the rule engine or machine learning model detects high risk or serious violations (such as fake invoices or over-budget expenditures), the real-time alarm submodule immediately activates, sending alarm information to relevant financial managers or project managers through preset channels (SMS, email, in-system pop-ups, DingTalk / WeChat push notifications). It also integrates with the workflow engine to automatically create audit task work orders, assigning them to designated personnel for processing, thus forming a closed-loop management system. This module supports a multi-tenant architecture, ensuring that the compliance data and rule settings of different construction companies are isolated from each other, protecting data privacy. After compliance checks are passed or data has been processed, its key information and hash value will be sent to the evidence chain generation module. The workflow begins with the hash calculation unit, which uses a cryptographically secure hash algorithm (such as SHA-256) to calculate a unique, fixed-length digital fingerprint (hash value) for each piece of important financial and tax data (such as an invoice and its associated contracts and acceptance forms). Any minor modification to the original data will cause a significant change in the hash value, thus ensuring data integrity. Subsequently, the timestamp service submodule, by calling the interface of the National Time Service Center or a globally trusted timestamp service provider, adds a legally valid timestamp accurate to milliseconds to this hash value, proving that the data existed at a specific point in time and has not been tampered with. Next, the digital signature submodule uses the enterprise's digital certificate (private key portion) obtained from an authoritative certificate authority to digitally sign the combination of the data hash value and timestamp, generating a unique signature value. This process is equivalent to affixing an unforgeable electronic seal to the enterprise-approved data, achieving non-repudiation. At this point, the electronic evidence package for a single piece of data (containing the original data, its hash value, timestamp, and digital signature) is ready. The evidence chain generation module then uses its blockchain... The storage interface packages these evidence packets according to business logic (e.g., by project, by month), and through smart contract calls, submits the hash value of the evidence packet (rather than the original sensitive data itself, to protect privacy) along with timestamps, signature information, and relevant business indexes (e.g., project ID, invoice number) to a pre-deployed permissioned consortium blockchain network in the form of a transaction. This blockchain network is jointly maintained by key participants such as construction companies, tax authorities, auditing firms, and banks, and adopts a practical Byzantine fault-tolerant consensus algorithm to ensure that the network can still reach consensus on the ledger state even in the event of partial node failure or malicious behavior. The written data is immutable and irrevocable. The smart contract encodes the logic for evidence verification, allowing anyone (with the appropriate permissions) to recalculate the original data hash value and compare it with the hash value stored on the chain.To verify the authenticity and completeness of evidence, the evidence chain visualization submodule extracts information from the blockchain and underlying database to generate an intuitive graphical evidence chain. This clearly displays all key evidence nodes and their relationships throughout the entire process of a financial and tax transaction, from contract signing, procurement, acceptance to payment, invoicing, and accounting, forming a complete and interconnected evidence loop. The evidence query engine supports rapid retrieval and verification of on-chain and off-chain evidence based on various conditions (such as transaction hashes, time ranges, and business keywords). Furthermore, advanced features such as zero-knowledge proofs allow one party to prove to the auditor that an expenditure complies with budget regulations without disclosing specific transaction details (such as sensitive amounts), further balancing audit requirements and business privacy. The final user interface module serves as the window for interaction between the platform and users, presenting all the complex processing results in an intuitive and easy-to-use format. It provides a customized interface based on role-based permissions: enterprise administrators can view the overall system health status, user management, etc. The platform offers a global view including rule base configuration; finance personnel use a daily work interface for data entry, compliance status checks, pending alerts, and report generation and export; auditors have access to powerful audit trail tools, enabling them to trace the complete and tamper-proof digital evidence chain behind any transaction. The data dashboard module uses visualization libraries such as ECharts and D3.js to transform key indicators like compliance rate, risk distribution, and project cost structure into dynamic charts, supporting drill-down analysis. The report generator automatically generates detailed financial and tax compliance reports that meet internal and external audit requirements based on preset templates or custom needs, and can export them to PDF or Excel format with one click. A mobile adaptation module ensures users can handle emergency alerts, upload documents by photo, or manage approval processes anytime, anywhere via a smartphone app, supporting offline operation and automatic synchronization after network recovery. The platform also integrates instant messaging tools for collaborative discussions on compliance issues within the team. The entire platform is driven by artificial intelligence, with predictive analytics, natural language processing (for parsing regulatory texts), and intelligent optimization recommendations permeating every stage from data cleaning to compliance analysis. Leveraging federated learning technology, it aggregates intelligence to improve model accuracy while protecting the data privacy of each enterprise. A security module is integrated throughout, from data transmission encryption and static data encryption to role-based access control and intrusion detection, ensuring the system's own security.

[0026] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the spirit of the present invention. Therefore, the scope of protection of the present invention is not limited to the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. An integrated digital evidence chain platform for financial and tax compliance in the construction industry, characterized in that, The platform is deployed based on a cloud computing architecture and includes a distributed server cluster, a network communication module, and a database storage system, wherein the platform further includes: The data acquisition module is used to collect financial and tax-related data of the construction industry in real time from multiple heterogeneous data sources, including enterprise resource planning systems, electronic invoice platforms, tax declaration systems, engineering project management software, IoT sensor devices, and manual input interfaces. The data acquisition module supports multiple data protocols, including HTTP / HTTPS, FTP, MQTT, and API interfaces, and has data encryption transmission function, using TLS / SSL protocol to ensure data transmission security. The data processing module is used to clean, deduplicate, standardize the format, and perform semantic parsing on the collected raw data. The cleaning process includes outlier detection, missing value imputation, and logical consistency verification. Format standardization converts the data into a unified format. Semantic parsing uses natural language processing technology to extract key fields, such as invoice amount, tax rate, project number, and supplier information. The compliance inspection module is used to automatically analyze the processed data based on a pre-set financial and tax regulations knowledge base. The knowledge base stores national and local financial and tax regulations, policy documents and historical cases of the construction industry, and integrates a rule engine and a machine learning model. The rule engine executes based on compliance rule matching, and the machine learning model is trained on historical compliance data using deep learning algorithms to identify potential violation patterns, such as false invoices, inflated costs or abnormal tax deductions. The evidence chain generation module is used to generate an immutable digital evidence chain. This module calculates the hash value of the data, adds timestamps and digital signatures, and stores the evidence information in the blockchain network to ensure the integrity, traceability and non-repudiation of the data. The blockchain adopts a consortium blockchain structure, and the participating nodes include construction companies, tax authorities and third-party auditing institutions. The user interface module provides a graphical user interface that supports role-based access control, including roles such as administrator, finance personnel, and auditor. Interface functions include data visualization dashboards, compliance report generation, evidence retrieval, and alarm notifications.

2. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The data acquisition module further includes: a multi-source adapter submodule, used to dynamically adapt to different data source formats. The adapter automatically parses data based on template configuration, supporting structured data such as database tables and unstructured data such as scanned documents. The document processing integrates optical character recognition technology to convert paper invoices and contracts into digital text; a real-time streaming data processing submodule, using a streaming processing framework to achieve high-throughput data and setting up a data buffer to handle peak traffic; a data verification submodule, used to perform preliminary verification during the acquisition phase, including format checking, range verification, and business logic verification, such as verifying the legality of invoice codes and the existence of project numbers; the data acquisition module also includes a data lineage tracing function, recording data sources, acquisition time, and operation logs to ensure data traceability transparency, and reducing network load through an incremental acquisition strategy, only synchronizing changed data.

3. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The data processing module further includes: a data cleaning engine, which uses statistical methods and clustering algorithms to identify abnormal data and performs manual review in conjunction with business rules. During the cleaning, deduplication, format standardization, and semantic parsing processes, the data cleaning engine simultaneously records and associates the cleaning history and data source information for each data item. The data source information includes the original system identifier, data collection timestamp, and operation logs, ensuring the traceability of the cleaning process. A data transformation unit is used to map heterogeneous data to a standard data model. This model defines unified fields, such as project ID, cost category, and tax identifier. The transformation process includes data type conversion, unit unification, and encoding mapping. A data enrichment submodule supplements data by calling external APIs, such as obtaining real-time tax rate information from the tax system or querying supplier credit scores from a credit database. A data quality monitoring submodule calculates data quality indicators in real time, such as completeness, accuracy, and consistency scores, and triggers an automatic repair process.

4. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The compliance inspection module further includes: a rule base management submodule, used to dynamically update financial and tax regulations and rules, supporting a graphical rule editing interface, allowing users to customize rule priorities and effective times; and a rule execution engine, based on Drools or a similar engine, to implement parallel rule evaluation, generate compliance scores and detailed reports, including descriptions of violations, risk levels, and recommended measures. The rule base management submodule has a built-in special rule set for verifying the integration of the four flows (invoice flow, fund flow, contract flow, and goods flow), whereby the four flows are matched and cross-validated. The rule execution engine compares key data in different business flows... Identifiers, such as invoice numbers, contract numbers, project numbers, and time logic, automatically detect inconsistencies, breakpoints, or logical conflicts between data flows and generate a unified compliance analysis report. The machine learning analysis submodule uses supervised learning models, such as random forests or gradient boosting trees, trained on historical audit data to predict high-risk transactions. The model is periodically retrained to adapt to policy changes. The scenario simulation unit is used to build virtual compliance scenarios to test the compliance impact under different policies. The real-time alert submodule sends immediate alerts via SMS, email, or mobile push when serious violations are detected and integrates with the workflow engine to automatically allocate processing tasks.

5. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The evidence chain generation module further includes: a hash calculation unit that generates data hashes using SHA-256 or similar algorithms to ensure uniqueness and tamper-proofness; a timestamp service submodule that adds trusted timestamps by calling an authoritative timestamp agency API; a digital signature submodule that signs evidence using enterprise digital certificates based on public key infrastructure technology; a blockchain storage interface that packages evidence data into blocks and automatically executes storage logic through smart contracts, with the smart contracts encoding evidence verification rules such as hash comparison and signature verification; an evidence chain visualization submodule that generates a graphical evidence chain diagram to display the data flow path and key event points; and an evidence query engine that supports fast retrieval based on keywords, time ranges, or blockchain transaction IDs.

6. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The user interface module further includes: a role-customization interface, providing a system configuration panel for administrators, data entry and report viewing functions for finance personnel, and comprehensive audit trajectory queries for auditors; a data dashboard module, using ECharts or similar libraries to implement interactive charts, displaying compliance rates, risk trends, and project financial and tax overviews; a report generator, supporting custom report templates and outputting detailed compliance reports in PDF or Excel format; a mobile adaptation submodule, based on responsive design, providing mobile APP access and supporting offline data collection and synchronization; collaboration tool integration, such as integrating instant messaging software to facilitate team discussions on compliance issues; and a user experience optimization unit, continuously improving the interface design through A / B testing and integrating a voice assistant to support voice command operations.

7. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The blockchain network adopts a permissioned consortium blockchain, with nodes consisting of construction companies, tax authorities, banks, and certification bodies. It uses a practical Byzantine fault-tolerant consensus algorithm to ensure high throughput and low latency. The smart contract code has automatic compliance check logic, such as automatically triggering tax declarations or evidence archiving. The cross-chain interoperability submodule allows interaction with other blockchain systems, such as supply chain finance chains, to achieve data sharing; the blockchain explorer integration provides a publicly verifiable evidence browsing interface.

8. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The core of artificial intelligence further includes: a predictive analytics submodule, which uses time series analysis models to predict financial and tax risks of construction projects, such as cash flow risks or the impact of policy changes; a natural language processing engine, which parses regulatory texts, automatically extracts rules, and updates the knowledge base; an anomaly detection algorithm, which identifies abnormal transaction patterns based on isolated forests or autoencoders; an optimization recommendation submodule, which provides financial and tax planning suggestions for enterprises through reinforcement learning; and an AI model management unit, which supports model version control, A / B testing, and automated deployment.

9. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The platform also includes a report generation module for automatically generating comprehensive financial and tax compliance reports. The report content includes data summaries, compliance analysis, evidence chain summaries, and recommended measures. Report templates are customizable and support multilingual output. The report generation module integrates an automatic evidence verification unit to assess the authenticity, legality, and relevance of key evidence cited in the report: authenticity assessment is based on blockchain hash verification and digital signature verification results; legality assessment is based on the authority of the data source, such as the matching results of data directly connected to the tax system and the rules of the compliance inspection module; relevance assessment is based on data lineage tracing technology to analyze the logical correlation strength between evidence and specific business scenarios, projects, and financial conclusions. The assessment results are presented in the report in a visual scoring format. The report distribution submodule automatically sends reports to relevant parties via email or message queue; The report archiving unit stores reports in a distributed file system and links them to a chain of evidence for auditing purposes.

10. The integrated digital evidence chain platform for financial and tax compliance in the construction industry according to claim 1, characterized in that, The platform also includes a security protection module, which involves data encryption, access control, and intrusion detection; data encryption uses the AES-256 algorithm to encrypt static and transmitted data; access control is based on a role-based permission model to achieve fine-grained permission management, such as attribute-based access control; the intrusion detection system uses machine learning to monitor security events in real time. The security audit submodule records all operation logs and generates security reports periodically; Disaster recovery mechanisms ensure business continuity through data backup and off-site disaster recovery.

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