289 results about "Canonicalization" patented technology

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

Various embodiments relate to normalizing raw data by mapping the raw data to a computer-readable tag. A computer-readable tag may be an identifier that at least partially represents a category (e.g., a department) and / or the raw data itself. In response to receiving the raw data, some embodiments perform the mapping by, for example, performing natural language processing (NLP) on each particular department's raw data to associate natural language words in the raw data to its corresponding computer-readable tag and then populating, at a data structure that includes the computer-readable tag, an entry with data (representing the raw data) in a standardized format. In this way, regardless of whether different sets of raw data come from disparate sources that have diverse formats, protocols, or structures relative to each other, the normalized data and standardized form makes the data compatible.

The invention discloses a large model scheduling multi-agent power grid fault coping strategy knowledge graph extension method and system, and the method comprises the steps: carrying out the modeling of task distribution as a mixed integer programming problem, and achieving the solving through a relaxation-correction algorithm; extracting and normalizing a core task of knowledge, and outputting structured data; summarizing the obtained text segments, and prompting a large language model to retain key entities, relationships and domain-specific terms; using an LLM-based named entity recognition technology, combining with prompt and dictionary / ontology filtering in the power dispatching field, recognizing related entities in a text, and normalizing the related entities into a standard form in a knowledge graph; detecting logic contradictions between the newly extracted triples and existing relationships in the knowledge graph, and classifying and solving the contradictions by utilizing debate prompts based on LLM (Logistics Library Model); summarizing the plurality of verification signals, and calculating the global confidence, definition and correlation score of each triple;

The invention relates to the technical field of natural language processing, and discloses a knowledge base question and answer platform construction method based on a large language model, which comprises a knowledge acquisition module, a data preprocessing module, a text processing module, a vectorization module, a question understanding module, a mixed retrieval module, a prompt generation module, an answer generation module and an answer quality analysis module. A secondary inquiry processing module and a feedback learning module; according to the method, a semantic segmentation algorithm is combined with semantic retrieval and keyword retrieval, so that the flexibility is high; normalized prompts are constructed, input is performed according to correlation sorting, and the accuracy of answers is improved; multi-dimensional confidence evaluation is introduced, strict multi-layer security and compliance filtering is set, and the reliability of the system is ensured; the relevance of multiple rounds of dialogues is judged and complemented, so that interaction is more natural and efficient; knowledge is collected and updated in real time, a knowledge base and a retrieval strategy are continuously optimized, and a closed loop of data-application-feedback-tracing-optimization is formed.

The invention discloses a fault diagnosis method and system for a coil cutting all-in-one machine based on a large language model, and relates to the technical field of equipment fault diagnosis, and the system comprises a table data intelligent processing module, a knowledge base construction module, a diagnosis reasoning module and a user interaction module. The table data intelligent processing module is used for carrying out automatic fault mode labeling and description standardization on a cutting and rolling all-in-one machine fault diagnosis case library provided by a user; the domain knowledge graph construction module is used for integrating the standardized table database and automatically constructing a domain knowledge graph; the diagnosis reasoning module is used for performing fault diagnosis reasoning based on a large language model and a knowledge graph; and the user interaction module provides a multi-modal user interaction interface and supports visual display of fault diagnosis. Therefore, by adopting the fault diagnosis method and system for the cutting and winding all-in-one machine based on the large language model, the intelligent level of fault diagnosis can be improved, the knowledge base construction and expansion cost can be reduced, and the usability and maintainability of the system can be enhanced.

The invention discloses a data processing method for industrial equipment for industrial control Internet of Things, which comprises the following steps: acquiring industrial equipment sensor data, and carrying out standardization processing to obtain a standardized matrix; initializing a sparse dictionary learning model, setting an initial dictionary structure and a sparse coefficient, and executing preliminary sparse decomposition; constructing a flower pollination algorithm search space, optimizing a layering number, a regular weight and a switching threshold, and updating dictionary parameters; calculating an optimal sparse coefficient matrix, and performing sparse reconstruction processing; and carrying out data denoising, compression and feature extraction, and outputting a final processing result. According to the method, the flower pollination optimization algorithm and the sparse representation dictionary learning model are fused, so that efficient denoising, compression and abnormal feature extraction processing of the industrial equipment data are realized.

The invention discloses a two-stage electric power vertical field large model fine tuning method based on supervised fine tuning and reinforcement learning, and relates to the field of artificial intelligence large model fine tuning training in the electric power field. Performing supervision and fine tuning on the language model according to the field knowledge guide prompt words; constructing a reinforcement learning objective function by using an importance sampling ratio and a cutting mechanism; and constructing an output reward model and a standardized dominant function, and performing fine tuning on the adjusted language model in the initial stage in combination with a reinforcement learning objective function to obtain a fine-tuned power vertical field language model. According to the method, an electric power professional data set is constructed, the advantages of supervision fine tuning and reinforcement learning are combined, a technical path of staged optimization is formed, a standardized output format of electric power problem reasoning is designed, and the interpretability and engineering practicability of a model reasoning result are ensured.

The invention relates to a knowledge graph construction method, device and equipment based on multi-source data. The method comprises the following steps: acquiring original data from different data sources; preprocessing the original data to obtain target text data; wherein the preprocessing comprises format conversion, text cleaning and normalization and / or sentence segmentation and segmentation; performing knowledge extraction on the target text data through a pre-optimized large language model to obtain original structured data including an original entity, an original relationship and an original attribute; post-processing the original structured data to obtain target structured data including a target entity, a target relationship and a target attribute; wherein the post-processing comprises format analysis, entity standardization and ambiguity elimination, and relation and attribute verification; and updating nodes and edges of the current knowledge graph according to the target structured data. The method can adapt to multi-source heterogeneous data, and the accuracy and consistency of the knowledge graph are improved.

A system for real-time detection and mitigation of morphing malware in high-density edge networks, consisting of: a data acquisition unit configured to receive, normalize, and encode multimodal telemetry data streams originating from at least one of the following domains: network traffic, process behavior, system call sequences, binary instruction traces, and control flow graphs; the data acquisition unit is further configured to compute feature embeddings over sliding time windows and apply privacy-preserving redactions prior to storage; a generative neural processor that is operationally coupled to the data acquisition unit and configured to generate synthetic morphing malware variants by learning probabilistic transformations of previously observed malicious data representations, maintaining semantic functionality while varying structural and behavioral features; a discriminative neural processor trained adversarially with the generative neural processor, wherein the discriminative neural processor is configured to detect morphing malware by evaluating a probability distribution over multimodal telemetry embeddings and classifying anomalous process and flow behaviors in real time; a coordination processor that is communicatively connected to both the generative neural processor and the discriminative neural processor and is configured to orchestrate adversarial co-training, regulate detection thresholds, calculate reinforcement-based penalties for false negative results, and trigger countermeasures as soon as a detection confidence level exceeds a predefined adaptive threshold; a secure, system-integrated inference and enforcement unit configured to perform low-latency countermeasures at the network edge, including selective packet filtering, flow isolation, process interruption, or system microsegmentation, based on instructions from the coordinating processor; and a hardware-embedded security enclave that is embedded in the system and configured to store cryptographic keys, neural model parameters, and integrity affirmation data to ensure the confidentiality, authenticity, and immutability of model artifacts and policy configurations.

The invention relates to a geology vertical field large language model construction method and system fused with a knowledge graph, and the method comprises the steps: obtaining multi-source geology data, and carrying out the standardization processing, and obtaining a structured geological corpus; constructing a first knowledge graph and a vector index database supporting semantic retrieval based on the structured geological corpus; extracting first path information and constructing a first cue word, and performing fine tuning training on a pre-trained large language model according to the first cue word; and receiving an original text input by a user, and performing normalization processing to obtain first prompt information. And constructing a second cue word in the vector index database and the first knowledge graph based on the first cue information, and inputting the second cue word into a large language model subjected to fine tuning training to obtain a geoscience domain question and answer with a controlled structure. Compared with the prior art, the technical problem of semantic deviation and uncontrollability of question and answer generation content can be solved.

The invention relates to the technical field of OA examination and approval, in particular to a multi-modal file intelligent examination and approval method and system based on a large language model, and the method comprises the steps: configuring exclusive examination and approval templates for different examination and approval roles, and generating a role exclusive intelligent examination model; the method comprises the following steps: receiving a to-be-approved multi-modal file, analyzing file content, and outputting structured JSON (JavaScript Object Notation) data; inputting the structured JSON data and a configured approval template into a corresponding intelligent review model, performing step-by-step reasoning through a thinking chain prompt strategy, and outputting a structured review intermediate result; encoding the review intermediate result into a semantic vector, and retrieving Top-K similar historical approval cases from a knowledge base module based on a vector database to generate a reference suggestion set; fusing the review intermediate result with the reference suggestion set to generate a review result; based on the review result and the feedback of the approver, the standardized approval opinions are generated, and the accuracy and consistency of review are improved.

The invention belongs to the technical field of natural language processing and geographic information systems, and particularly relates to a region address standardization method based on knowledge graph enhanced retrieval. Cleaning and preprocessing the original address text input by the user; utilizing a fine-tuned large language model to identify a geographic entity and performing standardized expansion on variant expression to generate a query candidate set; entity linking and context retrieval are carried out based on the knowledge graph, and attributes, hierarchy and spatial topology information of associated entities are obtained; in combination with the original input and the map context, an enhanced retrieval query text is generated through large language model reconstruction; vectorized semantic retrieval is carried out through the fine-tuned embedding model, and a preliminary candidate address set is obtained; carrying out multi-dimensional refined sorting by adopting a resorting model; and generating a structured standard address by using a large language model, and outputting the structured standard address after multi-level verification. According to the method, the problems of ambiguity resolution, alias recognition and context understanding in address processing are solved, and the accuracy and robustness of address standardization are improved.

The invention provides a multi-modal data pairing method and system based on deep learning, and relates to the technical field of data processing, and the method comprises the steps: obtaining a video multi-frame sequence and a target text, and respectively extracting an overlapped frame group set and a standardized text sequence; performing spatio-temporal feature extraction and text dependency relationship coding to obtain a video time sequence vector sequence and a text vector sequence; executing cross-modal alignment search, and constructing a monotonic matching path set; calculating a semantic and action entity relationship consistency score of the paired elements on the path to obtain a comprehensive score; and determining an alignment relationship between the video and the text based on the optimal path. According to the method, accurate matching of the video and the text is realized, and the cross-modal retrieval efficiency is improved.

The invention relates to the technical field of chemical safety intelligent analysis, and discloses a chemical safety production safety intelligent analysis system based on a large language model, and the system comprises a semantic data construction module which is used for obtaining multi-source data and building a standardized database, a semantic anchor point library and a causal fragment set; the mechanism constraint modeling module is used for generating a mechanism constraint library and mapping the mechanism constraint library with the device unit; the semantic compiling constraint module is used for compiling the natural language intention into a formalized constraint set; the interlocking generation matching module is used for generating a semantic interlocking table and an action list; the check amplitude limiting decision module is used for determining a reserved action list, an amplitude limiting parameter and an execution sequence; the simulation verification and evaluation module is used for carrying out full-track simulation and outputting a feasibility result; and the deviation revision backfilling module is used for comparing the actual working condition with the simulation track and generating a revision proposal. According to the method, self-adaptive closed-loop optimization of semantic knowledge unified modeling and safety analysis in the chemical safety production process is realized.

The invention discloses an index optimization and compression storage system and method for a large-scale literature set, and the method comprises the following steps: S1, collecting and preprocessing literature data, and generating a standardized text data set; s2, carrying out keyword semantic vector coding, and constructing a keyword semantic vector matrix; s3, constructing an initial Gaussian mixture model to obtain a clustering center, a covariance matrix and a weight; s4, introducing a sea elephant optimization algorithm to optimize clustering parameters, and outputting an optimal clustering result; s5, constructing a semantic clustering structure, and generating an index tree structure; s6, performing bitmap compression and inverted coding, and constructing an index table supporting Boolean logic; and S7, dynamically accessing the newly added literature, and completing incremental updating of the index structure. The method is used for improving the index construction efficiency and the storage compression rate of a large-scale literature set, and efficient and semantic literature retrieval service capable of being incrementally updated is achieved.

The invention provides a file positioning management method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. According to the method, files are collected from multiple sources and subjected to standardization processing, an element set is generated in combination with multi-modal analysis of texts, images, audios, videos and tables, cross-modal alignment is achieved through a semantic representation model, hierarchical indexes of semantics, keywords and relations are constructed, and a unique traceability identifier is generated; in the query stage, intention recognition and joint retrieval are carried out, a result subjected to permission verification and traceability information labeling is output, online optimization and incremental reconstruction are executed based on user feedback, and comprehensiveness, accuracy, traceability and self-adaptive optimization of file positioning are achieved.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention relates to the technical field of knowledge maps, in particular to a steel manufacturing knowledge map construction method based on multi-source heterogeneous data, which comprises the following steps: converting message data into a standardized semantic triple through a semantic mapping rule, and complementing missing attributes by using physical and chemical component balance logic to form a standardized entity; meanwhile, a semantic processor is used for extracting unstructured report text logic, cross-modal coupling is carried out on the unstructured report text logic and SEM image features, and a failure mechanism enhanced entity is generated. Then, full-process dynamic parameters are collected and regularized into equal-interval time sequence parameters; and finally, taking the normalized entity as an index feature and the mechanism enhanced entity as an association constraint, executing semantic fusion by utilizing a topological feature mapping engine, and constructing a dynamic weight graph structure by combining with a dynamic probability distribution correction operator iteration parameter weight. According to the method, efficient association and semantic organization of manufacturing whole-process multi-modal data are realized, and the retrieval precision and knowledge discovery capability of steel manufacturing data are greatly improved.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

The invention discloses a book data processing and intelligent service system based on artificial intelligence, and relates to the technical field of artificial intelligence and digital library crossing, and the technical scheme is characterized in that a multi-format document intelligent conversion and regular cleaning mechanism is adopted, a multi-level metadata extraction algorithm is combined, and a structured knowledge base is accurately constructed; vector semantic retrieval, BM25 keyword retrieval and a cross encoder reordering model are creatively fused, and high-precision context retrieval is achieved through dynamic weight configuration and locality sensitive hash de-duplication. A parallel data processing architecture supporting GPU acceleration is constructed based on a Chroma vector database, the knowledge response capability of a local large model is enhanced in combination with an RAG technology, and verifiable standardized content is generated. The system is compatible with a Linux / Windows platform and containerized deployment, has hundred million-level data throughput efficiency, can be widely applied to the fields of intelligent libraries, knowledge questions and answers and personalized recommendation, and remarkably improves the retrieval accuracy of book data and the intellectualization level of knowledge services.

A system may receive, from a client device, a query requesting quality information of a target device. The system may access a set of data records associated with the target device, pre-process the set of data records for extracting raw data associated with the target device from the set of data records, and convert the pre-processed data to normalized data using a first large language model (LLM). The system may apply a second LLM to the normalized data for generating an output result, which includes the requested quality information of the target device. Applying the second LLM may include: retrieving contextual information related to the target device; generating a prompt to the second LLM, the prompt comprising at least the normalized data, the retrieved contextual information, and the query requesting quality information of the target device; and providing the generated prompt to the second LLM to receive the output result.

The invention discloses a compiler optimization method and device and a storage medium, and belongs to the technical field of computers. The method comprises the steps of obtaining a vector length corresponding to an access operation in a compiling language; the vector length is related to the number of elements included in the memory address to be accessed; under the condition that the vector length is not the power of the preset numerical value and the vector length is greater than a preset threshold value, splitting the access operation according to the vector length to obtain a plurality of sub-access operations; and according to the plurality of sub-access operations, optimizing repeated operations in the compilation language to obtain a compilation optimization result. By standardizing the access operation in the intermediate language of the compiler and splitting the access operation into a plurality of sub-access operations, the repeated sub-access operations in the intermediate language can be eliminated; moreover, in the instruction scheduling process after register allocation, based on the instructions corresponding to the multiple sub-access operations, access conflicts specific to hardware can be eliminated, and the accuracy of compiler optimization results is improved.

The invention relates to the technical field of informatization system development, in particular to a natural language-based complex data model generation method, which comprises the following steps of receiving business requirement input in a natural language form, preprocessing and normalizing input contents, processing ambiguity and incompleteness of requirements through a multi-round dialogue complementation mechanism, and generating a complex data model. Obtaining a complete and clear business demand description; and carrying out deep semantic analysis on the normalized business requirements by adopting a large language model. Aiming at the pain points that an existing data modeling technology is high in threshold, low in efficiency, difficult in quality guarantee, weak in integration adaptation and the like, the method has the remarkable advantage of multiple dimensions, non-technical background personnel can directly input service requirements through natural language interaction and deep semantic analysis on the premise of reducing the technical threshold, database knowledge and SQL specifications do not need to be elaborated, and the method is suitable for large-scale popularization and application. The cognitive gap of business and technology is spanned, the dependence on professional design talents is reduced, and the learning cycle of green hands is shortened.

The invention discloses an education question answering model routing method based on deep learning and a knowledge graph, and aims to solve the problems that the existing multi-model selection depends on text similarity, lacks knowledge points and first repair constraints, is easy to route by mistake and generates illusion. The method comprises the following steps: generating problem sub-graphs on an educational knowledge graph, carrying out relation perception graph neural network coding, constructing a model capability graph containing expression distribution and service attributes, forming masked cost under the constraints of types, privacy, time delay, cost and first repair coverage, and obtaining soft assignment by adopting unbalanced optimal transmission with entropy regularization; and interpretable routing is completed in combination with the meta-path attention subjected to interpretation consistency regularization training, so that the technical effects of reducing wrong routing and illusion rate, improving preferred correct routing rate and outputting meta-path interpretation are achieved.

The invention discloses an intelligent and automatic test case generation method based on a large language model, and relates to the technical field of large language model application, and the method comprises the following steps: collecting a software demand description file and an interface standardization file of a project, carrying out semantic analysis and mapping, generating a logic constraint set, and establishing a path mapping table; inputting the path mapping table into a large language model, generating a test scene and assertion, and executing semantic drift detection; when semantic drift is detected, a drift report is generated, a reverse correction process is executed, a patch prompt is generated, and the generation chain is executed again; and after the semantic distance of the prompt chain is detected to reach a convergence state, outputting a stable test case set. According to the method, the generation structure is controlled through the deterministic prompt sequence, so that the stability and controllability of test scene and assertion generation are realized; and through semantic drift detection and a reverse correction mechanism, real-time correction of semantic offset of the prompt chain is realized, and the accuracy and stability of the test case are improved.

The invention belongs to the technical field of computer data security, and particularly relates to a data contract-oriented strategy real-time compiling and consistency hot deployment method and system.The method comprises the steps that according to a strategy input by a user, strategy definition and standardized representation are based on formalized semantics, and a data contract-oriented strategy is compiled in real time; constructing a strategy abstract syntax tree with complete semantic information; inputting the abstract syntax tree into a multi-stage compilation optimization pipeline, and generating a high-performance intermediate representation (IR) code by using a cost model driven optimization decision mechanism; a policy distribution architecture based on a publishing-subscribing model is adopted, and an improved Gossip protocol and a Paxos atomic submission algorithm are combined to realize distributed consistency synchronization and real-time hot deployment of a policy; and efficient and safe execution of the strategy is realized through a register type micro virtual machine and a self-adaptive execution mode. The problems that in the prior art, execution performance has bottleneck, strategy updating is delayed, complex strategy supporting capacity is weak, and cluster strategy consistency is difficult to guarantee are solved.

The invention discloses a method and system for automatically generating headless test cases based on behavior description. The method comprises the following steps: obtaining an original behavior description text, and carrying out standardization processing on the original behavior description text to generate a standardized behavior description text; performing semantic analysis on the standardized behavior description text to generate a structured intermediate representation; matching each IR step in the structured intermediate representation with a preset step library, and generating a corresponding target step for each IR step; according to all the target steps, corresponding code templates are selected from a preset template library and combined in sequence, and headless automatic test cases are generated; and for an interface on which the headless automatic test case depends, selecting a matched Mock data version from the Mock snapshot warehouse according to the interface contract hash, and performing contract consistency verification. According to the method, through semantic analysis, step library matching and template combination, BDD description is fully automatically converted into executable scripts, and testers are liberated from repeated coding work.

The invention discloses a product recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to a recommendation scene of financial products. According to the method, end-to-end data standardization, semantic analysis and privacy protection are carried out; performing disturbance and noise injection based on differential privacy; the method comprises the following steps: accurately capturing short-term fluctuation and long-term trend of customer demands by virtue of a multi-level time sequence algorithm, and extracting a typical demand change mode through segmented clustering and modeling; when significant demand fluctuation is detected, the online learning and deep reinforcement learning model can update a recommendation strategy in real time; the interpretability analysis module transparently presents a recommendation decision basis; user feedback is continuously absorbed through incremental learning, model parameters are continuously optimized, and finally it is guaranteed that an accurate personalized recommendation result is output under millisecond-level response. According to the method, the recommendation accuracy, the user satisfaction and the service conversion rate are remarkably improved, and the compliance and the expansibility are considered at the same time.

The invention provides a data processing method and related equipment. The method comprises the steps of obtaining an original code file; determining a coding format corresponding to each code block in the original code file according to the compiling instruction; and compiling the at least one code block into a target execution file according to the coding format corresponding to each code block. The target execution file comprises at least one segment, and each segment comprises a segment identifier and at least one sub-module. The segment identifier is used for indicating the type of the code block corresponding to the segment. The sub-module includes a sub-module option and a sub-module code. The sub-module options are used for indicating coding formats corresponding to the sub-module codes. The sub-module code comprises at least one data sequence or identification information corresponding to the at least one data sequence, and each data sequence is determined according to a coding format indicated by the sub-module option and a corresponding code block. According to the method, the code blocks are coded in a standardized mode to generate the target execution file, so that cross-platform operation of the target execution file is realized, and the operation efficiency is improved.

The invention relates to a test system based on a multi-modal knowledge base, belongs to the technical field of software testing, and solves the problems of insufficient reuse of historical project data and expert knowledge, lack of professional domain knowledge, illusion, low test case adoption rate and low practicability. The system comprises a knowledge base construction module used for constructing a hierarchical multi-mode professional field knowledge base; the preprocessing module is used for forming a standardized project development document and a software source code file of a to-be-tested project; the test case generation module is used for generating a test case and a software test description thereof based on the multi-modal professional domain knowledge base, the project development document of the project to be tested and the software source code file; and the test execution module prepares execution conditions according to the test case and the software test description thereof, and then executes the software test steps in sequence according to the input and operation description steps described by the test process to obtain an execution result of the test case. And knowledge-based and deep structured multiplexing of test assets is realized, and the test quality and efficiency are improved.