AI-driven government affair industry intelligent response and execution system

Abstract

The invention provides an AI-driven government affair industry intelligent response and execution system, relates to the technical field of artificial intelligence and government affair informatization, and aims to solve the problems of low efficiency, poor quality, unscientific decision and the like of traditional government affair services. The system comprises a data acquisition module, a data analysis module, an intelligent response module and an execution module. The data acquisition module acquires multi-source government affair data at high frequency, and the data analysis module performs accurate processing by using a deep learning model. The intelligent response module quickly generates an accurate response strategy, and the execution module is efficiently implemented. The intelligent monitoring module guarantees operation, the cloud computing platform guarantees efficient utilization of resources, the block chain technology guarantees data security, and the intelligent agent technology promotes service innovation. According to the invention, government affair service efficiency and quality are improved, decision scientificity is enhanced, resource configuration is optimized, cost is reduced, data security is guaranteed, and intelligent development of government affair service is powerfully promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of artificial intelligence and e-government informatization. More specifically, it particularly relates to an AI-driven intelligent response and execution system for the e-government industry. Background Art

[0002] In today's digital age, e-government services are facing increasing demands and challenges. With the development of society and the continuous improvement of the public's requirements for the quality of e-government services, the limitations of traditional e-government processing methods are gradually emerging.

[0003] In the past, the collection of e-government data mainly relied on manual filling and limited information systems. The data collection frequency was low, the scope was narrow, and errors and incomplete data were prone to occur. This led to a lack of comprehensive, accurate, and timely data support for government departments in decision-making, making it difficult to make scientific and reasonable decisions.

[0004] In terms of data analysis, traditional methods usually based on simple statistics and empirical judgments were unable to deeply explore the potential correlations and trends in the data. This made it difficult for the government to discover potential problems and needs, and unable to carry out effective planning and responses in advance.

[0005] The response mechanism of e-government services was also relatively slow and passive. It often dealt with problems or public demands after they occurred, and was unable to actively predict and meet the needs of the public. Moreover, the response content often lacked personalization and accuracy, and could not effectively solve practical problems.

[0006] There were also many problems in the execution link, such as low efficiency caused by manual operations, inconsistent execution standards, and difficulty in tracking and evaluating the execution effect. This not only affected the quality and efficiency of e-government services, but also reduced the public's satisfaction and trust in government services.

[0007] In addition, with the continuous expansion of the scope of e-government services and the increasing variety of business types, traditional e-government processing methods were difficult to cope with complex and changeable situations, and could not achieve the optimal allocation and efficient utilization of resources.

[0008] Against this background, it has become an urgent task to use artificial intelligence technology to realize the intelligence, automation, and high efficiency of e-government services. The AI-driven intelligent response and execution system for the e-government industry emerged as the times require, aiming to solve the pain points and difficulties in traditional e-government services, improve the level and quality of e-government services, and meet the growing needs of the public. Summary of the Invention

[0009] In order to solve the above technical problems, the present invention provides an AI-driven intelligent response and execution system for the e-government industry to solve the above problems.

[0010] An AI-driven intelligent response and execution system for the government affairs industry, including a data collection module, a data analysis module, an intelligent response module, and an execution module. The data collection module is used to collect government affairs-related data, with a data collection frequency of once every 15 minutes, covering government service windows, online platforms, mobile terminals, and sensor devices. The amount of data collected through the distributed data collection algorithm reaches 100,000 pieces per day. The data analysis module analyzes and processes the collected data based on artificial intelligence algorithms to extract key information and insight into potential needs, using deep learning models such as convolutional neural networks (CNNs), with a data processing speed of 1,000 pieces per second and an accuracy rate of 95%. The intelligent response module generates corresponding response strategies according to the analysis results, using natural language processing technology and intelligent reasoning algorithms, with an accuracy rate of the generated response content not less than 90% and a response time not exceeding 5 seconds. The execution module is responsible for converting the response strategy into actual government affairs execution actions, with an execution success rate of 98%.

[0011] Preferably, the sensor devices in the data collection module include temperature sensors, humidity sensors, and pressure sensors, with measurement accuracies of ±0.5°C, ±2%RH, and ±0.1 Pa respectively, and the data transmission uses the TCP / IP protocol with a transmission rate not less than 100 Mbps.

[0012] Preferably, the deep learning model in the data analysis module is trained through the Stochastic Gradient Descent (SGD) algorithm, with the scale of the training dataset reaching 500,000 samples, and the overfitting rate of the model not higher than 5%.

[0013] Preferably, the natural language processing technology in the intelligent response module uses Bidirectional Long Short-Term Memory Networks (BiLSTMs), which can recognize and understand at least 10 languages, and the semantic understanding accuracy rate is not less than 92%.

[0014] Preferably, the execution module is integrated with the business systems of government departments at all levels through WebService interfaces to achieve seamless docking and efficient execution of response strategies, with an execution delay time not exceeding 100 milliseconds.

[0015] Preferably, it further includes an intelligent monitoring module for real-time monitoring of the system operation status and government affairs execution effects, and timely discovery and warning of potential problems. The intelligent monitoring module is based on machine learning algorithms such as Support Vector Machines (SVMs), and performs predictive analysis on the monitoring data, with a prediction accuracy rate of 90% and a false alarm rate not higher than 3%.

[0016] Preferably, the warning mechanism in the intelligent monitoring module sets different levels of warning thresholds according to the deviation degree of system performance indicators. When the system performance indicators deviate from the normal range by more than 10%, a first-level warning is issued; when it exceeds 20%, a second-level warning is issued.

[0017] Preferably, the system is built on a cloud computing platform to achieve elastic allocation and efficient utilization of resources, ensuring high availability and scalability of the system. The resource allocation response time of the cloud computing platform does not exceed 3 seconds, and the downtime during system expansion does not exceed 5 minutes.

[0018] Preferably, the system uses blockchain technology to ensure the security, integrity, and immutability of data, protecting the privacy of government affairs data. The hash algorithm of the blockchain uses SHA-256, the generated hash value length is 256 bits, and the data encryption strength reaches the AES-256 standard.

[0019] Preferably, the system simulates the work processes and decision-making patterns of government affairs personnel through agent technology to achieve intelligent government affairs services and management. The decision accuracy rate of the agent is not less than 85%, and it can adaptively adjust the decision-making strategy according to different government affairs scenarios, with the adjustment time not exceeding 3 minutes.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve the efficiency of government affairs processing: The data collection frequency is as high as once every 15 minutes, enabling timely acquisition of the latest government affairs-related information and avoiding decision-making delays caused by information lag.

[0021] 2. The application of deep learning models and intelligent algorithms has significantly improved the data analysis speed. For example, 1000 pieces of data can be processed per second, quickly obtaining accurate analysis results and providing timely support for decision-making.

[0022] 3. The intelligent response module generates a response strategy within 5 seconds, and the success rate of the execution module reaches 98%, significantly shortening the government affairs processing cycle and improving the overall work efficiency.

[0023] 4. Improve the quality of government affairs services: The application of natural language processing technology and intelligent reasoning algorithms results in a response content accuracy rate of not less than 90%, ensuring that the information provided to the public is accurate, clear, and useful.

[0024] 5. It can provide personalized services according to different government affairs scenarios and needs, meeting the diverse needs of the public and improving the public's satisfaction with government affairs services.

[0025] 6. Enhance the scientific nature of government affairs decision-making: Large-scale training data sets (such as 500,000 samples) and advanced model training algorithms (such as stochastic gradient descent algorithm) enable the data analysis model to have stronger generalization ability and prediction ability.

[0026] 7. In-depth analysis and mining of government affairs data can discover potential problems and trends, providing strong data support for government departments to formulate scientific and reasonable policies and decisions.

[0027] 8. Optimize resource allocation: The intelligent monitoring module monitors the system operation status and the effect of government affairs execution in real time, dynamically allocates resources according to actual needs, and avoids waste and shortage of resources.

[0028] 9. The elastic allocation and efficient utilization of resources on the cloud computing platform ensure the stable operation of the system under high load conditions and improve the resource utilization efficiency.

[0029] 10. Reduce government affairs costs: The automated government affairs processing flow reduces manual intervention and lowers labor costs. Precise decision-making and efficient execution reduce errors and repetitive work, lowering error correction costs and time costs. The adoption of blockchain technology ensures the security, integrity, and immutability of data, protects the privacy of government affairs data, and reduces the risk of data leakage. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a schematic diagram of the system composition of the present invention; Figure 2 is a schematic diagram of the functions of each module in the present invention; Figure 3 is a schematic diagram of each sensor in the data acquisition module of the present invention; Figure 4 is a schematic diagram of the data analysis module of the present invention; Figure 5 is a schematic diagram of the intelligent response module of the present invention; Figure 6 is a schematic diagram of the connection between the execution module and other business systems of the present invention; Figure 7 is a schematic diagram of the intelligent monitoring module of the present invention; Figure 8 is a schematic diagram of the warning mechanism of the intelligent monitoring module of the present invention; Figure 9 is a schematic diagram of the connection between the system and the cloud computing platform of the present invention; Figure 10 is a schematic diagram of the connection between the system and the blockchain technology of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] With the rapid development of information technology and the continuous growth of government affairs service demands, the traditional government affairs processing method has been difficult to meet the requirements of high efficiency, precision, and intelligence. The AI-driven intelligent response and execution system for the government affairs industry proposed by the present invention aims to solve this problem. By integrating advanced technical means, it realizes the intelligence, automation, and high efficiency of government affairs services. The following further describes the implementation manner of the present invention in detail with reference to the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0032] Please refer to Figures 1 - 10 , the present invention provides an AI-driven intelligent response and execution system for the government affairs industry, including a data collection module, a data analysis module, an intelligent response module, and an execution module. The data collection module is used to collect government affairs-related data, with a data collection frequency of once every 15 minutes, covering government service windows, online platforms, mobile terminals, and sensor devices. The amount of data collected through the distributed data collection algorithm reaches 100,000 pieces per day. The data analysis module analyzes and processes the collected data based on artificial intelligence algorithms to extract key information and insight into potential needs, using deep learning models such as convolutional neural networks (CNNs), with a data processing speed of 1,000 pieces per second and an accuracy rate of 95%. The intelligent response module generates corresponding response strategies according to the analysis results, using natural language processing technology and intelligent reasoning algorithms, with the accuracy rate of the generated response content not less than 90% and the response time not exceeding 5 seconds. The execution module is responsible for converting the response strategy into actual government affairs execution actions, with an execution success rate of 98%.

[0033] The sensor devices in the data collection module include temperature sensors, humidity sensors, and pressure sensors, with measurement accuracies of ±0.5°C, ±2%RH, and ±0.1 Pa respectively, and the data transmission uses the TCP / IP protocol with a transmission rate not less than 100 Mbps.

[0034] The deep learning model in the data analysis module is trained through the Stochastic Gradient Descent (SGD) algorithm, with the scale of the training dataset reaching 500,000 samples, and the overfitting rate of the model not higher than 5%.

[0035] The natural language processing technology in the intelligent response module uses Bidirectional Long Short-Term Memory Networks (BiLSTMs), which can recognize and understand at least 10 languages, and the semantic understanding accuracy rate is not less than 92%.

[0036] The execution module is integrated with the business systems of government departments at all levels through WebService interfaces to achieve seamless docking and efficient execution of response strategies, with the execution delay time not exceeding 100 milliseconds.

[0037] It also includes an intelligent monitoring module for real-time monitoring of the system operation status and government affairs execution effects, and timely discovery and warning of potential problems. The intelligent monitoring module is based on machine learning algorithms such as Support Vector Machines (SVMs), performs predictive analysis on the monitoring data, with a prediction accuracy rate of 90% and a false alarm rate not higher than 3%.

[0038] The early warning mechanism in the intelligent monitoring module sets different levels of early warning thresholds according to the deviation degree of system performance indicators. When the system performance indicators deviate from the normal range by more than 10%, a first-level early warning is issued; when it exceeds 20%, a second-level early warning is issued.

[0039] The system is built on a cloud computing platform to achieve elastic allocation and efficient utilization of resources, ensuring the high availability and scalability of the system. The resource allocation response time of the cloud computing platform does not exceed 3 seconds, and the downtime during system expansion does not exceed 5 minutes.

[0040] The system uses blockchain technology to ensure the security, integrity, and immutability of data, protecting the privacy of government affairs data. The hash algorithm of the blockchain uses SHA-256, the generated hash value length is 256 bits, and the data encryption strength reaches the AES-256 standard.

[0041] The system simulates the work processes and decision-making modes of government affairs personnel through agent technology to achieve intelligent government affairs services and management. The decision accuracy rate of the agent is not less than 85%, and it can adaptively adjust decision-making strategies according to different government affairs scenarios, with the adjustment time not exceeding 3 minutes.

[0042] Example 1: Tax declaration service: Data collection: Through channels such as the tax system interface, enterprise financial software, and online declaration platforms, information such as the enterprise's financial data and tax declaration records is collected at a frequency of once every 15 minutes.

[0043] Sensor devices are used to collect environmental data such as temperature and humidity in the enterprise's production and operation sites as auxiliary analysis factors.

[0044] Data analysis: Convolutional neural network (CNN) is used to extract features and classify the collected data to identify the enterprise's tax behavior patterns and potential risks.

[0045] Combined with historical data and industry standards, a regression analysis algorithm is used to predict the enterprise's taxable amount.

[0046] Intelligent response: According to the analysis results, personalized tax declaration reminders and suggestions are generated and pushed to the enterprise through methods such as text messages, emails, and government affairs APPs.

[0047] For enterprises with tax risks, a risk warning report is generated and corresponding solutions are provided.

[0048] Execution: Automatically complete the filling and submission of tax declaration data to achieve one-key declaration.

[0049] Track and provide feedback on the application results to ensure the smooth completion of the application process.

[0050] Example 2: Urban traffic management service: Data collection: Collect traffic flow, vehicle speed, road conditions and other data from traffic cameras, sensors, in-vehicle devices and mobile terminals, etc., and update it every 15 minutes.

[0051] Integrate the operation data of public transportation such as buses, subways and taxis.

[0052] Data analysis: Use deep learning models to analyze traffic data and predict traffic congestion sections and periods.

[0053] Through correlation analysis, find out the relationship between traffic congestion and factors such as weather and holidays.

[0054] Intelligent response: According to the analysis results, dynamically adjust the traffic signal duration to optimize traffic flow.

[0055] Push real-time traffic information and best travel route suggestions to the public.

[0056] Execution: Automatically control traffic facilities such as variable lanes and intelligent street lights.

[0057] Automatically monitor and punish traffic violations.

[0058] Example 3: Social assistance service: Data collection: Collect basic information, income status, medical expenses and educational needs of vulnerable groups such as low-income families, disabled people and elderly people living alone from civil affairs departments, communities, hospitals and educational institutions, etc., and summarize it every 15 minutes.

[0059] Use social media and online platforms to collect relevant public opinions and help-seeking information.

[0060] Data analysis: Use clustering analysis algorithms to classify vulnerable groups and evaluate the urgency of their assistance needs.

[0061] Combine policies, regulations and financial budgets to analyze the distribution of assistance resources.

[0062] Intelligent response: Generate assistance application forms and material lists for eligible vulnerable groups and provide application guidance through online and offline channels.

[0063] For emergency relief needs, activate the rapid response mechanism and coordinate relevant departments and social forces to provide assistance.

[0064] Execution: Automatically review relief applications and distribute relief funds and materials.

[0065] Track and evaluate the relief effect, and adjust the relief plan in a timely manner.

[0066] Comparison Example 1: Traditional tax filing service: Data collection: Enterprises submit tax information to the tax department through paper forms or spreadsheets, usually on a monthly or quarterly basis.

[0067] The tax department manually collects and organizes data, which is inefficient and error-prone.

[0068] Data analysis: Mainly rely on manual experience and simple statistical analysis methods, making it difficult to discover deep-seated problems and risks.

[0069] Intelligent response: Tax payment reminders and suggestions are conveyed through paper notices or phone calls, which are not timely and personalized enough.

[0070] Execution: Tax filing requires enterprise financial personnel to manually fill out a large number of forms, and the filing process is cumbersome. Comparison Example 2: Traditional urban traffic management service: Data collection: Traffic data is mainly collected by fixed-position cameras and sensors, with limited coverage and low data update frequency.

[0071] Public transportation operation data is scattered among different departments and enterprises, making it difficult to integrate and utilize.

[0072] Data analysis: The analysis methods are mainly based on historical data and empirical formulas, with low prediction accuracy.

[0073] Intelligent response: Traffic signal control and route planning mainly rely on fixed patterns and manual intervention, lacking flexibility.

[0074] Execution: The control and adjustment of traffic facilities require manual operation, with a slow response speed.

[0075] Comparison Example 3: Traditional social assistance service: Data collection: Relief application information is mainly submitted through paper materials, and the collection and organization process is long.

[0076] Community workers visit households door-to-door to obtain some information, which is labor-intensive and the information is incomplete.

[0077] Data analysis: The analysis method is mainly based on manual screening and simple calculations, making it difficult to accurately assess the relief needs.

[0078] Intelligent response: Relief notices and guidance are conveyed through community announcements or phone calls, which are prone to omissions and delays.

[0079] Execution: The distribution of relief funds and materials requires multiple levels of approval, with a long cycle and low efficiency.

[0080] To verify the effectiveness of the AI-driven intelligent response and execution system for the government affairs industry of the present invention, we conducted the following experiments: Experimental example 1: Evaluation of the effect of tax declaration service: Experimental design: Select 100 enterprises as the experimental group and use the system of the present invention for tax declaration services; select another 100 enterprises as the control group and use the traditional tax declaration service method.

[0081] Set evaluation indicators, including declaration accuracy rate, declaration time, accuracy rate of tax payment risk identification, and enterprise satisfaction, etc.

[0082] Experimental results: The declaration accuracy rate of the experimental group reached 98%, while that of the control group was 85%.

[0083] The average declaration time of the experimental group was 30 minutes, while that of the control group was 2 hours.

[0084] In terms of the accuracy rate of tax payment risk identification, the experimental group was 90%, while the control group was 60%.

[0085] The enterprise satisfaction survey showed that the satisfaction of the experimental group was 95%, while that of the control group was 70%.

[0086] Experimental example 2: Evaluation of the effect of urban traffic management service: Experimental design: Select two areas with similar traffic conditions as the experimental group and the control group respectively. The experimental group uses the system of the present invention for traffic management, and the control group uses the traditional traffic management method.

[0087] The evaluation indicators include traffic congestion index, average vehicle speed, traffic accident incidence rate, and citizen satisfaction, etc.

[0088] Experimental results: After a one-month experiment, the traffic congestion index of the experimental group decreased by 30%, while that of the control group decreased by 10%.

[0089] The average vehicle speed of the experimental group increased by 25%, while that of the control group increased by 10%.

[0090] In terms of the accident incidence rate, the experimental group decreased by 20%, and the control group decreased by 5%.

[0091] The public satisfaction survey shows that the satisfaction rate of the experimental group is 85%, and that of the control group is 60%.

[0092] Experimental Example 3: Evaluation of the Effect of Social Assistance Services: Experimental Design: Select 200 vulnerable families as the experimental group to receive the social assistance services provided by the system of the present invention; select another 200 families as the control group to receive traditional social assistance services.

[0093] The evaluation indicators include the review time of assistance applications, the accuracy rate of assistance fund disbursement, the accuracy rate of assistance effect evaluation, and the satisfaction of assisted families, etc.

[0094] Experimental Results: The average review time of assistance applications in the experimental group is 5 working days, while that in the control group is 20 working days.

[0095] In terms of the accuracy rate of assistance fund disbursement, the experimental group is 99%, and the control group is 90%.

[0096] In terms of the accuracy rate of assistance effect evaluation, the experimental group is 95%, and the control group is 80%.

[0097] The satisfaction survey of assisted families shows that the satisfaction rate of the experimental group is 90%, and that of the control group is 75%.

[0098] By statistically analyzing the data of the above-mentioned examples, comparative examples and experimental examples, we can draw the following conclusions: It can be clearly seen from the above data that the AI-driven intelligent response and execution system for the government affairs industry of the present invention shows significant advantages in various services, can greatly improve the efficiency and quality of government affairs services, and enhance the satisfaction of the public.

[0099] Through the detailed elaboration and analysis of the above-mentioned multiple embodiments, comparative examples, and experimental examples, the advantages of the AI-driven intelligent response and execution system in the government affairs industry of the present invention are clearly demonstrated. With advanced data acquisition technology, the system realizes multi-source and high-frequency data acquisition, ensuring the comprehensiveness and timeliness of information. Its powerful data analysis ability, using cutting-edge deep learning models and algorithms, deeply mines the potential patterns and correlations in the data, providing a solid foundation for accurate decision-making. The intelligent response module can quickly generate personalized and targeted strategies to meet diverse government affairs needs. The efficient execution module ensures the accurate and rapid implementation of strategies, forming a complete service loop.

[0100] Compared with traditional government affairs service methods, the system has achieved a qualitative leap in key indicators such as efficiency, accuracy, adaptability, and satisfaction. It not only significantly shortens the business processing time and improves work efficiency, but also significantly enhances the accuracy and quality of services, effectively reducing the error rate and risks. At the same time, its personalized service mode and real-time response ability greatly enhance user satisfaction and trust.

[0101] In future development, the system will continue to keep up with the pace of technological development, continuously optimize and improve its own functions. It will continuously introduce new artificial intelligence technologies and algorithms to further enhance the system's performance and intelligence level. Strengthen the integration and collaboration with other government affairs systems to build a more integrated and efficient government affairs service ecosystem. Through continuous innovation and improvement, the system is expected to play a more important leading role in the field of government affairs services, making more significant and lasting contributions to promoting the digital and intelligent transformation of government affairs services, enhancing the government's governance ability and public service level.

[0102] In short, the system of the present invention brings new development opportunities and possibilities to the government affairs industry, setting a new benchmark for achieving more intelligent, efficient, and high-quality government affairs services.

[0103] The embodiments of the present invention are given for purposes of illustration and description, and are not exhaustive or limit the invention to the disclosed form. Many modifications and variations are obvious to those of ordinary skill in the art. The embodiments are selected and described to better illustrate the principles and practical applications of the present invention, and to enable those of ordinary skill in the art to understand the present invention and design various embodiments with various modifications suitable for specific purposes.

Claims

1. An AI-driven intelligent response and execution system for the government affairs industry, characterized in that: It includes a data acquisition module, a data analysis module, an intelligent response module, and an execution module; The data acquisition module is used to collect government-related data. Its data acquisition frequency is once every 15 minutes and it is collected through a distributed data acquisition algorithm; The data analysis module analyzes and processes the collected data based on artificial intelligence algorithms and adopts a deep learning model; The intelligent response module generates corresponding response strategies according to the analysis results, using natural language processing technology and intelligent reasoning algorithms; The execution module converts the response strategy into actual government execution actions.

2. The AI-driven intelligent response and execution system for the government affairs industry according to claim 1, wherein The sensor devices in the data acquisition module include temperature sensors, humidity sensors, and pressure sensors, and data transmission uses the TCP / IP protocol.

3. An AI-driven intelligent response and execution system for the government affairs industry as claimed in claim 1, characterized in that, The deep learning model in the data analysis module is trained through the Stochastic Gradient Descent (SGD) algorithm, and the scale of the training dataset is 500,000 samples.

4. The AI-driven intelligent response and execution system for the government affairs industry according to claim 1, characterized in that, The natural language processing technology in the intelligent response module adopts a Bidirectional Long Short-Term Memory Network (BiLSTM).

5. An AI-driven intelligent response and execution system for the government affairs industry according to claim 1, characterized in that, The execution module is integrated with the business systems of government departments at all levels through a WebService interface.

6. The AI-driven intelligent response and execution system for the government affairs industry according to claim 1, wherein It also includes an intelligent monitoring module, and the intelligent monitoring module is based on machine learning algorithms.

7. An AI-driven intelligent response and execution system for the government affairs industry as claimed in claim 1, characterized in that, The early warning mechanism in the intelligent monitoring module sets different levels of early warning thresholds according to the deviation degree of system performance indicators.

8. An AI-driven intelligent response and execution system for the government affairs industry according to claim 1, characterized in that, The system is built on a cloud computing platform, and the resource allocation response time of the cloud computing platform does not exceed 3 seconds.

9. The AI-driven intelligent response and execution system for the government affairs industry according to claim 1, characterized in that, The system adopts blockchain technology, and the hash algorithm of the blockchain adopts SHA-256.

10. The AI-driven intelligent response and execution system for the government affairs industry according to claim 1, characterized in that, The system simulates the work processes and decision-making modes of government personnel through agent technology, and the agent can adaptively adjust decision-making strategies according to different government scenarios.

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