353 results about "Data normalisation" patented technology

A computing system is disclosed for generating fulfillment-ready coordination sessions based on tokenized item data and user-submitted participation parameters. The system ingests structured, semi-structured, or unstructured item data from merchant sources and applies schema-aligned transformation logic to normalize the data into structured item representations. The normalized records are tokenized into machine-readable item tokens that encode fulfillment constraints and canonical attributes. The system receives user item selections and associated participation parameters, encodes the item tokens and participation data into structured vector embeddings, and applies compatibility scoring logic using vector comparison and rule-based threshold evaluation. Compatibility scores are evaluated against session eligibility constraints derived from the item tokens. When eligibility conditions are met, the system generates a coordination session payload comprising match outcomes, proportional pricing, and fulfillment metadata, and issues orchestration instructions to external fulfillment systems for group-based delivery or preparation execution.

The invention discloses a heat pump system state anomaly detection method based on a depth auto-encoder, and the method comprises the steps: collecting compressor data, and carrying out the standardization processing to construct a multi-dimensional time sequence; a spatial-temporal feature extraction depth auto-encoder with a thermodynamic coupling attention mechanism is constructed, coupling attention is utilized to calculate physical parameter coupling strength weights to extract spatial features, time features are extracted in combination with a long and short-term memory network, and normal state data are reconstructed and predicted through a decoder after fusion; residual vectors of predicted normal state data and original data are calculated, and a weighted mahalanobis distance is calculated by using a covariance matrix to generate an abnormal score; and constructing a sliding probability distribution model based on historical normal data, calculating a current score occurrence probability, and comparing the current score occurrence probability with a preset threshold to output an anomaly detection result. According to the method, a multi-physical parameter space coupling relationship and a time evolution rule are captured through a thermodynamic coupling attention mechanism, and the anomaly detection accuracy and robustness are improved.

The invention relates to the field of water environment pollution detection and analysis, in particular to a water environment pollution detection and analysis system and method based on the Internet of Things, and solves the problems of sampling redundancy, missing detection, high-dimensional data over-fitting, difficulty in traceability and early warning lag in traditional detection. The system comprises a monitoring acquisition module, a water quality anomaly detection module, a pollution tracing module, a pollution early warning module and a database. The monitoring acquisition module adopts chaotic adaptive sampling to adjust sensor frequency, and entropy optimization water quality data is obtained by combining permutation entropy and collaborative filtering denoising; the water quality anomaly detection comprises the following steps: standardizing data into high-dimensional point clouds, calculating a Betti number and a non-equilibrium state entropy, and fusing to generate a comprehensive pollution topological entropy index; according to pollution traceability, a dynamic graph and a diffusion kernel matrix are constructed by using GNN, and the optimal source intensity is solved through quantum annealing; the range is predicted through a diffusion equation for early warning, and information is integrated to generate a report to be pushed to users. The method corresponds to a module flow and provides support for pollution prevention and control.

The invention relates to the technical field of financial data analysis and artificial intelligence, in particular to a multi-source heterogeneous financial data fusion and intelligent analysis system which comprises a data standardization processing module, a time sequence event fusion module, a knowledge graph construction module, a relation reasoning module and a self-adaptive anomaly detection module. The data standardization processing module is used for converting heterogeneous financial data from different sources into unified tensor representation; the time sequence event fusion module adopts a double-clue cooperation mechanism to establish a mapping relation between continuous time sequence data and discrete events; the knowledge graph construction module extracts financial entities and relationships thereof, and constructs a multi-level knowledge graph; the relation reasoning module performs deep reasoning based on a graph attention mechanism; and the adaptive anomaly detection module dynamically adjusts the detection threshold according to the market environment. According to the system, implicit association in heterogeneous financial data can be deeply mined, market anomalies are recognized in advance, and comprehensive support is provided for financial decision making.

The embodiment of the invention provides a knowledge graph fusion method and system based on a large model, and the method comprises the steps: carrying out the data standardization, entity feature enhancement and relation semantic annotation of heterogeneous knowledge graphs from different sources; through semantic similarity calculation, context reasoning and alignment confidence evaluation, a multi-stage and multi-mode entity alignment mechanism is constructed in combination with a large language model, and cross-source entity matching is performed on entities in heterogeneous knowledge maps of different sources; based on conflict detection, dynamic weight distribution and a conflict resolution strategy, generating a data fusion result, and unifying multi-source data; and generating a high-quality unified knowledge graph through missing relationship prediction, logic consistency verification and an incremental updating mechanism. According to the knowledge graph fusion method and device, full-process automation, precision and dynamics of knowledge graph fusion are realized, the problems of high manual dependence, weak semantic processing capability, poor cross-domain adaptability and the like in the prior art are effectively solved, and the efficiency and quality of knowledge graph fusion are remarkably improved.

The invention relates to the technical field of environment monitoring, in particular to an urban carbon emission intelligent prediction method based on multi-modal data and machine learning integration, which comprises the following steps: feature acquisition and preprocessing: acquiring multi-source heterogeneous data and processing the multi-source heterogeneous data into a standardized data set; constructing an LSTM (Long Short Term Memory) and XGBoost hybrid prediction model; predicting future data on the basis of existing data, and predicting input features of future time steps of the weight integration prediction model on the basis of historical data in a standardized data set; and predicting carbon emission in the future. Industrial structure indexes, environment variables and social and economic substitution indexes are integrated through time characteristic engineering, and emission driving factors in different urban environments can be comprehensively captured; a recursive prediction mechanism is established, development trajectories of different cities are considered, reliable emission prediction can be carried out, meanwhile, time consistency is kept, and prediction uncertainty is quantified; interference influences under different policies are quantified, and a quantitative basis is provided for carbon management decisions.

The invention relates to the technical field of intelligent data correction, and discloses an intelligent accounting data correction method and system based on data difference identification, and the method comprises the steps: S, synchronously executing data standardization and data portrait construction based on accounting data and a preset business rule, business rule injection type feature extraction is carried out on the standardized accounting data to obtain a difference composite feature vector, multi-level confidence classification is carried out based on the vector, difference type labels are generated, a cost-risk-efficiency optimization correction decision is carried out, an optimal correction strategy instruction and a traceability record are obtained, and the traceability record is obtained. Performing directional correction process management and control on the data, then searching similar cases, performing case auxiliary verification on the corrected accounting data, generating a correction verification result, and finally outputting a correction traceability report; according to the invention, the accuracy of intelligent accounting data correction based on data difference identification can be improved.

The invention discloses a ward dynamic rule decision-making method based on the Internet of Things and multiple agents, and the method comprises the following steps: collecting related data of a ward, and carrying out the data standardization, and obtaining a standardized event stream; constructing three types of agent agents, establishing an intention and instruction channel, and forming an inter-agent communication channel; semantic fusion is executed based on the inter-agent communication channel, and a disposal trigger condition is generated; arranging and forming a rule template set according to a processing trigger condition and a specified field; based on the rule template set and a preset security constraint, a trinuclear-GR (1) reaction formula is called for synthesis, and a dynamic rule set is generated; executing mutual exclusion and resource constraint check to form a disposal scheme; performing formalized security verification on the disposal scheme to obtain a verified disposal decision; and translating the verified disposal decision into an equipment protocol instruction, issuing the equipment protocol instruction, and recording a trigger event and a compliance log. According to the invention, ward dynamic rule decision is realized.

The invention discloses a pulsed eddy current sandwich gold plate purity evaluation method and device based on temperature compensation, computer equipment and a medium. The method comprises the following steps: selecting test gold; collecting original data in a first environment temperature range, and collecting second original data of the test gold in at least one second environment temperature range; standardizing the first raw data; inputting the standardized original data into a classification model for training to obtain a trained classification model; collecting to-be-detected data of to-be-detected gold; standardizing the to-be-measured data according to the coincidence range of the environment temperature and the number of the to-be-measured gold; and inputting the standardized to-be-detected data into the classification model, and converting according to an output result of the classification model to obtain the predicted purity of the to-be-detected gold. According to the method, a closed-loop data preprocessing system is constructed, so that the processed data is directly adaptive to an existing classification model, and the influence caused by temperature drift can be effectively counteracted without modifying a model structure or retraining the model.

The invention discloses a cross-heterogeneous platform law supervision system based on XML and semantic matching. The cross-heterogeneous platform law supervision system comprises a data standardization module and a cross-heterogeneous platform execution system. The cross-heterogeneous platform execution system comprises a heterogeneous platform data import module, an XML file verification module and a supervision function execution module; the data standardization module is used for realizing standardization of cross-platform legal supervision data on the basis of intermediate description language technical specifications; the heterogeneous platform data import module comprises two independent heterogeneous platform data import platforms and is used for realizing data import and converting heterogeneous data of different platforms into a unified XML (Extensible Markup Language) file; the XML file verification module is used for carrying out mutual matching of an XML file and an XSD file based on an XSD specification file given by an XML specification and the XML format file so as to realize XML file verification; and the supervision function execution module is used for executing a legal supervision function corresponding to the XML file after the XML file passes verification.

The invention discloses an electric power total-factor unified load prediction method and system based on a time sequence large model, and relates to the technical field of machine learning, and the method comprises the steps: triggering a prediction process through a timed task, and obtaining historical load data and external covariable data; constructing a time sequence sample required by training and prediction according to a time window, and performing batch pulling and processing according to a fixed number of days when the data size exceeds a single-batch threshold value; performing data standardization and data cleaning on the data, and generating structured time sequence input; loading a time sequence large model as a unified prediction engine, and inputting a load sequence and an external covariable into a covariable fusion component for collaborative modeling; and outputting fine-grained prediction results of the electric power elements in the target time period at the same time under a single model framework, and post-processing and storing the prediction results. Through the technical scheme of the invention, the model fragmentation and maintenance cost is reduced, the generalization and stability are improved, and the operation stability and the engineering availability are improved.

The invention provides a multi-source data fused water quality pollution monitoring method and device and a storage medium, and relates to the technical field of water quality monitoring. According to the method, through the key steps of data preprocessing, cross-modal feature fusion, pollution concentration estimation, graph neural network traceability and the like, multi-source water quality data and monitoring point space topology information are deeply integrated, and a whole-process automatic monitoring system is constructed. According to the method, end-to-end processing from data standardization to pollution emission source positioning is realized, a multi-source heterogeneous data barrier is effectively broken, the complementary value and the time-space association rule of different modal data are fully mined, the integrity, accuracy and efficiency of water quality pollution monitoring are improved, the emission source position and the pollution diffusion path can be accurately positioned, and the water quality pollution monitoring efficiency is improved. Full-link and targeted technical support is provided for water quality pollution control and treatment decision, and the problems that traditional monitoring data is low in utilization rate and insufficient in traceability accuracy are solved.

A computing platform performs automated due diligence by acquiring project scope information and documents, standardizing all data across all document types into a uniform format optimized for analysis and subjecting the data to repeated querying by large language model agents to triangulate it against a body of human content expertise coded into the platform. The platform compares variables with a project proforma, verifies permits by parsing permit data and cross checking a permitting authority portal, evaluates design completeness by detecting standards of readiness status, and measures the bankability of project agreements compared to industry standards. The platform computes environmental, social, and governance metrics, assesses counterparty risk and community sentiment using searches of public sources, and validates a reported capital stack by analyzing documents and applying industry standards thereto. The platform calculates risk metrics, applies weights to produce two scores, and generates a report that includes remediation steps.

The invention discloses a multi-protocol conversion method, system and device between fire alarm devices and a medium, and belongs to the technical field of communication. The method comprises the following steps: S1, loading a protocol template; s2, establishing communication with a source device; s3, analyzing and converting the original data; s4, writing the data into a database in a standardized manner; s5, standardized data mapping and frame construction; s6, establishing communication with the target; the system part comprises the following modules: a protocol configuration module used for storing and managing template information of all converted protocols; the data acquisition and analysis engine is used for establishing communication with source equipment; the data standardization and database module is used as a data transit hub of the system; the data mapping and packaging engine is used for preparing communication data for target equipment; the data sending engine is used for communicating with target equipment; the problems that in the prior art, protocols between new fire alarm equipment and old fire alarm equipment are not compatible, a flexible conversion mechanism is lacked, and the new fire alarm equipment and the old fire alarm equipment are difficult to uniformly access an Internet of Things platform are solved.

The invention discloses a quality inspection and labeling system for bid invitation file generation based on deep learning, and the system comprises a data standardization module which is used for collecting items and historical texts and executing data standardization, and generating a standardized text sequence; the semantic coding and candidate generation module is used for tensorizing the standardized text sequence and generating a candidate text of the bid invitation file; the text decoding module is used for executing limited CRF decoding on the candidate text and outputting an aligned labeling result file; the hypergraph labeling module is used for constructing a hypergraph and globally decoding the hypergraph to generate a multi-level labeling result; the selective re-checking module is used for calculating uncertainty, triggering manual re-checking and outputting a confirmed labeling result and an uncertainty record; the consistency verification module is used for verifying hierarchical consistency to form a final labeling result; and the structured export module is used for exporting the bid invitation file finished product text and the structured annotation data. According to the invention, quality inspection and labeling of bid invitation file generation are realized.

The invention relates to the field of lubricating equipment management, and particularly provides a lubricating equipment health management method and system, a terminal and a medium, and the method comprises the steps: collecting multi-modal data, time sequence structured data including vibration and the like, non-structured data including texts and the like, and oil detection data of lubricating equipment; carrying out standardization processing on the data to obtain multi-modal features; aligning data through timestamp calibration and space mapping, and constructing a multi-modal heterogeneous graph containing multi-modal nodes and multi-type edges; generating a uniform equipment state representation vector through cross-modal diagram attention calculation; inputting a pre-trained neural network model, and outputting the fault type and probability, the remaining service life and the health score; and receiving a query through a natural language interface based on the result, and generating a visual report and / or an operation and maintenance work order. According to the invention, precise identification and early warning of lubricating equipment faults are realized, and the accuracy and operation and maintenance efficiency of equipment health management are improved.

The invention relates to the technical field of tunnel engineering informatization and intelligent construction safety monitoring, in particular to a tunnel construction-oriented blasting data acquisition management platform system, which comprises a data standardization acquisition module for obtaining a standardized management input data set; the compliance conflict sensing module is used for generating a space-time mismatching degree index; the dynamic trust decision module is used for generating a security adjustment threshold window allowing the execution parameter to deviate from the reference value; the execution supervision module compares and verifies the to-be-executed construction parameters with the safety adjustment threshold window, judges compliance attributes of the to-be-executed construction parameters, and generates a corresponding release instruction or a blocking instruction; the process tracing module is used for recording the authorization basis and the environment background of each parameter adjustment to form a non-tampering audit log; according to the method, the problem that the geological uncertainty cannot be accurately represented by the traditional means is effectively solved, and the predictability and the accuracy of construction risk identification are remarkably enhanced.

The invention relates to the field of enterprise application integration, in particular to a supply chain finance-oriented multi-protocol conversion and semantic verification system and method, which comprises a protocol adaptation layer, a core processing layer, a management support layer and a distributed data storage layer, the protocol adaptation layer adopts a plug-in and metadata driving design, and a protocol plug-in is an independent JAR packet and supports multi-protocol encoding and decoding and hot updating; the core processing layer realizes data standardization and financial-level semantic verification through a general message model, a dynamic routing engine, a visual data conversion module and a rule engine; and the management support layer provides a low-code portal and supports rule configuration, simulation testing and full-link monitoring. According to the technical scheme, through the seven steps of configuration, decoding, routing, conversion, verification, coding and auditing, the method adapts to a multi-specification heterogeneous system, data intercommunication accuracy and compliance are improved, docking cost is reduced, and supply chain financial service requirements are met.

The invention discloses a patient rehabilitation effect evaluation system based on nursing information processing. The system comprises an evaluation data acquisition module, a data preprocessing module, an effect evaluation model construction module, a model hyper-parameter optimization module and a rehabilitation effect evaluation module. The invention relates to the technical field of intelligent patient rehabilitation effect evaluation, in particular to a patient rehabilitation effect evaluation system based on nursing information processing, and the system comprises the steps: obtaining an effect evaluation original data set through data collection; a data preprocessing method of data cleaning, feature extraction, time sequence alignment, data standardization and data set segmentation is adopted; a double-flow deep network model is adopted as an effect evaluation model, and it is ensured that an evaluation result conforms to clinical logic while feature collaboration of heterogeneous medical data is achieved; an improved leapfrog algorithm is adopted to optimize model hyper-parameters, dynamic balance exploration and development are achieved, meanwhile, the stability of the algorithm is enhanced through population division, and stable performance of an evaluation system in different clinical scenes is ensured.

Embodiments of systems and methods of this disclosure provide a multi-tenant user interface for uploading a variety of different documents over a network to a content server; analyze the uploaded documents on the content server using an AI-powered document analysis engine to identify, for each document, a document type and industry, the AI powered document analysis engine including one or more AI machine learning models trained on documents within the industry and an operator-specific adaptation corresponding to a pre-determined transaction; generate a tailored data extraction prompt based on the document type- and industry using an adaptive prompting system on the content server; extract, from each document by way of the AI-powered document analysis engine, data corresponding to the tailored data extraction prompt; and standardize the extracted data using a data standardization module on the content server.

The invention relates to a medical waste supervision process risk early warning system based on artificial intelligence. The system comprises a whole process data acquisition module, a data preliminary processing module, a preliminary early warning model construction module, a risk grading model construction module and a supervision process risk early warning module. According to the invention, original data is obtained through data acquisition; a data preliminary processing method of multi-source data cleaning, graph sequence construction, time sequence attribute graph construction, data standardization and data set segmentation is adopted; a clustering model is adopted as a preliminary early warning model, potential risk process nodes are quickly identified through an unsupervised learning mode, high-quality input is provided for subsequent grading, and meanwhile, a graph structure is introduced to facilitate fusion of data attributes, structural relationships and time sequence behaviors; a deep learning model is adopted as a risk grading model, dynamic risk quantification is realized by integrating sub-graph features, time sequence context and environmental factors, and meanwhile, the fine grading precision is improved by utilizing data to drive threshold adjustment.

The invention relates to the technical field of artificial intelligence, and discloses a traceability and supervision integrated method and system based on a knowledge graph. The method comprises the steps of collecting multi-dimensional dealer operation data; carrying out standardization and entity alignment; constructing a multi-layer knowledge graph covering network points, products, consumers, cost and inventory; deploying a graph neural network to perform node embedding; calculating a dealer comprehensive confidence score in combination with a belief propagation algorithm; and generating a visual traceability report containing evidence chain backtracking. The system comprises a multi-source data acquisition module, a data standardization module, a knowledge graph construction module, a graph neural network embedding module, a confidence coefficient propagation calculation module and a traceability report generation module. Through graph structure modeling and an automatic scoring mechanism, objective, dynamic and interpretable dealer health degree and potential evaluation is realized, and supervision decision scientificity and efficiency are improved.

The invention discloses a medical document processing method and system based on a double-pipeline architecture, and relates to the technical field of document processing. According to the medical document processing method based on the double-assembly-line architecture, through a closed-loop process of document classification, preprocessing, double-assembly-line directional parallel processing and hierarchical vectorization storage, precise adaptation and efficient processing of multi-format and multi-type medical documents are achieved, the information loss rate and the key information truncation rate are greatly reduced, and the medical document processing efficiency is improved. According to the method, the document processing efficiency and the data standardization degree are improved, the warehousing success rate and the data traceability of the vector library are ensured, high-quality and structured data source support is provided for subsequent medical intelligent retrieval, clinical question and answer and retrieval enhancement generation system application, the knowledge base construction and maintenance cost is remarkably reduced, and the method is suitable for popularization and application. The problems that in existing medical document processing, medical semantics are not taken into consideration, so that key clinical information is easy to cut off, and a single processing flow cannot adapt to a structured guide and an unstructured case are solved.

The invention relates to the technical field of gastrointestinal tumor chemotherapy risk assessment, and particularly discloses a gastrointestinal tumor chemotherapy risk scoring model and a construction method thereof, and the method comprises the steps: obtaining the individualized feature data and basic physiological data of a historical patient, and constructing an individualized difference library with a unique ID, and a core influence library; the method comprises the following steps: standardizing and coding double-library data, synchronizing clinical data containing treatment effect codes, forming a data dictionary, acquiring and coding current gastrointestinal tumor patient data, and matching the data dictionary to judge individual difference abnormity; when the first data set is abnormal, constructing a first data set, screening core independent variables through Logistic regression, and constructing a scoring model by using an LSTM (Long Short Term Memory) model; when no abnormity exists, redundant codes are removed to obtain a second data set, and modeling is conducted through the same method. Through double-library linkage and scene-divided modeling, the three-level toxicity risk prediction precision is improved, data support is provided for clinical chemotherapy dose adjustment and toxicity prevention, and the risk of excessive treatment or insufficient treatment is reduced.

The invention provides a cross-border commodity adaptive compliance data standardization system and method. Comprising a data preprocessing module used for mapping and converting field names and data formats of different sources; the semantic recognition module is used for recognizing and obtaining initial standardized commodity information; the compliance processing module is used for generating a supervision rule data structure associated with commodity categories, customs clearing fields and customs codes; the standard commodity archive generation module is used for filling customs clearance fields which can be deduced according to the cross-border commodity knowledge graph and the initial standardized commodity information, marking customs clearance fields which cannot be automatically determined, and generating standard commodity archive data; and the electronic tag docking module is used for establishing a corresponding relationship between the electronic tag code and the standard commodity archive data. According to the invention, unified standardized modeling of heterogeneous commodity data can be realized, the generation accuracy of compliance fields and customs clearance information is improved, and the electronic tag application and commodity file maintenance cost is reduced.

The invention discloses an unsafe hoisting behavior identification method based on multi-sensor data fusion, which comprises the following steps of: 1, acquiring multi-dimensional monitoring data in real time through multi-sensor data acquisition; 2, data preprocessing and data fusion are carried out, data synchronization and alignment, data cleaning and abnormal value processing, filtering and denoising, feature extraction and data standardization normalization processing are carried out on the multi-dimensional monitoring data in sequence, and then a multi-source fusion feature matrix is constructed by adopting a weighted fusion algorithm; 3, screening core features associated with the unsafe hoisting behaviors from the multi-source fusion feature matrix; 4, establishing a quantitative safety rule base based on deterministic identification of the safety rule base, matching the core features with rule base judgment logic, and outputting a preliminary identification result; and 5, identification result verification and early warning: correcting the preliminary identification result through multi-source data consistency verification and time sequence continuity verification, and triggering graded early warning according to the correction result.

The invention relates to the technical field of automobile lightweight design and intelligent databases, and provides an automobile lightweight intelligent decision database system and method based on artificial intelligence. The invention aims to solve the technical problems of low design efficiency, low performance prediction and parameter optimization precision and difficulty in balancing multi-target requirements caused by data heterogeneous dispersion, difficulty in intercommunication and information splitting in the existing automobile lightweight design. According to the scheme, an intelligent decision database system is constructed through five core modules: a multi-source heterogeneous data acquisition module realizes data standardization processing and secure storage; the lightweight knowledge graph construction module establishes an association system and dynamically updates a standard; the intelligent algorithm module integrates a deep residual network, Bayesian optimization and a Transform architecture, and completes performance prediction, parameter optimization and scheme generation; the multi-objective optimization decision module screens an optimal scheme through multi-dimensional evaluation and constraint processing; the visual interaction module provides a visual interaction function and supports multi-mode deployment.

The invention discloses an enterprise financial credit granting and increasing method and device and a medium, and the method comprises the steps: carrying out the standardization of the whole-chain dynamic data of enterprise production and operation, and generating a time series data set; carrying out weighted fusion through a preset five-dimensional data fusion framework to obtain a holographic state vector; and inputting the industry risk template, the holographic state vector and the knowledge graph into a pre-trained multi-modal artificial intelligence inference model to obtain a credit score and risk conduction probability matrix. Matching the credit score with a preset strategy rule base to obtain credit granting parameters, and determining a risk disposal strategy according to probability matrix nodes; the matrix is rendered into an interactive topological graph through a force guiding algorithm, and a risk checking report is generated by comparing the space-time deviation of the internet of things GPS and the block chain logistics coordinates. And finally, visually integrating the credit granting parameters, the processing strategy, the topological graph and the checking report to form an enterprise credit granting and credit increasing management scheme. Through multi-source data fusion and dynamic modeling, the evaluation real-time performance and risk control accuracy are improved, the period is shortened, and service customer groups are expanded.

The invention discloses a method and a system for quality inspection of change survey data. The method comprises the following steps of 1, performing format identification on the change survey data; 2, calling an improved MacBERT model to carry out error correction processing, and generating standardized data; 3, performing integrity check and authenticity check on the attachment file corresponding to the standardized data; 4, performing field-by-field comparison on the material verification data and the data model retrieved from the database to generate comparison data; 5, inputting the spatial features of the comparison data into a cross-modal topology adapter of the improved MacBERT model, and generating topology verification data; and step 6, recording quality inspection result information. The method is suitable for data standardization processing and topology compliance verification scenes in the fields of natural resources, urban and rural construction and the like.

The invention belongs to the technical field of image processing and privacy protection, and particularly relates to a deep face anonymization standardization processing and evaluation system. The system comprises a data preprocessing module, a multi-technology integration anonymization module, a cross-scene test set module, a multi-dimensional evaluation module and a visual interaction module. The data preprocessing module realizes face data standardization processing by unifying image size, resolution and color depth standards and combining Dlib face detection and feature point positioning; the multi-technology integration anonymization module packages mainstream anonymization technologies such as FIT, G2Face, DeepPrivacy and the like, and provides an extensible interface; the cross-scene test set module covers face samples of different illumination, postures and ages; the multi-dimensional evaluation module constructs an evaluation system from privacy protection intensity (re-recognition rate, cosine similarity and the like), image quality (FID, SSIM and the like), attribute maintenance (difference measurement of expressions, hairstyles and the like) and processing efficiency (time / memory consumption); and the visual interaction module supports data uploading, technology selection, result viewing and automatic report generation. According to the method, the problems of no unified architecture and evaluation fragmentation in the prior art are solved, standardized processing and fair evaluation of a face anonymization technology are realized, and a privacy protection technology is promoted to be applied to the ground.