A government affair service analysis management system

Abstract

This invention discloses a government service analysis and management system, belonging to the field of government service management technology. The system includes a user profile construction module, a demand prediction and proactive service module, a data control and permission adaptation module, a process monitoring and root cause tracing module, a policy compliance supervision module, an inclusive service adaptation module, and a central dispatch module. This system improves upon the limitations of existing government service systems' passive service architecture, which is centered on pre-set items, by constructing a user lifecycle profile and a causal inference logic chain for government service needs. It can accurately uncover the inherent causal relationship between user characteristics and government service items, effectively eliminating the interference of pseudo-correlation features on demand prediction, achieving accurate prediction and compliance verification of users' potential service needs. This realizes the transformation of government services from an item-driven "people looking for services" to a demand-driven "services finding people" model, reducing the time and communication costs for the public from the source and improving the government service experience.

Description

Technical Field

[0001] This invention relates to the field of government service management technology, specifically a government service analysis and management system. Background Technology

[0002] Government service items refer to the general term for administrative power items and public service items that are handled upon application by administrative organs at all levels and their authorized organizations. Its scope includes administrative power items such as administrative licensing and administrative confirmation, as well as public service items in the fields of public education, labor and employment, and social insurance. With the in-depth advancement of the digital transformation of government affairs nationwide, government service platforms at the provincial, municipal, and county levels have achieved widespread coverage, and more than 70% of government service items can be handled online. "One-stop online service" has basically solved the basic problem of "running around to multiple places and running offline" for the public to handle affairs.

[0003] Existing government service systems generally adopt a passive service architecture centered on a pre-set list of government items. They neither construct causal inference models of user characteristics and service needs based on the full lifecycle government data of natural persons and market entities, nor establish complete technologies for full-link lineage tracing, homology verification, and dynamic fine-grained access control of cross-departmental, multi-source, and heterogeneous government data. Therefore, they cannot achieve a fundamental architectural upgrade of government services from "people looking for services" to "services looking for people," nor can they achieve reliable and efficient sharing of cross-departmental government data while ensuring data security and compliance. Ultimately, this results in serious mismatch between the supply and demand of government services, cumbersome and costly procedures for the public, low efficiency in approval processes, and frequent data compliance and security risks. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a government service analysis and management system that solves the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: a government service analysis and management system, including a user profile construction module, a demand prediction and proactive service module, a data control and permission adaptation module, a process monitoring and root cause tracing module, a policy compliance supervision module, an inclusive service adaptation module, and a central dispatch module; The user profile building module is used to build a full life cycle timeline profile of natural persons and market entities, complete the real-time update of profile features based on an event-driven mechanism, and mine the causal relationship feature set between user features and government affairs. The demand prediction and proactive service module is used to construct a causal inference model of government demand based on the causal relationship feature set, predict users' potential service needs in combination with current policies and rules, generate personalized service guidance and pre-filled application forms, and complete the proactive push and pre-processing of government services. The data management and permission adaptation module is used to build a full-link lineage tracing system for government data elements, complete the sameness verification and dynamic consistency update of multi-source heterogeneous data, and realize fine-grained access control and compliance desensitization of government data based on the four-dimensional permission model. The process monitoring and root cause tracing module is used to collect time-series data from all nodes of the government affairs handling process, build a time-series feature library of the entire process, and realize real-time early warning and multi-dimensional root cause tracing of bottlenecks in handling affairs based on the Bayesian causal network model. The policy compliance supervision module is used to perform structured analysis and element extraction of policy and regulatory documents, synchronously update the handling rules and approval standards for government affairs, build a full-process compliance verification engine, and realize full-cycle compliance supervision of case handling. The inclusive service adaptation module is used to identify the identity of special groups based on user profiles, generate a full-link personalized service adaptation solution, and complete the full-dimensional adaptation of government services. The central scheduling module is used to realize the coordinated scheduling of various modules, the closed-loop management of cross-departmental government service processes, and the monitoring of system configuration and operation status.

[0006] Preferably, the user profile construction module includes: a natural person profile submodule, a market entity profile submodule, an event-driven update submodule, and a causal feature mining submodule; The Natural Person Profile submodule is used to aggregate government data of a natural person from birth to death, using the natural person's identity identifier as the unique primary key, and to construct a timeline profile of the entire life cycle with timestamps. The market entity profiling submodule is used to aggregate government data of market entities throughout their entire lifecycle, from establishment to cancellation, using the unified social credit code as the unique primary key, and to construct a timeline profiling of the entire lifecycle with timestamps. The event-driven update submodule is used to monitor change events of all government data sources. When data is added, changed, or cancelled, it triggers the synchronous update of profile features and records the entire change log. The causal feature mining submodule is used to construct the causal relationship feature set by identifying the causal relationship between user characteristics and government affairs processing based on historical case data and user profile features, eliminating false correlation features.

[0007] Preferably, the demand prediction and proactive service module includes: a causal inference model construction submodule, a policy and rule adaptation submodule, a proactive service generation submodule, and a pre-processing submodule; The causal inference model construction submodule is used to train a causal inference model of government affairs demand based on dual robust learning, with the causal association feature set as input and user government affairs handling behavior as output, to quantify the average processing effect of user features on the demand for handling affairs, and output the probability ranking of users' potential service needs. The policy rule adaptation submodule is used to connect to the policy element library of the policy compliance supervision module, match the current policy rules corresponding to potential service needs, verify the compliance and scope of application of the needs, and eliminate the predicted needs that do not meet the policy requirements. The proactive service generation submodule is used to generate personalized service guidance matching user characteristics based on compliance-based predicted needs, and to pre-fill the service form based on user profile data to generate a pre-filled service package. The aforementioned pre-approval submodule is used to automatically complete the pre-approval process for government affairs that do not require users to submit additional materials, undergo on-site verification, or obtain subjective confirmation from users, and generate the processing results and proactively push them to users.

[0008] Preferably, the data management and permission adaptation module includes: a data tracing submodule, a homogeneity verification submodule, a consistency update submodule, and a four-dimensional permission management submodule; The data tracing submodule is used to assign a unique lineage identifier to each smallest granularity government data element, record the full-link data lineage information of the data element's generating department, data standard, update cycle, and call chain, and construct a government data lineage map; The homology verification submodule is used to verify the source consistency of the data element based on the lineage identifier when calling government data elements across departments, compare the value, update time and standard specifications of the same data element from different data sources, identify data conflicts and anomalies, and output the verification results. The consistency update submodule is used to monitor data change events of the source department of the data element. When the source data changes, it triggers the synchronous update of the corresponding data elements of all related departments in the whole system, and synchronously updates the government data lineage map. The four-dimensional permission control submodule is used to construct a four-dimensional permission model based on data sensitivity level, service scenario, user identity and approval process, dynamically allocate data access permissions based on the corresponding parameters of the current case, and perform compliance de-identification processing on sensitive fields that exceed the permission scope.

[0009] Preferably, in the four-dimensional access control submodule, the sensitivity level of government data elements is divided into four levels: public information, internal information, sensitive information and top secret information, and corresponding access permission rules and desensitization rules are set for data elements of different sensitivity levels. For top-secret information, access to the corresponding fields will only be granted when all preset conditions for the service scenario, user identity, and approval process are met simultaneously. The entire process will be recorded, and downloading and forwarding will be prohibited.

[0010] Preferably, the process monitoring and root cause tracing module includes: a full-process node embedding submodule, a time-series feature library construction submodule, a bottleneck early warning submodule, and a causal root cause tracing submodule; The full-process node data collection submodule is used to break down the processing flow of each government matter into the smallest granular processing node, set up data collection points for each node, and collect time-series operation data of each node's operator, operation time, dwell time and flow status. The time-series feature library construction submodule is used to construct a standard time-series feature baseline and an abnormal time-series feature sample library for each processing node of each government matter based on the time-series operation data of historical cases. The bottleneck early warning submodule is used to compare the currently collected case processing time-series data with the corresponding standard time-series feature baseline. When the node running data exceeds the preset threshold, a bottleneck early warning is triggered in real time and pushed to the corresponding processing and supervision departments. The causal root cause tracing submodule is used to, when a blockage warning is triggered, quantify the causal contribution of each variable to the occurrence of the blockage based on a pre-trained Bayesian causal network model, with the abnormal characteristics of the blockage as the target variable and the influencing factors of the whole process as candidate causal variables, and output the root cause ranking and tracing results of the blockage.

[0011] Preferably, the causal root cause tracing submodule generates government service process optimization suggestions based on the bottleneck tracing results; To address systemic bottlenecks caused by flawed process design, we generate process optimization solutions that include node merging, process reduction, and material simplification. These solutions are then pushed to government service management departments for continuous iterative optimization of government service processes.

[0012] Preferably, the policy compliance supervision module includes: a policy structured parsing submodule, a matter rule update submodule, a compliance verification submodule, and a risk handling submodule; The policy structured parsing submodule is used to perform full-text parsing of policy and regulatory documents through a fine-tuned government affairs domain big language model, extract structured elements related to government services such as processing conditions, approval standards and processing time limits, and generate a policy element library; The rule update submodule is used to compare and verify the newly formulated policy element library with the existing rules for handling government affairs, identify rule differences and generate update suggestions. After review and confirmation, the rules for handling government affairs and approval standards are updated synchronously throughout the system. The compliance verification submodule is used to build a compliance verification rule library for the entire process before, during and after the application. It completes compliance prediction before the application is submitted, verification during the approval process and risk tracing after the application is completed, so as to realize compliance supervision throughout the entire application cycle. The risk handling submodule is used to automatically generate corresponding handling instructions based on the risk level identified by compliance verification, track the handling progress, and form a closed-loop management of risk identification, early warning, handling and review.

[0013] Preferably, the inclusive service adaptation module includes: a special group identification submodule, an adaptation scheme generation submodule, a service channel adaptation submodule, and an assistance and agency scheduling submodule; The special group identification submodule is used to identify the identity and personalized needs of special groups based on the full life cycle timeline profile output by the user profile construction module, and to construct a special group exclusive profile. The adaptation scheme generation submodule is used to generate a personalized service adaptation scheme for the entire chain, from the interactive interface, the process, the material filling and the approval mode, based on the special profile of the special group. The service channel adaptation submodule is used to match the optimal government service processing channel based on the behavioral habits and needs of special groups. The assistance and agency scheduling submodule is used to intelligently match the corresponding assistance and agency personnel and service stations based on the service needs and geographical location of special groups, generate and dispatch assistance and agency tasks, and track service progress and service evaluation.

[0014] Preferably, the central scheduling module includes: a cross-departmental collaboration submodule, a collaborative scheduling submodule, a system management submodule, and an operation monitoring submodule; The cross-departmental collaboration submodule is used to build a closed-loop management mechanism for government service processes across departments, levels, and regions, enabling automatic distribution of processes, synchronization of nodes, tracking of progress, and mutual recognition of results for jointly handled matters; The collaborative scheduling submodule is used to intelligently schedule the computing power and resources of each module according to the handling process and system operation status, so as to realize data interaction and collaborative work between modules. The system management submodule is used to provide visual operation functions for government affairs management, user permission management, system configuration management, and rule configuration management; The operation monitoring submodule is used to monitor the system's operating status, the call status of each module, the data flow status and the progress of case processing in real time, and to trigger alarms in real time when the system malfunctions.

[0015] This invention provides a government service analysis and management system. It has the following beneficial effects: (1) By constructing a user lifecycle profile and a causal inference logic chain for government affairs needs, the limitations of the existing government service system’s passive service architecture centered on pre-set items are improved. It can accurately explore the inherent causal relationship between user characteristics and government affairs, effectively eliminate the interference of pseudo-related characteristics on demand prediction, achieve the effect of accurate prediction and compliance verification of users’ potential service needs, realize the fundamental transformation of government services from "people looking for services" driven by items to "services looking for people" driven by needs, reduce the time cost and communication cost of the public from the source, and greatly improve the accuracy, adaptability and user experience of government services.

[0016] (2) By constructing a full-link lineage tracing system and a four-dimensional permission control mechanism for government data elements, a trusted sharing link for cross-departmental multi-source heterogeneous government data can be opened up. This enables real-time verification of data homogeneity and dynamic consistency updates across the entire system. It achieves the effect of compliant desensitization of sensitive data while controlling data access permissions at a fine-grained level. This improves the industry pain point of government data not daring to share and not being able to share in the existing technology, breaks down the implicit data barriers between departments, and realizes efficient and compliant sharing and reliable application of government data under the premise of ensuring the security and controllability of government data.

[0017] (3) By utilizing the full-process time-series monitoring of case handling, the root cause tracing of bottlenecks, and the full-cycle compliance supervision driven by policies, dynamic control and risk identification of the entire process of government service handling can be achieved. This can accurately locate the core causes of bottlenecks in handling affairs, and at the same time achieve dynamic synchronization of policies, rules and standards for handling matters. This achieves the effect of closed-loop management of the entire process, including pre-judgment, in-process control and post-event traceability. It effectively solves the problems of lagging performance analysis and insufficient coverage of compliance supervision in existing government services, systematically improves the efficiency of government service handling, prevents corruption and compliance risks in the approval process, and promotes the standardization and high-quality development of government services. Attached Figure Description

[0018] Figure 1 This is a system block diagram of the analysis and management system of the present invention; Figure 2 This is a structural block diagram of the user profile building module of the present invention; Figure 3 This is a structural block diagram of the demand prediction and proactive service module of the present invention; Figure 4 This is a structural block diagram of the data management and permission adaptation module of the present invention; Figure 5 This is a structural block diagram of the process monitoring and root cause tracing module of the present invention; Figure 6 This is a structural block diagram of the policy compliance supervision module of the present invention; Figure 7 This is a structural block diagram of the universal service adaptation module of the present invention; Figure 8 This is a structural block diagram of the central scheduling module of the present invention. Detailed Implementation

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

[0020] Example 1 Please see Figure 1-8 This invention provides a government service analysis and management system. To achieve the above objectives, this invention is implemented through the following technical solutions: including a user profile construction module, a demand prediction and proactive service module, a data control and permission adaptation module, a process monitoring and root cause tracing module, a policy compliance supervision module, an inclusive service adaptation module, and a central dispatch module. The user profile building module is used to build a full lifecycle timeline profile of natural persons and market entities, and to complete the real-time update of profile features based on an event-driven mechanism, and to mine the causal relationship feature set between user features and government affairs. The demand prediction and proactive service module is used to build a causal inference model of government demand based on the causal relationship feature set, predict users' potential service needs in combination with current policies and rules, generate personalized service guidance and pre-filled application forms, and complete the proactive push and pre-processing of government services. The data governance and permission adaptation module is used to build a full-chain lineage traceability system for government data elements, complete the sameness verification and dynamic consistency update of multi-source heterogeneous data, and realize fine-grained access control and compliance de-identification of government data based on a four-dimensional permission model. The process monitoring and root cause tracing module is used to collect time-series data from all nodes of the government affairs process, build a time-series feature library of the entire process, and realize real-time early warning and multi-dimensional root cause tracing of bottlenecks in the process based on the Bayesian causal network model. The policy compliance supervision module is used to perform structured analysis and element extraction of policy and regulatory documents, synchronously update the handling rules and approval standards for government affairs, build a full-process compliance verification engine, and realize full-cycle compliance supervision of the handling of cases. The inclusive service adaptation module is used to identify the identities of special groups based on user profiles, generate personalized service adaptation solutions across the entire chain, and complete the full-dimensional adaptation of government services. The central dispatch module is used to achieve coordinated dispatch of various modules, closed-loop management of cross-departmental government service processes, and monitoring of system configuration and operation status.

[0021] In this embodiment, when providing government services, after the system starts, the data management and permission adaptation module first completes the standardized access of all government data, assigns a globally unique lineage identifier to each government data element at the smallest granularity, constructs an end-to-end full-link data lineage graph, completes the sameness verification of cross-departmental multi-source data in real time, and continuously monitors the change events of the data source to trigger the synchronous update of the related data of the entire system, ensuring the consistency of the data of the entire system. Based on the four-dimensional permission model, it provides unified fine-grained permission control and compliance de-identification services for all data access operations in the entire system, thus building a solid foundation for trusted data in the system. Based on this, the user profile building module constructs full lifecycle timeline profiles of natural persons and market entities based on trusted government data that has been verified by the same source. Through an event-driven mechanism, it monitors changes in government data in real time and synchronously completes incremental updates of profile features and full-link log recording. Then, the causal forest algorithm is used to mine the causal relationship between user characteristics and government affairs processing, eliminate spurious features, and construct a standardized causal relationship feature set to provide an interpretable core basis for subsequent demand prediction. Subsequently, the demand prediction and proactive service module takes the causal relationship feature set as input and completes the accurate prediction of users' potential service needs through a dual robust causal inference model. After connecting with the standardized policy element library of the policy compliance and supervision module to complete the compliance verification of the demand, it generates personalized service guidance and pre-filled application packages that match user characteristics and actively pushes them to users through multiple channels. For matters that meet the conditions of "no application required to enjoy", the entire process of pre-approval is automatically completed. Simultaneously, the inclusive service adaptation module identifies special groups based on user profiles, generates a full-link personalized service adaptation plan, matches the optimal service channels and assistance resources, and completes the adaptation of inclusive services. After a case is initiated, the process monitoring and root cause tracing module collects time-series data from all nodes of the case process. It compares the real-time collected time-series data with the standard time-series feature baseline. When an anomaly occurs, it triggers a bottleneck warning in real time. It completes multi-dimensional root cause tracing of bottlenecks through a pre-trained Bayesian causal network model and generates process optimization suggestions simultaneously. The policy compliance supervision module simultaneously completes full-cycle compliance supervision, including pre-process compliance prediction, real-time verification during the process, and post-process risk tracing. It also handles identified compliance risks in a graded and closed-loop manner, continuously analyzes policy and regulatory documents, and dynamically updates the rules for handling government affairs to ensure that the handling of affairs is completely consistent with current policies. In the process of government services, the central dispatch module runs through the entire process, realizes dynamic scheduling and collaborative work of computing resources of each module, completes the closed-loop management of cross-departmental and cross-level joint handling matters, monitors the system's full-dimensional operation status in real time, and triggers alarms in real time when anomalies occur, ensuring the overall stable, efficient and secure operation of the system.

[0022] Example 2 Specifically, the user profile building module includes: a natural person profile submodule, a market entity profile submodule, an event-driven update submodule, and a causal feature mining submodule; The Natural Person Profile submodule is used to aggregate government data on a natural person's entire life cycle from birth to death, using the natural person's identity identifier as the unique primary key, and to construct a timeline profile of the entire life cycle with timestamps. The Market Entity Profile submodule is used to aggregate government data on market entities throughout their entire lifecycle, from establishment to cancellation, using the unified social credit code as the unique primary key, and to construct a timeline profile of the entire lifecycle with timestamps. The event-driven update submodule is used to listen for change events in all government data sources. When data is added, changed, or cancelled, it triggers the synchronous update of profile features and records the entire change log. The causal feature mining submodule is used to identify the causal relationship between user characteristics and government affairs processing based on historical case data and user profile features, eliminate spurious correlation features, and construct a causal relationship feature set.

[0023] The demand prediction and proactive service module includes: a causal inference model construction submodule, a policy and rule adaptation submodule, a proactive service generation submodule, and a pre-processing submodule; The causal inference model construction submodule is used to train a causal inference model of government affairs demand based on dual robust learning, with the causal association feature set as input and the user's government affairs handling behavior as output. It quantifies the average processing effect of user features on the demand for handling affairs and outputs the probability ranking of users' potential service needs. The policy and rule adaptation submodule is used to connect to the policy element library of the policy compliance and supervision module, match the current policy and rule corresponding to potential service needs, verify the compliance and scope of application of the needs, and eliminate the predicted needs that do not meet the policy requirements. The proactive service generation submodule is used to generate personalized service guidance that matches user characteristics based on compliance-based anticipated needs, and to pre-fill the service application form based on user profile data to generate a pre-filled service application package. The pre-approval submodule is used to automatically complete the pre-approval process for government affairs that do not require users to submit additional materials, undergo on-site verification, or obtain subjective confirmation from users, and generate the processing results and proactively push them to users.

[0024] The data governance and permission adaptation module includes: a data traceability submodule, a homogeneity verification submodule, a consistency update submodule, and a four-dimensional permission control submodule; The data tracing submodule is used to assign a unique lineage identifier to each government data element at the smallest granularity, record the full-link data lineage information of the data element's generating department, data standard, update cycle, and call chain, and construct a government data lineage map; The homology verification submodule is used to verify the source consistency of data elements based on lineage identifiers when calling government data elements across departments. It compares the values, update times and standard specifications of the same data element from different data sources, identifies data conflicts and anomalies, and outputs the verification results. The consistency update submodule is used to monitor data change events of the source department of data elements. When the source data changes, it triggers the synchronous update of the corresponding data elements of all related departments in the entire system, and synchronously updates the government data lineage map. The four-dimensional access control submodule is used to build a four-dimensional access model based on data sensitivity level, service scenario, user identity and approval process. It dynamically allocates data access permissions based on the corresponding parameters of the current case and performs compliance de-identification processing on sensitive fields that exceed the permission scope.

[0025] In the four-dimensional access control submodule, the sensitivity level of government data elements is divided into four levels: public information, internal information, sensitive information and top secret information. Corresponding access permission rules and desensitization rules are set for data elements of different sensitivity levels. For top-secret information, access to the corresponding fields will only be granted when all the preset conditions for the service scenario, user identity, and approval process are met simultaneously. The entire process will be recorded and downloading or forwarding will be prohibited. In this embodiment, the user profile construction module adopts a hybrid modeling approach that combines wide table modeling and graph modeling. It constructs a three-level hierarchical storage architecture consisting of a basic attribute layer, a behavior event layer, and a feature indicator layer. It uses a time-series database to store the entire lifecycle timeline data. It monitors incremental changes in government data sources through the CDC permission model change data capture technology. It completes incremental updates of profile features based on a stream computing engine without recalculating all data. At the same time, it uses an immutable chain structure to store the entire change log. The causal feature mining submodule controls for confounding variables through propensity score matching, calculates the average treatment effect of features based on the causal forest algorithm, eliminates spurious features through placebo testing, and generates a standardized causal association feature set. The demand prediction and proactive service module employs a dual robust learning framework to construct a causal inference model, combining the robustness of outcome regression and propensity score models to address confounding bias issues. A rule engine enables automated compliance verification of policy rules and predicted demands, while a template engine combined with a dynamic content generation mechanism completes the personalized generation of service guides and pre-filled forms. A "no-application-required" access rule base and an automated process engine enable pre-processing. The data governance and permission adaptation module constructs a data lineage map through end-to-end full-link lineage tracing technology, completes homology verification using a multi-dimensional verification rule system, achieves consistent data updates across the entire system through a source-driven incremental synchronization mechanism, builds a dynamic permission calculation engine, calculates access permissions in real time based on a four-dimensional permission model, completes compliant processing of sensitive fields using real-time dynamic de-identification technology, and ensures that all data access operations are fully traceable and auditable.

[0026] Example 3 Specifically, the process monitoring and root cause tracing module includes: a full-process node tracking submodule, a time-series feature library construction submodule, a bottleneck early warning submodule, and a causal root cause tracing submodule; The full-process node data collection submodule is used to break down the processing flow of each government matter into the smallest granular processing node, set up data collection points for each node, and collect time-series operation data of each node's operator, operation time, dwell time and flow status. The time-series feature library construction submodule is used to construct standard time-series feature baselines and abnormal time-series feature sample libraries for each processing node of each government matter based on the time-series operation data of historical cases. The congestion warning submodule is used to compare the currently collected case processing time-series data with the corresponding standard time-series feature baseline. When the node running data exceeds the preset threshold, a congestion warning is triggered in real time and pushed to the corresponding processing and supervision departments. The causal root cause tracing submodule is used to quantify the causal contribution of each variable to the occurrence of the blockage when a blockage warning is triggered. Based on a pre-trained Bayesian causal network model, it uses the abnormal characteristics of the blockage as the target variable and the influencing factors related to the whole process as candidate causal variables. It then outputs the root cause ranking and tracing results of the blockage.

[0027] The causal root cause tracing submodule generates suggestions for optimizing government service processes based on the results of bottleneck tracing. To address systemic bottlenecks caused by flawed process design, we generate process optimization solutions that include node merging, process reduction, and material simplification. These solutions are then pushed to government service management departments for continuous iterative optimization of government service processes.

[0028] The policy compliance supervision module includes: a policy structured analysis submodule, a rule update submodule, a compliance verification submodule, and a risk handling submodule; The policy structured analysis submodule is used to perform full-text analysis of policy and regulatory documents through a fine-tuned government affairs domain big language model, extract structured elements related to government services such as processing conditions, approval standards and processing time limits, and generate a policy element library; The item rule update submodule is used to compare and verify the newly formulated policy element library with the existing rules for handling government affairs, identify rule differences and generate update suggestions. After review and confirmation, the rules for handling government affairs and approval standards are updated synchronously throughout the system. The compliance verification submodule is used to build a compliance verification rule library for the entire process of pre-application, during-application and post-application. It completes compliance prediction before the application is submitted, verification during the approval process and risk tracing after the completion, so as to realize full-cycle compliance supervision of the application. The risk handling submodule is used to automatically generate corresponding handling instructions based on the risk level identified by compliance verification, track the handling progress, and form a closed-loop management of risk identification, early warning, handling and review.

[0029] In this embodiment, the process monitoring and root cause tracing module adopts non-intrusive full-process data collection technology, which automatically completes the time-series data collection based on the life cycle events of the process engine nodes without intruding into the business code. The processing node decomposition follows the atomicity principle, with each node corresponding to a single processing action and approval authority. The collected time-series data is uniformly written into the time-series database using a standardized event format. The time-series feature library construction submodule calculates the probability distribution of normal duration of nodes using kernel density estimation, determines the standard time-series feature baseline based on a pre-set confidence level, and constructs an abnormal feature sample library by clustering historical abnormal data using an unsupervised learning algorithm. The congestion early warning submodule completes real-time comparison of time-series data through a real-time stream computing engine, triggers graded early warnings based on feature deviation and abnormal pattern matching, and different levels of early warning correspond to differentiated push and handling processes; The causal root cause tracing submodule is based on a pre-trained Bayesian causal network model of historical bottleneck cases. It constructs the causal dependency relationship between abnormal features and candidate causal variables, completes causal reasoning through the belief propagation algorithm, calculates the causal contribution of each variable and outputs the root cause ranking, and automatically generates process optimization schemes based on rule reasoning. The policy compliance supervision module adopts a hybrid parsing architecture that combines a fine-tuned large language model in the government sector with rule matching to complete the structured element extraction of policy texts. The extracted elements are then standardized and corrected through a rule engine to generate a machine-readable policy element library. The item rule update submodule constructs a mapping relationship library between policy elements and item rules, automatically identifies rule differences and generates update suggestions with policy basis, and automatically synchronizes item rules across the entire system after review; The compliance verification submodule constructs three levels of compliance verification rules: pre-event, during-event, and post-event, to achieve full-cycle compliance verification for case handling. The risk handling submodule constructs a three-level risk classification and control system, and formulates differentiated handling procedures for different levels of risk to achieve closed-loop risk management.

[0030] Example 4 Specifically, the inclusive service adaptation module includes: a special group identification submodule, an adaptation scheme generation submodule, a service channel adaptation submodule, and an assistance and agency scheduling submodule; The special group identification submodule is used to identify the identity and personalized needs of special groups based on the full life cycle timeline profile output by the user profile building module, and to build a special group exclusive profile. The adaptation solution generation submodule is used to generate personalized service adaptation solutions for the entire chain, from the interactive interface, service process, material filling to the approval mode, based on the exclusive profile of special groups. The service channel adaptation submodule is used to match the optimal government service processing channel based on the behavioral habits and needs of special groups; The assistance and agency scheduling submodule is used to intelligently match the corresponding assistance and agency personnel and service stations based on the service needs and geographical location of special groups, generate and dispatch assistance and agency tasks, and track service progress and service evaluation.

[0031] The central dispatch module includes: a cross-departmental collaboration submodule, a collaborative dispatch submodule, a system management submodule, and an operation monitoring submodule; The cross-departmental collaboration submodule is used to build a closed-loop management mechanism for government service processes across departments, levels, and regions, enabling automatic distribution of processes, synchronization of nodes, progress tracking, and mutual recognition of results for jointly handled matters; The collaborative scheduling submodule is used to intelligently schedule the computing power and resources of each module according to the handling process and system operation status, so as to realize data interaction and collaborative work between modules. The system management submodule provides visualized operation functions for government affairs management, user permission management, system configuration management, and rule configuration management; The operation monitoring submodule is used to monitor the system's operating status, the call status of each module, the data flow status and the progress of case processing in real time, and to trigger alarms in real time when the system malfunctions. In this embodiment, the inclusive service adaptation module constructs special group feature tags, and automatically identifies the special group identity through feature matching and rule reasoning based on the user's full life cycle profile. It extracts personalized features to construct a special group exclusive profile, and adopts a dynamic update mechanism to optimize the profile features in real time. At the same time, it constructs a full-process privacy protection mechanism. The collection, storage and use of profile data comply with personal information protection regulations throughout the process, and it is only used for government service adaptation scenarios. The data is encrypted and stored throughout the process and access permissions are strictly controlled. The adaptation scheme generation submodule builds a full-link service adaptation rule library, presets differentiated adaptation rules for different types of special groups, and automatically generates full-dimensional adaptation schemes covering interactive interfaces, operation processes, material filling and approval modes through the rule engine; The service channel adaptation submodule builds a multi-channel service capability library and automatically matches the optimal service channel through a multi-attribute decision algorithm, supporting seamless connection and data interoperability of multi-channel services. The assistance and agency scheduling submodule builds an assistance and agency resource library. Through an intelligent scheduling algorithm that combines proximity matching and capability matching, it achieves the optimal matching of personnel and tasks, realizing closed-loop management of the entire process of assistance and agency tasks. The central scheduling module adopts a visual process orchestration engine based on the BPMN2.0 standard, which supports no-code configuration and dynamic orchestration of cross-departmental joint processes, and realizes automatic process distribution, node synchronization, result mutual recognition and closed-loop management. The collaborative scheduling submodule adopts a microservice governance framework to achieve elastic scaling of services, circuit breaking and degradation, traffic control and load balancing, and dynamically allocate computing resources based on system load; The system management submodule provides a visual, multi-dimensional management backend, supporting code-free management of government affairs, user permissions, and rule configurations; The operation monitoring submodule constructs a four-level, all-dimensional monitoring system encompassing the infrastructure layer, platform layer, application layer, and business layer, enabling real-time monitoring of system operation status and multi-level intelligent alarms to ensure stable 24 / 7 operation of the system.

[0032] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A government service analysis management system, characterized by: It includes modules for user profiling, demand prediction and proactive service, data management and permission adaptation, process monitoring and root cause tracing, policy compliance and supervision, inclusive service adaptation, and central dispatch. The user profile building module is used to build a full life cycle timeline profile of natural persons and market entities, complete the real-time update of profile features based on an event-driven mechanism, and mine the causal relationship feature set between user features and government affairs. The demand prediction and proactive service module is used to construct a causal inference model of government demand based on the causal relationship feature set, predict users' potential service needs in combination with current policies and rules, generate personalized service guidance and pre-filled application forms, and complete the proactive push and pre-processing of government services. The data management and permission adaptation module is used to build a full-link lineage tracing system for government data elements, complete the sameness verification and dynamic consistency update of multi-source heterogeneous data, and realize fine-grained access control and compliance desensitization of government data based on the four-dimensional permission model. The process monitoring and root cause tracing module is used to collect time-series data from all nodes of the government affairs handling process, build a time-series feature library of the entire process, and realize real-time early warning and multi-dimensional root cause tracing of bottlenecks in handling affairs based on the Bayesian causal network model. The policy compliance supervision module is used to perform structured analysis and element extraction of policy and regulatory documents, synchronously update the handling rules and approval standards for government affairs, build a full-process compliance verification engine, and realize full-cycle compliance supervision of case handling. The inclusive service adaptation module is used to identify the identity of special groups based on user profiles, generate a full-link personalized service adaptation solution, and complete the full-dimensional adaptation of government services. The central scheduling module is used to realize the coordinated scheduling of various modules, the closed-loop management of cross-departmental government service processes, and the monitoring of system configuration and operation status.

2. The government service analysis and management system according to claim 1, characterized in that: The user profile building module includes: a natural person profile submodule, a market entity profile submodule, an event-driven update submodule, and a causal feature mining submodule; The Natural Person Profile submodule is used to aggregate government data of a natural person from birth to death, using the natural person's identity identifier as the unique primary key, and to construct a timeline profile of the entire life cycle with timestamps. The market entity profiling submodule is used to aggregate government data of market entities throughout their entire lifecycle, from establishment to cancellation, using the unified social credit code as the unique primary key, and to construct a timeline profiling of the entire lifecycle with timestamps. The event-driven update submodule is used to monitor change events of all government data sources. When data is added, changed, or cancelled, it triggers the synchronous update of profile features and records the entire change log. The causal feature mining submodule is used to construct the causal relationship feature set by identifying the causal relationship between user characteristics and government affairs processing based on historical case data and user profile features, eliminating false correlation features.

3. The government service analysis and management system according to claim 1, characterized in that: The demand prediction and proactive service module includes: a causal inference model construction submodule, a policy and rule adaptation submodule, a proactive service generation submodule, and a pre-processing submodule; The causal inference model construction submodule is used to train a causal inference model of government affairs demand based on dual robust learning, with the causal association feature set as input and user government affairs handling behavior as output, to quantify the average processing effect of user features on the demand for handling affairs, and output the probability ranking of users' potential service needs. The policy rule adaptation submodule is used to connect to the policy element library of the policy compliance supervision module, match the current policy rules corresponding to potential service needs, verify the compliance and scope of application of the needs, and eliminate the predicted needs that do not meet the policy requirements. The proactive service generation submodule is used to generate personalized service guidance matching user characteristics based on compliance-based predicted needs, and to pre-fill the service form based on user profile data to generate a pre-filled service package. The aforementioned pre-approval submodule is used to automatically complete the pre-approval process for government affairs that do not require users to submit additional materials, undergo on-site verification, or obtain subjective confirmation from users, and generate the processing results and proactively push them to users.

4. The government service analysis and management system according to claim 1, characterized in that: The data management and permission adaptation module includes: a data tracing submodule, a sameness verification submodule, a consistency update submodule, and a four-dimensional permission management submodule; The data tracing submodule is used to assign a unique lineage identifier to each smallest granularity government data element, record the full-link data lineage information of the data element's generating department, data standard, update cycle, and call chain, and construct a government data lineage map; The homology verification submodule is used to verify the source consistency of the data element based on the lineage identifier when calling government data elements across departments, compare the value, update time and standard specifications of the same data element from different data sources, identify data conflicts and anomalies, and output the verification results. The consistency update submodule is used to monitor data change events of the source department of the data element. When the source data changes, it triggers the synchronous update of the corresponding data elements of all related departments in the whole system, and synchronously updates the government data lineage map. The four-dimensional permission control submodule is used to construct a four-dimensional permission model based on data sensitivity level, service scenario, user identity and approval process, dynamically allocate data access permissions based on the corresponding parameters of the current case, and perform compliance de-identification processing on sensitive fields that exceed the permission scope.

5. The government service analysis and management system according to claim 4, characterized in that: In the four-dimensional access control submodule, the sensitivity level of government data elements is divided into four levels: public information, internal information, sensitive information and top secret information. Corresponding access permission rules and desensitization rules are set for data elements of different sensitivity levels. For top-secret information, access to the corresponding fields will only be granted when all preset conditions for the service scenario, user identity, and approval process are met simultaneously. The entire process will be recorded, and downloading and forwarding will be prohibited.

6. The government service analysis and management system according to claim 1, characterized in that: The process monitoring and root cause tracing module includes: a full-process node embedding submodule, a time-series feature library construction submodule, a bottleneck early warning submodule, and a cause-and-effect root cause tracing submodule; The full-process node data collection submodule is used to break down the processing flow of each government matter into the smallest granular processing node, set up data collection points for each node, and collect time-series operation data of each node's operator, operation time, dwell time and flow status. The time-series feature library construction submodule is used to construct a standard time-series feature baseline and an abnormal time-series feature sample library for each processing node of each government matter based on the time-series operation data of historical cases. The bottleneck early warning submodule is used to compare the currently collected case processing time-series data with the corresponding standard time-series feature baseline. When the node running data exceeds the preset threshold, a bottleneck early warning is triggered in real time and pushed to the corresponding processing and supervision departments. The causal root cause tracing submodule is used to, when a blockage warning is triggered, quantify the causal contribution of each variable to the occurrence of the blockage based on a pre-trained Bayesian causal network model, with the abnormal characteristics of the blockage as the target variable and the influencing factors of the whole process as candidate causal variables, and output the root cause ranking and tracing results of the blockage.

7. The government service analysis and management system according to claim 6, characterized in that: The root cause tracing submodule generates suggestions for optimizing government service processes based on the results of bottleneck tracing. To address systemic bottlenecks caused by flawed process design, we generate process optimization solutions that include node merging, process reduction, and material simplification. These solutions are then pushed to government service management departments for continuous iterative optimization of government service processes.

8. The government service analysis and management system according to claim 1, characterized in that: The policy compliance supervision module includes: a policy structured analysis submodule, a matter rule update submodule, a compliance verification submodule, and a risk handling submodule; The policy structured parsing submodule is used to perform full-text parsing of policy and regulatory documents through a fine-tuned government affairs domain big language model, extract structured elements related to government services such as processing conditions, approval standards and processing time limits, and generate a policy element library; The rule update submodule is used to compare and verify the newly formulated policy element library with the existing rules for handling government affairs, identify rule differences and generate update suggestions. After review and confirmation, the rules for handling government affairs and approval standards are updated synchronously throughout the system. The compliance verification submodule is used to build a compliance verification rule library for the entire process before, during and after the application. It completes compliance prediction before the application is submitted, verification during the approval process and risk tracing after the application is completed, so as to realize compliance supervision throughout the entire application cycle. The risk handling submodule is used to automatically generate corresponding handling instructions based on the risk level identified by compliance verification, track the handling progress, and form a closed-loop management of risk identification, early warning, handling and review.

9. The government service analysis and management system according to claim 1, characterized in that: The inclusive service adaptation module includes: a special group identification submodule, an adaptation scheme generation submodule, a service channel adaptation submodule, and an assistance and agency scheduling submodule. The special group identification submodule is used to identify the identity and personalized needs of special groups based on the full life cycle timeline profile output by the user profile construction module, and to construct a special group exclusive profile. The adaptation scheme generation submodule is used to generate a personalized service adaptation scheme for the entire chain, from the interactive interface, the process, the material filling and the approval mode, based on the special profile of the special group. The service channel adaptation submodule is used to match the optimal government service processing channel based on the behavioral habits and needs of special groups. The assistance and agency scheduling submodule is used to intelligently match the corresponding assistance and agency personnel and service stations based on the service needs and geographical location of special groups, generate and dispatch assistance and agency tasks, and track service progress and service evaluation.

10. A government service analysis and management system according to claim 1, characterized in that: The central dispatch module includes: a cross-departmental collaboration submodule, a collaborative dispatch submodule, a system management submodule, and an operation monitoring submodule; The cross-departmental collaboration submodule is used to build a closed-loop management mechanism for government service processes across departments, levels, and regions, enabling automatic distribution of processes, synchronization of nodes, tracking of progress, and mutual recognition of results for jointly handled matters; The collaborative scheduling submodule is used to intelligently schedule the computing power and resources of each module according to the handling process and system operation status, so as to realize data interaction and collaborative work between modules. The system management submodule is used to provide visual operation functions for government affairs management, user permission management, system configuration management, and rule configuration management; The operation monitoring submodule is used to monitor the system's operating status, the call status of each module, the data flow status and the progress of case processing in real time, and to trigger alarms in real time when the system malfunctions.

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