98 results about "Knowledge utilization" patented technology

The invention discloses a traffic large model construction and decision-making method and device based on multi-modal two-way map reasoning, and the method comprises the steps: constructing a multi-modal data set of a text, an image and a track, generating fusion features through spatial-temporal clustering and cross-modal Transform coding, carrying out the two-way map reasoning in combination with a traffic knowledge map, and carrying out the decision-making of the traffic large model. The method comprises the following steps: generating an embedded representation through a forward graph neural network, reversely mapping a decision scheme generated by a language model to a graph to verify consistency, outputting knowledge to enhance embedding, fusing multi-modal features and knowledge embedding by adopting an LoRA multi-task joint fine tuning technology, adapting to traffic field tasks, deploying a real-time inference engine, and carrying out real-time inference on the traffic field. And processing the dynamic data flow through an aging perception attention mechanism, and outputting traffic event identification, path planning and scene question and answer results in parallel. Compared with the prior art, the method has the advantages that the problems of insufficient multi-source heterogeneous data fusion, low knowledge utilization efficiency and poor real-time decision consistency can be solved, and the semantic understanding and decision accuracy of the traffic large model is effectively improved.

The invention discloses a power transformer fault auxiliary decision-making method and system based on a knowledge graph and a large language model, and the method comprises the steps: obtaining power transformer fault text data, carrying out the preprocessing, obtaining fault-related text and table data, preliminarily defining an ontology, selecting a part of text data for marking, and carrying out the recognition of the ontology; a plurality of named entity recognition and relation extraction models are trained, an optimal model is determined, then triple extraction is carried out on unlabeled text data, and a knowledge graph is constructed; predicting a potential entity relationship in the knowledge graph based on a link prediction model, and complementing the knowledge graph under the large language model and human assistance; constructing a semantic search model training data set based on the large language model and training a semantic search model; based on the complemented knowledge graph, the trained semantic search model is utilized to retrieve knowledge graph sub-graphs according to the problem, and auxiliary decision making is completed. According to the method, the problem of low potential knowledge utilization level in the existing power transformer fault auxiliary decision-making based on the knowledge graph is solved.

The invention discloses a federal large model knowledge collaborative training method supporting multi-modal heterogeneous clients, which comprises the following steps: each client receives a model initialization parameter issued by a central server, and applies adaptive differential privacy noise to independently train a heterogeneous lightweight model based on local private data; updating the model to which the noise is applied and uploading a modal identifier of the model to a central server side; after model updating and modal identification of each client are received, based on a modal perception weighted consensus fusion mechanism, knowledge of each client is fused to update a global large model; and the central server side issues the updated presentation layer parameters of the global large model to the client side for initialization of the next round of local training. According to the method, on the premise that a public data set or specific task setting is not needed, comprehensive compatibility of data isomerism, client dynamic participation, model diversity and privacy protection requirements is achieved, and the adaptability, stability and knowledge utilization efficiency of large model federation training are remarkably improved.

The invention provides a large language model knowledge preference alignment method and system based on self-supervised learning. The method comprises the following steps: defining knowledge availability comprising three dimensions; generating and constructing preference data based on retrieval enhancement, obtaining constructed data according to the generated questions, answers and context paragraphs, evaluating the data through a defined knowledge utilization degree, and screening out samples for training; and training samples through a preference optimization method comprising a self-supervision module and knowledge perception to perform preference alignment, wherein the self-supervision module integrates knowledge utilization degree as a supervision signal into model training. Based on the technical scheme of the invention, the data acquisition cost is effectively reduced, the problems of scarcity of field data and difficulty in labeling are solved, and the data quality is improved; and the understanding and application capability of the model on professional knowledge is improved, and effective alignment of the model and domain expert knowledge application is realized.

The invention relates to a knowledge graph enhanced multi-modal file retrieval method, and belongs to the field of artificial intelligence and multi-modal information retrieval. The invention aims to solve the problems of multi-modal information semantic segmentation, weak semantic reasoning ability and low semantic matching precision in the existing multi-modal archive resource retrieval process. Through four stages of archive multi-modal data preprocessing and feature extraction, knowledge graph construction and enhancement, semantic retrieval request analysis and intention modeling, and multi-modal semantic matching and sorting, a semantic relationship is enhanced by utilizing a knowledge graph, so that the semantic relevancy and context consistency of a retrieval result are remarkably improved; a user is allowed to input and inquire in various forms such as texts, images and voices, and semantic extension retrieval is supported. According to the method, more efficient and accurate archive resource retrieval can be realized, and the user retrieval experience and archive knowledge utilization are improved.

The invention discloses a global knowledge extraction method and system based on a large model and an RAG technology, and belongs to the technical field of large model data processing. According to the global knowledge extraction method and system based on the large model and the RAG technology, local sensitive hashing is adopted for duplicate removal, similar texts are combined through dynamic thresholds, and redundant data interference is reduced; a text segmentation strategy based on separator confidence ensures that segmented text blocks maintain semantic integrity and are adaptive to large model input length limitation, similarity retrieval and full-text retrieval are fused, results are reordered in combination with an RRF algorithm, semantic relevance and accuracy are considered, the multi-scene knowledge construction requirement is met, and the multi-scene knowledge construction efficiency is improved. High-correlation entity pairs are screened through mutual information, context knowledge is retrieved from a vector database in combination with an RAG system, semantic reasoning is conducted through a large language model, hidden logic relations among entities are generated, and jumping from data association to knowledge generation is achieved.

The invention discloses a DeepSeek-based biomass gasifier control optimization method and a DeepSeek-based biomass gasifier control optimization system. The DeepSeek-based biomass gasifier control optimization method comprises the following steps of: acquiring operation data of a biomass gasifier in real time and in all directions by utilizing multiple sensors and preprocessing the operation data; collecting and organizing professional knowledge related to biomass gasification, and constructing a knowledge graph in combination with the preprocessed biomass gasifier operation data; taking the preprocessed data as input, taking the constructed knowledge graph as a knowledge base, designing a training objective function of a comprehensive performance index through transfer learning, and combining reinforcement learning with a DeepSeek model to carry out model training and optimization; the DeepSeek model utilizes the trained model to predict an optimal control strategy according to real-time gasifier operation data and knowledge graph related knowledge and converts the optimal control strategy into an instruction for execution; s5, adjusting a control strategy in real time through online monitoring and feedback optimization; according to the method, the control precision and adaptability are remarkably improved, and the model generalization and adaptability are enhanced.

The invention discloses a postpartum rehabilitation intelligent evaluation and early warning system, and the system comprises a postpartum rehabilitation knowledge base which is used for carrying out the knowledge extraction and structuring of postpartum rehabilitation data based on a medical coding marker technology, obtaining a medical vector representation, constructing a postpartum rehabilitation knowledge graph based on the medical vector representation, and obtaining a postpartum rehabilitation knowledge graph; the postpartum rehabilitation knowledge graph is stored and updated based on the graph database and knowledge representation learning; and the intelligent evaluation and personalized recommendation system is used for performing fusion and depth feature extraction on the multi-modal data based on the trans-attention auto-regression Transform multi-modal electronic health record to obtain multi-modal depth features, performing risk prediction on the multi-modal depth features by using a deep learning model, and performing recommendation on the multi-modal depth features. And generating a personalized rehabilitation scheme based on case reasoning and expert rules. The early warning decision support system is used for carrying out risk early warning by using a multi-dimensional early warning model in combination with the risk prediction result; and the remote expert consultation and cooperation platform is used for remote cooperation guidance. In this way, the problems of experience dependence, insufficient knowledge utilization and the like of traditional postpartum rehabilitation can be solved.

The embodiment of the invention provides a knowledge base processing method and device, computer equipment, a medium and a program product. The knowledge base processing method comprises the steps of receiving a sharing voucher of a shared knowledge base; in response to an access operation on the sharing voucher, displaying a shared sharing knowledge base which comprises at least one knowledge file; questioning information of the shared knowledge base is received; and displaying answer information of the question information, wherein the answer information is obtained by answering the question information based on the knowledge file in the shared knowledge base. By adopting the embodiment of the invention, the knowledge utilization rate of the knowledge base can be improved.

The invention discloses an assembly process knowledge graph construction and updating method based on graph attention network mapping, and the method mainly comprises the following steps: standardization system design construction, data collection, data classification, multi-modal data preprocessing, multi-source information extraction and graph node feature initialization. Semantic modeling and vector space mapping driven by a graph attention network, knowledge storage, construction of an assembly process knowledge graph by using stored knowledge data, and semantic complementation and dynamic updating of a process information model. Through the unique self-attention mechanism of the GAT, the weight can be dynamically distributed for the relationship among different information nodes, so that knowledge reasoning and expression are more accurately carried out, and the problems of knowledge fragmentation, management rigidity, semantic understanding deficiency, low knowledge utilization efficiency and the like in the prior art are systematically solved.

The invention discloses a power grid equipment health management and operation and maintenance platform based on knowledge federation and a language model, and relates to the technical field of power grid equipment health management and intelligent operation and maintenance. Comprising a multi-source data fusion module, a knowledge federal management system, a large language model, an intelligent analysis and prediction module, an operation and maintenance decision support module and a visual display module, and the large language model can be a DeepSeek-R1 large model. According to the method, the knowledge federation technology and the breakthrough AI capacity of the DeepSeek-R1 large model are creatively fused, remarkable technical innovation and function breakthrough are achieved in the aspects of data fusion, knowledge utilization, man-machine interaction, intelligent decision making and the like, and the limitation of an existing power grid operation and maintenance technology can be effectively overcome; the intelligent and active preventive level of operation and maintenance of power grid equipment is comprehensively improved, and key technical support is provided for constructing a safer, more reliable and more efficient intelligent power grid.

In a domain knowledge utilization system in which a utilization algorithm for utilizing domain knowledge described by a graph for construction and training of a prediction model can be selected, a utilization algorithm that is selectable by an algorithm selection unit includes at least one or more of a first utilization algorithm using a feature derived from the graph as an explanatory variable of the prediction model, a second utilization algorithm applying a relationship between nodes in the graph to a relationship between explanatory variables of the prediction model, and a third utilization algorithm applying a definition of a node in the graph to a training condition of the prediction model.

The invention discloses a role playing model fusion system and method based on retrieval and reflection, and belongs to the technical field of natural language processing. The implementation method comprises the following steps: 1, performing data cleaning on original role historical dialogue data, and performing semantic screening by using semantic similarity to form historical dialogue context data; 2, event knowledge associated with the roles is constructed through association degree matching; 3, constructing a role reflection strategy mode by using historical dialogue data, and generating role reflection data; 4, when a user inputs a question, replying by adopting a multi-source fusion mode based on role thinking; compared with the prior art, in a multi-scene task, a dynamic retrieval role knowledge base is utilized to screen task related background knowledge, and a thinking mode modeling framework based on a role reflection mechanism and a context fusion strategy are constructed to generate high-quality answers conforming to the context; and thus, the fusion ability of the role playing model is improved, and the consistency of role words and language styles is realized.

The invention relates to a converter steelmaking knowledge graph construction method and device and computer equipment, and the method comprises the steps: constructing an ontology hierarchy according to collected multi-source heterogeneous data, then carrying out corresponding knowledge extraction on different types of data based on an ontology hierarchy structure, employing a similarity calculation hierarchical clustering method based on TF-IDF and SBERT, and carrying out knowledge extraction on different types of data; and converter steelmaking knowledge is processed and fused, so that the data quality of the knowledge graph is ensured. Multi-source knowledge dispersed in a technical document, an operation manual, an enterprise informatization database and the like is systematically integrated to form a structured knowledge base, which has positive influence on improving information retrieval and knowledge utilization and acquisition processes of converter steelmaking and provides solid knowledge data support for subsequent implementation of converter intelligent steelmaking.

The invention discloses a pollutant toxicity assessment method and device based on RAG enhancement and knowledge graph reasoning, equipment and a medium, and relates to the technical field of pollutant assessment, and the method comprises the steps: constructing a standardized toxicological knowledge graph through multi-source data collection and preprocessing; by combining RAG enhanced retrieval and knowledge graph reasoning, a toxicity reasoning path and an evaluation index are rapidly generated, meanwhile, interpretable content is provided, multi-source heterogeneous data fusion and self-adaption to spectral characteristic differences of different sites are achieved, the problems that traditional evaluation is low in efficiency, high in cost, insufficient in knowledge utilization and the like are effectively solved, rapid and accurate evaluation is achieved, and the evaluation efficiency is improved. And the credibility and the interpretability of the evaluation result are enhanced.

The invention relates to a knowledge graph construction and retrieval optimization method based on water affair data, and the method comprises the steps: taking the unified knowledge expression of a water affair object and an event thereof as a target, determining a water affair business field and business range, and constructing a water affair knowledge graph conceptual model covering a static knowledge ontology and a dynamic event ontology; performing knowledge extraction on the obtained multi-source heterogeneous water affair data, converting the data into a unified triple form, and constructing a data layer of a water affair knowledge graph; and on the basis of the data layer, node, path and region query is performed on the knowledge graph by adopting a graph algorithm, and semantic enhancement retrieval and reasoning are performed in combination with a large language model. By constructing the water affair knowledge graph and introducing knowledge extraction and retrieval enhancement capability of a large language model, dynamic modeling and semantic reasoning of water affair objects, events and relationships thereof are realized, high-risk area identification, emergency path planning and emergency early warning are supported, and the knowledge utilization rate and intelligent decision level in the water affair field are improved.

The invention relates to the technical field of big language model reasoning, and provides a robust retrieval enhancement method and device oriented to a big language model. According to the method, semantic analysis is carried out on user query in a real scene, a key entity is positioned, then a core entity is taken as an initial retrieval point, a credible iterative retrieval strategy is executed, associated information in a knowledge graph is obtained step by step, factual verification is carried out, the limitation of a traditional RAG framework on the key entity in the real scene is broken through, and the reliability of the key entity in the real scene is improved. Robust reasoning and knowledge utilization of the LLM under the conditions that reliable predefined associated entities are lacked and query semantics are complex are achieved, and robustness and fact consistency of the model in a complex knowledge graph question and answer task are improved.

The invention discloses a retrieval enhancement generation method and system based on long-term memory and multi-source knowledge iterative fusion. The method comprises the following steps: firstly, after user inquiry is received, triggering long-term memory database retrieval, external knowledge base retrieval and large language model internal knowledge generation in parallel to construct a multi-source candidate knowledge base, and improving result correlation by adopting a self-adaptive retrieval method based on a dynamic top-k value; secondly, inputting user query and multi-source knowledge into the large language model for iterative fusion, and generating a final answer through conflict detection, fusion processing and confidence evaluation; and finally updating the long-term memory database. The method can improve the accuracy and stability of the generated answers in a multi-round interaction and complex task scene, reduces the risk of answer one-sidedness, inference incompleteness or fact deviation, enhances the multi-source knowledge utilization ability and long-term learning ability of the system, and improves the user experience. Therefore, the comprehensive performance and reliability of the system in applications such as intelligent question answering, information retrieval assistance and text generation are obviously expanded.

The invention provides an electric power complex question-answering method and device based on large-model multi-agent, and relates to the technical field of electric power natural language processing. The method comprises the steps that a domain knowledge base is constructed, and a large-model multi-agent collaborative reasoning framework is constructed and comprises a service processing agent, a domain knowledge agent and an action planning agent; acquiring a natural language problem of a target electric power complex task input by a user by using a business processing agent; interaction between the business processing agent and the action planning agent is utilized to obtain action planning knowledge of the natural language problem; the business processing agent interacts with the domain knowledge agent, and domain knowledge of the target electric power complex task is obtained from the domain knowledge base; and generating an analysis result of the target electric power complex task in combination with the action planning knowledge and the domain knowledge in the business processing agent. According to the invention, the accuracy and reliability of power complex problem results can be improved.

The embodiment of the invention provides a chemical device question answering method and system fused with a knowledge graph, and belongs to the technical field of semantic recognition. The method comprises the following steps: collecting knowledge information of a chemical device, and performing vector representation on words in the knowledge information; performing feature extraction on the knowledge information after vector representation based on a pre-trained entity relationship joint extraction model, and constructing a knowledge graph based on extracted features; performing entity linking by taking the constructed knowledge graph as a knowledge base; and recovering question text information of the user, performing answer matching based on the knowledge base, and feeding back a matched answer to the user side. According to the scheme, the question-answering system is constructed based on the knowledge graph, users can answer questions and answers conveniently, and the knowledge utilization rate is increased.

The invention relates to the technical field of medical informatization, in particular to a clinical aid decision-making optimization method, device and equipment based on guide knowledge, and the method comprises the steps: obtaining electronic medical record data from a hospital information system and a laboratory information management system communication system, data standardization is achieved through the steps of duplicate removal, missing value processing, abnormal value processing, format standardization and the like; according to authoritative knowledge such as clinical pathways, diagnostic guidelines and disease consensus provided by doctors, key feature information is extracted by utilizing a natural language processing technology, structured data is extracted by adopting a regular expression, and unstructured texts are marked by pre-training a medical word vector dictionary, performing word segmentation by a classifier and performing part-of-speech tagging. The weight is optimized based on the loss function; an initial case knowledge base is constructed, and high-quality cases are screened out according to evaluation indexes such as knowledge richness and curative effect; dividing the high-quality cases into well-known physician cases and rare disease cases through syntactic analysis according to the classification indexes; case matching is carried out by adopting selective weighted heterogeneous value distance measurement, discrete attributes adopt value difference measurement, continuous attributes adopt standardized difference square, and attribute weights are optimized by means of a genetic algorithm, so that auxiliary clinical decision and knowledge services are realized. The invention can provide a scheme which can efficiently construct a high-quality case library, automatically extract key feature information, fuse guide knowledge for classification and screening, and introduce more accurate measurement and weight optimization in matching.

The invention discloses a vertical domain intelligence analysis method based on a large language model. The method comprises the following steps: receiving a natural language analysis demand of a user; based on pre-constructed vertical domain specific task planning knowledge, intelligently decomposing the demand into a subtask list corresponding to a preset dimension by using a large language model, and semantically matching a proper information acquisition tool for each subtask to generate a structured task plan; the system schedules and executes tool sets of network search, webpage capture, internal query and the like according to a plan, and performs cross verification and cleaning on collected fragmented information; and inputting the verified data into a large language model for integration analysis, and optionally carrying out multi-round iteration self-evaluation optimization to automatically generate a high-quality vertical domain intelligence analysis report. According to the method, full-process and high-intelligence automatic processing of information analysis in the vertical field from demand understanding to report generation is realized, and the method has excellent planning capability, process interpretability, result reliability and excellent modular expansibility.

The invention provides an autonomous vulnerability analysis system and method based on knowledge graph guidance and hierarchical verification, and relates to the technical field of automatic software vulnerability analysis, and the method comprises the steps: constructing a structured vulnerability knowledge graph by using unstructured historical vulnerability cases; obtaining a to-be-analyzed project, inputting the to-be-analyzed project into the large language model, taking the structured vulnerability knowledge graph as priori knowledge, and performing information retrieval by using a macroscopic mode and statistical evidence in the structured vulnerability knowledge graph; guiding a large language model to identify a pollution source and a risk pool based on a retrieval condition, and driving a static analysis engine to generate a preliminary suspected path; and screening the suspected paths through a two-stage grading review mechanism, finally outputting to obtain a high-credibility vulnerability, and generating a final high-credibility vulnerability report. The method is based on statistical law inference, so that the false alarm rate can be greatly reduced, and the discovery precision of real vulnerabilities is remarkably improved.

The application discloses a kind of urban land remote sensing image classification methods based on evolution multitask optimization, comprising: 1: obtaining urban land cover remote sensing image sample and pre-processing, to build sample dataset;2: the sample dataset is decomposed into multiple training subsets, and the corresponding sub-population is constructed for each training subset;3: the state of sub-population is evaluated to adjust the probability of executing knowledge transfer operation online;4: different source populations are selected according to the state of sub-population to provide knowledge for it;5: more optimal classifier is obtained by using the model representing global information;6: select a part of poor performance classifier to carry out unbalanced adjustment;7: finally, the best individual is selected from the sub-population to form a multi-classifier, to identify different types of urban land use and cover situation.The application can quickly classify different urban land types by constructing and training the optimal multi-classifier, so as to improve the accuracy and precision of land type classification result.

The application discloses a pollution toxicity evaluation method and device based on RAG enhancement and knowledge graph reasoning, equipment and medium, relates to the technical field of pollution evaluation, and comprises the following steps: constructing a standardized toxicology knowledge graph through multi-source data acquisition and preprocessing. Combined with RAG enhanced retrieval and knowledge graph reasoning, the toxicity reasoning path and evaluation index are quickly generated, the explainability content is provided, the multi-source heterogeneous data fusion and the adaptive difference of different site spectrum characteristics are realized, the problems of low efficiency, high cost, insufficient knowledge utilization and the like of the traditional evaluation are effectively solved, the rapid and accurate evaluation is realized, and the credibility and explainability of the evaluation result are enhanced.

The application discloses a kind of based on measurement and knowledge water surface unmanned system operating state early warning method, belong to unmanned system safety monitoring technical field.To solve the problem of insufficient knowledge utilization, poor dynamic adaptability of existing early warning method, the method of the application includes: collecting position, attitude, ship body state and environment multi-source time series data, and obtaining text form expert knowledge;Adopt sliding window and standardization preprocessing data;Expert knowledge semantic vector is extracted using BERT model;Through Crossformer model coding time series features, knowledge vector is spliced and fused, and system state features are output;Risk membership degree is calculated based on fuzzy set theory, and early warning is triggered according to the maximum membership principle.The application realizes the deep integration of measurement data and expert knowledge, improves the early warning accuracy, robustness and generalization ability.

The invention discloses a visual inertial navigation radar fusion self-positioning method based on a self-supervised neural network, and relates to the technical field of artificial intelligence and sensor fusion positioning, and the method comprises the following steps: constructing a meta-learner based on a long short-term memory network; according to the method, dynamic topology adaptation is realized by constructing the LSTM-based meta learning device, and topology adjustment can be completed and environment characteristics can be accurately captured in response to environment change within about 2 minutes; efficient knowledge migration and inheritance are realized by utilizing a knowledge memory module of an attention mechanism and a related algorithm, and new and old knowledge can be accurately fused to guarantee tunneling safety; through joint training and optimization and multi-module collaborative optimization of parameters, multi-target balance optimization is performed by adopting an NSGA-II algorithm, performance, efficiency and knowledge utilization are considered under different geological conditions, and the overall level of tunneling operation is improved.

The invention discloses a knowledge management method and system based on document similarity detection and a multi-modal knowledge base. The method comprises the steps of obtaining a target document; when a target document is put in storage, a similarity detection method based on a resource self-adaption mechanism is adopted, and existing documents are screened out from the multi-modal knowledge base to serve as a candidate document set; a similarity detection method based on a precision and efficiency optimal mechanism is adopted, and the candidate document set is used for conducting duplicate removal on the target document or storing the target document which is judged to be a new document; obtaining a question asked by the user; based on the multi-mode knowledge base subjected to duplicate removal or updating, multi-path mixed retrieval is conducted on the problem, a multi-path mixed retrieval result is subjected to two-stage sorting to obtain a rearrangement result, reference data are selected from the rearrangement result, the problem, cue words, context and the reference data are assembled into a large model input instruction according to a preset cue word template, and the large model input instruction is input into the multi-mode knowledge base. An answer generated by the large model is obtained and returned to the user; and comprehensive improvement of the knowledge utilization rate, the retrieval precision and the efficiency is realized.

The invention discloses a knowledge management platform for a petrochemical engineering investigation and design enterprise, which stores knowledge documents of the enterprise into different knowledge bases in the platform for management according to classification, and is also provided with a search module, a knowledge application module, a knowledge community management module, an expert question and answer management module, a knowledge operation module and the like. The method is convenient for enterprises to search, utilize, share and manage knowledge, accumulate enterprise knowledge assets, avoid enterprise knowledge loss, promote reutilization and innovation of enterprise knowledge, improve the working efficiency of enterprise employees and enterprise knowledge management innovation quality, and comprehensively improve the enterprise knowledge informatization management level.

The invention discloses a ship equipment fault detection method and device, a medium and a product, and relates to the field of electric digital data processing, and the method comprises the steps: introducing a bidirectional gating circulation unit and a variational auto-encoder into a teacher-student network model, and obtaining an initial detection model; respectively inputting a fault-free signal in the first sample data set and a corresponding signal in the second sample data set into a student network and a teacher network, training an initial detection model to obtain a ship equipment detection model, and inputting a to-be-detected ship equipment signal to obtain an actual detection result; in the training process, a BM method and an extreme value theory are adopted to obtain GEV distribution so as to determine a fault detection threshold value, and then ship equipment fault detection is achieved in combination with an actual detection result. According to the method, the accuracy, the robustness and the practicability of detection are remarkably improved, the technical bottlenecks in the aspects of data dependence, priori knowledge utilization, detection precision and the like in the prior art are broken through, and the detection requirements of complex and changeable statistical characteristics in a real environment are met.