Linkage command decision and early warning method integrating grid patrol event and artificial intelligence

Abstract

The invention discloses a linkage command decision-making and early warning method fusing grid patrol events and artificial intelligence, and belongs to the technical field of crossing of smart cities and artificial intelligence. The method aims at solving the problems of low efficiency and insufficient early warning caused by the fact that multi-source data are difficult to fuse and event association depends on manpower in existing urban governance. According to the scheme, multi-source data such as grid patrol, Internet of Things monitoring and hotline are accessed and standardized to form a standardized event in a unified format; performing time-space and semantic fusion analysis on the events by using an artificial intelligence analysis engine, intelligently merging associated events and identifying high-risk events; early warning is automatically generated based on a preset rule base, and a processing task is distributed to a corresponding service system; and finally, the command system carries out unified scheduling. According to the method, intelligent association analysis, active early warning and cooperative command of urban governance events can be realized, and the management efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the field of interdisciplinary technology of smart cities and artificial intelligence. More specifically, the invention relates to a method for joint command, decision-making and early warning that integrates grid patrol events and artificial intelligence. Background Technology

[0002] With the acceleration of urbanization, the bottlenecks of traditional urban governance models are becoming increasingly apparent. Currently, urban governance relies on multiple parallel subsystems, such as grid patrol systems, public service hotline systems, and various independent IoT monitoring systems (such as smart trash cans). These systems have different technical architectures and inconsistent data standards, forming deep "information silos."

[0003] A more critical issue is the lack of a "decision-making hub" in current technology capable of intelligently analyzing this heterogeneous data. The massive amounts of information generated by various systems, including text and images reported by grid workers, voice descriptions from citizen hotlines, and status data uploaded by sensors, mostly remain at the level of independent display and simple alarms. The identification of urban management problems heavily relies on human experience for judgment and classification, resulting in low efficiency and poor consistency. Furthermore, there are significant gaps in the analysis of cross-system, multimodal events and root cause tracing, making it impossible to generate accurate decision-making instructions for execution.

[0004] Take a typical scenario as an example: an IoT-based smart trash can can automatically sound an alarm when it's overflowing, but this event cannot be automatically linked to subsequent issues discovered by grid workers, such as "scattered trash" caused by the overflow, or "odor nuisance" complaints from residents. This leads to the same root cause being repeatedly reported and handled by multiple departments, resulting in a serious waste of resources. Furthermore, due to the lack of intelligent analysis, it cannot learn patterns from historical data, thus failing to automatically trigger preventative patrols of surrounding areas based on a single event, nor can it generate coordinated disposal task instructions.

[0005] Therefore, there is an urgent need in this field for a new type of solution that can go beyond simple transactional process management, make full use of artificial intelligence technology to deeply integrate, intelligently correlate and analyze the root causes of urban governance events from multiple channels and multiple modalities, and automatically generate accurate early warning and handling decision instructions, thereby achieving a fundamental transformation from passive response to proactive early warning, and from isolated single-point handling to systemic collaborative decision-making. Summary of the Invention

[0006] One object of the present invention is to address at least the aforementioned deficiencies and to provide at least the advantages that will be described later.

[0007] The present invention provides a method for joint command, decision-making and early warning that integrates grid patrol events and artificial intelligence. One purpose of this invention is to solve the technical problems in existing urban governance, such as inconsistent data standards among various subsystems leading to information silos, reliance on manual methods for event identification and correlation analysis resulting in low efficiency, and the inability to achieve intelligent correlation analysis and automated early warning decision-making.

[0008] This invention provides a method for coordinated command, decision-making, and early warning that integrates grid patrol events with artificial intelligence, comprising the following steps: S1. Data Access and Standardization Steps: The system accesses diverse urban governance data sources via data interfaces. These data sources include at least: event data reported by the grid patrol subsystem, which contains text descriptions and on-site images; component status data reported by the IoT component monitoring subsystem; and data from the public service hotline subsystem. A unified data platform is constructed to receive and store the diverse urban governance data; the data interface includes a data format conversion and protocol adaptation module, which is used to convert heterogeneous data from different subsystems into a unified standard data format specified by the platform. The unified standard data format is a structured data format and is required to include the following fields: a unique event identifier, location coordinates based on a latitude and longitude coordinate system, a timestamp conforming to the ISO 8601 standard, and a question type code based on a predefined classification system to obtain a standardized event; S2. Intelligent Analysis and Decision-Making Steps: The artificial intelligence analysis engine running on the unified data platform performs the following operations sequentially on the standardized events from S1: Fusion analysis and collaborative decision-making: Based on preset association rules, multiple standardized events that are related in space, time and semantics are merged to generate fused events, and the remaining events are distinguished into specific types of independent events and ordinary independent events; Intelligent early warning and task derivation: Based on the fused event and / or the specific type of independent event, early warning information is automatically generated, and processing tasks are automatically derived to one or more related business processing subsystems according to the preset decision rule base. S3. Command and dispatch procedures: The command system performs unified command and dispatch and resource allocation based on the fusion events, early warning information and derived handling tasks generated by S2.

[0009] Preferably, the method for assigning the problem type code is as follows: The event data of the grid patrol subsystem is processed using a pre-trained multimodal neural network model. The processing includes: performing natural language understanding on the text description using a pre-trained language model based on the Transformer architecture to extract keywords, and performing computer vision analysis on the scene images using a convolutional neural network model to identify visual features. Subsequently, based on the fusion result of the keywords and visual features, the event problem is automatically identified, and each event is assigned a problem type code based on the predefined classification system. The component status data of the IoT component monitoring subsystem is converted into the corresponding problem type code through a preset status-type mapping table; The data from the public service hotline subsystem is classified using a natural language processing model, and question type codes are assigned. At this point, all data source outputs are standardized events with a unified issue type code.

[0010] Preferably, the fusion analysis and collaborative decision-making specifically includes: using the standardized events output by all subsystems as input, the artificial intelligence analysis engine performs fusion analysis; the fusion analysis is based on preset association rules, and its judgment process includes: Calculate the spatial distance between any two event locations and compare this distance with a dynamic distance threshold; the dynamic distance threshold is set based on the average farthest distance of the same type of urban components in the historical database that triggered related events in the past, with an additional standard deviation as a buffer value. Calculate the time difference between the occurrence times of the two events mentioned above, and compare this time difference with a preset time window; the preset time window is set based on the handling priority and average handling time of different problem types in the predefined classification system; Determine whether there is a semantic relationship between the question type codes of the two events mentioned above, as determined by a predefined question type mapping table; When the spatial distance is less than or equal to the dynamic distance threshold, the time difference is within the preset time window, and there is a semantic relationship, the engine will deduplicate and merge the multiple events to generate a fused event; events that cannot be merged will be retained as independent events. The independent events are categorized into specific types of independent events and ordinary independent events; Furthermore, the intelligent early warning and task derivation specifically include: Using the fused event and the specific type of independent event as input, the artificial intelligence analysis engine automatically generates early warning information; at the same time, based on the problem type code and attributes contained in the fused event or the specific type of independent event, and according to the preset decision rule base, the engine automatically derives processing tasks to one or more related business processing subsystems. For the aforementioned ordinary independent events, they are processed through the regular transaction processing flow.

[0011] Preferably, the predefined question type mapping table is a two-dimensional relation matrix, which records the markers indicating whether there are causal, concurrent, or hierarchical relationships between different question type codes.

[0012] Preferably, the specific type of independent event refers to an event whose problem type code is pre-marked as "urgent" or "major" in the predefined classification system, or an event that is judged to have the risk of triggering a chain reaction according to the pre-set decision rule base.

[0013] Preferably, the rules in the pre-set decision rule base define the logical relationship between conditions and actions; wherein the rule base includes at least the following rules: When the problem type of an event is garbage overflow, and the number of related events in its vicinity exceeds a preset threshold, a disposal task is assigned to the sanitation dispatch system, and the warning level of the event is upgraded. When the incident involves damage to public facilities and occurs at night, a response task is assigned to the emergency repair system, and a safety warning message is attached to the task instruction.

[0014] Preferably, the predefined classification system is an open classification system, including one or more of the following categories: waste disposal, urban environment, public facilities, public order, and traffic safety.

[0015] Preferably, the data format conversion and protocol adaptation module supports multiple communication protocols, including HTTP / HTTPS, MQTT, and WebService, and has a built-in data cleaning and validity verification submodule to ensure the quality of input data.

[0016] Preferably, the artificial intelligence analysis engine adopts a microservice architecture, in which its internal functions of intelligent event recognition and classification, fusion analysis and collaborative decision-making, intelligent early warning and task derivation are encapsulated as independent, horizontally scalable services.

[0017] Preferably, in the fusion analysis and collaborative decision-making step, when multiple events meet the merging conditions, the engine determines the dominant problem type and priority of the fused events based on the credibility weight of the event's source subsystem and the reporting time priority. The present invention has at least the following beneficial effects: This invention achieves standardized access and fusion of heterogeneous data from different sources and protocols, such as grid patrols, IoT monitoring, and public service hotlines, by constructing a unified data platform and mandating a structured data format that includes unique event identifiers, location coordinates, standard timestamps, and issue type codes. This technical approach fundamentally breaks down data barriers, transforming previously scattered and disorganized data into standardized events with unified formats and clear semantics, laying a reliable data foundation for subsequent intelligent analysis.

[0018] By employing a pre-trained multimodal neural network model to jointly analyze the text and images reported by grid workers, and combining the mapping and transformation of IoT status data with natural language processing of hotline text, a unified problem type code is automatically and accurately assigned to all events. This technical approach overcomes the subjectivity and inefficiency of previous reliance on manual judgment, achieving automation, standardization, and intelligence in understanding and classifying complex urban events, and providing core semantic understanding capabilities for precise decision-making.

[0019] By employing an AI-powered analytics engine to perform fusion analysis, multiple independent events that are spatiotemporally and semantically related are intelligently merged into a single event based on dynamically calculated spatial distance thresholds, time windows set according to business priorities, and predefined semantic association rules. This technique mimics the associative thinking ability of human experts but achieves it in an automated and quantitative manner. It proactively uncovers the root causes and systemic urban problems hidden behind fragmented reporting, accurately pinpoints the core of the problem, and avoids duplicate handling of the same issue and waste of resources.

[0020] By establishing a decision rule base based on production rules, early warning information can be automatically generated and dispatched to relevant business processing subsystems (such as sanitation dispatch and emergency maintenance) based on fused events or specific types of independent events (such as emergency, major, or events with cascading risks). This technology constructs an automated pipeline of "data → insight → command," automating the decision-making process that previously required manual judgment and dispatch. It achieves a fundamental shift from passively receiving alarms to proactively predicting and warning, and from single-point response to collaborative linkage, greatly shortening the response time from event occurrence to task issuance, and providing high-quality decision output for the entire command and dispatch system.

[0021] By adopting a microservice architecture to design the AI ​​analysis engine, its core functions are encapsulated as independent and scalable services, enabling the system to dynamically allocate resources based on business load. This technical approach ensures high availability and resilience of the system when facing sudden, high-concurrency urban events, guaranteeing the stable and efficient operation of the intelligent decision-making core.

[0022] By assigning credibility weights to different data sources and combining them with reporting time priorities, the dominant problem type and handling priority are intelligently determined when generating fused events. This technique fully leverages the characteristics of each data source, enabling the generated decision instructions (early warnings, tasks) to more accurately reflect the actual situation and urgency on-site, thereby improving the efficiency of resource utilization.

[0023] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0024] The present invention will be further described in detail below with reference to embodiments, so that those skilled in the art can implement it based on the description.

[0025] It should be noted that, unless otherwise specified, the experimental methods described in the following implementation plan are conventional methods, and the reagents and materials described are commercially available unless otherwise specified. In the following text, "ENV" represents "environmental".

[0026] This invention discloses a method for coordinated command, decision-making, and early warning that integrates grid patrol events with artificial intelligence. The implementation of this method is described in detail below: 1. System Overall Architecture and Data Flow The implementation of this invention is based on a unified cloud-native data platform. This platform accesses data from the grid patrol subsystem, the IoT component monitoring subsystem, and the public service hotline subsystem through multiple data interface modules (such as API gateways and message queues). The data first enters the data standardization and preprocessing module, and is then stored in a unified event database. The core artificial intelligence analysis engine consumes standardized event data from the database, performs subsequent intelligent analysis, and its output decision results (such as fused events, early warning information, and derived tasks) are finally delivered to the command and dispatch system for visualization and resource allocation.

[0027] 2. Specific implementation of data access and standardization steps (corresponding to step S1) 2.1 Data Source Access The grid patrol subsystem receives structured data packets reported by the mobile terminal app via the HTTPS protocol. These data packets contain event text descriptions, on-site image encodings, latitude and longitude coordinates collected by the GPS module, and device timestamps.

[0028] IoT Component Monitoring Subsystem: Subscribes to topic messages published by smart devices (such as trash can and manhole cover sensors) through message queue telemetry transmission protocol. The message body includes device identifier, status monitoring value (such as full capacity percentage), power status and reporting time.

[0029] Public service hotline subsystem: It obtains work order data from the municipal government platform on a regular basis through a web service interface. The data format includes work order number, text content after citizen voice transcription, acceptance timestamp, and preliminary system classification identifier.

[0030] 2.2 Data Format Standardization and Problem Type Coding The unified standard data format used in the construction adopts a serialized data format, and its structure definition mandates the inclusion of the following fields: Event identifier (generated using a distributed unique identifier generation algorithm) Geographic location information (including coordinate objects of longitude and latitude) Timestamp (a millisecond-level time record conforming to international standard time format) Problem type coding (type identifier based on a predefined classification system) Raw data (stores the original information used for traceability) The assignment of question type codes is achieved through the following technical means: a) Multimodal recognition model for grid patrol events: The model employs a dual-channel fusion multimodal neural network architecture. The model consists of a text processing channel and an image processing channel.

[0031] The text processing channel uses a pre-trained language model based on a self-attention mechanism as its core network. This channel takes an event text description as input and outputs a fixed-dimensional text feature representation.

[0032] The image processing channel uses a deep convolutional neural network pre-trained on a large image dataset as a feature extractor. After the input image is standardized and resized, the output is a fixed-dimensional visual feature representation.

[0033] The feature fusion and classification process concatenates text feature vectors and image feature vectors to form a higher-dimensional fused feature representation. This fused feature is then compressed in dimensionality by a fully connected layer before being input into a multi-class classification layer. The number of nodes in this classification layer matches the number of question types in the predefined classification system. Finally, a probability normalization function outputs the probability distribution for each question type, and the type with the highest probability is selected as the question type code for that event.

[0034] The model training process used 100,000 historical grid worker reports as training samples, which included correctly labeled question types manually. Training consisted of two phases: first, independent parameter optimization was performed on the text and image channels separately; then, the backbone network parameters for both channels were fixed, and the top fusion and classification layers were jointly trained. An improved version of the adaptive moment estimation optimization algorithm was used, with an initial learning rate of 0.02%, 32 samples per iteration, and a loss function commonly used in classification tasks. A total of 20 complete training epochs were conducted.

[0035] b) IoT component status data processing: Establish a mapping table between status values ​​and problem types. For example, when the device type is "smart trash can" and the status monitoring value is greater than 90%, it is directly converted to the corresponding problem type code "environmental type 001" (indicating a trash overflow status).

[0036] c) Public service hotline data processing: A lightweight text classification model based on convolutional neural networks is used to automatically classify transcribed text. This model is trained using 500,000 historical hotline work orders, following a training process similar to the text channel training method described above, and ultimately outputs the corresponding question type code.

[0037] 3. Specific implementation of intelligent analysis and decision-making steps (corresponding to step S2) 3.1 Fusion Analysis and Collaborative Decision Making Dynamic distance threshold calculation method: Each week, the average furthest distance at which a single event of the same type of urban component (such as a "smart trash can") triggers related events within a preset time window (such as six hours) is statistically analyzed from the historical database over the past thirty days. Based on this average value, a standard deviation is added as a buffer value to form the dynamic distance threshold for the week.

[0038] The principle for setting preset time windows is based on the priority of handling different problem types in a predefined classification system. For example, a two-hour window is set for high-priority "damage to public facilities" incidents, a six-hour window is set for medium-priority "city appearance and environment" incidents, and a twenty-four-hour window is set for low-priority "public order" incidents.

[0039] Semantic association determination mechanism: Query a predefined question type association table. This table records the semantic relationships between different question type codes, such as causal relationships, concurrency relationships, or hierarchical relationships.

[0040] Event merging logic: The analysis engine continuously monitors newly added standardized events. When a new event arrives, the engine searches for candidate related events across three dimensions: spatial distance, time interval, and semantic association. Multiple events are merged into a single fused event when the following conditions are met: spatial distance is less than or equal to a dynamic threshold, time difference is within a preset window, and a semantic association exists. The fused event is assigned a new event identifier and inherits the more reliable issue types and priorities from the source events.

[0041] 3.2 Intelligent Early Warning and Task Derivation Warning generation mechanism: When a merged event occurs, or when a specific type of independent event is identified and marked as emergency (such as "gas leak"), the engine automatically generates a warning message. The warning message includes the event location, type description, list of related events, and recommended handling plan.

[0042] Task derivation mechanism: The engine queries a pre-defined set of decision rules, which are expressed in a condition-action format. For example: The rule base defines the following: when the problem type of an event is garbage overflow and the number of related events in its vicinity exceeds a preset threshold, an emergency cleaning task is assigned to the sanitation dispatch system and the warning level is raised; when the problem type of an event is public facility damage and the event occurs at night, a nighttime maintenance task is assigned to the emergency maintenance system and a safety reminder is added to the task instruction.

[0043] Regarding the setting of thresholds in decision-making rules: The various thresholds involved in the decision rule base (such as the "surrounding event count threshold") are not fixed values, but are based on learning and statistical analysis of historical fused event data to ensure their scientific validity and business rationality. The specific setting method is as follows: Data foundation: The system collects data on all fusion events that have occurred within a certain period (e.g., 6 months).

[0044] Feature extraction: For each fusion event, analyze its constituent events, spatial distribution density, temporal aggregation rate, and other features.

[0045] Threshold Calculation: Taking the "surrounding event number threshold" as an example, this threshold is determined by multiplying the mode or average of the number of initial events contained in the correctly merged events in the statistical historical data by an empirical coefficient (such as 1.2 to 1.5). For example, if statistics show that the merged events related to "garbage overflow" usually consist of 2-4 initial events with a mode of 3, then the threshold can be set to 3 or 4.

[0046] Optimization and adjustment: The initial threshold will be A / B tested and fine-tuned during system operation based on the accuracy of the warning and the false alarm rate, eventually forming a stable value.

[0047] Task instructions are automatically dispatched through the application programming interfaces provided by each business subsystem.

[0048] Those skilled in the art will understand that the implementation of the decision rule base is not limited to a specific programming language or code form; its core lies in defining the mapping logic from event conditions to action. This rule base can be implemented using a generative rule engine, a decision tree, or any other software architecture capable of conditional judgment and task derivation.

[0049] Example 1 Taking "overflowing smart trash cans leading to a chain reaction of problems" as an example, the complete implementation process of this invention is demonstrated: Initially: The smart trash can reports its overflow status data via the Internet of Things protocol, and after standardization, it is assigned an "Environment Class 001" type code.

[0050] Thirty minutes later: The grid worker discovered scattered litter in the vicinity and reported a text description and photos of the scene via mobile terminal. After analysis by the multimodal recognition model, it outputs an "Environmental Class 005" type code (scattered litter).

[0051] Ninety minutes later: A citizen complained about an odor problem in the area through the hotline. The text classification model identified it as "Environmental Category 002" type code (odor nuisance).

[0052] The AI ​​engine performs a fusion analysis: it detects that these three events meet the merging criteria in three dimensions: spatial distance (within 50 meters), time window (within two hours), and semantic association (causal and concurrent relationships). Therefore, they are merged into a new event, with the theme defined as "a series of urban appearance problems caused by overflowing garbage cans".

[0053] Early warning and task derivation: The engine triggers the corresponding decision rules, generates orange early warning information, and automatically dispatches "emergency cleaning and surrounding area cleaning" tasks to the sanitation dispatch system, while pushing preventive inspection notices to the grid worker's terminal.

[0054] Command and Dispatch: The command center interface highlights the integrated event and AI suggestions, allowing dispatchers to quickly identify and allocate resources, significantly improving handling efficiency and avoiding multiple responses and resource waste.

[0055] Example 2 With the advancement of smart city construction, many cities have established preliminary urban operation management platforms, which constitute existing technology. This existing technology typically includes the following methods, structures, and steps: Method: A centralized information collection and distribution model is adopted.

[0056] Structure: The system typically includes a data receiving center and a human operator command center. The data receiving center receives data from the grid worker patrol app, some IoT sensors, and the 12345 hotline system through a simple data interface. This data is then displayed uniformly on a large electronic map screen.

[0057] Specific steps: Data aggregation: Data from various subsystems is pushed to the platform, but it is usually only parsed in a simple format without in-depth data standardization and semantic unification. For example, the grid worker's report of "the trash can is full" and the IoT sensor's report of "98% overflow" are treated by the platform as two independent and unrelated data entries.

[0058] Manual identification and classification: Operators at the command center need to rely on personal experience and visual observation to identify whether events from different sources describe the same problem on the large screen. For example, operators need to see "scattered garbage" events (from grid workers) and "odor complaints" events (from the hotline) appearing in close proximity on the screen at the same time, and infer from experience that they may originate from the same unreported "overflowing garbage bin" problem.

[0059] Manual correlation and decision-making: Call center staff manually confirm the correlation of events through telephone communication, reviewing historical records, etc., and then manually merge multiple tasks through the platform, or assign them to different handling departments.

[0060] Task dispatch: Finally, the task is dispatched to the corresponding business subsystem by manual click.

[0061] The technical problems with this existing technology are: Highly dependent on human experience: The accuracy and efficiency of event correlation analysis depend entirely on the operator's proficiency and mental state, resulting in poor consistency and high human resource costs.

[0062] Delayed response: From the occurrence of an event to its manual identification, association, and task assignment, the entire process is time-consuming, making it impossible to achieve proactive early warning and rapid response.

[0063] The problem of "information silos" has not been fundamentally solved: the platform has only achieved "simultaneous display" of data, but has not achieved deep integration and intelligent analysis of data at the semantic level. The data of each subsystem is still an isolated "information chimney".

[0064] To address the aforementioned technical problems, this invention provides a method for coordinated command, decision-making, and early warning that integrates grid patrol events with artificial intelligence. Compared to existing technologies, the difference lies in the construction of an "artificial intelligence analysis engine" possessing multimodal intelligent recognition and standardization capabilities, as well as intelligent fusion analysis and decision-making capabilities based on dynamic rules. This achieves a fundamental shift from passive reception and manual judgment to proactive perception and intelligent decision-making.

[0065] The following example, "overflowing smart trash cans leading to a chain reaction of problems," illustrates the specific implementation of this invention in detail: S1. Data Access and Standardization Steps Implementation Structure: This system employs a unified data platform, whose front-end integrates a data format conversion and protocol adaptation module. This module includes built-in parsers for various communication protocols such as HTTP / HTTPS, MQTT, and WebService, as well as a data cleaning and validity verification submodule for filtering duplicate and invalid data.

[0066] Specific steps and applicable scenarios: Data access: The IoT component monitoring subsystem reports the status data packet of "Smart Trash Can A" via the MQTT protocol. The content is {Device ID: Bin-A, Overflow: 98%, Timestamp: T1}.

[0067] The grid patrol subsystem reports an event data item via the HTTPS protocol, which includes a text description of "a large number of plastic bags are scattered at the XX intersection, suspected to be an overflowing and uncleaned trash can" and on-site images.

[0068] The public service hotline subsystem pushed a citizen complaint through the WebService interface, stating that "there is a serious odor near XX Street, which is affecting my life."

[0069] Data standardization and intelligent coding (core distinguishing features): All data is converted to a uniform Avro format and is required to include event ID, latitude and longitude, timestamp, and issue type encoding.

[0070] A method for assigning encoding to problem types (this is not available in existing technologies): For IoT data, by querying the pre-set state-type mapping table, "overflow > 90%" is directly mapped to the unified problem type code ENV001 (garbage overflow).

[0071] The text and images reported by grid workers are processed using a pre-trained multimodal neural network model. This model consists of a text branch and an image branch. The text branch uses a Transformer architecture model to perform natural language understanding on the text descriptions, extracting keywords such as "scattered garbage" and "suspected overflow." The image branch uses a convolutional neural network to perform visual analysis on the on-site images, identifying visual features such as "scattered garbage" and "full trash cans." Subsequently, the model fuses the text keywords and image features, ultimately automatically outputting the most matching problem type code, ENV005 (scattered garbage).

[0072] For hotline texts, a natural language processing classification model is used to analyze the complaint content and automatically output the problem type code ENV002 (odor nuisance).

[0073] S2, Intelligent Analysis and Decision-Making Steps Implementation Structure: On top of a unified data platform, an artificial intelligence analysis engine with a microservice architecture runs, the core of which includes an event recognition module, a fusion analysis module, and an early warning module.

[0074] Specific steps and applicable scenarios (core distinguishing features): Fusion analysis and collaborative decision-making: The engine takes the three standardized events (ENV001, ENV005, ENV002) mentioned above as input.

[0075] The engine performs the following automatic judgments (which is unmatched by manual operation in existing technology): Spatial judgment: Calculate the spatial distance between the three event locations (all < 50 meters) and compare it with a dynamic distance threshold (e.g., 170 meters, calculated by adding the standard deviation to the average farthest distance of related events triggered by "overflowing trash can" in historical data). The result meets the condition.

[0076] Time judgment: Calculate the time difference of events (all <2 hours) and compare it with the preset time window (6 hours, set according to the priority of handling urban appearance and environmental issues). The result meets the conditions.

[0077] Semantic judgment: Query a predefined problem type mapping table (two-dimensional relation matrix) and find that there is a "causal relationship" between ENV001 (garbage overflow) and ENV005 (garbage scattered), and a "concurrency relationship" between ENV001 (garbage overflow) and ENV005 (garbage scattered).

[0078] When all three conditions are met, the engine automatically merges these three independent events into a single event, with the theme defined as "a series of urban appearance problems caused by the overflowing of trash can A".

[0079] Intelligent early warning and task derivation: Using this fusion event as input, the engine automatically generates an orange alert, prompting the command center to pay attention to the area.

[0080] Simultaneously, the engine queries the pre-set decision rule library and matches the corresponding rule: when the problem type is garbage overflow and a fusion event has been formed, a disposal task is dispatched to the sanitation dispatch system and the warning level is upgraded. A specific implementation of this rule in the system is as follows: "IF Problem type = garbage overflow AND fusion event generated THEN Dispatch task to sanitation dispatch system and upgrade warning level".

[0081] Therefore, the system automatically dispatched a task to the sanitation dispatch system: "Please urgently remove garbage bin A and clean the surrounding area."

[0082] S3, Command and Dispatch Procedures The command system's large screen will highlight the fusion event, AI-generated early warning information, and automatically assigned tasks. The dispatcher's job changes from "full-process manual operation" under existing technology to "supervising and confirming AI decisions," requiring only one-click confirmation to complete the final allocation of resources.

[0083] This invention addresses the problems of reliance on human experience and poor consistency: Through a multimodal AI model and pre-defined association rules, it achieves automated and standardized identification of event types and relationships, eliminating the subjectivity and inconsistency of human judgment. Existing technologies, however, rely entirely on the experience and responsibility of agents, failing to guarantee accuracy and efficiency. The intelligent fusion analysis and automatic task assignment mechanism of this invention can complete the entire process from event perception to task assignment within minutes, achieving proactive warnings and rapid response. In contrast, existing technologies often take hours from agent discovery of associations to manual merging and assignment, resulting in severe response delays. This invention, through a unified data standardization process (especially issue type coding) and multi-dimensional (spatiotemporal, semantic) fusion analysis, completely breaks down the barriers between subsystems at both the technical and semantic levels, enabling data to truly generate aggregated value.

[0084] Furthermore, a semantic association matrix for question types is constructed and stored within the unified data platform. This matrix is ​​in the form of a two-dimensional table, with the number of rows and columns equal to the total number of question types in the predefined classification system. The row and column headers of the matrix consist of codes for all valid question types, including specific codes such as "Environment Class 001", "Environment Class 002", and "Facility Class 004".

[0085] Each cell in the matrix records the semantic relationship between the question types in the corresponding row and column. We use predefined semantic relationship types for annotation, specifically including the following three main relationships: Causality indicates that one type of problem may directly lead to another type of problem. For example, the problem of "overflowing garbage" is likely to lead to the problem of "scattered garbage".

[0086] Concurrency indicates that two types of problems often occur simultaneously under the same root cause; for example, "garbage overflow" and "odor nuisance" often occur together.

[0087] The hierarchical relationship indicates that one problem type is a specific manifestation of another problem type. For example, "damaged streetlights" is a specific manifestation of "public facility malfunction".

[0088] In practical applications, when the AI ​​analysis engine needs to determine the semantic relationship between two events, it will quickly determine whether there is a preset semantic relationship between the two types of questions by querying the tag value at the corresponding position of the matrix.

[0089] Furthermore, in the design of the predefined classification system, two important attributes were set for each question type code: The basic level attributes categorize problem types into three levels: "normal," "critical," and "urgent." Problems that directly endanger public safety, such as "gas leaks," are defined as "urgent," while problems with a wide impact, such as "severe flooding on main roads," are defined as "critical." All other problems are classified as "normal."

[0090] The cascading reaction risk indicator is used to mark whether a problem type has the potential to trigger secondary problems. For example, a "fallen tree" problem is marked as having a cascading reaction risk because it may further lead to traffic disruptions or damage to power facilities.

[0091] The system's judgment logic includes two parallel paths: First, it checks the basic level attribute of the event problem type. If it is "major" or "urgent", it is directly classified as an independent event of a specific type. Second, it makes a comprehensive judgment based on preset decision rules through a risk prediction mechanism. For example, when the system detects that the "tree falling" problem occurs on a traffic artery, it will automatically identify that it has the risk of triggering a chain reaction.

[0092] Once an event is identified as a specific type of independent event through the above-mentioned judgment process, it will enter the rapid response channel and directly jump to the intelligent early warning and task derivation stage to ensure a rapid response to high-risk events.

[0093] Furthermore, the decision rule base adopts a condition-action expression format, which is managed and executed by a dedicated rule parsing engine. The rule files are stored in an easy-to-maintain text format and support dynamic loading and updates.

[0094] Each rule consists of a condition judgment section and an action execution section. The condition judgment section supports various logical operations on event attributes, including numerical comparison, logical combination, and set inclusion relationship judgment, and also supports calling built-in functions such as time calculation and surrounding event statistics.

[0095] The action execution section defines specific task dispatch operations and interfaces with various business subsystems through a unified task dispatch interface. This interface encapsulates the communication protocols and data format conversions for business systems such as the sanitation dispatch system and the emergency maintenance system.

[0096] Typical rule examples are as follows: Emergency garbage collection rules: When the system identifies a garbage overflow problem and the number of related events in the surrounding area reaches or exceeds the threshold set based on historical data statistics, it will automatically dispatch an emergency collection task to the sanitation dispatch system and raise the warning level to orange.

[0097] Nighttime facility maintenance rules: When the system detects damage to public facilities during the nighttime period, it automatically dispatches a nighttime maintenance task to the emergency maintenance system and adds safety operation tips to the task instruction.

[0098] Furthermore, a hierarchical and scalable classification and coding system was established, adopting a three-level structure of major categories, intermediate categories, and minor categories. The coding rules use a mixed alphanumeric format, for example, "Environment Category.01.01" represents the "Overflowing Bins" subcategory within the "Waste Collection" subcategory under the "Environment" major category.

[0099] The system initially established five main categories: Waste Management, covering issues related to the collection, transportation, and treatment of various types of waste; Urban Environment, including issues related to environmental sanitation and city cleanliness; Public Facilities, involving the maintenance of public facilities such as roads and lighting; Public Order, including issues related to the management of public place order; and Traffic Safety, covering issues related to road traffic facilities and safety.

[0100] The openness of the system is reflected in two aspects: horizontal expansion supports the direct addition of major categories through the management system backend, such as the addition of the water management major category in the future; vertical refinement allows for the dynamic addition of medium and small categories under the existing major categories, such as the gradual expansion of subcategories such as postal facilities and shared bicycle parking facilities under the public facilities major category.

[0101] Furthermore, the data format conversion and protocol adaptation module supports multiple communication protocols and has a built-in data cleaning and validity verification submodule.

[0102] The data format conversion and protocol adaptation module is designed as a pluggable protocol processing framework. This framework includes independent adapters for different communication protocols, including: HTTP / HTTPS Adapter: Specifically designed to handle request-response communication from mobile terminals of the grid patrol subsystem and the public service hotline subsystem. This adapter can parse data packets transmitted via Hypertext Transfer Protocol and its secure version, and handle user authentication and data decryption.

[0103] MQTT Adapter: Specifically designed for accessing data from IoT component monitoring subsystems. This adapter receives lightweight messages from a massive number of sensor devices by subscribing to specific topics, making it suitable for low-power, high-concurrency data transmission scenarios in the IoT field.

[0104] WebService Adapter: Used for data exchange with traditional heterogeneous systems such as government hotlines. It can parse XML format data based on the Simple Object Access Protocol and achieve cross-platform and cross-language data integration.

[0105] Data cleaning and validity verification: Each protocol adapter is followed by a data cleaning and validity verification submodule. This submodule performs the following specific operations: Format compliance verification: Check whether the required fields of the data packet are complete and whether the data format conforms to the predefined specifications (such as whether the latitude and longitude coordinates are within a reasonable range).

[0106] Logical rationality verification: Determine the rationality of the data based on business rules. For example, verify whether the reported timestamp is before the current time (not a future time) and whether the battery voltage reported by the sensor is within the normal operating range.

[0107] Data cleaning: Processing identified invalid or problematic data, such as automatically correcting obviously erroneous latitude and longitude coordinates (e.g., correcting the coordinates of a city management event located in the center of the ocean to the center coordinates of its administrative region), or filtering out completely duplicate data entries.

[0108] In scenarios where smart trash cans are overflowing, IoT sensors report data via the MQTT protocol, while the grid worker app reports via the HTTPS protocol, and the hotline system provides data via WebService. This implementation's multi-protocol adaptation framework ensures that these three data sources with vastly different technical architectures can be seamlessly and stably connected to a unified platform. Simultaneously, the data cleaning submodule effectively identifies and corrects minor location errors caused by sensor GPS signal drift, or filters out duplicate reporting events caused by network jitter. This ensures the data quality and reliability of subsequent AI analysis from the data source, avoiding the problem of "trash in, trash out," and laying a solid foundation for ensuring the accuracy of the final decision.

[0109] Furthermore, the AI ​​analysis engine adopts a microservice architecture, with its core functions encapsulated as independent, horizontally scalable services.

[0110] The AI ​​analytics engine is not a single, massive software system, but rather a collaborative set of fine-grained microservices. Each service undertakes specific, cohesive business capabilities and interacts through lightweight communication mechanisms such as RESTful APIs or gRPC. Core services include: Event intelligent recognition and classification service: This service is specifically responsible for receiving raw event data, calling multimodal recognition models or text classification models, and outputting standardized event type codes.

[0111] Fusion Analysis and Collaborative Decision-Making Service: Specifically responsible for performing spatiotemporal semantic correlation analysis to achieve event merging and integration.

[0112] Intelligent early warning and task derivation service: Specifically responsible for generating early warning information and deriving handling tasks based on the rule base.

[0113] These services are all deployed on a containerized platform, allowing for independent version updates, scaling up and down, and fault recovery. For example, during the peak reporting period for grid workers from 9 to 11 a.m. each day, the system can automatically add more container instances for the "Intelligent Event Recognition and Classification Service" to cope with the surge in image and text recognition requests, while other services maintain their original resource allocation.

[0114] When responding to sudden urban environmental incidents (such as widespread flooding after heavy rain), the number of reported incidents can surge dramatically. Traditional monolithic architectures may experience system-wide response delays or even crashes due to resource bottlenecks in individual modules. However, the microservice architecture of this invention allows system administrators to rapidly scale up the "integrated analysis and collaborative decision-making service" individually, increasing its computing resources to ensure that massive amounts of events can be analyzed and correlated in a timely manner, while other services remain unaffected. This architecture endows the system with extremely high elasticity and reliability, ensuring that the command and decision-making system can maintain efficient and stable operation even when facing sudden pressures in urban governance.

[0115] Furthermore, in the fusion analysis step, when multiple events are merged, the engine determines the dominant issue type and priority of the fused events based on the credibility weight of the source subsystem and the reporting time priority.

[0116] The system pre-determines a reliability weight for each data source subsystem. This weight is set based on historical data accuracy statistics, for example: The credibility weight of reports submitted by grid workers (with on-site image evidence) is the highest, set to 1.0.

[0117] The credibility weight of IoT sensor data is the second highest, set at 0.9.

[0118] The credibility weight of public service hotlines (which are based on citizen descriptions and may contain subjective or inaccurate information) is relatively low, set at 0.7.

[0119] The principle of prioritizing reporting time is that, all other things being equal, events that occur later are more likely to reflect the current severity of the problem and therefore have a higher priority.

[0120] Decision-making logic: When multiple events are merged, the engine determines the properties of the merged event according to the following rules: Dominant Issue Type: Compare the issue type codes of all merged events and select the issue type of the event with the highest source credibility weight as the dominant issue type of the merged event. If multiple events have the same credibility, select the most recently reported event type.

[0121] Priority of handling: The initial priority of a merged event is determined by the dominant issue type, but it can be dynamically increased by events with high source credibility weight or the most recently occurring events.

[0122] Taking the overflowing trash can scenario again as an example, suppose three events are merged: the IoT sensor first reports "overflow" (weight 0.9), then the grid worker reports "scattered trash" with photos (weight 1.0), and finally, the citizen hotline complains about "odor" (weight 0.7). According to the rules of this implementation method, the dominant problem type of the merged event will be "scattered trash" reported by the grid worker, which has the highest credibility, because it provides the most reliable on-site evidence. At the same time, since it includes the root cause problem of "overflow" confirmed by the sensor, the priority of the event will be significantly increased. This mechanism ensures accurate characterization and reasonable classification of the merged events, enabling the assigned disposal tasks to directly address the core of the problem and match the appropriate level of urgency, greatly improving the scientific nature of command and decision-making and the efficiency of resource utilization.

[0123] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Further modifications can be readily implemented by those skilled in the art.

Claims

1. A method for linking grid patrol events with artificial intelligence to command decision and early warning, characterized in that, The method comprises the following steps: S1, data access and standardization step: Accessing multiple urban governance data sources through a data interface, the data sources including at least: event data reported by a grid patrol subsystem, containing text description and on-site images, component state data reported by an Internet of Things component monitoring subsystem, and public service hotline subsystem data; Building a unified data platform for receiving and storing the multiple urban governance data; the data interface includes a data format conversion and protocol adaptation module for converting heterogeneous data from different subsystems into a unified standard data format specified by the platform; The unified standard data format is a structured data format, and is forced to include the following fields: a unique event identifier, a location coordinate based on a latitude-longitude coordinate system, a timestamp conforming to the ISO 8601 standard, and a problem type code based on a predefined classification system, resulting in standardized events; S2, intelligent analysis and decision-making step: An artificial intelligence analysis engine running on the unified data platform performs the following operations on the standardized events from S1 in turn: Fusion analysis and collaborative decision-making: based on preset association rules, multiple standardized events associated in space, time, and semantics are merged to generate fusion events, and the remaining events are classified into specific type independent events and ordinary independent events; Intelligent early warning and task derivation: based on the fusion events and / or the specific type independent events, automatically generating early warning information, and automatically deriving handling tasks to one or more related business handling subsystems according to a preconfigured decision rule library; S3, command and dispatch step: The command system performs unified command and dispatch and resource allocation based on the fusion events, the early warning information, and the derived handling tasks generated by S2.

2. The method of claim 1, wherein, The method for assigning the problem type code is: For the event data of the grid patrol subsystem, a pre-trained multi-modal neural network model is used for processing; the processing includes: performing natural language understanding on the text description through a pre-trained language model based on the Transformer architecture to extract keywords, and performing computer vision analysis on the on-site images through a convolutional neural network model to identify visual features; then, based on the fusion result of the keywords and visual features, automatically identifying the event problem, and assigning a problem type code based on the predefined classification system to each event; For the component state data of the Internet of Things component monitoring subsystem, the state-type mapping table is used to convert the corresponding problem type code; For the public service hotline subsystem data, a natural language processing model is used to classify the text content and assign a problem type code; At this point, all data sources output standardized events with unified problem type codes.

3. The method of claim 1, wherein, The fusion analysis and collaborative decision-making specifically includes: taking the standardized events output by all subsystems as input, and performing fusion analysis by the artificial intelligence analysis engine; the fusion analysis is based on preset association rules, and its judgment process includes: calculating the spatial distance between any two event locations and comparing the distance with a dynamic distance threshold; the dynamic distance threshold is set based on the average farthest distance of the same type of urban components in the historical database that triggered associated events in the past, plus a standard deviation as a buffer value; calculating the time difference between the occurrence times of the above two events and comparing the time difference with a preset time window; the preset time window is set based on the priority of handling and the average handling time of different problem types in the predefined classification system; determining whether there is a semantic association relationship between the problem type codes of the above two events, as determined by a predefined problem type mapping table; when the spatial distance is less than or equal to the dynamic distance threshold, the time difference is within the preset time window, and there is a semantic association relationship, the engine merges the multiple events to generate a fused event; for events that cannot be merged, they are kept as independent events; distinguishing the independent events into specific type independent events and ordinary independent events; and the intelligent early warning and task derivation specifically includes: generating early warning information automatically by the artificial intelligence analysis engine with the fused event and the specific type independent event as input; at the same time, based on the problem type code and attributes contained in the fused event or specific type independent event, according to the pre-set decision rule library, automatically deriving handling tasks to one or more related business handling subsystems; for the ordinary independent events, they enter the regular transaction processing flow for processing.

4. The method of claim 1, wherein, The predefined problem type mapping table is a two-dimensional relationship matrix, which records the marks of whether there is a cause-and-effect, concurrent or superior-inferior relationship between different problem type codes.

5. The method of claim 1, wherein, The specific type independent event refers to an event whose problem type code is pre-marked as "urgent" or "major" in the predefined classification system, or an event that is judged to have the risk of triggering a chain reaction according to the pre-set decision rule library.

6. The method of claim 1, wherein, The rules in the pre-set decision rule library define the logical relationship between conditions and actions; at least the following rules are included in the rule library: when the problem type of an event is garbage overflow and the number of its surrounding associated events exceeds a preset threshold, a handling task is derived to the sanitation dispatching system, and the early warning level of the event is raised; when the problem type of an event is public facility damage and its occurrence time is in the night period, a handling task is derived to the emergency repair system, and safety warning information is attached to the task instruction.

7. The method of claim 1, wherein, The predefined classification system is an open classification system, including one or more of garbage disposal, city appearance and environment, public facilities, public order, and traffic safety.

8. The method of claim 1, wherein, The data format conversion and protocol adaptation module supports multiple communication protocols including HTTP / HTTPS, MQTT and WebService, and has built-in data cleaning and validity checking sub-modules to ensure the quality of input data.

9. The method of claim 1, wherein, The artificial intelligence analysis engine adopts a micro-service architecture, and event intelligent recognition and classification, fusion analysis and collaborative decision-making, intelligent early warning and task derivation functions inside the engine are encapsulated as independent, horizontally scalable services.

10. The method of claim 1, wherein, In the fusion analysis and collaborative decision-making step, when multiple events meet the merging conditions, the engine determines the dominant problem type and priority of the fusion event according to the source subsystem credibility weight and reporting time priority of the events.