Multi-terminal fusion management method and system for public communication channel

Abstract

The invention relates to the technical field of information management and processing, and discloses a multi-terminal fusion management method and system for a public communication channel, and the method comprises the steps: receiving multi-terminal original data, and carrying out the normalization of the multi-terminal original data into a standardized atomic event; constructing and dynamically evolving a dynamic public affair causal graph based on the atomic events; calculating an issue entropy for the composite issue, and carrying out complexity quantification and priority distribution; when the priority reaches a preset condition, generating a self-adaptive workflow according to a causal atlas context; and after the workflow is executed, performing cross-modal closed-loop verification, and solidifying a successful disposal scheme into a reusable treatment mode. The corresponding system comprises a data acquisition and normalization module, a causal atlas modeling module, a topic complexity analysis module, a self-adaptive workflow management module and a closed-loop verification and knowledge solidification module, and realizes refined complexity quantification of public affairs, and improves the flexibility and efficiency of a disposal process. And continuous accumulation and optimization of management knowledge are promoted.

Description

Technical Field

[0001] This invention relates to the field of information management and processing technology, specifically to a multi-terminal integrated management method and system for public communication channels. Background Technology

[0002] With the popularization of information technology and the improvement of public awareness, the volume of public opinions, problem reports, and demands expressed through various channels such as social media, government hotlines, and online platforms is increasing daily. While this multi-source, heterogeneous public communication data contains rich information about public sentiment, its diverse formats, complex content, and real-time nature present challenges for administrators in data processing and information integration.

[0003] Existing technologies often face challenges in effectively aggregating and uniformly analyzing such public communication data, leading to information silos and hindering a comprehensive understanding of public affairs. Furthermore, identifying key, interconnected public issues from massive datasets and accurately assessing their complexity and prioritizing them remains a challenge. Many systems rely primarily on keyword matching or human experience, failing to delve into the underlying causal relationships or potential impacts of events. This compromises the scientific rigor of decision-making and the timeliness of responses, resulting in inefficient resource allocation.

[0004] At the handling level, existing public affairs management processes are often relatively fixed and lack the ability to adapt to the dynamic evolution of events and personalized needs. When public affairs are complex or evolve rapidly, the workflow is difficult to adjust quickly to match actual needs, affecting the efficiency and effectiveness of handling.

[0005] Finally, existing methods lack a systematic and objective closed-loop verification mechanism for handling public affairs, making it difficult to accurately assess the effectiveness of solutions. Furthermore, successful experiences are often not effectively extracted, consolidated, and reused, leading to slow knowledge accumulation, limited organizational learning capabilities, recurring similar problems, and an inability to form a continuously optimized and self-evolving management model. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a multi-terminal integrated management method and system for public communication channels, aiming to solve problems such as scattered data sources, information silos, delayed event response, rigid processing procedures, and difficulties in performance evaluation in traditional public affairs management, and to achieve intelligent, refined, and closed-loop management of public affairs.

[0007] The first aspect of this invention provides a multi-terminal integrated management method for public communication channels, comprising the following steps: In the initial stage of data processing, heterogeneous data from multiple sources is aggregated and semantically normalized. This step receives raw data from multiple public communication channels, parses and transforms this raw data to generate atomic events containing standardized event elements; it eliminates data format differences and semantic ambiguities between different channels, laying the foundation for subsequent processing.

[0008] In the information integration and correlation phase, a dynamic public affairs causal graph is constructed and evolved. This step, based on the standardized atomic events, constructs and continuously updates a dynamic public affairs causal graph; the dynamic public affairs causal graph represents the correlation and causal relationship between public affairs, and is used to analyze the underlying causes and potential impacts of events.

[0009] Preferably, when constructing and evolving the causal graph, time series of the occurrence frequency are constructed for different event categories; the Granger causality test is used to perform pairwise tests on the time series to determine whether the past value of one event category has predictive power for the current value of another event category; if the test result rejects the null hypothesis at a preset significance level, a directed causal edge is created between the composite issue nodes representing the two event categories, and the causal weight and time delay of the directed causal edge are quantified according to the p-value and lag order of the test. The p-value refers to the probability of observing the current sample result or a more extreme result than the current sample result when the null hypothesis is true.

[0010] In the decision support phase, complexity quantification and priority allocation based on issue entropy are performed. This step calculates issue entropy—used to quantify the uncertainty and complexity of complex issues formed by the aggregation of one or more atomic events in the dynamic public affairs causal graph—and prioritizes these complex issues based on the value of the issue entropy. Issue entropy is used to assess the urgency and processing difficulty of different public affairs, thereby optimizing resource allocation.

[0011] Preferably, the issue entropy of the composite issue is calculated using a multi-dimensional weighted summation model, wherein the issue entropy is a weighted sum of at least the following four components: Causal complexity is used to measure the structural importance of complex issues in a causal network. Subject coupling degree is used to measure the complexity of cross-departmental collaboration required to solve complex issues; Evolution speed is used to measure the acceleration of the growth in public opinion intensity on complex issues. And semantic divergence, used to measure the degree of confusion and opposition in public opinion surrounding complex issues.

[0012] In one specific embodiment, the calculation of causal complexity is based on the in-degree and out-degree of the composite issue node in the causal graph; the calculation of evolution speed is performed by taking the second derivative of the heat value time series of the composite issue.

[0013] Preferably, when performing priority sorting, the system maintains a maximum priority queue with the issue entropy as the priority key value. When the issue entropy of a composite issue is updated, the position of the composite issue is updated in the maximum priority queue.

[0014] During the task execution phase, a graph-driven adaptive workflow is generated and executed. When the priority of the composite issue reaches a preset condition, the system dynamically generates an adaptive workflow based on the contextual information of the composite issue in the dynamic public affairs causal graph. This workflow adjusts the handling tasks and responsible parties according to the characteristics of the specific event to improve handling efficiency.

[0015] In one specific embodiment, generating an adaptive workflow specifically includes: automatically decomposing the complex issue into a series of atomic tasks with dependencies based on the causal structure in the dynamic public affairs causal graph; and organizing the dependencies between the atomic tasks into a directed acyclic graph to represent the workflow.

[0016] Preferably, the dependencies between the atomic tasks include causal dependencies, i.e., establishing a dependency from the handling task of the preceding cause issue to the handling task of the subsequent result issue based on the causal edges between the composite issues in the dynamic public affairs causal graph; logical dependencies, i.e. establishing dependencies based on the preset logical order of the tasks themselves; and resource dependencies, i.e., establishing corresponding resource dependency edges when multiple tasks need to share scarce resources to ensure that tasks are executed in sequence or resource allocation is coordinated.

[0017] During the feedback and optimization phase, cross-modal closed-loop verification and knowledge consolidation are performed. After the adaptive workflow is executed, the system triggers cross-modal verification of the handling effect, and based on the verification results, successful handling solutions are consolidated into reusable governance models to form knowledge accumulation and experience transfer. If the handling fails or inconsistencies occur, the workflow is reactivated for secondary handling.

[0018] Preferably, triggering cross-modal verification of the handling effect specifically includes: locating the original reporting user and surrounding affected users associated with the composite issue, and proactively pushing a verification request that includes text input and image upload functions; calling the natural language processing function component to analyze the text feedback from the user, and calling the computer vision function component to analyze the images returned by the user, so as to comprehensively determine the handling effect.

[0019] In one specific embodiment, solidifying a successful resolution solution into a reusable governance model specifically includes: encapsulating the graph subgraph corresponding to the problem, the directed acyclic graph workflow corresponding to the solution, and the verification evidence proving the success of the resolution into a standardized governance model data structure and storing it in a knowledge base.

[0020] A second aspect of the present invention provides a multi-terminal integrated management system for public communication channels, characterized in that the system comprises: The data acquisition and normalization module is configured to receive and parse raw data from multiple public communication channels and output standardized atomic events. This module implements front-end data access and preprocessing, solving the problem of heterogeneous data integration.

[0021] The causal graph modeling module is configured to construct and maintain a dynamic causal graph of public affairs, representing the associations and causal relationships between public affairs, based on the standardized atomic events. This module is used to dynamically discover and update the intrinsic connections between events.

[0022] The issue complexity analysis module is configured to calculate issue entropy for composite issues in the dynamic public affairs causal graph to quantify uncertainty and complexity, and to prioritize them accordingly. This module is used to identify and prioritize important and complex public affairs.

[0023] An adaptive workflow management module is configured to respond to composite issues that reach a preset priority and generate adaptive workflows based on contextual information in the causal graph. This module manages the public affairs handling process.

[0024] The closed-loop verification and knowledge solidification module is configured to perform cross-modal verification of the treatment effect after the adaptive workflow is executed, and solidify the successfully verified treatment plan into a reusable governance model. This module ensures the effectiveness of the treatment effect and promotes the accumulation and reuse of knowledge.

[0025] This invention provides a multi-terminal integrated management method and system for public communication channels. It has the following beneficial effects: 1. This invention constructs a dynamic causal graph of public affairs and introduces issue entropy calculation on this basis, enabling multi-dimensional and refined complexity assessment of complex public issues and prioritization accordingly. This mechanism allows managers to accurately identify the urgency and difficulty of handling different public affairs, thereby achieving rational resource allocation and scientific decision-making, avoiding the problems of insufficient understanding of issues or misallocation of resources in traditional models.

[0026] 2. This invention dynamically generates an adaptive workflow based on contextual information in a dynamic public affairs causal graph. This workflow can flexibly adjust task allocation and execution order according to the causal structure, task dependencies, and resource requirements of a specific issue. This graph-driven adaptability improves the response speed to sudden or complex public affairs and optimizes the collaborative efficiency in the handling process, breaking free from the rigid limitations of traditional fixed processes.

[0027] 3. This invention introduces a cross-modal closed-loop verification mechanism to objectively evaluate the treatment effect and solidify the verified treatment plan into a reusable governance model. This not only ensures that the effect of each treatment action can be effectively evaluated and fed back, but also builds a knowledge base for self-learning and iterative optimization, thereby realizing the accumulation and inheritance of management experience and providing experience for solving similar problems in the future. Attached Figure Description

[0028] Figure 1 This is a system framework diagram of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention.

[0029] Among them, 110 is the data acquisition and normalization module; 120 is the causal graph modeling module; 130 is the issue complexity analysis module; 140 is the adaptive workflow management module; and 150 is the closed-loop verification and knowledge solidification module. Detailed Implementation

[0030] The technical solutions in 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.

[0031] See attached document Figure 1 This invention provides a multi-terminal integrated management system for public communication channels, which includes the following logical functions: The data acquisition and normalization module 110 receives raw data from multiple configured heterogeneous public communication channels and converts the raw data into a standardized atomic event data structure according to preset parsing rules.

[0032] The causal graph modeling module 120, based on atomic event data streams, constructs and dynamically updates a dynamic public affairs causal graph containing multiple types of nodes and weighted directed edges, used to represent the associations and causal relationships between public affairs.

[0033] The issue complexity analysis module 130, based on causal graphs, calculates the issue entropy of complex issues formed by the aggregation of atomic events, in order to quantify the complexity, uncertainty and potential impact of complex issues.

[0034] The adaptive workflow management module 140 dynamically generates workflows that include task decomposition, responsible parties, and handling paths for highly complex issues based on the calculation results of issue entropy.

[0035] The closed-loop verification and knowledge solidification module 150 triggers a verification process for the handling effect after the workflow is executed, and solidifies the successful handling path and context information into a reusable governance model based on the verification results.

[0036] See attached document Figure 2 The present invention also provides a multi-terminal integrated management method for public communication channels. This method is executed through the aforementioned system, and its overall workflow may include the following steps: S101. Multi-terminal heterogeneous data collection and semantic normalization: Receive raw data from multiple public communication channels through a preset interface, and parse the raw data to generate standardized atomic events.

[0037] S102. Construction and Evolution of Dynamic Public Affairs Causal Graph: Based on atomic events, construct and dynamically update a dynamic public affairs causal graph. The update includes adding or updating nodes in the graph, and mining and establishing causal relationship edges between nodes through causal inference algorithms.

[0038] S103. Complexity Quantification and Priority Allocation Based on "Issue Entropy": For a complex issue formed by the aggregation of one or more atomic events in a causal graph, calculate its issue entropy to quantify its complexity, and prioritize the complex issues according to the value of the issue entropy.

[0039] S104. Graph-driven adaptive workflow generation and execution: In response to the priority of a composite issue meeting preset conditions, an adaptive workflow containing the handling task and responsible subject is dynamically generated based on the context information of the composite issue in the causal graph.

[0040] S105, Cross-modal closed-loop verification and knowledge solidification: After the adaptive workflow is executed, a closed-loop verification process for the handling effect is triggered, and based on the results of the verification process, successful handling paths are selectively solidified into reusable governance modes, or the workflow is reactivated for secondary handling.

[0041] To further clarify the technical solutions of the embodiments of the present invention, the specific implementation methods involved in the above steps will be described in detail below.

[0042] Step S101 is executed by the data acquisition and normalization module 110 to provide unified and high-quality data input for subsequent steps.

[0043] The data acquisition and normalization module 110 can interface with various public communication front-ends through two specific technical implementation methods: The first method uses a standardized application programming interface (API) based on a request-response model, such as a RESTful API. Each front-end application encapsulates the raw data submitted by the user in an HTTP request and sends it to the designated endpoint exposed by the data acquisition and normalization module 110. To ensure the confidentiality and integrity of data transmission, the entire communication process is encrypted using the HTTPS protocol. The second method uses a message queue system based on a publish-subscribe model, such as Apache Kafka. In this approach, each front-end application acts as a producer, publishing raw data as messages to a designated topic. The data acquisition and normalization module 110, as a consumer, subscribes to this topic to asynchronously acquire data. This method is suitable for high-concurrency, high-throughput data access scenarios and ensures data security through SASL authentication and TLS encryption.

[0044] After receiving raw data containing text, images, etc., the data acquisition and normalization module 110 uses its internal multimodal semantic normalization engine to parse and transform the data. The processing logic of the multimodal semantic normalization engine distinguishes data based on its modality: For text data, the multimodal semantic normalization engine invokes natural language processing (NLP) functional components, which perform tasks including but not limited to entity recognition, relation extraction, and event classification. For example, through entity recognition technology, from the sentence "The streetlights on Chengnan Road are broken, affecting nighttime travel safety," the location entity "Chengnan Road," the object entity "streetlights," and the problem entity "nighttime travel safety" are identified; through relation extraction, the state relationship between "streetlights" and "broken" is established. Regarding the specific implementation of the NLP functional components, those skilled in the art can use pre-trained language models (such as BERT) for fine-tuning, or use rule-based and dictionary-based methods. The specific implementations are well-known technologies in the field and will not be elaborated upon here.

[0045] For image data, the engine invokes computer vision (CV) functional components to perform tasks including but not limited to object detection and scene classification. For example, for a user-uploaded image, the object detection algorithm identifies objects such as "fallen trees" and "damaged guardrails" in the image. The scene classification algorithm determines whether the image scene is a "road" or a "park." The specific implementation of the computer vision functional components can use mature object detection models such as YOLO and Faster R-CNN. Their training and deployment are well-known technologies in this field and will not be elaborated here.

[0046] After processing single-modal data, the multimodal semantic normalization engine performs information fusion. If a piece of original data contains both text and an image, the engine will integrate the structured information extracted from both modalities. For example, if the text description is "The manhole cover here is missing," and the image is analyzed and identified as a "manhole," the multimodal semantic normalization engine will associate the two to generate a more accurate and richer structured description.

[0047] After the above processing, the multimodal semantic normalization engine ultimately transforms each valid piece of raw data into a standardized atomic event data structure, which serves as the basic information unit for all subsequent analysis steps. Its specific field definitions are as follows: A unique identifier representing an atomic event can be generated using a UUID (Universally Unique Identifier) ​​to ensure global uniqueness; The timestamp represents the event, recording the precise time when the event was received by the system or submitted by the user, using an ISO8601 format string or a Unix timestamp; It represents the geographical location information of an event, stored in GeoJSON format, and can represent a point or a region, accurately recording the spatial location where the event occurred; The category of the event is represented by a value taken from a predefined enumeration list, such as 'INFRASTRUCTURE_FAULT' (infrastructure failure), 'TRAFFIC_CONGESTION' (traffic congestion), 'POLICY_SUGGESTION' (policy recommendation), etc., which are determined by the classification results of the aforementioned NLP or CV components; The core object of the event is a string that describes the main entities involved in the event, such as "streetlight", "bus stop sign", or "draft regulation".

[0048] The state description of the event is a string that describes the state of the core object or the dynamics related to the event, such as "damaged", "missing", "suggested modification", or "running error".

[0049] The associated user identifier, a string or integer, is used to uniquely identify the public user who submitted the event.

[0050] Represents a raw data pointer, a URI (Uniform Resource Identifier) ​​that points to the raw data file stored in object storage or a file system, used for data tracing and verification.

[0051] Through the above methods, step S101 transforms diverse and unstructured public input into a stream of atomic events with a unified format and clear semantics.

[0052] The construction of the dynamic public affairs causal graph in step S102 is performed by the causal graph modeling module 120. The dynamic public affairs causal graph is the core data structure of the method of the present invention, which is used to formalize and structure the various entities involved in public affairs and the complex relationships between them.

[0053] Dynamic public affairs causal graphs are formally defined as a time-varying graph. A dynamically evolving, directed, weighted graph, denoted as ;in, Represents the set of all nodes in the graph. Represents time The set of all edges in the graph.

[0054] Node set It consists of four different types of nodes, namely The specific data structure and attribute definitions for each type of node are as follows: Atomic event nodes These nodes are the most basic data units in the graph, directly mapped from the atomic events generated in step S101. Their data structure is consistent with that of the atomic events, containing... , , , , , , , Each atomic event node represents an indivisible objective public event fact that has occurred and been recorded by the system.

[0055] Composite issue nodes These nodes represent public issues with a more macroscopic semantics, aggregated from one or more atomic event nodes. A composite issue node is an abstract generalization of a series of related micro-events, and its data structure includes: The topic has a unique identifier, a topic name (e.g., "Noise pollution from nighttime construction in the central urban area"), a topic status (e.g., active, being processed, resolved, archived), and a dynamically updated topic popularity value.

[0056] Responsible Entity Node These nodes represent entities or individuals that assume corresponding responsibilities in the public affairs processing flow. Specifically, they may include government departments, subdistrict offices, and liaison stations for people's congress representatives. Their data structure includes: a unique identifier for the entity, the entity name, the entity type (e.g., government agency, social organization, individual), and contact information or interface address for system integration.

[0057] Policy target nodes These nodes represent textual objects such as policies, regulations, and plans related to public affairs. The subject can include urban management regulations, transportation planning schemes, draft legislation, etc. Its data structure includes: a unique identifier for the subject, the name of the subject, the document number, the status (e.g., draft, currently in effect, repealed), and a link to the original policy text.

[0058] edge set Each edge in Each edge represents a specific relationship between nodes, and is directional and weighted. The edge set mainly consists of three different types of edges: Aggregate edges: This type of edge is used to represent a "belonging" relationship between an atomic event and a composite issue. The direction of the edge starts from an atomic event node. Pointing to a complex issue node The establishment of this edge indicates that the atomic event is a concrete instance constituting a macroscopic complex issue. The weight of the edge can represent the semantic relevance score of the atomic event to the topic of the complex issue.

[0059] Associative Edges: This type of edge is used to represent non-causal associations between nodes of different types. For example, an edge from a composite issue node... Pointing to the responsible entity node An associated edge indicates that the responsible entity is one of the responsible units for handling this issue; another example is an edge originating from a node of a complex issue. Nodes pointing to policy targets An edge representing a relationship indicates that the occurrence or resolution of an issue is related to that policy or regulation. The weight of the edge can represent the strength or certainty of the relationship.

[0060] Causal edges: This type of edge is a core feature of the graph in this invention, used to represent potential causal relationships between complex issues. The direction of the edge starts from a node of the complex issue that acts as a cause. Points to a composite issue node that is the result. The existence of a causal edge indicates The occurrence of such issues has a statistically significant impact on subsequent events. The probability of occurrence of a given issue; each causal edge contains the following key attributes: Causal weight : A numerical value that quantifies the strength of the causal relationship. This weight is determined by the statistical test results of the causal inference algorithm in the subsequent steps.

[0061] Time delay : A numerical value representing the average time interval from the occurrence of the cause issue to the manifestation of the result issue.

[0062] Through the above formal definitions of nodes and edges, the causal graph modeling module 120 can organize discrete public appeal data into a network structure rich in semantic and logical relationships.

[0063] In the process of constructing a dynamic causal graph of public affairs, a core step is the causal relationship mining performed by the causal graph modeling module 120, aiming to discover and quantify the non-obvious leading-lagging or predictive relationships existing between different categories of public affairs, and solidify this relationship with "causal edges" in the graph.

[0064] The execution process of causal relationship mining is as follows: Construction of time series: The causal graph modeling module 120 first constructs a time series of the occurrence frequency for different event categories from the accumulated atomic event data. This process includes: Determine a standard time aggregation unit , such as "day" or "week". For each predefined event category

[0065] (e.g.: For "drainage facility blockage", For "urban road waterlogging"), count the number of atomic events that occur within each time unit .

[0066] Thus, a discrete time series is generated for each event category , where is the total duration of the observation window.

[0067]

[0068] Time series preprocessing: To meet the requirements of the subsequent statistical model for data stationarity, the causal graph modeling module 120 preprocesses the constructed time series. This processing includes performing a stationarity test on the time series, such as using the Augmented Dickey-Fuller test (ADF Test); if the test result indicates that the series is non-stationary, then perform a differencing transformation on it until the series reaches stationarity. For the specific implementation of this test and transformation, it belongs to the well-known techniques in the field of time series analysis and will not be elaborated here.

[0068] Causality Test: The causal graph modeling module 120 employs the Granger causality test method based on the Vector Autoregression (VAR) model to test the causality between any two preprocessed time series (e.g., representative...). of and representatives of Perform pairwise tests to determine Are the past values ​​correct? The current value has predictive power.

[0069] This test is performed by comparing the goodness of fit of the following two regression models: Constrained model (only) Its own historical value prediction ): ; Unrestricted model (using simultaneously) and Historical value prediction ): ; in: and These represent two event categories at different points in time. The observed values.

[0070] It is the maximum lag order of the model, and the optimal value can be determined by methods such as the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC).

[0071] , , These are the regression coefficients of the model.

[0072] and These are the residuals of the model. The null hypothesis of the Granger causality test. for: All lagging items coefficient All are zero (i.e.) The causal graph modeling module 120 tests this hypothesis by calculating the F-statistic.

[0073] Causal edge generation and quantification: Based on the results of causal testing, the causal graph modeling module 120 creates or updates causal relationships in the graph.

[0074] If the result of the F-test is at the pre-set significance level Below (for example) The null hypothesis was rejected. That is, the p-value of the test is less than Then the event category is considered Event Category Granger's reason.

[0075] The F-statistic is an intermediate result of the Granger causality test. Its value determines the final p-value, which is used to determine whether a causal relationship exists and to quantify the strength of causality.

[0076] At this point, the causal graph modeling module 120, in the graph, corresponds to the event categories respectively. and Two complex issue nodes and Between, create a line from point to A directed causal edge, and the causal weight of that directed causal edge. The value is assigned to a monotonically decreasing function that tests the p-value to reflect the strength of the causal relationship. A specific calculation method could be... The smaller the p-value, the higher the statistical significance and the greater the causal weight.

[0077] The time delay of the causal edge The attribute is assigned a value, the value of which can be determined by the optimal lag order. The process of obtaining, or through analysis and The cross-correlation function is used to determine the average time span from the occurrence of the cause to the manifestation of the result.

[0078] This causal relationship mining process is executed periodically by the causal graph modeling module 120, or triggered when the accumulated atomic event data reaches a certain amount, to ensure that the causal relationships represented in the graph can reflect the latest data patterns.

[0079] After the causal graph modeling module 120 completes the construction of the dynamic public affairs causal graph, another core function is to maintain the dynamics of the graph to accurately reflect the changing trends of public affairs over time. This dynamics is mainly achieved through two aspects: real-time updates of the graph topology and dynamic calculation of node attributes. Among them, a key dynamic attribute is the popularity value of composite issue nodes, which is used to quantify the degree of attention or activity of the issue at the current moment.

[0080] When the causal graph modeling module 120 receives a new atomic event from step S101, it first treats the atomic event as a new atomic event node. Add it to the dynamic public affairs causal graph and establish its relationship with the corresponding complex issue nodes. The aggregation edge. Subsequently, the causal graph modeling module 120 immediately triggers the processing of this composite issue node. Popularity value Recalculation.

[0081] The popularity score is calculated using a time-decay-based cumulative effect model. The basic principle of this model is that every atomic event related to the topic contributes to its popularity, but the effectiveness of this contribution decays exponentially over time. An earlier event has a smaller impact on the current popularity than a more recent event.

[0082] At the current time point A complex issue node Popularity value The specific calculation formula is as follows: ; in: Representing nodes of complex issues At the current time The popularity value.

[0083] Represents the deadline , and complex issue nodes The total number of atomic events associated with the aggregation.

[0084] It is an index used to traverse these associated atomic events.

[0085] It is the first The timestamp of each associated atomic event, the value of which is provided by the data structure of the atomic event.

[0086] It is the current system time when the heat calculation is performed.

[0087] It is a pre-configured time decay coefficient, which is a positive real number. This coefficient controls the rate at which the influence of historical events decays.

[0088] A larger A value that indicates the heat decays faster means the system pays more attention to recent events; a smaller value means the heat decays faster. This value indicates that the impact of historical events can be retained for a longer period of time. This coefficient can be adjusted according to different application scenarios (e.g., emergency response or long-term planning).

[0089] The cumulative effect model shows that whenever a new atomic event occurs, the heat value of the related composite issue increases accordingly, reflecting the issue's growing popularity. During periods without new events, the heat value smoothly declines over time, reflecting the issue's natural cooling or being forgotten by the public.

[0090] The complexity analysis module 130 performs step S103 to quantify the complexity of complex issues in the dynamic public affairs causal graph. To achieve an objective and comprehensive assessment of the potential impact, difficulty of handling, and development trend of public issues, this invention introduces and defines a composite metric—issue entropy.

[0091] The fundamental principle of issue entropy lies in the fact that a complex social issue shares similarities in characteristics with a high-entropy system in thermodynamics or information theory. Specifically, this manifests as: the intertwining of causes and consequences, resulting in high structural uncertainty; the need for coordination among numerous departments to resolve the issue, leading to high uncertainty in collaboration; rapid changes in public opinion surrounding the issue, resulting in high uncertainty in its development; and the division and opposition among the public regarding the issue, leading to high uncertainty in viewpoints. Therefore, issue entropy is constructed as a comprehensive quantitative indicator to measure the overall uncertainty and complexity of a complex issue.

[0092] The issue complexity analysis module 130 addresses each complex issue in the graph. Complex issue nodes Calculate the issue entropy This calculation is achieved through a multi-dimensional weighted summation model, and the specific calculation formula is as follows: ; in: Representing complex issues The total entropy of the issue indicates that the higher the value, the greater the overall complexity and uncertainty of the issue, and the greater its potential impact.

[0093] The causal complexity of an issue measures its structural importance in a causal network. An issue is considered to have high causal complexity if it is both a cause and a consequence of multiple other issues.

[0094] The subject coupling degree of a problem is used to measure the complexity of cross-departmental and cross-unit collaboration required to resolve the problem. The more responsible parties involved, or the lower the historical collaboration efficiency, the higher the subject coupling degree.

[0095] The speed of an issue's evolution is used to measure the acceleration of its public opinion surge, capturing the urgency of its fermentation and outbreak. An issue whose popularity is rising rapidly has a high speed of evolution.

[0096] Semantic divergence is a measure of the degree of confusion, opposition, and diversity of public opinion surrounding an issue. The more divisive and opposing the viewpoints in a related discussion, the higher the semantic divergence.

[0097] , , , These represent the weighting coefficients of the four components mentioned above. These coefficients are all preset positive real numbers and satisfy the following conditions: By adjusting these weighting coefficients, different aspects of the complexity of the assessment issue can be emphasized according to different management objectives. For example, in an emergency management scenario, the evolution speed component can be appropriately increased. weight .

[0098] The issue complexity analysis module 130 performs periodic calculations on complex issues. Issue Entropy When calculating the causal complexity, it is necessary to calculate its four components separately. Main coupling degree Evolution speed and semantic divergence .

[0099] For causal complexity The calculation of issue complexity analysis module 130 is based on composite issue nodes. In the dynamic public affairs causal graph The topological structure of a causal network is quantified. The structural importance of an issue in a causal network is directly related to the number of edges it connects as a cause (out-degree) and as a result (in-degree). The more edges connected, the stronger the pivotal role of the issue in the entire causal chain, and the higher its causal complexity. The specific calculation formula is as follows: ; in: Representing nodes of complex issues In the map The in-degree in the middle, that is, the direction of the direction. The number of causal edges.

[0100] Representing nodes of complex issues In the map The out-degree, that is, from The specified number of causal edges. The addition of 1 is to prevent the logarithm calculation from being negative infinity or zero due to zero in-degree or out-degree, ensuring that causal complexity has a positive, non-zero metric range. The use of the logarithmic function slows down the growth of causal complexity, preventing a few highly connected nodes from excessively dominating the issue entropy.

[0101] For the main coupling degree The issue complexity analysis module 130 is designed to measure the complexity of the cross-departmental collaboration required to resolve the issue. This is related not only to the number of responsible parties involved, but also to the past efficiency of their collaboration. The specific calculation formula is as follows: ; in: Representative and Complex Issues The cardinality of the set of directly related responsible parties, i.e., the number of nodes related to complex issues. The number of responsible entity nodes with "related edges". The issue complexity analysis module 130 traverses the graph... Use connected edges to obtain this set.

[0102] This represents the historical average collaboration barrier index among these related responsible parties. This index is calculated based on historical task processing data accumulated over a long period by the system and is used to quantify efficiency bottlenecks or difficulties in collaboration among the responsible parties. For example, This can be determined by statistically analyzing the average overdue rate or rework rate of all relevant stakeholders involved in the task. A higher value indicates greater collaboration barriers and a higher difficulty in completing the task. Specific calculations... The algorithm can be comprehensively calculated based on indicators such as the average timeout duration of historical tasks, the number of cross-departmental coordinations, and the average rework rate. Its specific implementation is a well-known technology in this field and will not be elaborated here.

[0103] Regarding the rate of evolution The calculation, specifically the issue complexity analysis module 130, captures the acceleration of issue popularity over time to provide early warning of an issue's explosive growth or rapid cooling. This component represents the complex issue node. Popularity value Regarding time The second derivative of . The specific calculation formula is as follows: ; Due to heat value It is calculated and updated at discrete time points, and the issue complexity analysis module 130 approximates it using the finite difference method. For example, if we have an issue... At three consecutive time points The popularity values ​​are respectively , , Then at the point in time The evolution rate can be approximated as: ; in, The time interval for calculating the heat value.

[0104] For semantic divergence The calculation, the issue complexity analysis module 130, quantifies the complexity of complex issues. The degree of confusion and opposition in public opinion or sentiment. This component is measured using the concept of information entropy, reflecting the uniformity of the distribution of public viewpoints. If public opinion tends to be consistent, the degree of divergence is low; if viewpoints are diverse and evenly distributed, the degree of divergence is high. The specific calculation formula is as follows: ; in: It is the total number of predefined opinion or sentiment categories, such as "support", "oppose", and "neutral".

[0105] In relation to complex issues Of all the associated atomic events (especially their textual content), opinion or sentiment belongs to the category. The probability of this probability is calculated by the issue complexity analysis module 130 by calling the sentiment analysis or stance detection function component in natural language processing. This component performs analysis on all complex issues. The relevant text information is analyzed, and each text is categorized into a preset category. One of the categories is identified, and the frequency of occurrence of each category is statistically analyzed to obtain the corresponding probability distribution. For the specific implementation of sentiment analysis or stance detection, those skilled in the art can use deep learning models (such as recurrent neural networks, Transformers, etc.) for training and deployment. The specific implementation is a well-known technology in this field and will not be elaborated here.

[0106] The issue complexity analysis module 130 obtained the complex issues through the above calculation method. The four core complexity components are then summed using a weighted average to obtain the final issue entropy. value.

[0107] The topic entropy of all active complex topics is calculated in the topic complexity analysis module 130. Subsequently, one of its core functions is to use these entropy values ​​to sort and manage issues in real time, thereby forming a dynamic priority queue. This queue provides a basis for decision-making in subsequent resource allocation and workflow scheduling.

[0108] The issue complexity analysis module 130 maintains a priority queue data structure, which can be implemented based on a binary heap or a Fibonacci heap. In this priority queue, each element represents an active, complex issue. Its priority key value is set to the issue entropy of that issue. Since a higher issue entropy value indicates a greater complexity and urgency of the issue, this priority queue is configured as a maximum-priority queue, meaning that the head of the queue always contains composite issues with the highest issue entropy value.

[0109] The implementation of this dynamic priority queue involves the following specific mechanisms: Joining and updating topics: When a complex issue is first identified and its initial issue entropy is calculated... Subsequently, the issue complexity analysis module 130 inserts it as a new element into the priority queue. The queue maintenance operation ensures that the queue structure satisfies the characteristics of a priority heap after insertion.

[0110] The causal complexity of complex issues increases with the influx of new atomic events, the evolution of causal graphs, or changes in external conditions. Main coupling degree Evolution speed and semantic divergence Changes occur, leading to an increase in its issue entropy. The value has been updated.

[0111] The issue complexity analysis module 130 recalculates the issue's entropy after detecting any event that causes a change in issue entropy. The key is set to the value, and an update operation is performed on the priority queue. This operation typically involves removing the original issue from the queue and reinserting it with the new entropy value, or directly updating its key and adjusting the queue structure to maintain the priority order.

[0112] Topic selection and acquisition: When the adaptive workflow management module 140 is ready to process a new public transaction, it requests the highest-priority composite issue from the issue complexity analysis module 130. In response to this request, the issue complexity analysis module 130 extracts (or typically removes) the composite issue with the highest issue entropy at the head of the priority queue. .

[0113] Dynamic maintenance: The "dynamic" nature of this priority queue lies in its ability to continuously reflect changes in the status and complexity of issues. The calculation of issue entropy (including its four components) is periodic or event-driven. For example, whenever an atomic event related to a complex issue is received by the system, or when the system detects a significant change in the causal graph, the entropy value of the relevant issue may be reassessed and updated in the queue. This real-time update mechanism ensures that limited administrative attention resources are always directed towards the most urgent and complex public issues.

[0114] Through the implementation of the dynamic priority queue described above, the issue complexity analysis module 130 can effectively manage a large number of active public affairs, providing a clear, scientifically measured processing order, thereby optimizing the efficiency of administrative resource allocation.

[0115] The adaptive workflow management module 140 executes step S104, which dynamically generates an adaptive workflow that includes the handling tasks and responsible parties, based on the context information of the compound issues in the causal graph.

[0116] The adaptive workflow management module 140 initiates workflow generation based on issue priority. When a composite issue... Issue Entropy Exceeding the preset dynamic threshold The system will activate the workflow generation process when an issue reaches a predetermined high-priority interval in the priority queue. This triggering mechanism, based on real-time entropy and priority, ensures that limited administrative resources can prioritize the most urgent, complex, and potentially high-impact public issues.

[0117] After the workflow is triggered, the adaptive workflow management module 140 executes the generation of a disposal plan based on the graph context. This process includes two main steps: dynamic identification of responsible parties and collaborative relationships, and task decomposition and construction of a directed acyclic graph (DAG) workflow.

[0118] For the dynamic identification of responsible parties and collaborative relationships, the adaptive workflow management module 140 first extracts nodes related to the current complex issues from the dynamic public affairs causal graph. A local subgraph centered on this topic. This subgraph includes nodes representing complex issues. It itself, as well as all nodes directly or indirectly connected to it through aggregation edges, correlation edges, or causal edges, including atomic event nodes, other complex issue nodes, responsible entity nodes, and policy target nodes.

[0119] The adaptive workflow management module traverses the subgraph 140 times, identifying all related composite issues. Nodes with directly "related edges" are responsible entities. These responsible entities will be considered the primary responsible units or collaborating units for this issue. Furthermore, the adaptive workflow management module 140 further analyzes and relates to the issue. Related causal edges identify potential complex issues. The upstream causes of the issue, or the issues that may arise from it. The resulting downstream consequences are also issues. The responsible parties associated with these causes or consequences will also be included in the scope of potential collaborating entities to achieve source governance or early warning and response.

[0120] For task decomposition and DAG workflow construction, the adaptive workflow management module 140, based on the identified graph context information, transforms a complex composite issue... It is automatically decomposed into a series of atomic tasks with clear logical dependencies and organized into a workflow in the form of a directed acyclic graph (DAG).

[0121] The construction process is as follows: Initial task generation: Adaptive workflow management module 140 based on composite issues Category and associated atomic events The type is determined by matching the corresponding standard processing pattern or atomic task template from a predefined knowledge base. For example, if the issue category is "infrastructure failure" and the associated atomic event is "potholes," preliminary tasks such as "site investigation," "develop a repair plan," and "organize construction" may be generated. These preliminary tasks constitute the initial nodes of the DAG.

[0122] Task responsibility allocation: For each generated task, the adaptive workflow management module 140 assigns the task to the most suitable responsible entity based on the identified list of responsible entities, combined with historical processing data (e.g., the departments that have handled similar tasks in the past) and preset responsibility division rules.

[0123] Task dependency establishment: Causal dependence: If complex issues With another issue Existence from arrive The causal edge indicates the issue It is an issue The reason. At this point, resolving the issue... The relevant tasks (or at least the issues to be investigated) The task of addressing the root cause will be considered as resolving complex issues. The key prerequisite task is to establish a framework in the DAG that starts from resolving issues. The task is aimed at solving complex issues. The task's dependency edges.

[0124] Logical dependencies: These are based on the logical order of the tasks themselves, such as "site investigation" must precede "developing a repair plan". These logical dependencies are automatically established by the adaptive workflow management module 140 by parsing the preset dependencies in the task template.

[0125] Resource dependency: If multiple tasks need to share scarce resources or require specific policy approval (identified by the associated policy target node), the adaptive workflow management module 140 will establish corresponding resource dependency edges to ensure that tasks are executed in sequence or coordinate resource allocation.

[0126] DAG Structure Optimization and Verification: After generating the initial DAG, the adaptive workflow management module 140 performs loop detection to ensure it is acyclic. If a loop is detected, backtracking and adjustments are performed. Simultaneously, the parallelism of the DAG is analyzed to identify task groups that can be executed concurrently, thereby improving overall processing efficiency.

[0127] The generated workflow DAG is represented as ,in It is a collection of task nodes, each task Includes attributes such as task name, responsible entity, task description, and estimated time. It is a set of dependencies between tasks, represented as directed edges.

[0128] After the workflow is generated, the adaptive workflow management module 140 distributes the decomposed tasks to the internal management systems or business processing platforms of the corresponding responsible entities via API interfaces or message queues. For example, a "site survey" task will be sent to the task management system of the "municipal engineering department," while a "public opinion guidance" task will be sent to the corresponding system of the "publicity department." After task distribution, the adaptive workflow management module 140 continuously monitors the execution status of each task, providing data support for subsequent closed-loop verification.

[0129] The closed-loop verification and knowledge solidification module 150 performs the verification of the handling effect of step S105 and the accumulation of successful experience. This step is triggered when the adaptive workflow management module 140 reports that all key tasks in a workflow have been completed.

[0130] The closed-loop verification and knowledge solidification module 150 first executes a proactive cross-modal verification process. This process aims to obtain direct, objective evidence from the physical world regarding the effects of the action. The closed-loop verification and knowledge solidification module 150 locates the original complex issue that triggered the workflow by tracing back the dynamic public affairs causal graph and finds all atomic event nodes associated with that issue. This is achieved by reading the user identifiers from these atomic event nodes. The closed-loop verification and knowledge solidification module 150 obtained the identity of the original reporting user. Simultaneously, based on the geographical location information of these atomic events... The closed-loop verification and knowledge solidification module 150 can perform a spatial radius query in the user database to identify other potentially affected users who live or work in the vicinity of the incident site.

[0131] After locating these target users for verification, the closed-loop verification and knowledge consolidation module 150 proactively sends a verification request for the handling effect to them through their registered contact methods (e.g., mobile application push, SMS, or email). This request includes an interactive interface with at least three parts: first, explicit options such as "Problem Resolved" or "Problem Unresolved"; second, a text box for users to input supplementary information; and third, a functional component that allows users to instantly take a photo or upload an image from their album.

[0132] After receiving user feedback, the closed-loop verification and knowledge solidification module 150 performs a verification result determination based on multimodal analysis. This determination process aims to comprehensively analyze the various information submitted by the user to form an objective conclusion, rather than relying solely on the user's subjective choice of "satisfied" or "dissatisfied".

[0133] The specific judgment logic is as follows: Text Analysis: The closed-loop verification and knowledge consolidation module 150 calls upon the natural language processing component to perform sentiment analysis and keyword extraction on the supplementary information entered by the user in the text box. If the user selects "Problem solved," but the text content expresses negative emotions or contains keywords such as "still," "no," or "yet," the system will classify this as inconsistent feedback.

[0134] Image Analysis: The closed-loop verification and knowledge solidification module 150 calls upon computer vision components to analyze the images submitted by the user. This analysis is context-dependent and directly corresponds to the original problem. For example, if the original problem is "potholes in the road," the CV component will perform object detection in the submitted image to verify whether "pothole" objects still exist. Simultaneously, the processed image can be compared with the original reported image to confirm significant changes in physical features.

[0135] If the original issue is "garbage accumulation", the CV component will perform semantic segmentation on the returned image and calculate whether the area occupied by "garbage" category pixels has been significantly reduced to below an acceptable threshold.

[0136] Comprehensive Judgment: The closed-loop verification and knowledge solidification module 150 integrates the above multimodal analysis results according to a preset decision rule. A typical successful verification judgment condition is: the user selects "problem solved", the text feedback is neutral or positive, and the computer vision analysis results confirm the corresponding change in the physical world.

[0137] If the verification result indicates that the processing has failed or there is an inconsistency, the closed-loop verification and knowledge solidification module 150 will re-mark the status of the composite issue as "processing" and reactivate the adaptive workflow, or notify the relevant responsible parties of the situation for secondary processing.

[0138] If the verification result indicates successful handling, the closed-loop verification and knowledge solidification module 150 will execute the knowledge solidification mechanism. This mechanism aims to transform successful handling cases into reusable "governance models." A standardized governance model data structure is defined, containing the following fields: Problem Subgraph: A subgraph structure extracted from the dynamic public affairs causal graph, containing the compound issue nodes to be resolved, the related atomic event nodes, and the causal and correlation relationships between them.

[0139] Solution DAG: A directed acyclic graph workflow that is generated and successfully executed in step S104, containing task decomposition, responsibility assignment, and dependencies.

[0140] Verification evidence: A set of objective evidence used to prove the success of the handling, such as on-site photos returned by users and confirmed by CV analysis, as well as positive text feedback.

[0141] The closed-loop verification and knowledge solidification module 150 stores this structured governance model in a dedicated knowledge base. In the future, when the adaptive workflow management module 140 needs to generate a workflow for a new, high-priority composite issue, it will first construct a query subgraph of the graph context of the new issue and search it in the knowledge base. The search can use graph matching algorithms, such as those based on graph edit distance or subgraph isomorphism, to find the most similar historical success cases to the current problem. For the specific implementation of the graph matching algorithm, those skilled in the art can use algorithms such as VF2, which are well-known technologies in the field and will not be elaborated here. If a highly similar governance model is matched, the system can recommend or even directly reuse its corresponding solution DAG, thereby significantly improving decision-making efficiency and the success rate of handling.

Claims

1. A multi-terminal integrated management method for public communication channels, characterized in that, Includes the following steps: S101. Multi-terminal heterogeneous data collection and semantic normalization: Receive raw data from multiple public communication channels and convert the raw data into standardized atomic events containing event elements; S102. Construction and evolution of dynamic public affairs causal graph: Based on the standardized atomic events, construct and dynamically evolve a dynamic public affairs causal graph to represent the association and causal relationship between public affairs; S103. Complexity quantification and priority allocation based on issue entropy: For the composite issues in the dynamic public affairs causal graph, calculate the issue entropy used to quantify the uncertainty and complexity of the composite issues, and prioritize the composite issues based on the issue entropy. S104. Graph-driven adaptive workflow generation and execution: When the priority of the composite issue reaches the preset condition, an adaptive workflow is generated based on the context information of the composite issue in the dynamic public affairs causal graph. S105. Cross-modal closed-loop verification and knowledge solidification: After the adaptive workflow is executed, cross-modal verification of the handling effect is triggered, and based on the verification results, the successful handling solution is solidified into a reusable governance mode.

2. The multi-terminal integrated management method for public communication channels according to claim 1, characterized in that, In step S102, the step of constructing and dynamically evolving a dynamic public affairs causal graph includes: For different event categories, construct time series of event occurrence frequencies; The Granger causality test method is used to perform pairwise tests on the time series to determine whether the past values ​​of one event category have predictive power for the current values ​​of another event category. If the test result rejects the null hypothesis at a preset significance level, a directed causal edge is created between the composite issue nodes representing the two event categories, and the causal weight and time delay of the directed causal edge are quantified according to the p-value and lag order of the test.

3. The multi-terminal integrated management method for public communication channels according to claim 1, characterized in that, In step S103, the issue entropy of the composite issue is calculated using a multi-dimensional weighted summation model. The issue entropy of the composite issue is a weighted sum of at least the following four components: Causal complexity: a measure of the structural importance of complex issues in causal networks; Subject Coupling: Measures the complexity of cross-departmental collaboration required to resolve complex issues; Evolution speed: Measures the acceleration of the growth in public opinion intensity on complex issues; Semantic divergence: measures the degree of confusion and opposition in public opinion surrounding complex issues.

4. The multi-terminal integrated management method for public communication channels according to claim 3, characterized in that, The calculation of the causal complexity is based on the in-degree and out-degree of the composite issue node in the causal graph; the calculation of the evolution speed is achieved by taking the second derivative of the heat value time series of the composite issue.

5. The multi-terminal integrated management method for public communication channels according to claim 3, characterized in that, In step S103, the step of prioritizing the composite issues based on the issue entropy specifically includes: Maintain a maximum priority queue with the entropy of the aforementioned issue as the priority key; When the entropy of a composite issue is updated, the position of the composite issue in the highest priority queue is updated.

6. The multi-terminal integrated management method for public communication channels according to claim 1, characterized in that, In step S104, generating an adaptive workflow specifically includes: The complex issues are automatically decomposed into a series of atomic tasks with dependencies based on the causal structure in the dynamic public affairs causal graph. The dependencies between the atomic tasks are organized into a directed acyclic graph to represent the workflow.

7. The multi-terminal integrated management method for public communication channels according to claim 6, characterized in that, The dependencies between the atomic tasks include: Causal Dependence: Based on the causal edges between composite issues in the dynamic public affairs causal graph, establish the dependency between the handling tasks of the preceding cause issues and the handling tasks of the subsequent result issues; Logical dependency: Dependencies are established based on the pre-defined logical order of the task itself; Resource dependency: When multiple tasks need to share scarce resources, establish corresponding resource dependency edges to ensure that tasks are executed in sequence or to coordinate resource allocation.

8. The multi-terminal integrated management method for public communication channels according to claim 1, characterized in that, In step S105, the cross-modal verification step for the treatment effect is triggered, specifically including: The system locates the original reporting user and surrounding affected users associated with the complex issue and proactively pushes a verification request that includes text input and image upload functions. The system calls upon natural language processing components to analyze the text of user feedback and computer vision components to analyze the images returned by the user, in order to comprehensively determine the effectiveness of the action.

9. The multi-terminal integrated management method for public communication channels according to claim 8, characterized in that, In step S105, the successful treatment plan is solidified into a reusable governance model, which specifically includes: The subgraph corresponding to the problem, the directed acyclic graph workflow corresponding to the solution, and the verification evidence proving the successful handling are encapsulated into a standardized governance model data structure and stored in a knowledge base.

10. A multi-terminal integrated management system for public communication channels, applied to the multi-terminal integrated management method for public communication channels as described in any one of claims 1-9, characterized in that, include: The data acquisition and normalization module is configured to receive and parse raw data from multiple public communication channels and output standardized atomic events. The causal graph modeling module is configured to construct and maintain a dynamic causal graph of public affairs that represents the correlation and causal relationship between public affairs based on the standardized atomic events. The issue complexity analysis module is configured to calculate issue entropy for composite issues in the dynamic public affairs causal graph to quantify uncertainty and complexity, and to prioritize them accordingly. The adaptive workflow management module is configured to respond to composite issues that reach a preset priority and generate an adaptive workflow based on the context information in the causal graph. The closed-loop verification and knowledge solidification module is configured to perform cross-modal verification of the treatment effect after the adaptive workflow is executed, and solidify the successfully verified treatment scheme into a reusable governance mode.

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