160results about How to "Reduce ambiguity" patented technology

The invention provides a semantic element-based knowledge question and answer accuracy improving method, which comprises the following steps of: extracting information from text data, and constructing semantic nodes; word segmentation processing is performed on a question text input by a user, a word sequence is obtained, and semantic element representation of a question is formed; in the knowledge recall stage, a golden investment algorithm is applied to achieve knowledge retrieval; matching the candidate answers with semantic elements of the user question, and scoring and sorting according to keywords; and according to the query intention of the user question and the sorted candidate answers, constructing cue words of the large model, calling the large model to generate a final answer, and presenting the output answer according to a specified format. By integrating semantic analysis, knowledge recall, natural language processing and other technologies, the intellectualization level and response accuracy of the knowledge question-answering system are remarkably improved, and higher-quality service is provided for users.

The invention discloses a real-time three-dimensional modeling method and system based on 5G flight control and a medium, and belongs to the field of three-dimensional modeling, and the method comprises the steps: obtaining a modeling demand and task environment parameters of a user, and planning a route; controlling the unmanned aerial vehicle to fly according to the planned route; judging whether the position data and the attitude data of the unmanned aerial vehicle at each waypoint are within a preset threshold range, and if so, controlling the unmanned aerial vehicle to collect image data of the target area; the collected image data is preprocessed; extracting feature point data of the target area by utilizing machine learning according to the preprocessed image data; converting the feature point data of the target area into sparse point clouds by using a triangulation algorithm; converting the sparse point clouds into dense point clouds through an MVS algorithm; and according to the pre-processed dense point cloud, a Poisson surface reconstruction algorithm is adopted to establish a three-dimensional grid model of the target area. Aiming at the low precision of unmanned aerial vehicle three-dimensional modeling in the prior art, the method improves the precision of unmanned aerial vehicle three-dimensional modeling.

The invention discloses an automatic sleep staging method and system, and relates to the technical field of sleep detection. Comprising the following steps: acquiring electroencephalogram signal data, and enhancing the data; performing multi-scale feature extraction; and the classifier network predicts the sleep stage of the target time period according to the feature vector. According to the technical scheme, the automatic sleep stage staging network combining the feature pyramid and the supervised comparative learning shows a synergistic effect, so that the overall performance and the distinguishing capability of the classifier network are improved; the feature pyramid can process different time and frequency scales, so that the sleep stage in the original single-channel EEG signal can be better distinguished. Meanwhile, by adopting a training framework based on supervised comparative learning and by minimizing intra-class feature distance and maximizing inter-class feature distance, features with higher discrimination capability are extracted, and fuzziness in the sleep stage is relieved. According to the framework, the distinguishing ability of the sleep stage is remarkably improved by fusing the multi-level features and the class distinguishing features.

The invention relates to the technical field of tunnel engineering and engineering geophysical prospecting, and discloses a seismic wave continuous detection method and system for a double-line TBM construction tunnel, and the method comprises the steps: laying an observation system, and carrying out the data collection through employing a fixed receiving and transmitting distance; data continuous acquisition: performing data acquisition during heading of a previous tunnel, performing continuous data acquisition along with heading of a cutterhead, and continuously acquiring multi-source multi-wave-field data not lower than a set group number in a single test; refracted wave data are processed, and the continuous speed profile of the to-be-excavated area of the following tunnel is obtained according to the obtained speed distribution information; interpreting refracted wave data, and realizing geological interpretation of geophysical prospecting data according to the obtained continuous transverse velocity profile of the to-be-excavated area of the following tunnel in combination with geological information and structural distribution of the excavated hole body section of the preceding tunnel; and seismic multi-wave-field multi-view-field comprehensive detection is carried out to obtain the unfavorable geological distribution condition vertical to the line direction. According to the invention, multi-wave-field, multi-mode and multi-view comprehensive detection of the seismic wave method can be realized.

The invention relates to the technical field of computers, in particular to a method for improving question answering accuracy based on context and LLM. The method comprises the following steps that S1, a user puts forward a question; s2, generating question word embedding; s3, context retrieval: comparing the data vector in the S2 with a knowledge vector library, and retrieving a context related to the question through word embedding slices according to the similarity with the question; s4, question rewriting and enhancing: taking the question proposed by the user and the context in the step S3 as the input of LLM, and generating a rewritten and enhanced question by using a question rewriting prompt; s5, multi-level rewriting and enhancement: taking the rewriting and enhancement problem in the step S4 as the input of the word embedding model in the step S2, circulating the steps S2, S3 and S4 for N times, and finally forming the Nth rewriting and enhancement problem; and S6, question answering: taking the question after the Nth rewriting enhancement, the context of the (N + 1) th retrieval and the prompt word of the user, which are formed in the S5, as the input of LLM to form the result of the question.

The invention relates to a text knowledge enhanced scene graph generation method, and belongs to the field of computational vision. The method comprises the following steps: extracting text features of text information; extracting visual features and entity labels of the scene image, and combining the extracted visual features into paired entity pair features; introducing entity pair feature guidance to generate visual guidance text feature representation, so that the extracted text features are associated with visual entities; obtaining refined entity enhancement features and entity-to-visual predicate features based on the visual features and the entity tags; performing cross-modal alignment on the visual guidance text features and the entity pair visual predicate features to obtain graphical multi-modal feature representation; and inputting the multi-modal feature representation and the entity enhancement feature into a multi-layer perceptron for prediction to obtain prediction output of the entity pair and the entity pair relationship. According to the invention, the accuracy of entity object prediction and entity relationship prediction in the scene graph generation process can be improved.

The invention relates to the field of new energy vehicles, in particular to a vehicle state prediction method and related equipment. The vehicle is provided with a plurality of sensors, and the method comprises the following steps: collecting a plurality of sensor data from the plurality of sensors respectively; carrying out data fusion on various sensor data to obtain multi-modal fusion data; and predicting the vehicle state of the vehicle based on the multi-modal fusion data and a preset vehicle state prediction model to obtain a predicted vehicle state. According to the method and the device, the vehicle sensor data of different sources and different types can be integrated, so that the vehicle state prediction model can fully utilize the multi-modal fusion data to carry out operation and analysis so as to adapt to different driving environments and working conditions to carry out vehicle state prediction, and the accuracy of vehicle state prediction is improved.

The invention discloses a multi-source data assimilation method based on remote sensing in the technical field of remote sensing data, and the method comprises the following steps: S100: data preprocessing: carrying out the preprocessing of multi-source remote sensing data, including radiation correction, geometric correction and cloud detection, so as to eliminate the noise and error in the data; and S200, feature extraction: extracting useful feature information from the preprocessed data, including spectral features and texture features, so as to facilitate subsequent data analysis and application. And the prediction capability is improved: the model prediction capability can be improved through data assimilation, so that a model result is continuously close to an observation value, and the prediction accuracy is improved. The observation data of different sources and resolutions are fused through data assimilation, information more accurate and richer than single data can be obtained, fuzziness is reduced, and interpretation reliability is improved.

The invention discloses a strip mine area remote sensing image semantic segmentation method and device, a medium and a product, and relates to the field of remote sensing image semantic segmentation, and the method comprises the steps: carrying out the data enhancement of remote sensing image data in a strip mine area remote sensing semantic segmentation data set, and obtaining an expansion data set; constructing a remote sensing image semantic segmentation model based on a DeepLabV3 + model; according to the remote sensing image semantic segmentation model, on the basis of a DeepLabV3 + model, a backbone network is constructed based on a MobileNeXt architecture, a DenseASPP module in cross-layer connection is constructed, and two RGA modules are utilized in a decoder to carry out feature extraction on feature map information output by an encoder and shallow semantic features obtained by the backbone network. And training and verifying the remote sensing image semantic segmentation model by using the extended data set. According to the invention, the accuracy of remote sensing image semantic segmentation in a strip mine area scene can be improved.

The invention discloses a self-constraint magnetic anomaly physical property inversion method and system based on correlation coefficients, and relates to the field of geophysical data inversion interpretation, and the method comprises the steps: carrying out the grid processing of actual measurement data, and obtaining actual measurement two-dimensional data D; dividing the underground space into uniform grid units; correlation imaging calculation is carried out on the actually measured data, and a correlation imaging coefficient matrix C is calculated; obtaining a conversion coefficient matrix K according to the prior geological data, and calculating an inversion initial model M0 according to the conversion coefficient matrix K and the related imaging coefficient matrix C; and performing physical property inversion calculation on the inversion initial model M0 by using a conjugate gradient algorithm to obtain inversion magnetic information of the underground space. According to the method, the observation data correlation coefficient matrix is introduced as a self-constraint condition, and the fitting degree and roughness of the conjugate gradient inversion model are self-constrained through the correlation coefficient matrix, so that the multiplicity of solutions of the inversion result can be reduced, the stability of the inversion result can be enhanced, and the resolution of the inversion result can be improved.

The invention provides a method and device for determining the flow velocity of fluid in a well based on distributed optical fibers, and the method comprises the steps: carrying out the filtering processing of the pre-obtained distributed optical fiber sound wave data of a target well, so as to determine a filtering result; determining a velocity spectrum corresponding to the target well according to the filtering result; and determining the fluid velocity in the target well according to the velocity spectrum. Aiming at multiplicity of solution of production profile inversion of distributed optical fiber temperature sensing, firstly, a production logging measurement mode is specifically formulated, and the flow velocity of fluid in a shaft is extracted by collecting distributed optical fiber sound wave sensing data, so that multiplicity of solution of production profile inversion is reduced.

The invention provides an AI and VR nursing method and device integrating multiple senses, a medium and a program product, and relates to the field of intelligent medical treatment. The method comprises the steps that a first instruction for entering a virtual scene is received, and a subject enters the virtual scene to generate a virtual subject; the virtual scene comprises a foretaste area; receiving a second instruction that the virtual subject moves to the foretaste area in the virtual scene and moving the virtual subject to the foretaste area; receiving an instruction for a fake feeding object placed in the foretaste area, wherein the fake feeding object appears in the hand of the virtual subject; the category of the fake feeding object is recognized, and according to whether the distance between the fake feeding object and the mouth of the virtual subject in the virtual scene exceeds a threshold value or not or whether a feeding instruction is received or not, the smell spraying device receives a smell simulation instruction of the fake feeding object and starts the device to generate corresponding smell. A voice control module of the false feeding device is developed through AI, and a sniffing intention recognition algorithm is developed, so that accurate analysis and conversion of signals are realized, and the equipment intelligence is improved.

According to the multi-modal intelligent question answering method and device, the storage medium and the computer equipment, when the query question is received, the target query text of the query question is extracted to avoid input carrier limitation of the query question, then the target query text is vectorized into the target text vector, and the query question is queried according to the target text vector. The target text block is obtained based on the target query text and the target text vector, so that the retrieval accuracy is improved; and then optimizing the target text block by adopting the target text optimization model and outputting a high-accuracy target answer. If the target answer contains the picture address, a picture request is initiated to the server after a request address corresponding to the picture address is generated, and the server performs security verification on the request address and returns picture content corresponding to the picture address after the request address passes the security verification, so that the data security is improved; and finally, replacing the picture address in the target answer with the picture content, and displaying the final target answer to the user through a multi-modal mode of the text and the picture, so that the intuition and the accuracy are improved.

The invention relates to a retrieval problem optimization method, medium and system based on a multi-layer knowledge base, and the method comprises the steps: carrying out the level-to-level management of knowledge data, and constructing the multi-layer knowledge base; according to user query, retrieving the multi-layer knowledge base to generate an initial vector block set; performing first query rewriting on the query, and retrieving the multi-layer knowledge base again to generate an update vector block set; according to the source statistical result of the initial vector block set and the updated vector block set in the multi-layer data block, determining the strength direction of retrieval; and according to the strength direction of the retrieval, carrying out second query rewriting on the query, and outputting a query problem. Therefore, the intensity direction of the retrieval is positioned, and a query problem with both professionality and comprehensiveness can be generated. According to the method, through two rounds of optimization, the ambiguity of query is remarkably reduced, the accuracy and diversity of the generated content are enhanced, and a more efficient and more accurate solution is provided for application of the RAG system in complex tasks.

The invention provides a 3D human body posture estimation method based on a diffusion model and continuous frame time-frequency information fusion, and the method comprises the steps: inputting a 2D posture into an initial estimator for posture extraction, and obtaining an initial 3D human body posture; inputting the initial 3D human body posture into a diffusion model in an iteration form, and training in combination with noise to obtain an optimized 3D human body posture; performing mechanism processing on the long sequence data of the 3D human body skeleton by using an encoder of a space Transform module and a time frequency domain fusion module to obtain encoding features, and performing mechanism processing on the long sequence data of the 3D human body skeleton by using the time frequency domain fusion module; and the coding features are input into the time-frequency feature fusion module for mechanism processing, so that the accuracy of attitude estimation is improved. According to the method, the initial estimator is used for integrating constraints such as the bone length consistency and the joint connection relation, and fuzziness and ambiguity in 2D-3D inference can be reduced.

The invention relates to the technical field of semantic analysis, and discloses an intelligent semantic analysis system and method based on a data large model, and the method comprises the steps: converting an audio file into a text, dividing the text into a character group and a word group, numbering the character group and the word group, and extracting repeated content; training a deep learning model to process the character group and the word group to form a standard text; analyzing the audio to determine a language region, obtaining a dialect database and extracting dialect content; combining the dialects, generating a to-be-analyzed text, and performing semantic analysis and classification; and finally, obtaining the expression meaning of the standard text according to the analysis result. The existing marked dialects are combined and substituted into the standard text, so that potential multiple explanations caused by dialect differences in the text can be covered and revealed, all expression meanings existing in the standard text can be obtained, and the process can ensure that any possible expression meaning is not missed, so that the accuracy of the expression meanings is improved. And the final semantic analysis is more comprehensive and accurate.

The invention discloses a mineral reserve analysis method and system based on multi-parameter clustering, a storage medium and equipment, and belongs to the field of electrical data clustering analysis, and the method comprises the following steps: S1, obtaining the distribution of a plurality of parameters representing the electrical properties of rocks and ores through a geophysical inversion method; s2, extracting different parameters and recording corresponding positions to obtain data sets corresponding to all the parameters; s3, according to the data groups corresponding to all the parameters, carrying out mineral trap pre-division on the to-be-detected area; s4, clustering analysis is carried out in combination with the data sets corresponding to different parameters, iteration revision is carried out on the pre-division result of the mineral reservoir entrainment, and final mineral reservoir entrainment division based on multiple parameters is obtained after iteration is finished; and S5, estimating mineral reserves according to the final mineral trap division. According to the method, the mineral traps are divided by comprehensively utilizing various parameters which are relatively sensitive to electrical parameter distribution of rocks and ores, so that the mineral traps are divided more accurately, and meanwhile, the judgment accuracy and the reserve estimation precision are improved through clustering.

The invention discloses a method for carrying out simultaneous transmission call translation by using an earphone, relates to the field of call translation, and solves the problem that in the process of simultaneous transmission call translation, if only the paraphrase of original voice is rigidly transmitted in a target language, the connotation and context of an original text cannot be fully captured and expressed, and the interpretation of the original text cannot be fully captured and expressed. The problems of serious information distortion and connotation loss are often caused; by simulating the mood of a speaker, the quality of simultaneous transmission call translation can be remarkably improved, the emotion expression is enhanced, the information accuracy is improved, the communication effect is enhanced, and the user experience is improved; in the aspect of technology implementation, the speech synthesis and sentiment analysis technology can provide powerful support, so that tone simulation is more natural and real; according to the method, voice data of different accent, different speakers, different background noise and different speech speeds are collected, a more robust and accurate voice recognition model can be trained, the method is suitable for diversified voice input, and the generalization ability of the method in different scenes is improved.

The invention discloses a deep learning-based hillock and beach reservoir prediction method and system under a well-free condition. The method comprises the following steps: determining seismic data and logging data of an adjacent work area, and matching the seismic data of a research work area and the seismic data of the adjacent work area; extracting seismic attributes of an adjacent work area, and constructing a conversion relation between the seismic attributes and the reservoir porosity of the hillside and beach body through a deep learning algorithm; and seismic attributes of the research work area are extracted, and the mound and beach body reservoir of the research work area is predicted according to the conversion relation. According to the method, the reservoir of the hillock and beach body is predicted according to various attribute characteristics of the seismic data, so that the multiplicity of solutions of a reservoir prediction result is effectively reduced, and the reservoir prediction precision is improved.

The invention belongs to the technical field of transient electromagnetism, and particularly relates to a ground array type three-dimensional transient electromagnetic detection method, which comprises the following steps of: 1, designing and planning a three-dimensional measurement network according to a detection task, obtaining coordinates of each sensor node, and forming a sensor array; 2, conveying the sensor to a corresponding coordinate point, synchronously transmitting a signal, and carrying out data synchronous acquisition; and step 3, processing and visualizing the collected data so as to carry out interpretation. According to the invention, simultaneous detection of a plurality of measurement points in a measurement area can be realized, so that a three-dimensional electrical structure image of an underground medium can be constructed, the electromagnetic field intensity of excitation can be greatly enhanced, the signal-to-noise ratio and the detection depth of a measurement signal can be improved, and the electrical distribution characteristics of the underground medium can be reflected more truly; the multiplicity of solutions is reduced, and the accuracy and reliability of exploration results are improved.

The invention discloses a ship aging management object screening method, and the method comprises the following steps: 1), obtaining a ship composition element diagram for a ship product to be subjected to aging management; 2) constructing an aging management demand degree comprehensive evaluation model; 3) obtaining the evaluation grade of each evaluation index of the evaluation object; 4) constructing an evaluation decision evaluation matrix, and performing aging management priority evaluation; 5) determining a solution with the highest priority for elements in the evaluation matrix; 6) calculating the similarity of the cloud model; 7) calculating a correlation coefficient Ri based on cloud model similarity; 8) performing aging management priority evaluation on the m evaluation objects in the ship composition element graph according to the correlation coefficient Ri based on the cloud model similarity; and 9) obtaining the sequence of the aging management priorities of the basic elements in the whole ship range. According to the ship aging management object screening method based on the cloud model theory, fuzziness and randomness generated in the subjective evaluation process are reduced.

Embodiments of the present application relate to the technical field of image processing, and particularly relate to a contour fusion method, an electronic device and a storage medium. Embodiments of the present application determine the parent-child relationship between multiple contours, directionally constrain the contour points of the parent contour and the child contour, and determine the fusion direction, thereby fusing the nested multiple contours describing the same target region into a complete contour, eliminating the understanding ambiguity of the target region caused by the nested labeling of multiple contours, avoiding the misrecognition of the same defect region as multiple independent target regions by the segmentation network, improving the accuracy of the training data labeling, making the loss value calculation in the supervised training process more accurate, and significantly improving the convergence effect, generalization ability, and detection precision and robustness of the network model on tin hole defects.

The utility model belongs to the field of tissue embossing equipment, and particularly relates to a tissue production embossing device which comprises a rack. The rack is rotationally connected with a conveying roller and an embossing roller; a gear motor is fixedly connected to the side face of the rack. The output end of the gear motor is fixedly connected with the embossing roller; a housing is fixedly connected to the rack; an air knife nozzle is fixedly connected to the housing; a fan is fixedly connected to the top of the rack; by arranging the fan, the first pipe body, the housing and the air knife nozzle, when the embossing roller blowing device is used, the fan conveys air to the air knife nozzle through the first pipe body, air flow is sprayed out of the air knife nozzle, the air flow vertically sprayed out of the air knife nozzle blows an embossing roller, the air flow is blown out of the air knife nozzle, the air flow is blown out of the air knife nozzle, and the air flow is blown out of the air knife nozzle. Therefore, tissue fibers adhered to the surface of the knurling roller are reduced, and the situation that the grooves in the knurling roller are blocked, so that pressed patterns are not clear is reduced.

The invention discloses a method for advanced prediction of disaster-causing structures based on heat source tracing and hydraulic joint tomography inversion. The adopted test monitoring arrangement scheme is closely combined with the advanced geological drilling conditions and is related to the diameter of tunnels (holes) and laneways. The implementation basis is the advanced geological drilling holes carried out in underground projects, and it is universal for various underground projects. The adopted heat source tracing method has good application prospects due to its convenient monitoring and pollution-free. The filtering method based on the combination of smooth low-pass filtering and adaptive Kalman filtering realizes the dynamic adjustment of the covariance matrix and the gain matrix of the state prediction error, has the ability to track the sudden change state, and effectively avoids the filter divergence. The method makes full use of the data information obtained by the traditional hydrogeological test, carries out the hydrogeological joint tomography inversion, can significantly reduce the multi-solution of the inverse problem, realizes the interpretation of the non-redundant information of the water-richness of the disaster-causing structure, and provides a theoretical basis for accurately exploring the disaster-causing structure ahead of the project in advance.

This application relates to the field of code generation technology, and discloses a software code generation method and its apparatus, device, and medium. The method includes: responding to a code generation driving event and determining the multimodal information corresponding to the event, wherein the multimodal information corresponds to the original software development requirements; semantically aligning the different modal information in the multimodal information and fusing them to generate software function planning information; extracting each function planning unit as a code generation subtask of the corresponding code generation type according to the code generation type corresponding to the function planning unit described in the software function planning information; calling a large language model to execute each code generation subtask and synchronously triggering the corresponding coding constraint instructions to generate target code. This application, through the semantic alignment and fusion of multimodal information and the synchronous triggering mechanism of coding constraints in the code generation process, highly restores the development requirements of the software code and reduces the subsequent security audit costs and software maintenance risks of the software code.

The application discloses a kind of semantic boundary guided robot operation scene point cloud segmentation methods. Firstly, the three-dimensional point cloud of robot operation scene is obtained;Then, the semantic boundary point in the scene three-dimensional point cloud is predicted using point cloud learning network;The scene three-dimensional point cloud is input into point cloud Transformer model for feature extraction, and the semantic boundary point is used as the extended key point of self-attention mechanism in Transformer model, wherein for the point feature of each boundary region, a contrast learning loss function is added to reduce the semantic ambiguity of boundary point feature;Finally, based on the point feature of each point, the semantic class of each point is predicted to obtain the point cloud semantic segmentation result. The application enhances the learned point feature by introducing semantic boundary information in the point cloud feature extraction process, and improves the misclassification problem caused by boundary ambiguous features, effectively improves the point cloud segmentation quality of robot operation scene, and has good engineering practical value.

The present application belongs to the technical field of geotechnical engineering, and specifically relates to a tunnel underpassing a site adverse geological early warning method and system. A multi-source detection network is arranged in a set region of tunnel construction, original data reflecting geological abnormal conditions are obtained and preprocessed, a multi-dimensional feature index set including wave speed, dielectric constant, rock mass integrity coefficient, etc. is obtained, a three-dimensional perspective model containing spatial distribution and physical properties of adverse geological bodies is generated through a data fusion algorithm, and a geological anomaly grade G is determined accordingly. The structure vulnerability grade V of a target historical site is obtained, and a disturbance influence coefficient S of the site by current construction operation is calculated based on construction parameters. The geological anomaly grade G, the site vulnerability grade V and the construction influence coefficient S are input into a preset risk coupling evaluation model, a comprehensive risk value R is dynamically calculated, and corresponding graded early warning is triggered according to the threshold interval of the R value.

The invention discloses a regional geological disaster vulnerability evaluation method, device and equipment and a medium, and relates to the technical field of geological disaster evaluation, and the method comprises the following steps: obtaining a plurality of evaluation indexes used for evaluating the vulnerability of a geological disaster in a target region; dividing the terrain of the target area into a plurality of grids, and obtaining a plurality of evaluation index values of the corresponding terrain in each grid; fitting the plurality of evaluation index values in each grid to obtain a vulnerability value of each grid; taking the discourse domain in the cloud model as an abscissa and the membership degree as an ordinate, and constructing a standard cloud picture for representing the vulnerability level; inputting the vulnerability value of each grid into the cloud model to generate a sample cloud picture, comparing the membership degree of the sample cloud picture with a plurality of membership degrees of a standard cloud picture to obtain a comparison result, and obtaining the vulnerability level of the target area according to the comparison result. According to the method, fuzziness and randomness can be reflected through the difference value of the membership degrees, and the accuracy of vulnerability evaluation is improved.

The present invention provides a method, device, equipment and medium for regulating formation grouting. The method comprises: inputting formation data of a target grouting section into a prediction model, and outputting a grouting pressure target value from the prediction model; determining a grouting flow plan value for the next stage based on a steady-state error between a grouting flow measured value and a target value in a current stage and the grouting flow measured value; after the grouting flow plan value reaches the target value, constructing a grouting pressure adaptive PID control strategy based on a grouting pressure measured value and a target value in a current cycle, and determining a grouting pressure plan value for the next cycle based on the grouting pressure adaptive PID control strategy and a second steady-state error between a grouting pressure measured value and a grouting pressure target value in a current cycle and the grouting pressure measured value in the current cycle, until the grouting pressure plan value reaches the grouting pressure target value, so that the regulation of the grouting process has a target quantification, which is gentler and more accurate than manual regulation.

The invention relates to the technical field of text classification and grading evaluation, in particular to a specific text classification and grading evaluation system and method based on an information element extraction technology. The method comprises the steps that an extraction unit performs key element field extraction on text data based on a large model fine tuning technology; the standardization unit carries out standardization processing on each extracted element field; and the evaluation unit constructs a text classification and grading evaluation system through a decision tree based on the normalized fields. According to the technology, the accuracy and the coverage rate of key element field extraction can be improved through the large model fine tuning technology and the model, the model can be helped to better understand the context in a text, so that the situations of false report and missing report are reduced, the extraction quality is improved, the fine tuning technology can enable the model to better adapt to a new data set, and the extraction efficiency is improved. Even if the data set is slightly different from the pre-training data set, good performance can be kept, and the robustness of the model facing texts of different styles or formats can be enhanced.