491 results about "Target Response" patented technology

The invention discloses a knowledge base enhancement generation method and system based on hybrid retrieval and fact verification. The method comprises the following steps: receiving an original query of a user; performing intention analysis and rewriting on the query, identifying an intention type and generating a sub-query adapted to retrieval; executing mixed retrieval of vector retrieval, keyword retrieval and selectable knowledge graph retrieval based on the sub-query, and recalling related knowledge fragments; performing deduplication clustering, fact conflict recognition processing and correlation reordering on the knowledge fragments; according to the intention type and the reordered knowledge fragment, dynamically selecting a prompt template to construct an enhanced prompt; and sending the enhanced prompt into the large language model, and generating a target response with the reference source. According to the method, knowledge recall comprehensiveness is improved through mixed retrieval, knowledge reliability is ensured through fact verification, generation logicality is enhanced in combination with dynamic prompt construction, and response accuracy and credibility are improved.

The invention provides a cue word attack detection method and system of a large language model and electronic equipment, and relates to the technical field of text process.The method comprises the steps that a to-be-processed text block in an original text is determined; performing heuristic semantic analysis on the text block to obtain a first suspicious text block and a first risk score thereof; performing semantic analysis on the first suspicious text block and the context information thereof through a first language model to obtain a second suspicious text block and a second risk score thereof; performing structured verification of the attack intention on the second suspicious text block according to a preset meta prompt word through a second language model to obtain an attack intention verification result; the attack intention verification result comprises a third suspicious text block and a third risk score thereof; and executing a target response action on the original text based on the first risk score, the second risk score and the third risk score. According to the method, the efficient processing requirement of the long text can be considered, and the detection accuracy of complex attacks can be improved.

A method of performing a task based on a large model and an electronic device are provided, which relate to artificial intelligence technology, and in particular to fields of voice interaction, deep learning, large model, etc. The method includes: acquiring a demand feature characterizing a demand intention; performing a task by using the large model according to the demand feature, to obtain a response text, in which a target response word is determined based on: determining a query feature for each attention subtask in the task based on an associated response word feature; and performing, based on the demand feature read from a storage unit as a value feature and a key feature shared by the plurality of attention subtasks, the plurality of attention subtasks by using a computing unit according to a plurality of query.

The invention relates to the technical field of artificial intelligence, can be applied to the medical field and the financial science and technology field, and discloses a dialogue method and device based on a memory scheduling framework, equipment and a medium, which are applied to a bank virtual customer service scene or an intelligent triage and pre-inquiry scene. Obtaining a target dialogue page from all dialogue pages of the current dialogue chain in the short-term memory queue; a target topic segment and a target dialogue page are retrieved from the medium-term memory module, and target feature information is retrieved from the long-term personalized memory module; constructing a structured cue word, and generating a target response based on the structured cue word; storing the newly constructed dialogue page in a short-term memory queue; if the short-term memory queue is full, removing the dialogue page from the head of the queue; updating the topic segment in the medium-term memory module based on the removed dialogue page; and updating the long-term personalized memory module according to the popularity of the topic segment in the updated middle-term memory module. The dialogue response accuracy is improved.

Disclosed in the present invention are a method and apparatus for synthesizing spatially cross-correlated multi-point ground motions fitting a response spectrum. The method comprises: using an influence matrix method to obtain ground motion acceleration time histories fitting a target reaction spectrum with high precision, and calculating a Fourier amplitude spectrum and a phase spectrum thereof; and considering a calculation relationship between a power spectral density function and the Fourier amplitude spectrum, taking the power spectral density function of the obtained time histories as an initial value, using a random vibration theory and Cholesky decomposition to obtain spatially cross-correlated multi-point ground motion time histories, further taking into account a phase spectrum of the ground motion time histories fitting the target reaction spectrum with high precision, and performing correction and iteration to obtain spatially cross-correlated multi-point ground motions fitting a response spectrum. The present invention overcomes the shortcomings in current multi-point ground motion time history synthesis techniques, such as the difficulty in ensuring good fitting to a target response spectrum and the non-stationarity of synthesized ground motions, helping to improve the reliability of dynamic response analysis results for large-scale engineering structures under the action of multi-point ground motions.

The invention discloses an automatic extraction method for semiconductor device model parameters, and the method comprises the steps: obtaining a multi-target response through the key parameters of a semiconductor device model, and constructing and training an agent model to predict a target response mean value and uncertainty parameters; constructing a multi-objective optimization collaborative circulation framework assisted by an agent model; generating a candidate parameter population in key parameters according to a position updating mechanism of a multi-objective optimization algorithm, predicting candidate parameters by using a trained proxy model, selecting the candidate parameters according to a prediction result to carry out real simulation evaluation, adding an obtained real sample into a non-dominated file, and updating the file based on a non-dominated relationship. Adding a training set dynamic updating agent model into the real sample, generating a potential search trajectory, selecting a representative representative Pareto approximate solution from the potential search trajectory, and updating the position of the population and the direction of the next-generation search trajectory according to the representative solution selected from the file; and terminating the circulation, outputting the solution set in the file, and checking.

The invention discloses a large language model prejudice reduction method and system based on cross-model judgment, and the method comprises the steps: giving a group of multiple different LLM models, inputting preset prompts into the different LLM models, and obtaining a response generated by each LLM model; based on the composite prejudice measurement standard, evaluating the prejudice level of the response generated by all other LLM models in the same row group by using each LLM model, and obtaining a prejudice evaluation score; on the basis of the obtained prejudice evaluation scores, calculating a wave arrival score of each response by using an improved wave arrival counting mechanism, and selecting the response with the highest wave arrival score as a consensus target response which is accepted by collectively and has the least prejudice; and performing parameter fine tuning on each participated LLM model based on the obtained low-prejudice consensus target response. The invention provides a novel cross-model judgment framework for detecting and reducing prejudice in LLM by referring to concepts of human companion review and collaborative learning.

Provided are a model distillation method and apparatus. The method comprises: acquiring a fixed training dataset, wherein the fixed training dataset comprises a plurality of training text sequences; guiding, by means of a teacher model, a student model to generate target response text sequences on the basis of the training text sequences, so as to correct semantic differences between response text sequences output by the student model and response text sequences output by the teacher model; on the basis of the plurality of training text sequences and a target response text sequence corresponding to each of the plurality of training text sequences, obtaining a target training dataset; and on the basis of the target training dataset and the teacher model, performing distillation training on the student model. In the present application, semantic differences between outputs of the teacher model and outputs of the student model are marked and evaluated one by one at a sequence level, and errors are identified and corrected, such that the student model is guided to generate more reliable and diversified samples, thereby improving the training effect of the student model.

The invention discloses a cascade speculation inference method and system based on hierarchical decline KV cache compression, and the method comprises the steps: firstly inputting a context prompt text into a target model for coding, generating a KV cache, and calculating an attention score between tokens; secondly, descending sorting is carried out based on the attention scores of the final input token, KV cache blocks corresponding to the first k attention scores are selected as cascading middle layer cache, a lightweight large language model is loaded to serve as a draft model, and a hierarchical decline KV cache compression strategy is adopted to maintain draft model cache. Then, based on all the caches, a double-layer cascade speculation reasoning framework is constructed, a target reasoning path is obtained, and the caches are updated; and finally, repeating the operation until target response data corresponding to the context prompt text is output according to the target reasoning path. According to the method, the KV cache proportion is reduced, meanwhile, the draft token acceptance rate of the target model of the full KV cache is improved, and reduction of precision is reduced.

The application discloses a display panel driving method, a driving device and a readable storage medium, and relates to the technical field of display, which is characterized in that: an initial response square wave signal is converted based on a current display temperature to obtain a target response square wave signal; a target duty cycle in the target response square wave signal is used to determine a calling requirement, so as to determine a response duration corresponding to a normal output display picture of a liquid crystal display device at the current display temperature; a target response duration parameter table meeting the calling requirement is called; and the display panel at the current display temperature is driven according to the target response duration parameter table, so that the reaction speed of various pixel points to an input display signal at various temperatures is effectively accelerated, and the adverse situation that an animation picture is still blurred and ghosted due to the fact that different temperatures cannot be considered is avoided.

The invention belongs to the technical field of water traffic transportation safety and navigation mark navigation aiding, and mainly relates to a navigation mark anti-collision early warning method based on multi-sensor fusion, and the method comprises the steps: constructing a multi-sensor fusion architecture integrating an AIS terminal, a laser radar and an AI camera, and achieving the omnibearing monitoring and precise recognition of a ship in a navigation channel; a grading response mechanism is adopted, corresponding functions are dynamically started according to the real-time distance between the ship and the navigation mark, and targeted response from early warning to emergency warning is achieved; a legal evidence chain packaging mechanism is introduced, a unique ID is generated for each early warning event, formatted evidence is stored in an associated mode, and integrity and traceability of data are ensured; through a dynamic energy supply strategy, an equipment operation mode is intelligently adjusted according to a storage battery voltage threshold value, and continuous and stable work of the system under the conditions of cloudy and rainy days, night and the like is guaranteed; according to the invention, the accuracy, timeliness and reliability of navigation mark collision early warning information are effectively improved.

The invention relates to a seismic oscillation fitting method matched with a multi-damping-ratio response spectrum. According to the seismic motion fitting method, the seismic geology environment of the region is classified and the feature mother wave set is constructed, so that the seismic geology features of the region can be fully considered, and the fitting result can better conform to the actual condition of the target region and is more targeted and accurate. Through combination of characteristic seismic oscillation mother wave optimization and a dynamic parameterized particle swarm optimization algorithm, aiming at nonlinear characteristics of multi-damping ratio spectrum coupling matching, an improved particle swarm optimization PSO algorithm with a self-adaptive inertia weight and a learning factor is designed, and while the multi-target response spectrum fitting precision is improved, the multi-target response spectrum fitting precision is improved. And the time-frequency non-stationary characteristic of original seismic oscillation is reserved.

The invention discloses a flora prediction and petroleum pollution remediation method based on machine learning. The method comprises the following steps: collecting multiple groups of experimental data of a diesel oil pollution sample treated by a microbial agent, extracting environmental factors, microbial community characteristics and target response variables to construct a training data set after missing value processing, abnormal value detection and standardized pretreatment, and importing the data set into a preset machine learning model to obtain a training result; carrying out feature learning, classification training and hyper-parameter optimization by adopting a GridSearchCV method in combination with 10-fold cross validation; evaluating the correlation between a target response variable classification result and the features through a multivariable Pearson's correlation matrix, and constructing an optimal test set; and finally, selecting an optimal prediction model according to a preset index. According to the method, the model training quality is improved through data preprocessing and correlation analysis, efficient flora prediction and algorithm application evaluation are achieved by means of multiple machine learning algorithms, scientific support is provided for petroleum pollution remediation, and remediation accuracy and efficiency are improved.

The invention relates to a fine-grained target real-time image segmentation method and system based on a dynamic state modeling network, and belongs to the technical field of intelligent image processing. The method comprises the following steps: extracting multi-scale detail features by using a lightweight backbone network; through a dual-scale two-dimensional selective scanning module, the features are divided into a thin branch and a thick branch, and local scanning and global scanning are executed respectively; a dynamic cross-scale feature selection and aggregation module is adopted, redundancy is suppressed through reweighting and statistical filtering, and key target responses are highlighted; at a decoding end, local details and global semantics are fused through jump connection and an edge extractor; and finally, introducing a form-guided pseudo label hierarchical supervision strategy, and improving the structure learning ability of the model by using a coarse-to-fine morphological prior. According to the method, the segmentation precision, the boundary integrity and the tiny target recall rate of the fine-grained target under the scenes of ore separation, industrial defect detection, pavement crack recognition and the like are remarkably improved.

The invention provides an intelligent computing platform abnormal root cause positioning method fused with a large language model and related equipment, and relates to the technical field of anomaly detection. The method comprises the steps of firstly obtaining semantic expression features of interaction between a target student and a large language model, and judging whether the interaction obstacle degree exceeds a preset value or not; if yes, the feedback type of the obstacle is determined firstly, then dialogues in a preset window before the obstacle appears are searched, the starting point of the obstacle is positioned, and key semantics of the content of the obstacle in the period are extracted. And then based on key semantics, searching a target learning group and a matching dialogue of users in a set area, identifying a plurality of error types and determining a basic score, matching the error types with an obstacle feedback type to obtain a matching score, combining the error types with the obstacle feedback type to determine a basic error type, and finally modifying and generating a reply and feeding back the reply to students. By implementing the method, a closed loop from obstacle identification to root cause positioning to generation of targeted reply is realized, and the accuracy and efficiency of abnormal root cause positioning are greatly improved.

The invention belongs to the technical field of radar data processing, and particularly relates to a GPGPU-based time-varying target echo simulation method and device. Each thread in each Block in each Grid of the GPGPU is responsible for echo calculation of a target in a channel at each moment, and each echo calculation step comprises the following steps: S1, calculating an inherent frequency domain scattering coefficient of the target according to an incident angle of a radar to the target; s2, obtaining an inherent time domain scattering coefficient of the target through inverse Fourier transform; s3, carrying out amplitude-phase modulation on the inherent time domain scattering coefficient of the target; s4, calculating a target response signal based on the modulated time domain scattering coefficient; and S5, determining a time-varying target echo signal according to the target echo frequency, the time delay, the target Doppler frequency and the calculated target response signal. Important support is provided for testing and verification of a radar detection system, and the simulation effect is closer to a real scene.

The invention discloses a dynamic filtering and beamforming method for improving the anti-interference capability of a secondary radar. The method comprises the following steps: initializing original data; periodically monitoring the interference congestion degree of the downlink response signal of the secondary radar system in the current radio environment, and judging the interference intensity; dynamically adjusting the transmitting power and the PRF of an uplink interrogation signal of the secondary radar system; notifying the air target responder to reduce the sending rate and the transmitting power of a downlink response signal through the downlink control instruction; the general orientation of the interference source is determined in combination with external monitoring information, adaptive beamforming is performed based on the array antenna, and null is formed in the general direction of the interference source to realize interference suppression of a spatial domain; and performing dynamic filtering processing on the receiving signal after beam forming processing, extracting an effective target response signal sequence, and reconstructing the effective target response signal sequence into complete target response information data according to a predetermined protocol format. According to the invention, the accuracy and reliability of target signal detection and the anti-interference performance of the system are improved.

The invention relates to the technical field of intelligent Internet of Things platforms, in particular to a smart park management platform for the Internet of Things and an intelligent algorithm, and the system comprises a terminal state sensing module, a response path combination module, a control path sorting module, a communication window adjustment module and a distribution execution regulation and control module. According to the method, equipment response records with associated numbers are extracted, a control set with a parallel relationship in a scheduling period is constructed, a control chain structure with a sequential triggering relationship is formed through connection in combination with the consistency of signal emission and a target response sequence in an instruction track, and paths are divided according to control sources, a number sequence and target types; and the initial time sequence and the response interval characteristics are matched to carry out channel grouping, so that the coordination relationship among multiple chains is enhanced, the hierarchical structure of path organization is enhanced, and the coherent operation capability of a control process in a multi-device scheduling scene, the instruction passing efficiency and the distributed regulation and control capability of communication execution are improved.

The invention provides a strong physical unclonable circuit resistant to machine learning attacks, and relates to the technical field of information security and communication. The circuit generates an excitation signal through an external pulse signal generator; outputting a first response signal according to the challenge signal through a first confusion vector circuit; outputting a second response signal according to the challenge signal through a second confusion vector circuit; performing linear confusion on the first response signal and the second response signal through a chained block confusion circuit to generate a challenge signal; and finally, performing nonlinear confusion on the first response signal and the second response signal through a gating circuit, and outputting a target response signal. According to the strong physical unclonable circuit resisting the machine learning attack provided by the invention, the security of identity authentication between equipment is improved under the condition that a large amount of hardware resource overhead is not increased.

The invention discloses a remote communication method for a surgical robot, a storage medium and a program product, and the method comprises the steps: receiving first control data sent by a remote control end which carries out the remote control of the surgical robot in response to the starting of a remote mode; wherein the first control data is obtained by encrypting original control data by the remote control end by adopting a preset encryption algorithm; the preset encryption algorithm comprises a symmetric encryption algorithm and / or an asymmetric encryption algorithm; decrypting the first control data by adopting a target decryption algorithm corresponding to the preset encryption algorithm to obtain second control data, and determining a target response mode of the surgical robot for the second control data according to the second control data and preset control logic; wherein the preset control logic at least comprises the adjustment logic of the working state of the surgical robot, so that the transmission safety of the remote surgical data and the operation safety of the surgical robot are improved.

The invention provides a knowledge question-answering method and device for a long thinking chain, electronic equipment and a storage medium. The method comprises the following steps: receiving user input; based on a pre-constructed long thinking chain knowledge base, determining a target knowledge block corresponding to user input through a keyword-vector similarity mixed matching mode; wherein the long thinking chain knowledge base comprises a plurality of knowledge blocks with internal logic association; and determining a target reply input by the user according to the target knowledge block. According to the method, the target knowledge blocks are matched from the pre-constructed long thinking chain knowledge base in a keyword-vector similarity mixed matching mode to integrate and determine the target reply input by the user, so that the problem of insufficient understanding of a deep context is effectively improved, retrieval of irrelevant information is also effectively avoided, and the user experience is improved. Accuracy and relevance of answer content are ensured, and remarkable improvement of question and answer accuracy and intelligent degree in a complex context is realized.

The invention discloses a dialogue control method and system based on an artificial intelligence dialogue large model, and the method comprises the steps: receiving the dialogue content of a user, extracting features, and constructing an initial dialogue feature set; calling a dialogue dual processing model to generate intermediate processing features; determining a core feature vector through a topic tracking mechanism, updating a topic feature library, and calculating a similarity matrix; starting off-question detection based on the similarity matrix, and marking potential off-question content; processing off-question content in combination with dialogue control parameter intervention, and generating a subject regression prompt; and generating and outputting a target response, and storing dialogue related data. The system comprises a dialogue content processing unit, a feature construction unit, a dual processing unit, a theme tracking and off-question detection unit, a control intervention unit and a response generation output unit. According to the method and the system, through multi-mechanism cooperation, dialogue coherence and theme consistency are improved, and accurate control of the dialogue is realized.

The invention provides a charging load demand response method based on a virtual power plant technology. The charging load demand response method comprises the steps of generating a virtual load node corresponding to a to-be-regulated charging load and an initial response curve sequence in a preset response scheduling layer through a virtual power plant coordination system; and performing time sequence rearrangement on the initial response curve sequence according to a time axis, executing a coupling operation through a topological coupling engine, and outputting a coupling result. And verifying whether the coupling result is adaptive to a preset response time condition, generating a first to-be-optimized response curve by adopting a node replacement strategy when the coupling result is adaptive, and constructing a second to-be-optimized response curve when the coupling result is not adaptive. And executing optimization operation according to the processing mode adaptive to the to-be-optimized response curve, generating a target response curve, and replacing the to-be-regulated charging load with the target response curve to realize optimized regulation of the charging load. The method can improve the operation stability of the power grid, reduce the load response delay, optimize the space-time distribution of the charging load, and improve the adaptability and adjustment efficiency of the power grid to the large-scale charging load.

The invention provides an intelligent warehouse integrated management and control system for port logistics, and relates to the technical field of port warehouse management, and the system comprises a graph module which is configured to be used for constructing an operation relation knowledge graph based on the entity relation of a port warehouse, the nodes of the knowledge graph represent warehouse operation elements, and the edges of the knowledge graph represent the incidence relation between the warehouse operation elements; the analysis module is connected with the graph module and is configured to be used for monitoring the node state of each node in the knowledge graph and reasoning the potential influence of the abnormal node state on the global operation plan by analyzing the association relationship between the nodes when the abnormal node state is monitored; and the strategy generation module is connected with the analysis module and is configured to be used for generating a target coping strategy according to the potential influence and outputting a corresponding management and control instruction. According to the invention, the risk that local abnormity is not disposed in time and is evolved into global operation interruption is avoided, so that stable execution of a port logistics overall operation plan is guaranteed, and the throughput efficiency and operation toughness of a warehouse are improved.

The invention provides a model distillation method and device, and the method comprises the steps: obtaining a fixed training data set which comprises a plurality of training text sequences; guiding the student model to generate a target response text sequence based on the training text sequence through the teacher model so as to correct the semantic difference between the response text sequence output by the student model and the response text sequence output by the teacher model; obtaining a target training data set based on the plurality of training text sequences and the target response text sequence corresponding to each training text sequence in the plurality of training text sequences; and based on the target training data set and the teacher model, carrying out distillation training on the student model. According to the method, the semantic difference between the output of the teacher model and the output of the student model is marked and evaluated one by one at the sequence level, errors are recognized and corrected, the student model is guided to generate more reliable and diversified samples, and the training effect of the student model is improved.

To improve responsiveness while reducing the effect of noise, a valve control method for controlling a valve of a fluid control device includes generating a target response function converted from the flow rate setting using a target response transfer function; generating a target voltage function by making a correction in nonlinearity of a drive voltage for the valve and a flow rate, with respect to the target response function; generating a feedforward voltage signal from the target voltage function by making a correction corresponding to the delay characteristic of the valve; causing a feedback controller to generate a feedback voltage signal from a deviation between the target response function and a flow rate measurement of a flow rate sensor; generating a corrected command voltage signal using a feedforward voltage signal and the feedback voltage signal; and controlling the valve using the corrected command voltage signal.

The invention discloses an intelligent response strategy adaptive generation method and system, and relates to the technical field of network security. The method comprises the following steps: calling an information confrontation sample, and mining a causal chain set based on a network attack scene; according to the causal chain set, constructing a strategy generator and deploying the strategy generator in a network security center; the method comprises the following steps: performing real-time operation monitoring by taking a network attack element as an identification target, triggering a strategy generator, executing a multi-step decision, and determining a target response strategy, the multi-step decision comprising first causal matching of the triggering of the network attack element and the response strategy, and second judgment positioning based on strategy fitness; third combination optimization based on strategy point combination optimization; and performing network security management according to the target response strategy. The technical problem that in the prior art, network attack response strategy generation depends on a static rule, and consequently the response strategy is not accurate and effective enough is solved, and the technical effects that the self-adaptive response strategy is intelligently generated through multi-step decision making, and the network security protection effect is improved are achieved.

The invention relates to an interface adaptation method and system for reconstruction and integration of a complex information system module. The method comprises the following steps: acquiring a protocol mapping rule and constructing a rule base; monitoring and receiving a calling request which is sent by a source module through a transmission layer protocol and carries source module side interface protocol format data; analyzing the request, stripping a source module side transport layer protocol header to extract application layer data, and packaging the application layer data into an internal general data model; performing structure mapping, data conversion and dynamic logic processing on the model through a rule execution engine according to a request mapping rule of a rule base, reconstructing the model into target request data according to a target module interface format, and sending the target request data to a target module through a target side transport layer protocol; receiving target response data, stripping a transport layer protocol header of the target response data, analyzing the target response data to the internal general data model, and performing reverse conversion according to a response mapping rule to enable the data to conform to a source side interface format; and packaging the back-to-source module side transport layer protocol back to the source module. By adopting the method, the adaptation flexibility and the maintenance efficiency can be improved.

The invention provides a duty ratio detection device and a random access memory. The duty ratio detection device comprises a duty ratio detection module which is configured to receive a calibration clock signal in a calibration execution process and execute duty ratio detection on the calibration clock signal to obtain a duty ratio detection result; the adjustment control module is connected with the duty ratio detection module, the adjustment control module is configured to generate a corresponding target adjustment code based on a duty ratio detection result in the calibration execution process, and the duty ratio detection module adjusts a response threshold value based on the target adjustment code so as to eliminate the deviation between the response threshold value and a target response threshold value; and the duty ratio detection module performs duty ratio detection on the input clock signal based on the target response threshold value in the normal detection execution process. According to the scheme, the duty ratio detection module can respond when the duty ratio of the input clock signal is equal to the target response threshold value, that is, the response threshold value of the duty ratio detection module is adjusted to the target response threshold value, so that the detection deviation of the duty ratio detection device is reduced.

The invention relates to the technical field of artificial intelligence and medical information processing, and discloses a user session response system and method based on health data, equipment and a medium. The system comprises a session semantic recognition module, an entity information extraction module, an archive data alignment module, a health classification tree construction module, a data hierarchical matching module and a response text generation module. The method comprises the following steps: recognizing consultation semantics contained in a health consultation session of a target user; determining a target session text in the health consultation session according to the consultation semantics; performing data alignment on the medical entity information in the target session text according to the historical health file, constructing a structured health file based on a data alignment result, and generating a health state classification tree; performing classification matching on the consultation semantics by utilizing the health state classification tree to obtain a target health state classification; and generating a corresponding target response text based on the target health state classification, and pushing the target response text to the target user side. According to the invention, the healthy session response accuracy can be improved.