372 results about "Structured analysis" patented technology

The invention discloses a memory enhancement and task planning method and system for large-model multi-agent. The method comprises the steps of performing structured analysis on historical task data, extracting planning experience and an abstract mode, and constructing and indexing an external memory library; retrieving related experience and modes from a memory library based on the new task description, and generating a memory enhanced task planning book; the planning scheme is distributed to a multi-agent network to be executed, an actual execution track is monitored and recorded, and the deviation between the actual execution track and the planning expectation is calculated; and evaluating the planning scheme based on the execution result and the deviation, extracting a planning correction rule, and updating the experience and the rule of the task to a memory bank. By introducing an external memory and closed-loop learning mechanism which can be dynamically evolved, the problems that existing system planning lacks experience guidance, knowledge cannot be precipitated and reused, and planning and execution are disjointed are solved, and the planning quality, the system reusability and the self-adaptive optimization capability are remarkably improved.

The invention provides a mathematical question and answer process teaching system, which comprises a user interaction module, a question analysis and standardization module, a cognitive diagnosis engine, a generative teaching module, a mathematical knowledge graph database and a user portrait database, teaching content and guiding questions are presented to the user in a multi-mode form; the question analysis and standardization module is used for performing semantic understanding and structured analysis on mathematical questions and generating standardized question description and at least one standard question solving path; according to the method, the structured reasoning of the knowledge graph is combined with the natural language generation ability of the generative AI, so that a closed loop from'problem solving 'to'diagnosis' to'heuristic teaching 'is realized, thinking blockage points of students can be accurately positioned and personalized tutoring can be provided like human teachers, and the learning efficiency and the autonomous thinking ability are remarkably improved.

The invention provides a large model retrieval enhancement generation method fused with a multi-source knowledge base, and relates to the technical field of large model retrieval enhancement generation. The method comprises the steps that firstly, user query is analyzed, multiple implied independent retrieval intentions are recognized and separated, and a knowledge source identifier is distributed to each retrieval intention subtask based on a mapping rule; according to the method, related evidence fragments are acquired in parallel from a heterogeneous knowledge source library in a targeted manner, multi-level relation analysis is further performed on the original evidence fragments by introducing a cross validation engine, so that a structured evidence conflict graph is constructed, and then the evidence conflict graph is intelligently processed by applying a game theory excitation algorithm. The method comprises the following steps: obtaining an evidence set with internal consistency, forming a guide signal through structured analysis, and finally, inputting original user query, the synthesized evidence set and the guide signal into a large language model in combination with a pre-constructed cue word template to generate a final answer under constraint, so as to realize retrieval enhancement generation fused with multi-source knowledge base retrieval.

The invention discloses a mining equipment state prediction method based on digital twinning and multi-modal fusion, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: inputting a multi-modal alignment working condition window sequence into a digital twinning mechanism link, carrying out the same-window ideal response deduction, outputting an ideal multi-modal response, and constructing a twinning dynamic residual error; meanwhile, segmenting a residual structure event to generate a residual event token set; positioning a multi-modal data fragment based on the residual event token set, executing cross-modal attention alignment, and performing topology propagation aggregation in combination with a mechanical topology observation mapping table to generate a topology constraint fusion table set; and inputting the topological constraint fusion table set into the graph neural network, carrying out message passing in combination with a mechanical topological graph, outputting a component state and a complete machine state, and packaging the component state and the complete machine state into a mining equipment state prediction set. According to the method, structured analysis of the twinborn dynamic residual error is realized, and the method is used for accurately positioning an abnormal event and improving the sensitivity and timeliness of state prediction.

The invention discloses a video compliance monitoring system based on multistage event aggregation, which adopts a distributed two-stage architecture of an edge end and a center end, the edge end realizes video stream acquisition, dynamic sampling, multi-modal AI structured analysis and video segmentation buffer storage, and pushes an analysis result to the center end in real time, and the center end receives data and then sends the data to the edge end. Through a three-stage analysis system including atomic event extraction, session scene recognition and cross-time-period violation detection, violation detection is completed, a backtracking video with a superimposed mark can be generated, and a violation record and a video URL are pushed to a service system, so that efficient analysis and accurate backtracking of video compliance monitoring are realized; the invention further relates to a video compliance monitoring method based on multi-level event aggregation.

The invention relates to the technical field of emergency rescue intelligent decision, and discloses an emergency rescue management system and method based on rescue timeliness. The method comprises the following steps: constructing a space-time three-dimensional grid by fusing multi-source information, and performing dynamic scene reconstruction by using a multi-level mapping rule to obtain preliminary and deep rescue scenes. And after combined anomaly diagnosis is carried out on the double scenes, a task topological structure is driven to dynamically evolve according to a diagnosis report, and a rescue task sequence is generated. And then cross-dimension resource adaptation is executed, a resource list is formed, key bottleneck nodes are predicted and identified in combination with real-time feedback, and finally a rescue instruction chain is generated. According to the method, deep fusion and structured analysis of field information in a space-time dimension are realized, a closed-loop decision-making mechanism with task and resource dynamic cooperation and adaptive optimization is established, and timeliness and overall toughness of rescue actions in a complex disaster environment are enhanced.

The invention discloses a multi-model inference chain data analysis method and device based on natural language input, a medium and a program product. The method comprises the steps that an analysis problem text in a natural language form is analyzed into a semantic task structure, and the semantic task structure is mapped into a structured analysis model conforming to a preset specification; registering the structured analysis model as an instance to an analysis model library, allocating a unique identifier ID to the structured analysis model during registration, and writing attribution relation information of the structured analysis model; an upstream model ID and a downstream model ID in the attribution relation information of the instance are read, the attribution relation information of the upstream model and the downstream model is analyzed, layer-by-layer expansion is carried out in the mode, and a multi-model reasoning chain of the instance is constructed according to the dependency relation between the models; and according to a sequence determined by the multi-model reasoning chain, executing each model instance in sequence or in parallel, and integrating the obtained final output into a comprehensive analysis report. According to the invention, automatic execution from a natural language to full-process data analysis can be realized.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The invention relates to the crossing field of an artificial intelligence technology and an automotive electronic technology, and discloses a voice interaction question answering method and system based on a vehicle-mounted large model knowledge base, and the method comprises the following steps: receiving a user query text, and executing intention recognition and structured analysis; based on an analysis result, constructing and executing a mixed retrieval request including structured filtering, full-text keyword retrieval and semantic vector retrieval; fusing and reordering the plurality of retrieval result sets obtained respectively to obtain a high-quality final retrieval result set; and finally, constructing a context based on the final retrieval result set and a user query text, and calling a large language model to generate and output a final answer. By introducing a hybrid retrieval and fusion reordering mechanism, comprehensive context information is provided for a large language model, the accuracy and reliability of questions and answers are improved, and the ability of a system to understand and process complex fuzzy queries is enhanced.

The invention relates to the technical field of computer vision and edge computing, and discloses a multi-computing-power collaborative video structured analysis method, which comprises the following steps of: acquiring a plurality of paths of original video streams, and synchronously decoding the plurality of paths of original video streams; gradient analysis is adopted to evaluate the decoded multiple paths of original video streams, and a preprocessing algorithm combination is selected based on a quality feature vector; establishing a data transmission channel, and adopting an asynchronous communication protocol and a multi-level cache system; performing content complexity analysis, and performing model matching according to the content complexity analysis to obtain a scheduling decision; carrying out distributed deployment, and carrying out pipeline reasoning on the deployed model; pipeline reasoning results are fused, and consistency constraint verification is carried out on fusion results; optimizing system configuration parameters according to a video structured analysis result to realize video structured analysis; the real-time performance, the accuracy and the system stability of video analysis are improved.

The invention discloses a Transform-based digital textbook automatic hierarchical design method. The method comprises the steps of obtaining digital textbook content, performing structured analysis, dividing the digital textbook content into a plurality of content units, and allocating a unique identifier; an improved Transform model is input, semantic modeling is executed, the cognitive hierarchy is controlled, and folding representation is generated; based on the folded semantic representation, initial layering is executed, a layered structure is constructed, and a sequence and hierarchical relationship is generated; constructing a self-evolution hierarchical decision maker, detecting conflicts in a hierarchical structure, and identifying a hierarchical inconsistent relationship; re-executing hierarchical processing based on the conflict relation updating rule to generate an updated hierarchical structure; and performing disturbance processing on the textbook structure, repeating modeling and layering processes, and verifying a hierarchical stability result. According to the method, the stable and automatic hierarchical design of the digital textbook is realized by improving Transform semantic modeling and self-evolution hierarchical decision.

The invention discloses a multi-modal sensing edge intelligent monitoring system, which relates to the field of edge intelligent monitoring and comprises a multi-modal data acquisition module for acquiring multi-modal data; the edge intelligent processing module performs space-time registration, feature extraction and feature level fusion on the multi-modal data to generate a multi-modal feature vector; performing joint reasoning on the multi-modal feature vector by adopting an intelligent analysis model, outputting a structured analysis result, and generating a local early warning instruction when the danger level indicated by the structured analysis result exceeds a threshold value; and the central control module carries out early warning information issuing, data archiving and model iteration training according to the structured analysis result. Localized feature level fusion and intelligent analysis are carried out on multi-modal data through an edge intelligent processing module, and low-delay risk identification and early warning are realized. Meanwhile, continuous optimization and global situation awareness of the model are realized through a cloud edge coordination mechanism, and the defects of the traditional architecture in real-time performance, reliability and intelligent level are effectively overcome.

The invention provides a subject intelligent evaluation informatization system based on medical record home page data. Comprising a data integration and treatment module, a data standardization processing module, a core index intelligent calculation engine, a subject competitiveness multi-dimensional evaluation model, a DRG / DIP analysis application module, a medical quality and safety monitoring module, an intelligent report generation and visualization platform and a system management and configuration center. Through full-process automatic and intelligent analysis, multi-source data can be automatically integrated and strict quality control and standardization processing can be carried out, the accuracy and consistency of evaluation data are guaranteed from the source, complex analysis from basic index operation to comprehensive competitive power score ranking can be rapidly and accurately completed, and the method is suitable for large-scale popularization and application. And the result is visually presented through a visual platform, and even a structured analysis report is automatically generated, so that managers are liberated from tedious data processing and can concentrate on decision analysis, and the management efficiency and the response speed are remarkably improved.

The invention discloses a self-adaptive software test case generation optimization method, which comprises the following steps of: carrying out structured analysis on service requirements of a target system, extracting multi-dimensional test requirements of performance, resources, risks and coverage rates, and carrying out tagging and normalization processing to form a quantifiable index system; based on a meta-strategy layer decision engine, fusion reinforcement learning and fuzzy reasoning are carried out to realize multi-target weight distribution and dynamic compromise; generating and optimizing a preferred case set by means of a trade-off adaptive algorithm, performing conflict marking and constraint cooperative adjustment, collecting feedback data through automatic testing, correcting weight distribution and conflict detection rules according to actual results, and continuously iterating case generation strategies; the objective achievement degree is periodically evaluated, the strategy model is reconstructed or switched to adapt to scene changes, the accuracy of test requirement expression and the self-adaptability of the use case generation strategy are improved, and efficient collaborative optimization of multi-objective testing and dynamic adaptation of service scenes are achieved.

The invention discloses a mineral mill operation health condition evaluation method based on multi-source data fusion, and the method comprises the steps: firstly collecting vibration data, oil data and operation data, and carrying out the standardized preprocessing and time synchronization of the data; then classifying the multi-source data to a part hierarchy in a part-part-measuring point three-level classification system established based on an equipment physical structure; a part-level multi-dimensional feature matrix is constructed through timestamp matching, and heterogeneous data structured alignment is achieved; screening a strong correlation measuring point group through correlation analysis, and extracting comprehensive features through PCA dimension reduction; and finally, processing the fused feature vector based on an isolated forest algorithm, and quantitatively outputting a health degree score of the mineral mill. According to the method, through deep fusion of multi-source data and part-level structured analysis, the one-sidedness of single data source evaluation is overcome, the operation health condition of the mineral mill can be comprehensively, accurately and visually reflected, and a reliable basis is provided for predictive maintenance.

A nuclear power scene-oriented multi-source heterogeneous data acquisition and processing system realizes thorough separation of equipment access and data understanding through a decoupling architecture of acquisition side plug-in and service side normalization, acquisition plug-in only transmits original data, and semantic analysis is centrally processed by a unified physical model engine; the problem of hard coding coupling of the plug-in and the business logic is avoided; by defining a standardized equipment abstract model and shielding bottom layer manufacturer, protocol and format differences, various kinds of heterogeneous data are standardized on the semantic level, unified modeling and semantic alignment of the multi-source heterogeneous data are achieved, data of various internet of things equipment sources are effectively integrated, and the data processing efficiency is improved. The problems of unclear data semantics and difficulty in unified use caused by diverse equipment manufacturers, different communication protocols and heterogeneous data formats are solved, cross-system linkage is promoted, semantic understanding, structured analysis and standardized presentation of cross-protocol and cross-platform equipment data are realized, and the safety, efficiency and management level of a nuclear power station are improved.

The invention belongs to the technical field of virtual reality, and particularly relates to a virtual-real fusion scene automatic construction method based on AIGC script generation, which comprises the following steps: step 1, inputting a script text input by a user into a large language model for structured analysis, generating a corresponding three-dimensional geometric grid, a surface texture map and an initial placement position according to the updated feature vector of each node; 2, collecting an eye image sequence of a user through an eye movement tracking module, and executing boundary fusion processing on rendering results of all rendering areas to output adaptive rendering frames; and step 3, performing collision detection and iterative position correction on each three-dimensional geometric grid, and performing assembly and serialization export on each corrected three-dimensional geometric grid. According to the invention, end-to-end automatic construction from the script text to the interactive virtual-real fusion scene is realized.

The invention discloses a CVE-ATTamp (Cascade Vector Enhanced ATTamp) for a network security scene. The invention discloses a CK association fusion RAG construction method, and relates to the technical field of network security. The method comprises the following steps: (1) carrying out structured analysis and vectorization on CVE vulnerability information data, extracting features and fusing CVSS scores to carry out semantic embedding; (2) ATTamp; semantic modeling of a CK threat matrix is carried out, and a threat semantic space containing tactical level information is constructed; (3) heterogeneous knowledge association and graph construction: calculating cross-domain semantic similarity and establishing logic mapping between vulnerabilities and tactics in combination with expert rules to form a heterogeneous knowledge graph; and (4) attack logic-oriented RAG retrieval and generation: retrieving a key attack path sub-graph based on a hybrid scoring mechanism, and driving a large model to generate a security analysis report with logic depth. According to the method, attack logic is stored through mapping, so that the problems that the traditional RAG lacks inference ability and security data fragmentation are solved, and the accuracy and tactical value of automatic threat analysis are remarkably improved.

The invention relates to the technical field of system compliance management, and discloses a system compliance early warning method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting system file data, and carrying out the standardization processing and structural analysis; data preparation is carried out based on the system metadatabase and the supervision fine tuning corpus set, an initial model is constructed and trained, a trained system large model is formed, and version management is carried out; determining early warning types, establishing a system revision early warning model for different early warning types, and triggering early warning reminding according to an output result of the early warning model; performing difference comparative analysis on the same-theme systems based on the system large model, and generating a compliance inspection report; receiving a natural language question, generating an answer and providing system traceability information; a system outline is generated according to system revision requirements, a chapter summary and clause content are generated based on the system outline, and real-time compliance check and clause recommendation are supported. According to the invention, the intelligent demand of enterprise system full-life-cycle management can be met.

The invention relates to the technical field of computer data processing, in particular to an intelligent model system for automatically generating a standard protocol interface, which comprises the following steps: acquiring demand information input by a user through a demand acquisition module and converting the demand information into standardized demand parameters; the protocol knowledge base module outputs a compliance rule set according to the target protocol type, and the interface generation module automatically generates an interface source code, a configuration file and an interface document according to the standardized demand parameters and the compliance rule set. The verification module verifies and optimizes the generated interface, and the self-learning module updates system analysis logic and rule content according to a verification result. The system adopts a natural language processing and structured analysis double-path fusion technology to realize accurate analysis of requirements, realizes intelligent matching of protocol rules through a protocol knowledge graph, completes automatic generation of codes based on a symbol reasoning and neural code generation technology, forms a complete closed loop of requirement analysis, protocol adaptation, code generation and verification optimization, and realizes automatic analysis of the requirements. And the interface development efficiency and quality are effectively improved.

The invention discloses an information fusion and reasoning method and system based on multi-agent collaborative networking search, and relates to the technical field of network information, and the method comprises the steps: a deep research strategy scheduling agent deconstructs an unstructured request through a task formalization mechanism, generates a high-dimensional instruction code, and dynamically optimizes an execution path. And the network intelligence index generation agent extracts and screens the trusted URL by using a rule engine and a semantic evaluator based on the code, and generates a structured link instruction stream. The structured analysis intelligent agent implements multi-level semantic distillation and conversion on an original document to obtain key value pair representations, and the key value pair representations are packaged into machine readable data. And generating a comprehensive intelligence abstract with consistent logic through the information fusion analysis agent. And the natural language generation engine converts the information into a professional report, and returns the professional report to the scheduling module through a feedback interface to realize cognitive closed-loop iterative optimization. The technical problem that in the prior art, it is difficult for geologists to obtain accurate, comprehensive and credible professional conclusions is solved.

A cross-modal collaborative analysis method based on a multi-modal bid invitation file comprises the following stages: a data analysis stage: converting the multi-modal bid invitation file into a picture format, then identifying a content type and cutting to obtain fragment information; processing the table fragment information to obtain structured table data, and processing the picture fragment information to obtain structured picture data; then associating the text fragment information, the table fragment information and the picture fragment information to obtain integrated multi-modal data; a target item extraction stage: extracting a target item in the bid invitation file; and a consistency verification stage: generating a structured analysis result. According to the method, automatic analysis and association of multi-modal information of texts, tables and drawings in the bid invitation file and consistency check conflict detection work are realized, and the problems of splitting of the multi-modal information of the texts, the tables and the pictures, low efficiency and easiness in making mistakes in traditional manual analysis are solved.

The invention relates to the technical field of computer vision and artificial intelligence, in particular to a knowledge-guided meter reading method and system based on a visual language large model, and the method comprises the following steps: a template obtaining step: obtaining a meter image, and calling a structured reading prompt template containing metadata and an inference logic instruction based on the type; a multi-modal input step: taking the image and the template as multi-modal data and inputting the multi-modal data into the visual language large model; a reasoning generation step: extracting visual features, performing attention alignment on the visual features and the template, responding to an instruction to execute thinking chain reasoning, and generating a natural language description result containing an intermediate derivation basis; an analysis output step: performing structured analysis on the result, and extracting a meter reading value conforming to the format constraint; according to the invention, deep fusion of business logic and visual perception is realized, man-machine trust is established, and logic mistake of a pure visual model is avoided.

The invention discloses an equipment fault and maintenance knowledge intelligent association method based on a large model, and relates to the technical field of equipment intelligent operation and maintenance and industrial artificial intelligence, and the method comprises the steps: carrying out the structural analysis of historical fault work order data and maintenance record data of target equipment, and constructing a fault-maintenance knowledge pair data set; performing semantic coding on the fault description text and the maintenance scheme text by using a pre-trained large model, generating a corresponding semantic vector, and constructing a fault-maintenance semantic association map; and performing semantic coding on the real-time fault description, screening candidate fault nodes based on semantic similarity, calculating the semantic matching degree of the maintenance scheme in the semantic association map, and outputting a target maintenance scheme sequence after matching degree sorting. According to the invention, the intelligent level of equipment fault maintenance and the reuse efficiency of historical maintenance knowledge are improved.

The invention belongs to the technical field of intelligence analysis, and particularly discloses a knowledge graph driven trend intelligence prediction method, which comprises the steps of collecting multi-source heterogeneous intelligence data and constructing a target trend knowledge graph, generating and screening a prediction index group based on the knowledge graph, performing classification and quantitative weighting on the prediction index group, and obtaining a target trend information prediction result. Drawing a threat curve graph, and constructing a trend intelligence prediction model based on the threat curve graph to output an action trend prediction result of the monitored object; the method has the following advantages that multi-source intelligence can be systematically fused, structural analysis is achieved, the misjudgment risk caused by data noise and strategic deception is effectively reduced, the prediction accuracy and foresight are improved through quantitative weighting and probabilistic reasoning, and therefore intelligent, dynamic and high-reliability technical support is provided for strategic early warning.

The invention discloses a code change generation method based on a multi-agent proxy library migration scene, which can accurately locate all code action ranges related to library migration in a file range by performing structured analysis on a source code file and combining source library import information and code feature recognition, and can improve the code change generation efficiency. The problems of omission and misjudgment caused by manual analysis or simple rule matching are avoided, and the integrity and accuracy of migration site recognition are improved. According to the method, the corresponding migration plan is generated by combining key information of the source library and the target library aiming at the identified action range to be migrated, so that the code change process is not limited to simple interface replacement any more, targeted adjustment can be performed according to a specific use scene, and the consistency of migrated codes in terms of semantics and behaviors is ensured. According to the method, through a collaborative code change generation and verification mechanism, the code change related to library migration is automatically generated and evaluated, and the reliability and availability of a generation result are improved while manual intervention is reduced.

The invention relates to an automatic verification method and system for a power distribution station project file, and the method comprises the following steps: deep analysis of multi-modal information: receiving design assets of a power project of a power distribution station, carrying out the multi-modal structured analysis, and generating structured design data; performing rigidity specification verification: inputting the structured design data into a rule engine, executing rigidity specification verification in combination with rule terms, and outputting a first type of verification result; prediction-reasoning evaluation: performing risk prediction and optimization reasoning on the structured design assets based on a large language model to obtain a second type of evaluation result; and intelligent report generation: fusing the first type of verification result and the second type of evaluation result, and automatically generating a visual review report. Compared with the prior art, the system has the advantages of intelligent examination and the like.

The invention relates to the technical field of artificial intelligence and the field of financial services, in particular to a contract content verification method and device based on artificial intelligence, equipment and a medium. According to the method, a to-be-checked contract text is obtained, structured extraction is carried out on the contract text, a structured text representing paragraph marks, clause types and title levels is determined, compliance judgment results and result accuracy corresponding to each input collaborative agent are aggregated, and a check result with a comparison result being the corresponding contract text is obtained. The method can be applied to a financial service scene, the to-be-checked contract text is firstly obtained and subjected to structured extraction to obtain the structured text containing the paragraph mark, the clause type and the title level, and the contract text check result is obtained by comparing the aggregation result with the preset check condition. Therefore, the to-be-checked contract text is comprehensively subjected to structured analysis, and the contract text is automatically checked to obtain a check result.

The invention discloses a general document structured analysis method, and relates to the field of document intelligent analysis, and the method comprises the following steps: carrying out global layout analysis based on a thumbnail to obtain each layout area position; performing local content identification on each layout area based on the native resolution to obtain corresponding element contents; and sorting and splicing the element contents to obtain the structured document. Through decoupling global layout analysis and local content recognition, computing resources are accurately put into an information area, and unification of efficiency and precision is achieved; and multiple tasks are processed through a unified model and special elements are processed through a special algorithm, so that the integrity and accuracy of analysis are ensured.

The invention relates to the technical field of geological information, and discloses a mineral resource potential rapid evaluation method and system based on big data, and the method comprises the steps: automatically collecting geological, remote sensing, geophysical and geochemical exploration and mining data through a multi-source interface, executing intelligent format recognition and structural analysis, and storing the data into a distributed data lake; scheduling parallel computing nodes according to task parameters, synchronously operating five sub-models including space superposition, geochemical anomaly identification, construction of mine control factor extraction, remote sensing alteration enhancement and mining intensity regression; and generating a comprehensive potential evaluation map through dynamic weighted fusion, and outputting a visual result and an uncertainty quantitative index. According to the technical scheme, the evaluation period is shortened from several weeks to several hours, the data processing accuracy is improved to 97% or above, the hit rate of the evaluation result in the known ore deposit area reaches 89%, and the mineral exploration efficiency and decision scientificity are improved.