3323 results about "Data format" patented technology

The invention discloses a geographic information big data-oriented multi-source data processing system, and relates to the technical field of data acquisition and sensors, and the system comprises a data acquisition module which accesses a remote sensing satellite, an unmanned aerial vehicle, an Internet of Things sensor and social media in real time through a multi-source heterogeneous interface, and carries out the adaptive analysis of a data format and metadata marking; the distributed storage module is used for performing partition storage on the geographic information data based on a space-time database and an object storage architecture, and establishing dynamic space-time index and version control; the data fusion module is used for realizing coordinate system conversion, time sequence calibration and semantic knowledge graph matching based on a multi-source data alignment method of dynamic weight distribution; and an intelligent analysis module and a security control module. According to the method, core pain points such as data splitting, low storage efficiency, extensive analysis and compliance risks in the geographic information field are systematically solved, and a full-stack type technical base is provided for scenes such as smart cities, emergency disaster relief and environment monitoring.

The invention belongs to the technical field of artificial intelligence and big data, and discloses a customer data processing and insight system based on a big language model. The system is composed of a multi-source data access module, a data preprocessing and label fusion module, a large language model semantic understanding module, a knowledge enhancement and semantic linkage module, an insight generation and visualization module, an intelligent strategy output module and a feedback learning and self-optimization module. According to the method, multi-source heterogeneous data such as texts, voices and structured behaviors are integrated, and the deep semantic analysis capability of a large language model is combined, so that global modeling of customer behaviors and intentions is realized; a multi-modal synchronous acquisition and standardization mechanism eliminates data format barriers, and a dynamic label mechanism adapts to context changes, so that the system can capture deep semantic association in customer expression, and compared with a traditional keyword matching method, the semantic understanding accuracy is improved by more than 40%, and a more complete data base is provided for insight generation.

The invention discloses an industrial equipment fault prediction method based on multi-modal data, and belongs to the technical field of specific calculation models, and the method comprises the steps: carrying out the preprocessing according to the collected multi-modal data of the operation of industrial equipment, so as to unify the format of the multi-modal data, and obtaining the structural data; extracting features of the structured data one by one according to data categories, and obtaining a multi-modal fusion feature through a dynamic fusion mechanism; according to the multi-modal fusion features, a fault prediction classification score is obtained through a deep neural network model to perform fault prediction; and when the drift parameter of the multi-modal data is greater than a preset threshold value, performing incremental training on the deep neural network model through the multi-modal data collected in real time to update parameters of the deep neural network model. Through multi-modal data unified processing, dynamic feature fusion, deep neural network modeling and an online learning mechanism, the problems of insufficient multi-modal data fusion, prediction uncertainty quantization deficiency, poor model adaptability and the like are solved.

The invention discloses a multi-dimensional carbon flux monitoring method, and particularly relates to the technical field of carbon flux monitoring, and the method comprises the following steps: collecting multi-source heterogeneous sensor data, and carrying out spatial resolution unification, time synchronization alignment and data format standardization preprocessing to ensure data consistency; executing fusion validity detection, and correcting time synchronization deviation and data redundancy conflicts; according to an abnormal detection result, dynamically adjusting a fusion weight or determining a weight based on historical clustering, and combining spatial adjacency interpolation and time interpolation to realize data fusion; and finally, inputting a carbon flux inversion model, and outputting a high-precision carbon flux monitoring value to realize dynamic monitoring and prediction. According to the method, a dynamic fusion weight adjustment mechanism is adopted, the fusion weight of each data source is dynamically optimized, adaptive fusion of multi-source observation data is realized, the problems of data discontinuity and insufficient data coverage in the prior art are solved, and a multi-dimensional, full-coverage and high-resolution carbon flux fusion data set is ensured to be formed.

The invention discloses a geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

The invention relates to a metro field large language model evaluation method and system. The method comprises the following steps: step 1, constructing a data set; the step 1 comprises the following steps of: 1.1, collecting public professional data; step 1.2, collecting internal professional data; 1.3, collecting a general data set; 2, data processing; in the step 2, the following steps are executed: step 2.1, data formatting; step 2.2, data cleaning is carried out; step 2.3, performing data enhancement; 3, fine adjustment of the model; in the step 3, the following steps are executed: step 3.1: constructing a training data set; step 3.2, fine adjustment of the model; step 3.3, carrying out model evaluation; 4, constructing an evaluation system; in the step 4, the following steps are executed: step 4.1, determining evaluation indexes; step 4.2, constructing an evaluation system; step 4.3, constructing an intelligent agent; step 4.4, multi-agent cooperation is carried out; and 5, performing model evaluation.

The invention provides an electronic information data association method and system based on the Internet of Things. The method comprises the steps that a parallel processing architecture is adopted to execute rapid protocol conversion on a high-priority data packet, data recombination is directly carried out through a pre-compiled conversion rule template, a complex format verification link is skipped, and the integrity and format correctness of the converted data packet are determined; response time and error rate statistical information in the data transmission process are obtained, protocol conversion rules and scheduling strategies are optimized through a feedback mechanism, and if the success rate of certain protocol conversion is lower than a standard value, corresponding mapping rules and verification algorithms are updated; and carrying out standardized packaging on all converted data by adopting a unified data exchange standard, adding an equipment identifier, a timestamp and a data type label, establishing a cross-protocol general data format, and carrying out final distribution after judging whether a data packet meets a standard format requirement.

The invention discloses an article recognition system based on computer vision. The article recognition system comprises a multi-modal data acquisition module, a multi-modal data processing module and a computer vision processing module, wherein the multi-modal data acquisition module is used for acquiring multi-modal data through a multi-modal sensor array; the data preprocessing module is used for standardizing a multi-modal data format and generating a time-space aligned multi-modal tensor; the feature extraction module is used for respectively extracting modal specific features from texture, spectrum and geometric dimensions by adopting ResNet50, 3D-CNN and PointNet + +; the multi-modal fusion module is used for constructing cross-modal joint representation; the adaptive sensing module is used for modeling illumination invariance and scene dynamics based on self-supervised comparative learning and a 3D-STMN space-time memory network, predicting a shielded target trajectory by using Kalman filtering in combination with the shielding sensing propagation module, and generating an environment sensing parameter set; and the recognition engine module is used for integrating YOLOv8 detection, Mask R-CNN segmentation and multi-modal decision tree classification, outputting a target bounding box, a category and confidence in combination with the depth data, and generating three-dimensional space coordinates combined with the depth data.

The invention discloses a digital financial data sharing system and method, and belongs to the technical field of digital financial data analysis, and the system comprises a data standardization management module, a data access conversion interface module, a cross-mechanism authentication authority management module, a safety audit risk control module and an intelligent analysis cooperation platform module. The data standardization management module is used for unifying a data format to be XBRL, metadata definition and cleaning rules and eliminating data semantic differences between mechanisms, and the data access conversion interface module is used for supporting multi-source heterogeneous data access and providing a standardized output interface. The cross-institution authentication authority management module is used for realizing decentralized identity authentication based on a block chain technology, zero-knowledge proof and a federated learning technology, and controlling a data access range in a hierarchical manner in combination with role authority, and the security audit risk control module is used for monitoring data flow in real time. According to the invention, on the basis of realizing digital financial data sharing, the problem of data islands can be solved, and cross-mechanism data authentication can be facilitated.

The invention discloses a multi-source heterogeneous data dynamic protocol security exchange adaptation system, which comprises a protocol analysis engine, a data encryption module, a data format standardization engine and a routing strategy engine, and is characterized in that the protocol analysis engine is respectively connected with a data source, the data encryption module and the data format standardization engine; the data format standardization engine is connected with the routing strategy engine, and the routing strategy engine is connected with the nodes. The invention discloses a multi-source heterogeneous data dynamic protocol secure exchange adaptation method. Comprising the following steps of protocol analysis and data conversion, data encryption and sensitive data desensitization, integrity verification and anomaly detection, routing strategy and intelligent scheduling, data distribution and storage, and dynamic extension and protocol adaptation, and the dynamic conversion of different protocol data ensures that various types of data can be smoothly interacted. Meanwhile, an advanced data security enhancement technology is integrated, multiple protections such as encryption and authentication are carried out on transmitted and stored data, and the data security is effectively improved.

The invention belongs to the field of data analysis, and discloses a data analysis method for engineering consultation digital intelligent management, which comprises the following steps: mapping multi-project heterogeneous data to a unified three-dimensional space-time grid through space-time grid division to form a grid mapping data set; detecting data format and attribute conflicts based on neighborhood correlation analysis, and distributing calibration data through dynamic weight; missing values and abnormal fluctuations are processed by using a time sequence autoregression model and a smoothing technology, and a complete data set with continuous time is constructed by combining linear interpolation and multi-dimensional data fusion; key indexes are extracted to construct a digital mirror image containing spatial positions, time sequences and attribute association, real-time data are synchronized, and outlier grid points are corrected; and multi-scene operation conditions are constructed based on the mirror image data, and a dynamic adjustment scheme is realized. According to the method, heterogeneous data are integrated through space-time grids, digital twinning and multi-scene simulation technologies are combined, and full-period management and risk intelligent decision-making of engineering data are achieved.

The invention relates to the technical field of data acquisition, and discloses a multi-protocol extensible data acquisition system architecture and method with a unified interface standard, and the architecture comprises a data source management module which is used for storing data source configuration information and distributing protocol identifiers; the protocol adaptation module comprises an analysis unit and a matching unit and is used for realizing data format conversion and protocol compatibility verification; the data conversion module is used for generating target data through format conversion and semantic mapping; the data verification module and the anomaly detection module are used for comparing and monitoring data integrity based on an integrity algorithm and a threshold value; the data quality monitoring module and the early warning module are used for comparing and evaluating data quality through a consistency model and a threshold value; and the result output module transmits the early warning information to a target system. The method comprises the steps of data source configuration, protocol adaptation, data conversion, integrity and quality monitoring, early warning and the like. The system expansibility and the data reliability are improved, and the method is suitable for multi-scene data acquisition.

An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

The invention discloses a market supervision data asset multi-dimensional evaluation method and system based on a hierarchy-network dynamic fusion model, and relates to the technical field of data asset evaluation. According to the method, a semantic association and index alignment relationship between data assets is comprehensively described by constructing a semantic map of the data assets; a hierarchy-network dynamic fusion model is formed by combining an analytic hierarchy process and a network analysis process, so that the determination of the index weight is more scientific and reasonable; a graph neural network is utilized to perform embedded representation learning on a data asset graph structure, comprehensive feature expression of each data asset is extracted, a multi-objective optimization method is combined, a Pareto optimal solution set is generated, scoring, sorting and grading are performed on the data assets, and reliability and interpretability of an evaluation result are ensured; the evaluation report is output in a structured data format and is provided for a market supervision system through an interface, thereby facilitating effective management and decision support for data assets by a supervision department, and having a wide application prospect.

The invention discloses a heterogeneous data processing method and device and a storage medium, and relates to the technical field of data processing.The method comprises the steps that to-be-processed heterogeneous data are obtained, field identifiers in the heterogeneous data are extracted, and then the field identifiers in the heterogeneous data are identified on the basis of a pre-trained semantic identification model; performing semantic matching on the field identifier and a target field in a preset database to generate a field-level mapping relationship, generating a step-by-step processing logic adaptive to a heterogeneous data format according to the field-level mapping relationship, executing verification operation on the step-by-step processing logic in a code sandbox, and performing verification operation on the step-by-step processing logic in the code sandbox. And after the verification is passed, storing the output target field data to a structure database. According to the heterogeneous data processing method and device, the extracted field identification is subjected to semantic matching, and the extracted target field is verified in combination with the code sandbox, so that the accuracy of heterogeneous data processing is improved.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention relates to the technical field of data fusion, in particular to a multi-source heterogeneous data fusion method and system based on cloud computing, and the method comprises the steps: carrying out the feature extraction of multi-source heterogeneous data, and generating standardized metadata comprising a data type, a transmission protocol and a semantic tag; an adaptive interface is dynamically generated based on the metadata, the adaptive interface comprises a data format conversion module, a protocol packaging module and a semantic analysis module, and cross-domain compatible data streams are output; performing data fragment encryption and priority marking on the cross-domain compatible data stream to generate a secure transmission block carrying a transmission feature vector; incremental fusion is executed at the cloud fusion node, semantic conflicts are detected by constructing a knowledge graph, and a fusion intermediate state is generated; and performing dynamic verification on the fusion intermediate state based on the credibility evaluation model, and outputting final fusion data with a credibility label. According to the method, the dynamic verification and hierarchical labeling of the fusion result are realized, and the semantic consistency and expandability of the fusion intermediate state are ensured.

The invention relates to an automatic protocol adaptation method for a real-time access system of multi-source equipment. The data acquisition layer receives original protocol data sent by equipment and transmits the original protocol data to the protocol adaptation layer. And the protocol metadata analysis module extracts, analyzes, compares and matches the data, and automatically identifies the protocol type of the equipment. The rule engine converts original protocol data into a system unified internal data format according to a conversion rule and then transmits the original protocol data to the data processing layer, and after cleaning, integration, analysis and other operations are carried out, the original protocol data are transmitted to the application interface layer to be called by an upper-layer application system. And when new protocol equipment is accessed, the hot plug function allows the system to dynamically load the new protocol adapter module in a state of not stopping running. According to the method, automatic adaptation of a new protocol is achieved through rapid deployment of the containerized micro-service architecture in combination with newly added protocol rules and metadata of the protocol management module, the whole process does not need to compile and publish the system again, and efficient and stable operation of the multi-source equipment real-time access system is guaranteed.

The invention provides a cloud platform docking service implementation method, system and device based on an adaptation layer and a medium, belongs to the technical field of cross-platform resource management and service integration, and particularly relates to a method for configuring an adapter for each cloud platform and mapping an API (Application Program Interface) of the platform to a standard RESTful API interface; performing standardization processing on interface return formats of different cloud platforms through a data conversion module, converting a heterogeneous format into a unified format, and outputting a unified data format; packaging and abstracting services of different cloud platforms; identity authentication is realized by randomly generating a secret key in cooperation with an encryption and decryption algorithm, and a verification mechanism of a source request IP is preset; and recording an operation log and an error log in the docking process, and recording an interface calling frequency, a requester IP and error information of an abnormal logic node. According to the method, the adaptation layer is introduced, the isomerism of the bottom cloud platform is shielded, and unified interfaces and services are provided, so that efficient and flexible cloud platform docking is realized.

The invention relates to a BIM-based building structure intelligent management method. The method comprises the following steps: step 1, dynamic monitoring factor mining and multi-modal sensing: collecting meteorological hydrology, geological conditions, microenvironment and natural disaster historical data; 2, neural operator-driven virtual-real fusion dynamic analysis: converting the format of the collected data, mapping the data into a BIM model, integrating a virtual-real synchronization engine, and pre-training a Fourier neural operator through real-time displacement and strain data reverse calibration model boundary conditions in a construction stage, and establishing a direct mapping relationship between an external load and a structural response; 3, multi-modal data collaboration and hierarchical permission interaction: forming a multi-dimensional feature matrix through association of timestamps and space coordinates; 4, multi-modal graph convolutional network damage identification: learning dynamic relevance between nodes through an adaptive graph convolutional layer, and extracting damage sensitive features; and step 5, dynamic life early warning of digital twinning drive: predicting the remaining service period of the structure.

The invention discloses an ocean multi-source observation data fusion assimilation method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining multi-source ocean observation data; uniformly converting the timestamps and the space grids of the multi-source ocean observation data into a data format of an FVCOM numerical model; constructing a data fusion assimilation model; the data fusion assimilation model comprises an FVCOM physical model and an AC-PKAN neural network; training the data fusion assimilation model, and adjusting the weight parameter of the data fusion assimilation model according to the dynamic observation data currently acquired by the edge device; and fusing and assimilating the multi-source marine observation data by using the data fusion assimilation model to obtain multi-source fusion marine observation data. According to the method, the timestamps and the space grids of the multi-source marine observation data are synchronized, and then the data fusion assimilation model is constructed for fusion assimilation, so that the fusion accuracy of the multi-source marine observation data can be improved.

The invention relates to the technical field of deep learning and defect detection, and discloses a multi-modal fusion defect perception and identification method based on deep learning, and the method comprises the steps: 1, obtaining multi-modal data: obtaining the multi-modal data of a target object through a collection device, and obtaining the multi-modal data of the target object; comprising visual data and non-visual data, and the data format covers two-dimensional images, three-dimensional point clouds, time series data and the like. According to the multi-modal fusion defect perception and identification method based on deep learning, the advantages of visual data and non-visual data can be integrated through multi-modal data fusion, the characteristics of a target object are described more comprehensively, information loss caused by single-modal data is reduced, the accuracy of defect detection is improved, and in industrial part detection, the detection efficiency is improved. By fusing the image and the point cloud data, defects such as cracks and holes on the surface of the part can be identified more accurately, and the detection precision is obviously improved compared with that of a single-mode method.

The invention discloses a heterogeneous equipment intelligent management system and method based on an artificial intelligence technology, and relates to the technical field of computers, and the system comprises a heterogeneous equipment intelligent management platform: a heterogeneous equipment layer is provided with multiple types of equipment; the equipment access layer supports a plurality of communication protocols; the device adaptation layer is used for converting communication protocols and data formats of different devices into model context protocols, and abstracting the devices by using a heterogeneous device unified description language; the resource management layer is responsible for monitoring the resource use condition of each device in real time and carrying out dynamic resource allocation according to a preset strategy; the task scheduling layer is used for intelligently distributing tasks according to task requirements and equipment capabilities and realizing cooperative work among equipment; and the user interface layer is used for providing a user interface, so that a user can conveniently configure, monitor and manage the equipment. According to the invention, heterogeneous equipment can be effectively managed, the compatibility and efficiency of equipment management are improved, the management cost is reduced, and intelligent control is realized.

The invention relates to the technical field of vehicle-mounted communication, and discloses an intelligent identification and conversion method for a heterogeneous vehicle-mounted connector protocol. According to the method, after a vehicle-mounted connector is connected, preset disturbance signal sequences are sequentially injected, response behaviors of the preset disturbance signal sequences in a signal channel are recorded, and a corresponding response sample set is constructed by analyzing response level, time delay and feedback data. The system further archives and encodes the sample set, generates a disturbance response behavior track containing state jump characteristics, and reasones a communication protocol type and a corresponding state driving factor through track comparison. On the basis, a field stable region and a boundary change mode are extracted, a protocol adaptive sampling rule is generated, then real-time segmentation and extraction are carried out on subsequent communication data, and a standardized data frame is constructed. Finally, data format reconstruction and semantic conversion among heterogeneous vehicle-mounted protocols are realized, and communication compatibility and data intercommunication capability among various connectors are improved.

The invention discloses an ecological environment pollution source tracing method based on data analysis, and relates to the field of ecological environment, and the method comprises the steps: carrying out the type selection of equipment, obtaining original data, carrying out the arrangement of the equipment after the type selection of the equipment, carrying out the multi-source data access after the arrangement of the equipment, and carrying out the data format standardization based on the obtained original data. And performing data anomaly detection based on the original data after data format standardization, and performing vacancy value filling based on the original data after data anomaly detection. According to the method, through multi-source data fusion, data cleaning, pollution diffusion modeling, pollution source identification and contribution rate calculation and visual early warning, the precision and reliability of pollution tracing are improved, firstly, format standardization, time synchronization and anomaly detection are carried out on pollution monitoring data of different sources, and data consistency and integrity are ensured; and then combining a Gaussian plume model, a fluid dynamic model and a Fick diffusion model.

The invention relates to a data cleaning method and device based on a Drools rule engine. The method comprises the following steps: acquiring original data from various data sources through a data acquisition module; converting the collected original data into a data format which can be processed by a Drools rule engine, and inserting the data format into a working memory; the data cleaning module carries out cleaning processing on the data according to a predefined cleaning rule, wherein the cleaning processing comprises operations such as duplicate removal, missing value filling and format correction; outputting the cleaned data to a target system through a data output module; and recording a cleaning log through the monitoring module, counting a cleaning result, and providing visual display. According to the scheme, a Drools rule engine is adopted, dynamic loading and updating rules are supported, and the dynamic loading and updating rules can take effect without restarting a system; the flexibility is high, and updating of various data sources and dynamic rules is supported; the cleaning efficiency is high, and large-scale data can be rapidly processed through the rule engine; the monitoring function is complete, real-time log recording and visual display are provided, and the cleaning process is convenient to manage and optimize.

The invention discloses a database question and answer model training method and device, a storage medium and computer equipment, and the method comprises the steps: associating standard structured query language statements, standard execution result answers and standard natural language questions, and generating training annotation data; and collecting simulation derivation problems possibly proposed for the database to obtain non-labeled data for training. Based on a GRPO reinforcement learning framework and a scoring reward function provided by a double-tower model, training is carried out on the scoring reward function by utilizing training labeling data, supervised fine tuning training is carried out on a database question and answer model, and non-labeling data for training, format rewards, executable rewards and scoring rewards of the scoring reward function are combined, so that the scoring reward function of the database question and answer model is obtained. And continuing to train the database question and answer model after supervised fine tuning training. Preliminary training is carried out through a small amount of annotation data, then subsequent training is carried out through non-annotation data, the reasoning ability of the model can be stimulated, the annotation cost is reduced, and the training efficiency is improved.

The invention relates to the technical field of data processing, in particular to an enterprise financial data processing system and method. The method comprises the following steps: obtaining multi-source enterprise financial data, and carrying out dynamic data type identification and structure transcoding to obtain preprocessed enterprise financial data; performing graph embedded structure conversion on the preprocessed enterprise financial data to obtain enterprise financial semantic fusion data; constructing a financial data auditing path according to the enterprise financial semantic fusion data; tense version mapping is carried out on the data format fluctuation characteristics in the financial data auditing path to record the structure change process of various vouchers and asset and liability data in the financial data auditing path, hierarchical file compression is carried out on a tense version mapping result, and a structured financial data auditing path is obtained; and time sequence causal link construction is carried out according to the structured financial data auditing path, and a financial cognitive inference model is obtained. The auditing efficiency and the risk monitoring capability can be improved.

The invention relates to the technical field of electric power management and control, and discloses an electric power BIM type data GIS discount classification method and system, and the method comprises the following steps: 1, constructing an electric power industry exclusive classification system and semantic mapping, and building a three-stage classification frame based on a GIS and an electric power industry standard; 2, unifying a space coordinate reference and a topological relation, and verifying topological consistency between components and between the components and topography and roads through a GIS (Geographic Information System) tool; 3, multi-scale classification and dynamic LOD model generation are carried out, and LOD model details are automatically switched according to a GIS scale; 4, performing data format conversion and intelligent lightweight processing, performing lossless compression on key components, and performing simplified compression on secondary components; and step 5, performing classification verification and dynamic updating mechanism, and comparing whether BIM and GIS base map position classification is correct or not, thereby realizing data synchronization. According to a dynamic classification framework and an intelligent lightweight strategy in the power industry, the BIM model efficiently adapts to the spatial analysis requirement of the GIS, and the data fusion and application efficiency is improved.

The invention discloses an indoor air quality monitoring method and system based on multi-dimensional analysis, and belongs to the technical field of air quality monitoring. The indoor air quality monitoring method based on multi-dimensional analysis comprises the steps that various data of a chemical sensor, an optical sensor and an acoustic sensor are collected; preprocessing the data, including unifying sampling frequency and normalizing different data formats; performing multi-dimensional feature extraction to obtain a multi-modal feature; carrying out feature fusion by adopting a machine learning algorithm; the air quality state is evaluated based on the fusion features; through multi-source data fusion analysis, the problems of one-sided monitoring result and insufficient reliability of a single sensor are solved, the monitoring accuracy and reliability are improved, and the method can be widely applied to scenes such as smart homes and office buildings.