Medical expense multi-mode payment integration method and system

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a medical expense multi-mode payment integration method and system, and the method comprises the steps: analyzing an analysis result of a multi-mode payment request, the analysis result comprises a payment mode identifier, user identity information and expense details, and the payment mode identifier is a payment mode identifier; creating a payment channel set of the multi-modal payment request based on the payment mode identifier, screening out a target payment channel in the payment channel set based on a real-time load parameter corresponding to the payment channel set, and performing joint verification on the user identity information and the expense details, and packaging the analysis result into a security data packet according to the verification result of the joint verification, inputting the security data packet into the target payment channel, and submitting the input result to a medical terminal, thereby solving the problem of how to improve the security and efficiency of the multi-modal payment integrated system.

Description

Technical Field

[0001] The present invention relates to the technical field of medical payment management, and in particular to a multi-modal payment integration method and system for medical expenses. Background Art

[0002] Existing technologies have difficulty efficiently processing diverse payment information when processing multimodal payment requests for medical expenses. In the creation and screening of payment channels, existing methods lack multi-dimensional attribute analysis of payment method identifiers, cannot trace the historical clearing flow of the main institution code based on the institution clearing topology map, and cannot perform spatial coverage verification of geographical response areas and core node links, making the creation of payment channel sets unreasonable. The screening of real-time load parameters also lacks a dynamic adjustment mechanism, making it difficult to ensure the stability and efficiency of payment channels. In addition, existing technologies cannot fully verify the validity of user identity credentials, nor can they accurately match rules for expense details, resulting in low security and reliability of payment authorization decisions, which seriously affects the security and efficiency of medical expense payments. Summary of the Invention

[0003] The present invention provides a method and system for integrating multimodal payment of medical expenses, the main purpose of which is to solve the problem of how to improve the security and efficiency of the multimodal payment integration system.

[0004] To achieve the above objectives, the present invention provides a multi-modal payment integration method for medical expenses, the method comprising: S1: Parsing the multimodal payment request to obtain a parsing result, wherein the parsing result includes: a payment method identifier, user identity information, and fee details; S2: Creating a payment channel set for the multimodal payment request based on the payment method identifier; S3: Filtering out a target payment channel from the payment channel set based on the real-time load parameters corresponding to the payment channel set; S4: performing joint verification on the user identity information and the expense details, and encapsulating the parsing result into a secure data packet according to the verification result of the joint verification; S5: Input the secure data packet into the target payment channel, and submit the input result to the medical terminal.

[0005] Optionally, the parsing the multimodal payment request to obtain a parsing result includes: Segmenting the data set of the multimodal payment request into semantic units to obtain an initial semantic unit set; Performing predefined label mapping on the initial semantic unit set to obtain a labeled semantic unit set; Compliance verification is performed on the set of labeled semantic units to obtain a parsing result of the multimodal payment request.

[0006] Optionally, creating the payment channel set for the multimodal payment request based on the payment method identifier includes: The payment method identifier is subjected to multi-dimensional payment attribute analysis to obtain three layers of payment attributes: Based on the preset institutional clearing topology, the historical clearing flow of the main institution code in the three-layer payment attributes is traced to obtain the core node link; Performing spatial coverage verification on the geographical response areas of the three-layer payment attributes and the core node links to obtain a subset of channels with matching locations; Reorganize the link architecture of the channel subset based on the cross-institution settlement type in the three-layer payment attributes to obtain a dynamic channel sequence inserted with a proxy node; Verify the stability index of the dynamic channel sequence to obtain a payment channel set that meets the survivability threshold.

[0007] Optionally, screening out a target payment channel from the payment channel set based on the real-time load parameter corresponding to the payment channel set includes: Mark the load health status of the payment channel set to obtain a list of healthy channels; Performing node risk filtering on the health status channel list to obtain a candidate channel subset; Dynamically correcting the weights of the candidate channel subset based on a preset time-sensitive factor to obtain a weighted sorted channel sequence; Verify the heartbeat response delay of the weighted sorted channel sequence to obtain a target payment channel candidate set that passes core verification; Uniquely select the target payment channel candidate set to obtain the target payment channel.

[0008] Optionally, verifying the heartbeat response delay of the weighted sorted channel sequence to obtain a target payment channel candidate set that passes core verification includes: Sending a heartbeat detection packet to the weighted sorted channel sequence to obtain a channel detection result with a response delay; Verify the channel detection result for delay compliance to obtain a target payment channel candidate set that meets the response requirements.

[0009] Optionally, the joint verification of the user identity information and the fee details includes: Verifying the validity of the credentials of the user identity information to obtain an identity verification result set; Matching the expense details with medical billing rules to obtain expense compliance determination results; Performing a joint authority audit on the identity verification result set and the fee compliance determination result to obtain a payment authorization decision; A risk control blacklist screening operation is performed on the payment authorization decision to obtain a verification result of the joint verification.

[0010] Optionally, the performing a risk control blacklist screening operation on the payment authorization decision to obtain a verification result of the joint verification includes: Matching the payment authorization decision with high-risk behavior characteristics to obtain a preliminary risk tagging result; The payment authorization decision is subjected to real-time blacklist screening based on the preliminary risk marking result to obtain a verification result of the joint verification.

[0011] Optionally, encapsulating the parsing result into a secure data packet according to the verification result of the joint verification includes: Extracting sensitive data from the verification result to obtain a payment field set that can be securely transmitted; The payment field set is encrypted to obtain an encrypted security data packet.

[0012] Optionally, inputting the secure data packet into the target payment channel and submitting the input result to the medical terminal includes: Transmitting the secure data packet through the target payment channel, receiving the transmission processing result of the transmission in real time, and saving the transmission processing result as an original response file; Converting the original response file into a standard format required by the medical terminal; The standard format is sent to the callback interface of the medical terminal to complete the final submission.

[0013] A multimodal payment integrated system for medical expenses, comprising: A data processing module, configured to parse the multimodal payment request to obtain a parsing result, wherein the parsing result includes: a payment method identifier, user identity information, and fee details; a channel generation module, configured to create a payment channel set for the multimodal payment request based on the payment method identifier; a channel screening module, configured to screen out a target payment channel from the payment channel set based on the real-time load parameters corresponding to the payment channel set; an encapsulation module, configured to perform joint verification on the user identity information and the expense details, and encapsulate the parsing result into a secure data packet according to the verification result of the joint verification; The submission module is used to input the security data packet into the target payment channel and submit the input result of the input to the medical terminal.

[0014] Beneficial effects 1. Multi-dimensional payment attribute analysis is performed based on the payment method identifier. The historical clearing flow is traced in combination with the institutional clearing topology map. The spatial coverage verification of the geographical response area and core node links is completed, thereby creating a reasonable set of payment channels. At the same time, the payment channels are screened according to real-time load parameters. Through operations such as marking the load health status, node risk filtering, dynamic weight correction, and heartbeat response delay verification, the selected target payment channels are ensured to be stable and efficient.

[0015] 2. Joint verification of user identity information and expense details, including verification of voucher validity, expense compliance determination, joint authority audit, and risk control blacklist screening, ensures the security and reliability of payment authorization decisions. The parsing results are then encapsulated into a secure data package and submitted to the medical terminal as required, further improving the security and standardization of the entire payment process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flowchart of a multi-modal payment integration method for medical expenses provided by one embodiment of the present invention; Figure 2 This is a functional module diagram of a multimodal payment integration system for medical expenses provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] The embodiment of the present application provides a method for integrating multimodal payment of medical expenses. The execution subject of the method for integrating multimodal payment of medical expenses includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for integrating multimodal payment of medical expenses can be executed by software or hardware installed on a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0019] Reference Figure 1FIG. 1 is a flow chart of a method for integrating multimodal payment of medical expenses according to an embodiment of the present invention. In this embodiment, the method for integrating multimodal payment of medical expenses includes: S1: Parsing the multimodal payment request to obtain a parsing result, wherein the parsing result includes: a payment method identifier, user identity information, and fee details.

[0020] In this embodiment, the parsing result of the multimodal payment request includes: Segmenting the data set of the multimodal payment request into semantic units to obtain an initial semantic unit set; Performing predefined label mapping on the initial semantic unit set to obtain a labeled semantic unit set; Compliance verification is performed on the set of labeled semantic units to obtain a parsing result of the multimodal payment request.

[0021] Specifically, semantic unit segmentation is performed on the dataset of multimodal payment requests, which is to divide the content in the dataset according to semantic meaning to obtain an initial semantic unit set composed of various semantic units, each of which is the smallest unit with independent semantics.

[0022] Specifically, the text area in the image is scanned, such as using OCR technology to locate form columns, converting handwritten / printed content into standard text, and parsing the QR code to obtain embedded payment link parameters.

[0023] The user's audio instructions are converted into structured text through the speech recognition engine, such as using XX wallet to pay the inspection fee of 200 yuan.

[0024] Link different modal data of the same request based on timestamps or session IDs, such as ensuring that a user's voice description and an uploaded medical document image belong to the same transaction.

[0025] Furthermore, semantic unit segmentation splits the string units according to the delimiter, for example: medical insurance card, Zhang San, CT scan 1=580 yuan is divided into [medical insurance card, Zhang San, CT scan 1=580 yuan].

[0026] Tag matching is to mark cells containing payment tool keywords, such as WeChat, credit card, and medical insurance, as payment method identifiers, cells matching the ID card number / mobile phone number format or name keywords as user identity information, and cells containing currency symbols or amount numbers as expense details.

[0027] Specifically, a predefined label mapping is performed on the initial semantic unit set, and a corresponding label is assigned to each initial semantic unit according to the pre-set labeling rules, thereby obtaining a labeled semantic unit set, so that each semantic unit has a clear label to identify its meaning and attributes.

[0028] Specifically, compliance verification is performed on the set of tagged semantic units. According to relevant specifications and standards, each tagged semantic unit is checked to see whether it meets the specified requirements. After verification, the parsing result of the multimodal payment request is obtained, which contains key information such as payment method identification, user identity information and fee details.

[0029] Specifically, the system receives original payment requests in various forms including text, such as a payment application form, images, such as a handwritten payment slip photo / QR code, and audio, such as a voice payment instruction.

[0030] S2: Creating a payment channel set for the multimodal payment request based on the payment method identifier.

[0031] In this embodiment, creating the payment channel set for the multimodal payment request based on the payment method identifier includes: The payment method identifier is subjected to multi-dimensional payment attribute analysis to obtain three layers of payment attributes: Based on the preset institutional clearing topology, the historical clearing flow of the main institution code in the three-layer payment attributes is traced to obtain the core node link; Performing spatial coverage verification on the geographical response areas of the three-layer payment attributes and the core node links to obtain a subset of channels with matching locations; Reorganize the link architecture of the channel subset based on the cross-institution settlement type in the three-layer payment attributes to obtain a dynamic channel sequence inserted with a proxy node; Verify the stability index of the dynamic channel sequence to obtain a payment channel set that meets the survivability threshold.

[0032] Specifically, multi-dimensional payment attribute analysis is carried out on the payment method identifier, information is extracted and sorted from the payment method identifier, three layers of payment attributes are analyzed, and the specific content of each layer of attributes is clarified.

[0033] Furthermore, the payment method identifier is decomposed into payment instrument attributes to obtain the three attributes contained in the identifier: the principal institution code, the geographical response area, and the cross-institution settlement type. For example, if the identifier is a provincial medical insurance card, the decomposition results are: principal institution = provincial medical insurance center, response area = designated hospitals in the province, and settlement type = cross-hospital tiered settlement.

[0034] Specifically, based on the preset institutional clearing topology, the past clearing flows of the main institution codes in the three-layer payment attributes are searched along the topological relationship, and the node paths that funds and other funds pass through during the clearing process are sorted out to form a core node link.

[0035] Specifically, the step of obtaining the institutional clearing topology is to identify various institutions related to medical expense payment, including but not limited to medical insurance centers, cooperative banks, medical institutions, etc. These institutions are the basic nodes that constitute the clearing topology.

[0036] Furthermore, we analyze the business transactions and capital flows of various institutions during the medical expense settlement process, for example, the settlement links between the medical insurance center and its partner banks, and the settlement relationships between medical institutions and the medical insurance center.

[0037] Furthermore, the clearing chain hierarchy is divided according to the business relationships between institutions. The relationship between the main institution and its subsidiaries, as well as the connection between institutions at different levels, are clarified.

[0038] Furthermore, detailed data on each institution’s historical liquidation process is obtained, including liquidation time, amount, participating institutions, etc. This data is an important basis for constructing the topology map.

[0039] Furthermore, using the collected historical clearing data and the identified business relationships, a graphical tool or professional topology modeling software is used to construct an initial model of the institutional clearing topology. In this model, nodes represent institutions, and edges represent clearing links between institutions.

[0040] Furthermore, the constructed topology map is compared with the actual clearing business process to check whether the topology map accurately reflects the clearing relationships and capital flows between institutions. If there are any deviations, the topology map is adjusted and corrected in a timely manner.

[0041] Furthermore, since institutional business may change, such as adding new cooperative banks, institutional liquidation policy adjustments, etc., it is necessary to regularly update and maintain the institutional liquidation topology map to ensure that it can always accurately reflect the actual liquidation situation. Furthermore, the historical clearing flow is traced back to the institution's clearing topology to identify the current institution's core nodes and subsidiary links in the clearing network. For example, it was discovered that provincial medical insurance centers must transit through the core nodes of the national medical insurance platform and have subsidiary clearing links to three partner banks.

[0042] Specifically, the geographical response area corresponding to the three-layer payment attributes is checked for spatial coverage with the core node links obtained previously to see the coverage matching degree between the geographical area and the link nodes, and to filter out the channel subset with matching locations.

[0043] Furthermore, a spatial coverage verification operation is performed on the physical location information of the medical terminal and the geographical response area to obtain a subset of location matching channels that effectively cover the terminal. At the same time, a time constraint is injected: the 7×24 hour clearing channel is activated only when the terminal is in the emergency service period.

[0044] Specifically, according to the cross-institutional settlement type in the three-layer payment attributes, the link architecture of the channel subset is reorganized and adjusted, and proxy nodes are added to construct a new dynamic channel sequence.

[0045] Furthermore, dynamic channel chain reorganization operations are performed on the multi-level institutions involved in the cross-institutional settlement type: when the settlement type is cross-institutional hierarchical settlement, the hierarchical review agent channel is automatically inserted into the existing link to form a new channel sequence consisting of bank clearing → hierarchical review agency → medical insurance center settlement.

[0046] Specifically, the stability-related indicators of the dynamic channel sequence are verified to determine whether they meet the survivability threshold requirements. After verification, the set of payment channels that meet the conditions is determined.

[0047] S3: Filtering out a target payment channel from the payment channel set based on the real-time load parameter corresponding to the payment channel set.

[0048] In this embodiment, the step of selecting a target payment channel from the payment channel set based on the real-time load parameter corresponding to the payment channel set includes: Mark the load health status of the payment channel set to obtain a list of healthy channels; Performing node risk filtering on the health status channel list to obtain a candidate channel subset; Dynamically correcting the weights of the candidate channel subset based on a preset time-sensitive factor to obtain a weighted sorted channel sequence; Verify the heartbeat response delay of the weighted sorted channel sequence to obtain a target payment channel candidate set that passes core verification; Uniquely select the target payment channel candidate set to obtain the target payment channel.

[0049] Specifically, multi-dimensional payment attribute analysis is performed based on the payment method identification, and the historical settlement flow is traced in combination with the institutional settlement topology map. The spatial coverage verification of the geographical response area and the core node links is completed, thereby creating a reasonable set of payment channels. At the same time, the payment channels are screened according to the real-time load parameters, and operations such as marking the load health status, node risk filtering, dynamic weight correction and heartbeat response delay verification are carried out to ensure that the selected target payment channels are stable and efficient.

[0050] Specifically, the load of each channel in the payment channel set is evaluated, and its health status is determined based on the load data. After marking these health statuses, a list of healthy channels is compiled to clearly present the health status of each channel.

[0051] Furthermore, based on preset load health thresholds and real-time load parameters such as latency and throughput change rate, a static threshold comparison operation is performed on each channel, marking it as healthy, where the load is within a safe range and the change rate is low, or warning, where the load exceeds the limit or changes dramatically.

[0052] At the same time, channel type weights are integrated. For example, the credit card network is preset as a high-priority payment channel type. This operation takes into account the instantaneous fluctuations of load parameters and matches inherent performance, generating a vector containing health / alert status and weight factors as the basic screening layer, rather than directly using numerical sorting.

[0053] Specifically, for the channels in the healthy channel list, we analyze the possible risks of each channel node, such as node failure, network fluctuation and other risk factors, filter out the channels with high-risk nodes, and the remaining channels form a candidate channel subset.

[0054] Specifically, based on the preset time-sensitive factor, that is, considering the factors that affect the channel weight at different times, the weights of the channels in the candidate channel subset are adjusted, and the channels are sorted according to the adjusted weights to obtain a weighted sorted channel sequence.

[0055] Furthermore, time-sensitive factors such as whether the medical terminal is currently in emergency mode are preset, and the delay weight is increased by 50% in emergency mode and normal in routine mode, and a context-based dynamic weighting operation is performed.

[0056] For example, in emergency mode, low-latency channels are given additional priority. This operation simultaneously monitors the rate of change of load parameters (such as the delay rising slope) and dynamically adjusts their weights to prevent future overloads. A weighted sequence is output, in which the channel order reflects the load stability and adaptability to the immediate scenario. The self-evolution of screening is achieved through the "time-load collaboration" logic, breaking through the static threshold rules.

[0057] Specifically, a heartbeat detection signal is sent to each channel in the weighted sorted channel sequence, the response delay of the channel is measured and verified, and the channels whose response delay meets the requirements and passes the core verification are selected to form the target payment channel candidate set.

[0058] Specifically, the channels in the target payment channel candidate set are selected according to the uniqueness principle, and one of the channels is determined as the final target payment channel.

[0059] In this embodiment, verifying the heartbeat response delay of the weighted sorted channel sequence to obtain a target payment channel candidate set that passes core verification includes: Sending a heartbeat detection packet to the weighted sorted channel sequence to obtain a channel detection result with a response delay; Verify the channel detection result for delay compliance to obtain a target payment channel candidate set that meets the response requirements.

[0060] Specifically, heartbeat detection packets are sent to each channel in the weighted sorted channel sequence in turn, and the time it takes for each channel to receive the detection packet and return a response is recorded. These results containing response delay information are sorted together to form a channel detection result with response delay, clarifying the response time of each channel.

[0061] Specifically, the response delay of each channel in the channel detection results is compared with the pre-set compliance delay standard to check whether it meets the requirements. The channels whose response delays meet the standards are screened out to form a candidate set of target payment channels that meet the response requirements.

[0062] S4: Perform joint verification on the user identity information and the fee details, and encapsulate the parsing result into a secure data packet according to the verification result of the joint verification.

[0063] In this embodiment, the joint verification of the user identity information and the expense details includes: Verifying the validity of the credentials of the user identity information to obtain an identity verification result set; Matching the expense details with medical billing rules to obtain expense compliance determination results; Performing a joint authority audit on the identity verification result set and the fee compliance determination result to obtain a payment authorization decision; A risk control blacklist screening operation is performed on the payment authorization decision to obtain a verification result of the joint verification.

[0064] Specifically, extract data such as the item name, quantity, unit price, etc. from the expense details, match them one by one with the medical billing rule library, query the medical insurance catalog for medical treatment items to determine whether they are reimbursed and the corresponding reimbursement ratio, verify whether the hospital charges exceed the standards approved by the price department, distinguish between Class A, Class B, and Class C drugs for drug expenses, determine the reimbursement scope according to the medical insurance policy, check whether the unit price of the drug is within the purchase price limit and the retail price limit, and for service expenses, check the hospital charging standards and medical insurance payment policies, summarize the compliance / non-compliance results of each expense, form the expense compliance determination result, and indicate the violation points of the non-compliant expenses.

[0065] Specifically, the validity of the user's identity information credentials is verified by retrieving the identity information credentials submitted by the user, such as identity cards, medical insurance cards, etc., and then comparing them with the identity authentication database in real time to check whether the issuing agency of the credentials is legal and whether the information on the credentials is consistent with the database records. At the same time, it is verified whether the credentials are within the validity period and whether they have been reported lost or revoked. After this series of verifications, an identity authentication result set is formed, which clearly records the pass or fail status of each verification item.

[0066] Specifically, the expense details are matched with medical billing rules, and each charge item in the expense details is compared one by one with the pre-set medical billing rule library.

[0067] Furthermore, the medical billing rule library contains detailed rules such as charging standards, item coding correspondence, billing methods, etc. for various medical service items stipulated by the state and the industry. It checks whether the items in the expense details have corresponding legal codes in the rule library, whether the charging standards comply with the regulations, whether the calculation of quantity and unit price is correct, and whether there are violations such as duplicate charges and split charges. Finally, the cost compliance judgment result is drawn based on the comparison results to clarify whether the expenses are compliant and the specific circumstances of the violations.

[0068] Specifically, a joint permission audit is conducted on the identity authentication result set and the fee compliance determination result. The system will conduct a comprehensive analysis of these two results based on the preset permission audit strategy.

[0069] Furthermore, the permission audit policy specifies the payment permissions of users with different identity types under different fee compliance conditions. For example, for users whose identity authentication is fully passed and whose fees are fully compliant, the system will determine whether to allow them to make payment operations of the corresponding amount based on the permission level corresponding to their identity; if there are some problems with the identity authentication or some non-compliance with the fees, the corresponding payment authorization decision will be generated according to the specific rules in the policy, which clearly indicates whether the payment is allowed, rejected or requires further review.

[0070] Specifically, payment authorization decisions are screened against a risk control blacklist, comprehensively comparing the user identity and account information involved in the payment authorization decision with a risk control blacklist database. The risk control blacklist database contains information about users with a history of fraud, illegal payments, and other negative records. The system then checks each user's information to see if it exists on the blacklist.

[0071] If the user information is found to match any record in the blacklist, the payment is determined to be risky and a verification result indicating that the joint verification failed is generated. If no matching record is found in the blacklist, a verification result indicating that the joint verification passed is generated.

[0072] In this embodiment, the risk control blacklist screening operation is performed on the payment authorization decision to obtain the verification result of the joint verification, including: Matching the payment authorization decision with high-risk behavior characteristics to obtain a preliminary risk tagging result; The payment authorization decision is subjected to real-time blacklist screening based on the preliminary risk marking result to obtain a verification result of the joint verification.

[0073] Specifically, the payment authorization decision is matched with high-risk behavior characteristics, and the transaction information involved in the payment authorization decision, including transaction amount, transaction time, transaction location, transaction frequency, transaction object, etc., is compared one by one with the pre-set high-risk behavior characteristic library.

[0074] Specifically, the high-risk behavior feature library contains various transaction patterns and characteristics that may pose risks, such as single transaction amounts exceeding preset thresholds, large transactions during non-business hours, frequent transactions in different regions, and transactions with trading partners with risk records.

[0075] Specifically, when checking whether the transaction information meets any high-risk feature in the feature library, if it does, the payment authorization decision will be marked with the corresponding risk type and level to form a preliminary risk marking result, which clearly records whether there is a risk in the transaction and the specific circumstances of the risk.

[0076] Specifically, based on the preliminary risk marking results, the payment authorization decision is screened in real time by the blacklist. According to the risk type and level recorded in the preliminary risk marking results, the scope of the blacklist that needs to be screened is determined. Then, the user identity information, account information, transaction object information, etc. involved in the payment authorization decision are compared with the corresponding real-time blacklist database.

[0077] Specifically, the real-time blacklist database contains the latest user information, account information, and transaction object information that involve risky behaviors such as fraud, illegal payments, and money laundering. The relevant information is checked one by one to see if it exists in the blacklist. If any information is found to match a record in the blacklist, the payment authorization decision is judged to be risky and a verification result of joint verification failure is generated; if no matching record is found in the blacklist, the payment authorization decision is judged to have passed the risk control screening and a verification result of joint verification passing is generated.

[0078] In this embodiment, encapsulating the parsing result into a secure data packet according to the verification result of the joint verification includes: Extracting sensitive data from the verification result to obtain a payment field set that can be securely transmitted; The payment field set is encrypted to obtain an encrypted security data packet.

[0079] Specifically, sensitive data of the verification results are extracted, and the verification results are comprehensively scanned. Based on pre-set sensitive data identification rules, sensitive data including user identity information, account information, transaction amount, payment password, etc. are accurately located and extracted from the verification results. These extracted sensitive data are then integrated and classified, and redundant information is eliminated, ultimately forming a payment field set that can be securely transmitted. This payment field set only contains payment-related sensitive data that has been strictly screened and confirmed to be necessary.

[0080] Specifically, the payment field set is encrypted, each field in the payment field set is encrypted, the payment field set is converted into a specific data format, and then the data format is encrypted using an encryption key, so that the originally readable sensitive data is converted into ciphertext form.

[0081] Specifically, during the encryption process, the encryption algorithm iterates and transforms the data multiple times to ensure the security of the encrypted data packet. After encryption is complete, the system combines all encrypted fields into a complete encrypted data packet, known as an encrypted secure data packet. This data packet effectively prevents sensitive data from being stolen or tampered with during transmission.

[0082] S5: Input the secure data packet into the target payment channel, and submit the input result to the medical terminal.

[0083] In this embodiment, inputting the secure data packet into the target payment channel and submitting the input result to the medical terminal includes: Transmitting the secure data packet through the target payment channel, receiving the transmission processing result of the transmission in real time, and saving the transmission processing result as an original response file; Converting the original response file into a standard format required by the medical terminal; The standard format is sent to the callback interface of the medical terminal to complete the final submission.

[0084] Specifically, the security data packet is transmitted through the target payment channel, and a connection with the target payment channel is established according to the pre-set communication protocol and interface specifications of the target payment channel. Then, the encrypted security data packet is sent out accurately in the format and transmission method required by the channel to ensure that the security data packet can smoothly enter the target payment channel for processing.

[0085] Specifically, the system receives the transmitted transmission processing results in real time and saves them as a raw response file. During the secure data packet transmission process, the system continuously monitors the feedback from the target payment channel. Once the target payment channel completes processing the secure data packet and returns the processing results, the system immediately captures these results. The system then saves the received transmission processing results in their entirety as a raw response file, following a fixed file storage format and naming convention. This file records all information about the target payment channel's processing of the secure data packet.

[0086] Specifically, the original response file is converted into the standard format required by the medical terminal. All contents in the original response file are first read, and then the data in the original response file is reorganized and formatted according to the standard data format specification pre-defined by the medical terminal.

[0087] For example, the field name, data type, arrangement order, etc. of the data are adjusted to meet the requirements of the medical terminal, and the non-standard or non-compliant data in the original response file are converted and corrected, and finally a file that meets the standard format of the medical terminal is generated.

[0088] Specifically, the standard format is sent to the callback interface of the medical terminal to complete the final submission, and a connection with the callback interface of the medical terminal is established according to the callback interface address and communication protocol provided by the medical terminal.

[0089] The data in the converted standard format file is encapsulated according to the interface requirements to ensure that the data can be correctly received and parsed.

[0090] Finally, the encapsulated data is sent to the callback interface of the medical terminal through the established connection. After the medical terminal receives the data and completes the verification, the entire submission process is completed.

[0091] like Figure 2 FIG. 1 is a functional module diagram of a multimodal payment integration system for medical expenses provided by an embodiment of the present invention.

[0092] The multimodal medical expense payment integrated system 100 described in the present invention can be installed in an electronic device. Depending on the functionality implemented, the multimodal medical expense payment integrated system 100 may include a data processing module 101, a channel generation module 102, a channel screening module 103, a packaging module 104, and a submission module 105. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0093] In this embodiment, the functions of each module / unit are as follows: A multimodal payment integrated system for medical expenses, comprising: A data processing module, configured to parse the multimodal payment request to obtain a parsing result, wherein the parsing result includes: a payment method identifier, user identity information, and fee details; a channel generation module, configured to create a payment channel set for the multimodal payment request based on the payment method identifier; a channel screening module, configured to screen out a target payment channel from the payment channel set based on the real-time load parameters corresponding to the payment channel set; an encapsulation module, configured to perform joint verification on the user identity information and the expense details, and encapsulate the parsing result into a secure data packet according to the verification result of the joint verification; The submission module is used to input the security data packet into the target payment channel and submit the input result of the input to the medical terminal.

[0094] In the several embodiments provided by the present invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0095] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0096] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0097] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0098] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A multimodal payment integration method for medical expenses, characterized in that: The method comprises: S1: Parsing the multimodal payment request to obtain a parsing result, wherein the parsing result includes: a payment method identifier, user identity information, and fee details; S2: Creating a payment channel set for the multimodal payment request based on the payment method identifier; S3: Filtering out a target payment channel from the payment channel set based on the real-time load parameters corresponding to the payment channel set; S4: performing joint verification on the user identity information and the expense details, and encapsulating the parsing result into a secure data packet according to the verification result of the joint verification; S5: Input the secure data packet into the target payment channel, and submit the input result to the medical terminal.

2. A multimodal payment integration method for medical expenses as claimed in claim 1, characterized in that: The parsing result of the multimodal payment request includes: Segmenting the data set of the multimodal payment request into semantic units to obtain an initial semantic unit set; Performing predefined label mapping on the initial semantic unit set to obtain a labeled semantic unit set; Compliance verification is performed on the set of labeled semantic units to obtain a parsing result of the multimodal payment request.

3. A multimodal payment integration method for medical expenses as claimed in claim 1, characterized in that: The payment channel set for creating the multimodal payment request based on the payment method identifier includes: The payment method identifier is subjected to multi-dimensional payment attribute analysis to obtain three layers of payment attributes: Based on the preset institutional clearing topology, the historical clearing flow of the main institution code in the three-layer payment attributes is traced to obtain the core node link; Performing spatial coverage verification on the geographical response areas of the three-layer payment attributes and the core node links to obtain a subset of channels with matching locations; Reorganize the link architecture of the channel subset based on the cross-institution settlement type in the three-layer payment attributes to obtain a dynamic channel sequence inserted with a proxy node; Verify the stability index of the dynamic channel sequence to obtain a payment channel set that meets the survivability threshold.

4. A multimodal payment integration method for medical expenses as claimed in claim 1, characterized in that: The step of selecting a target payment channel from the payment channel set based on the real-time load parameter corresponding to the payment channel set includes: Mark the load health status of the payment channel set to obtain a list of healthy channels; Performing node risk filtering on the health status channel list to obtain a candidate channel subset; Dynamically correcting the weights of the candidate channel subset based on a preset time-sensitive factor to obtain a weighted sorted channel sequence; Verify the heartbeat response delay of the weighted sorted channel sequence to obtain a target payment channel candidate set that passes core verification; Uniquely select the target payment channel candidate set to obtain the target payment channel.

5. A multi-modal payment integration method for medical expenses as claimed in claim 4, characterized in that: The verification of the heartbeat response delay of the weighted sorted channel sequence to obtain a target payment channel candidate set that passes core verification includes: Sending a heartbeat detection packet to the weighted sorted channel sequence to obtain a channel detection result with a response delay; Verify the channel detection result for delay compliance to obtain a target payment channel candidate set that meets the response requirements.

6. A multi-modal payment integration method for medical expenses as claimed in claim 1, characterized in that: The joint verification of the user identity information and the fee details includes: Verifying the validity of the credentials of the user identity information to obtain an identity verification result set; Matching the expense details with medical billing rules to obtain expense compliance determination results; Performing a joint authority audit on the identity verification result set and the fee compliance determination result to obtain a payment authorization decision; A risk control blacklist screening operation is performed on the payment authorization decision to obtain a verification result of the joint verification.

7. A multimodal payment integration method for medical expenses as claimed in claim 6, characterized in that: The performing of a risk control blacklist screening operation on the payment authorization decision to obtain a verification result of the joint verification includes: Matching the payment authorization decision with high-risk behavior characteristics to obtain a preliminary risk tagging result; The payment authorization decision is subjected to real-time blacklist screening based on the preliminary risk marking result to obtain a verification result of the joint verification.

8. A multi-modal payment integration method for medical expenses as claimed in claim 1, characterized in that: The step of encapsulating the parsing result into a secure data packet according to the verification result of the joint verification comprises: Extracting sensitive data from the verification result to obtain a payment field set that can be securely transmitted; The payment field set is encrypted to obtain an encrypted security data packet.

9. A multimodal payment integration method for medical expenses as claimed in claim 1, characterized in that: Inputting the secure data packet into the target payment channel and submitting the input result to the medical terminal includes: Transmitting the secure data packet through the target payment channel, receiving the transmission processing result of the transmission in real time, and saving the transmission processing result as an original response file; Converting the original response file into a standard format required by the medical terminal; The standard format is sent to the callback interface of the medical terminal to complete the final submission.

10. A multi-modal payment integrated system for medical expenses, characterized in that: The system comprises: A data processing module, configured to parse the multimodal payment request to obtain a parsing result, wherein the parsing result includes: a payment method identifier, user identity information, and fee details; a channel generation module, configured to create a payment channel set for the multimodal payment request based on the payment method identifier; a channel screening module, configured to screen out a target payment channel from the payment channel set based on the real-time load parameters corresponding to the payment channel set; an encapsulation module, configured to perform joint verification on the user identity information and the expense details, and encapsulate the parsing result into a secure data packet according to the verification result of the joint verification; The submission module is used to input the security data packet into the target payment channel and submit the input result of the input to the medical terminal.

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