Rural intelligent governance method and system integrated with digital human

Abstract

The invention provides a village intelligent governance method and system integrated with digital humans in the technical field of intelligent villages and digital governance, and the method comprises the steps: S1, collecting village monitoring data through an Internet of Things device, a mobile terminal and a third-party system, encrypting the village monitoring data into encrypted monitoring data, and transmitting the encrypted monitoring data to a cloud platform; s2, the cloud platform decrypts the encrypted monitoring data to obtain rural monitoring data, pre-processes each piece of rural monitoring data, inputs the pre-processed rural monitoring data into a machine learning model to obtain a data analysis result, and stores each piece of rural monitoring data and the data analysis result into a database; s3, after the cloud platform executes identity authentication, an input interaction instruction is obtained, the interaction instruction is analyzed through the digital human engine to obtain instruction content, the rural governance service is executed based on the instruction content, and an execution result is fed back through the digital human engine; and S4, the cloud platform records the interaction log in real time and stores the interaction log in a database. The system has the advantages that the intelligent level of rural governance and the user experience are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of smart village and digital governance technology, and in particular to a smart village governance method and system that integrates digital humans. Background Technology

[0002] Currently, some rural areas have initially established information management systems for business areas such as land management, agricultural resource statistics, and crop planting planning, aiming to promote the online transformation of public services and improve government transparency and efficiency. However, most existing systems are still limited to specific business modules, with relatively simple functions, and have not yet formed a comprehensive platform covering all aspects of rural governance, resulting in significant information silos and difficulties in cross-departmental collaboration. Specifically, existing technologies mainly have the following shortcomings:

[0003] First, the system's functions are relatively basic, mostly providing only routine services such as service guide inquiries, form downloads, and online consultations. While this improves the convenience of handling affairs to some extent, the overall level of intelligence is low, lacking advanced functions such as in-depth data analysis, decision support, and personalized recommendations. Furthermore, the system's interface design and operating procedures are not user-friendly for the elderly or villagers with limited digital literacy, resulting in a high barrier to entry for actual use.

[0004] Secondly, existing systems are typically built around a single business area (such as land management, agricultural statistics, or planting planning), lacking cross-business collaboration and functional integration mechanisms. Inconsistent data standards and poor interface interoperability between systems further exacerbate the problems of information isolation and resource sharing.

[0005] Third, the lack of unified data governance standards and integration mechanisms leads to fragmented data sources and heterogeneous storage structures, making it difficult to achieve cross-system data fusion and global analysis, which severely restricts the data's ability to support macro-level decision-making.

[0006] Although some economically developed or digitally pilot villages have begun to explore the application of technologies such as big data and artificial intelligence in governance aspects such as resource allocation, disaster early warning, and public opinion monitoring, the relevant practices are still mainly focused on the dimension of agricultural production. The integrated and intelligent application in the field of social governance is still in its initial stage, and the overall maturity is limited.

[0007] In summary, while existing technologies have made some progress in promoting the informatization of rural governance, they still generally suffer from insufficient intelligence and poor user experience. In particular, there is a lack of a comprehensive smart rural governance solution that can integrate data fusion, intelligent analysis, visualization, and user-friendly interaction to meet the multi-dimensional and in-depth governance needs of rural areas.

[0008] Therefore, how to provide a method and system for smart rural governance that integrates digital humans to improve the level of intelligence in rural governance and enhance user experience has become an urgent technical problem to be solved. Summary of the Invention

[0009] The technical problem to be solved by this invention is to provide a method and system for smart rural governance that integrates digital humans, thereby improving the level of intelligence in rural governance and enhancing user experience.

[0010] In a first aspect, the present invention provides a method for smart rural governance integrating digital humans, comprising the following steps:

[0011] Step S1: The edge server collects rural monitoring data through IoT devices, mobile terminals and third-party systems, encrypts the rural monitoring data into encrypted monitoring data and sends it to the cloud platform.

[0012] Step S2: The cloud platform decrypts the received encrypted monitoring data to obtain rural monitoring data. After preprocessing each rural monitoring data, it inputs it into a pre-deployed machine learning model to perform data analysis operations, obtains data analysis results, and stores each rural monitoring data and data analysis results in the database.

[0013] Step S3: After the cloud platform performs identity authentication, it obtains the user's input interaction instructions, parses the interaction instructions through the digital human engine to obtain the instruction content, executes rural governance services based on the instruction content, and feeds back the execution results through the digital human engine.

[0014] Step S4: The cloud platform records the interaction logs in real time and stores the interaction logs in the database.

[0015] Furthermore, step S1 specifically includes:

[0016] The edge server collects rural monitoring data through IoT devices, WeChat mini-programs or apps running on mobile terminals, and third-party systems; the IoT devices include at least video surveillance cameras that support AI recognition, temperature sensors, humidity sensors, laser particulate matter sensors, and water quality sensors;

[0017] The edge server encrypts the village monitoring data into encrypted monitoring data using the SM2 or SM3 algorithm, and then sends the encrypted monitoring data to the cloud platform via a 5G communication module or fiber optic broadband based on the TLS protocol.

[0018] Furthermore, step S2 specifically includes:

[0019] The cloud platform decrypts the received encrypted monitoring data using the SM2 or SM3 algorithm to obtain rural monitoring data. It performs preprocessing on each rural monitoring data, including at least data cleaning, normalization, and tagging. The preprocessed rural monitoring data is then input into a pre-deployed machine learning model to perform data analysis operations, obtain data analysis results, and store the rural monitoring data and data analysis results in a database.

[0020] The database includes a structured sub-database, an unstructured sub-database, and a graph sub-database; the structured sub-database uses MySQL; the unstructured sub-database uses MongoDB; and the graph sub-database uses Neo4j.

[0021] Furthermore, step S3 specifically includes:

[0022] The cloud platform obtains a JWT access token through the OAuth 2.0 authorization code process. After performing identity authentication using the signature and content of the JWT access token, it obtains the user's interactive instructions input in the form of text, voice, or gesture. The digital human engine parses the interactive instructions to obtain the instruction content. Based on the instruction content, it executes rural governance services such as real-time data display, heat map analysis, trend analysis, early warning prompts, policy interpretation, transaction guidance, psychological counseling, event reporting, event classification, task assignment, processing feedback, risk warning, trend prediction, decision suggestion generation, user profile generation, or service push. The execution results are recorded, and the execution results are fed back in the form of text, voice, or gesture through the digital human engine. The execution results are also synchronized through large screens, mobile terminals, and PCs.

[0023] Furthermore, step S4 specifically includes:

[0024] The cloud platform records interaction logs in real time, including at least the instruction content, execution result, and interaction time, and stores the interaction logs in a structured database.

[0025] Secondly, this invention provides a smart rural governance system integrating digital humans, comprising the following modules:

[0026] The rural monitoring data acquisition module is used by the edge server to collect rural monitoring data through IoT devices, mobile terminals and third-party systems, and encrypt the rural monitoring data into encrypted monitoring data before sending it to the cloud platform.

[0027] The rural monitoring data analysis module is used to decrypt the encrypted monitoring data received by the cloud platform to obtain rural monitoring data, preprocess each rural monitoring data and input it into a pre-deployed machine learning model to perform data analysis operations, obtain data analysis results, and store each rural monitoring data and data analysis results in the database.

[0028] The user interaction module is used to obtain the user's input interaction commands after the cloud platform performs identity authentication, parse the interaction commands through the digital human engine to obtain the command content, execute rural governance services based on the command content, and feed back the execution results through the digital human engine.

[0029] The interaction log management module is used by the cloud platform to record interaction logs in real time and store the interaction logs in the database.

[0030] Furthermore, the rural monitoring data acquisition module is specifically used for:

[0031] The edge server collects rural monitoring data through IoT devices, WeChat mini-programs or apps running on mobile terminals, and third-party systems; the IoT devices include at least video surveillance cameras that support AI recognition, temperature sensors, humidity sensors, laser particulate matter sensors, and water quality sensors;

[0032] The edge server encrypts the village monitoring data into encrypted monitoring data using the SM2 or SM3 algorithm, and then sends the encrypted monitoring data to the cloud platform via a 5G communication module or fiber optic broadband based on the TLS protocol.

[0033] Furthermore, the rural monitoring data analysis module is specifically used for:

[0034] The cloud platform decrypts the received encrypted monitoring data using the SM2 or SM3 algorithm to obtain rural monitoring data. It performs preprocessing on each rural monitoring data, including at least data cleaning, normalization, and tagging. The preprocessed rural monitoring data is then input into a pre-deployed machine learning model to perform data analysis operations, obtain data analysis results, and store the rural monitoring data and data analysis results in a database.

[0035] The database includes a structured sub-database, an unstructured sub-database, and a graph sub-database; the structured sub-database uses MySQL; the unstructured sub-database uses MongoDB; and the graph sub-database uses Neo4j.

[0036] Furthermore, the user interaction module is specifically used for:

[0037] The cloud platform obtains a JWT access token through the OAuth 2.0 authorization code process. After performing identity authentication using the signature and content of the JWT access token, it obtains the user's interactive instructions input in the form of text, voice, or gesture. The digital human engine parses the interactive instructions to obtain the instruction content. Based on the instruction content, it executes rural governance services such as real-time data display, heat map analysis, trend analysis, early warning prompts, policy interpretation, transaction guidance, psychological counseling, event reporting, event classification, task assignment, processing feedback, risk warning, trend prediction, decision suggestion generation, user profile generation, or service push. The execution results are recorded, and the execution results are fed back in the form of text, voice, or gesture through the digital human engine. The execution results are also synchronized through large screens, mobile terminals, and PCs.

[0038] Furthermore, the interaction log management module is specifically used for:

[0039] The cloud platform records interaction logs in real time, including at least the instruction content, execution result, and interaction time, and stores the interaction logs in a structured database.

[0040] The advantages of this invention are:

[0041] 1. Rural monitoring data is collected via edge servers through IoT devices, mobile terminals, and third-party systems. This data is then encrypted and sent to the cloud platform. The cloud platform decrypts the received encrypted data, performs preprocessing, and inputs it into a pre-deployed machine learning model for data analysis. The analysis results are then stored in a database. After authentication, the cloud platform receives user input commands, parses them using a digital human engine, executes rural governance services based on these commands, and provides feedback on the results through the digital human engine. It records interaction logs in real time and stores them in the database; that is, it integrates multi-source data from IoT devices, mobile terminals and third-party systems through edge servers, and generates intelligent decision-making results such as early warning and prediction through unified decryption and machine learning analysis by the cloud platform; at the same time, it uses a digital human engine to parse user text, voice or gesture commands, and provides personalized interaction covering 18 types of services such as policy interpretation, transaction guidance and risk management, and realizes closed-loop governance from data perception to decision execution through multi-terminal synchronous feedback, which significantly improves the accuracy of analysis and the convenience of operation, especially solving the digital divide problem for the elderly and other groups, and thus greatly improving the level of intelligence of rural governance and user experience.

[0042] 2. Significantly enhance the level of intelligence in rural governance: By integrating big data, artificial intelligence and digital human technology, a highly intelligent rural governance platform is built, realizing closed-loop management of the entire process from data collection and intelligent analysis to visualization and user interaction; the digital human engine, as the front-end interactive entry point, has the ability of voice recognition, natural language understanding and emotional expression, and can proactively guide villagers to participate in village affairs management and consultation, lower the threshold for use, and make grassroots governance more automated, precise and efficient.

[0043] 3. Creating a new smart governance model that is visual, interactive, and decision-making-oriented: The built-in data cockpit module (real-time data display, heat map analysis, trend analysis, and early warning prompts) can monitor and dynamically display key indicators such as rural population structure, economic operation, environmental conditions, and public security incidents in real time. Combined with machine learning models, it can realize trend prediction, risk warning, and generation of auxiliary decision-making suggestions. At the same time, the introduction of the digital human engine enhances affinity and interactivity, improves user participation and satisfaction, and truly promotes the transformation of rural governance from the traditional "passive response" to a modern "proactive service" model.

[0044] 4. Promote the innovative application of digital human technology in the field of public governance: Take the lead in introducing digital human technology into rural grassroots services, expand its application scenarios in publicity, service guidance, psychological counseling, etc., verify its feasibility and effectiveness in the field of public services, and provide a practical foundation and technical accumulation for subsequent promotion and application in more public fields such as community governance, education and medical care, and government service halls.

[0045] 5. Helping to narrow the urban-rural digital divide and promote social equity and inclusive development: Taking into full account the diverse characteristics of users in rural areas, we have adopted technologies such as age-friendly design, multilingual support (including dialect recognition), and barrier-free interactive interfaces to ensure that the elderly, people with disabilities, ethnic minorities, and residents in remote areas can also conveniently access services. This not only improves the accessibility and fairness of services, but also helps to enhance the public's trust and sense of gain in our work.

[0046] 6. It not only effectively solves problems such as information asymmetry, low response efficiency, and insufficient service coverage in traditional rural governance, but also demonstrates significant technological advantages and social value in improving governance efficiency, optimizing user experience, and promoting social equity, and has good application prospects and promotion potential. Attached Figure Description

[0047] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0048] Figure 1 This is a flowchart of a rural smart governance method integrating digital humans, as described in this invention.

[0049] Figure 2 This is a schematic diagram of the structure of a smart rural governance system integrating digital humans according to the present invention. Detailed Implementation

[0050] The technical solution in this application embodiment follows the following general approach: It integrates multi-source data from IoT devices, mobile terminals, and third-party systems via an edge server, and then uses a cloud platform for unified decryption and machine learning analysis to generate intelligent decision-making results such as early warnings and predictions. Simultaneously, it utilizes a digital human engine to parse user text, voice, or gesture commands, providing personalized interactions covering 18 categories of services including policy interpretation, transaction guidance, and risk management. Through multi-terminal synchronous feedback, it achieves closed-loop governance from data perception to decision execution, significantly improving analytical accuracy and operational convenience. It particularly addresses the digital divide for groups such as the elderly, thereby enhancing the intelligence level of rural governance and improving user experience.

[0051] Please refer to Figures 1 to 2 As shown, a preferred embodiment of the present invention, a method for smart rural governance integrating digital humans, includes the following steps:

[0052] Step S1: The edge server collects rural monitoring data through IoT devices, mobile terminals and third-party systems, encrypts the rural monitoring data into encrypted monitoring data and sends it to the cloud platform; the cloud platform is responsible for large-scale data processing and model training, and builds a batch and stream processing pipeline based on Hadoop+Spark+Flink, supporting real-time ETL, data cleaning and label modeling.

[0053] Step S2: The cloud platform decrypts the received encrypted monitoring data to obtain rural monitoring data. After preprocessing each rural monitoring data, it inputs it into a pre-deployed machine learning model to perform data analysis operations, obtains data analysis results, and stores each rural monitoring data and data analysis results in the database. In specific implementation, TensorFlowServing / TorchServe is used to deploy the trained machine learning model, LSTM / GRU is used for trend prediction, YOLOv5 / v8 is used for video behavior recognition, and BERT / ERNIE is used for policy text understanding and summary generation.

[0054] Step S3: After the cloud platform performs identity authentication, it obtains the user's input interaction commands, parses the interaction commands through the digital human engine to obtain the command content, executes rural governance services based on the command content, and feeds back the execution results through the digital human engine; the digital human engine integrates third-party digital human SDKs (such as Baidu Xiling, Tencent Zhiying) to achieve: a) multimodal input: voice + text + gesture; b) emotional expression: adjusting tone, expression, and speech rate according to semantics; c) dialect recognition: an end-to-end ASR model based on CTC loss, supporting Sichuan and Chongqing dialects, Cantonese, Wu dialects, etc.

[0055] The user experience design principles of the digital human engine are: (1) Voice priority: 90% of operations can be completed by voice; (2) Minimalist interface: No more than 5 items in the first-level menu, with double labels of icons and text; (3) Age-friendly mode: Font enlargement, contrast enhancement, and voice prompts for operation guidance; (4) Multilingual support: Mandarin, dialects, and minority languages ​​can be switched.

[0056] Step S4: The cloud platform records the interaction logs in real time and stores the interaction logs in the database.

[0057] This invention aims to build an intelligent rural governance platform with data as the core, AI as the driving force, and a digital human engine as the interaction entry point. It adopts the design concept of "end-edge-cloud collaborative architecture" and "microservice + middle platform" to realize a closed loop of intelligent services from the perception layer to the application layer.

[0058] Step S1 specifically involves:

[0059] The edge server collects rural monitoring data through IoT devices, WeChat mini-programs or apps running on mobile terminals, and third-party systems (such as agricultural databases); the IoT devices include at least video surveillance cameras (H.265 encoded) that support AI recognition, temperature sensors, humidity sensors, laser particulate matter sensors, and water quality sensors; in specific implementations, the edge server uses LoRa / NB-IoT for low-power sensor communication.

[0060] The edge server encrypts the village monitoring data into encrypted monitoring data using the SM2 or SM3 algorithm, and then sends the encrypted monitoring data to the cloud platform via a 5G communication module or fiber optic broadband based on the TLS protocol. The edge server is deployed in townships or village committees and can use Huawei Atlas 500, capable of performing low-latency tasks such as local video analysis and speech recognition.

[0061] Step S2 specifically involves:

[0062] The cloud platform decrypts the received encrypted monitoring data using the SM2 or SM3 algorithm to obtain rural monitoring data. It performs preprocessing on each rural monitoring data, including at least data cleaning, normalization, and tagging. The preprocessed rural monitoring data is then input into a pre-deployed machine learning model to perform data analysis operations, obtain data analysis results, and store the rural monitoring data and data analysis results in a database.

[0063] The database comprises a structured sub-database, an unstructured sub-database, and a graph sub-database. The structured sub-database uses a MySQL database (master-slave replication + read-write separation). The unstructured sub-database uses a MongoDB database (logs, audio and video) and is combined with HDFS (archived data). The graph sub-database uses a Neo4j database for relationship mining (such as public opinion propagation paths).

[0064] Step S3 specifically involves:

[0065] The cloud platform obtains a JWT access token through the OAuth 2.0 authorization code process. After performing identity authentication using the signature and content of the JWT access token, it obtains the user's interactive commands input in the form of text, voice, or gestures. The digital human engine parses the interactive commands to obtain the command content. Based on the command content, it calls the database to execute rural governance services such as real-time data display, heat map analysis, trend analysis, early warning prompts, policy interpretation, transaction guidance, psychological counseling, event reporting, event classification, task assignment, processing feedback, risk warning, trend prediction, decision suggestion generation, user profile generation, or service push. The execution results are recorded, and the digital human engine provides feedback on the execution results in the form of text, voice, or gestures. The execution results are synchronized through large screens, mobile terminals, and PCs. The multi-terminal synchronization service is implemented based on WebSocket + RedisPub / Sub.

[0066] Identity and access control is based on OAuth 2.0+JWT to implement single sign-on; it adopts the RBAC (role-access-resource) model to support fine-grained access control.

[0067] In practical implementation, the application of rural governance services can be divided into a data dashboard module, a digital human service module, an event closed-loop management module, an AI-assisted decision-making module, and a personalized service recommendation module. The data dashboard module is used to visualize core indicators such as population, economy, environment, and public security, supporting heat maps, trend charts, and early warning pop-ups, rendered using ECharts / DataV+WebGL. The digital human service module provides an integrated service of "policy Q&A—service guidance—psychological counseling," supporting 24 / 7 online access, built on NLU+Dialogue Management+TTS. The event closed-loop management module achieves full-process tracking of "reporting—classification—distribution—handling—feedback—evaluation" and dialect recognition: based on CTC. The end-to-end ASR model for loss supports Sichuan and Chongqing dialects, Cantonese, Wu dialects, etc., and is built on a workflow engine (such as Activiti) + message queue (RabbitMQ / Kafka); the AI-assisted decision-making module generates risk warnings and disposal suggestions based on historical data and model predictions. Access control is implemented through a rule engine (Drools) + reinforcement learning (RL); the personalized service recommendation module pushes customized services based on user profiles (age, occupation, service history), and is implemented through collaborative filtering + content recommendation algorithms.

[0068] In specific implementation, the interactive entry points include: (1) Digital human interactive interface, which supports voice wake-up, multi-turn dialogue and emotional interaction, and is suitable for village committee hall, mobile terminal and smart speaker settings; (2) Mobile terminal mini program / App, mainly WeChat mini program, which supports offline caching and message push, and is suitable for villagers' daily affairs; (3) Village committee large screen cockpit, 4K large screen + touch interaction + automatic carousel mode, which is suitable for cadres' daily monitoring and decision-making; (4) PC terminal management backend, which is developed based on Vue + Element UI, supports multi-level permission management, and is suitable for system configuration and data management.

[0069] The target groups of users include: (1) villagers (with a focus on the elderly, people with disabilities, and those with low levels of education); (2) village cadres (village party secretaries, village directors, etc.); (3) grid members (responsible for incident patrol and reporting); and (4) township government personnel (responsible for supervision and coordination).

[0070] Step S4 specifically involves:

[0071] The cloud platform records interaction logs in real time, including at least the instruction content, execution result, and interaction time, and stores the interaction logs in a structured database.

[0072] A preferred embodiment of the present invention, a rural smart governance system integrating digital humans, includes the following modules:

[0073] The rural monitoring data acquisition module is used by the edge server to collect rural monitoring data through IoT devices, mobile terminals and third-party systems, and encrypt the rural monitoring data into encrypted monitoring data and send it to the cloud platform. The cloud platform is responsible for large-scale data processing and model training, and builds a batch and stream processing pipeline based on Hadoop+Spark+Flink, supporting real-time ETL, data cleaning and label modeling.

[0074] The rural monitoring data analysis module is used to decrypt the encrypted monitoring data received by the cloud platform to obtain rural monitoring data. After preprocessing each rural monitoring data, it is input into a pre-deployed machine learning model to perform data analysis operations, obtain data analysis results, and store each rural monitoring data and data analysis results in a database. In specific implementation, TensorFlow Serving / TorchServe is used to deploy trained machine learning models, LSTM / GRU is used for trend prediction, YOLOv5 / v8 is used for video behavior recognition, and BERT / ERNIE is used for policy text understanding and summary generation.

[0075] The user interaction module is used by the cloud platform to perform identity authentication, obtain user input interaction commands, parse the interaction commands through the digital human engine to obtain command content, execute rural governance services based on the command content, and provide feedback on the execution results through the digital human engine. The digital human engine integrates third-party digital human SDKs (such as Baidu Xiling and Tencent Zhiying) to achieve: a) multimodal input: voice + text + gesture; b) emotional expression: adjusting tone, facial expression, and speech rate according to semantics; c) dialect recognition: an end-to-end ASR model based on CTC loss, supporting Sichuan and Chongqing dialects, Cantonese, Wu dialect, etc.

[0076] The user experience design principles of the digital human engine are: (1) Voice priority: 90% of operations can be completed by voice; (2) Minimalist interface: No more than 5 items in the first-level menu, with double labels of icons and text; (3) Age-friendly mode: Font enlargement, contrast enhancement, and voice prompts for operation guidance; (4) Multilingual support: Mandarin, dialects, and minority languages ​​can be switched.

[0077] The interaction log management module is used by the cloud platform to record interaction logs in real time and store the interaction logs in the database.

[0078] This invention aims to build an intelligent rural governance platform with data as the core, AI as the driving force, and a digital human engine as the interaction entry point. It adopts the design concept of "end-edge-cloud collaborative architecture" and "microservice + middle platform" to realize a closed loop of intelligent services from the perception layer to the application layer.

[0079] The rural monitoring data acquisition module is specifically used for:

[0080] The edge server collects rural monitoring data through IoT devices, WeChat mini-programs or apps running on mobile terminals, and third-party systems (such as agricultural databases); the IoT devices include at least video surveillance cameras (H.265 encoded) that support AI recognition, temperature sensors, humidity sensors, laser particulate matter sensors, and water quality sensors; in specific implementations, the edge server uses LoRa / NB-IoT for low-power sensor communication.

[0081] The edge server encrypts the village monitoring data into encrypted monitoring data using the SM2 or SM3 algorithm, and then sends the encrypted monitoring data to the cloud platform via a 5G communication module or fiber optic broadband based on the TLS protocol. The edge server is deployed in townships or village committees and can use Huawei Atlas 500, capable of performing low-latency tasks such as local video analysis and speech recognition.

[0082] The rural monitoring data analysis module is specifically used for:

[0083] The cloud platform decrypts the received encrypted monitoring data using the SM2 or SM3 algorithm to obtain rural monitoring data. It performs preprocessing on each rural monitoring data, including at least data cleaning, normalization, and tagging. The preprocessed rural monitoring data is then input into a pre-deployed machine learning model to perform data analysis operations, obtain data analysis results, and store the rural monitoring data and data analysis results in a database.

[0084] The database comprises a structured sub-database, an unstructured sub-database, and a graph sub-database. The structured sub-database uses a MySQL database (master-slave replication + read-write separation). The unstructured sub-database uses a MongoDB database (logs, audio and video) and is combined with HDFS (archived data). The graph sub-database uses a Neo4j database for relationship mining (such as public opinion propagation paths).

[0085] The user interaction module is specifically used for:

[0086] The cloud platform obtains a JWT access token through the OAuth 2.0 authorization code process. After performing identity authentication using the signature and content of the JWT access token, it obtains the user's interactive commands input in the form of text, voice, or gestures. The digital human engine parses the interactive commands to obtain the command content. Based on the command content, it calls the database to execute rural governance services such as real-time data display, heat map analysis, trend analysis, early warning prompts, policy interpretation, transaction guidance, psychological counseling, event reporting, event classification, task assignment, processing feedback, risk warning, trend prediction, decision suggestion generation, user profile generation, or service push. The execution results are recorded, and the digital human engine provides feedback on the execution results in the form of text, voice, or gestures. The execution results are synchronized through large screens, mobile terminals, and PCs. The multi-terminal synchronization service is implemented based on WebSocket + RedisPub / Sub.

[0087] Identity and access control is based on OAuth 2.0+JWT to implement single sign-on; it adopts the RBAC (role-access-resource) model to support fine-grained access control.

[0088] In practical implementation, the application of rural governance services can be divided into a data dashboard module, a digital human service module, an event closed-loop management module, an AI-assisted decision-making module, and a personalized service recommendation module. The data dashboard module is used to visualize core indicators such as population, economy, environment, and public security, supporting heat maps, trend charts, and early warning pop-ups, rendered using ECharts / DataV+WebGL. The digital human service module provides an integrated service of "policy Q&A—service guidance—psychological counseling," supporting 24 / 7 online access, built on NLU+Dialogue Management+TTS. The event closed-loop management module achieves full-process tracking of "reporting—classification—distribution—handling—feedback—evaluation" and dialect recognition: based on CTC. The end-to-end ASR model for loss supports Sichuan and Chongqing dialects, Cantonese, Wu dialects, etc., and is built on a workflow engine (such as Activiti) + message queue (RabbitMQ / Kafka); the AI-assisted decision-making module generates risk warnings and disposal suggestions based on historical data and model predictions. Access control is implemented through a rule engine (Drools) + reinforcement learning (RL); the personalized service recommendation module pushes customized services based on user profiles (age, occupation, service history), and is implemented through collaborative filtering + content recommendation algorithms.

[0089] In specific implementation, the interactive entry points include: (1) Digital human interactive interface, which supports voice wake-up, multi-turn dialogue and emotional interaction, and is suitable for village committee hall, mobile terminal and smart speaker settings; (2) Mobile terminal mini program / App, mainly WeChat mini program, which supports offline caching and message push, and is suitable for villagers' daily affairs; (3) Village committee large screen cockpit, 4K large screen + touch interaction + automatic carousel mode, which is suitable for cadres' daily monitoring and decision-making; (4) PC terminal management backend, which is developed based on Vue + Element UI, supports multi-level permission management, and is suitable for system configuration and data management.

[0090] The target groups of users include: (1) villagers (with a focus on the elderly, people with disabilities, and those with low levels of education); (2) village cadres (village party secretaries, village directors, etc.); (3) grid members (responsible for incident patrol and reporting); and (4) township government personnel (responsible for supervision and coordination).

[0091] The interaction log management module is specifically used for:

[0092] The cloud platform records interaction logs in real time, including at least the instruction content, execution result, and interaction time, and stores the interaction logs in a structured database.

[0093] In summary, the advantages of this invention are as follows:

[0094] 1. Rural monitoring data is collected via edge servers through IoT devices, mobile terminals, and third-party systems. This data is then encrypted and sent to the cloud platform. The cloud platform decrypts the received encrypted data, performs preprocessing, and inputs it into a pre-deployed machine learning model for data analysis. The analysis results are then stored in a database. After authentication, the cloud platform receives user input commands, parses them using a digital human engine, executes rural governance services based on these commands, and provides feedback on the results through the digital human engine. It records interaction logs in real time and stores them in the database; that is, it integrates multi-source data from IoT devices, mobile terminals and third-party systems through edge servers, and generates intelligent decision-making results such as early warning and prediction through unified decryption and machine learning analysis by the cloud platform; at the same time, it uses a digital human engine to parse user text, voice or gesture commands, and provides personalized interaction covering 18 types of services such as policy interpretation, transaction guidance and risk management, and realizes closed-loop governance from data perception to decision execution through multi-terminal synchronous feedback, which significantly improves the accuracy of analysis and the convenience of operation, especially solving the digital divide problem for the elderly and other groups, and thus greatly improving the level of intelligence of rural governance and user experience.

[0095] 2. Significantly enhance the level of intelligence in rural governance: By integrating big data, artificial intelligence and digital human technology, a highly intelligent rural governance platform is built, realizing closed-loop management of the entire process from data collection and intelligent analysis to visualization and user interaction; the digital human engine, as the front-end interactive entry point, has the ability of voice recognition, natural language understanding and emotional expression, and can proactively guide villagers to participate in village affairs management and consultation, lower the threshold for use, and make grassroots governance more automated, precise and efficient.

[0096] 3. Creating a new smart governance model that is visual, interactive, and decision-making-oriented: The built-in data cockpit module (real-time data display, heat map analysis, trend analysis, and early warning prompts) can monitor and dynamically display key indicators such as rural population structure, economic operation, environmental conditions, and public security incidents in real time. Combined with machine learning models, it can realize trend prediction, risk warning, and generation of auxiliary decision-making suggestions. At the same time, the introduction of the digital human engine enhances affinity and interactivity, improves user participation and satisfaction, and truly promotes the transformation of rural governance from the traditional "passive response" to a modern "proactive service" model.

[0097] 4. Promote the innovative application of digital human technology in the field of public governance: Take the lead in introducing digital human technology into rural grassroots services, expand its application scenarios in publicity, service guidance, psychological counseling, etc., verify its feasibility and effectiveness in the field of public services, and provide a practical foundation and technical accumulation for subsequent promotion and application in more public fields such as community governance, education and medical care, and government service halls.

[0098] 5. Helping to narrow the urban-rural digital divide and promote social equity and inclusive development: Taking into full account the diverse characteristics of users in rural areas, we have adopted technologies such as age-friendly design, multilingual support (including dialect recognition), and barrier-free interactive interfaces to ensure that the elderly, people with disabilities, ethnic minorities, and residents in remote areas can also conveniently access services. This not only improves the accessibility and fairness of services, but also helps to enhance the public's trust and sense of gain in our work.

[0099] 6. It not only effectively solves problems such as information asymmetry, low response efficiency, and insufficient service coverage in traditional rural governance, but also demonstrates significant technological advantages and social value in improving governance efficiency, optimizing user experience, and promoting social equity, and has good application prospects and promotion potential.

[0100] While specific embodiments of the present invention have been described above, those skilled in the art should understand that the specific embodiments described are merely illustrative and not intended to limit the scope of the present invention. Equivalent modifications and variations made by those skilled in the art in accordance with the spirit of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A rural wisdom governance method of integrated digital people, characterized in that: The method comprises the following steps: Step S1, the edge server collects rural monitoring data through Internet of Things devices, mobile terminals and third-party systems, encrypts each rural monitoring data into encrypted monitoring data and sends the encrypted monitoring data to the cloud platform; Step S2, the cloud platform decrypts the received encrypted monitoring data to obtain rural monitoring data, pre-processes each rural monitoring data and inputs the pre-processed rural monitoring data into a pre-deployed machine learning model to perform data analysis operations, obtains data analysis results, and stores each rural monitoring data and the data analysis results in a database; Step S3, after the cloud platform performs identity authentication, the cloud platform obtains an interactive instruction input by a user, analyzes the interactive instruction through a digital human engine to obtain instruction content, performs rural governance services based on the instruction content, and feeds back the execution result through the digital human engine; Step S4, the cloud platform records interactive logs in real time and stores the interactive logs in the database.

2. The integrated digital human rural wisdom governance method of claim 1, wherein: The step S1 is specifically: The edge server collects rural monitoring data through Internet of Things devices, mobile terminal running WeChat mini programs or APPs and third-party systems; the Internet of Things devices at least include AI recognition supported video monitoring cameras, temperature sensors, humidity sensors, laser particulate matter sensors and water quality sensors; The edge server encrypts each rural monitoring data into encrypted monitoring data through SM2 algorithm or SM3 algorithm, and sends the encrypted monitoring data to the cloud platform through a 5G communication module or an optical fiber broadband based on a TLS protocol.

3. The integrated digital human rural wisdom governance method of claim 1, wherein: The step S2 is specifically: The cloud platform decrypts the received encrypted monitoring data through SM2 algorithm or SM3 algorithm to obtain rural monitoring data, pre-processes each rural monitoring data at least including data cleaning, normalization and labeling, inputs the pre-processed rural monitoring data into a pre-deployed machine learning model to perform data analysis operations, obtains data analysis results, and stores each rural monitoring data and the data analysis results in a database; The database is provided with a structured sub-database, an unstructured sub-database and a graph sub-database; the structured sub-database adopts a MySQL database; the unstructured sub-database adopts a MongoDB database; and the graph sub-database adopts a Neo4j database.

4. The integrated digital human rural wisdom governance method of claim 1, wherein: The step S3 is specifically: The cloud platform obtains a JWT access token through an OAuth 2.0 authorization code process, performs identity authentication by signing and content of the JWT access token, obtains an interactive instruction input by a user in the form of text, voice or gesture, analyzes the interactive instruction through a digital human engine to obtain instruction content, performs rural governance services based on the instruction content, records execution results, feeds back the execution results through the digital human engine in the form of text, voice or gesture, and synchronizes the execution results through a large screen, a mobile terminal and a PC terminal.

5. The integrated digital human rural wisdom governance method of claim 1, wherein: The step S4 is specifically: The cloud platform records an interaction log in real time, which at least includes instruction content, execution result, and interaction time, and stores the interaction log in a structured manner to a database.

6. A rural wisdom governance system integrated with digital people, characterized in that: Comprise the following modules: The village monitoring data acquisition module is configured to: The edge server collects village monitoring data through Internet of Things devices, mobile terminals, and third-party systems, encrypts each village monitoring data into encrypted monitoring data, and sends the encrypted monitoring data to the cloud platform; The village monitoring data analysis module is configured to: The user interaction module is configured to:

7. The integrated digital human rural wisdom governance system as claimed in claim 6, wherein: The interaction log management module is configured to: The village monitoring data acquisition module is specifically configured to: The edge server collects village monitoring data through Internet of Things devices, mobile terminals, and third-party systems, encrypts each village monitoring data into encrypted monitoring data, and sends the encrypted monitoring data to the cloud platform; 8. The integrated digital human rural wisdom governance system as claimed in claim 6, wherein: The village monitoring data analysis module is specifically configured to: The database is provided with a structured sub-database, an unstructured sub-database, and a graph sub-database; the structured sub-database adopts a MySQL database; the unstructured sub-database adopts a MongoDB database; and the graph sub-database adopts a Neo4j database. The user interaction module is specifically configured to:

9. The integrated digital human rural wisdom governance system as claimed in claim 6, wherein: ​ The cloud platform obtains a JWT access token through an OAuth 2.0 authorization code process, performs identity authentication by using the signature and content of the JWT access token, obtains an interactive instruction input by a user in the form of text, voice or gesture, obtains instruction content by analyzing the interactive instruction through a digital human engine, and performs a rural governance service based on the instruction content, such as real-time data display, heat map analysis, trend analysis, early warning, policy interpretation, transaction guidance, psychological counseling, event reporting, event classification, task assignment, processing feedback, risk warning, trend prediction, decision suggestion generation, user portrait generation or service pushing, records the execution result, feeds back the execution result in the form of text, voice or gesture through the digital human engine, and synchronizes the execution result through a large screen, a mobile terminal and a PC terminal.

10. The integrated digital human rural wisdom governance system as claimed in claim 6, wherein: The interactive log management module is specifically used for: The cloud platform records an interactive log in real time, and stores the interactive log in a structured manner to a database, the interactive log at least including instruction content, execution result and interactive time.

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