AI intelligent affair handling application platform for college service

Abstract

The invention discloses an AI intelligent affair handling application platform for college services, relates to the technical field of educational informatization, and eliminates the dependence of complex certification manual auditing, solves the problems of flow fragmentation and payment mismatching and realizes full-flow automatic closed loop through a multi-modal sensing module and a data fusion and transaction closed loop module. An electronic certificate with a digital fingerprint is generated by using SM2 or RSA dynamic signature and a block chain technology, and a platform real-time comparison mechanism is established, so that the problems of certificate verification breakpoint and tampering risk are solved; besides, a service stability evaluation index is fitted in combination with indexes such as a hardware failure rate and a packet loss rate, terminal failure early warning and self-repairing are realized, the cooperative efficiency and global credibility of college work are remarkably improved, and a safe, efficient and credible campus digital service system is constructed.

Description

Technical Field

[0001] This invention relates to the field of educational informatization technology, specifically to an AI-powered intelligent service application platform for higher education institutions. Background Technology

[0002] Traditional university administrative services originated from early paper-based archives and manual window-based office work, with core processes heavily reliant on offline physical workflows between various functional departments. With the advancement of educational informatization, the system has evolved from stand-alone databases and departmental management software to digital campus portals. While initial online information management has been achieved, the system has long remained fragmented due to cross-departmental data silos. The first-generation service system could only handle basic data queries and simple certificate printing. For complex tasks such as GPA calculations and average grade certificates in both Chinese and English, faculty and students still needed to travel to various colleges for manual preliminary review and physical stamping. This traditional architecture, centered on departmental functions, suffers from limitations such as disconnected data interaction, high reliance on manual labor, and weak anti-counterfeiting verification capabilities, making it difficult to meet the urgent needs of modern smart campuses for highly credible, fully closed-loop services.

[0003] However, existing technologies often have the following technical shortcomings in traditional university administrative service systems: Fragmented business processes and high reliance on manual labor result in extremely low collaboration efficiency and payment experience. The existing first-generation self-service system can only handle simple, routine certificates. For complex materials such as GPA and average score certificates, it still uses a serial model of "preliminary review by the secondary college, review by the academic affairs department, and offline manual stamping," resulting in a deep disconnect between data acquisition, business review, and terminal output, and a heavy reliance on manual intervention. At the same time, there is a serious mismatch between payment scenarios and hardware facilities. The payment process is highly anchored to physical cards and cannot adapt to the current situation of "cardless" campuses and mobile payments. It has failed to build an automated closed loop of "online triggering of business, cloud-based payment completion, and offline self-service printing," resulting in long processing times and redundant processes for teachers and students.

[0004] The lack of credible certificates and the existence of gaps in the verification mechanism make it difficult to meet the anti-counterfeiting requirements of high credibility. Traditional paper-based certificates lack effective digital anti-counterfeiting technologies, and existing electronic certificate systems have failed to establish real-time communication and verification tunnels with authoritative platforms such as the China Higher Education Student Information System (CHESICC), resulting in both electronic and paper materials facing the potential risk of forgery or tampering. The lack of an authoritative verification feedback loop not only creates a burden of repeated verification when certificates are used across departments and regions, but also hinders universities from establishing a globally recognized and credible electronic certificate system, failing to effectively support the security and supervision needs under the digital transformation of campuses. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an AI-powered intelligent service application platform for university services, thereby resolving the aforementioned technical deficiencies.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: an AI-powered intelligent service application platform for university services, comprising a multimodal perception and verification module, a business data fusion module, a trusted electronic certificate generation module, a transaction closed-loop control module, and an intelligent terminal feedback module; The multimodal perception and verification module is used to obtain the applicant's identity feature information through multimodal biometric technology, and to identify the security level of the current business request by combining geographical location and terminal status, and to mark the legitimate identity. The business data fusion module is used to retrieve student status and grade data from the academic affairs system and payment status data from the financial system in real time, and clean and format the raw data according to the preset business template library, and summarize and generate a business dataset to be processed. The trusted electronic certificate generation module is used to dynamically match encryption algorithms for digital signature processing based on the business type of the business dataset to be processed, and to generate electronic certificates with unique digital fingerprints using blockchain evidence storage technology, while constructing a certificate credibility evaluation model. The transaction closed-loop control module is used to calculate the billing amount and free quota offset of this business by combining the applicant's role and permissions and historical printing frequency, and release the physical printing or electronic issuance instruction after payment is completed. The intelligent terminal feedback module is used to monitor the hardware consumable status and network communication quality of offline printing terminals in real time, and comprehensively evaluate and display the stability index of business processing based on the results of the credibility assessment model, so as to execute the corresponding fault warning or business reissue logic.

[0007] Preferably, the multimodal perception and verification module includes an identity recognition unit and an environment perception unit; The identity recognition unit is used to collect facial features through binocular liveness detection technology and compare them with the campus unified identity authentication platform to obtain the applicant's student ID and role label; The environmental perception unit is used to monitor the authenticity of the applicant's credentials through terminal sensors and determine whether the current login environment belongs to a trusted campus LAN segment. If an abnormal login path is detected, a secondary enhanced authentication command is triggered.

[0008] Preferably, the business data fusion module includes a template matching unit and a data verification unit; The template matching unit is used to automatically extract the corresponding Chinese-English comparison fields and formatting rules from the background according to the type of proof selected by the applicant, including Chinese and English transcripts, GPA certificates, and teaching assignment sheets. The data verification unit is used to automatically retrieve missing items and logically contradictory items in the student record before generating the file, and to determine the data completeness through a logical expression algorithm. If the completeness is lower than the preset completeness threshold, the printing process is interrupted and a data maintenance request is sent to the academic affairs office.

[0009] Preferably, the trusted electronic certificate generation module includes a dynamic signature unit and an evidence storage and analysis unit; The dynamic signature unit is used to execute algorithm switching logic based on whether the business destination is domestic or international. Specifically: If the destination is domestic, the SM2 domestic cryptographic algorithm is used to perform national cryptographic signature and timestamp affixing; If the destination is overseas, the RSA international standard algorithm is used for digital signature, and an Adobe trusted root certificate is embedded. The evidence storage analysis unit is used to extract the feature digest of the generated PDF file and synchronize its hash value to the school's private blockchain node to build a traceable evidence storage index table.

[0010] Preferably, the credibility assessment model obtains the credential credibility coefficient by calculating the linear relationship between the certificate algorithm weight and the blockchain verification success rate; The calculation logic for the credibility coefficient of the certificate is as follows: taking into account the timeliness of the digital signature, the impact of algorithm strength correction, and the verification status of the blockchain distributed consensus, the level of trust in the legal validity of the certificate is determined.

[0011] Preferably, the transaction closed-loop control module includes a quota calculation unit and a remote authorization unit; The quota calculation unit is used to automatically deduct the free printing quota for the academic year based on the applicant's identity and role. If the quota is exhausted, it will automatically generate a deduction order by associating with the mobile payment module. The remote authorization unit is used to implement the entrusted printing logic. After the applicant completes the process online, the system generates an encrypted authorization code with a time limit. The authorized person can use this code to complete the physical printing output at the designated terminal.

[0012] Preferably, the intelligent terminal feedback module includes a status monitoring unit and an intelligent early warning unit; The status monitoring unit is used to collect voltage fluctuation data, current stability data, paper balance and toner concentration of the offline printer in real time, and standardize the units of each monitoring indicator after processing. The intelligent early warning unit is used to automatically calculate the hardware risk assessment value when any monitoring indicator deviates from the safe range. If the value exceeds the preset red line, the terminal is automatically marked as "under maintenance" on the map view and the user is guided to the nearest available device.

[0013] Preferably, the intelligent terminal feedback module includes an external verification linkage unit; The external verification linkage module is used to establish a dedicated communication tunnel with the online academic qualification verification platform, and compares the generated electronic transcript summary with the online academic qualification verification platform interface in real time to ensure the consistency between the data issued by the school and the inventory data.

[0014] Preferably, the intelligent terminal feedback module further includes an intelligent early warning unit; The intelligent early warning unit is used to construct a service stability assessment index; the service stability assessment index is obtained by fitting the terminal hardware health, data interaction latency, and business processing success rate. The specific calculation logic of the service stability assessment index is as follows: the hardware failure rate and the network packet loss rate are weighted and summed, and then the response time difference of the system under high concurrency is corrected to obtain the comprehensive stability index of the current service platform.

[0015] Preferably, the intelligent early warning unit performs multi-level feedback actions by comparing the stability index with a preset evaluation threshold: If the stability index falls below the warning threshold, the system will automatically execute self-repair logic, including restarting application services, clearing system cache, and synchronizing offline print records. If the stability index is in an abnormal range, the system will automatically remind the user of potential delays in the current business process through voice guidance and simultaneously send maintenance response instructions to the management backend.

[0016] This invention provides an AI-powered intelligent service application platform for universities. It offers the following advantages: (1) This AI-powered intelligent service application platform for university services achieves rapid verification of applicants' identity features and security level marking through a multimodal perception and verification module and its identity recognition unit, effectively eliminating the reliance on manual review in the traditional serial process; it automatically extracts the comparison fields and layout rules of Chinese and English transcripts, GPA certificates and teaching assignments by using the template matching unit in the business data fusion module, solving the technical bottleneck of deep disconnect between data acquisition, business review and terminal output; through the quota calculation unit and remote authorization unit in the transaction closed-loop control module, it realizes the leap from physical physical cards to the "mobile payment + time-sensitive encrypted authorization code" mode, successfully building an automated closed loop of "online triggering of business, cloud-based payment completion, and offline self-service printing"; combined with the service stability evaluation index fitted by hardware failure rate, network packet loss rate and response time difference under high concurrency in the intelligent terminal feedback module, and the data verification logic with preset complete threshold, it significantly improves the collaborative efficiency of university administrative services and the service experience of teachers and students.

[0017] (2) This AI-powered intelligent service application platform for university services utilizes the dynamic signature unit in the trusted electronic certificate generation module to call the SM2 domestic cryptographic algorithm or the RSA international standard algorithm integrated with Adobe trusted root certificates for domestic and foreign business destinations respectively. In conjunction with the evidence analysis unit, the feature digest hash value of the PDF file is synchronized to the school's private blockchain node, which fundamentally solves the risk that the proof materials are easily forged and tampered with. By constructing a certificate credibility assessment model that comprehensively considers the impact of the timeliness of digital signatures, the impact of algorithm strength correction, and the blockchain distributed consensus verification status, a quantitative level of legal validity trust is provided for the proof materials, which greatly reduces the burden of repeated review when using certificates across regions. The dedicated communication tunnel of the online academic qualification verification platform established by the external verification linkage module realizes the real-time consistency comparison between the school's issued data and the inventory data, eliminates the mechanism breakpoint of authenticity verification, and builds a globally credible security supervision support system for the digital campus. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system application process of an AI-powered intelligent service application platform for university services according to the present invention. Figure 2 This is a schematic diagram of the system framework of an AI-powered intelligent service application platform for university services according to the present invention. Figure 3 This is a schematic diagram of the process structure of the transaction closed-loop control module and the intelligent terminal feedback module in an AI-powered intelligent service application platform for university services according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1 Please see Figure 1 This invention provides an AI-powered intelligent service application platform for university services, including a multimodal perception and verification module, a business data fusion module, a trusted electronic certificate generation module, a transaction closed-loop control module, and an intelligent terminal feedback module. The multimodal perception and verification module is used to obtain the applicant's identity feature information through multimodal biometric technology, and to identify the security level of the current business request by combining geographical location and terminal status, and to mark the legitimate identity. The business data fusion module is used to retrieve student status and grade data from the academic affairs system and payment status data from the financial system in real time, and clean and format the raw data according to the preset business template library, and summarize and generate a business dataset to be processed. The trusted electronic certificate generation module is used to dynamically match encryption algorithms for digital signature processing based on the business type of the business dataset to be processed, and to generate electronic certificates with unique digital fingerprints using blockchain evidence storage technology, while constructing a certificate credibility evaluation model. The transaction closed-loop control module is used to calculate the billing amount and free quota offset of this business by combining the applicant's role and permissions and historical printing frequency, and release the physical printing or electronic issuance instruction after payment is completed. The intelligent terminal feedback module is used to monitor the hardware consumable status and network communication quality of offline printing terminals in real time, and comprehensively evaluate and display the stability index of business processing based on the results of the credibility assessment model, so as to execute the corresponding fault warning or business reissue logic.

[0021] In this embodiment, the AI-powered intelligent service application platform for universities constructed by the present invention deeply integrates multimodal biometrics, geolocation, and terminal status recognition technologies through a multimodal perception and verification module. This ensures accurate classification of the security level of business requests and rigorous verification of legitimate identities, thus solidifying the security of system access from the source. Relying on the business data fusion module to retrieve student records and grades from the academic affairs system and payment data from the financial system in real time, and in conjunction with the business template library to perform cleaning and formatting processing on the raw data, the collaborative efficiency of heterogeneous data in the process of summarizing and generating business datasets to be processed is greatly improved. The trusted electronic certificate generation module, through the organic combination of dynamic matching encryption algorithms and blockchain evidence storage technology, not only endows the generated electronic certificates with a unique digital fingerprint, but also establishes a quantitative standard for the credibility of proof materials by constructing a certificate credibility assessment model; the transaction closed-loop control module performs deep logical operations based on the applicant's role and permissions and historical printing frequency to accurately determine the billing amount and free quota offset of this business, and completely opens up the automated control logic from fee settlement to physical printing or electronic command release; The intelligent terminal feedback module tracks the status of offline printing terminal hardware consumables and network communication quality in real time, and combines the results of the credibility assessment model to comprehensively fit a stability index for business processing. It can efficiently execute targeted fault warnings or business reissue logic, thereby ensuring the stable operation of the entire service chain, significantly reducing manual maintenance costs and improving the user's service experience.

[0022] Example 2 Please see Figures 2 to 3The multimodal perception and verification module includes an identity recognition unit and an environment perception unit; The identity recognition unit is used to collect facial features through binocular liveness detection technology and compare them with the campus unified identity authentication platform to obtain the applicant's student ID and role label; The environmental perception unit is used to monitor the authenticity of the applicant's credentials through terminal sensors and determine whether the current login environment belongs to a trusted campus LAN segment. If an abnormal login path is detected, a secondary enhanced authentication command is triggered.

[0023] The business data fusion module includes a template matching unit and a data verification unit; The template matching unit is used to automatically extract the corresponding Chinese-English comparison fields and formatting rules from the background according to the type of proof selected by the applicant, including Chinese and English transcripts, GPA certificates, and teaching assignment sheets. The data verification unit is used to automatically retrieve missing items and logically contradictory items in the student record before generating the file, and to determine the data completeness through a logical expression algorithm. If the completeness is lower than the preset completeness threshold, the printing process is interrupted and a data maintenance request is sent to the academic affairs office.

[0024] The trusted electronic certificate generation module includes a dynamic signature unit and an evidence storage and analysis unit. The dynamic signature unit is used to execute algorithm switching logic based on whether the business destination is domestic or international. Specifically: If the destination is domestic, the SM2 domestic cryptographic algorithm is used to perform national cryptographic signature and timestamp affixing; If the destination is overseas, the RSA international standard algorithm is used for digital signature, and an Adobe trusted root certificate is embedded. The evidence storage analysis unit is used to extract the feature digest of the generated PDF file and synchronize its hash value to the school's private blockchain node to build a traceable evidence storage index table.

[0025] The credibility assessment model obtains the credibility coefficient of the credential by calculating the linear relationship between the certificate algorithm weight and the blockchain verification success rate. The calculation logic for the credibility coefficient of the certificate is as follows: taking into account the timeliness of the digital signature, the impact of algorithm strength correction, and the verification status of the blockchain distributed consensus, the level of trust in the legal validity of the certificate is determined.

[0026] The transaction closed-loop control module includes a quota calculation unit and a remote authorization unit; The quota calculation unit is used to automatically deduct the free printing quota for the academic year based on the applicant's identity and role. If the quota is exhausted, it will automatically generate a deduction order by associating with the mobile payment module. The remote authorization unit is used to implement the entrusted printing logic. After the applicant completes the process online, the system generates an encrypted authorization code with a time limit. The authorized person can use this code to complete the physical printing output at the designated terminal.

[0027] The intelligent terminal feedback module includes a status monitoring unit and an intelligent early warning unit; The status monitoring unit is used to collect voltage fluctuation data, current stability data, paper balance and toner concentration of the offline printer in real time, and standardize the units of each monitoring indicator after processing. The intelligent early warning unit is used to automatically calculate the hardware risk assessment value when any monitoring indicator deviates from the safe range. If the value exceeds the preset red line, the terminal is automatically marked as "under maintenance" on the map view and the user is guided to the nearest available device.

[0028] The intelligent terminal feedback module includes an external verification linkage unit; The external verification linkage module is used to establish a dedicated communication tunnel with the online academic qualification verification platform, and compares the generated electronic transcript summary with the online academic qualification verification platform interface in real time to ensure the consistency between the data issued by the school and the inventory data.

[0029] The intelligent terminal feedback module also includes an intelligent early warning unit; The intelligent early warning unit is used to construct a service stability assessment index; the service stability assessment index is obtained by fitting the terminal hardware health, data interaction latency, and business processing success rate. The specific calculation logic of the service stability assessment index is as follows: the hardware failure rate and the network packet loss rate are weighted and summed, and then the response time difference of the system under high concurrency is corrected to obtain the comprehensive stability index of the current service platform.

[0030] The intelligent early warning unit performs multi-level feedback actions by comparing the stability index with a preset evaluation threshold: If the stability index falls below the warning threshold, the system will automatically execute self-repair logic, including restarting application services, clearing system cache, and synchronizing offline print records. If the stability index is in an abnormal range, the system will automatically remind the user of potential delays in the current business process through voice guidance and simultaneously send maintenance response instructions to the management backend.

[0031] In this embodiment, the credibility assessment model performs the following logic to determine the credibility coefficient C of the credential: Extract the pre-stored algorithm strength coefficient table from the external data carrier, where the SM2 algorithm is set to 1.0 and the RSA-2048 algorithm is set to 0.85; calculate the timeliness decay factor, specifically by: obtaining the difference between the signing time and the current business time; if the difference is within one year, the decay factor is set to 1.0; if it exceeds one year, it decreases by 0.1 annually; obtain the blockchain consensus verification status; return 1.0 if verification passes, and 0 if verification fails; the processing module performs a weighted summation calculation: The algorithm strength coefficient is multiplied by the first weight of 0.4, the timeliness decay factor is multiplied by the second weight of 0.3, and the blockchain state value is multiplied by the third weight of 0.3. The sum of the three is the certificate credibility coefficient. In this embodiment, if the certificate credibility coefficient is greater than 0.85, the certificate is determined to have "high legal validity".

[0032] The stability index S is obtained by performing the following steps: obtaining the normalized hardware failure rate F (between 0 and 1) and the network packet loss rate P; performing initial weight aggregation: calculating the product of the hardware failure rate and the first weight 0.6, and the product of the network packet loss rate and the second weight 0.4, and adding them together to obtain the basic risk value; introducing response time difference correction logic: obtaining the average response time of the system under the current concurrency, and calculating the ratio of this time to the preset baseline response time (set to 500ms in this embodiment); multiplying the basic risk value by the square root of the response time ratio to obtain the final service stability assessment index. If the index exceeds the preset red line value of 0.75, subsequent self-repair actions are triggered.

[0033] Furthermore, the S generated by the intelligent early warning unit has a clearly defined quantitative range to characterize the operating status of the offline printing terminal. When S approaches its theoretical minimum value of 0, it indicates that the offline printing terminal is in an ideal steady state. At this time, the hardware voltage and current parameters are stable, paper and toner consumables are sufficient, and the network packet loss rate is zero, with extremely short system response time, ensuring that the business processing process has extremely high continuity and reliability. When S approaches and exceeds the preset trigger threshold of 0.75 and evolves towards the maximum value, it indicates that the terminal has entered a critical fault state or failure state, reflecting hardware consumables depletion, physical link congestion, or severe response lag caused by high system concurrency. At this time, the risk of business processing failure increases exponentially, and the system accordingly forces the execution of self-repair or business path redirection logic.

[0034] The calculation logic of S deeply couples physical failure patterns with business operation logic. Specifically, the hardware failure rate F is positively correlated with the exponent S, integrating physical indicators such as voltage fluctuations and consumable reserves. This ensures the system can capture business interruption risks caused by hardware wear and tear in real time, conforming to the evolution process of electrical control system failures. The network packet loss rate P is also positively correlated with the exponent S, directly reflecting the impact of communication quality on certificate issuance and hash synchronization atomicity. Furthermore, the response time difference correction ratio establishes a non-linear positive correlation between the actual response time and the 500ms baseline time, and uses a square root function for smoothing correction. This aims to filter out instantaneous network jitter under high concurrency conditions, preventing oversensitivity in the early warning logic. This ensures the accuracy of system risk identification while improving the redundancy of the service platform under load pressure.

[0035] By employing an objective threshold derivation methodology, the continuous change of S is mapped to discrete intervention strategies, the logic of which is rooted in the co-evolution of terminal hardware failure and network congestion. By selecting the Success Rate of Service (TSR) as a verifiable performance indicator, this invention constructs a causal relationship model between the S exponent and TSR, and uses first-order derivative analysis based on the rate of change to identify the abrupt slope change point of TSR with S, thus mathematically determining S=0.75 as the critical point for system robustness.

[0036] Based on this technological boundary, the present invention divides the application space into three levels: When S is in the stable operating range of 0 to 0.4, the system performs routine monitoring and maintains normal service release. When S is in the performance warning range of 0.4 to 0.75, the system will activate voice guidance and send maintenance instructions to the management backend. When S exceeds the circuit breaker self-repair range of 0.75, the system immediately performs service restart, cache clearing, and terminal map marker redirection to ensure the certainty of user service results even under extreme conditions.

[0037] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AI-powered intelligent service application platform for university services, characterized in that, It includes a multimodal perception and verification module, a business data fusion module, a trusted electronic certificate generation module, a transaction closed-loop control module, and a smart terminal feedback module; The multimodal perception and verification module is used to obtain the applicant's identity feature information through multimodal biometric technology, and to identify the security level of the current business request by combining geographical location and terminal status, and to mark the legitimate identity. The business data fusion module is used to retrieve student status and grade data from the academic affairs system and payment status data from the financial system in real time, and clean and format the raw data according to the preset business template library, and summarize and generate a business dataset to be processed. The trusted electronic certificate generation module is used to dynamically match encryption algorithms for digital signature processing based on the business type of the business dataset to be processed, and to generate electronic certificates with unique digital fingerprints using blockchain evidence storage technology, while constructing a certificate credibility evaluation model. The transaction closed-loop control module is used to calculate the billing amount and free quota offset of this business by combining the applicant's role and permissions and historical printing frequency, and release the physical printing or electronic issuance instruction after payment is completed. The intelligent terminal feedback module is used to monitor the hardware consumable status and network communication quality of offline printing terminals in real time, and comprehensively evaluate and display the stability index of business processing based on the results of the credibility assessment model, so as to execute the corresponding fault warning or business reissue logic.

2. The AI-powered intelligent service application platform for university services according to claim 1, characterized in that: The multimodal perception and verification module includes an identity recognition unit and an environment perception unit; The identity recognition unit is used to collect facial features through binocular liveness detection technology and compare them with the campus unified identity authentication platform to obtain the applicant's student ID and role label; The environmental perception unit is used to monitor the authenticity of the applicant's credentials through terminal sensors and determine whether the current login environment belongs to a trusted campus LAN segment. If an abnormal login path is detected, a secondary enhanced authentication command is triggered.

3. The AI-powered intelligent service application platform for university services according to claim 1, characterized in that: The business data fusion module includes a template matching unit and a data verification unit; The template matching unit is used to automatically extract the corresponding Chinese-English comparison fields and formatting rules from the background according to the type of proof selected by the applicant, including Chinese and English transcripts, GPA certificates, and teaching assignment sheets. The data verification unit is used to automatically retrieve missing items and logically contradictory items in the student record before generating the file, and to determine the data completeness through a logical expression algorithm. If the completeness is lower than the preset completeness threshold, the printing process is interrupted and a data maintenance request is sent to the academic affairs office.

4. The AI-powered intelligent service application platform for university services according to claim 1, characterized in that: The trusted electronic certificate generation module includes a dynamic signature unit and an evidence storage and analysis unit. The dynamic signature unit is used to execute algorithm switching logic based on whether the business destination is domestic or international. Specifically: If the destination is domestic, the SM2 domestic cryptographic algorithm is used to perform national cryptographic signature and timestamp affixing; If the destination is overseas, the RSA international standard algorithm is used for digital signature, and an Adobe trusted root certificate is embedded. The evidence storage analysis unit is used to extract the feature digest of the generated PDF file and synchronize its hash value to the school's private blockchain node to build a traceable evidence storage index table.

5. The AI-powered intelligent service application platform for university services according to claim 4, characterized in that: The credibility assessment model obtains the credibility coefficient of the credential by calculating the linear relationship between the certificate algorithm weight and the blockchain verification success rate. The calculation logic for the credibility coefficient of the certificate is as follows: taking into account the timeliness of the digital signature, the impact of algorithm strength correction, and the verification status of the blockchain distributed consensus, the level of trust in the legal validity of the certificate is determined.

6. The AI-powered intelligent service application platform for university services according to claim 1, characterized in that: The transaction closed-loop control module includes a quota calculation unit and a remote authorization unit; The quota calculation unit is used to automatically deduct the free printing quota for the academic year based on the applicant's identity and role. If the quota is exhausted, it will automatically generate a deduction order by associating with the mobile payment module. The remote authorization unit is used to implement the entrusted printing logic. After the applicant completes the process online, the system generates an encrypted authorization code with a time limit. The authorized person can use this code to complete the physical printing output at the designated terminal.

7. The AI-powered intelligent service application platform for university services according to claim 1, characterized in that: The intelligent terminal feedback module includes a status monitoring unit and an intelligent early warning unit; The status monitoring unit is used to collect voltage fluctuation data, current stability data, paper balance and toner concentration of the offline printer in real time, and standardize the units of each monitoring indicator after processing. The intelligent early warning unit is used to automatically calculate the hardware risk assessment value when any monitoring indicator deviates from the safe range. If the value exceeds the preset red line, the terminal is automatically marked as "under maintenance" on the map view and the user is guided to the nearest available device.

8. The AI-powered intelligent service application platform for university services according to claim 1, characterized in that: The intelligent terminal feedback module includes an external verification linkage unit; The external verification linkage module is used to establish a dedicated communication tunnel with the online academic qualification verification platform, and compares the generated electronic transcript summary with the online academic qualification verification platform interface in real time to ensure the consistency between the data issued by the school and the inventory data.

9. The AI-powered intelligent service application platform for university services according to claim 1, characterized in that: The intelligent terminal feedback module also includes an intelligent early warning unit; The intelligent early warning unit is used to construct a service stability assessment index; the service stability assessment index is obtained by fitting the terminal hardware health, data interaction latency, and business processing success rate. The specific calculation logic of the service stability assessment index is as follows: the hardware failure rate and the network packet loss rate are weighted and summed, and then the response time difference of the system under high concurrency is corrected to obtain the comprehensive stability index of the current service platform.

10. The AI-powered intelligent service application platform for university services according to claim 9, characterized in that: The intelligent early warning unit performs multi-level feedback actions by comparing the stability index with a preset evaluation threshold: If the stability index falls below the warning threshold, the system will automatically execute self-repair logic, including restarting application services, clearing system cache, and synchronizing offline print records. If the stability index is in an abnormal range, the system will automatically remind the user of potential delays in the current business process through voice guidance and simultaneously send maintenance response instructions to the management backend.

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