150 results about "Retrieval algorithm" patented technology

The invention discloses an intelligent question answering method and device based on a large model, a storage medium and a server, and relates to the field of information retrieval. The method comprises the steps of receiving a to-be-answered question input by a user; according to formats indicated by a plurality of preset retrieval algorithms, converting the question to be answered into query statements respectively corresponding to the plurality of retrieval algorithms; according to the generated multiple query statements, retrieval is performed in a knowledge base, and candidate document sets corresponding to the multiple query statements are obtained; fusing the candidate document sets according to a preset fusion rule to obtain a fused document set; extracting a target answer from the fusion document set according to a pre-trained generation large model; according to the method and the device, the candidate document sets generated by fusing different algorithms are balanced between accuracy and diversity, so that the answer is enabled to be related to the question, and rich information is provided.

The invention discloses a multi-modal data automatic processing and information extraction method, and belongs to the technical field of data processing. Comprising the steps of establishing an original knowledge base according to original training data in a business scene; preprocessing the original multi-modal data in the original knowledge base to obtain a preprocessed knowledge base; inputting the preprocessing knowledge base into a knowledge retrieval unit; a text to be queried is converted into a query vector, the distance between the query vector and a knowledge fusion vector representation vector in the knowledge retrieval unit is calculated, and a retrieval result is obtained through an approximate nearest neighbor retrieval algorithm; and performing multi-modal data fusion on a retrieval result through a cross-modal Transform model, and combining fused semantics with user query to generate an answer. According to the method, data of multiple modes such as texts, images, audios and videos can be processed, multi-mode fusion and reasoning are carried out through the visual language model, accurate extraction and structured storage of information are achieved, and the efficiency and quality of data analysis and mining are improved.

The invention discloses a large model-based government affair official document quotation retrieval method, device and equipment and a medium, and relates to the technical field of artificial intelligence, the method comprises the following steps: obtaining a government affair official document, determining a document slice corresponding to the government affair official document, and creating a document knowledge base and a vector knowledge base based on the document slice; identifying the terminologies in the government affair document by using a target pre-training natural language processing model to obtain a corresponding identification result; based on the document knowledge base and the vector knowledge base, performing keyword matching on the recognition result by using a target information retrieval algorithm to obtain a plurality of corresponding candidate quotation documents; and performing semantic correlation scoring on each candidate citation document and the recognition result through a Rianker model, reordering each candidate citation document according to a corresponding scoring result, and outputting a target citation document according to a corresponding reordering result. Therefore, the quotation retrieval efficiency can be improved.

The invention relates to the technical field of natural language processing and artificial intelligence, and discloses a silk path knowledge base intelligent question answering system based on a large language model, comprising a silk path data acquisition module used for acquiring multi-source silk path knowledge data; the silk path knowledge base construction module is used for processing the multi-source silk path knowledge data to construct a structured knowledge base and an unstructured knowledge base; the data storage system module is used for storing a structured knowledge base and an unstructured knowledge base and providing a structured retrieval interface and an unstructured retrieval interface; and the man-machine interaction module is used for identifying a user intention according to the user question and outputting an answer corresponding to the user question by adopting a corresponding answer generation mode based on the user intention. According to the method, efficient and accurate silk path knowledge questions and answers are realized by constructing the multi-modal knowledge base and optimizing a retrieval algorithm and a prompt engineering technology.

The invention discloses an automatic driving test case generation method based on a large model retrieval enhancement technology, and aims to improve the generation efficiency of an automatic driving test scene. The method comprises the following steps: firstly, cutting a preprocessed data text into blocks, and vectorizing the text through an embedded model to construct an efficient vector database; secondly, preliminarily retrieving related text blocks by using an improved hybrid retrieval algorithm, and reordering by using a BERT cross encoder to improve the relevance of retrieval results; aiming at the complex task requirement in the automatic driving test, the method is divided into a plurality of sub-tasks to be executed in parallel, so that the retrieval efficiency and accuracy are improved. And then, a LoRA strategy is adopted to finely adjust the large language model so as to adapt to an automatic driving scene generation task. And finally, jointly inputting the cue word template and the reordering result into a fine tuning model to generate a test case. According to the invention, a brand new technical scheme is provided for automatic driving test scene generation, and the method is of great significance in improving the test evaluation level of automatic driving and accelerating the landing of the automatic driving technology.

The invention relates to the technical field of operation and maintenance automation, and provides an operation and maintenance field professional question and answer and diagnosis Agent design method and system, and the method comprises the steps: receiving and structurally analyzing an original query request of a user, carrying out the parameter validity check and safety verification, and extracting the query content and a session identifier; identifying task types through an intention classification algorithm based on the pre-training language model and performing content risk assessment; performing semantic extension on the query to generate an extended query word, performing similarity matching in the operation and maintenance knowledge base by using a hybrid retrieval algorithm, and fusing related knowledge fragments; inputting the enhanced query information and the task type into an inference engine for intelligent inference to obtain a diagnosis result; the reasoning result is stored in a historical memory library, and session context state information is updated; and performing formatting processing and security check on the reasoning result, packaging the result and context information, and outputting a standard response result. According to the method, the accuracy and the intelligent level of operation and maintenance professional question answering and diagnosis are improved.

The invention provides a three-dimensional object collision detection parcel grid generation method and system, a storage medium and a program product, and the method comprises the steps: extracting all vertex data of a first object according to a sub-Mesh data set after serialization of a three-dimensional model, and determining an initial center point; each vertex of each piece of sub Mesh data is converted from a local coordinate system to a global world coordinate system, and a point cloud data set is generated; constructing an initial convex hull by adopting a matrix hierarchical extremum retrieval algorithm, and generating a final convex hull by adopting a conflict graph and incremental convex hull algorithm; initializing a radius coverage model by taking the geometric center of the model as a sphere center; performing subdivision according to longitude and latitude to generate a hierarchical ball grid; emitting rays, correcting vertex positions layer by layer, approaching the surface of the model, and outputting a collider network. Through the overall point cloud set, the incremental convex hull algorithm and the spherical grid contraction technology, unified topological wrapping of the multiple three-dimensional objects is achieved, the visual fidelity and the collision precision are ensured, and resource consumption is reduced.

The invention relates to the technical field of artificial intelligence, and discloses an enterprise-level large-model agent application system supporting multi-modal collaboration, and the system comprises a user interaction terminal, an agent engine server, a knowledge engine server, a plug-in integration center, and a distributed storage unit. The agent engine server is responsible for intention recognition and task arrangement of a multi-modal input signal, and dynamically loads a differential reasoning strategy based on an environment isolation mechanism. And the knowledge engine server constructs a cross-modal semantic anchor point, analyzes an unstructured document into a tetrad knowledge unit, and realizes accurate recall of images and texts by using a hybrid retrieval algorithm. And the plug-in integration center executes outbound replacement and inbound restoration of the sensitive data through the context-aware dynamic desensitization gateway. According to the method, through a multi-modal semantic association and closed-loop verification mechanism, the problems of low complex document retrieval precision and leakage of external calling data are solved, and the service processing capacity and safety of the system are improved.

The invention discloses an AI (artificial intelligence) agent construction system based on hybrid retrieval and father-child segmentation, which comprises the following steps of: dividing a subclass knowledge base according to domain knowledge, performing father-child segmentation processing, and constructing a hierarchical semantic network; vectorization embedding and deep semantic reconstruction are carried out on the user question text; retrieving the reconstructed problem by adopting a mixed retrieval algorithm combining sparse retrieval and dense retrieval, and forming a high-score sub-segment set according to a comprehensive score obtained by dynamic weight distribution; mapping the sub-segments to the parent segment through a hierarchical backtracking algorithm, aggregating brother nodes to form an extended candidate set, and generating an associated sub-segment set after duplicate removal and re-retrieval; and finally inputting a large language model to generate a complete answer. According to the method, the problems of context segmentation, low retrieval accuracy and complicated knowledge base maintenance of traditional document segments are solved, the answer coverage and accuracy of an intelligent question-answering system are remarkably improved, and the method is suitable for knowledge question-answering scenes in the complicated technical fields such as intelligent network connection automobiles and the like.

The invention is suitable for the technical field of natural language processing, relates to a hierarchical memory and context awareness retrieval method of a large role model and a related product, and aims to solve the problems of limited model memory duration, insufficient retrieval correlation and insufficient personality consistency in a long dialogue. According to the invention, a short-term-middle-term-long-term three-level memory architecture is adopted, and a memory attenuation and migration mechanism is combined, so that dynamic metabolism of memory is realized; related memories are recalled accurately through a context semantics and role personality double-sensitive double-stage retrieval algorithm; relying on a personality-linked memory fusion and response generation strategy, the reply is ensured to fit personality setting; and a closed-loop adaptive learning mechanism of dialogue-memory-retrieval-generation-feedback is constructed, and the memory quality is continuously optimized. According to the method, the role large model can have the human-like continuous memory ability, the continuity, retrieval accuracy and personality consistency of long dialogues are remarkably improved, and the long-term personalized interaction requirements of scenes such as digital personality assistants and dialogue agents are met.

The present invention relates to the technical field of data retrieval. Disclosed are a data retrieval method and a network-attached storage device. The method comprises: acquiring data to be retrieved, wherein said data comprises at least one piece of sub-data to be retrieved, and each piece of said sub-data comprises one of text data, image data, and video data; on the basis of a feature extraction model, performing a feature extraction fusion operation on the preprocessed data to be retrieved to obtain a fusion feature vector corresponding to the data to be retrieved; on the basis of a target index structure corresponding to a vector database, determining a vector index corresponding to the fusion feature vector; and on the basis of a determined retrieval algorithm, performing a retrieval operation on the vector index in the vector database to obtain a retrieval result corresponding to the data to be retrieved. Therefore, by implementing the present invention, the data retrieval flexibility can be improved, so that more efficient retrieval services can be provided for users.

The invention relates to the technical field of computers, and provides an educational resource personalized recommendation method and device, equipment and a medium. The method comprises the steps of collecting learning historical data, interest preference data and knowledge mastery degree data of students in real time, and constructing a multi-dimensional student portrait through a machine learning algorithm; based on the multi-dimensional student portraits, matched data are retrieved from various kinds of data of an education resource library through a multi-modal retrieval algorithm, and learning content is generated through fusion and provided for students; and collecting feedback scores of the students on the learning content, taking the feedback scores as rewards, adjusting weight parameters of the multi-modal retrieval algorithm on retrieval of various types of data through a reinforcement learning algorithm, returning to the step of retrieving the matched data until the feedback scores meet a threshold condition, and generating personalized learning content based on the final weight parameters. According to the scheme, the disjunction rate of the recommended learning content and the student portrait is reduced, and the adaptability of the recommended learning content and the student individual is improved.

The embodiment of the invention discloses a contract review method and a related product, which are used for improving the efficiency and accuracy of contract review. According to the embodiment of the invention, the to-be-examined contract is fragmented, and at least one first feature tag is allocated to each fragment, so that the content of the whole to-be-examined contract can be split into smaller structured units, and the precision and speed of subsequent retrieval can be improved through the first feature tags. By determining the target review scheme corresponding to the contract to be reviewed, the review items can be flexibly adjusted according to different contract types, and the applicability is improved. And the to-be-reviewed data set is extracted in combination with multiple retrieval algorithms of different dimensions, so that missing detection of key to-be-reviewed data can be avoided, and the contract content matching accuracy is improved. As the data set input into the contract review model is screened and only contains the to-be-reviewed data set related to the review item, the misjudgment rate of the contract review model can be effectively reduced finally, and the accuracy of the review result is improved.

The invention relates to a PCB component type selection method and system based on a knowledge graph and intelligent retrieval. Relates to the technical field of PCB design, and the method integrates knowledge in multiple aspects of performance parameters, packaging forms, supplier information and the like of components by constructing a PCB component knowledge graph, and establishes semantic association among the knowledge. And performing intelligent retrieval in the knowledge graph by using an information retrieval algorithm based on design requirements and constraint conditions input by a user, and quickly and accurately screening out components meeting requirements. Meanwhile, the multi-mode large model is combined to carry out comprehensive evaluation on the components, factors such as cost and supply stability are considered, optimal component type selection suggestions are provided for users, the efficiency and accuracy of component type selection in PCB design are improved, the cost is reduced, and intelligent development in the field of PCB design of electronic products is promoted.

The invention discloses an unmanned aerial vehicle frequency hopping signal sorting and identification method based on spectrum characteristics, and the method comprises the following steps: carrying out the short-time Fourier transform of a signal received by a receiver, and obtaining a time-frequency matrix; eliminating fixed-frequency signal interference by adopting an energy cancellation method to obtain a time-frequency matrix; partial background noise is processed and eliminated through a threshold value method, and a binary matrix is obtained; removing interference by adopting a morphological filtering method to obtain a binary matrix; estimating parameters of the unmanned aerial vehicle by adopting a time-frequency analysis method to obtain a frequency hopping signal parameter set; matching the frequency hopping signal parameter set with features in an unmanned aerial vehicle signal feature library to obtain a pre-classification cluster; performing frequency hopping period estimation on the frequency hopping moment sequence in the pre-classification clusters by adopting a repetition period transformation method to obtain the frequency hopping period of each pre-classification cluster; adopting a sequence signal retrieval algorithm to screen out a frequency hopping sequence conforming to a frequency hopping period estimation value in the pre-classification cluster to obtain a main classification cluster; and matching the parameter set of the main classification cluster with the features in the unmanned aerial vehicle feature library and a frequency hopping signal number threshold, and if matching succeeds, identifying a specific unmanned aerial vehicle model. The method can solve the problems that interference cannot be effectively eliminated and multi-unmanned aerial vehicle frequency hopping signals cannot be sorted in a limited technology.

The invention discloses a Cuckoo search algorithm-based grid-connected inverter multi-objective optimization control method, which comprises the following steps of: constructing a three-level grid-connected inverter mathematical model, and calculating to obtain an inverter side current reference value; constructing a current prediction model of the inverter side; constructing a value function control item of the inverter side current by adopting a value function in a square error form; constructing a cost function by taking the neutral-point potential balance and the switching frequency as control items; and iteratively calculating a weight factor by adopting an improved self-adaptive cuckoo retrieval algorithm to realize multi-objective optimization control of the grid-connected inverter. According to the multi-objective optimization control method for the grid-connected inverter based on the cuckoo search algorithm, the problem that the control performance is limited due to the fact that weight factors are difficult to set in multi-objective optimization of the grid-connected inverter in the prior art is solved.

The invention relates to a rail transit large model retrieval enhancement generation method and system, an application and a medium, and the method comprises the steps: receiving a user query text, converting the user query text into a triad sequence through a large language model, and sorting triads based on a semantic relationship; the method comprises the following steps: obtaining embedding vectors of entities and relationships in a sequence through an embedding model, and retrieving candidate entities and relationship sets from a knowledge graph according to the embedding vectors; and calculating a triple sequence semantic distance between the sequenced sequence and the candidate sequences by adopting a triple sequence retrieval algorithm, and selecting the first K candidate sequences meeting the structural constraint. And finally, inputting the user query and the first K candidate sequences into the large language model to generate an answer, thereby avoiding knowledge fragmentation and structure mismatch, and realizing reliable and accurate response to complex multi-hop query in the urban rail transit field.

The invention discloses a verifiable retrieval method and device, equipment, a medium and a product, and relates to the technical field of block chains. Obtaining a query request; performing data positioning from a retrieval algorithm tree based on the query request to obtain at least one target leaf node and a data index set corresponding to the target leaf node; generating a target node proof and a path proof based on the retrieval algorithm tree, the target leaf node and the data index set; and verifying the target node proof and the path proof based on the retrieval algorithm tree. By the adoption of the technical scheme, under-chain retrieval and verification are carried out through the retrieval algorithm tree, on-chain and off-chain cooperation of query verification is achieved, it is ensured that the certification size is small, verification calculation complexity is lowered, on-chain storage overhead is reduced, and the problems that at present, request query certification is large in size and low in verification efficiency are solved.

The invention provides an intelligent retrieval and question-answering system for knowledge base context awareness and traceability enhancement, and the system obtains an analysis result, an optical character recognition result and a semantic analysis result through the analysis of PDF physical layout, Word / Excel paragraphs, titles, tables and style attributes thereof, and the optical character recognition. Structured knowledge blocks, positions and levels of path images and vector representation are generated and stored in a vector database, and related results of user query requests are extracted by executing a mixed retrieval algorithm with keyword retrieval and vector semantic retrieval. Performing intelligent reordering by considering semantic similarity, keyword matching, knowledge block type weight, source knowledge base weight and page position weight to obtain a candidate knowledge block list, constructing cue words according to an intelligent reordering result, and calling an external large language model to generate answers; source labels in answers are managed to be associated with metadata of corresponding numbers in a candidate knowledge block list, and the problem that text blocks and traceability are not accurate is solved.

The invention relates to the technical field of artificial intelligence, and provides a domain relation extraction method and system based on a large language model. The method comprises the following steps: identifying entity information in a field text set, and obtaining a labeled entity set with type labels; on the basis of entity pairs in the labeled entity set and predefined relation types, a question set is constructed by utilizing a judgment question generation algorithm, and a domain relation judgment question set is obtained; reconstructing the structured domain data into training data through a dialogue format conversion algorithm, and training a large language model based on the training data by adopting a QLoRA quantization fine tuning algorithm to obtain a domain fine tuning model; a double-layer retrieval algorithm is applied to retrieve and obtain related information from the knowledge graph, the domain relation judgment question set and the related information are combined and input into the domain fine tuning model for reasoning, and a domain relation triple is obtained. According to the method, high-precision and interpretable domain relation extraction is realized, and the accuracy and robustness of the model in the vertical domain are improved.

The invention discloses a sentiment analysis method and system based on a multi-modal enhanced retrieval generation technology, and belongs to the field of sentiment analysis and natural language processing. The method comprises the following steps: constructing an external multi-modal vector database oriented to an emotion analysis task; converting the user input data to obtain a query vector; aiming at a query vector corresponding to user input data, using a vector retrieval algorithm to retrieve a target feature vector from the external multi-modal vector database; constructing an emotion analysis prompt word template; according to the target feature vector, extracting other field data corresponding to the target feature vector from the external multi-modal vector database as a retrieval result; and embedding user input data and an external multi-modal vector database retrieval result into a prompt word template, calling a large language model, and generating an emotion analysis result. The invention provides an efficient and reliable multi-modal solution for the field of sentiment analysis.

The invention discloses an intelligent question and answer method and device and a storage medium. The method comprises the following steps: in response to an information inquiry request, obtaining first inquiry information input at a target terminal, analyzing the first inquiry information to obtain second inquiry information, and performing retrieval in a data cache layer based on the second inquiry information and a preset retrieval algorithm; under the condition that the first question data matched with the second inquiry information is retrieved, displaying answer data corresponding to the first inquiry information on the target terminal; wherein the data caching layer is used for caching the first question data of which the historical question query frequency is greater than the preset query frequency and the first answer data corresponding to the first question data. The problems that the query efficiency is low and the computing power consumption is too high are solved, and the beneficial effects of remarkably improving the query efficiency and reducing the computing power consumption are achieved.

The invention provides a policy intelligent question-answering method and system based on a knowledge graph and a storage medium. The method comprises the following steps: performing intention recognition and key information extraction on user query content to obtain a query intention and a key entity corresponding to the user query content; determining a plurality of seed entities in a pre-constructed knowledge graph according to the key entities; based on a multi-hop retrieval algorithm, according to the seed entities, determining an importance score of each entity in the knowledge graph; according to the importance score, determining a target entity in the knowledge graph and a target relationship directly associated with the target entity; and generating a query result corresponding to the user query content according to the target entity, the target relationship and the user query content by applying a pre-trained large language model, and outputting the query result. The method is used for achieving the effect of efficiently and accurately outputting the query result.

The present invention discloses a sentiment analysis method and system based on multimodal enhanced retrieval generation technology, belonging to the fields of sentiment analysis and natural language processing. The method comprises: constructing an external multimodal vector database for sentiment analysis tasks; converting user input data to obtain a query vector; using a vector retrieval algorithm to retrieve a target feature vector from the external multimodal vector database for the query vector corresponding to the user input data; constructing a sentiment analysis prompt template; based on the target feature vector, extracting other field data corresponding to the target feature vector from the external multimodal vector database as a retrieval result; embedding the user input data and the external multimodal vector database retrieval result into the prompt template, and invoking a large language model to generate a sentiment analysis result. The present invention provides an efficient and reliable multimodal solution for the field of sentiment analysis.

The invention discloses an intensive data scene-oriented RAG retrieval generation system, and provides a novel system aiming at the problems that in an intensive data scene, retrieval is inaccurate, generated contents deviate from themes and the like in an existing RAG technology, and the system comprises a block database module, a context construction module, a related information aggregation module and an optimization algorithm module. Wherein the block database module can automatically generate and store document metadata information, and supports multi-path query; the context construction module can enhance the accuracy of a query result; the RIA module can extract related text fragments from a plurality of query results and combine the related text fragments into a meta-document; and the optimization algorithm module is responsible for adjusting the correlation and length of the segments. According to the method, the retrieval efficiency and accuracy are improved, parameter configuration is supported to adapt to different application scenes, meanwhile, the retrieval algorithm can be continuously optimized through a user feedback mechanism, a data security guarantee function is achieved, and the accuracy of knowledge base question and answer data can be remarkably improved.

This invention relates to a semantically aware cross-modal encrypted retrieval method, belonging to the fields of information retrieval and data encryption. By introducing deep learning, it enhances the semantic feature mining capability for multimodal data, replacing the traditional keyword retrieval mode with semantic feature retrieval. A low-overhead multimodal data encrypted retrieval method is introduced, designing a parallel retrieval tree structure based on the block-based approach to achieve low-overhead, privacy-preserving, and fast semantic similarity retrieval. The rationality of the scheme is analyzed from aspects such as precision, search time, and storage overhead. This invention obtains multimodal features by extracting features from query requests; it uses a similarity-hiding encrypted retrieval algorithm to generate encrypted query trapdoors for multimodal query features; and it enables secure multimodal data retrieval in the medical IoT, combining the powerful edge computing capabilities and fast response of traditional scenarios, overcoming the problems of insufficient end-user computing resources and key leakage.

This application relates to the field of program repair processing technology, and in particular to an automatic program repair method and system based on a multi-level tree structure of a large language model. The method includes: using an information retrieval algorithm to retrieve a set of similar code examples corresponding to the program code to be repaired from a pre-defined code library; determining the model input content based on the set of similar code examples and the program code to be repaired; constructing a thought forest based on the model input content; performing error cause analysis based on the thought forest to obtain a set of high-confidence error causes; constructing a repair forest based on the set of high-confidence error causes; formulating repair strategies based on the repair forest to obtain a set of repair strategies; and performing autoregressive decoding operations based on the program code to be repaired, the set of similar code examples, and the set of repair strategies to obtain the patch code corresponding to the program code to be repaired. This application facilitates improvements in the accuracy and stability of the program repair process.

This invention discloses a method and device for auxiliary diagnosis of gas turbine faults, belonging to the field of industrial equipment fault diagnosis technology. Utilizing intelligent retrieval algorithms and efficient database support, it can quickly match relevant cases from a massive database of historical cases and provide diagnostic suggestions when a gas turbine experiences a warning or anomaly, shortening the fault diagnosis time. By characterizing historical fault cases of the gas turbine and combining them with current operating background parameters for multi-dimensional, dynamically weighted similarity matching, it can more accurately identify historical cases most similar to the current abnormal operating conditions, thereby improving the accuracy of fault diagnosis. In the specific application scenario of gas turbine power plants, it demonstrates unique technical contributions and significant practical value, which is of great significance for ensuring the safe and stable operation of gas turbines, improving power generation efficiency, and reducing operation and maintenance costs.

The present invention discloses a ciphertext retrieval method based on multi-key homomorphic encryption, which can perform retrieval on ciphertexts encrypted with different keys. The data owner and the data user respectively use homomorphic encryption technology to encrypt the data and the query value, and upload the ciphertexts to the server. The server will expand the ciphertexts according to the total number k of participants (including the data owner and the data user) to obtain a multi-key form ciphertext containing k + 1 elements. The expanded ciphertext can perform homomorphic operations even under different key encryptions and supports distributed decryption, and the decryption process is jointly executed by all participants. Based on this, the present invention realizes the ciphertext retrieval function. At the same time, the present invention also optimizes the retrieval function by applying compression coding technology, and uses a Bloom filter set to return all retrieval results at one time, without repeatedly executing the retrieval algorithm, reducing the computational and storage overheads, and greatly improving the retrieval performance.

The invention relates to the technical field of electronic reconnaissance, and discloses a pulse description word sorting method based on a pulse repetition interval transformation method, and the method comprises the steps: firstly carrying out the pre-sorting based on the phase, pulse width, frequency and other characteristics of pulse description words, and achieving the preliminary classification of pulse data; then, on the basis of pre-sorting, an improved PRI transformation method is adopted for main sorting, and through accurate calculation and optimized PRI sequence analysis, the operation speed is increased, and a corresponding PRI value is rapidly determined; and finally, extracting a matched pulse sequence from the original sequence by using the target PRI in combination with a sequence retrieval algorithm, and ensuring that the pulse description words are efficiently and accurately sorted and extracted in a complex signal environment.