Urban operation management scheme based on AI center

Abstract

The invention discloses a city operation management scheme based on an AI center, belongs to the technical field of city operation management, and optimizes a current city operation management platform by introducing an artificial intelligence technology, building a city brain AI center platform and integrating the platform with a big data platform. And particularly, accurate prediction and efficient aid decision making are carried out on urban operation events, so that social adverse effects caused by misjudgment are reduced, the accuracy and the releasability of commercial intelligent content results are enhanced, and the ability of a management platform to quickly, conveniently and accurately process events in various fields of urban operation is improved.

Description

A city operation management solution based on AI hub Technical Field

[0001] This invention belongs to the field of urban operation management service technology, specifically relating to an urban operation management solution based on an AI hub. Background Technology

[0002] Currently, improving urban operational efficiency through digitalization and continuously solving practical problems in urban governance is an important path to enhancing the level of urban governance. Research has revealed a strong demand for the construction of an integrated urban operation management platform.

[0003] Currently, mainstream government systems still revolve around big data platforms, collecting data from traditional IoT, business application systems, and OA systems. This data is then cleaned, processed, and aggregated into a data warehouse for business intelligence visualization. However, with the increasing diversity and complexity of business data, current traditional platform construction solutions are no longer sufficient to meet the intelligent requirements of smart city governance. Mainstream big data platforms lack efficient, flexible, and secure data processing capabilities, and are unable to effectively mine and analyze massive amounts of data, thus failing to provide valuable decision-making support for city managers. Furthermore, traditional digital modeling, simulation, and analysis methods urgently need optimization in areas such as architecture design, predictive maintenance, and iterative evolution.

[0004] The root cause lies on the business side: current urban operation management services suffer from insufficient supply capacity, low professional standards, high data acquisition costs, and inadequate service convenience, failing to provide residents with practical, accurate, convenient, and efficient services. On the technology side, traditional data platforms, with their focus on visual business intelligence, suffer from simplistic data structures, uninspired data models, and an inability to fully reflect data value; their outdated technical architecture lacks intelligent analysis tools. Therefore, there is an urgent need to introduce next-generation technological concepts, break with conventional thinking, and improve the efficiency of digital urban operations. Summary of the Invention

[0005] The purpose of this invention is to provide an AI-based urban operation management solution to reduce the negative social impact caused by misjudgments, enhance the accuracy and interpretability of business intelligence results, and improve the management platform's ability to quickly, conveniently, and accurately handle events in various fields of urban operation.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: On one hand, this specification provides an urban operation management solution based on an AI hub, including: Step 102, determining the platform business objectives of the urban brain AI hub; the platform business objectives are the services provided by the urban brain AI hub to the urban operation management platform; Step 104, based on the business flow of the urban operation management platform, determining the overall architecture and platform business flow of the urban brain AI hub; the overall architecture of the urban brain AI hub includes a big data platform, an AI hub platform, and an integrated event handling platform; the platform business flow is that business data flows from the big data platform to the AI ​​hub platform and then to the integrated event handling platform; the big data platform is used to collect various... The system performs big data analysis on the data to obtain preliminary analysis results, which are then pushed to the AI ​​central platform. The AI ​​central platform is used to classify and deeply analyze the preliminary analysis results, obtain event warnings and analysis results, and push them to the integrated event handling platform. The integrated event handling platform is used to process events based on the event warnings and analysis results and feed the processing results back to the AI ​​central platform. Step 106: Based on the platform business objectives, overall architecture, and platform business flow of the city brain AI central platform, the core functional components of the city brain AI central platform are determined to obtain the target city brain AI central platform. Step 108: The target city brain AI central platform is connected to the city operation management platform to obtain the city operation management platform based on the AI ​​central platform.

[0007] Based on the above technical solutions, this specification achieves the following technical effects: This method targets typical urban operation scenarios, constructs an AI central platform for the city's brain, introduces data mining and algorithm recommendation model rapid construction technology based on artificial intelligence, selects actual scenarios that can be matched with urban operations using artificial intelligence, comprehensively identifies factors inducing emergency events, reduces adverse social impacts caused by misjudgments, enhances the accuracy and interpretability of business intelligence results, and improves the management platform's ability to quickly, conveniently, and accurately handle events in various fields of urban operation. Focusing on emergency incidents, it conducts scenario data collection and analysis in fields such as hazardous chemicals, fireworks and firecrackers, construction, transportation, fire protection, gas, roads and bridges, special equipment, floods and droughts, meteorological disasters, geological disasters and forest fires, infectious disease outbreaks, and food and drug safety, guiding application development and improving the city's comprehensive perception, dynamic monitoring, risk identification, intelligent judgment, precise prevention and control, and command and rescue capabilities in urban operation management. Attached Figure Description

[0008] Figure 1 is a flowchart illustrating an AI-based urban operation management scheme according to an embodiment of the present invention.

[0009] Figure 2 is an overall architecture diagram of an AI-based urban operation management platform according to an embodiment of the present invention.

[0010] Figure 3 is a schematic diagram of the platform business flow of the City Brain AI hub in one embodiment of the present invention. Detailed Implementation

[0011] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and are not to a precise scale, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0012] It should be noted that, in order to clearly illustrate the content of this invention, several embodiments are provided to further explain different implementations of the invention. These embodiments are enumerated rather than exhaustive. Furthermore, for the sake of brevity, content mentioned in the preceding embodiments is often omitted in the following embodiments. Therefore, content not mentioned in the later embodiments can be referred to in the preceding embodiments.

[0013] Example 1: Please refer to Figure 1, which shows a city operation management solution based on an AI hub provided in this embodiment. In this embodiment, the method includes: Step 102, determining the platform business objectives of the city brain AI hub; the platform business objectives are the services provided by the city brain AI hub to the city operation management platform; in this embodiment, the platform business objectives of the city brain AI hub include: AI intelligent application service objectives, multi-source data access service objectives, daily management and resource allocation service objectives, and algorithm deployment service objectives.

[0014] Step 104: Based on the business flow of the city operation management platform, determine the overall architecture and platform business flow of the city brain AI hub; the overall architecture of the city brain AI hub includes a big data platform, an AI hub platform, and an integrated event handling platform; the platform business flow is that business data flows from the big data platform to the AI ​​hub platform and then to the integrated event handling platform; the big data platform is used to collect various types of data and perform big data analysis, obtain preliminary analysis results, and push them to the AI ​​hub platform; the AI ​​hub platform is used to classify and deeply analyze the preliminary analysis results information into subject domains, obtain event warnings and analysis results, and push them to the integrated event handling platform; the integrated event handling platform is used to... The event warning and analysis results are processed and the processing results are fed back to the AI ​​central platform. In this embodiment, the big data platform is used to collect various types of data and perform big data analysis to obtain preliminary analysis results and push them to the AI ​​central platform, including: Step 202, the big data platform collects and integrates various types of data to obtain data summary information; the various types of data include IoT data, theme application system data, and OA system data; wherein, the IoT data includes video database and drone-collected data; Step 204, the big data platform performs big data analysis on the data summary information to obtain preliminary analysis results; Step 206, the big data platform pushes the preliminary analysis results to the AI ​​central platform in the form of information.

[0015] In this embodiment, the AI ​​central platform is used to classify and deeply analyze the preliminary analysis results to obtain event warnings and analysis results, and push them to the integrated event handling platform. This includes: Step 302, the AI ​​central platform classifies the received preliminary analysis results to obtain thematic business data corresponding to different thematic domains; Step 304, based on AI machine learning technology, the AI ​​central platform assigns target algorithms matched by the algorithm center to the thematic business data corresponding to different thematic domains and performs deep analysis to obtain event warnings and analysis results for different events; Step 306, the AI ​​central platform pushes the event warnings and analysis results for different events to the corresponding event handling personnel in the integrated event handling platform in the form of a report.

[0016] Step 106: Based on the platform business objectives, overall architecture, and platform business flow of the City Brain AI Hub, determine the core functional components of the City Brain AI Hub to obtain the target City Brain AI Hub. In this embodiment, the core functional components include an access center, an algorithm center, a sharing center, and a management center. In this embodiment, one implementation of step 106 is as follows: Step 402: Based on the goal of multi-source data access services, set up an access center in the big data platform. The access center is used to access various types of structured data, semi-structured data, and unstructured data. Step 404: Based on the goal of algorithm deployment services, set up an algorithm center in the AI ​​Hub platform. The algorithm center is used to store various types of urban operation AI algorithms and provide algorithm installation and deployment. In this embodiment, the various types of urban operation AI algorithms include traffic mitigation algorithms, smoke recognition algorithms, snow melting and antifreeze algorithms, waterlogging prevention algorithms, license plate recognition algorithms, street cleaning algorithms, and pedestrian flow recognition algorithms.

[0017] Step 406: Based on the AI ​​intelligent application service goals, a sharing center is set up in the AI ​​hub platform; the sharing center is used to provide users with various AI-based intelligent applications and to provide application developers with API interfaces based on algorithm services; Step 408: Based on the daily management and resource configuration service goals, a management center is set up in the AI ​​hub platform; the management center is used to manage and monitor the platform business goals, computing resources, policies, clusters, users and permissions, and logs of the city brain AI hub.

[0018] In this embodiment, the core functional components also include: a data dashboard, used for gridded management of the system based on data from different dimensions, intuitively displaying the statistical and real-time information of the city brain AI hub; in this embodiment, the data dashboard supports dynamic page refresh, and performs real-time monitoring and visualization of alarm data, number of devices, device usage, resource usage, algorithm usage, high-incidence locations, and alarm trends.

[0019] The early warning center, located within the AI ​​central platform, utilizes AI visual analysis technology to analyze and track targets within a scene by separating the background from the target, and then issues an alarm. In this embodiment, the early warning center uses AI visual analysis technology to separate the background from the target in the scene and, based on different preset alarm rules in different camera scenes, analyzes whether the target in the scene violates the prescribed behavior corresponding to the preset alarm rules. If so, the early warning center automatically issues an alarm, displays alarm information, and emits a warning sound.

[0020] The video aggregation section, located within the big data platform, is used to access various video source devices using streaming media transmission protocols, enabling the unified aggregation, integration, and centralized management of diverse video resources.

[0021] In this embodiment, the video aggregation module includes: a custom grouping module for creating custom groups or importing / exporting groups, and incorporating different devices into corresponding groups; a tag management module for managing cameras or devices by tagging them based on scenarios; a video polling module for polling videos based on polling intervals; a deployment and early warning module for setting deployment time, deployment range, and withdrawal; performing precise and fuzzy deployment based on target feature attributes; receiving deployment instructions; sending alarm information to the deployment party after discovering target objects that match the deployment target feature attributes; querying early warning time, early warning type, and early warning location; a trajectory playback module for playing back the historical trajectories of captured vehicles; and a vehicle trajectory fusion module for fusing the captured vehicle trajectories with BeiDou positioning data.

[0022] Step 108: Connect the target city brain AI hub to the city operation management platform to obtain the city operation management platform based on the AI ​​hub.

[0023] Specifically, an AI-based urban operation management solution includes: (1) Architecture design reference Figure 2. The city brain AI hub platform relies on AI algorithm models to establish a unified and diverse data entry point, provide edge-cloud model on-demand deployment capability, realize AI capability self-closed loop, and achieve the integration of AI and business at low cost through visual interactive window, intelligent application service and standard API interface, and quickly build intelligent applications.

[0024] (2) Implementation Steps First step, determine the business objectives of the city brain AI hub platform. First, it is necessary to determine the business objectives of the city brain AI hub platform, which means to understand the position of the city brain AI hub in the entire city operation and management platform and the tasks it can complete.

[0025] After detailed research and summarization, the City Brain AI Central Platform, unlike traditional big data platforms, can mainly achieve the following objectives: (1) Provide full-process services for AI model deployment and AI application release; (2) Support unified access standards and self-service access functions for intelligent devices and diverse data; (3) Provide automated hosting and resource adaptation capabilities; (4) Support intelligent AI applications and standard API services; (5) Support one-click end-to-end on-demand deployment of any algorithm; (6) Support unified algorithm access and distribution. The second step is to determine the business flow of the City Brain AI Central Platform. This step is crucial. Only by clarifying the business flow of urban operation management can the position of the AI ​​Central Platform in the entire business platform be determined, and the direction of data access and information output be determined simultaneously to give full play to the real effectiveness of the AI ​​Central Platform.

[0026] Because the AI ​​hub plays a unique role—timely analysis of transaction data or information, and pushing the results of intelligent analysis to city operation managers for transaction processing and judgment—the business flow should be: Big Data Platform → AI Hub Platform → Integrated Event Handling Platform. The Big Data Platform is responsible for collecting various types of data, generally including IoT data (video aggregation data, drone-collected data, etc.), main application system data, OA system data, etc. After integrating and aggregating the data, it performs big data analysis and pushes information with preliminary analysis results to the AI ​​Hub Platform. The AI ​​Hub Platform categorizes the pushed information into thematic business data based on subject domains. Then, using AI machine learning technology, different thematic data are assigned to the algorithm center according to the business type, matched with corresponding algorithms for in-depth analysis, generating corresponding event warnings and analysis results, which are pushed to the event handling platform in the form of reports. It is important to note that the processing results are automatically assigned to the relevant responsible departments or even individuals, ensuring that the person responsible for event handling receives the warning information promptly and makes an accurate judgment. The entire business process is constantly in a state of judgment, analysis, and rapid push. The system adopts an uninterrupted data transfer strategy to deliver messages to users as quickly as possible. Timeliness and analyzability are also the biggest highlights of the AI ​​hub. This allows the entire city operation platform to act like a brain, cooperating with various business departments to handle various tasks, which is particularly effective in handling emergency events. This differs from the traditional platform architecture centered on big data platforms. By centering on the AI ​​hub platform, it breaks the previous transaction processing pattern, greatly improving transaction processing efficiency and making the entire platform intelligent. The business flow diagram of the AI ​​hub is shown in Figure 3. The third step is to decompose the business functions of the city brain AI hub platform. After determining the business flow and the core position of the AI ​​hub, the core functional components within the AI ​​hub can be constructed. The AI ​​hub platform built using this method has a total of 7 departments.

[0027] (1) AI Central Hub Main Functional Components—AI Dashboard The AI ​​Dashboard uses data from different dimensions to perform grid-based management of the system, intuitively displaying the statistical and real-time information covered by the system. It supports dynamic page refresh and comprehensively monitors core indicators such as alarm data, number of devices, device usage, resource usage, algorithm usage, high-incidence locations, alarm trends, and other prominent issues. Through visual analysis, it comprehensively displays the current status of AI management, assisting managers in fully grasping the operational situation and improving supervision and administrative efficiency.

[0028] (2) The main functional component of the AI ​​hub—the early warning center—uses AI visual analysis technology to analyze and track targets appearing in the camera scene by separating the background and the target in the scene.

[0029] Users can use the video content analysis function to preset different alarm rules in different camera scenes. Once the target violates the predefined rules in the scene, the system will automatically issue an alarm, pop up alarm information and sound an alarm. Users can click on the alarm information to reconstruct the alarm scene and take relevant measures.

[0030] By leveraging real-time video analysis and alarm information, and utilizing AI-powered intelligent detection methods, a closed-loop process for handling early warning and monitoring events can be efficiently completed.

[0031] (3) Main functional components of AI hub - access center The AI ​​hub can access structured data, semi-structured data and unstructured data, which enables the AI ​​hub to integrate multiple types of data and obtain more accurate judgment and analysis results, greatly improving the effectiveness of transaction processing. The following are the core functions of the access center: 1) Support unified access of device data, business data and other platform data 2) Support unified management of smart devices of different brands, models and types 3) Support conversion of data with different protocols and encodings into a unified format for output and control 4) Support access using standard protocols 5) Support access and management using device SDK and private protocol (4) Main functional components of AI hub - algorithm center The algorithm center is the most important functional module of the AI ​​hub. This patent builds the algorithm center into an algorithm warehouse, with various AI algorithms built-in for users to choose from, such as traffic relief algorithm, smoke recognition algorithm, snow melting and antifreeze algorithm, waterlogging prevention algorithm, license plate recognition algorithm, street cleaning algorithm, pedestrian flow recognition algorithm and other common urban operation algorithms. Data closure is achieved through business flow, allowing the AI ​​hub to maintain a continuous learning state and gradually improve the ability to analyze and process event situations.

[0032] Furthermore, as AI technology matures, it is expanding from fields such as security and transportation to all domains. The algorithm repository provides installation and deployment services for long-tail algorithms, customized algorithms, and third-party algorithms, achieving unified management through the AI ​​platform. Platform users no longer need to worry about different algorithm protocols or instance runtime resources; they can complete operations such as algorithm information creation, algorithm image import, algorithm installation and deployment, and runtime resource allocation through the platform, lowering the barrier to algorithm management and improving the efficiency of algorithm usage.

[0033] (5) AI Hub Main Functional Components - Shared Center The shared center gathers AI-based intelligent applications and serves as a window to provide end users with AI applications and tools that can be used directly. It also provides application developers with API interfaces based on algorithm services. Developers do not need to worry about algorithm installation and deployment or data access management, and can focus on the implementation of the business side.

[0034] Developers' algorithms can feed back into the AI ​​hub and be uploaded to the application center, thereby enhancing the platform's business capabilities.

[0035] (6) AI Hub Main Functional Components - Management Center The management center is responsible for the configuration of each link in the AI ​​application development process and the management of the platform itself, including: service, computing resources, policies, cluster, user and permission management, log management, monitoring management and other functions.

[0036] (7) Main Functional Components of the AI ​​Hub—Video Aggregation The video aggregation module uses streaming media transmission protocols to access various video source devices, unifying, integrating, and centrally managing various scattered video resources. It processes the collected video data and integrates it into a unified database or content library. This includes metadata tagging, classification, archiving, and indexing of the videos, enabling users to quickly retrieve and access them based on different conditions such as device and channel. Specific functions are shown in Table 1: Table 1 Functions of the Video Aggregation Module The fourth step is to connect the completed AI hub platform to the city operation management platform. This solution explains the necessity of connecting to the city operation platform from four aspects: construction scenarios, algorithm center, emergency event handling, and platform integration.

[0037] Construction Scenario: The City Brain AI Central Platform can leverage big data, AI, and other technologies to efficiently handle emergency events in Dongxihu District. This phase of construction combines an event handling platform, a video aggregation and video analysis platform to build a City Brain AI Central Algorithm Library. Utilizing technologies such as video recognition, IoT sensing, and AI analysis, it achieves comprehensive perception of urban vital signs such as meteorological safety, traffic, and the environment, improving the intelligent discovery and handling capabilities of events and supporting various intelligent application scenarios in the city.

[0038] Algorithm Center: The AI ​​hub automatically identifies data types and pushes them to the algorithm center for analysis (including snow melting models, license plate recognition, waterlogging models, etc.), quickly providing event handling decision-making solutions or timely warnings of event situations, completing the event handling closed loop, and greatly improving the efficiency of emergency event handling.

[0039] Emergency Response: Construct an emergency response platform with functions such as real-time monitoring, map spatial monitoring, alarm push, alarm response tracking, and alarm analysis to achieve real-time visibility, controllability, and traceability of perceived events.

[0040] Platform Integration: Integrate with the event management platform and upgrade the video aggregation platform. Utilize AI technology to analyze real-time data to predict potential future risks. This predictive capability helps city managers take proactive measures to avoid or mitigate losses from potential risks. Integrating with the event management platform (3) Effect Analysis 1) Technical Benefits This solution realizes the implementation, application, and promotion of cutting-edge technologies such as digital twins, IoT, big data, and artificial intelligence in vertical fields, continuously updating and upgrading algorithm models, expanding model parameters, and improving technical performance.

[0041] 2) Social benefits: This plan creates an urban operation management platform application and intelligent interactive software products that integrate multiple urban operation scenarios, using intelligent technology innovation to drive a new paradigm of service and governance in vertical fields.

[0042] 3) Economic benefits: This plan builds professional knowledge service capabilities for urban management, water conservancy, sanitation and other fields, reduces costs and increases efficiency, and is expected to generate more than 5 million yuan in economic benefits annually.

[0043] In summary, this solution targets typical urban operation scenarios, constructing a city brain AI central platform. It introduces AI-based data mining and algorithm recommendation model rapid construction technology, selects AI-matched actual urban operation scenarios, comprehensively identifies emergency event triggering factors, reduces adverse social impacts caused by misjudgments, enhances the accuracy and interpretability of business intelligence results, and improves the management platform's ability to quickly, conveniently, and accurately handle events across various urban operation sectors. Focusing on emergency incidents, it conducts scenario data collection and analysis in areas such as hazardous chemicals, fireworks and firecrackers, construction, transportation, fire protection, gas, roads and bridges, special equipment, floods and droughts, meteorological disasters, geological disasters and forest fires, infectious disease outbreaks, and food and drug safety. This data guides application development and enhances the city's comprehensive perception, dynamic monitoring, risk identification, intelligent judgment, precise prevention and control, and command and rescue capabilities in urban operation management.

[0044] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and still achieve the desired result. Furthermore, the specific order or sequential order shown in the drawings is not necessarily required to achieve the desired result; in some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0045] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A city operation management method based on an AI hub, characterized in that, include: The platform business objectives of the City Brain AI Hub are defined; these objectives are the services provided by the City Brain AI Hub to the city operation management platform. Based on the business flow of the city operation management platform, the overall architecture and platform business flow of the City Brain AI Hub are determined. The overall architecture of the City Brain AI Hub includes a big data platform, an AI hub platform, and an integrated event handling platform. The platform business flow is that business data flows from the big data platform to the AI ​​hub platform and then to the integrated event handling platform. The big data platform is used to collect various types of data and perform big data analysis to obtain preliminary analysis results and push them to the AI ​​central platform; the AI ​​central platform is used to classify and deeply analyze the preliminary analysis results information by subject domain, obtain event warnings and analysis results, and push them to the integrated event handling platform. The integrated event handling platform is used to process events based on event warnings and analysis results and feed the processing results back to the AI ​​hub platform; based on the platform business objectives, overall architecture and platform business flow of the city brain AI hub, the core functional components of the city brain AI hub are determined to obtain the target city brain AI hub; the target city brain AI hub is connected to the city operation management platform to obtain the city operation management platform based on the AI ​​hub.

2. The urban operation management scheme based on an AI hub according to claim 1, characterized in that, The platform business objectives of the City Brain AI Hub include: AI intelligent application service objectives, multi-source data access service objectives, daily management and resource allocation service objectives, and algorithm deployment service objectives.

3. The urban operation management scheme based on an AI hub according to claim 1, characterized in that, The big data platform is used to collect various types of data and perform big data analysis to obtain preliminary analysis results and push them to the AI ​​central platform. This includes: the big data platform collecting and integrating various types of data to obtain aggregated data information; the various types of data include IoT data, thematic application system data, and OA system data; wherein, the IoT data includes video databases and drone-collected data; the big data platform performing big data analysis on the aggregated data information to obtain preliminary analysis results; and the big data platform pushing the preliminary analysis results to the AI ​​central platform in the form of information.

4. The urban operation management scheme based on an AI hub according to claim 1, characterized in that, The AI ​​central platform is used to classify and deeply analyze preliminary analysis results, obtain event warnings and analysis results, and push them to the integrated event handling platform. This includes: the AI ​​central platform classifying the received preliminary analysis results into subject domains to obtain subject business data corresponding to different subject domains; the AI ​​central platform, based on AI machine learning technology, assigning target algorithms matched by the algorithm center to the subject business data corresponding to different subject domains and performing deep analysis to obtain event warnings and analysis results for different events; and the AI ​​central platform pushing the event warnings and analysis results for different events to the corresponding event handling personnel in the integrated event handling platform in the form of reports.

5. The urban operation management scheme based on an AI hub according to claim 2, characterized in that, The core functional components include an access center, an algorithm center, a sharing center, and a management center. Based on the platform business objectives, overall architecture, and platform business flow of the City Brain AI Hub, the core functional components of the City Brain AI Hub are determined, resulting in the following: Based on the goal of multi-source data access services, an access center is set up in the big data platform; the access center is used to access various types of structured, semi-structured, and unstructured data. Based on the goal of algorithm deployment services, an algorithm center is set up in the AI ​​Hub platform; the algorithm center is used to store various city-running AI algorithms and provide algorithm installation and deployment. Based on the goal of AI intelligent application services, a sharing center is set up in the AI ​​Hub platform; the sharing center is used to provide users with various AI-based intelligent applications and to provide application developers with API interfaces based on algorithm services. Based on the goal of daily management and resource configuration services, a management center is set up in the AI ​​Hub platform; the management center is used to manage and monitor the platform business objectives, computing resources, policies, clusters, users and permissions, and logs of the City Brain AI Hub.

6. The urban operation management scheme based on an AI hub according to claim 5, characterized in that, The core functional components also include: a data dashboard, used for gridded management of the system based on data from different dimensions, intuitively displaying statistical and real-time information of the city brain AI hub; an early warning center, located in the AI ​​hub platform, used to use AI visual analysis technology to analyze and track targets in a scene by separating the background from the target and issue alarm prompts; and a video aggregation module, located in the big data platform, used to access various video source devices using streaming media transmission protocols, and to uniformly aggregate, integrate, and centrally manage various scattered video resources.

7. The urban operation management scheme based on an AI hub according to claim 6, characterized in that, The data dashboard supports dynamic page refresh and provides real-time monitoring and visualization of alarm data, number of devices, device usage, resource usage, algorithm usage, high-incidence locations, and alarm trends.

8. The urban operation management scheme based on an AI hub according to claim 6, characterized in that, The early warning center uses AI visual analysis technology to separate the background and target in the scene and analyzes whether the target in the scene violates the prescribed behavior corresponding to the preset alarm rule based on different alarm rules in different camera scenes. If so, the early warning center will automatically issue an alarm and automatically pop up alarm information and issue a warning sound.

9. The urban operation management scheme based on an AI hub according to claim 5, characterized in that, The various urban operation AI algorithms include traffic mitigation algorithms, smoke recognition algorithms, snow melting and antifreeze algorithms, waterlogging prevention algorithms, license plate recognition algorithms, street cleaning algorithms, and pedestrian flow recognition algorithms.

10. The urban operation management scheme based on an AI hub according to claim 6, characterized in that, The video aggregation module includes: a custom grouping module for creating custom groups or importing / exporting groups, and including different devices in corresponding groups; a tag management module for managing cameras or devices by tagging them based on scenarios; a video polling module for polling videos based on polling intervals; a deployment and early warning module for setting deployment time, deployment range, and withdrawal; performing precise and fuzzy deployment based on target feature attributes; receiving deployment instructions; sending alarm information to the deployment party after discovering target objects that match the deployment target feature attributes; querying early warning time, early warning type, and early warning location; a trajectory playback module for playing back the historical trajectories of captured vehicles; and a vehicle trajectory fusion module for fusing the captured vehicle trajectories with BeiDou positioning data.

Images