Intelligent air security processing method and device based on life insurance business

Abstract

The application provides an intelligent air security processing method and device based on life insurance business, and relates to the technical field of life insurance business digital service system, wherein the method comprises the following steps: a multi-channel access module receives customer reservation, verifies the completeness and accuracy of personal information and security demand, and matches multi-modal identity verification according to the type of certificate; an AI algorithm dynamically allocates tasks, and informs both parties according to the working time, load and customer emergency degree of the business personnel; in remote audio and video interaction, identity verification is completed, an OCR identifies data to fill in an electronic form, and a smart engine checks compliance; after the business is completed, relevant data is stored and encrypted by block chain, and is associated with a policy number, a security number and an archiving core system. The method realizes online, intelligent and secure life insurance security whole process, improves service efficiency and experience, supports remote handling of the whole project, and guarantees compliance and data tamper resistance.

Description

Technical Field

[0001] This invention relates to the field of digital service systems for life insurance business, and in particular to an intelligent over-the-air security processing method and device based on life insurance business. Background Technology

[0002] Life insurance policy maintenance services, as a crucial business support for the insurance industry, are widely used in key aspects such as changes to customer policy information and adjustments to benefits. Among related technologies, a traditional policy maintenance service system is built through the collaborative operation of counter services, self-service platforms, and manual services. Specifically, this system covers the entire process from customer identity verification to business processing, including key steps such as information collection, manual review, and paper archiving. With the advancement of digital transformation in the insurance industry, existing technologies typically handle policy maintenance business through a single channel, but this has limitations such as fragmented service models and insufficient technological collaboration. While traditional policy maintenance can meet basic needs, it is constrained by the distribution of physical branches, the efficiency of manual review, and the compatibility of document types, making it difficult to build an intelligent service ecosystem covering a global customer base.

[0003] However, existing policy maintenance services rely heavily on in-person counter processing, failing to fully integrate internet technology and AI algorithms. This can lead to problems such as long waiting times for customers to visit branches and low utilization of service resources. Specifically, current technologies typically employ single verification methods (such as ID card OCR recognition) but lack a multimodal identity authentication system, making it ineffective in addressing the identity verification needs of customers with non-mainland Chinese documents. The service gap is particularly significant for cross-border customers and the elderly (who account for 18% of China's life insurance customers). Traditional methods suffer from high operational barriers and slow service response times among customers over 60. Consequently, existing technology systems have systemic flaws in service channel scalability (supporting only 30-50% of policy maintenance items), business processing efficiency (average 20 minutes per case for manual review), and security (risk of identity forgery), thus impacting the accessibility of insurance services and operational cost control (in-person branches account for over 40% of annual costs). Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose an intelligent over-the-air security processing method based on life insurance business.

[0006] The second objective of this invention is to propose an intelligent air-based security processing device based on life insurance business.

[0007] The third objective of this invention is to provide an electronic device.

[0008] The fourth objective of this invention is to provide a computer-readable storage medium.

[0009] The fifth objective of this invention is to provide a computer program product.

[0010] To achieve the above objectives, a first aspect of the present invention proposes an intelligent over-the-air security processing method based on life insurance business, comprising:

[0011] S1 receives customer appointment requests through a multi-channel access module, verifies the completeness and accuracy of the personal information and security requirements filled in by the customer, and selects a multimodal authentication method based on the type of customer's identification document. S2 dynamically assigns security tasks to salespersons based on AI algorithms, taking into account the salesperson's working hours, task load, and customer urgency, generating task assignment results and notifying customers and salespersons. S3 verifies customer identity through an identity verification module during remote audio and video interaction, automatically identifies uploaded information using OCR technology and fills it into electronic forms, and combines an intelligent audit engine to verify the compliance of form content. S4. After the business is completed, the audio and video recordings, electronic forms and electronic signature data are encrypted and stored through the blockchain evidence storage module, and automatically linked and archived to the life insurance core system with the policy number and policy maintenance number.

[0012] Optionally, S11, the multi-channel access module supports uploading and parsing of multiple file formats such as PDF, JPG, and PNG; S12, the blacklist mechanism filters abnormal appointment requests through a customer historical appointment behavior scoring model. The scoring model includes a weighted calculation of customer appointment frequency, task cancellation rate and data submission compliance rate.

[0013] Optionally, in step S21, the AI ​​algorithm adopts a multi-dimensional matching model, and the weight parameters include the matching degree of the salesperson's professional skills and the overlap between the customer's geographical location and the salesperson's service scope. S22, the allocation failure handling includes triggering a resource release warning and starting a backup task allocation strategy, with the backup strategy prioritizing salespersons whose task load is below a threshold.

[0014] Optionally, in S31, the identity verification module uses a real-time comparison algorithm with the public security system database for mainland ID cards, and uses multi-factor authentication technology for non-mainland documents, including dual verification of biometrics and SMS verification codes. S32, the OCR recognition engine supports semantic verification of document information.

[0015] Optionally, S41, the blockchain evidence storage module adopts the Hyperledger Fabric architecture and implements access control for different data preservation services through channel isolation; S42, the data archiving process automatically generates watermark information, the watermark content includes timestamp, salesperson ID and hash value of customer IP address.

[0016] Optionally, S5 can monitor audio and video connection quality, salesperson response speed, and customer satisfaction indicators in real time. When an audio or video interruption is detected or a salesperson fails to respond within a timeout period, an early warning model is triggered and management personnel are notified to intervene. S6, managers initiate emergency response procedures based on the type of warning, including reassigning tasks to backup salespersons or adjusting the workload threshold of salespersons.

[0017] To achieve the above objectives, a second aspect of the present invention provides an intelligent over-the-air security processing device based on life insurance business, comprising: The multi-channel access and identity verification module is used to receive customer appointment requests through multiple channels, verify the completeness and accuracy of the personal information and security requirements filled in by the customer, and select a multimodal identity verification method according to the type of customer's identification document. The AI ​​dynamic task allocation module is used to dynamically allocate security tasks to salespersons based on AI algorithms. It takes into account the salesperson's working hours, task load and customer urgency, generates task allocation results and notifies customers and salespersons. The remote interaction and intelligent review module is used to complete customer identity verification during remote audio and video interaction. It uses OCR technology to automatically recognize uploaded information and fill it into electronic forms, and combines the intelligent review engine to perform compliance verification on the form content. The blockchain-based evidence storage and data archiving module is used to encrypt and store audio and video recordings, electronic forms, and electronic signature data through the blockchain evidence storage module, and automatically link them with the policy number and policy maintenance number to the life insurance core system.

[0018] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of the first aspects.

[0019] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of the first aspects.

[0020] To achieve the above objectives, a fifth aspect of the present invention provides a computer program product that, when executed by a processor, implements the method described in any one of the first aspects.

[0021] The technical solutions provided by the embodiments of the present invention bring at least the following beneficial effects: The methods, apparatus, electronic devices, and computer-readable storage media of this invention enable the online and intelligent processing of the entire life insurance policy maintenance business, significantly improving service efficiency, coverage, and security, and effectively solving the problems of limited channels, low efficiency, and inconvenience for special groups in traditional policy maintenance services.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating an intelligent over-the-air security processing method based on life insurance business, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram of an intelligent over-the-air security processing method based on life insurance business provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an intelligent over-the-air security processing device based on life insurance business, provided in an embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] To address the problems of low efficiency, high labor and time costs, and geographical limitations in counter services, embodiments of this invention provide an intelligent over-the-air policy maintenance method based on life insurance business. Figure 1 This is a flowchart illustrating an intelligent over-the-air security processing method based on life insurance business, provided as an embodiment of the present invention. Figure 1 As shown, the method includes the following steps: S1 receives customer appointment requests through a multi-channel access module, verifies the completeness and accuracy of the personal information and security requirements filled in by the customer, and selects a multimodal authentication method based on the type of customer's identification document.

[0026] Specifically, in some implementations, receiving customer appointment requests through a multi-channel access module and verifying the completeness and accuracy of the personal information and security needs filled in by the customer is a key preliminary step in the intelligent air security service system of this invention. Its technical implementation is based on multi-protocol adaptation interfaces and multi-dimensional data verification mechanisms. This module supports access from various terminal devices, including but not limited to mobile apps, WeChat mini-programs, web pages, and smartwatches. It adopts a RESTful API architecture and uses HTTPS protocol for encrypted data transmission to ensure the security and integrity of information during transmission.

[0027] In terms of specific operation, the personal information customers fill in on the appointment interface includes fields such as name, ID number, and contact information. Policy maintenance needs include the type of maintenance (such as beneficiary change, address update, etc.) and the expected processing time. Upon receiving the appointment request, the system first performs a data integrity check to ensure all required fields have been filled in. Secondly, the system calls the core life insurance system interface to verify the consistency of the policy number, ID number, and other information provided by the customer, ensuring they match the customer information stored in the system. For customers using a resident ID card, the system further calls the OCR recognition engine to automatically extract key fields from the document image and compares them with the facial recognition database of the public security system to complete multimodal identity verification. If the document type is a passport or other international document, the system uses a multi-factor authentication mechanism, combining document OCR recognition with manual review to ensure the authenticity of the customer's identity.

[0028] At the parameter level, the system sets information integrity thresholds, such as requiring at least five fields, and requiring document numbers to conform to the ISO / IEC 7810 standard (e.g., 18-digit ID card number, 10-digit passport number, etc.). OCR accuracy must reach over 98%, and facial recognition confidence level must be above 95% to meet financial-grade identity verification requirements. Furthermore, the system supports a blacklist mechanism to automatically block customers who frequently cancel appointments or submit false information. Blacklist records are updated synchronously through the life insurance core system to prevent malicious appointment behavior.

[0029] This process is widely applicable in practice to various user groups, including domestic and international clients, senior citizens, and high-net-worth individuals. For example, for overseas clients, the system supports multilingual interfaces and international document type recognition, ensuring they can successfully submit appointment requests. For senior clients, the system provides auxiliary functions such as large fonts and voice guidance to lower the operational threshold.

[0030] From a technical perspective, this step achieves standardized collection of customer information and multimodal identity verification, providing a reliable data foundation for subsequent task allocation, remote audio and video interaction, and data archiving. Through automated verification and intelligent recommendation mechanisms, the system significantly improves appointment efficiency and customer satisfaction, while reducing manual review costs and enhancing the intelligence and compliance of the service process.

[0031] Furthermore, S1 includes: S11, the multi-channel access module supports uploading and parsing of multiple file formats such as PDF, JPG, and PNG.

[0032] Specifically, in some implementations, the multi-channel access module supports the uploading and parsing of multiple file formats such as PDF, JPG, and PNG. Its technical implementation is based on a multimodal file processing architecture, combining key technologies such as file format recognition, image preprocessing, OCR recognition, and structured data extraction to achieve compatible processing and intelligent parsing of various document formats. As the information collection entry point for the entire air-to-air security service system, this module undertakes the core functions of uploading customer information, automatic identification, and data pre-filling, and is a prerequisite for achieving automated review and intelligent recommendation.

[0033] At the technical implementation level, the system first uses a front-end multi-format file upload control to allow users to select and upload files in formats such as PDF, JPG, and PNG from local devices or cloud storage. During the upload process, the system performs format validation to ensure that the file type conforms to a preset whitelist (such as application / pdf, image / jpeg, image / png) and limits the file size to a reasonable range (such as no more than 20MB for a single file) to ensure system performance and transmission efficiency. After the upload is complete, the system calls the image processing engine to perform preprocessing operations such as grayscale conversion, binarization, noise reduction, and rotation correction on the image files (JPG, PNG) to improve the accuracy of OCR recognition.

[0034] For PDF files, the system uses a PDF parser to extract page content. If the PDF is a scanned document, it is converted into an image format before OCR recognition. The OCR recognition module is based on a deep learning model (such as a CNN+Transformer structure), supports multilingual recognition (such as Chinese, English, and Traditional Chinese characters), and optimizes the model for commonly used fields in the insurance industry (such as name, ID number, policy number, date of birth, etc.), achieving an accuracy rate of over 95% (under standard lighting and clarity conditions). The recognition results are then structured and extracted using Natural Language Processing (NLP) technology and automatically populated into system forms, reducing manual input by users and improving operational efficiency.

[0035] In terms of specifications, the system supports file formats including but not limited to PDF, JPG, and PNG; the maximum file size is 20MB; the supported resolution range is 300dpi to 600dpi; the recognition accuracy is no less than 95% under standard conditions; and the OCR recognition response time is controlled within 2 seconds. The system also supports multi-threaded concurrent processing, with a single node capable of handling no fewer than 10 file upload tasks simultaneously, ensuring stability in high-concurrency scenarios.

[0036] At the application level, this module is widely used in scenarios where customers remotely submit documents such as identity verification, original policies, and proof of relationship, especially suitable for overseas customers, elderly customers, and other user groups who find it inconvenient to submit documents offline. By supporting multiple file formats, the system achieves cross-platform and cross-terminal compatibility. Customers can upload documents through various channels such as mobile apps, WeChat mini-programs, and web pages, while sales staff can view and process them centrally in the backend.

[0037] The technical benefits of this step lie in the fact that, through intelligent parsing of multi-format files, the system automates and standardizes data collection, significantly improving customer convenience and business processing efficiency. Simultaneously, the extraction of structured data provides high-quality data input for subsequent intelligent review, recommendation engines, and blockchain-based evidence storage, enhancing the system's overall intelligence and data security.

[0038] S12, the blacklist mechanism filters abnormal appointment requests through a customer historical appointment behavior scoring model. The scoring model includes a weighted calculation of customer appointment frequency, task cancellation rate and data submission compliance rate.

[0039] Specifically, in some implementations, the blacklist mechanism filters abnormal appointment requests through a customer historical appointment behavior scoring model. Its technical implementation is based on weighted calculations of multi-dimensional behavioral characteristics to identify potential malicious or inefficient appointment behaviors, thereby optimizing system resource allocation and improving service efficiency. This scoring model comprehensively considers three key indicators: customer appointment frequency, task cancellation rate, and document submission compliance rate, assigning different weight coefficients to each to reflect their impact on system stability and service quality.

[0040] At the technical implementation level, customer appointment frequency is determined by counting the number of appointments made by customers within a preset time window (such as the last 30 days). and compare it with the threshold set by the system. Compare. If If this occurs, a frequency anomaly flag will be triggered. Task cancellation rate. Defined as the percentage of customers who proactively cancel their scheduled tasks, calculated using the following formula:

[0041] in To cancel the number of tasks, Total number of scheduled tasks. Document submission compliance rate. The completeness and accuracy of information submitted by customers in their historical appointments are measured and calculated as follows:

[0042] in To ensure compliance with the number of submissions, This represents the total number of submissions. The system uses a weighted scoring function based on the above three metrics:

[0043] in The weighting coefficients for each indicator are typically dynamically adjusted through historical data analysis and business rule configuration to adapt to risk preferences under different business scenarios.

[0044] In application scenarios, this blacklist mechanism is primarily used during the booking phase of over-the-air security services. When a customer submits a booking request, the system automatically invokes a scoring model for real-time evaluation. If the score... Exceeding the preset abnormal threshold If the system flags the customer as a high-risk user, it will reject their current appointment request and record it in the blacklist database for subsequent manual review or automatic blocking.

[0045] The technical effect of this step is that by quantifying customer behavior characteristics, the system can effectively identify and intercept abnormal appointment behavior, reduce the generation of invalid tasks, improve task allocation efficiency and resource utilization, and enhance system security and service quality, providing a reliable prerequisite for subsequent remote audio and video interaction and business processing.

[0046] S2 dynamically assigns security tasks to salespersons based on AI algorithms, taking into account the salesperson's working hours, task load, and customer urgency, generating task assignment results and notifying both the customer and the salesperson.

[0047] Specifically, in some implementations, the dynamic task allocation step based on AI algorithms is one of the core functions of the intelligent air security service system of this invention. Its technical implementation principle is based on a combination of multi-dimensional feature modeling and real-time scheduling optimization algorithms. This step constructs a task allocation model by collecting key parameters such as the salesperson's working time, current task load, and customer urgency, thereby achieving efficient and reasonable allocation of security tasks.

[0048] From a technical implementation perspective, the system first receives customer appointment requests through the task center module and transforms them into structured task objects. Each task object includes fields such as customer ID, policy type, appointment time, customer urgency level (e.g., a rating of 1-5), and required service duration. Salesperson information includes the current number of tasks, estimated available time, service history ratings, and professional skill tags (e.g., "beneficiary change," "policy loan," etc.). During task allocation, the system uses a combination of rule-based priority ranking and machine learning-based matching algorithms to match tasks with salespeople in real time.

[0049] At the parameter level, the system introduces a weight based on the urgency of the task. Salesperson workload coefficient Task matching degree ,in Integers between 1 and 5 Indicates salesperson Current task load, Indicates task With salesperson The matching degree is usually calculated based on the similarity between skill tags and task types. The objective function for task assignment can be expressed as:

[0050] in For the total number of customer tasks, The total number of salespersons. This function aims to minimize task allocation mismatch while considering urgency and load balancing.

[0051] In application scenarios, this step is widely applicable to life insurance customers handling policy maintenance business remotely, such as address changes, beneficiary changes, and policy loans. Especially in complex scenarios such as multilingual support, cross-border services, and services for the elderly, the system can intelligently match agents with appropriate service capabilities based on customer attributes (such as language preferences, geographical location, age, etc.), thereby improving service efficiency and customer satisfaction.

[0052] The technical benefits of this step are a significant improvement in the intelligence level of task allocation and resource utilization. By dynamically adjusting the task allocation strategy, the system can reduce task waiting time, improve the work efficiency of sales staff, and ensure that high-priority tasks receive timely responses, thereby optimizing service processes and enhancing customer experience.

[0053] Furthermore, S2 includes: S21, the AI ​​algorithm adopts a multi-dimensional matching model, and the weight parameters include the matching degree of the salesperson's professional skills and the overlap between the customer's geographical location and the salesperson's service scope.

[0054] Specifically, in some implementations, the AI ​​algorithm employs a multi-dimensional matching model. Its core lies in achieving intelligent task allocation between customers and salespersons by quantifying matching features across multiple dimensions. This model calculates similarity based on multi-dimensional feature vectors of customer appointment information and salesperson attribute data, thereby dynamically matching the optimal salesperson and improving the accuracy of task allocation and service efficiency.

[0055] From a technical implementation perspective, this multi-dimensional matching model typically employs a weighted vector similarity algorithm, such as cosine similarity or Euclidean distance, to match the feature vectors of customers and salespersons. Specifically, the salesperson's professional skills matching degree is determined by constructing a feature vector based on their historical handling of policy maintenance cases, success rate, and customer feedback ratings, and then matching it against the type of policy maintenance the customer is currently applying for. For example, if the customer is applying for a "beneficiary change," the system will extract a weight from the salesperson's experience in handling that type of business. And calculate its matching degree with the customer request. ,in It indicates that the salesperson is on the Weighting in preservation-like business Indicates the first in the customer request Matching strength of similar business types.

[0056] In addition, the overlap between the customer's geographical location and the salesperson's service area. This is achieved through geofencing technology, where the system uses the customer's current GPS coordinates. polygonal boundary with the salesperson's service area Spatial matching is performed. If the customer is within the salesperson's service area, the overlap is 1; if they are outside the area, the overlap is determined by the distance decay function. Calculate the matching degree, such as ,in The maximum tolerable distance threshold set for the system is typically 50 kilometers.

[0057] At the parameter level, the system sets a matching degree threshold. The value is typically 0.7, used to determine whether to assign the task to the salesperson. If... If the task is successfully assigned, it will be placed in the allocation queue, waiting for resources to be released or re-matched.

[0058] This step is widely used in the task allocation module of life insurance air travel insurance service systems, especially in complex service scenarios involving multiple languages, regions, and business types, such as foreign customers, elderly customers, and emergency insurance needs. Through a multi-dimensional matching model, the system can effectively reduce the subjectivity of task allocation, improve resource utilization, and shorten customer waiting time, thereby enhancing overall service efficiency and customer satisfaction.

[0059] Furthermore, the innovation of this technical solution lies in the joint modeling of salespersons' skill dimensions and geographical dimensions, combined with AI algorithms to achieve dynamic and accurate task allocation, solving the problems of low efficiency and inaccurate matching in traditional manual allocation, and has significant practicality and promotional value.

[0060] S22, the allocation failure handling includes triggering a resource release warning and starting a backup task allocation strategy, with the backup strategy prioritizing salespersons whose task load is below a threshold.

[0061] Specifically, in some implementations, the allocation failure handling includes triggering a resource release warning and initiating a backup task allocation strategy. The backup strategy prioritizes business personnel whose task load is below a threshold. This step is a key fault-tolerance mechanism in the task allocation module, designed to improve the system's task scheduling efficiency and service quality under high concurrency or resource-constrained conditions.

[0062] From a technical implementation perspective, when the system detects during dynamic task allocation that the load of currently available salespersons has exceeded a preset threshold (e.g., ...), If the resource allocation fails, the allocation is considered a failure. At this point, the system will trigger a resource release early warning mechanism, sending an early warning signal to the task management module via a message queue or event bus, indicating that resources are currently scarce and task reallocation or resource scheduling optimization is necessary. Simultaneously, the system will activate a backup task allocation strategy, which is based on a load balancing algorithm and prioritizes tasks with lower loads. The system selects target assignment objects from among the sales personnel. In further implementation, the system can employ algorithms such as Weighted Round Robin or Least Connections, combined with the sales personnel's skill tags (e.g., ...). Indicates salesperson (Professional skills matching) and task urgency (e.g.) Indicates task (Priority) to make comprehensive decisions.

[0063] At the parameter level, the system defines several key parameters, including but not limited to: the maximum load threshold for salespersons. Allocation failure warning trigger threshold Task priority Salesperson skill matching degree Maximum number of task retries etc. Among them, Typically set to The threshold is set at 70% to 85% to allow for a buffer and prevent frequent triggering of warnings. In the task retry mechanism, if consecutive... If the assignment fails, the system will automatically mark the task as "awaiting manual assignment" and notify the security administrator to intervene.

[0064] At the application level, this step is widely used in high-concurrency appointment scenarios, such as holidays and policy changes, where a surge in customer appointments leads to resource constraints for sales staff. The system uses an early warning mechanism to promptly identify resource bottlenecks and redistributes tasks to sales staff with lower workloads using backup strategies, thereby avoiding task backlog and improving overall service response speed. Furthermore, in cross-border service scenarios, due to time zone differences and language barriers, the system needs to dynamically adjust task allocation strategies to ensure the rationality and timeliness of task distribution.

[0065] The technical advantage of this step lies in the fact that, by introducing early warning and backup strategies, the system can automatically adjust its task allocation logic when resources are strained, preventing tasks from remaining unprocessed for extended periods, thereby improving customer satisfaction and system stability. Simultaneously, this mechanism effectively supports collaborative work among multiple sales personnel, enhancing the system's resilience and fault tolerance, and ensuring the efficient operation of intelligent over-the-air security services.

[0066] During remote audio and video interaction, S3 verifies customer identity through an identity verification module, automatically identifies uploaded information using OCR technology and fills it into electronic forms, and combines an intelligent audit engine to verify the compliance of form content.

[0067] Specifically, during remote audio and video interaction, the customer identity is verified through the identity verification module, and the uploaded information is automatically recognized and filled into the electronic form using OCR technology. Combined with the intelligent audit engine, the form content is verified for compliance. This is one of the key steps in ensuring business security and efficiency in the intelligent over-the-air security service system of this invention.

[0068] At the technical implementation level, this step first establishes a real-time connection between the customer and the security personnel through a remote audio-visual interaction module. When the customer accesses the system, the identity verification module is activated, employing a multimodal identity verification mechanism, including but not limited to facial recognition, OCR document recognition, and multi-factor authentication (such as SMS verification codes and biometric comparison). For residents of mainland China, the system calls the facial recognition interface of the public security system to compare the real-time captured facial image of the customer with the photo on their ID card, ensuring consistency between the person and the document. The OCR recognition module, based on a deep learning model (such as a CNN+Transformer structure), extracts text from documents such as ID cards and insurance policies uploaded by the customer, achieving an accuracy rate of over 98% (tested under the ISO / IEC24615 standard). It automatically fills the recognized fields (such as name, ID number, and policy number) into electronic forms, reducing manual input errors and improving data entry efficiency.

[0069] In terms of parameters, the OCR recognition module supports multiple image formats (such as JPEG, PNG, and PDF), and an image resolution of at least 300 DPI is recommended to ensure recognition quality. In the identity verification module, the facial recognition similarity threshold can be set to 0.85 or higher to meet the compliance requirements of the financial industry for identity verification. The intelligent review engine, based on a preset rule engine and knowledge graph, performs compliance checks on the filled-in form, including field completeness, document validity, and policy status, with a review response time controlled within 2 seconds to meet real-time interaction needs.

[0070] In application scenarios, this step is widely used in various remote policy maintenance businesses, such as address changes, beneficiary changes, and policy loan applications. It is especially suitable for elderly customers, overseas customers, or users with mobility difficulties, enabling barrier-free and intelligent business processing through remote video guidance and automated data processing.

[0071] The technical benefits of this step are a significant improvement in the security and efficiency of business processing. Through OCR automatic recognition and intelligent review, the system can reduce manual input workload by more than 80%, while reducing the false identity verification rate to below 0.5%, effectively preventing identity theft and data forgery risks, and providing a high-quality data foundation for subsequent business approvals and data archiving.

[0072] Furthermore, S3 includes: S31, the identity verification module uses a real-time comparison algorithm with the public security system database for mainland ID cards, and multi-factor authentication technology for non-mainland documents, including dual verification of biometrics and SMS verification codes.

[0073] Specifically, in some implementations, the identity verification module uses a real-time comparison algorithm with the public security system database for mainland ID cards, while multi-factor authentication technology, including dual verification of biometrics and SMS verification codes, is used for non-mainland documents. This step is technically implemented based on OCR image recognition, facial recognition algorithms, multi-factor authentication mechanisms, and API interface integration with the public security system database, ensuring the authenticity and legality of customer identities. It is a crucial link in the security and compliance of the entire over-the-air security system.

[0074] At the technical implementation level, for mainland China ID card users, the system first uses OCR technology to extract text from the uploaded ID card image, recognizing fields such as name, ID number, date of birth, and issuing authority. Then, the system calls the real-time identity verification interface provided by the public security system to compare the extracted ID card information with the public security database. This interface typically follows the GB / T 28181-2016 standard, supports HTTPS encrypted communication, and its response time is generally controlled within 500ms to ensure a good user experience. If the comparison results match and the ID card is valid, the identity verification is successful.

[0075] For users with non-mainland China identification documents, the system employs multi-factor authentication technology, including biometric recognition (such as facial recognition and fingerprint recognition) and SMS verification code verification. For facial recognition, the system uses a deep learning-based LivenessDetection algorithm, analyzing facial micro-expressions, lighting changes, head movements, and other features to determine if the user is a live person, preventing attacks using photos or videos. The accuracy rate is typically above 99.5%, with a false recognition rate of less than 0.01%. SMS verification codes are sent via the SMPP protocol or REST API provided by the operator. The verification code is 6 digits long and valid for 5 minutes, ensuring timeliness and security.

[0076] In application scenarios, this identity verification module is widely used in key business processes such as customer appointments, remote audio and video interactions, and electronic signatures. For example, before establishing a remote audio and video connection, the system must complete identity verification to ensure that the customer's information matches the policy information, preventing impersonation or identity forgery. For overseas customers, the system supports multilingual interfaces and international SMS services, ensuring compliance and availability globally.

[0077] The technical effect of this step is that, through real-time comparison with the public security system database and a multi-factor authentication mechanism, the system achieves a high-precision and high-security identity verification process, effectively preventing identity theft, ensuring the legality of preservation business and the integrity of data, and providing a reliable identity foundation for subsequent business processing, electronic signatures and data archiving.

[0078] S32, the OCR recognition engine supports semantic verification of document information and verifies the format of ID card number through regular expressions.

[0079] Specifically, in some implementations, the OCR recognition engine supports semantic verification of document information, using regular expressions to verify the format of ID card numbers. This step plays a crucial role in the system's identity verification function, ensuring that the document information submitted by customers complies with national or international standards, thereby improving the accuracy and compliance of the system's review process.

[0080] From a technical implementation perspective, after receiving the document image uploaded by the customer, the OCR recognition engine first uses image preprocessing techniques (such as grayscale conversion, binarization, and edge detection) to enhance image quality and improve recognition accuracy. Then, it uses deep learning models (such as CNN and Transformer) to recognize the text information on the document and extract key fields, such as the document number, name, and date of birth. After recognition, the system matches and validates the document number against a preset regular expression. For mainland China resident ID cards, the system uses a regular expression for format validation, requiring the ID number to consist of 17 digits plus one digit or the letter X, conforming to the encoding standards for ID card numbers in the "Law of the People's Republic of China on Resident Identity Cards".

[0081] In terms of parameters, the OCR recognition engine should achieve an accuracy rate of over 98%, and the response time for document number format verification should be controlled within 500ms to ensure overall system response efficiency. The system supports document types including, but not limited to, resident ID cards, passports, and Hong Kong, Macao, and Taiwan resident permits, with each document type corresponding to different regular expressions and field extraction rules.

[0082] In terms of application scenarios, this step is widely used in the document review process of remote policy maintenance services, especially in business scenarios such as beneficiary changes, policy information updates, and identity verification. For example, when a foreign customer uploads their passport through the system, the system automatically recognizes and verifies the passport number format. If it does not conform, the system prompts the customer to re-upload or correct the information, thereby avoiding subsequent business processing failures due to format errors.

[0083] The technical effect of this step is that, by combining structured verification rules with OCR recognition technology, the system achieves automatic recognition and compliance verification of document information, reduces manual intervention, improves review efficiency and data quality, provides a reliable data foundation for subsequent identity authentication, business processing and data archiving, and enhances the system's intelligence level and business processing capabilities.

[0084] S4. After the business is completed, the audio and video recordings, electronic forms and electronic signature data are encrypted and stored through the blockchain evidence storage module, and automatically linked and archived to the life insurance core system with the policy number and policy maintenance number.

[0085] Specifically, after the business transaction is completed, the audio and video recordings, electronic forms, and electronic signature data are encrypted and stored through a blockchain evidence storage module, and automatically linked to the policy number and policy maintenance number and archived in the life insurance core system. This is a crucial step in achieving data traceability and business integrity in this invention. This step, through the combination of multi-dimensional data integration and blockchain technology, ensures the immutability, security, and compliance of policy maintenance business data.

[0086] At the technical implementation level, the system first performs structured processing on the audio and video recordings generated during business transactions, including the extraction and encapsulation of metadata such as timestamps, operation nodes, and interaction content. Electronic form data is standardized and encoded using JSON or XML formats to ensure a clear and easily parsed data structure. Electronic signature data is encrypted using digital signature algorithms (such as RSA and ECDSA) to generate a signature hash value, which is then bound to the form content. All data is digested using hash algorithms such as SHA-256 before archiving, serving as input data for blockchain notarization.

[0087] The blockchain notarization module adopts a consortium blockchain architecture, with nodes consisting of the insurance company's core system, policy maintenance service system, and third-party certification authorities, ensuring multi-party consensus and trusted storage of data. Before data is written to the blockchain, it must undergo verification logic through smart contracts, including data integrity verification, signature validity verification, and timestamp consistency checks. After notarization is completed, the system will generate a unique blockchain hash value and bind this hash value with business identifiers such as the policy number (PolicyNo) and policy maintenance number (ClaimNo), forming a queryable metadata index.

[0088] At the parameter level, the system supports archiving of various data formats, such as H.264 encoded video files, PNG / JPG screenshots, and PDF electronic forms. Archived data must meet the requirements of ISO / IEC 26324 (electronic signature standard) and ISO / IEC 27001 information security management system. The blockchain evidence storage module supports a TPS (transactions per second) of no less than 1000 transactions per second, with storage latency controlled within 500ms, ensuring the real-time performance and consistency of business data.

[0089] This step is widely applicable in practice for various life insurance policy maintenance businesses, such as beneficiary changes, policy loans, and contact information updates. Especially in cross-border policy maintenance, elderly customer service, and high-risk business scenarios, the automatic linking of blockchain-based evidence storage with the core system can effectively meet regulatory compliance requirements and improve data auditing efficiency.

[0090] In terms of technical effectiveness, this step achieves permanent storage and traceability of business data, ensuring that complete, authentic, and tamper-proof business records can be quickly retrieved in subsequent audits, dispute resolution, and compliance checks. Simultaneously, by automatically associating with policy numbers and policy maintenance numbers, it improves data retrieval efficiency, providing a solid foundation for subsequent business processing and data analysis.

[0091] The intelligent over-the-air policy maintenance processing method based on life insurance business in this invention realizes remote and intelligent processing of life insurance policy maintenance business, improves service efficiency and customer experience, and ensures the security and traceability of business processing through technologies such as OCR recognition, AI review, and blockchain evidence storage.

[0092] Furthermore, S4 includes: S41, the blockchain evidence storage module adopts the Hyperledger Fabric architecture and realizes access control of different security business data through channel isolation.

[0093] Specifically, in some implementations, the blockchain evidence storage module adopts the Hyperledger Fabric architecture, achieving access control for different data preservation businesses through channel isolation. Its technical implementation is based on distributed ledger technology and a multi-channel mechanism, ensuring data security, privacy, and traceability with multi-party participation. Hyperledger Fabric is a modular, scalable, permissioned blockchain platform that supports multi-organizational collaboration, chaincode (smart contract) execution isolation, and channel mechanisms, making it particularly suitable for enterprise-level business scenarios.

[0094] In this system, the blockchain evidence storage module isolates different types of policy preservation business data into independent channels by constructing multiple channels. Each channel is jointly maintained by a group of authorized organizations. For example, for different policy preservation business types such as "address change," "beneficiary change," and "policy loan application," the system can create independent channels, with each channel containing only data and operation records related to that business. The member organizations of the channels include the insurance company's core system, policy preservation operator organizations, and customer identity verification organizations. Each organization uses the MSP (Membership Service Provider) mechanism for identity authentication and access control.

[0095] Furthermore, the chaincode (smart contract) deployed in each channel is responsible for handling the data storage logic within that channel, including operations such as data upload, querying, and auditing. The chaincode is implemented in Go or Java, conforms to the Hyperledger Fabric chaincode interface specification, and supports CouchDB as a state database, enabling efficient storage and querying of structured data. Before data is uploaded to the chain, the system hashes key data such as audio and video recordings, electronic signatures, and OCR recognition results to generate unique digest values. These digest values, along with business metadata (such as policy numbers, policy maintenance numbers, and timestamps), are packaged and submitted to the ordering service (Orderer) in the channel. The ordering service sorts the transactions and packages them into blocks, which are then verified and written to the ledger by the endorsing nodes in the channel.

[0096] Optionally, the system supports channel-level access control policies. By configuring endorsement policies and read / write policies, it ensures that only authorized organizations or users can read or write data in a specific channel. For example, customers can only access channel data related to their policies, while policyholders can access channel data for the tasks they are responsible for, thus achieving fine-grained access control.

[0097] In terms of specific metrics, the system supports a throughput (TPS) of 1000+, a block size configurable from 1MB to 10MB, and a block generation interval set from 1 second to 5 seconds, meeting the needs of high-concurrency and low-latency businesses. Simultaneously, the system supports PBFT or Raft consensus algorithms to ensure transaction consistency and reliability.

[0098] In practical applications, this step is primarily deployed between the life insurance core system and the online policy preservation platform to ensure the immutability and traceability of policy preservation data, thereby guaranteeing the compliance and traceability of business operations. For example, after a customer completes their electronic signature, the system packages the signature data and policy preservation task information onto the blockchain for subsequent auditing and regulatory purposes.

[0099] In terms of technical effectiveness, this step, through a channel isolation mechanism, effectively solves the security risks and access control problems caused by centralized data storage in traditional insurance preservation systems. It realizes distributed storage and fine-grained access control of business data, improves the security, compliance and data credibility of the system, and builds a reliable digital insurance preservation service infrastructure for insurance companies.

[0100] S42, the data archiving process automatically generates watermark information, the watermark content includes timestamp, salesperson ID and hash value of customer IP address.

[0101] Specifically, during the data archiving process, the system automatically generates watermark information. The watermark content includes a timestamp, salesperson ID, and hash value of the customer's IP address. Its technical implementation is based on the combination of digital watermarking and hash algorithm, which aims to enhance the anti-tampering capability and traceability of archived data, and ensure the integrity and security of business data during storage and transmission.

[0102] In some implementations, the watermark generation process first obtains the precise time of the current processing moment through the system timestamp module, typically in ISO 8601 standard format, such as 2024-11-05T14:30:45Z, where Z represents UTC time. The timestamp precision can be set to milliseconds to ensure that the data processing time can still be uniquely identified under high-concurrency scenarios. The salesperson ID is provided by the system authentication module, usually a unique identifier of 16 characters or longer, in the format PICC-AGENT-20241105-001234, used to identify the specific operator.

[0103] The client's IP address is obtained through network layer protocols (such as the X-Forwarded-For field in the HTTP request header or the source IP of the TCP connection). After receiving a client request, the system extracts the IP address and encrypts it using a secure hash algorithm (such as SHA-256) to generate a fixed-length hash value, for example, hash_ip = SHA256("192.168.1.100"). This hash value serves as additional verification information for the client's identity, preventing the IP address from being tampered with or forged.

[0104] Furthermore, the system combines the above three pieces of information (timestamp, salesperson ID, and IP hash value) into a single string and overlays it onto archived images, videos, or PDF documents using image watermark embedding technology. The watermark embedding method can employ frequency domain transformation (such as DCT transform) or spatial domain overlay (such as alpha channel overlay), and the embedding strength can be configured from 0.1 to 0.5 (0 for complete transparency, 1 for complete coverage) to achieve tamper-proof functionality without affecting readability.

[0105] This step plays a crucial role in the entire airborne data preservation system, particularly in data quality management and compliance auditing. By automatically adding watermark information, the system can effectively prevent archived data from being illegally tampered with or replaced, while providing a reliable basis for subsequent business traceability, responsibility allocation, and audit verification.

[0106] The intelligent over-the-air policy maintenance processing method based on life insurance business in this invention realizes remote and intelligent processing of life insurance policy maintenance business, improves service efficiency and customer experience, and ensures the security and traceability of business processing through technologies such as OCR recognition, AI review, and blockchain evidence storage.

[0107] S5 monitors audio and video connection quality, salesperson response speed, and customer satisfaction metrics in real time. When an audio or video interruption is detected or a salesperson fails to respond within a timeout period, an early warning model is triggered, and management personnel are notified to intervene.

[0108] Specifically, this step involves the monitoring, early warning, and emergency response mechanism within the intelligent air security service system. Its core lies in timely detection and handling of anomalies by real-time monitoring of key indicators such as audio and video connection quality, salesperson response speed, and customer satisfaction, thereby ensuring the continuity and quality of the service process. In some implementations, this mechanism is based on a distributed monitoring architecture and multi-dimensional data collection, combined with AI early warning models and automated notification systems to achieve dynamic intervention in the execution of security tasks.

[0109] At the technical implementation level, the system continuously monitors the audio and video connection status through a real-time monitoring module deployed on the server. Specifically, the system uses the RTCP (Real-Time Transport Control Protocol) to evaluate the quality of the audio and video streams, including parameters such as packet loss rate, latency, frame rate, and audio jitter. For example, if the latency of the audio and video connection exceeds a preset threshold... or continuous packet loss rate At this time, the system determines it as a connection error. Simultaneously, the system monitors whether the salesperson responds to customer requests within a specified time using a task status polling mechanism; if the response time... If this happens, a timeout warning will be triggered.

[0110] The early warning model is built based on the statistical analysis of historical task data and abnormal events, and uses supervised learning algorithms (such as LSTM, XGBoost, etc.) to predict task risks. When audio or video interruptions are detected or salespersons fail to respond within the time limit, the system pushes early warning information to the terminal devices of management personnel through message queues (such as Kafka, RabbitMQ), and notification methods include SMS, email, and APP push, to ensure that management personnel can intervene and handle the situation in the shortest possible time.

[0111] In application scenarios, this step is widely used in the real-time management of remote security services, especially in scenarios such as cross-border audio and video interaction, elderly customer service, and handling complex security projects, where it has significant emergency response value. For example, when a customer is overseas and an unstable network environment causes audio and video interruptions, the system can immediately notify administrators to reassign tasks or switch communication channels to ensure uninterrupted service.

[0112] The technical benefits of this step are that it significantly improves service stability and response efficiency, reduces the risk of customer loss due to technical failures or personnel response delays, and enhances the system's proactive control over service quality, providing insurance companies with efficient, secure, and traceable over-the-air insurance services.

[0113] S6, managers initiate emergency response procedures based on the type of warning, including reassigning tasks to backup salespersons or adjusting the workload threshold of salespersons.

[0114] Specifically, in some implementations, when the system detects anomalies during remote audio and video interactions (such as salespersons failing to respond within a timeout period, audio / video connection interruptions, or task backlogs), administrators can initiate emergency response procedures based on the warning type to ensure the continuity and quality of security services. This step is technically implemented based on a collaborative mechanism between the system's built-in monitoring and warning module and task allocation module. By dynamically adjusting task allocation strategies or optimizing salesperson workload thresholds, it enables rapid response and handling of abnormal tasks.

[0115] At the technical implementation level, the system determines whether an emergency response needs to be activated by collecting and analyzing multiple key performance indicators (KPIs) in real time. For example, the system monitors the number of tasks currently being completed by each salesperson. Task response time Audio and video connection status Parameters such as... When... (in The preset workload threshold is typically set to a maximum of 20 tasks per day. ( When the maximum response time (usually set to 15 minutes) is reached, the system will trigger an early warning mechanism and push early warning information to the administrator.

[0116] The emergency response process includes two main operations: task reallocation and workload threshold adjustment. In task reallocation, the system, through the task center module, removes currently unprocessed or timed-out tasks from the original salesperson's task queue and reallocates them to backup salespersons based on parameters such as the availability of the current salesperson, their professional skills matching, and the urgency of the task. The selection of backup salespersons follows a priority strategy, for example, prioritizing those with fewer than [number] current tasks. Salespeople, or based on historical response efficiency Sort and select.

[0117] In workload threshold adjustment, the system allows administrators to dynamically modify the workload limit for salespersons based on real-time business pressure. For example, during peak business periods, it can be Increase the value from 20 to 25 to enhance overall processing capacity; however, it can be appropriately reduced during off-peak periods to avoid overloading.

[0118] This step is widely applicable in practical situations involving handling anomalies in life insurance over-the-air policy maintenance services, such as sudden task backlogs, temporary absence of sales personnel, and urgent customer needs. Through this rapid response mechanism, the system can effectively reduce task delays, improve customer satisfaction, and ensure the continuity and compliance of business processes.

[0119] From a technical perspective, this emergency response mechanism significantly enhances the system's fault tolerance and service resilience, ensuring efficient service operation even under high concurrency or unexpected situations. Furthermore, through its integration with the blockchain archiving module, all emergency response operations are recorded and documented, strengthening the system's traceability and auditability.

[0120] The intelligent over-the-air policy maintenance method based on life insurance business in this invention further improves the stability of remote policy maintenance services and customer satisfaction by monitoring the quality of audio and video connections, the response speed of salespersons and customer satisfaction in real time, and automatically triggering an emergency response mechanism in conjunction with an early warning model.

[0121] To achieve the above embodiments, the present invention also proposes an intelligent air security processing device based on life insurance business. Figure 3 This is a schematic diagram of the structure of an intelligent over-the-air security processing device based on life insurance business, provided as an embodiment of the present invention. Figure 3 As shown, the device includes: The multi-channel access and identity verification module 100 is used to receive customer appointment requests through multiple channels, verify the completeness and accuracy of the personal information and security requirements filled in by the customer, and select a multimodal identity verification method according to the type of customer's document. The AI ​​dynamic task allocation module 200 is used to dynamically allocate security tasks to salespersons based on AI algorithms. It takes into account the salesperson's working hours, task load and customer urgency, generates task allocation results and notifies customers and salespersons. The remote interaction and intelligent review module 300 is used to complete customer identity verification during remote audio and video interaction, automatically recognize uploaded information using OCR technology and fill it into electronic forms, and combine it with an intelligent review engine to perform compliance verification on the form content. The Blockchain Evidence Storage and Data Archiving Module 400 is used to encrypt and store audio and video recordings, electronic forms, and electronic signature data through the blockchain evidence storage module, and automatically link them with the policy number and policy maintenance number to the life insurance core system.

[0122] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0123] To implement the above embodiments, the present invention also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer execution instructions; the processor executes the computer execution instructions stored in the memory to implement the method provided in the foregoing embodiments.

[0124] To implement the above embodiments, the present invention also proposes a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the methods provided in the foregoing embodiments.

[0125] To implement the above embodiments, the present invention also proposes a computer program product, including a computer program that, when executed by a processor, implements the methods provided in the foregoing embodiments.

[0126] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in this invention all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0127] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0128] This invention is intended to provide implementation schemes for users to selectively prevent the use or access to personal information data. That is, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information can be de-identified to protect user privacy.

[0129] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0130] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0131] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0132] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0133] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0134] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0135] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0136] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

[0137] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0138] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for intelligent over-the-air security processing based on life insurance business, characterized in that, include: S1 receives customer appointment requests through a multi-channel access module, verifies the completeness and accuracy of the personal information and security requirements filled in by the customer, and selects a multimodal authentication method based on the type of customer's identification document. S2 dynamically assigns security tasks to salespersons based on AI algorithms, taking into account the salesperson's working hours, task load, and customer urgency, generating task assignment results and notifying customers and salespersons. S3 verifies customer identity through an identity verification module during remote audio and video interaction, automatically identifies uploaded information using OCR technology and fills it into electronic forms, and combines an intelligent audit engine to verify the compliance of form content. S4. After the business is completed, the audio and video recordings, electronic forms and electronic signature data are encrypted and stored through the blockchain evidence storage module, and automatically linked and archived to the life insurance core system with the policy number and policy maintenance number.

2. The method as described in claim 1, characterized in that, The S1 receives customer appointment requests through a multi-channel access module, verifies the completeness and accuracy of the personal information and security requirements filled in by the customer, and selects a multimodal authentication method based on the customer's document type. It also includes: S11, the multi-channel access module supports uploading and parsing of multiple file formats such as PDF, JPG, and PNG; S12, the blacklist mechanism filters abnormal appointment requests through a customer historical appointment behavior scoring model. The scoring model includes a weighted calculation of customer appointment frequency, task cancellation rate and data submission compliance rate.

3. The method as described in claim 1, characterized in that, The S2 method dynamically allocates security tasks to salespersons based on AI algorithms, comprehensively considering salespersons' working hours, task load, and customer urgency, generating task allocation results and notifying both the customer and the salesperson. It also includes: S21, the AI ​​algorithm adopts a multi-dimensional matching model, and the weight parameters include the matching degree of the salesperson's professional skills and the overlap between the customer's geographical location and the salesperson's service scope; S22, the allocation failure handling includes triggering a resource release warning and starting a backup task allocation strategy, with the backup strategy prioritizing salespersons whose task load is below a threshold.

4. The method as described in claim 1, characterized in that, During remote audio and video interaction, the S3 verifies customer identity through an identity verification module, automatically identifies uploaded data using OCR technology and fills it into electronic forms, and combines an intelligent audit engine to perform compliance verification of the form content. It also includes: S31, the identity verification module uses a real-time comparison algorithm with the public security system database for mainland ID cards, and multi-factor authentication technology for non-mainland documents, including dual verification of biometrics and SMS verification codes. S32, the OCR recognition engine supports semantic verification of document information.

5. The method as described in claim 1, characterized in that, The S4 encrypts and stores audio and video recordings, electronic forms, and electronic signature data through a blockchain notarization module, and automatically links and archives them to the life insurance core system along with the policy number and policy maintenance number. It also includes: S41, The blockchain evidence storage module adopts the Hyperledger Fabric architecture and implements access control for different data preservation services through channel isolation; S42, the data archiving process automatically generates watermark information, the watermark content includes timestamp, salesperson ID and hash value of customer IP address.

6. The method as described in claim 1, characterized in that, Also includes: S5 monitors audio and video connection quality, salesperson response speed, and customer satisfaction indicators in real time. When an audio or video interruption is detected or a salesperson fails to respond within a time limit, an early warning model is triggered and management personnel are notified to intervene. S6, managers initiate emergency response procedures based on the type of warning, including reassigning tasks to backup salespersons or adjusting the workload threshold of salespersons.

7. An intelligent over-the-air security processing device based on life insurance business, characterized in that, include: The multi-channel access and identity verification module is used to receive customer appointment requests through multiple channels, verify the completeness and accuracy of the personal information and security requirements filled in by the customer, and select a multimodal identity verification method according to the type of customer's identification document. The AI ​​dynamic task allocation module is used to dynamically allocate security tasks to salespersons based on AI algorithms. It takes into account the salesperson's working hours, task load and customer urgency, generates task allocation results and notifies customers and salespersons. The remote interaction and intelligent review module is used to complete customer identity verification during remote audio and video interaction. It uses OCR technology to automatically recognize uploaded information and fill it into electronic forms, and combines the intelligent review engine to perform compliance verification on the form content. The blockchain-based evidence storage and data archiving module is used to encrypt and store audio and video recordings, electronic forms, and electronic signature data through the blockchain evidence storage module, and automatically link them with the policy number and policy maintenance number to the life insurance core system.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

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