139 results about "Ambiguity resolution" patented technology

The invention provides a multi-language cross-culture communication assisting method and system based on a large model. The method comprises the following steps: receiving a source language audio stream during a call, calling a multi-language sound frequency harmonic modulation feature library to extract fundamental frequency harmonic intensity distribution and tone turning features, and generating a cultural acoustic fingerprint vector; based on the vector, controlling a microphone array phase difference, directionally enhancing a fundamental frequency harmonic component of a speaker and suppressing noise, and outputting a high signal-to-noise ratio spectrogram; analyzing the pronunciation rhythm and tone turning characteristics of the spectrogram, capturing the pitch jump and duration of the syllable boundary, and generating an acoustic culture label; associating the spectrogram with a target semantic library, matching harmonic distribution and a cultural context rule based on a large model, and outputting a cultural interpretation prompt containing an ambiguity resolution suggestion; and generating a calibration result according to the acoustic tag and the semantic prompt, and overlapping the dynamic floating subtitles to the face area of the speaker in the video conference picture. According to the invention, cultural tone ambiguity in multi-language communication is eliminated.

The invention discloses an intelligent auditing method and system based on a multi-modal large model, and relates to the field of artificial intelligence, and the method comprises the following steps: a field adaptive model is constructed by injecting financial and accounting field corpora into an LLaMA-3 architecture, accounting criteria, policies and regulations can be automatically analyzed, executable auditing rules can be generated, the system adopts a multi-channel structure, and the auditing efficiency is improved. According to the method, key entity extraction, logic relation identification and ambiguity resolution are synchronously completed, the accuracy and robustness of rules are ensured, multi-source heterogeneous data are integrated by constructing a finance and accounting knowledge graph, generated SPARQL query is executed to realize automatic auditing testing, block chain anchoring rule hash and a three-dimensional evidence matrix are innovatively introduced, and the auditing efficiency is improved. And the traceable and non-tampering decision traceability of the whole process is realized. According to the method, the auditing efficiency, the coverage and the automation level are remarkably improved, the auditing risk is effectively avoided, and a highly credible solution is provided for intelligent auditing of man-machine cooperation.

The invention relates to a work order material intelligent tracking and dynamic updating system based on multi-source data fusion. The system comprises an acquisition module, a tracking module, an updating module, a man-machine interaction module and a storage module, the acquisition module is used for acquiring work order information from a bill of material, scheduling information and inventory and supplier data, and constructing a material data model; the tracking module monitors material inventory, consumption and material shortage risks based on the model, and realizes automatic state updating and graphical path recording in combination with bar code recognition and ambiguity analysis; the updating module predicts material requirements through a machine learning algorithm, optimizes a distribution plan and outputs a dynamic report; the interaction module supports multi-format data import, unstructured field classification and interrupted operation control; the storage module ensures credible synchronization and tamper-proofing performance of data among different systems through a block chain technology; according to the invention, high-efficiency management and dynamic allocation of work order materials can be realized in an environment without an MES system, and the production transparency and the resource utilization efficiency are improved.

Aspects of the invention provide a method, system, and computer program product for retrieval augmented generation. In one aspect, the method includes receiving a query. The method further includes classifying the query to a first domain within a plurality of domains. The method additionally includes determining an ambiguity associated with classifying the query. The method also includes retrieving an index of domain-specific vector embeddings corresponding to the domains when the ambiguity does not exceed a threshold for ambiguity. The method further includes prompting a large language model with the query and the domain-specific vector embeddings. The method also includes receiving a query response from the large language model as grounded with the most relevant index results. The method further includes forwarding the query response.

The invention relates to the technical field of natural language processing, in particular to an NLP-based multi-source data fusion system and method.The NLP-based multi-source data fusion system comprises a data collecting and processing unit, an NLP processing unit, a data fusion unit and an output unit.The data collecting and processing unit collects multi-source heterogeneous data and executes standardization processing to obtain preprocessed data; the NLP processing unit extracts entity, relation and emotion features through deep semantic analysis, a context-aware entity relation graph is constructed by adopting a bidirectional attention mechanism model and a graph attention network, and the data fusion unit realizes cross-source entity ambiguity resolution through an iterative graph neural network based on a graph topological structure. The emotion confidence weight is dynamically distributed to generate a fusion vector, a rule feedback reconstruction map is extracted, and the output unit converts the fusion vector into a target format for output, so that the problem of semantic conflict of multi-source data is solved, and semantic coherence and availability of fusion data 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 discloses an intelligent voice question-answering system and a knowledge reasoning method for operation and maintenance of power equipment. The system comprises a knowledge base construction module and a core question and answer engine module, the knowledge base construction module is used for constructing a power equipment knowledge graph and a fault case library, and the core question and answer engine module comprises a natural language understanding unit, a multistage semantic retrieval unit and a voice interaction ambiguity resolution unit. A natural language understanding unit identifies intent categories and power entities. The multi-stage semantic retrieval unit executes the following steps: matching in an ontology layer of the knowledge graph; performing extended query to obtain detailed information; and similar case matching is carried out in the fault case library. The voice interaction ambiguity resolution unit is used for executing disambiguation processing; analyzing the confidence and clarifying the dialogue; and triggering knowledge retrieval and reasoning and generating a sorting answer. The operation and maintenance efficiency is improved, the dependence on experts is reduced, the operation and maintenance quality and safety are improved, knowledge precipitation and sharing are promoted, the field work experience is improved, and scientific decision making is assisted.

The invention relates to a mine monitoring method based on millimeter-wave radar multi-feature fusion, and the method comprises the steps: transmitting an FMCW signal through a millimeter-wave radar, receiving a target echo, sequentially carrying out the signal preprocessing, 1DFFT distance measurement, 2DFFT speed measurement, velocity ambiguity resolution and DOA estimation angle measurement, and extracting the distance, velocity, angle and energy variation features of a target point cloud; then millimeter-level deformation is obtained through phase analysis; obtaining a vibration frequency through band-pass filtering and spectrum analysis; fusing the six types of features of the target point cloud, adopting a confidence counter weighting decision mechanism, and triggering early warning when a fusion score exceeds a preset threshold value; according to the invention, through the advantages of high reliability, strong anti-interference performance and high non-contact safety of multi-feature fusion decision, the false alarm rate is significantly reduced, and the real-time accurate early warning capability of the mine dangerous state is improved.

The application discloses an improved radar two-dimensional ambiguity resolution method for multi-targets, comprising the following steps: arranging point trail data after radar signal processing according to pulse repetition period from small to large; constructing a reference lookup table for resolving range ambiguity based on the arranged data; constructing a reference lookup table for resolving velocity ambiguity based on the arranged data; traversing all the frequency combinations of radar transmission, selecting N groups from M groups of frequency for multi-target pairing; obtaining a range pairing table based on the reference lookup table for resolving range ambiguity, calculating the standard deviation between rows of the range pairing table, and solving the real range of targets; obtaining a velocity pairing table based on the reference lookup table for resolving velocity ambiguity, calculating the standard deviation between rows of the velocity pairing table, and solving the real velocity of targets; and merging and processing output target data. The application can solve the problem that the traditional one-dimensional set method cannot meet the real-time requirement when the number of targets is large and the ambiguity is large.

The invention discloses a comprehensive quality evaluation method and system for Beidou deformation monitoring observation data in a complex environment, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: carrying out the comprehensive evaluation of an elevating angle, a signal-to-noise ratio, an epoch difference and an environment model of the observation data of a Beidou single station, and determining the initial quality of the data; based on the double-difference observation values among the multiple stations, the data quality evaluation precision is further improved by combining a double-difference ambiguity resolving method; according to the method, ionosphere delay, three-difference errors and inter-epoch difference errors are used for data fusion, a more accurate quality evaluation result is obtained, and finally, the data are optimized through comprehensive analysis based on the quality evaluation results of various types of data so as to ensure accurate positioning and deformation monitoring. The technology aims at improving the accuracy and reliability of data quality evaluation by comprehensively analyzing influence factors of Beidou observation data in a complex environment, and provides support for geological disaster monitoring, smart city construction and other high-precision application scenes.

The invention relates to the technical field of navigation positioning, and discloses an INS-assisted positioning system and method under a GNSS / INS positioning system, and a medium, and the system comprises a prediction pseudo-range confirmation module, a floating point solution calculation module, a first optimal fixed solution calculation module, a second optimal fixed solution calculation module, and a fusion solution module. According to the scheme, the floating point ambiguity resolving and fixing process is optimized by correcting inertial information in real time, specifically, inertial prior information is corrected by using a combined positioning high-precision result, a traditional GNSS pseudo-range estimation method is replaced, an improved partial ambiguity fixing strategy is introduced, the fixing efficiency of non-optimal subset ambiguity is improved, and the positioning accuracy of the GNSS pseudo-range estimation method is improved. According to the method, the positioning precision, the stability and the real-time performance can be remarkably improved in a complex urban environment.

The invention relates to the technical field of GNSS high-precision positioning, and provides a BIE-based real-time dynamic positioning integer ambiguity resolving method and system, and the method comprises the steps: obtaining a preliminary ambiguity subset through the screening of a satellite elevation angle and a carrier-to-noise ratio, and carrying out the correlation reduction processing; a multi-level screening mechanism is adopted and comprises three stages of dynamic optimization of coarse screening, fine screening and candidate group generation; a weight distribution model is improved based on Laplacian distribution to calculate candidate solution weights and probability weighted fusion is carried out; in combination with an ILS and BIE mixed fixing strategy, BIE weighted averaging is adopted when ILS fixing fails; a positioning result is optimized through dynamic satellite screening and multi-system data fusion, and a jump detection threshold value is set to realize abnormal recalculation. The method improves the reliability of real-time dynamic positioning, and guarantees the continuous and stable operation of the system in a signal shielding or multi-path interference scene through a jump detection and recalculation mechanism.

The invention discloses a domestic PC terminal cross-modal natural interaction method and system fused with generative AI, and relates to the technical field of man-machine interaction, and the method comprises the steps: obtaining multi-modal data, and carrying out the preprocessing and feature extraction; constructing a modal adapter, mapping the extracted multi-modal features to a shared semantic space, and generating a multi-modal semantic vector; calculating a dynamic weight coefficient of each mode according to the current scene features; fusing the multi-modal semantic vectors to generate cross-modal fusion features, and carrying out ambiguity resolution processing on the fusion features; inputting the disambiguated fusion features into a lightweight domestic generative AI model, carrying out intention understanding, and generating a multi-modal response; and the generated multi-mode response is adapted to domestic PC terminal hardware and an operating system, and natural interaction is completed. According to the method, intention understanding and response generation are performed by introducing the generative AI domestic large model, so that the accuracy and naturalness of cross-modal interaction of the domestic PC terminal are improved.

The invention discloses a real-time PPP whole-cycle ionosphere elimination combination ambiguity fixing method and device based on PPP-B2b, terminal equipment and a storage medium, and belongs to the technical field of GNSS positioning and navigation, and the method comprises the steps: obtaining original observation data of a plurality of satellites and PPP-B2b correction information; according to the original observation data and the correction information of each row, generating wide lane and narrow lane floating point ambiguity resolution; calculating wide lane and narrow lane FCB of each satellite according to the wide lane and narrow lane floating point ambiguity resolution; and according to the wide-lane floating point ambiguity resolution, the wide-lane FCB, the narrow-lane floating point ambiguity resolution and the narrow-lane FCB, generating the whole-cycle ionosphere elimination combined ambiguity. According to the method, the PPP-B2b service broadcasted in real time is used for resolving, the method does not depend on post precision products, and the real-time performance can be guaranteed. By implementing the invention, the problem that the whole-cycle ionosphere elimination combination ambiguity cannot be solved in real time in the prior art can be solved.

The invention relates to the field of robot technology and artificial intelligence, and discloses a robot fine operation method and system based on a visual language model and semantic key point characterization, a closed-loop multi-body Agent system is constructed, GPT-4o and Ground DINO are combined to accurately understand and disambiguate a natural language instruction of a user, an innovative bridging layer is introduced, and the robot fine operation method and system based on visual language model and semantic key point characterization are obtained. Abstract decision information is converted into bottom execution parameters represented by three-dimensional semantic key points, the problem of decision transfer distortion is solved, meanwhile, a KMP algorithm is improved, and the stiffness and contact force of trajectory tracking are dynamically optimized by introducing a learning-based time-varying hybrid impedance / force control strategy, so that the accuracy of trajectory tracking is improved. According to the method, the generalization success rate of fine operation tasks of the robot and the stability of physical interaction are remarkably improved, seamless connection from high-level semantic understanding to low-level action generation is achieved, and an efficient and reliable technical scheme is provided for autonomous operation of the robot in a complex scene.

The invention discloses a GPU two-stage parallel implementation method for multi-target distance and speed ambiguity resolution, and relates to the technical field of radar ambiguity resolution, and the method comprises the steps: obtaining multiple repetition frequencies, randomly selecting the multiple repetition frequencies each time, and generating multiple repetition frequency groups; each repetition frequency corresponds to a plurality of plots, one plot is selected from the plots corresponding to each repetition frequency in the repetition frequency group each time to form a plot combination, a plurality of plot combinations corresponding to the repetition frequency group are generated, and identifiers of the plots in each plot combination are obtained; allocating a group of thread blocks to each trace point combination, and allocating a plurality of threads to each trace point; for the plurality of trace point combinations, performing coarse-grained parallel processing to obtain a minimum variance corresponding to each trace point combination; and for any trace point combination, performing fine-grained parallel processing to obtain a minimum variance corresponding to the trace point combination. According to the invention, the real-time performance of the defuzzification algorithm can be enhanced.

The present invention relates to a method and device for measuring the attitude of an array antenna, comprising obtaining a solution baseline vector through satellite observation of array elements on the array antenna to be measured, inputting the solution baseline vector into a three-dimensional attitude solution algorithm to obtain the previous three-dimensional attitude angle, obtaining the attitude matrix of the array antenna to be measured based on the previous three-dimensional attitude angle, eliminating the phase center error using the attitude matrix and phase direction diagram, using the three-dimensional attitude solution algorithm to obtain the current three-dimensional attitude angle of the array antenna to be measured, determining whether the current three-dimensional attitude angle has converged based on the current three-dimensional attitude angle and the previous three-dimensional attitude angle, and using the current three-dimensional attitude angle as the attitude angle solution result. The present invention eliminates the influence of the phase center error, improves the accuracy of the attitude solution result, improves the success rate of ambiguity solution and the reliability of the attitude measurement result, and improves the accuracy of the roll angle solution. The present invention also relates to a device and a storage medium.

This invention discloses a product classification and recognition method and system based on AI technology, belonging to the field of AI recognition technology. By constructing a semantic alignment master vector Edis, the system achieves structured extraction and ambiguity resolution of the overall semantic representation of the product. On this basis, the system introduces the construction of a product classification label semantic vector set Gvec and a semantic similarity score set Sset, enabling the system to perform fine-grained matching of multiple classification labels based on contextual semantic features. This constructs a confidence score Conf reflecting the classification credibility, which is then compared with a preset classification judgment threshold Thrs to ensure that product labels are output only when the classification judgment credibility is sufficient. Through the connection of the above processes and the linkage of output results, this method not only significantly reduces the probability of misjudgment caused by expression ambiguity and word order reversal, but also provides a reliable rejection mechanism when accurate classification is not possible, effectively improving the overall robustness and practical value of the classification system.

The invention provides a PPPRTK / LiDAR / INS fusion positioning method based on hierarchical optimization. The PPPRTK / LiDAR / INS fusion positioning method comprises the following steps: firstly, constructing a front-end tight coupling module of LiDAR and INS, extracting point cloud geometric constraints, and combining the point cloud geometric constraints with IMU pre-integration to form LIO odometer factors; then obtaining multi-system multi-frequency GNSS original observation, unifying time, coordinates and antenna reference, and carrying out multi-stage correction on the observation; a PPP-RTK / INS tight coupling observation model is established under an SRIF framework, linearization and residual construction are carried out on a non-difference and non-combination equation, a joint state is constructed, and a floating point-fixed two-stage ambiguity resolving and quality control method is adopted; and finally, performing joint optimization on the front-end LIO factor and the PPP-RTK / INS factor by utilizing a factor graph, eliminating abnormal observation in combination with a robust kernel and a residual error threshold, and outputting a continuous and accurate vehicle pose. The method can effectively improve the continuity, convergence speed and precision of positioning in complex scenes such as urban canyons and elevated interchange, and is suitable for the fields of navigation, automatic driving, free flow charging and the like.

The application relates to an antenna calibration method and device, comprising the following steps: selecting qualified visible satellites from GNSS satellites based on carrier phase observations, calculating unit observation vectors of the visible satellites, and determining reference satellites of each epoch according to the unit observation vectors of all the visible satellites; constructing a set of triple difference phase center error measurements; constructing a spherical harmonic function fitting model according to the set of triple difference phase center error measurements, and obtaining a phase pattern of the antenna to be calibrated after solving. The application is suitable for large-scale calibration of low-cost antennas. The carrier phase ambiguity is eliminated, the complex ambiguity resolution and verification process is avoided, the complexity of the phase center error calibration is reduced, the influence of triple difference noise on the fitting accuracy of the phase pattern is reduced, and the accuracy of the antenna calibration result is improved. The application also relates to an equipment and a storage medium.

This invention relates to an inter-satellite laser multi-standard signal fusion time-frequency synchronization method and system. It integrates frequency synchronization, time synchronization, and communication functions by using a single laser source to carry a frequency signal, a single-frequency signal, a low-pass filtered signal, and a pseudo-random code signal with communication data via spectrum shifting. The three signal spectra are separated and do not interfere with each other. The frequency signal is used for inter-satellite frequency synchronization; the single-frequency signal is used for high-precision time delay measurement to achieve inter-satellite time synchronization; and the pseudo-random code signal serves both communication and time delay measurement ambiguity resolution. This invention employs a bidirectional time delay measurement and joint calculation method, combined with frequency signal link jitter compensation, to achieve parallel and collaborative operation of the three functions. Compared with existing technologies, this invention improves system integration, reduces the number of modules and external interfaces, and effectively reduces the weight, power consumption, and volume of onboard equipment. It is suitable for high-precision time-frequency synchronization applications such as satellite navigation, deep space exploration, and integrated space-ground networks.

The invention provides a lightweight anaphora ambiguity resolution multi-task learning method, and relates to the technical field of intelligent language processing of a power system, and the method comprises the steps: S1, collecting power production command related documents to construct a lightweight power semantic knowledge base; s2, on the basis of the lightweight electric power semantic knowledge base, constructing an ultra-lightweight semantic analysis model through double-layer knowledge distillation; s3, performing lightweight deployment on the ultra-lightweight semantic analysis model based on an INT8 quantification technology to obtain a quantification model capable of running on edge equipment; s4, inputting the electric power semantic analysis task into a quantitative model obtained by lightweight deployment by adopting an end-side collaborative reasoning strategy, performing complexity evaluation and intelligent shunting processing, and outputting a semantic analysis result with high confidence; according to the method, high-precision semantic understanding under the condition of extremely low computing resources can be realized, and a practical and reliable intelligent language analysis capability is provided for a power production command system.

The invention relates to the field of radar distance ambiguity resolution processing, in particular to a radar distance ambiguity resolution implementation method based on a GPU. According to the technical scheme, the method comprises the steps of obtaining echo data sets under multiple groups of different PRFs, extracting a target distance ambiguity solution set corresponding to each group of PRFs from the echo data sets, performing extension calculation on target ambiguity distances under each PRF, generating all possible physical distance sets, forming a two-dimensional data structure, and obtaining a target distance ambiguity solution set; the two-dimensional data structure is stored in a shared table of a GPU global memory, sliding window statistics of a plurality of distance units are calculated in parallel through threads in the GPU, then sliding window clustering judgment is carried out, and if the windows are judged to be consistent, all distance values in the sliding windows are averaged to serve as a target real distance estimated value. The method is suitable for radar distance ambiguity resolution processing.

A multiple-pulses-in-air (MPiA) laser scanning system configured for measuring the time-of-flight of laser pulses, wherein the computing unit is configured for triggering the transmitter to transmit the send pulses with a varying pulse frequency, wherein the transmitter is configured to have a dead mode, wherein in the dead mode an increase in pulse frequency is effected, wherein in the course of increasing the pulse frequency, a reduction in pulse energy per individual send pulse of the send pulses is provided such that the pulse energy of the resulting return pulses is reduced such that it is below a detection threshold, and a measurement mode, wherein in the measurement mode a reduction in pulse frequency compared to the dead mode is effected.

The present application relates to the technical field of database construction, and particularly relates to a construction method of a foraminifera morphological feature database, comprising the following steps: S1, collecting foraminifera images from multiple sources, identifying scale drift and shooting deviation based on principal axis normalization and local vorticity field, performing structure-preserving image enhancement, and outputting an enhanced image set consistent in shell substance, shell arrangement, shell opening, suture line and micro-pore features; S2, extracting a multi-scale shell structure atlas, constructing a structure-semantic coupled label nested tree, performing polysemy recognition and ambiguity resolution, and generating a structured annotation set; S3, fusing the image set and the annotation set, and constructing a three-layer database model containing a part layer, an attribute layer and a variation layer according to the label nested tree. Through image structure enhancement, semantic ambiguity resolution and three-layer database construction, the present application effectively improves the recognition accuracy of foraminifera morphological features, the label organization logicality and the information retrieval efficiency.

The application discloses an online medical intelligent doctor guiding system based on text graph embedding and belongs to the technical field of medical doctor guiding. Through fine-grained medical entity recognition and ambiguity resolution on online medical consultation texts, fine-grained entities can be accurately recognized, and the problem of fine semantic granularity can be solved. Through matching of a medical dictionary and context, the ambiguity generated by the entity can be effectively eliminated. Through a dynamic updating mechanism, the latest medical knowledge can be ensured to be incorporated into the graph, and the knowledge can be prevented from being outdated. From text preprocessing to graph construction, a complete link is formed, which can provide a high-quality knowledge base for subsequent graph embedding and doctor guiding reasoning. The entity vector obtained through processing contains graph structure, deep semantics and clinical logic, and can effectively solve the defects of single mode of traditional embedding. Through a constraint loss function, the vector can be ensured to conform to the path rules of authoritative clinical guidelines, clinical logic consistency can be realized, and vectors that violate medical common sense can be avoided.

The invention relates to the technical field of data processing, and discloses a heterogeneous data semantic alignment method and system, electronic equipment and a storage medium, and the method comprises the steps: collecting Web front-end heterogeneous data, carrying out the type labeling according to a preset dimension, constructing a mapping relation between a data type and an extraction template, generating an extraction instruction corresponding to the data type, and carrying out the semantic alignment of the Web front-end heterogeneous data. Inputting the Web front-end heterogeneous data subjected to type labeling and a corresponding extraction instruction into a large language model, and outputting an extraction result; constructing a dynamic semantic knowledge base for supporting semantic alignment, performing field normalization, cross-data association and semantic ambiguity resolution processing on the extraction result through a large language model based on the dynamic semantic knowledge base, and outputting a semantic alignment result; and performing rule verification on a semantic alignment result, optimizing the large language model through a fine adjustment mode based on a verification result, and updating the dynamic semantic knowledge base at the same time. According to the invention, the adaptability and reliability of heterogeneous data processing can be improved.

The application discloses a dual-polarization weather radar speed ambiguity resolution method based on lag-1 cross-correlation, which utilizes lag-1 cross-correlation of adjacent radial directions instead of radial velocity of the adjacent radial directions in a traditional algorithm to calculate an ambiguity resolution reference speed. This makes the ambiguity resolution speed range of the ATAR mode expand to twice that of the traditional algorithm, so that the speed measurement range is consistent with that of the single-polarization mode. Application of the algorithm improves the reliability of speed data of the ATAT mode weather radar in severe weather detection.

A method performed by a computing device includes identifying a symbolic representation of a phrase to produce a plurality of tokens. The method further includes generating a first equation package for the plurality of tokens that corresponds to a first permutation of a plurality of permutations of interpretation of the plurality of tokens based on one or more different meanings of the symbolic representations of the phrase. The method further includes updating, utilizing a knowledge database, the first equation package for the plurality of tokens that optimizes an interpretation confidence level for the plurality of tokens to produce a second equation package that includes a sequence of second selected equation elements that corresponds to a second permutation of the plurality of permutations of interpretation of the plurality of tokens representing a most likely interpretation of the phrase.

The application discloses a satellite-ground communication time offset and Doppler frequency offset pre-compensation device, which has high estimation accuracy and can complete wide-range time offset and frequency offset estimation and pre-compensation without acquiring satellite ephemeris information of a terminal. The application is realized by the following technical scheme: a DoA estimation unit estimates the arrival angle DoA of a satellite signal by using an array signal processing algorithm; the estimation unit estimates the arrival angle DoA of a satellite signal; the acquired satellite azimuth angle and elevation angle information is divided into two paths, one path is sent to a distance calculation unit, a time offset calculation unit and a time offset pre-compensation unit, the propagation time delay between the satellite and the terminal is calculated and time offset pre-compensation is performed; the other path is output to a velocity vector calculation unit, a Doppler frequency offset calculation unit, a Doppler ambiguity resolution unit and a frequency offset pre-compensation unit, the Doppler frequency offset caused by satellite movement is calculated and frequency offset pre-compensation is performed, the residual time offset and frequency offset are estimated, and the residual time offset and frequency offset are accurately adjusted.