1130 results about "Data preparation" patented technology

The invention belongs to the technical field of intelligent traffic, and particularly relates to a commuting regular bus operation line intelligent early warning system, which comprises a data acquisition module, a data communication module, a processing analysis module, an early warning output module and a feedback module, and a commuting regular bus operation line intelligent early warning method comprises a risk prediction model training process, the specific process is as follows: step 1, a data preparation stage; 2, a model construction stage; 3, a model training stage; 4, a reinforcement learning optimization stage; 5, a verification and deployment stage; and step 6, a closed loop iteration stage. According to the invention, the limitation of a single data source can be broken through, the early warning precision is improved, and accidents such as sudden traffic congestion, vehicle faults, driver misoperation and dangerous behaviors of passengers can be timely handled; a processing feedback continuous iteration model is combined to adapt to a complex traffic scene; emergency resources are automatically matched based on the early warning level, and the operation loss is reduced.

The invention discloses a building elevator detection, diagnosis and decision-making method based on a graph retrieval enhanced agent. The method comprises the steps that 1, elevator detection data are prepared and processed; step 2), knowledge extraction; step 3), knowledge fusion; step 4), visualization and optimization of the knowledge graph; 5) performing graph retrieval enhancement generation; step 6), diagnosing a decision-making agent; according to the method, triple information can be extracted from structural data, text data, visual data and other multi-modal data in the elevator detection field by guiding a multi-modal large model through an elevator detection technical specification, and an elevator detection visual target entity and a text named entity are automatically aligned based on a pre-trained vision-language model; the multi-modal knowledge graph in the field of elevator detection is accurately and efficiently generated, and building elevator detection intelligent diagnosis is carried out on the basis of the multi-modal knowledge graph and the fusion graph retrieval enhancement technology.

A method based on a multi-task learning model comprises the following steps: acquiring and processing histopathological image data of gastritis through a data preparation and preprocessing step; feature extraction is performed by using a self-supervised learning pre-trained model, and image blocks are coded into high-dimensional feature vectors; and constructing a multi-task learning model, learning feature representation through a full connection layer module and an attention layer module, and outputting a classification result of each task. And carrying out model training and optimization by using an optimizer, adding multitask loss through a loss function, and dynamically adjusting the model performance. The trained model can automatically detect and grade gastritis, atrophy, acute activity, intestinal metaplasia and other pathological indexes, outputs a standardized evaluation result, and assists in pathological diagnosis. According to the method, a multi-task deep learning framework based on self-supervised learning pre-training is constructed, a traditional single-task modeling mode is broken through, deep learning framework design is driven through pathological index association, and accuracy and clinical practicability of non-neoplastic disease assessment of the stomach are remarkably improved.

The invention provides a lightweight real-time semantic segmentation method for a three-dimensional Gaussian scene, and belongs to the field of computer vision and three-dimensional scene understanding. The process comprises the following steps: S1, data preparation: acquiring multi-modal information data including point cloud, images and poses, and preprocessing the data; s2, carrying out two-dimensional semantic segmentation, and generating a pixel-level semantic segmentation map and a semantic tag for the image by utilizing PP-LiteSeg; s3, performing three-dimensional scene modeling and semantic projection, fusing multi-modal information to complete three-dimensional Gaussian scene reconstruction, and projecting two-dimensional semantics to a Gaussian function to form preliminary Gaussian function semantic features; and S4, semantic optimization and segmentation output: considering image difference, semantic difference and smoothness, establishing a loss function, optimizing semantic features and outputting a segmentation result. According to the method, efficient, real-time and accurate three-dimensional Gaussian semantic segmentation work can be completed, and the method is suitable for scene understanding tasks of light-weight and resource-limited equipment.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention relates to the technical field of traffic safety, in particular to a beforehand prevention and control strategy generation method for traffic safety risks of a highway network in a mountainous area. Comprising the following steps: risk diagnosis and data preparation: collecting road basic information, historical traffic flow data, meteorological data and accident-prone point data of a mountainous area expressway network; constructing a risk assessment model: selecting road alignment, traffic flow, weather and environmental toughness indexes, determining index weights by adopting an improved analytic hierarchy process, constructing the risk assessment model through a fuzzy comprehensive evaluation method, and calculating the traffic safety risk level of each road section; a prevention and control strategy is generated; and strategy verification and iteration. According to the method, the total factor evaluation model is constructed by integrating the road network topological structure, the traffic flow characteristics and the meteorological sensitive section data, so that the problem of one-sided risk identification caused by single factor analysis in the prior art is solved, and systematic description of the mountainous area highway composite risk scene is realized.

The invention belongs to the technical field of geological engineering and environmental science, and relates to a rock freeze-thaw damage prediction method based on deep learning, which comprises the steps of data preparation, data preprocessing, data set generation, model construction, model training, model verification and prediction output. According to the method, the deep learning model is applied, the rock freeze-thaw damage rule is automatically extracted by using the deep learning model, and the large parameter quantity is achieved; adopting a missing value processing method, and marking a missing value through a two-dimensional vector; for sample distinguishing of different sources, hot independent coding is adopted; in the aspect of a mask mechanism, the model focuses on real data. According to the rock freeze-thaw damage prediction method, a deep learning model can be constructed, the model not only improves the prediction precision, but also enhances the robustness and generalization ability of the model, multiple defects of a traditional model can be effectively overcome, the output of the model can be used for evaluation and engineering design of rock freeze-thaw damage, and the method is suitable for large-scale popularization and application. Scientific basis and decision support are provided, and the method has a wide application prospect.

The invention relates to the technical field of laser surface modification, and discloses a water turbine laser cladding process parameter intelligent optimization system and method, and the system comprises a data preparation and preprocessing module which is used for obtaining and preprocessing water turbine laser cladding process parameters and corresponding cladding layer quality index data; a physical information guided multi-task prediction network construction and training module; a causal inference analysis module; and a causal perception multi-objective optimization algorithm module. By constructing a multi-task prediction network guided by physical information, the internal physical law of the laser cladding process is fused into a deep learning model, and the accuracy and reliability of predicting a plurality of key quality indexes of the laser cladding layer of the water turbine are remarkably improved. Compared with a traditional pure data driving model, the method can generate a prediction result more conforming to an actual cladding mechanism by means of guidance of physical constraints even under the condition of limited experimental data, and lays a solid data foundation for subsequent process optimization.

The invention provides an SQL statement analysis method and device, equipment and a storage medium, and relates to the technical field of databases. According to the specific scheme, the method comprises the steps that SQL statements and metadata thereof are obtained, wherein the metadata comprises table names, column names and predicates of the SQL statements; target statistical information corresponding to the metadata is obtained from a preset statistical information base, and the target statistical information comprises table statistical information, column statistical information and index statistical information. And loading the statistical information to a test environment database to obtain a simulation database corresponding to the SQL statement. And based on the statistical information, determining an execution plan of the SQL statement, and executing the SQL statement in the simulation database according to the execution plan to obtain a statement analysis result. The method can effectively solve the problem of analysis distortion caused by the distribution difference of test and production environment data, and can obtain a more accurate SQL analysis result without complex data preparation.

The invention discloses a laser radar point cloud real-time three-dimensional target detection method suitable for an unmanned vehicle. The method comprises the following steps: preparing a target detection data set; model training; updating parameters; simulation data preparation; performing model reasoning and result evaluation based on simulation data; model deployment and real vehicle data preparation; performing model reasoning and data storage based on real vehicle data; uploading and marking the data; and incremental learning and parameter updating. The three-dimensional target detection model provided by the invention specifically comprises a preprocessing module; a backbone network module; a neck network module; a feature selection module; and a detection head module. According to the three-dimensional target detection model provided by the invention, the point cloud data is converted into various aerial view images, and richer spatial information is mined; a two-dimensional distance sensing sparse convolutional layer is adopted to perform feature extraction, and multi-view feature fusion is realized through a double-branch network; a multi-scale feature pyramid network and a feature selection module are combined, so that the detection precision of a long-distance target and a small target is effectively improved; by constructing an efficient neural network architecture and combining training, simulation testing and real data incremental learning strategies, the robustness, precision and adaptive capacity of a target detection model are effectively improved, and powerful perception support is provided for an unmanned driving system.

The invention discloses a fragmented cultivated land remote sensing extraction method based on space-time deep learning, and the method comprises the following steps: 1, data preparation: selecting a high-spatial-resolution remote sensing image, and capturing the space-time change of cultivated land in different growth cycles and environment conditions in combination with multi-temporal data of different times and seasons; a second step of model construction: constructing a deep learning model fusing ConvNeXt and a U-Net framework, namely a ConvNeXt-U model; 3, model training is carried out; 4, crushing cultivated land extraction; and 5, analyzing and outputting a result. Through multi-dimensional technical innovation, remarkable performance breakthrough and application value improvement are realized in the field of fragmented cultivated land remote sensing monitoring, and the method is specifically embodied in the following three aspects: 1, high precision and robustness improvement are realized; 2, optimizing calculation efficiency and generalization ability; and 3, dynamic evolution analysis and decision support capability.

The invention relates to the technical field of data preparation pipeline recommendation, in particular to a context semantic perception-oriented data preparation pipeline recommendation method and system. The method comprises the following steps: performing data preprocessing on an obtained automatic pipeline construction data set; aiming at the preprocessed data set, carrying out double-view feature fusion based on a self-attention mechanism; for the preprocessed data set, context semantic information extraction based on a large language model is carried out; based on feature fusion and information extraction, a pipeline recommendation result is obtained through a deep reinforcement learning network; evaluating and optimizing a pipeline recommendation result; and an output pipeline. Through double-view feature fusion based on a self-attention mechanism, end-to-end dynamic integration is carried out on statistical attributes of a data set and a historical pipeline component sequence.

The invention provides a knowledge graph construction and semantic query control method and system of a BIM (Building Information Modeling). Comprising the following steps of BIM model data preparation and attribute extension, IFC data analysis and knowledge graph modeling rule definition, knowledge graph automatic construction tool development, Neo4j graph database storage and management, natural language processing and large model semantic understanding, Cyber query statement generation and IOT interface calling, and query and control of an interaction process. According to the knowledge graph construction and semantic query control method for the BIM model, the BIM model is converted into the knowledge graph, and unified query and control from building space information to semantic level are realized by utilizing a natural language processing technology and large model capability.

The invention belongs to the technical field of ocean temperature data identification, and discloses a thermocline identification method and system based on hyperbolic tangent function fitting. The method comprises the steps of data preparation and preprocessing; selecting a section range; fitting a curve; and determining a thermocline upper bound and a thermocline lower bound through a rolling function. According to the method, the hyperbolic tangent function is used for fitting the S-shaped characteristic curve of the temperature-depth profile, and the structure of the ocean thermocline is accurately described. Compared with a traditional Sigmoid function, the hyperbolic tangent function is better in performance when fitting a steep change area, and rapid temperature change of a thermocline central area can be caught more finely. According to the method, fitting parameters (amplitude, steep degree, central position and vertical offset) are optimized by adopting a trust region reflection algorithm, so that a fitting result is ensured to have higher precision and robustness, and the method is suitable for complex ocean profile data.

The present invention relates to the field of image processing technology, and discloses a method for automatic segmentation and recognition of ultrasound images based on deep learning, including the following steps: S1, data preparation and preprocessing, S2, self-supervised learning pre-training, S3, transfer learning and incremental learning, S4, multi-task joint learning, S5, image quality enhancement and super-resolution reconstruction: S6, multi-modal information fusion, information fusion by combining modal images, S7, model training and optimization, increasing data diversity by using data enhancement technology. By fusing ultrasound images with modal image information, the segmentation accuracy and lesion recognition ability of ultrasound images are improved, and different modal images provide complementary features, showing significant advantages in the recognition of complex lesions, complex locations, complex morphology and early lesions. At the same time, by fusing images of different modalities through a deep learning model, image quality can be improved and diagnostic accuracy can be improved.

The invention relates to a method for size measurement and appearance detection of an O-shaped ring. The size measurement of the O-shaped ring is carried out through the modes of image preprocessing, region-of-interest segmentation, edge extraction, central point determination and the like. And then, appearance detection is carried out, edge type defect detection and internal defect detection are respectively carried out, and then result fusion and screening are carried out. Therefore, high-precision measurement can be achieved, the device is suitable for O-shaped rings of different types, existing defects can be found in time, and detection of edge defects such as burrs and deckle edges is met. Moreover, a deep learning algorithm is adopted for internal defect detection, and internal defects such as glue shortage, impurities, damage and fracture can be accurately recognized through the steps of data preparation, model training, model optimization and the like.

Knowledge graph guide and entity controlled techniques for generating synthetic doctor-patient conversations. In one particular aspect, a method is provided that includes obtaining an original dataset containing textual dialogue associated with a plurality of individual doctor-patient conversations for training a machine learning model, constructing input data by using named entity recognition to capture and categorize named medical entities present in the dialogue, generating prepared input data by arranging the input data in an annotated turn-by-turn conversation format using an input data preparation algorithm having various control parameters, training the machine learning model using the prepared input data, utilizing a knowledge graph to identify a plurality of symptoms mapped to a randomly selected disease, and causing the trained machine learning model to generate a synthetic doctor-patient conversation by inputting the plurality of symptoms to the machine learning model as a first control parameter of a conversation generation control algorithm.

The present application relates to the technical field of electric digital data processing, and provides a data preparation method, system, and device for AIGC-based interaction analysis, and a medium. The method comprises: by means of a pre-trained text label recognition model, determining a label sequence corresponding to an interaction statement having experienced first word segmentation processing; on the basis of a preset expression construction rule, determining a label combination sequence corresponding to the label sequence and a conditional expression corresponding to the label combination sequence; by means of a preconfigured database candidate text dictionary and a preset matching rule, determining a strictly matched keyword and a fuzzily matched keyword corresponding to the interaction statement having experienced second word segmentation processing; on the basis of the strictly matched keyword and a similar candidate text corresponding to the fuzzily matched keyword, determining a word segmentation result corresponding to the second word segmentation processing; on the basis of a plurality of preset SQL statement rule matching templates, generating an interaction SQL query statement, so as to determine, on the basis of the interaction SQL query statement, interaction response information corresponding to the interaction statement.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

The invention provides a data weaving platform-oriented self-service data preparation method, which comprises the following steps of: accessing original data and associated structured metadata of a target data source into a data weaving platform to generate standardized accessed original data and associated structured metadata; based on a semantic model of the data weaving platform, performing classified semantic annotation on the associated structured metadata, and positioning a transfer node of the associated structured metadata to generate a data blood relationship record; performing bidirectional quality verification on the data consanguinity record according to semantics and path continuity, and matching with a federated governance rule of a data weaving platform to generate a metadata governance list; and generating a unified metadata directory based on the metadata governance list, so that a user of the data knitting platform can obtain key segments containing complete data consanguinity records. According to the method, the problem of insufficient accuracy and efficiency in self-service data preparation can be effectively relieved, and the efficient preparation requirement of a data weaving platform for multi-source data is met.

The invention provides a radar target detection method based on contrast self-supervised learning, and the method comprises the steps: S1, data preparation: carrying out the preprocessing of an echo signal collected by a radar receiver, obtaining an R-D image corresponding to the signal, and carrying out the data augmentation operation, and obtaining an augmented R-D image; s2, building a self-supervised pre-training model: building a self-supervised comparative learning pre-training network based on a large number of unlabeled R-D graphs and augmented R-D graphs, and training to obtain a basic model; and S3, a supervised fine tuning model: inputting a small amount of target detection annotation data into the pre-training basic model, carrying out supervised fine tuning training to obtain a radar target detection model, adopting a pre-training-fine tuning two-stage training normal form, and fully utilizing a large amount of label-free data to carry out pre-training based on self-supervised learning to obtain a radar target detection model. Supervised fine tuning is carried out on a small number of labeled samples, dependence on a large amount of high-quality labeled data is effectively relieved, and radar target detection performance in a complex environment is further improved.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention relates to the field of medical image processing, and discloses a VF value multi-modal prediction method and system based on deep learning, and the method comprises the steps: data preparation; the method comprises the following steps: constructing a basic ophthalmology model based on a Vision Transform model; performing first-stage pre-training on the basic ophthalmology model by using the first data set to obtain a first pre-trained ophthalmology model; performing second-stage pre-training on the first pre-training ophthalmology model by using the second data set to obtain a second pre-training ophthalmology model, and taking the second pre-training ophthalmology model as an image encoder; the method comprises the steps that a unified multi-modal framework is constructed, the unified multi-modal framework comprises an image encoder, a text encoder, a multi-modal unified fusion module and a prediction module, and the multi-modal unified fusion module fuses an image token output by the image encoder and a text token output by the text encoder to obtain a fusion token; training the prediction module by adopting the fusion token to obtain a trained unified multi-modal framework; and importing to-be-predicted data into the framework, and outputting a predicted VF value. The visual field value prediction efficiency and accuracy can be remarkably improved.

The invention discloses a zero sample document level relation extraction method based on a general large language model, which extracts entity relations in a document level text based on the large language model by establishing a cue word template, reduces the demand for a large amount of annotation data, reduces the cost and time of data preparation, and improves the extraction efficiency. And the relation extraction technology can be more easily applied to the field of data scarcity. According to the embodiment, the process of training through a training sample and parameter tuning is not needed, the result is obtained on the existing large model directly through the cue word, and dependence on the sample is reduced. And meanwhile, the influence of the quality of the labeled text on the extraction result is also avoided. Meanwhile, the relation extraction process can be carried out more quickly, and the model does not need to be trained / finely adjusted, so that the development period is accelerated. The demand of manual intervention is reduced, the labor cost is reduced, and the processing speed is improved. The method can adapt to different document types and fields.

The invention provides a counterfeited audio detection system and method based on mask reconstruction and time-frequency feature fusion, and the system comprises a data preparation module, a spectrum feature extraction module, a time-frequency feature fusion and prediction module, a training module, and a counterfeited audio detection module. Through a double-branch structure of an audio mask auto-encoder, features are extracted from two angles of reconstruction learning and direct encoding, enhancement and sequence correlation modeling are performed on the double-branch features, the ability of capturing forged clues is enhanced, frequency domain information and time domain context are combined, fusion features with higher discriminative ability are formed, and the accuracy and the reliability of the audio mask auto-encoder are improved. Through unified optimization of the total loss function, effective learning extraction and fusion of audio features are enhanced, the accuracy of forged audio detection is remarkably improved, and the method is suitable for actual forged audio detection application.

The invention relates to a construction organization design word document intelligent generation method and system. The method comprises the steps of establishing a preset template, loading and displaying the preset template, loading data, replacing placeholders, generating a temporary intermediate scheme file, editing communication settings in a web mode, editing the temporary intermediate scheme file, editing cover information of a scheme file to be generated, and generating and storing a final document. According to the method, the problems of poor template reusability, disordered format, low collaboration efficiency, low compiling efficiency, version conflict and the like in traditional construction scheme compiling are solved, intelligent and standardized generation of construction organization design documents is realized, multi-person collaboration data editing operation can be realized through editing of Excel structured data, and the method is high in practicability and easy to popularize. The data preparation difficulty of scheme editors is greatly reduced, the team efficiency is improved, and the data editing efficiency is also improved through multi-person cooperation.

The invention discloses an unmanned aerial vehicle low-altitude photography image enhancement method and system. The method comprises the steps of data preparation, motion joint deblurring optimization, multi-source data registration, scene adaptive enhancement and image comprehensive enhancement. The invention relates to the technical field of unmanned aerial vehicle image enhancement, and realizes high-precision deblurring, precise registration and adaptive visual enhancement of an unmanned aerial vehicle image by fusing inertial measurement unit guided blurring kernel prediction, cross-modal graph structure matching and elastic transformation optimization and an enhancement strategy dynamic adjustment mechanism based on scene analysis. The image definition, the structure consistency and the scene readability are remarkably improved, and the method is suitable for image processing tasks in a complex low-altitude flight environment.

The invention particularly relates to a method and a system for automatically generating legal instruments through a large model. The method comprises the following steps: preparing and marking data; training a large model; information processing; generating a document; and outputting and feeding back. According to the method, large model training and a multi-dimensional data processing mode are combined, so that automatic generation of the legal document is realized; in the data preparation stage, comprehensiveness and accuracy of legal knowledge are ensured through hierarchical labeling and knowledge base construction; a plurality of evaluation indexes, optimization algorithms and regularization means are applied in large model training, so that the model can accurately learn a legal language mode and a logic relationship; in the information processing link, the case information input by the user can be deeply understood by means of the natural language processing technology and knowledge graph construction, the legal document writing time is greatly shortened through the series of operations, errors and omissions caused by manual writing are reduced, and the legal document generation efficiency and quality are remarkably improved.

Disclosed in the present application are an aircraft final assembly and integration testing platform and testing method. On the basis of a rational platform architecture, structured process data preparation, model-based operation guideline document preparation, and dispatch assistance decision-making are realized by means of an application layer, such that uniform data sources in the field of final assembly can be achieved. Data uploading and issuing between an industrial control network and an internal network, and data interaction control between layers are implemented by means of the mutually collaborative operation of the application layer, a storage layer, an edge layer and an execution layer, thereby improving the information acquisition efficiency and the operating efficiency. The edge layer and the execution layer collaboratively operate, so as to realize the interactive collaboration between the assembly guidance work for operators and automatic testing, thereby improving the efficiency of data information acquisition, operation instruction and fault handling during a final assembly and testing process of an aircraft.

The invention discloses a remote sensing image change detection method based on deep learning and a GIS, and relates to the technical field of image processing and geographic information system application. Comprising the steps of 1, carrying out data preparation and preprocessing, 2, constructing a deep learning network model, carrying out feature extraction on preprocessed remote sensing image data by adopting a multi-layer convolution layer and a pooling layer, and aiming at obtained GIS data, carrying out feature extraction on the remote sensing image data by adopting different feature extraction methods according to data types, 3, performing feature fusion: fusing the remote sensing image feature vector and the GIS feature vector in a deep learning network model by using a feature fusion layer to obtain a fused feature vector; and step 5, performing change detection and result output: applying the trained deep learning network model to the preprocessed to-be-detected remote sensing image data and GIS data, outputting a change probability value of each pixel point through forward propagation calculation of the model to obtain a change probability graph, and performing post-processing operation on an obtained binary change detection result to obtain a change probability graph. And finally, outputting a clear and accurate remote sensing image data change detection result, and displaying the position, range and type information of a change area in the form of image or vector data.