223 results about "Confidence measures" patented technology

The invention provides a cement equipment maintenance decision-making method and device based on a knowledge graph and large model reasoning, relates to the field of cement industry intelligent operation and maintenance, and solves the technical problem of decision-making response delay caused by knowledge fragmentation. The method comprises the following steps: extracting real-time characteristics from vibration spectrum signals, temperature curves and torque waveform data collected by an edge gateway, and extracting a work order entity triple from a natural language work order text of an EAM system; based on an equipment BOM list, a historical maintenance record and an FMEA analysis table, physical assembly constraint conditions are defined through ontology modeling to generate a cement equipment topological relation and a fault rule chain, and a knowledge graph is created to output a fault rule base with confidence coefficient weights. And inputting the real-time feature vector and the work order entity triple into a multi-modal collaborative inference engine, triggering a matched fault rule chain by combining real-time features and semantic features, outputting a fault root cause and an associated maintenance strategy ID, and labeling a logic chain. And activating the associated maintenance strategy ID and obtaining the real-time characteristic deviation degree of the maintenance strategy ID, quantifying the decision credibility through a tracing rule matching path, obtaining an executable maintenance instruction packet with a logic chain, executing the maintenance instruction packet and dynamically updating the knowledge graph based on a maintenance result. The method is used in the maintenance decision-making process of the cement equipment.

The invention relates to the technical field of artificial intelligence, and discloses a Bluetooth communication intelligent speech translation method and system based on multi-mode enhancement, and the method comprises the steps: collecting a multi-channel audio signal through a built-in multi-microphone array of a Bluetooth device, carrying out the dynamic direction self-adaptive beam forming of the multi-channel audio signal, and carrying out the self-adaptive beam forming of the multi-channel audio signal; extracting a Mel spectrogram feature of the direction enhancement signal, identifying lip regions of a plurality of candidate speakers in each frame of real-time speaking video captured by a camera, performing time sequence convolution on the lip regions to obtain a lip movement time sequence embedded vector, calculating a correlation score with the Mel spectrogram feature, separating the direction enhancement signal, and obtaining a lip movement time sequence embedded vector; and performing text transcription and conversion on the high-confidence separation voice to obtain a translation language text, and sending the synthesized target translation voice to a preset mobile terminal through the Bluetooth device to obtain a target translation result. According to the method, the real-time performance and accuracy of speech translation are improved in a multi-person scene, far-field speech, noise interference and accent difference.

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 discloses an intelligent education robot question answering system based on voice recognition and a knowledge graph, and particularly relates to the technical field of artificial intelligence. The method comprises the following steps: performing feature extraction on a user voice signal to obtain a text sequence and a multi-modal context feature; recognizing subject domain judgment and question answering intentions based on supervised classification and keyword rule fusion, and outputting a subject domain prior probability and a question answering target vector; determining polysemy words in the text sequence by using a context window, generating a semantic item sequence and semantic item confidence, constructing a subject domain sub-graph based on a subject domain prior probability, and obtaining a candidate reasoning path and a path scoring vector; determining a target teaching concept and an optimal reasoning path by combining Bayesian inference and consistency verification; generating a personalized question answering result aiming at the question asked by the user through fact retrieval and knowledge derivation in combination with the question answering target vector; accurate and efficient intelligent teaching question answering can be realized, and question answering accuracy and intelligent interaction capability of the education robot are effectively improved.

The invention discloses an Internet enterprise multi-mode identity verification method and system, and belongs to the technical field of Internet enterprise security, and the method comprises the steps: obtaining user historical behavior data, current transaction request data, equipment environment parameters and initial biological signal data, carrying out the risk assessment, and generating a verification path; obtaining a personalized verification sequence instruction, collecting a user face dynamic video stream, a real-time voice stream and response action time sequence data, carrying out cross-modal association comparison with a user reference biological feature template, outputting a biological feature confidence matrix, carrying out association analysis in combination with the obtained structured identity feature vector and risk assessment, and obtaining a personalized verification result; and generating a verification decision feature vector to judge a verification result state, and obtaining a pass instruction, a rejection instruction or a manual auditing request instruction. According to the method, dynamic risk-driven multi-modal verification path generation, cross-modal biological feature association decision and incremental learning mechanisms are adopted, so that the optimal balance between security and user experience can be realized in a complex network environment.

A system can include a server having a processor configured to receive, from a first computing device, first data associated with at least one historical data attack. The processor can encode the first data into at least one fixed-size representation. The processor can generate at least one anonymized vector representation based on the at least one fixed-size representation. The processor can receive, from a second computing device, second data associated with a potential attacker. The processor can encode the second data into at least one additional fixed-size representation. The processor can generate a second anonymized vector representation based on the at least one additional fixed-size representation. The processor can generate a confidence measure of the potential attacker as an attacker based on a comparison between the at least one anonymized vector representation and the second anonymized vector representation.

According to the voice transliteration text error correction method and device, the storage medium and the computer equipment, after the voice transliteration text is received in real time, the text is subjected to primary error detection, and the first error confidence coefficient is obtained; when the first error confidence coefficient is smaller than a fast error correction threshold value, the voice transcription text is directly output, and unnecessary error correction is avoided; otherwise, performing word-level rapid error correction on the voice transcription text to obtain a rapid error correction text, and performing secondary error detection on the rapid error correction text to obtain a second error confidence coefficient. Judging whether the second error confidence is smaller than a depth error correction threshold value or not; if yes, the rapid error correction text is directly output, and if not, semantic-level deep error correction is conducted on the rapid error correction text through a deep error correction model, and a deep error correction text is obtained and then output. Through a hierarchical error correction processing strategy, the method can be adapted to different service scenes, and meanwhile, dynamic balance between quality and efficiency of different service scenes can be realized by adjusting two error correction thresholds.

The invention discloses a real-time voice stream dialogue interaction method and system based on a large language model, and belongs to the technical field of voice recognition, natural language processing and human-computer interaction. The method comprises the following steps: S1, segmenting a voice signal according to a 320 millisecond time window; s2, through double-channel parallel processing, generating a preliminary text hypothesis locally and uploading a frame to a server side to accurately reconstruct a text; s3, fusing to generate a final text with a confidence label; s4, inputting the text into a large language model with a context management mechanism to generate a natural language response; s5, performing speech synthesis output of rhythm perception; s6, updating a dialogue cache and synchronizing a language model state; s7, predicting the semantic trend in advance by adopting a delayed slow release mechanism; and S8, introducing round-level intonation / rhythm parameters for semantic adjustment. The method has the beneficial effects that the response delay is reduced, the recognition accuracy is improved by fusing the confidence label, and meanwhile, the continuity and naturalness of multi-round interaction are enhanced through context and intonation modeling.

The invention discloses an intelligent control method and device, electronic equipment and a readable storage medium, and belongs to the technical field of intelligent control. Under the condition that the gesture instruction does not conflict with the voice instruction, performing multi-dimensional normalization on the gesture feature vector to obtain a to-be-fused gesture vector; performing normalization processing on the voice feature vector based on context semantic perception to obtain a to-be-fused voice vector; fusing the to-be-fused gesture vector and the to-be-fused voice vector to obtain a fusion vector, and determining a fusion instruction corresponding to the fusion vector as a target execution instruction; and under the condition that the gesture instruction conflicts with the voice instruction, selecting a target execution instruction according to a first confidence coefficient corresponding to the gesture instruction and a second confidence coefficient corresponding to the voice instruction. The self-adaptive normalization strategy is designed for different modal data, the data fusion accuracy is improved, meanwhile, the gesture and voice instruction conflict can be intelligently and flexibly processed, and the accuracy of determining the real intention of the user is improved.

The invention relates to an audio recognition method and device based on marking and backtracking correction, and the method comprises the steps: segmenting a target audio, and enabling adjacent segments after segmentation to have a partial overlapping region; if a slice point exists in a non-mute segment of the target audio and the acoustic feature similarity of a preset number of frames before and after the slice point is smaller than a set similarity threshold value, the slice point is marked as a cut-off risk point, and the cut-off risk point is used for indicating that continuous semantics before and after the slice point has a cut-off risk; after a text corresponding to the audio of each slice is recognized through the speech recognition model, the language confidence degree of an overlapping area where the truncation risk point is located is determined, and the language confidence degree is used for indicating context language logic of the overlapping area; and if it is determined that the language confidence is lower than a set confidence threshold, correcting the recognition texts of the slices before and after the truncation risk point. According to the invention, the accuracy of audio recognition is improved.

The invention provides a tracking method of dialogue-related knowledge points and electronic equipment. According to the embodiment, the knowledge points related to the dialogue are predicted through multiple methods such as the dialogue knowledge point prediction model, the retrieval enhancement generation method and the large language model LLM hierarchical prompt method, then the knowledge points predicted through the methods are fused to track the knowledge points related to the dialogue, the deviation of a single method is reduced, and the confidence coefficient of dialogue tracking is also improved; according to the embodiment of the invention, an autoregressive target knowledge tracking model is trained by means of the fusion result of the knowledge point set of each dialogue, the historical dialogues before each dialogue and the problems in each dialogue, so that the change of knowledge points in multiple rounds of dialogues can be timely captured when the target knowledge tracking model is subsequently utilized to perform knowledge tracking on the dialogues; and the knowledge tracking accuracy is improved.

The invention discloses a voice recognition method and device and electronic equipment. The method comprises the following steps: acquiring a voice to be recognized, the voice to be recognized being a voice in a dialect form; performing voice recognition on the to-be-recognized voice through the voice recognition model to obtain an initial recognition result of the to-be-recognized voice, the initial recognition result including a word sequence of the to-be-recognized voice and a confidence coefficient corresponding to the word sequence; under the condition that the confidence coefficient is smaller than a confidence coefficient threshold value, a plurality of candidate vectors with the similarity with the word sequence in a preset number are determined in the professional domain knowledge base and the dialect knowledge base respectively; and determining a target prompt word at least according to the to-be-recognized voice, the confidence coefficient and the plurality of candidate vectors, and correcting the word sequence according to the target prompt word through a voice correction model to obtain a target recognition result. According to the invention, a technical problem of low speech recognition accuracy in a scene of combining a professional field and a dialect in a speech recognition method in the related technology is solved.

The invention relates to the technical field of artificial intelligence and voice recognition, and discloses a doll intelligent voice recognition method and system based on artificial intelligence, and the method comprises the steps: collecting audio through a multi-microphone array; locally executing speech enhancement, acoustic feature extraction and lightweight wake-up word detection; local speech recognition is started, semantic confidence is evaluated, and desensitized data are uploaded to a cloud only when the confidence is insufficient; and the cloud end performs secondary recognition of context perception by using the large model in combination with the language habits of the children, and returns an optimization result. The system comprises an audio acquisition module, a local processing module, a cloud collaboration module and a state management module. According to the invention, through an end-cloud collaborative architecture, the identification robustness and the interaction intelligence level in a complex environment are significantly improved while privacy and low delay are guaranteed.

The invention relates to a multi-agent cooperation-based language model reasoning resource scheduling method and system, and the method achieves the intelligent simulation of multi-view iterative thinking in a complex reasoning task through the construction of a multi-agent system which is clear in division of labor and is provided with a special knowledge base. View limitation and decision deviation of a single model in long-sequence and multi-step reasoning are effectively overcome; depending on the fusion of the general capability of the large language model and the task special knowledge base, the professionality and accuracy of the reasoning result are improved. Meanwhile, the problems of resource waste, redundant calculation and unstable convergence caused by cognitive overload in the cooperation process are solved by combining multi-round dynamic cooperation with a convergence mechanism of cognitive load perception and a dynamic token number limitation and low-confidence branch pruning strategy; therefore, on the premise that the reasoning quality is guaranteed, the utilization efficiency of computing resources is remarkably optimized, peak value occupation is reduced, the overall reasoning time delay is shortened, and reliable technical support is provided for efficient and stable deployment of a large language model in a complex task.

The invention relates to the field of voice questions and answers, and particularly discloses a large model customer service system based on multi-modal double-layer shunt reasoning, which introduces semantic complexity evaluation on a text input by a user, and forms a three-dimensional decision matrix in combination with confidence of front-end voice recognition. By dynamically weighting the intention confidence, the semantic complexity score and the speech recognition confidence, the system can accurately recognize queries which seem to be simple and actually complex. In this way, the traditional confidence trap is solved, even if a complex query generates virtually high intention confidence due to the fact that the complex query contains keywords, the high complexity score of the complex query can correct the shunting decision, and the query is guided to a heavy weight model capable of conducting deep semantic understanding. Finally, it is ensured that all kinds of user requests can be properly processed by the system level, and the accuracy and the problem solving rate of a customer service system are remarkably improved.

The invention relates to the technical field of pneumatic conveying, discloses a pneumatic conveying system exception handling method and system based on multi-source sensing fusion, and aims to solve the problems that in the prior art, exception recognition lags behind, exception types cannot be distinguished, and real-time online intervention is lacked. According to the scheme, the method mainly comprises the following steps: constructing a three-dimensional input tensor through multi-source information synchronous acquisition of pressure, current and acoustic sensors; adopting a pre-trained anomaly detection model to realize anomaly type identification and confidence coefficient output; and performing abnormal grading judgment based on the confidence coefficient in the sliding window, and executing corresponding grading fault-tolerant control operation according to the abnormal grade. According to the method, early-stage accurate diagnosis and type recognition of the abnormity are achieved, differential response can be automatically triggered according to the abnormity severity degree, and the operation continuity of the pneumatic conveying system is improved.

The invention relates to the technical field of intelligent door locks, in particular to an AI-based intelligent door lock control method, which comprises the following steps: collecting biological characteristic information of a user in real time, and respectively generating confidence indexes of fingerprint characteristics, face characteristics and voiceprint characteristics based on signal credibility and characteristic integrity of the biological characteristic information; acquiring real-time environment interference parameters of the door lock; determining the priority of the authentication modes based on the confidence index, and obtaining the matching degree evaluation value of each authentication mode to determine a comprehensive risk index of authentication identification; judging whether the authentication is passed based on the comprehensive risk index, and judging whether the environment has an influence on a confidence index based on an environment interference parameter under the condition that the authentication is not passed; and executing a targeted interference elimination and authentication recovery strategy based on the category of the environmental interference parameter under the condition that the environment has an influence on the confidence index. The authentication success rate and safety of the intelligent door lock in a complex real environment are improved.

The invention discloses an electric power personnel illegal behavior identification method and system based on multi-frame multi-level joint discrimination, which is applied to the technical field of electric power operation safety supervision, and comprises the following steps: detecting the positions of all personnel in each frame of image in a video stream, adopting a ByteTrack algorithm as a core tracker, introducing a score grading matching strategy, and carrying out classification matching on the positions of all personnel in each frame of image in the video stream; taking the weighted sum of the spatial consistency and appearance feature similarity of the new detection frame as a final association degree quantity, distributing a unique identity code for a successfully associated personnel target, and recording track information in continuous frames; performing illegal behavior classification and illegal article detection on each frame of personnel area image; and calculating a joint confidence score of a frame where the personnel region image with the behavior category and the article category having a logic correspondence relationship is located, counting the number of frames exceeding a preset joint confidence threshold in each power personnel trajectory, and calculating an illegal behavior existence rate to judge an illegal behavior. According to the invention, the illegal behavior identification precision of the power personnel is effectively improved.

The invention discloses a text generation-oriented large language model dynamic self-distillation method, which comprises the following steps of: firstly, adding a task instruction prefix to input text data, automatically aligning an input text sequence to input a large language model, and introducing confidence to generate a text sequence; secondly, for a text sequence generated by the large language model, confidence-driven loss optimization is carried out on the large language model in combination with confidence loss and distillation loss; then, in a large language model self-distillation generation stage, a confidence-quality joint evaluation framework is constructed, and adaptive weight distribution is performed on a large language model generation text sequence; and finally, evaluating a self-distillation training effect based on the multi-dimensional global evaluation index in a self-distillation training period gap. According to the method, high-reliability knowledge migration and self-distillation are realized under the condition of low resources, and the text is accurately and efficiently generated.

The invention provides a vulnerability discovery method and system based on symbol-semantic hybrid reasoning, and belongs to the technical field of software security. The method comprises the steps that a target program is analyzed, and an event sequence is extracted; converting the event into a symbol with a time sequence label and confidence, and constructing a symbol dependency graph; reasoning based on the dependency graph, calling a large language model to generate a semantic reasoning action when the certainty is insufficient, and updating the state after symbol verification; new rules are extracted from the inference chain passing verification through reinforcement learning, and self-evolution is achieved; and outputting a vulnerability report containing the reasoning path and the confidence coefficient. The system correspondingly comprises a time sequence probability symbol module, a symbol-semantic bidirectional coupling reasoning module, a reinforcement learning self-evolution module and a report generation module. According to the method, the preciseness of symbol logic and the generalization ability of a semantic model are fused, the accuracy, interpretability and adaptive ability of vulnerability detection are effectively improved, and the method is suitable for security audit and code review of a complex software system.

An AI-powered automated recruitment system for intelligent talent identification and selection, consisting of: a processing unit configured to execute machine learning procedures for adaptive candidate evaluation; a storage unit that is operationally connected to the aforementioned processing unit and is configured to store candidate data, trained model parameters, and records of the recruitment process; a video interaction unit consisting of a camera and an audio recording unit configured for conducting real-time or asynchronous interviews, wherein the processing unit analyzes the captured audiovisual data using natural language processing, facial recognition and sentiment analysis to generate behavioral assessment metrics; a speech analysis unit consisting of a speech recognition processor and an acoustic characterization subsystem configured to receive and analyze telephone candidate responses, with the processing unit deriving linguistic competence, tonal clarity, and confidence indices from the responses; a technical evaluation unit configured to deliver interactive programming tasks to candidate terminals, with the processing unit evaluating code execution output and logic efficiency and detecting plagiarism through code similarity comparison techniques; a data analysis unit configured to combine the outputs of the aforementioned video interaction unit, speech analysis unit, and technical evaluation unit to generate predictive suitability scores for candidates and recruitment performance metrics; and a communication interface unit configured to establish secure data transmission connections between the system and external personnel databases via encrypted network protocols.

The invention provides a voice robot voice receiving, recognition and error correction method, which comprises the following steps: constructing an intention stability sensing unit, and realizing dynamic judgment on the state of a deliberate map in combination with multi-granularity semantic coding and sliding window variance analysis; when it is detected that the intention is unstable or multiple intentions compete, a parallel intention matching mechanism is adopted, and the accuracy of intention optimization is improved through confidence, semantic continuity and activation frequency fusion; through context significance weight and pruning strategies, historical semantic information is effectively managed, low-contribution content is compressed, and resource overhead is reduced; gating network parameters are continuously optimized on line, the intention recognition effect is improved in real time, the continuity and stability of dialogue intention modeling are enhanced, and the recognition accuracy and the system resource efficiency are both achieved.

Computer-implemented method (500) for training a model to improve speech recognition, wherein the computer-implemented method comprises: Receiving (502) an utterance by one or more processors, wherein the receiving is performed by a virtual assistant in a specific node of the virtual assistant, wherein frequently occurring terms have been identified for the specific node over a period of time; Transcription (504) of the utterance into text by the one or more processors; Generating (506) a transcription confidence score based on transcription and audiometry by one or more processors; in response to the transcription confidence score being below a threshold, comparing (510) phonemes in the utterance with phonemes in at least one term from a list of frequently occurring terms by one or more processors; Generating a sound similarity score for phonemes in which at least one term from a list of frequently occurring terms is selected by one or more processors; and Replacing (512) the transcription with at least one term from the list of frequently occurring terms if the sound similarity score is above a threshold, by one or more processors.

The invention discloses a parcel clamping stagnation diagnosis and processing system and method based on a large-scale language model, and the system comprises an acquisition module, a multi-modal fusion layer, a prompt construction module, an LLM large-scale language model, a decision module, an execution module, a feedback collection module and a reinforcement learning module. The matching method comprises the steps of monitoring, sorting, parcel state diagnosis, automatic or human intervention execution of corresponding decisions according to diagnosis results, summarization and feedback, and enhancement of LLM large-scale language model learning. The parcel sorting condition is monitored in a data fusion mode, abnormal numerical values are responded and diagnosed, the model judges whether clamping stagnation occurs or not, measures such as confidence analysis, decision execution of distribution of affected target groups, manual troubleshooting and continuous monitoring are given, an operator is allowed to know diagnosis information and then conduct manual intervention, and the accuracy and the reliability of parcel sorting are improved. And the feedback is recorded in time for the model to update the strategy, the architecture is optimized, and the accuracy and response efficiency of subsequent diagnosis are improved.

The invention provides methods and systems for identifying shape-based atypical segmentations. In one example, a method includes receiving a segmentation of a region of interest (ROI) of a medical image, the segmentation output by a segmentation model; computing a confidence measure for the segmentation, the confidence measure indicating how well a shape of the segmentation can be encoded by one or more dominant shape variation patterns of a set of predetermined segmentations of the ROI; and in response to the confidence measure satisfying a predetermined condition with respect to a threshold, displaying the segmentation, storing the segmentation, and / or using the segmentation for one or more downstream processes; otherwise, prompting a user to perform a manual segmentation.

The invention relates to the technical field of information processing, and discloses an interactive guiding method and system of an intelligent question answering system and a storage medium. The method comprises the steps that the intelligent question-answering system analyzes and processes expression mode data and rhythm preference data to generate a user reaction sequence and an emotion fluctuation sequence; judging the trend of user intention confidence evaluation, adjusting a dialogue guide strategy, and generating an expression ambiguity mark and a clarification path template; extracting question type classification statistical data of multiple rounds of dialogues, determining an effective guidance opportunity, matching dialogue data of similar scenes, and optimizing a hierarchical progressive strategy and a problem boundary narrowing strategy of a guidance problem; and processing the dialogue data of the current interaction round, judging the narrowing degree of the expression ambiguity mark, generating a multi-round dialogue fluency index, and obtaining a stable state of user intention confidence evaluation. The intention recognition accuracy, ambiguity clarification efficiency and multi-round interaction fluency of the intelligent question answering system are improved.

The application discloses a speech enhancement method, device and electronic equipment, the method comprises the following steps: acquiring speech recognition confidence and a to-be-enhanced speech signal, and converting the to-be-enhanced speech signal into a frequency domain signal, wherein the speech recognition confidence is a confidence degree representing that the speech signal contains a user speech instruction; calculating a noise power estimation value based on the speech recognition confidence and the frequency domain signal; and performing adaptive beamforming by using the frequency domain signal and the noise power estimation value to obtain an enhanced speech signal. According to the technical scheme provided by the application, the speech recognition confidence of the speech signal is introduced to adjust the noise power estimation value, so that a more accurate noise power estimation value is obtained, and then the adaptive beamforming algorithm is executed based on the noise power estimation value, so that the speech enhancement effect can be improved in a low signal-to-noise ratio scene, and a more reliable enhanced speech signal is obtained.

This invention relates to the field of natural language processing technology, specifically to a method and system for matching the intent of intelligent NPC dialogues in multimodal interaction. The method involves real-time acquisition of multimodal data, generating multimodal semantic features through submodal preprocessing; integrating the semantic features of each modality using a cross-modal fusion module based on semantic association, generating a candidate intent set based on semantic context representation and combined with a semantic parsing module and predefined intent templates; tracking changes in user intent in real time through a continuous semantic learning mechanism and an interactive memory module, combined with a Bayesian update method, dynamically adjusting the confidence level of each intent in the candidate intent set, and filtering the final intent; constructing an NPC semantic cognition model, and performing semantic consistency analysis to perform semantic checks on user input and NPC dialogue state; combining the final intent and a decision engine to generate a dialogue strategy and output synchronized response content; this invention improves the accuracy of dynamic matching of intelligent dialogue intents.

The invention relates to the technical field of data analysis and processing, and discloses an intelligent evaluation method for a project road presentation, which comprises the following steps of: acquiring multi-modal data of a road presentation video in real time, including speech text, voice audio, expression video and PPT content, and analyzing and processing the multi-modal data through a multi-modal fusion technology to ensure feature alignment; generating a core challenge question set based on the analysis data, and verifying the payment willingness deviation ratio of a target user for commercial logic, technical implementation and a financial model; executing three rounds of progressive questioning; when the multi-modal data analysis of the project road performance is carried out, the analysis deviation of voice, text and visual data can be recognized in real time by constructing a collaborative alignment mechanism of multi-modal feature fusion, and the high conformity of inquiry problem generation and real commercial risks is ensured; and meanwhile, a confidence-driven redundancy check process is established, expression data is actively fused to correct and output when the voice recognition credibility is insufficient, and the accuracy of dynamic questioning and the key vulnerability capturing capability are improved.

The invention provides an intelligent seat assisting method and system, electronic equipment and a storage medium, and relates to the technical field of intelligent customer service and human-computer interaction. According to the method, the session trust degree is determined by integrating factors such as the operation behavior of the seat, the confidence and security of the candidate reply, the session emotion and the system operation condition, and the risk level of the main candidate reply is combined, so that the self-adaptive control of the automatic sending strategy is realized, the error sending risk is remarkably reduced, and meanwhile, the overall processing efficiency is improved; an intelligent seat auxiliary process is divided into independent links such as a planning agent, an execution agent, a reply agent and a security auditing agent, so that an interpretable and pluggable Copilot architecture is realized, and the interpretability and controllability are improved.