This paper focuses on the three core engines of intelligent acceptance, intelligent approval, and multi-modal interaction, supplemented by a user experience optimization system and a closed loop of efficiency evaluation, and deeply discusses how AI can systematically empower government services, from mass errands to data running, from passive response to active service, and from capable to intelligent and easy to do.
As the core interface of interaction between the government and the people and enterprises, the efficiency and experience of government services are directly related to the level of governance modernization. With its powerful data analysis, pattern recognition and intelligent decision-making capabilities, AI is reconstructing the process chain and value chain of government services from the underlying logic.
1. AI-driven process reengineering engine
1. OCR+NLP-driven intelligent acceptance
Technical architecture and implementation path
1) Collaborative acceptance:The core is based on the deep integration of OCR (Optical Character Recognition) and NLP (Natural Language Processing) technologies. OCR is responsible for “looking” and accurately capturing text information on certificates and forms; NLP is responsible for “understanding” the semantics and associations of this information.
2) Millisecond-level structured extraction:For 20+ high-frequency licenses such as ID cards, business licenses, and real estate certificates, the system has a built-in refined template engine and an adaptive image preprocessing module.
- For ID photos that are blurry, tilted, and unevenly lit, the system will automatically enhance the image (such as denoising, sharpening, and correcting debits) to ensure the image quality of the OCR input.
- OCR models using deep learning (such as CRNN+Attention, or Transformer-based models) can not only recognize printed fonts, but also have high robustness for complex situations such as handwriting and stamp embossing.
- After recognition, the NLP module will structure the extraction according to the preset license knowledge graph (including field types, format rules, and logical relationships). For example, from the ID card picture, not only can the name and ID number be extracted, but also the gender (verified according to the ID number rules), date of birth, and the address information can be split into structured fields such as province, city, district, and street.
3) Intelligent verification rule engine:
- Field Integrity Check:Automatically compare the list of materials required for the application items, mark the missing items (such as “missing a copy of the legal person ID card”), and give clear prompts.
- Logical consistency check:Utilize the semantic understanding capabilities of NLP to cross-verify multi-source data. For example, check whether the name of the legal representative on the business license is consistent with the name on the submitted ID card; Whether the business address filled in the application form matches the address of the property deed/lease contract.
- Initial screening for authenticity:Combined with authoritative databases (such as public security population database, industrial and commercial enterprise database) for online verification (under the premise of obtaining user authorization). For example, real-time verification of the validity of ID card numbers and the status of unified social credit codes of enterprises.
- Reuse of historical materials:For repetitive services of the same user (enterprise), the system can intelligently retrieve materials (such as ID cards and business licenses) that have been submitted in history and are still valid, avoiding repeated uploads by users and improving the experience.
4) Localized deployment and integration:For example, intelligent identification services are provided in the form of microservice APIs, which are seamlessly connected to existing business approval systems (such as administrative approval systems and market supervision business systems) through standardized data interfaces (such as RESTful APIs) to achieve “recognition and entry” and greatly reduce manual secondary entry.
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Application scenarios and results
- Self-service terminal in the government hall:Users no longer need to fill out lengthy forms item by item. Just take a picture of the ID card, business license and other core documents on the terminal, and the system will instantly (usually < 2 seconds) to complete the identification, extraction, and automatically fill in more than 90% of the form fields. Users only need to check, add a small amount of information, or sign to confirm.
- Lightweight collection of community information:Pilot community staff use mobile terminals or special equipment, and residents can complete information registration by showing their ID cards and taking photos. The system automatically structures the storage and updates it against existing databases.
- Streamlined Review Process:Intelligent pre-examination and material verification pre-exist, solving problems such as incomplete materials and inconsistent information before the user submits the application. When submitting online, the system will immediately feedback material problems; When handling offline, the one-time qualification rate of materials has been greatly improved.
2. Intelligent approval of rules + large model collaboration
Hybrid decision engine build
1) Standardized rules engine:
- Digitization of rules and knowledge solidification:Structure and code the clear black and white approval conditions (such as the minimum limit of registered capital, business scope restrictions, and specific qualification requirements) in laws, regulations, policy documents, and departmental rules to form a rule base. The rules engine makes logical judgments based on this library.
- Efficient execution of “IF-THEN”:For standardized matters such as enterprise registration, establishment of individual industrial and commercial households, and some simple license filings, after the system receives complete and compliant materials, the rule engine instantly completes all conditional judgments (such as: registered capital ≥ statutory minimum limit? The business scope does not involve a negative list? The legal person has no record of dishonesty? ), if it is met, it will automatically “batch in seconds” and instantly generate electronic licenses or approval documents.
2) Large model engine:
(1) Understand policy intentions and situations:For matters such as policy subsidy applications, special industry access, and complex administrative reconsideration, which have vague rule boundaries and require comprehensive research and judgment, the rule engine is inadequate. At this time, a fine-tuned large language model in the government field is introduced.
(2) Core competencies:
- Semantic Understanding and Association Analysis:LLMs deeply understand the application descriptions and supporting materials submitted by users (such as project plans and audit reports). Combined with the embedded government knowledge graph (including policy provisions, historical cases, industry norms, departmental functions, etc.), automatically associate relevant policy basis.
- Corporate/Personal Portrait Fusion:Integrate the historical behavior data (credit history, tax status, past application records, etc.) and basic attribute data of the applicant (enterprise or individual) into a dynamic portrait to provide more comprehensive background information for approval decisions.
- Generative review recommendations:LLMs synthesize the above information to generate structured reports on review points or recommendations for decisions. For example: “The enterprise applies for scientific and technological innovation subsidies, and the proportion of its R&D investment meets the requirements of Article X of Policy A, but it needs to supplement the third-party R&D expense audit report required by Policy B; Based on their good credit history, it is recommended to prioritize review. This provides strong decision-making support for manual approvers and greatly shortens the time they spend reviewing policies and comparing materials.
3) RPA+OCR process automation blessing:For example, Zhongshan City’s “intelligent approval 2.0” in the pension treatment approval scenario. RPA robots automatically log in to social security, public security, civil affairs and other business systems, and capture key information such as applicants’ age, insurance records, and household registration status according to rules; OCR quickly identifies the application form and identity certificate submitted by the applicant. The combination of the two automatically completes most of the information collection and verification work, compressing the material collection process that would otherwise take days or even weeks into a very short time.
Risk prevention and control and collaborative optimization
1) Abnormal path map:
- Machine learning (such as unsupervised learning and graph neural network) is used to analyze massive historical approval data streams and construct a “normal approval path” model.
- Once an abnormal pattern deviating from the “normal path” is found (such as: intensive application by specific personnel, abnormally concentrated material submission time, high similarity of materials for different applicants, and frequent modification of key fields), the system automatically triggers risk warnings and marks high-risk points (such as “suspected material fraud” and “risk of benefit transmission”).
- Move risk control from the traditional “post-event accountability” to “in-process blocking”, such as using intelligent identification and data comparison to effectively identify false evidence materials and intervene before problems cause substantial losses.
2) Smart Routing Engine:
- For matters that require multi-departmental joint review (such as engineering construction project approval), the system automatically assigns tasks to the most appropriate department or personnel based on preset rules (departmental responsibilities, item relevance, current load) and intelligent algorithms (such as shortest path, load balancing).
- Built-in intelligent supervision clock: set a reasonable time limit for each link, and automatically trigger a graded reminder (system reminder – > supervisor supervision – > superior notification) when it is approaching or overtime is not processed, and push the joint review through SMS, workbench messages, emails, etc.
3. Multi-modal interactive government assistant
Technology integration and functional innovation
1) Full-stack voice interaction capabilities:
- ASR (Speech Recognition) Evolution:The deep neural network model is adopted, combined with acoustic model and language model adaptive technology. The key lies in strong dialect and accent support (such as iFLYTEK Spark V4.0 supports 37 dialects), and through massive government voice data training with dialect labels, the recognition accuracy in noisy environments (such as office halls) and Mandarin with accents is significantly improved (more than 86% in complex environments). Switch-free recognition technology eliminates the need for users to manually select dialect modes, and the system automatically recognizes and adapts.
- TTS (Speech Synthesis) Anthropomorphism:No more cold machine sounds. End-to-end deep learning synthesis technology (such as TacotronVITS) is used to generate voice broadcasts that are clear, smooth, natural, and even with appropriate emotion (such as notification solemnity, consultation affinity). Supports a wide range of timbre options.
- Multi-Round Conversation Understanding Engine:At the heart is Conversation State Management (DST) and contextual understanding. The system remembers the user’s previous questions and answers, and handles complex situations such as referents (such as “the policy mentioned above”, “what materials does it require”), ellipses, and more. Combined with intent recognition technology, it accurately captures the real needs of users (is it the query progress?). Or consult the policy? Or a complaint? ）。
2) Large model + chain of thought + knowledge graph:
- Large model base:Provides strong language understanding, generation, and reasoning capabilities.
- Chain-of-Thought Technology:Let AI simulate human thinking processes when answering complex policy consultations and “reason” answers step by step, rather than just matching keywords. For example, a user asks, “My company has just been established, what subsidies can I apply for?” The system will think step by step: 1) Determine the type of company (technology?) Small? Startup? ）； 2) Locate the place of registration (different regional policies); 3) Matching the establishment time (some subsidies have establishment requirements); 4) Comprehensively output the list of eligible subsidies and a brief description. This makes the answers more precise and logical.
- Dynamic Knowledge Graph:As a system “memory”, it stores structured and related policies and regulations, service guides, frequently asked questions (FAQs), departmental information, etc. The knowledge graph is updated in real-time to ensure the timeliness and accuracy of answers. The entities (e.g., policies, departments, materials) and relationships in the graph (e.g., “a policy is responsible for a department”, “Application A requires material B”) is the basis for accurate answers.
3) Emotion Recognition and Interaction Optimization:By analyzing the acoustic characteristics of speech (speech speed, intonation, volume) and the semantic emotion of the text, the system can preliminarily judge the user’s emotional state (such as anxiety, confusion, dissatisfaction). For the identified negative emotions, the assistant can actively adjust the tone (gentler and more soothing), prioritize the problem, or guide the transfer to manual service in a timely manner to improve the service temperature.
Scenario-based services and user reach
- Intelligent Process Navigation:In complex business processes such as social security transfer, business start-up, and real estate registration, assistants are no longer simple Q&A machines, but dynamic guides. It can actively push next action prompts, required material lists, handling locations/links, and even estimated processing time based on the user’s current link, submitted materials, and to-do items. For example, it can be linked with a dynamic knowledge base through multimodal intent recognition (users may express their needs through text, voice, or even uploading images) to accurately locate user problems and guide them to be solved.
- Seamless Omnichannel Access:The assistant capability is embedded in all user touchpoints such as government affairs apps, WeChat official accounts, mini programs, government portals, hotline IVR systems, and self-service terminals. No matter which channel users initiate consultation or handling, they can get a consistent and coherent service experience.
- Dialect Hotline:After the government hotline is connected to strong dialet recognition capabilities, a large number of users who are accustomed to using dialects (especially the elderly and residents in rural areas) are no longer forced to transfer or give up consultation due to language barriers.
2. User experience optimization system
1. Personalized recommendations for “thousands of people”
User portrait construction
- Multi-dimensional data fusion:Integrate basic user attributes (identity: corporate legal person/self-employed/natural person; Types: new citizens/elderly/entrepreneurs, etc.), historical service records (high-frequency matters, preferred channels, handling results), behavior trajectory (APP clickstream, website browsing path, consulting keywords), feedback evaluation (satisfaction, complaints and suggestions) and other data.
- Dynamic Image Engine:The portrait is not static. The system analyzes the latest behavior of users in real time (such as recently searching for “talent introduction policy” and visiting the social security query page multiple times), and dynamically adjusts the weight of portrait tags. For example, if a user frequently queries about science and technology innovation policies recently, the weight of his “followers of technology-based enterprises” label will increase significantly.
- Group portraits and intelligent grouping:In addition to individual portraits, the system also constructs group portraits (such as “start-up technology enterprises”, “flexible employment personnel”, and “retired elderly”) to understand the common needs of the group and guide service resource planning and policy formulation.
Scenario-based active service
1) Accurate Matching:Based on user portraits, the most relevant services and information are dynamically presented at the service entrance (APP homepage, website personal center, self-service terminal interface).
2) Smart Push:
- Policy Express:Taxpayers automatically receive “bank-tax interaction” financial product recommendations that match their industry and scale; The newly employed youth will see policy pushes such as “talent apartment application” and “vocational skills training subsidy”; Near-retirees will receive “pension calculation” and “qualification certification reminder”.
- Reminder:Reminder of license expiration, annual report submission, subsidy declaration period, etc.
- Related Recommendations:After handling the “enterprise establishment”, it will automatically recommend “one thing” joint services such as “seal engraving”, “social security account opening”, and “tax registration”.
3) Feedback-Driven Recommendation Optimization:The system continuously learns user feedback (click, ignore, praise, negative). If a type of recommendation (such as the “Environmental Approval Guidelines”) is ignored by the same user multiple times, the system will automatically reduce its recommendation weight and explore other services that may be of interest. Form a closed loop of “recommendation – > feedback – > optimization”.
2. Human-machine collaboration fault tolerance mechanism
Human-machine confidence threshold
- Quantifying uncertainty:For the answers, judgments, or recommendations given by AI, the system calculates a confidence score (0%-100%) to reflect its level of grasp. This score is calculated based on multiple factors such as model prediction probability, input data quality, and knowledge base coverage.
- Smart Switching:Set a clear confidence threshold (e.g., 85%). When the confidence level of the AI processing item is below the threshold, the system automatically and seamlessly transfers the human agent to avoid mishandling or adverse experiences. At the same time, key information such as user questions, interaction records conducted by AI, preliminary analysis results, and low-confidence reasons are completely pushed to the human agent as a reference for decision-making and reduce user repetition.
- Adjustable threshold:The confidence threshold can be dynamically adjusted according to the risk sensitivity of different business scenarios (such as simple consultation vs. fund approval).
AI+ feedback closed-loop
1) Convenient feedback channels:After each AI interaction (whether it is converted to human or not), provide a concise feedback entry (such as “Is it solved?” button, five-star rating, text opinion box). After the manual service is over, users are also encouraged to evaluate the overall service.
2) Log mining and root cause analysis:The system automatically records all interaction logs (user input, AI output, confidence level, conversion to manual record, final result, user feedback). Through NLP and log analysis technology, it automatically identifies high-frequency error points, user complaint points, and knowledge blind spots.
3) Continuous iteration of models and knowledge:Based on the results of the analysis, targeted:
- Optimize the model:Adjust model parameters, add scenario-specific training data, and improve intent recognition or semantic understanding modules.
- Expanded Knowledge Base:Supplement missing policy interpretations, update outdated service guidelines, and add new answers to high-frequency questions.
- Perfect the rules:Fixed the validation rule logic in Intelligent Acceptance or Approval.
- Adjust thresholds or interaction policies:If it is found that a certain type of problem is transferred too much, the analysis is whether the threshold setting is unreasonable or the AI ability is insufficient.
3. Data-driven performance evaluation
Multi-dimensional indicator system
- Core Efficiency Indicators:The online handling rate of matters (the proportion of online handling possible), the online handling rate (the proportion of actual online processing), the average processing time (from application to completion), the proportion of that is, the handling process (the proportion of matters that can be completed on the spot), and the material streamlining rate.
- Service quality indicators:User satisfaction (CSAT), net promoter score (NPS), one-time completion rate, problem resolution rate, complaint rate, and negative review rectification rate.
- Resource Efficiency Indicators:The average processing volume of window personnel/back-office approval personnel, the proportion of AI services (the proportion of AI processing matters to the total volume), the manual transfer rate (for AI), and the resource utilization rate (such as the utilization rate of self-service terminals).
- Business value indicators:The realization rate of “running at most once”, the integrated volume of “one thing”, and the efficiency of policy implementation (such as the timeliness of subsidy issuance).
- Benchmarking Reference:Strictly benchmark national standards and industry best practices such as the “Government Service Efficiency Evaluation Specification”.
Data cockpit
1) Multi-level visual management:Develop an integrated visual data platform (cockpit) that supports a multi-level view from city leaders, department heads, to window administrators. the mayor can grasp the efficiency of the city macroscopically; The Director can gain insight into the performance of the department; The hall manager can monitor the queue of each window and the usage rate of AI terminals in real time.
2) Real-time monitoring and early warning:Core indicators (such as satisfaction and processing time) are refreshed in real time. Set threshold alarms (such as the average duration of an event exceeding the promised time limit, or the satisfaction of a window continues to be below the threshold), and push the responsible person in real time through large screen color change, text messages, messages, etc.
3) Deep drilling analysis:Support multi-dimensional drilling analysis by region, department, type of matter, time period, etc., to locate efficiency bottlenecks (such as the registration time of enterprises in XX District is significantly higher than the city’s average?). The online handling rate of XX types of licensing matters is low? ）。
4) Intelligent Assisted Decision-Making:Decision support based on historical data and real-time monitoring. For example:
- Dynamic resource allocation:For example, the algorithm predicts the peak flow of people at each service outlet and time period, intelligently recommends the optimal service time to users (pushed through the APP), and dynamically adjusts the number of window openings and guides personnel allocation to effectively shave peaks and fill valleys.
- Process optimization suggestions:Analyze the time-consuming distribution map of cross-departmental joint review matters, identify stuck links, and promote process reengineering.
- Policy effect evaluation:Monitor the volume of handling of relevant matters and changes in user feedback after the new policy is launched, and evaluate the effect of policy implementation.
3. Implementation challenges of deep intelligence
1. Data security and compliance
Security-first architecture:The core adopts a privatized deployment model, and data sovereignty is firmly in the hands of the government.
Full coverage of national secret algorithms:Data transmission (TLS) and storage (database encryption, file encryption) are all certified by the National Cryptography Administration (e.g., SM2, SM3, SM4).
Blockchain Certificate Credit Enhancement:Key operations (user authorization records, material submission, approval conclusions, electronic license issuance) are stored on the blockchain to ensure that the operation cannot be tampered with, the whole process is traceable, and meets the requirements of judicial evidence collection.
Level 3 reinforcement of equal protection: It strictly meets the requirements of the third level of network security protection, covering all-round protection such as physical security, network security, host security, application security, data security, and backup and recovery.
2. Cross-departmental collaboration and process reconstruction
Top-level design and strong promotion:Set up a “Digital Government Reform Special Class” led by municipal leaders to break down the barriers of departmental interests and strongly promote data sharing and business collaboration.
Data-Based Process Reengineering (D-BPR):It is not simply to take the offline process online, but to reconstruct the end-to-end government service process with data flow as the core. For example, by clearly defining and connecting the data flow of all links of government services (application – > acceptance – > distribution – > approval – > decision – > delivery), the transformation of administrative operation to flat and intelligent is promoted.
Algorithm + departmental collaboration network:Establish a cross-departmental data sharing and exchange platform and a business collaboration platform, use intelligent routing algorithms to automatically connect upstream and downstream department tasks, use the status synchronization mechanism to let all parties know the progress in real time, and use intelligent supervision rules to ensure collaboration efficiency.
3. User acceptance and technology adaptation
Inclusive Design Principles:
- Multi-terminal entry:Ensure that services can be reached through smartphones (APPs/mini programs), computers (websites), self-service terminals, hotlines (including traditional phones), and even community agencies.
- Simplified interactions:Age-appropriate transformation of the APP/website interface (large font, high contrast, concise navigation, voice broadcasting); provide large buttons, clear guidance, and voice assistance at self-service terminals; The hotline retains clear traditional menu button navigation options.
- Offline assistance:Retain and optimize the manual window in the government affairs hall, equipped with guides, to provide assistance to groups that are unwilling or unable to use intelligent services (especially the elderly), and remote video assistance mode can be adopted.
Continuous User Education and Onboarding:Through a variety of offline and online channels such as brochures, official accounts, websites, short videos, community lectures, and hall experience areas, the use and advantages of intelligent services are popularized, and users’ willingness to try and operate can be improved.
4. Build a digital governance community with active service
Through intelligent acceptance, zero entry and intelligent control of materials are realized, through intelligent approval to realize the second batch of standardized matters and intelligent assistance for complex matters, and through multi-modal interaction to realize the anytime, anywhere and natural intimacy of services.
The establishment of a user experience optimization system (personalized recommendation, fault tolerance mechanism) and a closed-loop performance evaluation (index system, data cockpit) is the key guarantee to ensure that this change truly serves the people and continues to optimize. They shift the focus of services from the government supply side to the user experience side, and promote the steady upgrade of government services from basic “can do” to efficient “smart office” and satisfactory “easy to do”.
With the gradual upgrading of AI technology, the government service process will continue to upgrade:
- Multimodal large model deepening:Models with better understanding and generation capabilities will be able to handle more complex policy consultations, write more standardized administrative documents, provide more anthropomorphic interactive experiences, and even proactively discover potential user needs.
- Edge computing empowerment:Deploy lightweight AI models at edge nodes such as office halls and community service stations to achieve faster and more secure localized real-time responses (such as material pre-approval and simple consultation), reducing network dependency and latency.
- Proactive service, predictive service:Based on deep learning and predictive analysis of user portraits and behavioral data, the system will be able to actively push services that users have not yet noticed but urgently need (such as: “According to your business data, it is recommended that you apply for XX job stabilization subsidy, which is expected to receive YY million yuan”), or predictive intervention before problems occur (such as: “Detect an abnormal decline in your company’s electricity consumption, there may be business difficulties, and recommend relevant relief policies for you”).
- Digital Governance Community:The ultimate goal is to build a digital governance ecology with in-depth interaction, co-construction, co-governance and sharing between the government, enterprises and citizens. the government provides a transparent, efficient and intelligent service platform; Enterprises can easily obtain policies, handle affairs, and feedback demands; Citizens can enjoy public services and participate in social governance equally and conveniently. The safe, orderly and efficient flow of data in it has become the core element driving the modernization of governance.
