Business implementation method and system of tax intelligent self-service terminal

Abstract

The invention discloses a business implementation method and system of a tax intelligent self-service terminal, and relates to the technical field of tax business processing. The problems that in the prior art, deep mining and meeting of personalized requirements of users are lacked, data security guarantee is insufficient, and security threats such as network attacks and data leakage are difficult to effectively cope with are solved. According to the method, the user portrait is constructed by using the historical business record, personalized business recommendation is realized, and the tax handling efficiency and satisfaction are improved according to the risk preference adjustment strategy; the secure network channel monitors traffic in real time and deals with abnormal and hostile attacks, a data integrity verification mechanism ensures accurate data transmission, and a local backup mechanism ensures data storage security; according to the tax self-service terminal, the data is acquired and matched with the template, the rationality is verified, the accuracy and compliance of business handling are ensured, the intelligence, safety and compliance of the tax self-service terminal are improved, efficient, convenient and safe tax handling service is provided for users, and the burden of tax staff is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of tax business processing, and in particular to a business implementation method and system for a tax intelligent self-service terminal. Background Art

[0002] Tax processing is gradually shifting towards intelligent and self-service operations. Chinese patent application publication number CN105046551A discloses a method and system for implementing services on a self-service terminal. The method includes: the self-service terminal receiving a banking service processing request, sending the banking service processing instruction to a bank server, and providing the banking service processing results sent by the bank server; and the self-service terminal receiving a tax processing request, sending the tax processing request to a pre-connected tax system front-end server located on the tax system's external network, and receiving the tax processing results from the tax system front-end server.

[0003] Although the above patents can expand the use of tax services, they still have the following problems: Existing technologies lack the ability to deeply explore and meet users' personalized needs, and are unable to achieve accurate business recommendations and service customization. As a result, users have to screen complex business options on their own when handling business, which increases the difficulty and time cost of operation; and there are insufficient data security protections, making it difficult to effectively respond to security threats such as network attacks and data leaks. Summary of the Invention

[0004] The purpose of the present invention is to provide a business implementation method and system for a tax intelligent self-service terminal, realize personalized tax business recommendations by building user portraits, and adjust strategies according to risk preferences. At the same time, it uses secure network channels and data integrity verification mechanisms to ensure the security and accuracy of tax processing, optimize business processes, and improve the intelligence, security and compliance of tax self-service terminals to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The business implementation method of the tax intelligent self-service terminal includes: Conduct user identity verification based on the tax intelligent self-service terminal, obtain user instructions after passing the identity verification, and provide corresponding business guidance; Collect and process relevant business data based on the business type selected by the user, and generate documents required for tax declaration and business handling based on the corresponding data template; Perform local backup of the original data and generated files to determine the current available bandwidth of the backup node. The final available bandwidth is determined by testing the data packets and throughput fluctuation ratio. The tax intelligent self-service terminal transmits the generated tax declaration and business processing documents to the server through a secure network channel, reviews and processes the user's declared data in the documents, and feeds back to the tax intelligent self-service terminal; The secure network channel includes: Obtain traffic changes for each tax smart self-service terminal when transmitting data within a secure network channel, build a traffic model based on the traffic changes, and analyze network traffic characteristics; Once traffic anomalies are detected, the anomaly detection mechanism is immediately triggered to determine whether there is malicious attack behavior; Obtaining historical transmission success data for each tax intelligent self-service terminal, parsing the historical transmission success data to determine its integrity and security, and assessing the threat risk index and vulnerability risk index of the tax intelligent self-service terminal based on the integrity and security; Based on the working intensity of each tax intelligent self-service terminal, the security index of the tax intelligent self-service terminal is calculated using the preset risk assessment system in combination with the threat risk index and vulnerability risk index; The security index of the tax intelligent self-service terminal is compared with the preset security threshold. If it is lower than the preset security threshold, it is judged that there is a security risk in the tax intelligent self-service terminal and the business operation of the tax intelligent self-service terminal is suspended.

[0006] Furthermore, the tax smart self-service terminal performs user identity authentication, which also includes: if the user identity authentication fails more than three times, the user's operation instructions will be locked through the tax smart self-service terminal, and the camera will be activated to capture the user's facial image data in real time, and the identity authentication exception information and facial image data will be sent to the corresponding supervision system; at the same time, the user will be prompted on the tax smart self-service terminal to contact the tax staff for processing.

[0007] Furthermore, obtaining user instructions after identity verification also includes: After the user's identity verification is passed, the user's business preference model is built based on the user's historical business processing records. Based on the user's business preference model, the user is classified and labeled; At the same time, a user classification mapping table is established to associate and store the user's unique identifier with the classification category to which it belongs, and a tag database for the user is built based on the user's unique identifier, and the user's historical business records are regularly reclassified and the tags are updated; Build a user profile based on the user's tag database, and conduct in-depth mining and analysis of the information in the user profile to generate predicted tags; According to the predicted labels of user portraits and business preferences, the corresponding business recommendation strategy is matched to provide users with personalized business recommendations, and the business recommendation strategy is adjusted according to the risk preferences and actual needs of different users.

[0008] Furthermore, relevant business data collection and data processing are performed based on the business type selected by the user, specifically including: Based on the business type selected by the user, the corresponding professional data collection template is matched from the preset template library, and the contents of the data collection template are displayed to the user, with detailed filling instructions and examples; Read the business data entered by the user, check whether there are any missing mandatory items in the data collection template, and highlight the missing items based on the check results; Perform logical rationality checks on entered business data according to tax policy regulations. If any logical errors are found in the business data, a pop-up window will be displayed to inform the user of the error and possible corrections, guiding the user to make data adjustments. Import the verified business data into the corresponding professional data collection template to generate the documents required for tax declaration and business processing, and make local backups of the original data and generated files.

[0009] Furthermore, local backup of original data and generated files also includes: Preprocess the original data and generated files, classify and integrate the preprocessed data according to business type, and package them to generate backup sets; Extracting valid data blocks from the backup set, determining location information of valid data packets in the backup set and encapsulating them into data packets, and determining attribute information of the data packets; According to the storage architecture of the tax intelligent self-service terminal, backup nodes are set, where each backup node corresponds to a different storage partition within the terminal; Determine the current available bandwidth of each backup node and monitor the dynamic changes of bandwidth in real time; Matching is performed based on attribute information and available bandwidth, and an association relationship between the backup node and the corresponding data packet is established based on the matching results; Performing backup based on the association relationship through a backup node; During the backup process, each backup node is detected in real time to determine the change information of each data packet in the backup node, and the backup progress of each backup node is determined based on the change information; Determine whether all backups are completed based on the backup progress of each backup node, and generate a backup completion prompt message when all backups are completed.

[0010] Furthermore, determining the current available bandwidth of each backup node includes: Extracting the maximum theoretical bandwidth and the preset safety redundant bandwidth of the backup node; wherein the ratio of the preset safety redundant bandwidth to the maximum theoretical bandwidth ranges from 3% to 18%; Extract the used bandwidth of the backup node; Obtain the current basic available bandwidth of each backup node using the maximum theoretical bandwidth of the backup node, the preset safety redundant bandwidth, and the used bandwidth; The basic available bandwidth is obtained by the following formula: Basic available bandwidth = maximum theoretical bandwidth - safety redundancy bandwidth - used bandwidth After obtaining the basic available bandwidth, multiple test data packets with different data volumes are sent to the backup node to test the round-trip time and throughput of the data packets. After the test data packet completes the data transmission test, the maximum floating ratio of the throughput is compared with the ratio between the safety redundancy bandwidth and the maximum theoretical bandwidth, and the current available bandwidth of each backup node is determined based on the comparison result.

[0011] Furthermore, the maximum floating ratio of the throughput is compared with the ratio between the safety redundancy bandwidth and the maximum theoretical bandwidth, and the current available bandwidth of each backup node is determined based on the comparison result, including: When the comparison result indicates that the maximum floating ratio of the throughput does not exceed the ratio between the safety redundant bandwidth and the maximum theoretical bandwidth, the basic available bandwidth is used as the current available bandwidth of the backup node; When the comparison result indicates that the maximum floating ratio of the throughput exceeds the ratio between the safety redundant bandwidth and the maximum theoretical bandwidth, the data volume of multiple test data and the corresponding test data packet round-trip time and throughput are retrieved; Obtaining an available bandwidth adjustment coefficient using the data volume of the plurality of test data and the round-trip time and throughput of the corresponding test data packets; The available bandwidth adjustment coefficient is obtained by the following formula:

[0012] Where K is the available bandwidth adjustment coefficient; n is the number of test data packets; C i and C i+1 Represent the data volume of the i-th test data and the i+1-th test data respectively; T i and T i+1 Denotes the round trip time of the test data packets of the i-th test data and the i+1-th test data respectively; B i and B i+1 They represent the throughput of the i-th test data and the i+1-th test data respectively; Obtaining the current available bandwidth of the backup node using the available bandwidth adjustment coefficient combined with the basic available bandwidth; The current available bandwidth of the backup node is obtained by the following formula:

[0013] Where S represents the current available bandwidth of the backup node; S0 represents the basic available bandwidth; D represents the ratio of the preset safety redundancy bandwidth to the maximum theoretical bandwidth; and K represents the available bandwidth adjustment coefficient.

[0014] Furthermore, the secure network channel also includes introducing a data integrity verification mechanism during the data transmission process: Obtain parameter validation rules for each sub-data in the file based on the data type of the file required for tax declaration and business processing; Generate a data validation profile for each sub-data based on the parameter validation rules for the sub-data, and build a data validation component based on the data validation profile to receive the sub-data feature information and validate it according to the rules; Extract the data features of each sub-data, upload it to the corresponding data verification component through a secure network channel, and obtain the first verification result of whether the sub-data complies with the rules; Determine whether there is outlier data in each sub-data according to the first verification result; if so, determine that the parameter verification rule is unreasonable; otherwise, determine that the parameter verification rule is reasonable; When it is determined that the parameter validation rules are unreasonable, the outlier data time, business scenario distribution characteristics, numerical abnormality patterns, and associated abnormality characteristics of the outlier data in each sub-data are obtained to determine the constraint conditions of the corresponding data type of each sub-data; Determine the compatibility coefficient between the outlier data and the logical parameters corresponding to the parameter validation rule based on the constraint condition; If the compatibility coefficient is greater than or equal to a preset threshold, the numerical range in the parameter verification rule is adjusted by no more than 10% according to the numerical abnormality pattern and the associated abnormality characteristics; if the compatibility coefficient is less than the preset threshold, the numerical range in the parameter verification rule is adjusted by more than 10% according to the numerical abnormality pattern and the associated abnormality characteristics, and a modified parameter verification rule is obtained; detecting a data packet size of each sub-data, and setting a verification timestamp for the sub-data according to the data packet size; Performing data verification on each sub-data based on the verification timestamp of each sub-data and the modified parameter verification rule of the sub-data to obtain a second verification result; By comparing the logical relationship of the sub-data and checking the verification status of the required items, the data integrity of the second verification result is detected. If the data is complete, the verification is determined to be completed. If the data is incomplete, each sub-data is re-verified until the data is detected to be complete.

[0015] The present invention provides another technical solution, a business system of a tax intelligent self-service terminal, comprising: The authentication module is configured to integrate multiple authentication devices for user authentication, lock out users who fail authentication more than three times and report abnormal information. After the authentication is passed, the module obtains user business instructions through the touch screen and voice recognition and provides business guidance; The user portrait and business recommendation module is configured to collect and analyze user historical business data, build a business preference model and classify labels, build a label database, mine and analyze user portraits and generate predicted labels, and match business recommendation strategies; The data collection and processing module is configured to match the data collection template according to the business type, read the input data, verify the required items and logical rationality, generate business files from the verified data, and simultaneously back up the original data and files; A secure network transmission module is configured to monitor network traffic during data transmission on the tax intelligent self-service terminal, build traffic models, process abnormal traffic, identify and respond to malicious attacks, assess terminal security based on historical transmission data, and verify the integrity of transmitted data based on a data integrity verification mechanism; The server interaction module is configured to interact with the server through a secure network transmission module, upload business files, receive the server's review results on the declared data and feed them back to the tax intelligent self-service terminal.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By using historical business records to build user portraits, personalized business recommendations are achieved, strategies are adjusted according to risk preferences, and tax processing efficiency and satisfaction are improved; secure network channels monitor traffic in real time to respond to anomalies and malicious attacks, data integrity verification mechanisms ensure accurate data transmission, and local backup mechanisms ensure data storage security; business processes are optimized, and identity authentication, business instruction acquisition, data collection and processing, and other links are strictly standardized; data collection and matching templates, verification rationality, compliance with tax policies, and accurate and compliant business processing are guaranteed. This invention improves the intelligence, security and compliance of tax self-service terminals, provides users with efficient, convenient and safe tax processing services, reduces the burden on tax staff, and promotes the digital transformation of tax processing processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of the business implementation method of the tax intelligent self-service terminal of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] To address the technical issues that existing technologies lack the ability to deeply explore and meet users' personalized needs, and are unable to achieve accurate business recommendations and service customization, resulting in users having to screen complex business options on their own when handling business, increasing operational difficulty and time costs; and insufficient data security protection, making it difficult to effectively respond to security threats such as network attacks and data leaks, please refer to Figure 1 , this embodiment provides the following technical solutions: The business implementation method of the tax intelligent self-service terminal includes: User identity verification is performed on the tax intelligent self-service terminal, including ID card swiping, fingerprint recognition, and facial recognition. After passing the identity verification, the terminal receives user instructions, such as tax declaration, invoice collection, and tax payment certificate printing. Users can click the corresponding business option on the touch screen or use voice input to say the name of the business to be handled, and the corresponding business guidance will be provided; If a user's identity verification fails more than three times, the tax intelligent self-service terminal will lock the user's operation instructions, activate the camera to capture the user's facial image data in real time, and send the identity verification exception information including user identity information, number of verification attempts, failure time, etc. and facial image data to the corresponding supervision system. At the same time, the tax intelligent self-service terminal will prompt the user to contact tax staff for processing; Perform local backup of the original data and generated files to determine the current available bandwidth of the backup node. The final available bandwidth is determined by testing the data packets and throughput fluctuation ratio. Collect and process relevant business data based on the business type selected by the user, including basic information of the enterprise, tax period, income, deduction items, etc., and generate documents required for tax declaration and business handling based on the corresponding data template; The tax intelligent self-service terminal transmits the generated tax declaration and business processing documents to the server through a secure network channel, reviews and processes the user-declared data in the documents, and feeds back to the tax intelligent self-service terminal.

[0020] In this embodiment, through identity authentication and intelligent guidance, user experience and data security are improved, personalized services and automated data processing are realized, files are transmitted through secure channels, and real-time review feedback is performed, which effectively reduces the difficulty and time cost of user operations, while enhancing supervision efficiency, and providing an efficient, safe and convenient solution for intelligent tax construction, which not only improves the efficiency and security of tax processing, but also optimizes the user's service experience.

[0021] In this embodiment, obtaining user instructions after identity authentication is passed also includes: After the user's identity verification is passed, a business preference model for the user is built based on the user's historical business records (tax declaration, invoice application, tax registration change, tax incentive application and other business operations). Based on the user's business preference model, the user is classified and labeled into different categories, such as "high-frequency tax declaration users", "large-value invoice application users", "active users of comprehensive business processing", and "users with a preference for specific tax types". At the same time, a user classification mapping table is established to associate and store the user's unique identifier with the classification category to which it belongs. A tag database for the user is built based on the user's unique identifier. Additional tags are added in combination with other user information, such as company type and industry attributes. The user's historical business records are regularly reclassified and the tags are updated. A user profile is constructed based on the user's tag database, including information such as the user's business type, tax size, specific tax business, and taxpayer behavior (e.g., active tax filing vs. passive tax payment). This information is then deeply mined and analyzed to generate predictive labels. For example, based on the user's historical tax filing records and business development trends, a "potential tax incentive applicant" predictive label is generated, predicting that the user is likely to apply for a certain tax incentive policy in the next quarter. Based on the predicted labels of the user portrait and the business preferences, the corresponding business recommendation strategy is matched to provide users with personalized business recommendations, and the business recommendation strategy is adjusted according to the risk preferences and actual needs of different users. If the user frequently handles the VAT invoice collection business, this business option will be displayed in a prominent position, and relevant reminder information will be displayed next to it, such as the current invoice inventory status, the latest invoice management policy, etc. For users with lower risk preferences, more stable and compliant tax businesses are recommended; for users with higher risk preferences, some innovative tax planning plans can be appropriately recommended.

[0022] In this embodiment, personalized tax services are achieved by constructing a user business preference model and classifying and labeling it, which effectively improves the user experience. The analysis of the user's historical business records forms a user portrait and predicts demand, providing a basis for precise marketing and resource optimization for tax agencies. At the same time, the recommendation strategy is adjusted according to the user's risk preference, which not only meets the user's actual needs but also controls risks and enhances the robustness and compliance of the service. The real-time and transparent information prompts and policy recommendations improve the transparency of tax processing and the efficiency of policy implementation, and promote the intelligent and personalized development of tax services.

[0023] In this embodiment, relevant business data collection and data processing are performed according to the business type selected by the user, specifically including: Based on the business type selected by the user, the system matches the corresponding professional data collection template from the preset template library. If the user selects the tax return business, the system will match the tax return data collection template covering the required items such as basic enterprise information, tax period, income status, deduction items, etc.; if the user selects the invoice collection business, the system will match the invoice collection data collection template containing information such as enterprise name, taxpayer identification number, required invoice type and quantity, etc. The system will then display the various contents of the data collection template to the user, providing detailed filling instructions and examples; Read the business data entered by the user, check whether there are any missing mandatory items in the data collection template, and highlight the missing items based on the check results; Perform logical rationality checks on entered business data according to tax policy regulations. For example, in tax returns, check whether deduction items comply with the scope and proportion stipulated by tax policy, and whether the logical relationship between income and costs is reasonable. If logical errors are found in business data, a pop-up window will be displayed to inform the user of the error and possible correction methods, guiding the user to make data adjustments. Import the verified business data into the corresponding professional data collection template to generate the documents required for tax declaration and business processing, and make local backups of the original data and generated files. For example, for tax declaration business, a standard tax return form is generated, and for invoice collection business, an invoice collection application form containing company information and invoice application details is generated. In this embodiment, performing local backup of original data and generated files also includes: Pre-process the original data and generated files, classify and integrate the pre-processed data according to business type, and package them into backup sets. For example, for tax declaration business, the original declaration data and generated tax return forms within the same tax period are grouped into one backup set; for invoice collection business, corresponding backup sets are generated according to invoice collection batches; Extracting valid data blocks from the backup set, which contain key business information, such as income amounts and deduction details in tax returns, and invoice types and quantities in invoice collection, determining the location of valid data packets in the backup set and encapsulating them into data packets, and determining attribute information of the data packets, including data type (tax return data, invoice collection data, etc.), business time, and data volume; According to the storage architecture of the tax intelligent self-service terminal, backup nodes are set, where each backup node corresponds to a different storage partition within the terminal; Determine the current available bandwidth of each backup node and monitor the dynamic changes of bandwidth in real time; Matching is performed based on attribute information (such as data volume, business urgency, etc.) and available bandwidth, and an association relationship between the backup node and the corresponding data packet is established based on the matching results; Performing backup based on the association relationship through a backup node; During the backup process, each backup node is tested in real time to determine the change information of each data packet in the backup node, such as whether the data has been successfully written, whether there are any errors during the writing process, etc. The backup progress of each backup node is determined based on the change information; Determine whether all backups are completed based on the backup progress of each backup node, and generate a backup completion prompt message when all backups are completed.

[0024] In this embodiment, through refined data collection, logical verification and backup processes, the accuracy and security of tax declaration and business processing are ensured, the accuracy and efficiency of data collection are improved, and user filling errors are reduced; through logical rationality verification, the compliance of business data is guaranteed; local backup ensures the safe storage of data, which is convenient for subsequent query and audit; real-time backup progress monitoring and completion prompts enhance the reliability of the system and user satisfaction, and effectively improve the overall quality and user experience of tax self-service.

[0025] Specifically, determining the current available bandwidth of each backup node includes: Extracting the maximum theoretical bandwidth and the preset safety redundant bandwidth of the backup node; wherein the ratio of the preset safety redundant bandwidth to the maximum theoretical bandwidth ranges from 3% to 18%; Extract the used bandwidth of the backup node; Obtain the current basic available bandwidth of each backup node using the maximum theoretical bandwidth of the backup node, the preset safety redundant bandwidth, and the used bandwidth; The basic available bandwidth is obtained by the following formula: Basic available bandwidth = maximum theoretical bandwidth - safety redundancy bandwidth - used bandwidth After obtaining the basic available bandwidth, multiple test data packets with different data volumes are sent to the backup node to test the round-trip time and throughput of the data packets. After the test data packet completes the data transmission test, the maximum floating ratio of the throughput is compared with the ratio between the safety redundancy bandwidth and the maximum theoretical bandwidth, and the current available bandwidth of each backup node is determined based on the comparison result.

[0026] The technical effect of the above technical solution is that the basic available bandwidth is calculated by extracting the maximum theoretical bandwidth of the backup node, the preset safety margin bandwidth, and the used bandwidth using a formula. This formula comprehensively considers the node's theoretical transmission capacity, the bandwidth reserved for safety reasons, and the bandwidth already occupied. This makes the calculation of the basic available bandwidth more realistic and more accurately reflects the node's current bandwidth resources available for backup data transmission than calculations that only consider a single factor, laying a more accurate foundation for further determining the available bandwidth. After obtaining the basic available bandwidth, test packets of varying data volumes are sent and round-trip time and throughput are measured. The maximum fluctuation ratio of the throughput is then compared to the ratio of the safety margin bandwidth to the maximum theoretical bandwidth. This method further incorporates actual transmission test results and takes into account the impact of dynamic factors such as network latency and fluctuations on available bandwidth during network transmission. Through comparative analysis, the basic available bandwidth can be corrected and adjusted, thereby more accurately determining the current available bandwidth of each backup node and improving the accuracy of bandwidth calculation.

[0027] A preset safety margin bandwidth percentage (ranging from 3% to 18%) reserves bandwidth resources for backup nodes. This bandwidth can be used to address unexpected network traffic fluctuations and ensure the transmission of critical services. It prevents interruptions or significant delays in backup data transmission due to excessive bandwidth usage, enhancing data transmission stability and reliability, and ensuring that backup operations can be performed in a relatively stable network environment. Available bandwidth is determined based on test packet transmission and a comparison of the throughput fluctuation ratio with the safety margin bandwidth ratio. This allows the system to dynamically adjust its assessment of backup node available bandwidth based on the actual network status. When network conditions are good, bandwidth resources can be more fully utilized. When the network fluctuates or becomes unstable, the available bandwidth estimation can be adjusted promptly to avoid data transmission failures caused by overestimation of available bandwidth, thus better adapting to dynamic network changes and ensuring data transmission stability.

[0028] Resource utilization efficiency optimization accurately determines the available bandwidth of the backup node, which helps to more reasonably allocate bandwidth resources in the backup system. Backup tasks can be more evenly distributed to different nodes based on the actual available bandwidth of each backup node, avoiding situations where the bandwidth of some nodes is overly tight while the bandwidth of other nodes is idle, improving the resource utilization efficiency of the entire backup system and the overall effectiveness of the backup work. By considering the safe redundant bandwidth and combining it with actual transmission tests to determine the available bandwidth, the waste of resources caused by blindly estimating the bandwidth is avoided. There will be no idle resources due to excessive bandwidth reservation, nor will the backup strategy be frequently adjusted or hardware equipment added due to underestimation of bandwidth. This effectively saves network and hardware resources and optimizes resource utilization efficiency while ensuring the smooth progress of backup work.

[0029] Specifically, the maximum floating ratio of throughput is compared with the ratio between the safety redundancy bandwidth and the maximum theoretical bandwidth, and the current available bandwidth of each backup node is determined based on the comparison result, including: When the comparison result indicates that the maximum floating ratio of the throughput does not exceed the ratio between the safety redundant bandwidth and the maximum theoretical bandwidth, the basic available bandwidth is used as the current available bandwidth of the backup node; When the comparison result indicates that the maximum floating ratio of the throughput exceeds the ratio between the safety redundant bandwidth and the maximum theoretical bandwidth, the data volume of multiple test data and the corresponding test data packet round-trip time and throughput are retrieved; Obtaining an available bandwidth adjustment coefficient using the data volume of the plurality of test data and the round-trip time and throughput of the corresponding test data packets; The available bandwidth adjustment coefficient is obtained by the following formula:

[0030] Where K is the available bandwidth adjustment coefficient; n is the number of test data packets; C i and C i+1 Represent the data volume of the i-th test data and the i+1-th test data respectively; T i and T i+1 Denotes the round trip time of the test data packets of the i-th test data and the i+1-th test data respectively; B i and B i+1 They represent the throughput of the i-th test data and the i+1-th test data respectively; Obtaining the current available bandwidth of the backup node using the available bandwidth adjustment coefficient combined with the basic available bandwidth; The current available bandwidth of the backup node is obtained by the following formula:

[0031] Where S represents the current available bandwidth of the backup node; S0 represents the basic available bandwidth; D represents the ratio of the preset safety redundancy bandwidth to the maximum theoretical bandwidth; and K represents the available bandwidth adjustment coefficient.

[0032] The technical effect of the above technical solution is that by comprehensively considering the maximum theoretical bandwidth of the backup node, the safety redundant bandwidth, the used bandwidth and the relevant parameters of the test data packet, the current available bandwidth of the backup node can be evaluated more accurately. Not only the theoretical bandwidth situation is taken into account, but also factors such as the throughput fluctuation ratio, data volume, round-trip time in actual data transmission are combined, so that the evaluation results are closer to the available resource conditions in the actual network environment. The setting of the safety redundant bandwidth and the comparison with the maximum throughput fluctuation ratio ensure that the network operates stably within a certain redundancy range. When the throughput fluctuation is small, the basic available bandwidth is directly used to ensure that the network has sufficient safety margin to deal with emergencies; when the throughput fluctuation is large, the available bandwidth is adjusted through complex calculations to avoid network congestion or instability due to excessive bandwidth use, which is conducive to maintaining stable network transmission and reducing the risk of data loss and communication interruption.

[0033] Dynamically adjusting the available bandwidth based on actual test data can avoid resource waste or insufficiency caused by static bandwidth allocation. When the network load changes, the solution can flexibly adjust the available bandwidth of the backup node, so that network resources can be more fully utilized, improving the resource utilization efficiency of the entire network system, and optimizing resource allocation while ensuring network performance. This technical solution can adapt to different network conditions and business needs. Whether the network load is light or heavy, the appropriate available bandwidth can be determined through the corresponding mechanism. For network environments with large changes in data volume and obvious fluctuations in throughput, obtaining multiple test data for analysis can more accurately reflect the actual network situation, thereby allocating reasonable bandwidth resources to the backup node to meet the network needs in various complex business scenarios.

[0034] In this embodiment, the secure network channel includes: Obtain traffic changes for each tax smart self-service terminal when transmitting data within a secure network channel, build a traffic model based on the traffic changes, and analyze network traffic characteristics such as rate, packet size distribution, and connection duration; Once traffic anomalies are detected, such as a large number of data requests in a short period of time, abnormal packet size or frequency, the anomaly detection mechanism is immediately triggered to determine whether there is a malicious attack. If an attack is detected, countermeasures such as restricting access to specific IP addresses and adjusting network bandwidth allocation strategies are taken; Obtaining historical transmission success data for each tax intelligent self-service terminal, parsing the historical transmission success data to determine its integrity and security, and assessing the threat risk index and vulnerability risk index of the tax intelligent self-service terminal based on the integrity and security; Based on the working intensity of each tax intelligent self-service terminal, the security index of the tax intelligent self-service terminal is calculated using the preset risk assessment system in combination with the threat risk index and vulnerability risk index; The security index of the tax intelligent self-service terminal is compared with the preset security threshold. If it is lower than the preset security threshold, it is judged that there is a security risk in the tax intelligent self-service terminal and the business operation of the tax intelligent self-service terminal is suspended.

[0035] In this embodiment, by establishing a secure network channel, real-time monitoring and risk assessment of data transmission of tax intelligent self-service terminals are achieved. By analyzing traffic models, detecting abnormal traffic, evaluating the integrity and security of historical data, and calculating the security index of the terminal, potential network attacks and data leakage risks are effectively identified and responded to, the security of data transmission is enhanced, and malicious attacks are timely discovered and prevented; through the risk assessment system, the security status of the terminal is quantified to facilitate the adoption of preventive measures; by comparing with the preset security threshold, it is ensured that business operations can be suspended in time when security risks exist, thereby protecting user data and system security.

[0036] In this embodiment, the secure network channel also includes introducing a data integrity verification mechanism during the data transmission process: Based on the data type of the documents required for tax declaration and business processing, obtain the parameter validation rules for each sub-data in the file (such as income and deduction items in tax declarations, type and quantity of invoices collected, etc.), including format, value range, business logic and other requirements; Generate a data validation configuration file for each sub-data based on the parameter validation rules for that sub-data, including a structured configuration file of the rule name, applicable data type, and validation conditions. Build a data validation component based on the data validation configuration file to receive sub-data feature information and validate it according to the rules. Extract the data features of each sub-data, upload it to the corresponding data verification component through a secure network channel, and obtain the first verification result of whether the sub-data complies with the rules; Based on the first validation results, determine whether there are outliers in each sub-data set, such as abnormally low tax return income or invoice collection volume far exceeding normal. If so, determine that the parameter validation rule is unreasonable; otherwise, determine that the parameter validation rule is reasonable. When it is determined that the parameter validation rules are unreasonable, the outlier data time, business scenario distribution characteristics, numerical abnormality patterns, and associated abnormality characteristics of the outlier data in each sub-data are obtained to determine the constraint conditions of the corresponding data type of each sub-data; Determine the compatibility coefficient between the outlier data and the logical parameters corresponding to the parameter validation rule based on the constraint condition; If the compatibility coefficient is greater than or equal to the preset threshold, the numerical range in the parameter validation rule is adjusted by no more than 10% based on the numerical abnormality pattern and the associated abnormality characteristics, the numerical range is fine-tuned, and the format details are optimized. If the compatibility coefficient is less than the preset threshold, the numerical range in the parameter validation rule is adjusted by more than 10% based on the numerical abnormality pattern and the associated abnormality characteristics, the data type determination standard is redefined, a new business logic rule is constructed, and the modified parameter validation rule is obtained; detecting a data packet size of each sub-data, and setting a verification timestamp for the sub-data according to the data packet size; Performing data verification on each sub-data based on the verification timestamp of each sub-data and the modified parameter verification rule of the sub-data to obtain a second verification result; By comparing the logical relationship of the sub-data and checking the verification status of the required items, the data integrity of the second verification result is detected. If the data is complete, the verification is determined to be completed. If the data is incomplete, each sub-data is re-verified until the data is detected to be complete.

[0037] In this embodiment, by introducing a data integrity verification mechanism, the data processing security and accuracy of the tax intelligent self-service terminal are significantly improved; the automated verification process ensures the correctness of the data format and logic, outlier data analysis promptly identifies anomalies, and dynamic rule adjustment improves the adaptability of the verification rules; the application of timestamps enhances the security of data transmission, closed-loop verification ensures data integrity, optimizes user experience, reduces operational difficulty, and improves the efficiency and reliability of business processing.

[0038] In order to better demonstrate the business implementation method of the tax intelligent self-service terminal, the present invention provides a business system of the tax intelligent self-service terminal, including: The authentication module is configured to integrate multiple authentication devices for user authentication, lock out users who fail authentication more than three times and report abnormal information. After the authentication is passed, the module obtains user business instructions through the touch screen and voice recognition and provides business guidance; The user portrait and business recommendation module is configured to collect and analyze user historical business data, build a business preference model and classify labels, build a label database, mine and analyze user portraits and generate predicted labels, and match business recommendation strategies; The data collection and processing module is configured to match the data collection template according to the business type, read the input data, verify the required items and logical rationality, generate business files from the verified data, and simultaneously back up the original data and files; A secure network transmission module is configured to monitor network traffic during data transmission on the tax intelligent self-service terminal, build traffic models, process abnormal traffic, identify and respond to malicious attacks, assess terminal security based on historical transmission data, and verify the integrity of transmitted data based on a data integrity verification mechanism; The server interaction module is configured to interact with the server through a secure network transmission module, upload business files, receive the server's review results on the declared data and feed them back to the tax intelligent self-service terminal.

[0039] In this embodiment, the identity authentication module effectively prevents unauthorized access, takes locking measures for continuous failed authentication, and ensures the security of user accounts; the secure network transmission module enhances the security of data transmission by monitoring network traffic and handling abnormal situations; the user portrait and business recommendation module provides users with customized business recommendations by analyzing user historical data, thereby improving service accuracy and user satisfaction; the data acquisition and processing module automatically completes data entry and verification, reduces human errors, and improves the speed and accuracy of data processing; the server interaction module realizes the rapid uploading of business files and timely feedback of audit results, thereby improving the efficiency of business handling.

[0040] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. The business implementation method of the tax intelligent self-service terminal is characterized by: include: Conduct user identity verification based on the tax intelligent self-service terminal, obtain user instructions after passing the identity verification, and provide corresponding business guidance; Collect and process relevant business data based on the business type selected by the user, and generate documents required for tax declaration and business handling based on the corresponding data template; Perform local backup of the original data and generated files to determine the current available bandwidth of the backup node. The final available bandwidth is determined by testing the data packets and throughput fluctuation ratio. The tax intelligent self-service terminal transmits the generated tax declaration and business processing documents to the server through a secure network channel, reviews and processes the user's declared data in the documents, and feeds back to the tax intelligent self-service terminal; The secure network channel includes: Obtain traffic changes for each tax smart self-service terminal when transmitting data within a secure network channel, build a traffic model based on the traffic changes, and analyze network traffic characteristics; Once traffic anomalies are detected, the anomaly detection mechanism is immediately triggered to determine whether there is malicious attack behavior; Obtaining historical transmission success data for each tax intelligent self-service terminal, parsing the historical transmission success data to determine its integrity and security, and assessing the threat risk index and vulnerability risk index of the tax intelligent self-service terminal based on the integrity and security; Based on the working intensity of each tax intelligent self-service terminal, the security index of the tax intelligent self-service terminal is calculated using the preset risk assessment system in combination with the threat risk index and vulnerability risk index; The security index of the tax intelligent self-service terminal is compared with the preset security threshold. If it is lower than the preset security threshold, it is judged that there is a security risk in the tax intelligent self-service terminal and the business operation of the tax intelligent self-service terminal is suspended.

2. The method for implementing the business of the tax intelligent self-service terminal according to claim 1, characterized in that: The tax intelligent self-service terminal performs user identity authentication, and also includes: if the user identity authentication operation fails more than three times, the tax intelligent self-service terminal will lock the user's operation instructions, activate the camera to capture the user's facial image data in real time, and send the identity authentication exception information and facial image data to the corresponding supervision system; at the same time, the tax intelligent self-service terminal will prompt the user to contact the tax staff for processing.

3. The method for implementing the business of the tax intelligent self-service terminal according to claim 2, characterized in that: After authentication is passed, user instructions are obtained, including: After the user's identity verification is passed, the user's business preference model is built based on the user's historical business processing records. Based on the user's business preference model, the user is classified and labeled; At the same time, a user classification mapping table is established to associate and store the user's unique identifier with the classification category to which it belongs, and a tag database for the user is built based on the user's unique identifier, and the user's historical business records are regularly reclassified and the tags are updated; Build a user profile based on the user's tag database, and conduct in-depth mining and analysis of the information in the user profile to generate predicted tags; According to the predicted labels of user portraits and business preferences, the corresponding business recommendation strategy is matched to provide users with personalized business recommendations, and the business recommendation strategy is adjusted according to the risk preferences and actual needs of different users.

4. The method for implementing the business of the tax intelligent self-service terminal according to claim 1, characterized in that: Collect and process relevant business data based on the business type selected by the user, specifically including: Based on the business type selected by the user, the corresponding professional data collection template is matched from the preset template library, and the contents of the data collection template are displayed to the user, with detailed filling instructions and examples; Read the business data entered by the user, check whether there are any missing mandatory items in the data collection template, and highlight the missing items based on the check results; Perform logical rationality checks on entered business data according to tax policy regulations. If any logical errors are found in the business data, a pop-up window will be displayed to inform the user of the error and possible corrections, guiding the user to make data adjustments. Import the verified business data into the corresponding professional data collection template to generate the documents required for tax declaration and business processing, and make local backups of the original data and generated files.

5. The method for implementing the business of the tax intelligent self-service terminal according to claim 4, characterized in that: Local backup of original data and generated files, including: Preprocess the original data and generated files, classify and integrate the preprocessed data according to business type, and package them to generate backup sets; Extracting valid data blocks from the backup set, determining location information of valid data packets in the backup set and encapsulating them into data packets, and determining attribute information of the data packets; According to the storage architecture of the tax intelligent self-service terminal, backup nodes are set, where each backup node corresponds to a different storage partition within the terminal; Determine the current available bandwidth of each backup node and monitor the dynamic changes of bandwidth in real time; Matching is performed based on attribute information and available bandwidth, and an association relationship between the backup node and the corresponding data packet is established based on the matching results; Performing backup based on the association relationship through a backup node; During the backup process, each backup node is detected in real time to determine the change information of each data packet in the backup node, and the backup progress of each backup node is determined based on the change information; Determine whether all backups are completed based on the backup progress of each backup node, and generate a backup completion prompt message when all backups are completed.

6. The method for implementing the business of the tax intelligent self-service terminal according to claim 5, characterized in that: Determining the current available bandwidth of each backup node includes: Extracting the maximum theoretical bandwidth and the preset safety redundant bandwidth of the backup node; wherein the ratio of the preset safety redundant bandwidth to the maximum theoretical bandwidth ranges from 3% to 18%; Extract the used bandwidth of the backup node; Obtain the current basic available bandwidth of each backup node using the maximum theoretical bandwidth of the backup node, the preset safety redundant bandwidth, and the used bandwidth; The basic available bandwidth is obtained by the following formula: Basic available bandwidth = maximum theoretical bandwidth - safety redundancy bandwidth - used bandwidth After obtaining the basic available bandwidth, multiple test data packets with different data volumes are sent to the backup node to test the round-trip time and throughput of the data packets. After the test data packet completes the data transmission test, the maximum floating ratio of the throughput is compared with the ratio between the safety redundancy bandwidth and the maximum theoretical bandwidth, and the current available bandwidth of each backup node is determined based on the comparison result.

7. The method for implementing the business of the tax intelligent self-service terminal according to claim 6, characterized in that: Compare the maximum floating ratio of throughput with the ratio of safety redundancy bandwidth to the maximum theoretical bandwidth, and determine the current available bandwidth of each backup node based on the comparison result, including: When the comparison result indicates that the maximum floating ratio of the throughput does not exceed the ratio between the safety redundant bandwidth and the maximum theoretical bandwidth, the basic available bandwidth is used as the current available bandwidth of the backup node; When the comparison result indicates that the maximum floating ratio of the throughput exceeds the ratio between the safety redundant bandwidth and the maximum theoretical bandwidth, the data volume of multiple test data and the corresponding test data packet round-trip time and throughput are retrieved; Obtaining an available bandwidth adjustment coefficient using the data volume of the plurality of test data and the round-trip time and throughput of the corresponding test data packets; The available bandwidth adjustment coefficient is combined with the basic available bandwidth to obtain the current available bandwidth of the backup node.

8. The method for implementing the business of the tax intelligent self-service terminal according to claim 1, characterized in that: The secure network channel also includes introducing a data integrity verification mechanism during the data transmission process: Obtain parameter validation rules for each sub-data in the file based on the data type of the file required for tax declaration and business processing; Generate a data validation profile for each sub-data based on the parameter validation rules for the sub-data, and build a data validation component based on the data validation profile to receive the sub-data feature information and validate it according to the rules; Extract the data features of each sub-data, upload it to the corresponding data verification component through a secure network channel, and obtain the first verification result of whether the sub-data complies with the rules; Determine whether there is outlier data in each sub-data according to the first verification result; if so, determine that the parameter verification rule is unreasonable; otherwise, determine that the parameter verification rule is reasonable; When it is determined that the parameter validation rules are unreasonable, the outlier data time, business scenario distribution characteristics, numerical abnormality patterns, and associated abnormality characteristics of the outlier data in each sub-data are obtained to determine the constraint conditions of the corresponding data type of each sub-data; Determine the compatibility coefficient between the outlier data and the logical parameters corresponding to the parameter validation rule based on the constraint condition; If the compatibility coefficient is greater than or equal to a preset threshold, the numerical range in the parameter verification rule is adjusted by no more than 10% according to the numerical abnormality pattern and the associated abnormality characteristics; if the compatibility coefficient is less than the preset threshold, the numerical range in the parameter verification rule is adjusted by more than 10% according to the numerical abnormality pattern and the associated abnormality characteristics, and a modified parameter verification rule is obtained; detecting a data packet size of each sub-data, and setting a verification timestamp for the sub-data according to the data packet size; Performing data verification on each sub-data based on the verification timestamp of each sub-data and the modified parameter verification rule of the sub-data to obtain a second verification result; By comparing the logical relationship of the sub-data and checking the verification status of the required items, the data integrity of the second verification result is detected. If the data is complete, the verification is determined to be completed. If the data is incomplete, each sub-data is re-verified until the data is detected to be complete.

9. The business system of the tax intelligent self-service terminal is applied in the business implementation method of the tax intelligent self-service terminal according to any one of claims 1 to 8, characterized in that: include: The authentication module is configured to integrate multiple authentication devices for user authentication, lock out users who fail authentication more than three times and report abnormal information. After the authentication is passed, the module obtains user business instructions through the touch screen and voice recognition and provides business guidance; The user portrait and business recommendation module is configured to collect and analyze user historical business data, build a business preference model and classify labels, build a label database, mine and analyze user portraits and generate predicted labels, and match business recommendation strategies; The data collection and processing module is configured to match the data collection template according to the business type, read the input data, verify the required items and logical rationality, generate business files from the verified data, and simultaneously back up the original data and files; A secure network transmission module is configured to monitor network traffic during data transmission on the tax intelligent self-service terminal, build traffic models, process abnormal traffic, identify and respond to malicious attacks, assess terminal security based on historical transmission data, and verify the integrity of transmitted data based on a data integrity verification mechanism; The server interaction module is configured to interact with the server through a secure network transmission module, upload business files, receive the server's review results on the declared data and feed them back to the tax intelligent self-service terminal.

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