Digital-intelligent integrated device for smart city management

Abstract

The invention relates to the technical field of smart city management, in particular to a digital-intelligent integrated device for smart city management, which comprises an intelligent traffic management module, a city environment monitoring module, a public safety management module and a municipal facility operation and maintenance module, and is characterized in that the modules uniformly access data to a data sharing unit through an Internet of Things platform and an API (Application Program Interface); forming a standardized data set; and the business linkage unit is used for carrying out association modeling on the cross-module data by utilizing a big data technology, setting a triggering condition of cross-module linkage, and realizing automatic collaboration through an event response mechanism. By constructing a data sharing and business linkage unit architecture, business linkage of traffic, environment, public safety and municipal modules is realized, and when a traffic accident occurs in a certain area, the device can automatically and synchronously trigger cross-module response such as traffic signal optimization, environment monitoring encryption and emergency police scheduling, so that the safety of the traffic accident is improved. Compared with a traditional mode, the event handling efficiency is remarkably improved, the pain point of collaboration deficiency in existing management is fundamentally solved, and a systematic solution is provided for smart city governance.

Description

Technical Field

[0001] This invention relates to the field of smart city management technology, and in particular to a digital-integrated device for smart city management. Background Technology

[0002] Current urban management faces significant shortcomings in its digital transformation: data silos exist between various functional departments, with data from systems such as transportation, environment, and security difficult to share, creating "information islands" and resulting in fragmented perceptions of the city's operational status. Under traditional management models, cross-departmental business processes are fragmented; for example, handling environmental pollution incidents requires manual coordination among multiple departments such as environmental protection, transportation, and urban management, leading to response delays of several hours and missed opportunities for proactive intervention. Furthermore, decision-making relies on experience-driven approaches and lacks multi-dimensional data support; for instance, municipal facility maintenance depends solely on single fault alarms without considering surrounding traffic and safety data, resulting in inefficient resource allocation. Fundamentally, the existing urban management system lacks underlying data fusion and business linkage mechanisms, hindering cross-module collaborative responses. To address this, we propose a digital-integrated device for smart city management. Summary of the Invention

[0003] Based on the technical problems existing in the background technology, this invention proposes a digital and intelligent integrated device for smart city management. By constructing a "data sharing and business linkage unit" architecture, it realizes business linkage between traffic, environment, public safety and municipal modules. When a traffic accident occurs in a certain area, the device can automatically and synchronously trigger cross-module responses such as traffic signal optimization, environmental monitoring encryption, and emergency police force dispatch. Compared with the traditional mode, it significantly improves the efficiency of event handling, fundamentally solves the pain point of lack of collaboration in existing management, and provides a systematic solution for smart city governance.

[0004] This invention provides the following technical solution: a digital-integrated device for smart city management, comprising: The intelligent traffic management module is used for real-time traffic flow monitoring, predicting traffic congestion trends, optimizing traffic light timing in advance, easing traffic congestion, and handling traffic accidents and abnormal events. The urban environmental monitoring module is used for air quality and pollution source tracking, real-time monitoring of urban air quality, locating pollution sources using GIS maps, and water environment monitoring. The public safety management module is used for video surveillance and behavior analysis to monitor public places in real time, automatically identify suspicious behavior, trigger alarms in a timely manner, and provide emergency command and dispatch. The municipal facilities operation and maintenance module is used to monitor the status of municipal facilities in real time, collect energy data and analyze urban energy consumption, and optimize energy allocation. The intelligent traffic management module, urban environmental monitoring module, public safety management module, and municipal facility operation and maintenance module connect data to the data sharing unit through the Internet of Things platform and API interface to form a standardized dataset; The business linkage unit uses big data technology to model the correlation between cross-module data, sets the trigger conditions for cross-module linkage, and realizes automatic collaboration through an event response mechanism.

[0005] Each module collects data such as traffic flow and air quality in real time through various deployed sensors and devices. The data is then uniformly integrated into the data sharing unit via the IoT platform and API interface to form a standardized dataset. The business linkage unit performs correlation modeling on cross-module data based on big data technology.

[0006] Preferably, the operation process of the business linkage unit includes: Cross-domain data collection and standardized access: Data from nearby devices is collected through edge computing nodes, and data from different protocols is converted into a unified format using the standardized interface of the data platform; Data fusion and correlation analysis involves sequentially performing data processing steps such as cleaning and noise reduction, spatiotemporal correlation, and feature extraction. Linkage rule triggering and decision request: A preset linkage rule library is used to match data features in real time through the rule engine. When a rule is triggered, the business linkage unit sends a decision request to the collaborative decision model, along with the fused cross-domain data. Cross-module task scheduling and execution: Based on the solution output by the decision model, the business process engine automatically schedules resources of each module and uses a low-code platform to quickly configure cross-module processes.

[0007] First, data from various modules is collected via edge computing nodes. A data platform is then used to convert data from different protocols into a unified format, enabling cross-domain data collection and standardized access. Next, the data is cleaned, denoised, spatiotemporally correlated, and feature extracted to achieve data fusion and correlation analysis, uncovering potential connections between data. Then, a pre-defined linkage rule base is established, and the rule engine matches data features in real time. Once a rule is triggered, the business linkage unit sends a decision request with fused data to the collaborative decision model. Finally, based on the solution output by the decision model, the business process engine automatically schedules resources from each module, and a low-code platform is used to quickly configure cross-module processes, achieving efficient task execution.

[0008] By combining edge computing with a data platform, the efficiency of data collection and transformation is greatly improved, reducing transmission latency and data clutter. Deep data processing enables precise mining of data value, providing a reliable basis for decision-making. The setting of a pre-set rule base and rule engine ensures the timeliness and accuracy of decision requests. The application of a business process engine and low-code platform enables automated scheduling of cross-module resources and rapid configuration of processes, completely changing the situation of independent operation and low collaborative efficiency of traditional urban management modules. This allows urban management to quickly integrate resources and respond efficiently when facing complex situations, significantly improving the intelligence level and overall efficiency of urban management.

[0009] Preferably, after the cross-module task scheduling and execution of the business linkage unit, it also includes execution feedback and closed-loop optimization. The execution effect of the linkage measures is collected in real time through IoT devices and fed back to the business linkage unit. When the effect of any linkage scenario is not good, the rule base parameters are automatically optimized or the retraining of the collaborative decision model is triggered.

[0010] After cross-module task scheduling and execution, IoT devices collect real-time data on the execution effect of linkage measures, such as changes in road conditions after traffic diversion and improvements in environmental indicators after pollution control. This data is then fed back to the business linkage unit. The business linkage unit analyzes the feedback data, and if the execution effect of any linkage scenario does not meet expectations, it automatically initiates an optimization mechanism to adjust the linkage parameters in the rule base or triggers the retraining of the collaborative decision-making model, forming a closed-loop management process of "execution-feedback-optimization".

[0011] By providing real-time feedback and dynamic optimization, the traditional static decision-making model of "one-size-fits-all" in urban management is broken, enabling the coordinated strategies to continuously iterate and evolve based on actual results, adapting to the complex and ever-changing scenarios in urban operations.

[0012] Preferably, the training process of the collaborative decision-making model is as follows: Data annotation and sample construction: integrate historical cross-module data, and generate training samples through manual annotation or semi-supervised learning; The algorithm selection and architecture design adopt a multimodal fusion model, introduce reinforcement learning to optimize the decision-making strategy, and train the model by simulating different decision-making scenarios; Model training and tuning utilize a distributed training framework, iteratively optimizing model parameters on a GPU cluster, and avoiding overfitting through cross-validation.

[0013] First, historical data from various modules, including transportation and environment, are integrated. Through manual annotation or semi-supervised learning, the data is transformed into training samples containing different scenarios (such as congestion and pollution). Second, a multimodal fusion model (such as combining time series analysis and spatial correlation algorithms) is selected, and a reinforcement learning mechanism is introduced. By simulating multiple decision-making scenarios such as rainstorms and accidents, the model learns the optimal decision-making strategy. Finally, the model parameters are iteratively optimized on a high-performance computing cluster using a distributed training framework. At the same time, the generalization ability of the model is improved through a cross-validation mechanism to avoid prediction errors caused by data bias.

[0014] By integrating and labeling historical data across modules, the model is freed from the limitations of single-dimensional data and can learn decision-making logic based on the correlation of multi-source information. The combination of multimodal fusion and reinforcement learning gives the model the ability to simulate complex urban scenarios, enabling its decision-making strategies to efficiently adapt to dynamic changes in transportation, environment and other fields. The combination of distributed training and cross-validation technologies not only utilizes computing resources to shorten the training cycle, but also ensures the reliability of the model in practical applications through algorithm optimization.

[0015] Preferably, the model deployment and real-time inference process of the collaborative decision-making model is as follows: The trained model is compressed and deployed to the edge server to process high-bandwidth data such as real-time video streams, reducing the cloud load. Complex decisions are supported by the cloud server through a microservice architecture. When a business linkage unit sends a decision request, the model generates a solution according to the following steps: Input layer: Receives the fused cross-domain data; Feature layer: Extracts key features through a neural network; Decision-making level: Generates optimal linkage schemes based on reinforcement learning strategies; Output layer: Transforms the solution into executable instructions for each module.

[0016] Preferably, during the model iteration and evolution process of the collaborative decision-making model, federated learning technology is used to achieve continuous model evolution through encrypted gradient updates while protecting the data privacy of each module. When any module data is abnormal, the missing value is estimated by using historical data and data from neighboring devices to avoid decision failure.

[0017] The collaborative deployment model of edge and cloud computing not only reduces real-time data transmission latency by leveraging edge computing, but also supports complex decision-making through cloud computing power, achieving a balance between "real-time performance" and "accuracy". In the hierarchical inference architecture, the input layer ensures cross-domain data fusion, the feature layer mines deep data correlations through neural networks, the decision layer optimizes strategies with reinforcement learning, and the output layer ensures the executability of instructions, enabling the model to efficiently handle complex scenarios such as traffic congestion and environmental pollution.

[0018] Preferably, the intelligent traffic management module includes sensing devices, control devices, and communication devices; The traffic data collected by the sensing device is transmitted in real time to the data sharing unit through the communication device. The data sharing unit analyzes the data, predicts traffic congestion trends, and controls the device to take corresponding countermeasures through the business linkage unit.

[0019] The collaboration between sensing and communication devices enables real-time acquisition and transmission of traffic data, ensuring timely management responses; the combination of data sharing units and business linkage units enables traffic management to shift from passive handling to proactive prediction, changing the traditional inefficient model that relies on manual dispatch.

[0020] Preferably, the urban environmental monitoring module includes environmental sensing equipment, pollution source monitoring equipment, and communication and transmission equipment; The environmental sensing equipment and pollution source monitoring equipment continuously collect data, transmit it to the data sharing unit through the communication network, and clean, analyze and model the data through the business linkage unit to monitor the urban environmental quality in real time.

[0021] Environmental and pollution source data are continuously collected using environmental sensing devices (such as air quality sensors and water quality monitors) and pollution source monitoring devices (such as industrial exhaust gas emission monitors). The data is then transmitted in real time to the data sharing unit via a communication network. After the data sharing unit cleans and integrates the data, the business linkage unit uses big data analysis and modeling technology to analyze the urban environmental quality in real time. Once an abnormal pollution is detected, it triggers a linkage response with modules such as traffic management and public safety.

[0022] Preferably, the public safety management module includes monitoring equipment, alarm equipment, and communication and dispatching equipment; The monitoring equipment monitors public places in real time and transmits the data to the data sharing unit. It automatically identifies suspicious behavior and triggers alarms. When the alarm equipment detects an emergency, it sends alarm information to the business linkage unit. The business linkage unit achieves multi-department collaborative dispatch through communication and dispatch equipment, and directs rescue forces to respond quickly.

[0023] Real-time monitoring of public places is achieved through surveillance equipment (such as high-definition cameras and thermal imagers). The collected video data is transmitted to the data sharing unit, and intelligent algorithms automatically identify suspicious behavior and trigger alarms. At the same time, when alarm devices (such as smoke detectors and one-button alarm devices) detect an emergency, they send alarm information to the business linkage unit. The business linkage unit coordinates resources from multiple departments through communication and dispatch equipment (such as emergency command platforms and 5G walkie-talkies) to achieve rapid dispatch of rescue forces.

[0024] Preferably, the municipal facility operation and maintenance module includes facility monitoring sensors, data acquisition and transmission equipment, and maintenance dispatch terminal; The facility monitoring sensors monitor the equipment's operating status in real time. The data is transmitted to the data sharing unit via communication equipment. The business linkage unit analyzes the data, determines the fault type and severity, automatically generates a maintenance work order, and sends the work order to the maintenance dispatch terminal. After the maintenance is completed, the maintenance dispatch terminal sends the results back to the data sharing unit.

[0025] The deployment of monitoring sensors enables real-time, 24 / 7 awareness of the status of municipal facilities, changing the inefficient traditional manual inspection mode; the collaborative operation of data sharing units and business linkage units automates fault identification and work order generation, significantly shortening operation and maintenance response time; the application of maintenance dispatch terminals constructs a data-driven closed-loop management system, which not only improves maintenance efficiency but also provides historical data support for facility maintenance.

[0026] This invention provides a digital-integrated device for smart city management. Through the collaborative design of data sharing units and business linkage units, it completely breaks down the data barriers between departments in traditional urban management, transforming modules such as transportation and environment from "information silos" into an organic whole of "data interoperability." Unlike the inefficient response of traditional models that rely on manual coordination, its event-driven automatic collaboration mechanism enables cross-module linkage in scenarios such as pollution exceeding standards and traffic accidents, significantly shortening event response time. By leveraging big data correlation modeling, it transforms scattered single-module data into multi-dimensional decision-making basis, effectively improving the efficiency and intelligence level of urban governance. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the system of the present invention.

[0028] Figure 2 This is a schematic diagram of the intelligent traffic management module of the present invention.

[0029] Figure 3 This is a schematic diagram of the urban environmental monitoring module of the present invention.

[0030] Figure 4 This is a schematic diagram of the public safety management module of the present invention.

[0031] Figure 5 This is a schematic diagram of the municipal facility operation and maintenance module of the present invention.

[0032] Figure 6 This is a flowchart illustrating the operation of the business linkage unit of this invention.

[0033] Figure 7 This is a flowchart illustrating the operation of the collaborative decision-making model of this invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] like Figure 1 As shown, the present invention provides a technical solution: a digital-integrated device for smart city management, comprising: The intelligent traffic management module is used for real-time traffic flow monitoring, predicting traffic congestion trends, optimizing traffic light timing in advance, easing traffic congestion, and handling traffic accidents and abnormal events. like Figure 2 As shown, the intelligent traffic management module includes sensing devices, control devices, and communication devices; Sensing devices include geomagnetic sensors, microwave radar, high-definition traffic cameras, license plate recognition devices, and on-board OBD devices, which are used to collect data such as traffic flow, vehicle speed, vehicle type, and license plate information in real time.

[0036] The control equipment includes intelligent traffic signal controllers, variable message signs, and electronic police equipment, which enable traffic signal control and information dissemination.

[0037] The communication equipment includes 5G communication modules and fiber optic network equipment, ensuring fast and stable data transmission.

[0038] Traffic data collected by sensing devices is transmitted in real time to a data sharing unit via communication equipment. The data sharing unit analyzes the data using a business linkage unit to predict traffic congestion trends. Based on the prediction results, intelligent traffic signal controllers automatically adjust signal timings. If a traffic accident or abnormal event is detected, a high-definition camera automatically reports it using image recognition technology. The business linkage unit then notifies traffic police and rescue departments and plans the optimal rescue route. Simultaneously, variable message signs provide drivers with real-time traffic information to guide traffic flow.

[0039] The urban environmental monitoring module is used for air quality and pollution source tracking, real-time monitoring of urban air quality, locating pollution sources using GIS maps, and water environment monitoring. like Figure 3 As shown, the urban environmental monitoring module includes environmental sensing equipment, pollution source monitoring equipment, and communication and transmission equipment; Environmental sensing equipment includes air quality monitoring stations (monitoring indicators such as PM2.5, PM10, and sulfur dioxide), water quality sensors (monitoring pH, dissolved oxygen, and turbidity), noise monitoring instruments, and meteorological station equipment.

[0040] Pollution source monitoring equipment includes industrial exhaust gas emission monitors, wastewater discharge monitors, and oil fume monitoring equipment.

[0041] Communication and transmission equipment includes IoT communication modules and LoRa gateways, enabling remote data transmission.

[0042] Various environmental sensing and pollution source monitoring devices continuously collect data and transmit it to the data sharing unit via communication networks. The operational coordination unit cleans, analyzes, and models the data to monitor the city's environmental quality in real time. If air quality deteriorates or water pollution exceeds standards, the system automatically issues an alert and, using GIS maps, locates the pollution source, pushing the information to environmental enforcement departments. Enforcement departments then conduct on-site inspections and enforcement actions based on the data, forming a closed-loop management system of monitoring, early warning, and enforcement.

[0043] The public safety management module is used for video surveillance and behavior analysis to monitor public places in real time, automatically identify suspicious behavior, trigger alarms in a timely manner, and provide emergency command and dispatch. like Figure 4 As shown, the public safety management module includes monitoring equipment, alarm equipment, and communication and dispatching equipment; The surveillance equipment includes high-definition cameras, panoramic cameras, and thermal imaging cameras, covering urban public places, transportation hubs, and other areas.

[0044] Alarm devices include one-button alarm devices, smoke detectors, and gas leak detectors, used for alarming in case of emergencies.

[0045] Communication and dispatch equipment includes 5G walkie-talkies, emergency command and dispatch platforms, and video conferencing systems.

[0046] High-definition cameras utilize image recognition technology to monitor public places in real time, automatically identifying suspicious behaviors such as fights and gatherings of people and triggering alarms. When alarm devices detect emergencies such as fires or gas leaks, they immediately send alarm information to the operational coordination unit. The operational coordination unit integrates data such as on-site video, distribution of rescue resources, and personnel locations to provide visual decision support for the command center. Through 5G walkie-talkies and video conferencing systems, it enables multi-departmental collaborative dispatch and directs rescue forces to respond quickly.

[0047] The municipal facilities operation and maintenance module is used to monitor the status of municipal facilities in real time, collect energy data and analyze urban energy consumption, and optimize energy allocation. like Figure 5 As shown, the municipal facility operation and maintenance module includes facility monitoring sensors, data acquisition and transmission equipment, and maintenance dispatch terminal; Facility monitoring sensors include smart manhole cover displacement sensors, street light fault monitoring modules, pipeline pressure sensors, and cable temperature sensors.

[0048] The data acquisition and transmission equipment includes IoT gateways and NB-IoT communication modules, which upload device status data to the management platform.

[0049] The maintenance dispatch terminal includes a handheld terminal device for maintenance personnel to receive maintenance task instructions.

[0050] The facility monitoring sensors monitor the equipment's operating status in real time. The data is transmitted to the data sharing unit via communication equipment. The business linkage unit analyzes the data, determines the fault type and severity, automatically generates a maintenance work order, and sends the work order to the maintenance dispatch terminal. After the maintenance is completed, the maintenance dispatch terminal sends the results back to the data sharing unit.

[0051] Sensors on municipal facilities monitor equipment operating status in real time, such as manhole cover displacement, street light malfunctions, and abnormal pipeline pressure. Data is transmitted to a data sharing unit via communication equipment. The business linkage unit analyzes the data, determines the type and severity of the fault, automatically generates a maintenance work order, and sends the work order to the handheld terminal of the maintenance personnel. Maintenance personnel go to the site to carry out maintenance according to the work order information. After the maintenance is completed, they report the results through their terminals, and the data sharing unit updates the equipment status, realizing full-process digital management of facility operation and maintenance.

[0052] The intelligent traffic management module, urban environmental monitoring module, public safety management module, and municipal facility operation and maintenance module connect data to the data sharing unit through the Internet of Things platform and API interface to form a standardized dataset; The business linkage unit uses big data technology to model the correlation between cross-module data, sets the trigger conditions for cross-module linkage, and realizes automatic collaboration through an event response mechanism.

[0053] like Figure 6 As shown, the operation process of the business linkage unit includes: Step 1: Cross-domain data collection and standardized access Structured / unstructured data are acquired in real time from modules such as intelligent transportation (e.g., checkpoint cameras, RFID vehicle flow data), environmental monitoring (sensor networks), and public safety (surveillance video, alarm systems).

[0054] By collecting device data (such as video streams from traffic cameras and sensor signals from municipal facilities) from nearby edge computing nodes, transmission latency can be reduced.

[0055] By utilizing the standardized interfaces of the data platform, data from different protocols (such as MQTT) can be converted into a unified format.

[0056] When the environmental monitoring module detects that the air quality index (AQI) of a certain area exceeds the preset threshold, the data platform automatically collects traffic flow data of that area (from the intelligent transportation module) to prepare for subsequent linkage.

[0057] Step 2: Data Fusion and Correlation Analysis Cleaning and noise reduction: Remove abnormal data (such as jump values ​​caused by sensor failure) and fill in missing values ​​(such as by interpolating through historical data).

[0058] Data from different modules can be linked by timestamps and geographic locations, such as matching traffic congestion events with pedestrian flow data from nearby public safety cameras.

[0059] Machine learning algorithms (such as convolutional neural networks CNN) are used to extract features such as vehicle type and pedestrian density from video streams and establish a correlation with environmental data (such as noise and dust).

[0060] Use Spark Streaming for real-time data processing and build an "event-device-space" relationship graph using a graph database (such as Neo4j).

[0061] Step 3: Triggering Linkage Rules and Decision Requests A pre-defined linkage rule base (such as "when a traffic accident alarm is triggered and traffic flow drops sharply on a certain road section → trigger linkage between public safety and traffic management") is used to match data features in real time through a rule engine (such as Drools).

[0062] When a rule is triggered, the business linkage module sends a decision request to the collaborative decision-making model, along with the fused cross-domain data (such as accident location, surrounding road conditions, and police force distribution).

[0063] Step 4: Cross-module task scheduling and execution Based on the solution output by the decision model, the business process engine automatically schedules resources for each module: Intelligent Transportation Module: Adjusts traffic light timings at accident-prone sections and pushes detour navigation to the user's app; Public safety module: Automatically retrieves nearby surveillance video and links with traffic police and emergency medical centers; Public service module: Event notifications are sent via SMS / APP to guide citizens to avoid certain areas.

[0064] Use low-code platforms to quickly configure cross-module processes. For example, when the municipal facilities operation and maintenance module detects an abnormality in a manhole cover, it can automatically trigger the deployment of public safety warning signs and the notification of repair requests for public services.

[0065] Step 5: Execution Feedback and Closed-Loop Optimization The effectiveness of the coordinated measures is collected in real time through IoT devices (such as traffic flow recovery rate and environmental indicator improvement values) and fed back to the business coordination module.

[0066] If a certain type of collaborative scenario is not effective (such as traffic control causing more congestion on surrounding roads), the rules base parameters will be automatically optimized, or the collaborative decision-making model will be retrained.

[0067] like Figure 7 As shown, the training process of the collaborative decision-making model is as follows: Data annotation and sample construction: integrate historical cross-module data, and generate training samples through manual annotation or semi-supervised learning; The algorithm selection and architecture design adopt a multimodal fusion model, introduce reinforcement learning to optimize the decision-making strategy, and train the model by simulating different decision-making scenarios; Model training and tuning utilize a distributed training framework, iteratively optimizing model parameters on a GPU cluster, and avoiding overfitting through cross-validation.

[0068] The deployment and real-time inference process of the collaborative decision-making model is as follows: The trained model is compressed and deployed to the edge server to process high-bandwidth data such as real-time video streams, reducing the cloud load. Complex decisions are supported by the cloud server through a microservice architecture. When a business linkage unit sends a decision request, the model generates a solution according to the following steps: Input layer: Receives the fused cross-domain data; Feature layer: Extracts key features through a neural network; Decision-making level: Generates optimal linkage schemes based on reinforcement learning strategies; Output layer: Transforms the solution into executable instructions for each module.

[0069] During the model iteration and evolution of the collaborative decision-making model, federated learning technology is used to achieve continuous model evolution through encrypted gradient updates while protecting the data privacy of each module. When any module data is abnormal, the missing value is estimated by using historical data and data from neighboring devices to avoid decision failure.

[0070] In this invention, the deep integration of business linkage units and collaborative decision-making models forms a full-chain intelligent advantage from data collection to decision optimization: the business linkage units achieve real-time collection and standardized processing of cross-domain data through edge computing and data platforms, and construct data association graphs with the help of machine learning and graph databases, transforming scattered traffic, environmental, and other data into association information that can drive decision-making. Then, through a preset rule engine and business process engine, cross-module linkage is upgraded from manual coordination to event-driven automated response. Finally, the rule base is continuously optimized through an execution feedback mechanism, allowing management strategies to dynamically evolve with the city's operation. The collaborative decision-making model, through cross-module historical data annotation and multimodal fusion algorithms, endows the model with the ability to learn the association logic of complex urban scenarios. The collaborative deployment of edge and cloud not only ensures low-latency processing of real-time data, but also supports the accurate generation of complex decisions through cloud computing power. Combined with the privacy protection mechanism of federated learning and data anomaly fault tolerance technology, the model ensures dual advantages in data security and robustness.

[0071] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A digital-integrated device for smart city management, characterized in that, include: The intelligent traffic management module is used for real-time traffic flow monitoring, predicting traffic congestion trends, optimizing traffic light timing in advance, easing traffic congestion, and handling traffic accidents and abnormal events. The urban environmental monitoring module is used for air quality and pollution source tracking, real-time monitoring of urban air quality, locating pollution sources using GIS maps, and water environment monitoring. The public safety management module is used for video surveillance and behavior analysis to monitor public places in real time, automatically identify suspicious behavior, trigger alarms in a timely manner, and provide emergency command and dispatch. The municipal facilities operation and maintenance module is used to monitor the status of municipal facilities in real time, collect energy data and analyze urban energy consumption, and optimize energy allocation. The intelligent traffic management module, urban environmental monitoring module, public safety management module, and municipal facility operation and maintenance module connect data to the data sharing unit through the Internet of Things platform and API interface to form a standardized dataset; The business linkage unit uses big data technology to model the correlation between cross-module data, sets the trigger conditions for cross-module linkage, and realizes automatic collaboration through an event response mechanism.

2. The integrated digital and intelligent device for smart city management according to claim 1, characterized in that: The operation process of the aforementioned business linkage unit includes: Cross-domain data collection and standardized access: Data from nearby devices is collected through edge computing nodes, and data from different protocols is converted into a unified format using the standardized interface of the data platform; Data fusion and correlation analysis involves sequentially performing data processing steps such as cleaning and noise reduction, spatiotemporal correlation, and feature extraction. Linkage rule triggering and decision request: A preset linkage rule library is used to match data features in real time through the rule engine. When a rule is triggered, the business linkage unit sends a decision request to the collaborative decision model, along with the fused cross-domain data. Cross-module task scheduling and execution: Based on the solution output by the decision model, the business process engine automatically schedules resources of each module and uses a low-code platform to quickly configure cross-module processes.

3. The integrated digital and intelligent device for smart city management according to claim 2, characterized in that: After the business linkage unit schedules and executes cross-module tasks, it also includes execution feedback and closed-loop optimization. The execution effect of linkage measures is collected in real time through IoT devices and fed back to the business linkage unit. When the effect of any linkage scenario is not good, the rule base parameters are automatically optimized or the retraining of the collaborative decision model is triggered.

4. The integrated digital and intelligent device for smart city management according to claim 3, characterized in that: The training process of the collaborative decision-making model is as follows: Data annotation and sample construction: integrate historical cross-module data, and generate training samples through manual annotation or semi-supervised learning; The algorithm selection and architecture design adopt a multimodal fusion model, introduce reinforcement learning to optimize the decision-making strategy, and train the model by simulating different decision-making scenarios; Model training and tuning utilize a distributed training framework, iteratively optimizing model parameters on a GPU cluster, and avoiding overfitting through cross-validation.

5. The integrated digital and intelligent device for smart city management according to claim 4, characterized in that: The deployment and real-time inference process of the collaborative decision-making model is as follows: The trained model is compressed and deployed to the edge server to process high-bandwidth data such as real-time video streams, reducing the cloud load. Complex decisions are supported by the cloud server through a microservice architecture. When a business linkage unit sends a decision request, the model generates a solution according to the following steps: Input layer: Receives the fused cross-domain data; Feature layer: Extracts key features through a neural network; Decision-making level: Generates optimal linkage schemes based on reinforcement learning strategies; Output layer: Transforms the solution into executable instructions for each module.

6. The integrated digital and intelligent device for smart city management according to claim 5, characterized in that: During the model iteration and evolution process of the collaborative decision-making model, federated learning technology is used to achieve continuous model evolution through encrypted gradient updates while protecting the data privacy of each module. When any module data is abnormal, the missing value is estimated by using historical data and data from neighboring devices to avoid decision failure.

7. The integrated digital and intelligent device for smart city management according to claim 1, characterized in that: The intelligent traffic management module includes sensing devices, control devices, and communication devices; The traffic data collected by the sensing device is transmitted in real time to the data sharing unit through the communication device. The data sharing unit analyzes the data, predicts traffic congestion trends, and controls the device to take corresponding countermeasures through the business linkage unit.

8. The integrated digital and intelligent device for smart city management according to claim 1, characterized in that: The urban environmental monitoring module includes environmental sensing equipment, pollution source monitoring equipment, and communication and transmission equipment. The environmental sensing equipment and pollution source monitoring equipment continuously collect data, transmit it to the data sharing unit through the communication network, and clean, analyze and model the data through the business linkage unit to monitor the urban environmental quality in real time.

9. The integrated digital and intelligent device for smart city management according to claim 1, characterized in that: The public safety management module includes monitoring equipment, alarm equipment, and communication and dispatch equipment; The monitoring equipment monitors public places in real time and transmits the data to the data sharing unit. It automatically identifies suspicious behavior and triggers alarms. When the alarm equipment detects an emergency, it sends alarm information to the business linkage unit. The business linkage unit achieves multi-department collaborative dispatch through communication and dispatch equipment, and directs rescue forces to respond quickly.

10. A digital-integrated device for smart city management according to claim 1, characterized in that: The municipal facility operation and maintenance module includes facility monitoring sensors, data acquisition and transmission equipment, and maintenance dispatch terminal; The facility monitoring sensors monitor the equipment's operating status in real time. The data is transmitted to the data sharing unit via communication equipment. The business linkage unit analyzes the data, determines the fault type and severity, automatically generates a maintenance work order, and sends the work order to the maintenance dispatch terminal. After the maintenance is completed, the maintenance dispatch terminal sends the results back to the data sharing unit.

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