644 results about "Data vector" patented technology

The invention discloses a text travel sentiment analysis response method and system based on multi-modal fusion and incremental learning, and the method comprises the steps: obtaining a text, an image, an audio or a video input by a user, carrying out the query in a text and multi-modal knowledge base through employing a multi-modal retrieval technology, and optimizing a retrieval result through combining sentiment analysis, thereby achieving the purpose of improving the user experience. And finally generating a personalized tourism information response. The system integrates a large language model, text and multi-modal knowledge base construction, and a data vectorization processing and sentiment analysis technology, supports multi-modal input and output, can dynamically adjust retrieval and answer contents, and realizes personalized recommendation according to user feedback. The system also has the capabilities of multi-modal data expansion, dynamic knowledge base updating and voice interaction, can significantly improve the response speed and accuracy of tourism information service, is widely applicable to intelligent and personalized tourism information service scenes, and has relatively high innovativeness and practical value.

The invention provides a medical auxiliary diagnosis method and system based on time sequence and semantic weighting, and belongs to the technical field of medical information processing. Constructing the preprocessed current and historical medical record information of the patient into patient medical record data, and inputting the patient medical record data into a pre-trained medical language model to generate a preliminary diagnosis result; the comprehensive weight of the patient medical record data vector sequence is calculated through a time decay function and the content correlation weight, a medical record fusion vector is obtained through fusion, and the medical record fusion vector and the preliminary diagnosis result are spliced into a query vector; searching candidate fragments in a clinical guide knowledge base based on the query vector, calculating semantic evidence scores and coverage scores based on patient medical record data and tokens of the candidate fragments, obtaining sorting probabilities of the candidate fragments in combination with metadata prior scores of the candidate fragments, and screening out target guide fragments; and the preliminary diagnosis result and the target guide fragment are fused to generate final auxiliary diagnosis and treatment information, so that auxiliary diagnosis and treatment suggestions with traceability, verifiability and authoritative basis are provided.

The invention discloses a special large language model for a solid waste cementing material and an innovative hypothesis generation method thereof, which realize accurate understanding and reasoning of information such as material composition, process parameters, mechanical properties and the like by constructing a special knowledge graph in the field of materials, combining experimental data vector retrieval and fusing a mixed retrieval enhanced generation technology, so that the innovative hypothesis generation of the solid waste cementing material is realized. And a scientific reasoning module is used for deducing a material design rule to generate a potential innovation hypothesis. The method effectively breaks through the problems of long development period and low innovation efficiency of traditional materials, and is suitable for the fields of low-carbon building material development and solid waste high-valued utilization.

The invention relates to a construction method and system of an intelligent question-answering system based on a planning knowledge graph library. The method comprises the following steps: acquiring multi-source space planning data in real time; carrying out fusion on the planning data; vectorizing the fused planning data; a plurality of preset retrieval enhancement models are modularized, and interaction of vectorized planning data among the modules is realized through a message queue; through a natural language processing method and a message queue method, constructing a plurality of knowledge maps which can be accessed by the preset retrieval enhancement model; training a pre-training model through the knowledge graph; in response to a user question, retrieving an enhancement model through dynamic routing selection, and retrieving entities and relationships in the knowledge graph; and based on the retrieval result, generating a questioning result through a pre-training model. According to the method, intelligent question answering, scheme generation and evaluation of space planning are realized through the knowledge graph and the pre-training model constructed by the multi-source data, the working efficiency of the space planning is improved, and the manual workload and the error rate are reduced.

The invention discloses an equipment anomaly detection method based on deep learning, and relates to the field of equipment anomaly detection. The method comprises the following steps: acquiring and preprocessing original sensor data, and generating an equipment data vector; constructing a self-adaptive multi-dimensional time sequence self-error correction convolutional network, and carrying out equipment anomaly detection based on the equipment data vector; in the adaptive multi-dimensional time sequence self-error correction convolutional network, generating a comprehensive state vector by adopting an adaptive time window adjustment mechanism and a time weighted fusion mechanism; performing feature extraction on the comprehensive state vector to obtain a final feature, further calculating an initial abnormal score, and correcting the initial abnormal score by adopting a self-error correction mechanism in a self-adaptive multi-dimensional time sequence self-error correction convolutional network to obtain a final abnormal score; and performing anomaly judgment based on the final anomaly score. The problems that a traditional equipment anomaly detection method cannot effectively cope with the dynamic change of the equipment state and is easily influenced by equipment data fluctuation are solved.

The invention relates to a structured text analysis method and system based on title recognition and hierarchical abstracts, and the method comprises the steps: receiving a text, and obtaining sample data of a training language model; the vector database can perform vector dimension adjustment on the data of the stored text. The vector database takes vectors as basic storage units, and converts unstructured data into high-dimensional vectors through an embedding technology. And calling the data storage type information of the title text data set to determine the data storage type of the title text data set matched with the text storage mode. And obtaining a structure type of sample data of each text by utilizing a semantic segmentation script. And segmenting the document according to the identified chapter titles, and subdividing each chapter into a plurality of logic paragraphs to form structured text block units. The multi-level abstract generation model matched with the target model can fully mine level information and semantic association in the document. Therefore, the effect of analyzing the structured text by the intelligent question-answering system is improved.

The invention relates to the technical field of Internet of Vehicles communication, and discloses an Internet of Vehicles channel prediction method based on multi-modal fusion and related equipment. The method comprises the following steps: constructing an urban road scene of an Internet of Vehicles channel, and obtaining known time frame data and unknown to-be-predicted time frame data in the urban road scene in combination with a weather scene; carrying out data enhancement, data vector representation and position information processing to obtain a multi-modal vector, and carrying out feature extraction and splicing processing on the multi-modal vector to obtain a multi-modal feature vector; performing inter-modal deep fusion on the multi-modal feature vector to obtain a joint feature vector; and performing nonlinear mapping on the joint feature vector through a multi-layer perceptron module, outputting to obtain channel index probability distribution, and predicting the Internet of Vehicles channel according to the channel index probability distribution. The technical problems that in the prior art, the single-mode sensing capacity is limited, the original data quality is poor, and the robustness is insufficient in a complex scene are solved.

The invention discloses an RAG-based Deepseek-R1 dialogue question-answering method, which is characterized by comprising the following steps: carrying out knowledge base preprocessing, collecting information from a data source, converting the information into a uniform format, carrying out text analysis, partitioning, information extraction and labeling, vectorizing text data and storing the vectorized text data in a vector database; storing the preprocessed text data and the vectorized representation thereof by adopting a distributed vector database; receiving a question input by a user, understanding and rewriting the question, performing retrieval from the knowledge base by utilizing vectorization coding and similarity calculation, and optimizing a retrieval result through a reordering model; and based on the retrieved background knowledge, a Deepseek-R1 model is used to generate a natural language answer, and the natural language answer is output to the user after optimization and adjustment.

A method, apparatus and computer program product for homomorphic computation enables secure computation of determinants of a matrix under Fully Homomorphic Encryption (FHE). According to this disclosure, encrypted data that contains the values of a matrix is received at a server. The matrix is separated into at least a first portion, and a second portion. Each portion is configured as a square. A first data vector of ciphertext is computed for the first portion, and a second data vector of ciphertext is computed for the second portion. Under FHE, determinants of the first and second data vectors are computed as Single Instruction Multiple Data (SIMD) operations to generate a set of results. The set of results are then used to compute a determinant of the matrix. The determinant may then be used for FHE-based analytics.

The invention provides a full-automatic air compressor system integrating intelligent monitoring and fault diagnosis, and relates to the technical field of air compressor diagnosis. The management main system comprises an AI intelligent interaction module, an AR visual presentation module, a multi-source data fusion module, an intelligent diagnosis decision module, a fault prediction and prevention module, a user feedback learning module, an equipment linkage coordination module and a system core regulation and control unit. Operating parameters and microstructure change data of the air compressor are collected, and a high-precision comprehensive data vector is generated in cooperation with an algorithm based on tensor decomposition and generative adversarial network fusion. Compared with a traditional remote monitoring mode which depends on a single traditional sensor, the system can sense micro fault characteristics of a molecular level, and in cluster operation and maintenance of the air compressors in a factory, micro cracks of the air inlet valve are detected in advance, so that the problem of insufficient air pressure caused by delayed sensing is avoided.

The invention discloses a music stave sentiment classification method and system based on multi-level distillation, and belongs to the technical field of music information processing. The method comprises the steps of firstly collecting music stave data and converting the data into stave data vectors, then performing feature extraction by using a long short-term memory network, then constructing a teacher network and a student network for knowledge distillation, and realizing multi-level knowledge transmission through temperature scaling, KL divergence loss and mask feature distillation. And finally, training a lightweight classification model to complete sentiment classification. The knowledge distillation technology is creatively applied to staff sentiment classification, the classification accuracy is effectively improved through an online multi-level distillation mode, and the technical problems that a traditional method lacks semantic information and a self-supervised model is not suitable for sentiment tasks are solved. The method has the main advantages of high classification precision, light model weight, capability of effectively capturing music emotion features and the like.

The invention discloses a digital twin intelligent test run system based on a marine medium-speed diesel engine and a monitoring method, relates to the technical field of monitoring, and is used for solving the problems of degeneration identification lag and low emission early warning precision of an oil injector. The system comprises a multi-source heterogeneous data preprocessing module and a running state intelligent diagnosis module. The multi-dimensional data acquisition module is used for acquiring in-cylinder pressure, crankshaft torsional vibration and transient air-fuel ratio signals to construct multi-dimensional data vectors, and the multi-dimensional data acquisition module is used for analyzing combustion fluctuation characteristics based on an attention mechanism LSTM (Long Short Term Memory) network, identifying early deterioration of an oil injector and outputting an oil injection consistency coefficient and a fault type identifier. The method comprises the steps that according to an oil injection consistency coefficient, electromagnetic valve response delay and combustion parameter correlation are tracked, a degradation trend is predicted, and emission early warning is generated; and according to prediction and early warning results, fuel injection compensation parameters are dynamically adjusted in the digital twinborn model, a recursive least square method is adopted to identify combustion parameters and feed the combustion parameters back to an electric control unit, and self-adaptive optimization and closed-loop control in the test run stage are achieved.

An apparatus is described having processing circuitry to perform vector processing operations, a set of vector registers, and an instruction decoder to decode vector instructions to control the processing circuitry to perform the required operations. The instruction decoder is responsive to a given vector memory access instruction specifying a plurality of memory access operations, where each memory access operation is to be performed to access an associated data element, to determine, from a data vector indication field of the given vector memory access instruction, at least one vector register in the set of vector registers associated with a plurality of data elements, and to determine, from at least one capability vector indication field of the given vector memory access instruction, a plurality of vector registers in the set of vector registers containing a plurality of capabilities. Each capability is associated with one of the data elements in the plurality of data elements and provides an address indication and constraining information constraining use of that address indication when accessing memory. The number of vector registers determined from the at least one capability vector indication field is greater than the number of vector registers determined from the data vector indication field. The instruction decoder controls the processing circuitry: to determine, for each given data element in the plurality of data elements, a memory address based on the address indication provided by the associated capability, and to determine whether the memory access operation to be used to access the given data element is allowed in respect of that determined memory address having regard to the constraining information of the associated capability; and to enable performance of the memory access operation for each data element for which the memory access operation is allowed.

The invention relates to the technical field of airport safety guarantee, and particularly discloses an airport bird situation monitoring and early warning bird repelling equipment linkage control method comprising the following steps: obtaining bird situation data vectors collected by sensors, calculating fused bird situation data vectors, and obtaining bird flock information; constructing a bird situation risk assessment model based on machine learning, combining the fused bird situation data with an aircraft take-off and landing plan, a runway use state and airport surrounding environment information to form an input feature vector, calculating a risk value through a risk assessment function, and dividing risk levels according to the risk value; according to a risk grade result of the risk assessment module, corresponding bird repelling equipment is controlled to work according to a hierarchical linkage bird repelling strategy; and in the bird repelling process, the risk level is re-evaluated according to the new bird situation data, the parameters of the bird repelling equipment are adjusted, and the bird repelling strategy is optimized by using a reinforcement learning algorithm. According to the invention, the defects of monitoring range and precision of a single sensor are effectively overcome, and the reliability of bird monitoring is greatly improved.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention relates to the field of data processing, in particular to a data vectorization label processing method and device based on search self-feedback, and the method comprises the steps: extracting text clues through meta-file attribute extraction, optical character recognition, automatic voice recognition or subtitle analysis technologies; generating a content abstract by using the language model and the text clues; performing analysis processing on the content abstract by using an NER model based on a BERT architecture, and extracting features to obtain a tag set; processing the label set through a FastText algorithm, and performing dimension reduction to obtain candidate label vectors; matching the candidate tag vector and the basic vector through a tag system, and determining a search result corresponding to the candidate tag vector based on a search result set of the basic vector; and collecting user behavior data, calculating a user interest weight, and simulating and marking error cases by using the generative adversarial network to optimize the label system. In this way, for heterogeneous data, the accuracy rate of label marking can be greatly improved.

This gas leakage monitoring device comprises: an image data acquisition unit that acquires, as original image data, image data generated by capturing images at different times; a vector generation unit that selects two pieces of original image data in chronological order from the original image data, and performs predetermined image processing for calculating the movement direction and movement speed of an object included in the images, for the selected two pieces of original image data, to generate a vector indicating the movement direction and movement speed for each pixel included in the original image data; and a frequency distribution generation unit that classifies the vectors generated by the vector generation unit into sections of predetermined vector lengths, and generates a frequency distribution for gas leakage monitoring.

The invention belongs to the technical field of fire safety, and particularly relates to a control method of a fire detection alarm. Firstly, a distributed sensor network is deployed to collect temperature and smoke data, self-calibration is carried out, and abnormal data is eliminated. Then, through three-level progressive anomaly screening, namely, first-level preliminary screening monitoring mutation features and second-level feature verification, a space-time matrix is constructed, CNN analysis is used for the space-time matrix, third-level scene adaptation is combined with historical data to calculate the probability, and an anomaly screening signal is output. A visual camera is used for collecting images to generate a visual evidence matrix, a sensor data vector and an abnormal screening signal are combined to calculate a comprehensive confidence coefficient, and an alarm signal is triggered if the comprehensive confidence coefficient is larger than a threshold value; and finally, through time window sliding verification taking one minute as a period, determining an alarm when an alarm signal is greater than or equal to three periods. According to the method, the data accuracy is improved, fire hazards are accurately identified, the false alarm rate is reduced, and the alarm timeliness and accuracy are guaranteed.

The invention relates to the technical field of artificial intelligence, in particular to a vehicle condition data exception handling system based on clustering analysis, which comprises a data vector module for processing according to collected historical normal sample data and calculating a first data vector; the vector processing module comprises a data acquisition unit, a running state vector unit and a judgment unit; the data acquisition unit constructs a multi-dynamic sensor network and acquires real-time data of the motorcycle in accordance with the first data vector; the operation state vector unit processes frequency spectrum information of the data in the real-time data through Fourier transform, and calculates a multi-feature information fusion vector; the judgment unit establishes a clustering analysis model to process the obtained multi-feature information fusion vector to obtain a first clustering result and a second clustering result; and the controller obtains a second clustering result and performs automatic adjustment according to a reinforcement learning algorithm.

The invention belongs to the technical field of intelligent early warning, and particularly relates to an intelligent early warning method for a DMF waste liquid purification and recovery control platform, and the method comprises the steps: carrying out the principal component analysis of long-period historical data, and constructing a principal component transformation matrix of a static reference model; for a moment to be diagnosed, calculating a reconstruction value by using the static reference model to obtain a residual vector, carrying out eigenvalue decomposition on a covariance matrix of a residual matrix of a sliding time window, calculating a drift coherence index according to the distribution of drift eigenvalues, modulating a drift principal component vector of the sliding time window by combining the residual vector, and carrying out diagnosis on the moment to be diagnosed; and obtaining a drift compensation vector, superposing the drift compensation vector with a reconstruction value of a real-time data vector at a to-be-diagnosed moment to obtain an adaptive reconstruction value at the to-be-diagnosed moment, calculating a reconstruction error, comparing the reconstruction error with a fault alarm threshold, judging whether a fault exists at the to-be-diagnosed moment, and performing early warning. According to the invention, the early warning accuracy and robustness are improved.

The embodiment of the invention provides a data vectorization acceleration method and system, and the method comprises the steps: writing an optimizer rule into an optimizer, and carrying out the vectorization acceleration according to the optimizer rule, respectively fusing respective original operators in the first operator rule and the second operator rule with respective matched replacement operators of the first operator rule and the second operator rule to obtain a vectorized fusion operator; and converting the vectorization fusion operator into a vectorization execution operator according to the conversion logic, so that the vectorization execution operator calls a vectorization execution engine through a local interface JNI bridge to accelerate data vectorization. Therefore, through the embodiment of the invention, the problem that the calculation efficiency is relatively low in a large-scale data processing scene due to the fact that an existing Flink framework cannot fully utilize an instruction set and vectorization hardware resources in a batch processing mode can be solved.

The invention discloses an index data prediction method based on a time sequence large model and a related device, and belongs to the technical field of time sequence prediction. The method comprises the following steps: acquiring time sequence data and preprocessing the time sequence data to obtain a time sequence data D-dimensional vector; aligning the D-dimensional vector of the time sequence data through a cross attention mechanism to obtain an aligned time sequence data vector; generating a cue word with related information according to the D-dimensional vector of the time sequence data; converting the cue word into a D-dimensional vector, splicing the D-dimensional vector with the aligned time sequence data vector, and inputting the spliced vector into a pre-established large language model; and outputting predicted text data through the large language model, and converting and combining the text data to obtain a complete time sequence prediction data sequence. Under the practical constraints of insufficient computing power and insufficient fine adjustment data, efficient and accurate time sequence prediction is realized only through transformation of a large model, and the applicability of a time sequence prediction technology in different scenes is expanded.

The invention discloses a bank intelligent operation knowledge base implementation method and system based on a large language model and RAG, and the method comprises the steps: firstly extracting business terms from structured, semi-structured and unstructured multi-source heterogeneous multi-modal data of a bank, and then constructing a business dictionary containing term similarity data and an ontology tree; quantitatively calculating the release mechanism weight, the timeliness coefficient and the punishment association degree of the supervision clauses to obtain authority values, and sorting the authority values; then using a double-constraint loss function to finely adjust the pre-trained large language model as a vector representation model, vectorizing service data, and storing the vectorized service data into a vector database; and finally, aiming at user query, retrieving in a vector database, rearranging in combination with a supervision clause sorting result, and generating a business response after compliance verification. According to the method, the bank unstructured knowledge processing capability and retrieval precision can be improved, and efficient intelligent operation is realized.

The invention relates to a graph drawing method, a graph drawing device, electronic equipment and a storage medium, and belongs to the technical field of graph drawing. The method comprises the steps of obtaining user drawing board operation event information, obtaining a user drawing board operation element method through a predefined drawing board operation method mapping table, obtaining user drawing board operation element change data through drawing board updating logic calculation, and obtaining user drawing board operation element change cache data through caching of a data storage mechanism, the method comprises the steps of generating a real-time preview image through WebGL, determining user drawing board operation element difference data through data comparison, obtaining user drawing board operation element difference data vector diagram data through SVG format conversion, and performing drawing board local rendering through a Canvas API. The rendering precision of the drawing board is dynamically adjusted according to equipment performance data, multiple rendering tasks are calculated in parallel through a multi-core processor, and a next drawing board operation type label of a user is predicted through a user drawing board operation prediction model, so that related operation controls are preloaded, and the response speed is increased.

The invention relates to a synchronous acquisition and vector synthesis method for residual current data of multiple acquisition units, and the method comprises the steps: a collection unit which prepares to read the information of each acquisition unit; the acquisition unit interrupts multiple times of triggering acquisition and stores residual current data until residual current information of a complete sine period is acquired; processing the acquired residual current data, and judging whether the acquisition unit has a fault or not; and the collection unit completes residual current data vector synthesis and calculates the magnitude of the residual current. The invention further discloses a synchronous acquisition and vector synthesis system for the residual current data of the multiple acquisition units. According to the invention, the collection unit broadcasts the synchronization signal to the plurality of collection units, and the collection units can receive the synchronization signal at the same time and start to collect the residual current signal at the same time, thereby achieving the synchronous collection of the residual current, avoiding the phase error caused by the synchronization error in the residual current signal collection process, and improving the collection precision of the residual current signal.

An embodiment of the present application provides a data processing method, a data processing device, a processor and related devices thereof. The method is applied to a main processor core bound to a network card and configured with a cryptographic coprocessor. By receiving packets to be processed sequentially sent by a network end, network layer processing is then performed to obtain a data vector; the data vector is sequentially recorded in a circular buffer so that the main processor core and the slave processor core can obtain the data vector; after obtaining the data vector, the cryptographic coprocessor is used to perform encryption / decryption processing on the data indicated by the data vector to obtain first result data, and the result information is fed back to the circular buffer; based on the indication of the data vector in the circular buffer, the result data is sent to the network end; the result data includes the first result data and second result data obtained by the slave processor core performing encryption / decryption processing on the data indicated by the data vector using an encryption / decryption program, thereby realizing the full utilization of computing resources.

The invention provides an SQL (Structured Query Language) statement generation system, a multi-source knowledge base cluster module comprises a plurality of vector libraries, each vector library is used for storing a data vector of a data type corresponding to the vector library, a query understanding module can analyze and process a natural language query problem input by a user after receiving the natural language query problem, and the query understanding module can obtain a query result. According to the technical scheme, the vector engine module can perform collaborative retrieval on at least one vector library according to the processed query problem to obtain the target data information, multiple data types are respectively stored in different vector libraries, so that only the vector libraries related to the processed query problem need to be retrieved, and the retrieval efficiency is improved. According to the method, the retrieval accuracy is improved, multiple vector libraries correspond to multiple data vectors of different data types, and the accuracy of the generated SQL statements can be improved.

The present invention relates to the technical field of seismic exploration. Provided are a three-component seismic data vector anti-aliasing interpolation reconstruction method and an apparatus. The method comprises: representing three-component seismic data in a time domain as pure quaternions for vector joint; transforming the pure quaternion data in a time-space domain to a frequency-space domain to obtain frequency-space domain data; transforming the frequency-space domain data into a frequency-wave number domain; in the frequency-wave number domain, using an inclination angle scanning strategy to construct a mask matrix; performing vector anti-aliasing interpolation reconstruction on the frequency-space domain data and the mask matrix, so as to obtain a frequency-space domain reconstruction result; and inversely transforming the frequency-space domain reconstruction result to the time-space domain to obtain a time domain three-component three-dimensional data anti-aliasing reconstruction result. The present invention can effectively solve the problem of vector anti-aliasing reconstruction for three-component seismic data that contain both irregular and regular missing traces, thus improving the accuracy of effective wave event identification.

The invention is suitable for the technical field of intelligent wearable equipment, and provides a multi-modal physiological data fusion analysis method and device and intelligent wearable equipment. According to the multi-modal physiological data fusion analysis method, the various asynchronous original body data collected by the intelligent wearable device are subjected to preprocessing corresponding to the data types, and basic optimization of various data quality is ensured. The method comprises the steps of preprocessing various data, then performing space-time alignment fusion on the preprocessed various data, generating a space-time synchronous multi-dimensional fusion data vector, effectively overcoming the problem of time sequence misalignment caused by sampling frequency difference of different sensors, and reducing time sequence correlation errors caused by a traditional simple interpolation method. On the basis, a plurality of data features are extracted and fused to obtain a fused feature vector, so that the integrity and consistency of features dependent on subsequent analysis are guaranteed. And data features of different dimensions in the fusion feature vector are further independently derived, so that the state of each physiological index can be analyzed in a targeted manner.

The invention discloses an enteral nutritional complication risk grading early warning management method and a related device. The method comprises the following steps: generating a multi-modal data vector according to clinical data, environmental data and behavior data of a patient; inputting the multi-modal data vector and the individual feature information of the patient into a trained dynamic risk prediction model, and outputting occurrence probabilities of gastrointestinal complications, mechanical complications, infectious complications and metabolic complications; wherein the individual feature information comprises age, basic diseases and nutrition states; generating a personalized intervention scheme of the patient according to a risk grading result for reference of medical staff; and inputting the intervention effect of the medical personnel, the real-time data feedback of the patient and the enteral nutritional complication risk early warning system into the dynamic risk prediction model so as to adjust model parameters of the dynamic risk prediction model. Through the dynamic risk prediction model and the personalized intervention scheme, the prediction precision of the enteral nutritional complication risk is improved.