5552 results about "Data information" patented technology

A BIM-based prefabricated building design system and a simulated assembly method. The system comprises: a data collection module, a BIM modeling module, a drawing generation module, a component processing module, an assembly module, and a verification module; the data collection module is used for collecting data information of a prefabricated building; the BIM modeling module constructs a BIM model of the prefabricated building according to the data information; the drawing generation module is used for outputting a building component drawing according to the BIM model; the component machining module is used for performing machining and manufacturing according to the building component drawing; the assembly module is used for performing assembly path planning for a manufactured building component, and performing assembly on the basis of a planned path; and the inspection module is used for performing inspection on an assembled building. The present invention implements integration of prefabricated building design and implements prefabricated building installation intelligence, allowing assembly to be accurately completed.

The invention relates to a knowledge graph construction and intelligent prospecting prediction method based on multi-source heterogeneous geological data. The method comprises the steps that geological data information is acquired and preprocessed, and a geological information database is formed; geologic entities, attributes of the geologic entities and mutual relations of the geologic entities in the geologic information database are recognized through the natural language processing technology, and a geologic knowledge graph is constructed; training and optimizing the prediction model to obtain a metallogenic prediction model; and obtaining a prediction result of the metallogenic potential area by using the metallogenic prediction model, and generating a metallogenic analysis report. According to the method, the geological data quality is improved through data preprocessing, the knowledge graph is constructed to integrate geological knowledge, the deep learning model is utilized to accurately extract and predict the metallogenic characteristics, and finally the metallogenic analysis report is generated, so that the prospecting efficiency is improved, the cost is reduced, and the decision scientificity is enhanced.

The invention relates to the technical field of data information processing, in particular to an artificial intelligence-based index layer model construction analysis method and system, and the method comprises the following steps: 1, multi-source data synchronization and quality evaluation; 2, service context analysis and subject domain intelligent division are carried out, and service document and data table field semantics are analyzed; 3, index demand intelligent analysis and calculation logic generation: analyzing index demands input by a user through a natural language processing technology, matching service entities in the knowledge graph, generating initial calculation logic and optimizing execution efficiency; and 4, dynamically updating the index data and verifying the consistency, executing the optimized computational logic to generate the index data, tracking the blood relationship of the data, triggering dynamic updating, and setting a threshold value according to a historical fluctuation range to carry out abnormal alarm. Through automatic and intelligent processing, the probability of human intervention and human errors is reduced, and the efficiency and accuracy of data processing are improved.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

The invention discloses a multi-sensor data fusion control method for an automatic assembly line, and relates to the technical field of data fusion, and the method comprises the following steps: when multi-source data fusion is carried out, firstly, data information is obtained in real time through a multi-source sensor, the obtained data information is preprocessed, and the accuracy and integrity of data are ensured; by analyzing data quality and fusion performance indexes and system robustness and environmental adaptability indexes in real time, the system can accurately detect the influence of electromagnetic interference and environmental fluctuation on data and dynamically evaluate the state. And an early warning and adjustment strategy is automatically triggered in case of abnormality, so that data fusion continuity is guaranteed, and downtime and human intervention are reduced. And when the abnormity exceeds the self-repairing capability of the system, the system can give an early warning, so that the efficient and safe operation of the production line is ensured, and the capability of dealing with sudden problems is improved.

The invention discloses a Transform-based traffic flow prediction method and device capable of sensing a local-global time-space relationship, and the method comprises the steps: firstly obtaining the historical traffic data of all to-be-predicted road sections, the historical data comprising the traffic flow feature data of the predicted road sections, the static adjacent matrix data between road network nodes, and the sampling timestamp data of the historical data; secondly, a spatio-temporal information embedding layer is constructed to provide multiple types of embedding input for a model trunk, the learning ability of the model is enhanced, and three different types of embedding are respectively historical data information embedding, time information embedding and space node self-adaptive embedding; then, constructing a local-global time dependence extraction module, respectively learning short-time and long-time time dependence relationships in the data by using a multi-scale TCN and a self-attention mechanism in a time dimension, and meanwhile, introducing a double-path self-adaptive information gating fusion technology to realize effective fusion of time features of different hierarchies; then constructing a local-global spatial dependency extraction module, respectively learning local and global spatial dependency relationships in the data by using a dynamic-static graph convolutional network and a self-attention mechanism in spatial dimension, and realizing effective fusion of spatial features of different levels based on a two-way adaptive information gating fusion technology; and finally, mapping the potential spatial-temporal feature representation into a prediction result through a full connection layer network.

A method of tracking multi-obstacle objects in the field environment, a system, a device and a medium thereof are provided. The method includes using a Kalman filter to obtain prediction box center pixel coordinates and BEV object prediction center coordinates and using IMU data information to correct the prediction box center pixel coordinates. Obstacle object recognition is performed on an RGB image. Point cloud center coordinates are determined from the bird's eye view. The IMU data information is used to correct the point cloud center coordinates and an OCSORT model carries out minimum cost matching to obtain primary matching information. If detection boxes are matched with prediction boxes, the primary matching information is taken as successful matching information of the current frame, otherwise, an LAPJV algorithm is used to carry out minimum cost matching based on depth point cloud information to obtain the successful matching information of the current frame.

The invention discloses a four-dimensional-based resume and post matching analysis method and system, and relates to the technical field of data information processing. The method comprises the steps of obtaining an original JD text and performing text preprocessing; four-dimensional information is set, and information extraction is carried out based on four dimensions; establishing a mapping relation between the JD extraction field and the resume four-dimensional degree; performing resume matching based on four dimensions; carrying out dynamic weight distribution; performing comprehensive scoring based on the dynamic weight distribution information; and result verification and output are carried out. The system comprises a preprocessing module, a four-dimensional information extraction module, a matching calculation module, an interpretability module and a verification and output module. According to the method, a four-dimensional extraction framework directly corresponding to a resume system is proposed for the first time, a four-dimensional mapping model is formed, semantic alignment of recruitment requirements and talent resumes is realized, and the analysis accuracy of complex semantics is improved in combination with a rule engine and a machine learning model.

The invention discloses a road and bridge disease analysis and early warning method and system based on big data, and relates to the technical field of smart cities. Multiple groups of sensors are deployed, acquired related transverse wind load information data are associated with related vertical traffic load information data, and an external force load influence coefficient Xhz is constructed; after comparison, a preliminary early warning signal is sent out, related surface state data information and related vibration state data information of the current road and bridge are further collected and associated with an external force load influence coefficient Xhz, a comprehensive disease degree index Zbh is calculated through fitting in combination with a disease evaluation model, and the comprehensive disease degree index Zbh is compared with an evaluation threshold value K; according to the method and the system, the multi-dimensional data is acquired to judge whether the road bridge has a disease risk, a disease early warning instruction of a corresponding grade is generated and executed, and accurate identification and real-time early warning of the road bridge disease are realized through dynamic association and intelligent analysis of the multi-dimensional data.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention discloses a sensor fault detection and reconstruction method based on multi-sensor fusion, and the method comprises the steps: collecting data in a production process, and obtaining the data information of a detection point; carrying out anomaly detection on the collected data information through an LOF algorithm; classifying the detected abnormal data into normal data and abnormal data; inputting the abnormal data into a CNN-LSTM network sensor fault diagnosis model for principal component analysis dimensionality reduction, and reducing the input abnormal data from high-dimensional data to low-dimensional data by using principal component analysis; inputting sensor abnormal data subjected to principal component analysis dimensionality reduction into a CNN-LSTM-based sensor fault classification model, and determining the type of a sensor fault; and reconstructing the data of the fault sensor.

The invention relates to the technical field of battery thermal management, and provides a dynamic thermal management method and system for a battery module, and the method comprises the following steps: carrying out the local environment heat dissipation capability evaluation when a preset triggering condition is satisfied, and generating an evaluation result; generating a heat dissipation adjustment parameter of the heat dissipation system based on the evaluation result and the current thermal state of the local battery; carrying out data monitoring and recording, and generating reported data information; the reported data information comprises data related to local environment heat dissipation capability evaluation, evaluation result data, heat dissipation adjustment parameter data of a heat dissipation system and battery thermal state change data after the heat dissipation system executes adjustment; and uploading the reported data information to a cloud platform, so that the cloud platform executes continuous learning and optimization operation of the thermal management strategy based on the reported data information. The method has the advantages that the adaptability and safety of heat management are improved, and continuous optimization of a cloud strategy is supported.

The invention discloses a front road gradient estimation method based on multivariate information fusion. Relates to the technical field of intelligent driving, in particular to a front road slope estimation method based on multivariate information fusion. According to the method, road segmentation is performed through an improved Patchwork + + model, background and interference point clouds are eliminated, ground effective point cloud data information is extracted, and a road slope value is calculated based on the ground point cloud data information. The method comprises the following steps: performing time synchronization and space registration on a camera and a laser radar to obtain a multi-source information data set; the method comprises the following steps: constructing and improving a Patchwork + + model to obtain an improved Patchwork + + model: processing a multi-source information data set through the improved Patchwork + + model to obtain a coordinate # imgabs0 # of a ground point cloud; and calculating a road slope value according to the coordinate # imgabs1 # of the ground point cloud.

The embodiment of the invention provides a data processing method and device, electronic equipment and a storage medium, is suitable for a processor comprising a first integrated circuit unit and a second integrated circuit unit, and comprises the following steps: acquiring to-be-transmitted data and providing the to-be-transmitted data to the first integrated circuit unit; the verification information of the data to be transmitted is generated, the verification information comprises state information of a forward error correction code, data information of the forward error correction code and state information of cyclic redundancy check, and the data information of the forward error correction code and error data in the data to be transmitted have a corresponding relation; and sending the verification information and the to-be-transmitted data to the second integrated circuit unit, so that the second integrated circuit unit sends the to-be-transmitted data to the second integrated circuit unit under the condition that the state information of the forward error correction code is error-free or correctable error and the state information of the cyclic redundancy check is error-free. And a correct cyclic redundancy check code is calculated based on the received data to be transmitted and the data information of the forward error correction code, so that efficient data transmission is realized.

The invention relates to the technical field of laser SLAM, and particularly discloses a laser SLAM method based on ground segmentation, and the method comprises the steps: obtaining laser radar point cloud data information, carrying out the point cloud preprocessing of the laser radar point cloud data, and obtaining ground point cloud features and non-ground point cloud features; respectively carrying out feature extraction, feature matching and pose estimation on the ground point cloud features and the non-ground point cloud features to obtain a pose nonlinear optimization constraint result; selecting a key frame according to a pose nonlinear optimization constraint result, constructing a local map according to pose information of the key frame, and obtaining a local map construction result; loopback detection is carried out according to the local map construction result, and a loopback detection constraint result is obtained; and performing global pose optimization processing according to a loopback detection constraint result and a local map construction result to obtain a global pose and global map construction result. The laser SLAM method based on ground segmentation provided by the invention can improve the positioning precision and the operation stability.

The invention relates to the technical field of electric power operation and maintenance management, in particular to a transformer area intelligent operation and maintenance device and method based on a machine learning expert model, and the device comprises a logistic regression machine learning module and a robot process automation module. Real-time perception and data analysis of a business scene are realized by means of artificial intelligence, total data information of a master station measurement domain and an operation and maintenance work task is dynamically collected, incremental data is continuously utilized for data analysis and rule mining, a machine learning expert model is introduced, and the operation and maintenance efficiency is improved. A transformer area intelligent operation and maintenance system integrating human-computer interaction such as operation and maintenance index intelligent monitoring and analysis, intelligent abnormity diagnosis, intelligent operation list generation, intelligent dispatching, intelligent path planning and field operation guidance is constructed, service data is analyzed in real time, meanwhile, a robot process automation technology is integrated, automatic operation of a part of processes is achieved, and operation efficiency is improved. And support is provided for on-site acquisition operation and maintenance work.

The invention discloses a CAD automatic modeling method and system based on parameter driving. The method comprises the steps that model parameter information needing modeling is obtained; generating a CAD (Computer Aided Design) model on the basis of parameter analysis, geometric construction and surface recognition schemes; performing reverse reconstruction of the three-dimensional CAD model based on topology analysis, geometric feature extraction, pattern recognition and parameter relation inference; modeling, optimization and feedback evolution are carried out based on model analysis, feature learning and generation of parametric modeling rules; and according to the obtained data information, CAD automatic modeling based on parameter driving is completed. According to the method, CAD automatic modeling based on parameter driving is achieved, the reliability is higher, the accuracy is better, and the efficiency is higher.

The invention discloses a highway engineering road and bridge measurement method based on BIM, and belongs to the technical field of highway engineering measurement. According to the method, the problems that the modeling efficiency and accuracy are difficult to consider, the modeling precision of a non-important area is redundant and the like in the existing road and bridge measurement method for the highway engineering are solved. The method comprises the following steps: acquiring satellite positioning data by using a satellite positioning system in a measurement area, acquiring point cloud data by using laser scanning equipment, and acquiring road and bridge images by using image acquisition equipment; converting satellite positioning data into unified coordinate system data information, carrying out spatial registration on the unified coordinate system data information and the point cloud data, and dividing an important region and a non-important region; constructing a precise three-dimensional point cloud model for the important area, constructing a path point cloud model for the non-important area, and integrating road and bridge images; and finally fusing the model to obtain road and bridge parameters. The method can improve the model efficiency and reduce the model resource overhead while effectively improving the measurement precision, and is suitable for the measurement work of roads and bridges of highway engineering in various environments.

Provided is a terminal for transmitting information. The terminal includes a memory and a processor, wherein the memory stores at least one program code, and the processor is configured to load and execute the at least one program code, to cause the terminal to: report assistance information in a case that a first condition is satisfied, wherein the assistance information indicates service information of the terminal, the service information comprising at least one of a service characteristic of the terminal, remaining service information of the terminal, or remaining data information of the terminal.

The invention discloses a low-altitude atmosphere three-dimensional wind field inversion method based on wind profile radar vertical observation, relates to the technical field of three-dimensional wind field inversion, and aims to solve the problem of poor inversion effect caused by inaccurate model parameters in the wind field inversion process. The difference between simulation data and actual observation data is evaluated from multiple angles, a comprehensive optimization basis is provided, parameter calculation optimization is performed by adopting a particle swarm optimization method, the method has the characteristics of high convergence speed and high global search capability, is suitable for optimization of complex problems, and is used for confirming model requirements according to a wind field inversion rule. The model is selected by combining the evaluation result of the model library, it is ensured that the selected model has high pertinence and applicability, the prediction precision and reliability of the model are improved, model initialization is conducted through wind field fusion data, data information of multiple sources is fully fused, and the comprehensiveness and accuracy of the data are improved.

The invention relates to the technical field of terminal management, and particularly discloses a remote diagnosis management method and system based on a vehicle-mounted terminal, and the method comprises the steps: obtaining multi-source heterogeneous data collected by the vehicle-mounted terminal in real time; performing dynamic priority classification on the multi-source heterogeneous data to obtain a multi-source data priority set; constructing an edge-cloud collaborative multi-modal model according to the multi-source data priority set, and obtaining multiple pieces of dynamic data information according to the edge-cloud collaborative multi-modal model; and acquiring data association information and semantic information of each piece of dynamic data distribution information, and constructing a vehicle data association topological graph according to the data association information and the semantic information. According to the invention, the analysis and utilization capability of the vehicle data is effectively improved. The constructed edge-cloud collaborative multi-modal model can efficiently process multi-source data, reduce transmission delay and cloud burden, and improve real-time performance and decision accuracy.

The invention relates to the technical field of remote sensing monitoring equipment, and discloses tropical agricultural ecology remote sensing monitoring equipment which comprises an unmanned aerial vehicle platform, a remote sensing unmanned aerial vehicle is arranged on the unmanned aerial vehicle platform, and a monitoring sensor assembly is loaded on the remote sensing unmanned aerial vehicle. The monitoring sensor assembly comprises a high-resolution camera device, a hyperspectral sensor, a thermal infrared sensor and a laser radar, a data receiving module, a data processing module and a display module are arranged in the unmanned aerial vehicle platform, and the monitoring sensor assembly is mainly used for obtaining soil images and data information. The data processing module can judge soil ecological information according to images and data, and the monitoring sensor assembly and the data processing module are matched to obtain the erosion degree and fertility condition of soil in the planting area, the moisture content, the soil type, the moisture evaporation condition and the slope and direction information of the soil. The growth state of plants in a planting area and the ecological environment of soil are monitored, and producers are better assisted in formulating targeted agricultural management measures.

The invention discloses a high-concurrency API service-oriented AI gateway multi-level cache synchronization method and system, and the method comprises the steps: obtaining a request, extracting data information in the request, and carrying out the request routing, security authentication, service authentication and flow control in a memory; when the number of requests reaches a preset threshold value, writing the data information into a system cache and / or a database for updating; when a user carries out recharging operation, obtaining user data based on the multi-level cache architecture through an UpdatUser function to carry out balance calculation and updating; and updating the memory data information to a system cache and a database in real time based on a publishing and subscribing mechanism so as to carry out data consistency maintenance. Request routing, security authentication and other key operations are completed in the memory, the high-speed read-write characteristic of the memory is utilized, the request processing delay is greatly reduced, and ten thousands of concurrency per second or even higher concurrency is supported. The dependence on a back-end cache and a database is reduced, and the performance bottleneck is avoided.

The invention provides a power transmission line forest fire risk assessment method and system driven by a flame combustion model, relates to the technical field of electrical engineering, establishes a multi-source data fusion fire point monitoring system, and comprehensively utilizes three monitoring modes of satellite remote sensing, a ground monitoring station and unmanned aerial vehicle inspection to collect fire point data information. According to the method, multi-source cooperative complementary monitoring is realized, the satellite remote sensing, the ground monitoring station and the unmanned aerial vehicle are comprehensively used for routing inspection, the limitation of a traditional monitoring means is overcome, the satellite remote sensing coverage is wide, and large-area fire points can be found in time; a ground monitoring station carries out precise monitoring in a close-range and complex terrain area; the unmanned aerial vehicle is flexible in routing inspection and high in resolution, low-altitude detailed reconnaissance can be realized, and the three parts cooperate to comprehensively improve the monitoring accuracy and reduce missing report and false report.

The invention discloses an intelligent blasting sequence control system for mixed loading explosives, which is provided with a data acquisition module, a data preprocessing module, a model construction module, a parameter optimization module, a multi-target control module and a data storage module to intelligently control the blasting sequence of the mixed loading explosives. Distance attenuation features, delay features, topographic features and blasting energy features of blasting data are constructed, the blasting prediction model is updated and iterated based on a neural network algorithm and historical blasting data information, and the performance of the blasting prediction model is evaluated. The number of hidden layers and the number of nodes in the blasting prediction model are updated and optimized through a control variable method, the blasting prediction model is optimized, when mixed explosive is adjusted and the blasting sequence is optimized, the optimal mixed explosive composition is searched through a search optimization algorithm, and the optimal mixed explosive composition is obtained through control signals in combination with real-time blasting data information. And the blasting sequence and delay are dynamically adjusted, so that the blasting of the mixed explosive is intelligently and accurately controlled in real time.

The invention discloses an anti-drowning early warning system of a self-adaptive dynamic virtual electronic fence, and aims to solve the problems that an existing anti-drowning early warning system cannot adapt to water level changes and early warning lags behind. The virtual electronic fence setting module is used for setting static and dynamic virtual electronic fences, identifying and analyzing personnel behaviors and controlling alarms; the data acquisition module is used for acquiring environment data information and video monitoring information; and the data storage module is used for storing data, alarm information, virtual electronic fence setting information and water level change information of the virtual electronic fence according to the water level change adjustment range. The system is based on real-time detection of a YOLOv8 model and is combined with a virtual electronic fence technology, has the characteristics of high-precision identification, low-delay response, field environment adaptability and the like, can effectively reduce the occurrence rate of drowning accidents, and is a set of intelligent, efficient and convenient-to-maintain system.

The invention discloses a chemical material field knowledge graph construction method and device based on an inference large model prompt instruction. The method comprises the following steps: acquiring basic data information and experimental chart data; establishing a domain rule base and a knowledge graph basic framework document; establishing structured JSON data; constructing a Neo4j database according to the structured JSON data and a field rule base to perform structured data quality verification and dynamic enhancement so as to obtain the updated field rule base and the verified structured data; acquiring preprocessed atlas data according to the verified structured data; acquiring a multi-modal fusion preliminary map according to the preprocessed map data; and establishing a knowledge graph containing a reasoning result according to the multi-modal fusion preliminary graph. According to the method, the logical relationship in the article content can be better extracted, so that the content and the relationship of the established knowledge graph are more comprehensive.

The invention discloses a building engineering data information intelligent management method, and relates to the technical field of information management, and the method comprises the steps: collecting and aligning construction site multi-source heterogeneous data, forming an engineering alignment data set, extracting construction state features based on the alignment data set, and calculating a comprehensive deviation index; according to the method, the multi-source heterogeneous data and the dynamic twinborn model are fused, real-time monitoring, prediction and optimization control of the construction site state are achieved, the dynamic twinborn scene is constructed, the health state mapping of the key component is generated, and the construction optimization strategy is generated based on the health state mapping and the deviation index and is controlled. The structural deviation is quantitatively analyzed, the optimal construction strategy is automatically generated, model self-evolution and parameter self-optimization are achieved through feedback learning, the safety and intelligent level of constructional engineering are remarkably improved, the energy consumption and the construction period delay rate are reduced, and conversion from static monitoring to active decision making is achieved.

The invention relates to the technical field of industrial pollution monitoring, in particular to an industrial pollution source tracing method and system based on machine learning, which can accurately and efficiently determine a pollution source in an industrial park and meet high requirements of environmental protection laws and regulations and the public on environmental protection. The method is used in an industrial park, when an industrial pollution monitoring point monitors that pollution indexes exceed the standard and the emission of each factory in the park meets the emission standard for pollution discharge, pollution is subjected to traceability analysis, and a pollution source with the maximum participation degree in the pollution exceeding event is determined. The method comprises the following steps: acquiring pollution monitoring data information monitored by an industrial pollution monitoring point; carrying out standard-exceeding pollutant feature extraction on the pollution monitoring data information to obtain a park pollutant feature portrait; the park pollutant feature portrait comprises the components of the standard-exceeding pollutants in the park and the real-time concentration of each standard-exceeding pollutant; and matching the park pollutant feature portraits in a pre-established factory pollutant discharge database.