73 results about "Analysis center" patented technology

The invention provides an intelligent fire-fighting equipment self-inspection and fault early warning system based on an AI algorithm. The intelligent fire-fighting equipment self-inspection and fault early warning system comprises a sensing layer used for integrating a multi-source heterogeneous sensing unit; the edge layer is used for deploying intelligent preprocessing nodes; the transmission layer is used for constructing a safe and reliable transmission channel; the platform layer is used for establishing an intelligent analysis center; the application layer is used for providing intelligent decision service; and the five layers of architectures work cooperatively. According to the invention, full-dimension monitoring of fire-fighting equipment is realized through a multi-physics field cooperative sensing framework of a sensing layer, and the fault detection capability is remarkably improved. The multi-source heterogeneous sensing unit integrated in the sensing layer can capture various physical quantity information such as acoustics, vibration and thermal fields at the same time, information of different physical fields is complementarily enhanced through a cross-domain feature complementation enhancement mechanism, and more comprehensive equipment state features are obtained. In particular, the sensor self-adaptive excitation technology dynamically adjusts the excitation energy of the sensor according to the environmental interference degree, and ensures that high-quality signals can be obtained under different working conditions.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

The invention discloses a building facility detection data intelligent analysis and report system and method, and relates to the technical field of building structure health monitoring, and the method comprises the steps: achieving the high-precision data capture in a strong light dust raising environment through a laser radar and a dual-spectrum camera of a multi-mode anti-interference collection module; a millimeter wave radar array and an IMU inertial unit of the high-risk area strengthening module penetrate through severe working conditions to monitor key structure displacement; the credibility management engine is provided with an NFC timestamp chip and a double-chain block chain, and data judicial-level credibility and operation traceability are ensured; through humidity response gel packaging and Peltier semiconductor refrigeration of the self-maintenance sensing network, node environment self-adaption and drift suppression are achieved; and the multi-stage analysis center fuses edge-cloud computing power based on federal learning, generates a dynamic risk map and automatically outputs a compliance report. Accurate and timely reference is provided for operation and maintenance decisions, and the safety management level of building facilities is effectively improved.

The invention relates to the technical field of coal mine safety monitoring, and particularly provides a coal mine water disaster source intelligent identification system fused with a multi-source sensor. The system adopts a hierarchical architecture with edge intelligence and center decision collaboration, comprises an edge sensing and computing node, a region fusion decision node and a ground intelligent analysis center, and realizes real-time identification and early warning of water disaster risks through local feature extraction, multi-source information fusion and global situation research and judgment. According to the invention, the network load is reduced, and the autonomous emergency capability and the overall reliability of the system during communication interruption are improved.

The invention relates to the technical field of medical information management, in particular to a medical information management system and a medical data sharing method. Comprising a heterogeneous data access module, a distributed data lake module, a block chain core layer module, a dynamic authority control module, a cross-chain Internet gateway module, a big data analysis center module, a clinical decision support module, a credible audit tracking module, a patient sovereignty portal module and a system management console module. The system has the advantages that the block chain core layer module establishes a patient main index and data hash evidence storage mechanism, a data barrier between heterogeneous medical systems is broken through in combination with the heterogeneous data access module and the cross-chain Internet gateway module for standardized protocol conversion, and the dynamic authority control module responds to an intelligent contract to execute zero-knowledge proof verification, so that the safety of the system is improved. Permission verification is completed on the premise that original privacy data does not need to be transmitted, the cross-mechanism data retrieval response time is shortened, sensitive fields are automatically filtered through a data desensitization engine, and a technical basis is provided for hierarchical diagnosis and treatment and medical conjoined cooperation.

The invention relates to the technical field of e-commerce, in particular to a distributed data processing system based on big data, and the system comprises an internal data collection end which is used for collecting user transaction data, commodity data and behavior logs of an e-commerce platform in real time; the external data acquisition end is used for acquiring consumer credit data and social network behavior data of the third-party platform through an API (Application Program Interface); the data classification module is used for carrying out initial classification on the collected data by adopting a multi-level label system and generating classification labels with weight coefficients; the data screening module is used for dynamically filtering the classified data according to a preset threshold condition, and the threshold condition comprises data timeliness, integrity and confidence; the data error correction module is used for correcting the labels of the misclassification data set and feeding back the corrected data to the input end of the classification module; and the plurality of distributed data analysis centers generate 16 user portrait dimensions from A1 to D4, and the analysis centers execute data modeling of the corresponding dimensions in parallel.

The invention discloses a fence piece production line monitoring device and monitoring system, and relates to the technical field of production line monitoring. Comprising a data acquisition device, an edge computing device, a feedback control device, a whole-process quality tracing module and a cloud platform data analysis center. The data acquisition device acquires multi-station multi-mode real-time data, the edge computing device performs data fusion, preprocessing and preliminary abnormality diagnosis, the cloud platform develops deep analysis and model learning, the feedback control device generates an instruction to adjust production line parameters, and the whole-process quality tracing module realizes production whole-link data binding tracing. According to the method, accurate diagnosis of production abnormity, predictive maintenance of equipment, dynamic optimization of the process and closed-loop control of quality are realized, the production efficiency and the product quality are greatly improved, and the failure shutdown loss is reduced.

The invention discloses a database autonomous protection method based on a dual-system architecture, and belongs to the technical field of database information security. The static measurement agent calculates SM3-256 hash values for all key files of a database in a trusted environment, and the SM3-256 hash values serve as static trusted references to be uploaded and safely stored to a trusted analysis center. And the dynamic measurement agent loads an eBPF program to enter a baseline learning mode. The monitoring and intelligent response during operation comprises static monitoring, dynamic monitoring and correlation analysis and response. Through the design of the dual-system architecture, the protection system and the computing system run in parallel, and the continuous protection capability of the database system is realized. Therefore, the database system has deep security protection capability, and confidentiality, integrity and availability of the database system can be effectively guaranteed when the database system is faced with known and unknown threats.

The invention discloses a transformer substation multi-parameter intelligent monitoring and linkage control system which is based on an intelligent transformer substation internet of things management and control platform, a central control module is arranged in the management and control platform, and the management and control platform comprises an intelligent power monitoring system, a linkage control system and a digital twinborn simulation engine. The intelligent power monitoring system comprises a multi-parameter sensing terminal group, an edge computing node and a cloud analysis center which are arranged in a transformer substation target area, wherein the multi-parameter sensing terminal group collects electrical parameters, environmental parameters and equipment state parameters in real time; the linkage control system comprises an instruction analysis module, a multi-stage execution mechanism and a feedback verification circuit. The linkage control system receives an early warning signal of the intelligent power monitoring system through the Internet of Things management and control platform, and drives the multi-stage execution mechanism to execute a preset linkage strategy. The transformer substation intelligent management and control system integrates multi-parameter real-time monitoring, dynamic fault early warning, digital twin simulation and hierarchical linkage control, and is suitable for unmanned operation and maintenance of an intelligent transformer substation.

The invention relates to a Beidou No.3 precise point positioning method based on double-frequency combination and analysis center product optimization, and belongs to the technical field of precise point positioning, and the method comprises the following steps: S1, constructing three ionosphere-free combinations B1C-B2a, B1C-B3I and B1I-B3I by using four frequency point signals B1C, B2a, B1I and B3I; s2, through comparing a root mean square value RMS of the positioning deviation, the final positioning precision and the convergence time, respectively comparing and evaluating precision point positioning PPP performance of BDS-3 in different frequency combinations under precision products issued by a plurality of analysis centers; and S3, finding out the ionosphere-free combination and analysis center product with the optimal PPP calculation performance.

This invention discloses a method and apparatus for drill bit wear detection and remaining life prediction. The method includes: acquiring multimodal data of the drill bit; performing time-series alignment of the multimodal data using a dynamic time warping algorithm; inputting the time-series aligned multimodal data of the drill bit into an intelligent analysis center and outputting the remaining life prediction result of the target drill bit; using a visual prediction model to segment the wear region of the target drill bit; using a dynamic rating algorithm to classify the wear region; and using a digital twin simulation model, which is a multi-scale simulation system driven by digital twin simulation technology based on wear region segmentation results, classification results, real-time point cloud data of the target drill bit, and real-time sensor data, which can improve the detection accuracy, real-time performance, model generalization, and life prediction capability of drill bit wear.

The application discloses an insulation level online evaluation method applied to a vehicle-mounted metal closed device, which comprises an insulation main body, a grounding shielding layer, flexible insulation glue, a monitoring sensor, a terminal processor and a background data analysis center; the grounding shielding layer is uniformly arranged on the outer surface of the insulation main body, the local area of the insulation main body, where the monitoring sensor is arranged, is not provided with the grounding shielding layer, the shell of the monitoring sensor is annularly contacted with the grounding shielding layer after the monitoring sensor is arranged, the shell of the monitoring sensor and the grounding shielding layer completely wrap the insulation main body to form the metal closed device, the flexible insulation glue is filled between the bottom surface of the monitoring sensor and the insulation main body, the terminal processor is arranged near the insulation main body and is used with the vehicle, signal transmission is realized through wired connection between the terminal processor and the monitoring sensor, and data exchange is realized through wireless connection between the terminal processor and the background data analysis center. The metal closed grounding shielding effect and the partial discharge detection precision of the insulation main body can be considered, the monitoring principle is scientific and reasonable, the structure is simple and reliable, the shielding effect is good, the anti-interference capability is strong, the detection sensitivity is high, the cost is low, economic benefits are outstanding, and technical progress is remarkable.

The invention discloses an intelligent monitoring system and a monitoring and early warning method for an underwater composite material protection cover. The intelligent monitoring and early warning method comprises the following steps: (1) monitoring the deformation of a glass fiber reinforced plastic composite material in real time by installing a fiber bragg grating deformation monitoring sensor in a composite material structure layer of a protective cover; (2) mounting a displacement monitoring sensor on the inner side of the protective cover, paving the displacement monitoring sensor in the glass fiber reinforced plastic, and wrapping and fixing the displacement monitoring sensor with the glass fiber reinforced plastic; (3) installing a floating camera above the protective cover to carry out image monitoring in real time; (4) transmitting detection data to a signal collector and an oil-gas platform data analysis center which are arranged on the protective cover in a wired or wireless manner; and (5) setting an alarm program in an oil and gas platform data analysis center. According to the intelligent early warning method, the condition that the protection system is damaged can be effectively monitored in time, and meanwhile, the patrol pressure of the submarine robot can be reduced; in addition, an alarm mechanism and an image checking mechanism are designed, and the applicability of alarm prediction is improved.

The invention provides a voice interactive health monitoring system. The voice interactive health monitoring system comprises a voice interaction module, a physiological data acquisition module, a data analysis center and an early warning feedback module, the voice interaction module is connected with at least one wearable medical device and used for receiving and analyzing a natural language instruction of a user, generating a device operation instruction according to the natural language instruction, sending the device operation instruction to the corresponding wearable medical device and feeding back an operation result to the user; the physiological data acquisition module is used for acquiring physiological index data of a user from at least one wearable medical device and sending the physiological index data to the data analysis center; the data analysis center is used for performing dynamic analysis and anomaly detection on the received physiological index data of the user based on a machine learning algorithm, and sending an analysis and detection result to the early warning feedback module; and the early warning feedback module is used for sending graded early warning information to the user or the medical staff according to the received analysis and detection result. According to the invention, the health monitoring service can be provided for the user in a simpler and more convenient manner.

The invention discloses a data acquisition and evaluation method for a digital twin network, and designs a data acquisition and evaluation system for the digital twin network. Digital twin network management software, a virtual switch, virtual equipment, a network mirror image data acquisition and analysis terminal and a network mirror image data evaluation and analysis center are designed in a digital twin network data acquisition and evaluation system, and system composition, functions and processes are explained. According to the method, a safe, controllable and efficient research environment is realized by constructing a digital twin network environment, a brand new mode is provided for network data acquisition and evaluation, and the defects of actual physical network data acquisition and evaluation can be overcome.

The invention discloses an AI-driven natural resource data checking and cascading updating method and device based on a rule engine, and the method comprises the steps: decoupling a process into four technical components: 1) a field operator library which provides an atomized checking and updating function; the rule engine serves as a configuration and analysis center of a configuration rule; 3) an AI auxiliary module which assists a user in generating a process framework and rule parameters; and 4) a process executor which is responsible for driving the process, scheduling operators and managing transactions. According to the method, an AI auxiliary module assists a user in defining a process template and generating rule parameters, and the rule parameters are written into a rule engine; and the process executor starts the process and starts a transaction, requests rule configuration from the rule engine according to steps, calls the operator library to execute operation, runs through the process of'checking-updating-re-checking ', and ensures the atomicity of cascade updating through a transaction mechanism. According to the invention, decoupling of execution and logic is realized, and the definition threshold of business rules is obviously reduced.

The invention discloses an interactive response system based on artificial intelligence in the technical field of artificial intelligence, and the system comprises an intelligent analysis center, and input information is firstly processed to extract interaction features, including recognition of key elements inputted by a user and relationships thereof. Based on the characteristics, the system uses an advanced algorithm to carry out data analysis, and aims to find records or model output with the highest matching degree from a database. And generating accurate and related interaction feedback according to the optimal matching result. Interaction results are detected and analyzed in real time through connected sensor data, output content is optimized through environment, position information and the like, information is searched and displayed through intelligent analysis, if the information is internal data, the face and pupils of interaction personnel are analyzed through the sensor data, and the interaction effect is improved. After the analysis is completed, a visual report is generated and displayed after the external environment is confirmed through the sensor data, and if the data is the external data, the visual report is generated and displayed after the external environment is confirmed through the sensor data.

The invention relates to the technical field of data processing, and discloses a vehicle diagnosis function construction method based on multi-source data and intelligent analysis and related equipment, and the method comprises the steps: collecting original diagnosis data for a target vehicle diagnosis function from at least two mutually independent compliance data sources; standardizing the original diagnostic data to generate a unified standardized diagnostic data description file; the standardized diagnosis data description file is input into an intelligent analysis center for reasoning and verification, and verified diagnosis function logic is obtained; and according to the verified diagnosis function logic, automatically generating a diagnosis function framework for integrated use of the diagnosis platform. According to the invention, compliance and efficient development of the vehicle diagnosis function can be realized, and the problems of low development efficiency and insufficient law compliance in the prior art are solved.

The application discloses an Excel data analysis and extraction method, which comprises the following steps: receiving an Excel uploading request sent by a client by a server, performing eigenvalue calculation and data extraction on the uploaded Excel as a task and adding the task to a task queue, executing the task to be executed in the task queue by a data extraction and analysis center in the server, saving the extracted data, and feeding back the execution result to the task queue to provide the client with the execution result when the client queries the execution result. The technical effect of the application is to provide a method for analyzing and extracting Excel data, and the application can automatically identify the Excel structure to perform data extraction and conversion, greatly reducing the conversion cost and improving the conversion efficiency.

The embodiment of the invention provides a communication data fusion analysis method for abnormal fluctuation of enterprise on-duty personnel in a chemical industry park, which relates to the technical field of communication data analysis, and comprises the following steps: collecting a plurality of signaling data streams of a plurality of distributed communication base stations in real time by a plurality of edge stream processing engines in a dynamic geofence range, performing privacy-enhanced real-time hash matching based on the enterprise employee white list library, and generating a plurality of employee on-duty state vector sets; after the cloud analysis center receives a plurality of employee on-duty state vector sets, time sequence dynamic aggregation is carried out based on enterprise affiliation, M enterprise-level on-duty personnel time sequence maps corresponding to M park enterprises are constructed to execute on-duty personnel level abnormal fluctuation analysis, M on-duty abnormal risk factors are output, and the M on-duty abnormal risk factors are analyzed; the technical problems that in the prior art, counting precision of on-duty personnel of an enterprise is low, and safe production management decisions cannot be supported are solved. The effects of radically treating data misjudgment, accurately mapping the human fluctuation rule and providing a reliable decision basis for safe production management of the park are achieved.

The invention relates to the technical field of substation self-operation and state monitoring, in particular to a substation protection pressing plate switching process state test system which comprises a switching state analysis center, an operation routine unit, a state analysis module, a switching specification unit and a background monitoring module. According to the invention, the operation normalization of the protection pressing plate is discriminated from the angle of the difference between the switching tracks of the protection pressing plate, and the actual switching state of the protection pressing plate is further divided from the two dimensions of pressure and image, so that the consistency of the actual switching state of each protection pressing plate and the set switching state can be analyzed. According to the feedback result, the number and position information of the abnormal protection pressing plate are displayed on the monitoring interface, operation and maintenance personnel are prompted to check and process, input effectiveness analysis is deeply carried out on the protection pressing plate in the input state, and whether input of the protection pressing plate is effective or not is judged; and operation and maintenance personnel are prompted to carry out input abnormity management and control according to the information feedback.

The invention relates to the technical field of research and development project management, in particular to a metallurgical material research and development project digital management system which comprises a project experiment module, a template center module, an analysis center module and a system management module. The project experiment module is used for carrying out work flows of prototype product design, product authentication detection, product quality improvement, product big data benchmarking, process research and development entrustment, and technology development and technology cooperation according to research and development project types; the template center module is used for defining a data recording item and a data type through a template, standardizing a data recording standard and supporting data storage of the steel grade analysis center module; the analysis center module is used for centralized management and application analysis of data; the system management module is used by a system administrator for subject management, user management, role management, security audit and data recovery; the whole process of the metallurgical material research and development project is digitally and accurately managed, so that the research and development efficiency is improved, the cost is reduced, and the material performance is improved.

The invention discloses a new energy wind power monitoring system and method, equipment and a storage medium, and belongs to the technical field of new energy wind power monitoring. A sensor module of the system is used for collecting natural environment parameters and equipment operation data of a wind power system; the data acquisition and preprocessing unit is used for summarizing and preprocessing data, and transmitting the preprocessed data to the data processing and analysis center through a data transmission module in a mixed transmission mode; the data processing and analyzing center stores and analyzes the data; the early warning and alarm module triggers early warning or alarm according to the analysis result; the remote control and management platform is used for displaying the equipment state, monitoring data and early warning information and providing a remote operation interface; the system compatibility and expansion interface is used for being in butt joint with different types of wind turbine generators and management systems and supporting function expansion. The monitoring data of the system is comprehensive, and the system has relatively accurate and effective operation and maintenance real-time performance and early warning timeliness, has certain compatibility, and is convenient for operation and maintenance control and decision making.

The application provides a river and lake multi-medium environment pollution in-situ cooperative monitoring system and method, which comprises a floating intelligent monitoring platform, a three-phase interface environment in-situ monitoring unit and a data processing and intelligent analysis unit. The floating intelligent monitoring platform is a floating platform arranged on the water surface. The three-phase interface environment in-situ monitoring unit comprises an atmospheric interface environment monitoring unit, a water phase interface environment monitoring unit and a bottom mud interface monitoring unit. The data processing and intelligent analysis unit is a remote cloud platform and an intelligent analysis center arranged in a cloud server. Based on the application, in-situ, synchronous and continuous monitoring of the physicochemical indexes and pollutant concentrations of the key environmental interfaces of rivers and lakes can be realized. Through data fusion and model analysis, the migration and transformation rules of pollutants among the three-phase media are revealed, and comprehensive data support and decision basis for accurate tracing, risk assessment and comprehensive management are provided.

The invention discloses a supervision system, method, equipment, medium and product, and the system comprises a supervision platform and at least one monitoring unit, and the supervision platform is connected with each monitoring unit. The monitoring unit is used for collecting first monitoring information of a monitored object and uploading the first monitoring information to the supervision platform; and the supervision platform is used for constructing a security collaboration and intelligent analysis center, carrying out federated confrontation alignment and credible authority judgment across data sources, fusing multi-modal features to carry out decision application, and realizing a disposal feedback closed loop. According to the technical scheme, efficient, safe and accurate object supervision is realized.

This invention relates to an intelligent decision-making system and method for linens based on multi-source data fusion in the fields of industrial IoT, big data analytics, and asset management. The system comprises: a sensing and data acquisition layer that collects multi-source data in real time; an edge processing and communication layer that processes the multi-source data and outputs standardized data packets; a cloud-based data fusion and analysis center that receives and stores data, creating a digital twin for each piece of linen, and performing mining analysis on the digital twin cluster and real-time data stream to calculate key operational indicators; and an intelligent decision-making and optimization layer that dynamically generates optimal linen allocation plans and delivery routes for future cycles, outputs washing program suggestions, periodically assesses the remaining lifespan index of each linen digital twin, and generates cost analysis reports. This invention significantly reduces reliance on human experience, improves overall operational efficiency and economic benefits, achieves refined cost and process control, empowers full lifecycle value management of assets, and revolutionizes asset management transparency.

The invention discloses a metal repair defect online detection system and method, the system comprises six units including a multi-dimensional heterogeneous composite sensing acquisition unit and a precise dynamic motion adaptation unit, and multi-modal data is processed by integrating multiple detection technologies and utilizing an optimized generative adversarial network and a Transform model. Wherein the multi-dimensional heterogeneous composite sensing acquisition unit acquires data from multiple dimensions, the precise dynamic motion adaptation unit provides precise position control, the intelligent signal processing and analysis central unit uses a model to carry out signal processing and defect feature mining, and the multi-modal data fusion and visual presentation unit realizes data fusion and visual presentation. The method is based on the system, and detection is completed through the steps of equipment installation, data acquisition, signal processing, result presentation, parameter adjustment, standard exceeding alarm and the like. According to the invention, the accuracy and efficiency of metal repair defect detection are effectively improved.

The application discloses a BDS-2 / 3 precise point positioning tight combination ambiguity resolution method, which comprises the following steps: S1: fixing station coordinates, using international GNSS service (IGS) analysis center satellite orbit and clock error products, performing (MW) combination calculation and ionosphere-free (IF) floating point PPP resolution, and outputting station channel floating point wide lane ambiguity and pseudo-range OMC residual error; and S2: selecting a reference station and a BDS-3 reference star, and using the result of step S1 to calculate wide lane phase correction deviation and ionosphere-free combination signal distortion deviation correction number. The BDS-2 / 3 precise point positioning tight combination ambiguity resolution method has better model strength. Compared with a PPP-AR loose combination mode, the application adopts a tight combination mode, has better model strength, can fully play the advantages of BDS-2 and BDS-3 fusion processing, can eliminate the phase deviation between wide lane systems and the pseudo-range system deviation and the signal distortion deviation between channels of BDS-2 and BDS-3, and significantly improves the precise positioning performance of the Beidou system.

The embodiment of the application provides a kind of based on multi-modal fusion pipe network system, pipe data method and processor of effortful.The method comprises: edge service platform is used to obtain the real-time state and behavior information of entity pipe, and the real-time state and behavior information are introduced into the all-element data model resource pool;All-element data model resource pool is used to store the element data and model of entity pipe, and according to the real-time state and behavior information introduced by edge service platform, the stored element data and model are updated and optimized;Multi-domain modeling analysis center, including multi-modal fusion analysis engine, multi-domain modeling analysis center is used to determine target entity pipe according to demand, and the real-time element data and model of target entity pipe obtained by all-element data model resource pool are introduced into multi-modal fusion analysis engine.Drive kernel is provided by multi-domain modeling analysis center, and all-element data model resource pool provides data stream and model, and edge service platform realizes light weight operation.

The invention discloses an environmental noise and chemical pollution joint detection and big data comprehensive analysis device in the technical field of environmental pollution joint detection, and the device comprises a data collection part which is used for collecting environmental pollution data and image data in a set-point manner; an Internet of Things data transmission module; a big data analysis center; the data preprocessing module is used for preprocessing the collected data; the data analysis module is used for constructing, optimizing and training a long-short term memory network; the model prediction module is used for predicting the change trend of the environmental pollution data by using a long-short-term memory network; and the visual display module is used for reading the geographic data and visually displaying the predicted environmental pollution data change trend. According to the method, the influence of noise events on atmospheric chemical pollution and other environmental detection data changes is predicted, and the prediction result is visually expressed, so that the relation between automobile noise pollution and atmospheric chemical pollution and thermal pollution is deeply revealed, and the decision-making efficiency of urban managers is improved.