2468 results about "Speech input" patented technology

The present disclosure is directed to systems and methods to enhance the process of creating an artificial intelligence (AI) generated content or content items, such as images, text, video, sounds, etc., using a text or other suitable prompt, such as via voice input. The systems and methods disclosed provide streamlined content generation with, e.g., reduced processing power and computing time. In an embodiment the systems and methods receive a prompt for generating a first content item using a generative artificial intelligence (AI) model and retrieve, based on the prompt, a collection of matching content items. The systems and methods may then receive input selecting one of the content items from the collection and identify a prompt used to generate the selected content item. The systems and methods may then merge using a trained natural language processing model, the received prompt with the prompt of the selected content item to create a third prompt. In an embodiment the systems and methods may modify the third prompt based on additional input and, based on the modified third prompt, generate a second content item.

The invention discloses a scene interactive AI rehabilitation evaluation training and health monitoring system, and relates to the technical field of rehabilitation medical treatment and artificial intelligence, a semantic perception module is used for collecting and recognizing voice input, facial expressions, action tracks and eye movement paths of a user in a training process, and extracting context parameters; the knowledge-driven training generation module is used for calling a rehabilitation knowledge graph constructed by a graph neural network based on context parameters and individual training history, and generating a multi-path training scheme; training a feedback regulation engine, collecting posture offset, physiological stress and emotion feedback, and dynamically adjusting task difficulty, rhythm and prompt mode based on a dual-channel reinforcement learning model; the prediction module fuses training and monitoring data, and predicts a network identification function degradation risk through degradation driving; the cloud edge fusion platform is used for realizing task quick response and graph strategy iterative updating; according to the invention, the individuation, self-adaption and intelligent prediction capabilities of rehabilitation training are improved, and the rehabilitation effect and the system practicability are obviously optimized.

A real-time system for the multimodal generation of virtual reality scenes based on artificial intelligence for the creation of immersive three-dimensional environments from natural language narratives, consisting of: a speech capture module configured to continuously record a user's spoken narrative via one or more directional microphones, preprocesses the captured signal by noise reduction and temporal alignment, and outputs a digital speech stream; A speech-to-text processing unit that is operationally coupled to the speech capture module and configured for real-time speech recognition using a continuous neural transformer model. The unit is trained to transcribe natural language utterances into structured text data while maintaining contextual continuity throughout the evolving narrative. a semantic interpretation processing unit that is communicatively linked to the speech recognition unit and configured to perform natural language understanding techniques to extract contextual entities, spatial references, temporal relationships, and object attributes from the transcribed narrative; the engine includes a large language model that is fine-tuned for spatial reasoning tasks; a scene graph generation module configured to transform the interpreted semantic data into a structured, hierarchical representation that defines nodes for identified entities and edges for corresponding relationships, with each node associated with metadata describing geometry, position, orientation, texture, and linking attributes between objects; a multimodal image-language model processor coupled with the scene graph generation module, wherein the processor is configured to retrieve, adapt, or synthesize appropriate three-dimensional elements from a pre-trained visual-lexical embedding space and align these elements with their semantic and spatial definitions derived from the scene graph; a scene assembly and rendering controller configured to create a cohesive virtual scene from the aligned assets, perform real-time rendering using a GPU-accelerated ray tracing pipeline, and produce a stereoscopic visual output that corresponds to the evolving narrative; A head-mounted virtual reality visualization device connected to the rendering engine and configured to display the generated immersive environment to the user in real time. The device features motion sensors and inside-out tracking cameras to detect head and body movements, dynamically updating viewing angles and perspective within the rendered scene; and a bidirectional feedback module integrated into the head-mounted device and connected to the semantic interpretation processing unit; the module is configured to interpret corrective commands, gestures, or supplementary comments from the user to refine or modify specific scene elements without interrupting the real-time visualization; The system continuously updates the virtual scene as the narrative develops, ensuring temporal synchronization between speech input and rendered output below a defined latency threshold, thus enabling a natural, dialogic construction of complex three-dimensional virtual environments.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that edit digital images using combinations of speech input and gesture interactions. For instance, in some embodiments, the disclosed systems receive speech input from a client device displaying a digital image within a graphical user interface, the digital image portraying an object. Additionally, the disclosed systems detect, via the graphical user interface, one or more gesture interactions with respect to the object of the digital image. Based on the speech input, the disclosed systems determine an edit for the object of the digital image indicated by the one or more gesture interactions. Further, the disclosed systems modify the object within the digital image using the edit indicated by the one or more gesture interactions.

The invention discloses a cockpit active recommendation system and method based on a knowledge graph and semantic reasoning, and relates to the technical field of intelligent cockpits. The system receives natural language voice input of a user, executes voice recognition and semantic analysis, extracts user intention, keywords and slot entities, generates structured semantic information, constructs or calls a knowledge graph structure with semantic relation edges in combination with environment context information, and obtains the knowledge graph structure with the semantic relation edges. Semantic path reasoning is carried out based on the path dependence weight and the semantic similarity, a semantic edge label guided graph attention mechanism is introduced to calculate a path consistency score, a candidate recommendation set is generated, the semantic fitting degree and the path score are fused to sort and output recommendation content, and the graph edge weight and the user portrait are updated based on user feedback. According to the method, semantic understanding precision, recommendation path interpretability and system adaptive capacity are improved, and the method is suitable for personalized voice recommendation, man-machine interaction and scene linkage control tasks in an intelligent cockpit.

The invention provides a multi-mode-based man-machine interaction method and system, and relates to the technical field of man-machine interaction. Voice input of a user is received, environment information is collected, facial expressions of the user are captured, and voice features, environment features and facial features are obtained based on the input; utilizing a pre-trained emotion recognition model to generate emotion features of the user, and utilizing a pre-trained user behavior model to generate behavior features according to the current operation information of the user; after the features are obtained, the first weights and the second weights of the features are dynamically adjusted, feature weighted fusion is carried out, and comprehensive features are obtained; the multiple candidate responses are generated based on the comprehensive characteristics, the final response is screened out through a preset scoring mechanism and output, comprehensive understanding of the intention and the emotional state of the user is achieved through comprehensive utilization of multiple data sources, and meanwhile the weight of each characteristic can be dynamically adjusted so that more intelligent and humanized interaction experience can be generated.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention discloses an emotion-enhanced digital human driving and presenting system and method, and belongs to the technical field of computers.The system comprises an audio decoupling module, a potential diffusion model module, a human motion prior module, a training module and a rendered image generation module; predicting and reconstructing the facial expression and gesture action sequence to obtain a final action sequence; and generating a neural rendering image based on the final action sequence. According to the method, through voice input, emotion and style features in voice are captured, a cross attention feature fusion mechanism is introduced to enhance expression of emotion and personal style features, a voice-driven gesture generation task is expanded to a facial expression and gesture action synchronous generation task, the integrity of generated digital human actions is realized, and the user experience is improved. The emotional expressive force of facial expressions and gestures of the digital human is greatly improved.

The invention discloses a multi-mode instruction analysis system for a humanoid robot. Comprising a voice input module, a visual input module, a voiceprint feature extraction module, an object recognition and pose estimation module, a multi-modal alignment network based on a space-time attention mechanism, a scene semantic tree construction module, an instruction node mapping module, a confidence evaluation module and a decision module. According to the system, accurate alignment of voice and visual information is realized through a space-time attention mechanism, environment information is represented in combination with a scene semantic tree structure, and the instruction analysis accuracy is improved. And dynamically evaluating the confidence coefficient by adopting a fuzzy instruction backtracking algorithm, and if the confidence coefficient is lower than a threshold value, starting multi-round dialogue clarification to reduce misoperation. According to the method, multi-modal data are fused, the historical interaction learning ability is optimized, the understanding efficiency and interaction robustness of complex instructions are remarkably improved, the method is suitable for scenes such as family service and logistics storage, and the intelligent level of man-machine cooperation is enhanced.

The invention discloses a method and system for context semantic extraction and intention recognition in voice dialogue, and the method comprises the following steps: S1, obtaining a voice input signal, converting the voice input signal into text data, and carrying out the preprocessing; s2, constructing a dialogue context cache, and updating the cache content; s3, constructing a sparse hybrid expert Transform network, and carrying out semantic coding on the sparse hybrid expert Transform network; s4, calculating a semantic dependency relationship between dialogue statements by adopting an ordered graph neural network; s5, calculating intention probability distribution, and adjusting the weight of the sparse hybrid expert Transform network and the weight of the ordered graph neural network; and S6, updating a dialogue state, and generating a system response. According to the method, the sparse hybrid expert Transform network and the ordered graph neural network are combined, voice dialogue context semantic extraction and intention recognition are achieved, and the method has the advantages of being accurate in semantic understanding, high in calculation efficiency and high in context sensing capacity.

The invention provides an LLM technology-based real-time voice conversation method and system applied to an operation and maintenance platform, and relates to the technical field of data processing, the method comprises the following steps: receiving user voice input, carrying out noise reduction preprocessing, and converting a voice signal into text data; semantic analysis is conducted on the text data through an LLMAgent module, the intention of a user instruction is recognized, and a task execution plan is generated based on the intention; decomposing the task execution plan into a basic operation sequence which can be executed by a Unity platform, and determining the priority of task execution; executing the basic operation sequence in a Unity environment, synchronizing an operation state in real time and feeding back an execution result; user interaction data and system performance data are collected, and LLM model parameters and decision strategies are optimized. The technical problems of poor voice interaction effect, large response delay, low accuracy and the like in the existing Unity platform are solved.

The invention provides a bank financial management product recommendation method and system, and the method comprises the steps: firstly collecting a multi-modal dialogue data flow which is generated by interaction of a user terminal and an intelligent customer service system and contains voice input signals and text input contents, and then carrying out the semantic recognition of the voice input signals, and generating a structured demand description set; performing entity extraction and intention analysis on text input content to generate a multi-dimensional semantic tag set, performing time-space alignment and fusion on the text input content and the multi-dimensional semantic tag set to form a user portrait enhanced feature matrix, and performing multi-level association analysis on the user portrait enhanced feature matrix and a financial product knowledge graph based on a deep matching model to generate an initial recommendation result queue; and finally, according to the real-time environment perception data, queue context perception optimization is carried out, a dynamic adaptive recommendation list is generated, and visual rendering output is carried out through a multi-channel interaction interface, so that accurate and dynamic financial management product recommendation is realized by utilizing multi-modal data, deep analysis and combination with a real-time environment.

The invention discloses a medical image processing method and system based on artificial intelligence, and the method comprises the steps: carrying out the voice input of focus information through a voice input terminal, carrying out the preprocessing and character conversion output based on the inputted voice information, and dynamically generating a keyword set highly related to a current diagnosis task by combining structured priori knowledge of a medical knowledge graph and medical image-text multi-modal features, then generating a candidate diagnosis set based on the keyword set, calculating each semantic alignment score with the medical image features, generating a final diagnosis opinion through Langevin Dynamics sampling, and obtaining a final diagnosis result. And finally, the user terminal performs re-checking and enhanced correction, dynamically optimizes model parameters, and outputs a final formal diagnosis report based on an optimization correction result, thereby solving the accuracy and quality problems of traditional artificial intelligence on medical images, and realizing high-precision, high-robustness and interpretable artificial intelligence auxiliary diagnosis on the medical images.

The invention discloses a soothing lamp intelligent control method and system based on voice interaction, and relates to the technical field of data encryption, and the method comprises the steps: receiving a voice command inputted by a user through a voice interaction device, awakening a control system, and starting a voice emotion recognition unit; the voice emotion recognition unit is used for performing emotion analysis on the voice command, extracting emotion characteristics and judging real-time emotion of the user; on the basis of continuous voice input of a user, voice signals are collected regularly, the emotional state is updated dynamically, and light setting is optimized in combination with environmental data; by recording historical voice interaction data and environment data of a user, an emotion mode and light preference of the user are learned, and light setting is continuously optimized. Light setting is continuously optimized through a self-learning mechanism, it is ensured that the system can conduct self-adaptive adjustment along with changes of user requirements, and light control is more intelligent and personalized.

The invention relates to a method, a device, equipment and a product for executing tasks. The method comprises the following steps: acquiring an operation sequence of a user for a first task on a user interface, wherein the operation sequence comprises operation steps required for completing the first task; the method includes converting the operating steps into a model context protocol service based on the sequence of operations. The method includes obtaining a user input, the user input indicating a second task, the user input including at least one of a text input or a voice input. The method includes determining a model context protocol service to be executed based on the second task, the model context protocol service to be executed being associated with the second task. The method includes executing a second task based on task parameters of the second task and service parameters of a to-be-executed model context protocol service, the second task being composed of the to-be-executed model context protocol service associated with the second task, and the task parameters of the second task determining and displaying an execution result of the second task based on user input.

The invention relates to the technical field of intelligent voice interaction, in particular to an intelligent voice interaction system and an electronic student identity card, and the system comprises a voice input processing module, a semantic decomposition module, a task allocation module, a logic execution module, a dynamic response module and a voice interaction adjustment module. According to the invention, by analyzing the frequency domain and time domain features of the voice signal, accurately capturing voice details, improving the recognition accuracy, analyzing and recombining semantic content word by word, supporting accurate extraction and task description of complex semantics, analyzing resource consumption and dependency relationship, optimizing a task sequence, and improving the efficiency and adaptability of the system; task execution is combined with data retrieval and content classification, it is ensured that key tasks and associated tasks are carried out in a coordinated mode, uncompleted tasks are dynamically fed back and marked, supplementary information is assembled, dialogue continuity and response naturalness are enhanced, a voice interaction adjustment module analyzes uncompleted instructions and repeated calling, and adaptability to demand changes and service efficiency are improved.

The invention belongs to the technical field of vehicle state detection and early warning, and particularly relates to a vehicle state monitoring and early warning method and system.The monitoring and early warning method comprises the steps that a sensor obtains real-time data, an anomaly detection algorithm is applied, and an abnormal event is marked; calculating an information priority according to the abnormal severity and the driving scene, and distributing the information priority to a high-priority queue; multi-mode early warning is generated, and high-frequency sound and vibration are used during high-speed driving; extracting a voice prompt, and generating voice waveform data through a voice synthesis module; voice input of a driver is recognized, intention is analyzed, and abnormal information feedback is provided; according to the scene and the abnormal state, interactive output is optimized, and detailed information is displayed during low-speed congestion; dynamically adjusting the interface of the instrument panel, and amplifying the key area in case of abnormal severity; and integrating the image data and the voice data, generating a multi-mode signal, and transmitting the multi-mode signal after rendering processing. The vehicle abnormal information can be timely and accurately transmitted to a driver, and the driving safety is effectively improved.

The invention relates to an intelligent safety protection management method and system. The method comprises the following steps: acquiring behavior data and position data of personnel in an industrial site; analyzing the behavior data and the position data by using a pre-trained personnel behavior model to obtain a behavior recognition result; according to the behavior recognition result and the current operation state of the target equipment, judging a risk level corresponding to the personnel behavior; based on the risk level, a corresponding safety response strategy is matched in the edge computing node, a control instruction is generated according to the safety response strategy, and the control instruction is used for driving the target device to execute a corresponding response action; and the voice interaction module is used for acquiring voice input of an on-site operator, performing semantic recognition on the voice input to obtain a voice recognition result, and executing corresponding emergency control operation to cover a current control instruction when the voice recognition result meets a preset emergency instruction condition. The method has the effect of improving the accuracy of intelligent safety management in the industrial environment.

The invention discloses a multi-mode driven virtual digital human face animation generation method and system, and relates to the field of computer graphics, and the method comprises the steps: obtaining voice input and text input, and extracting voice features and text features; dynamically fusing the two features through an attention fusion model to generate facial expression and head posture control parameters, and dynamically adjusting contribution weights of a voice mode and a text mode to the control parameters by adopting a driving strategy of differentiation of facial upper expression and facial lower expression; and performing local deformation on the virtual digital human face image based on the control parameter to generate an initial animation frame, and performing refining processing on the initial animation by using a generative adversarial network to obtain a refined face animation. By means of the technical scheme, the natural and vivid virtual human face animation matched with the voice content and the text semantics can be generated.

The invention discloses a real-time digital human generation system and method based on low-calculation-amount voice driving, and relates to the technical field of digital human interaction, and the system comprises an audio processing module which is configured to receive voice input in real time and extract an audio feature vector; the driving and rendering module is used for mapping the audio feature vectors into parameters representing mouth movement, generating a dynamic mouth image based on the preprocessed static face reference data and the parameters, and fusing the dynamic mouth image with the reference image; the synchronous control module is used for ensuring the synchronization of the audio features and the rendering video frames according to a timestamp alignment mechanism and a PID feedback control algorithm; and the dynamic scheduling module is used for monitoring a hardware resource load in real time and realizing dynamic allocation of computing resources through multi-thread parallel and task priority adjustment. According to the technical scheme provided by the invention, the breakthrough of low delay, high fidelity and low energy consumption of the digital human can be realized in mobile terminal, embedded and multi-platform scenes.

The invention relates to the technical field of voiceprint recognition, and particularly provides a voiceprint recognition and voice synthesis combined intelligent dialogue method, which comprises the following steps: acquiring a user voice input signal in real time; voiceprint feature extraction is carried out on the voice input signal, and a composite voiceprint vector containing user identity features and rhythm features is obtained; user identity matching is carried out based on the composite voiceprint vector, and a dynamic user portrait database is associated and called; generating personalized semantic feedback content according to the weight-updated dynamic parameters in the dynamic user portrait database; generating a target timbre synthesis parameter set based on the timbre representation vector in the composite voiceprint vector in combination with the spatio-temporal characteristic parameters of the interaction scene; and performing voice synthesis on the personalized semantic feedback content by adopting the target timbre synthesis parameter set, adjusting rhythm expression parameters of the synthesized voice based on real-time rhythm characteristics in the composite voiceprint vector, and finally outputting personalized voice, thereby realizing natural interaction of one person, one voice and one strategy.

The invention relates to a vehicle-mounted user intention recognition and task generation system based on a large model, and relates to the technical field of automobile interaction. The user intention analysis module is used for obtaining voice input of a user in a vehicle and analyzing the voice input to obtain a plurality of user intentions; the situation awareness module is used for collecting current environment data of the vehicle and obtaining a current situation based on the current environment data through a large model; the task generation module is used for searching a task capability interface of a vehicle according to the user intention and the current situation to generate a task combination; the task combination is to call different agents; the reflection module is used for evaluating whether the generated task combination is consistent with the user intention and the current situation and adjusting the inconsistent task combination; and the task sequence arrangement module is used for confirming the control sequence of different series-parallel tasks according to the task combination and calling the corresponding agents.

In one embodiment, a method includes receiving a voice input having acoustic features from a first client system associated with a first user, determining emotions associated with the voice input based on one or more of the acoustic features by machine-learning models, determining facial features for a first extended-reality (XR) avatar representing the first user based on the emotions, and sending instructions for rendering the first XR avatar representing the first user to a second client system associated with a second user, wherein the first XR avatar is rendered with the determined facial features.

Provided in the embodiments of the present disclosure are a video generation method and a parameter generation model training method. The video generation method comprises: acquiring speech to be processed; inputting an emotion feature of a target object and said speech into a parameter generation model, so as to obtain an expression parameter, wherein the expression parameter is used for describing facial motion information of the target object under the influence of the emotion feature, the parameter generation model is obtained by means of performing training on the basis of a sample emotion feature, sample speech and an expression parameter label that corresponds to the sample speech, and the sample emotion feature and the sample speech are obtained on the basis of a sample video; and inputting an object image of the target object and the expression parameter into a video generation model, so as to obtain a target video of the target object. By means of generating an expression parameter on the basis of an emotion feature and speech to be processed, and further generating a target video on the basis of the expression parameter, diversified emotion information is fused into the target video while ensuring the synchronization between speech and expression in the target video, thereby improving the accuracy and vividness of the target video.

The present invention relates to a system for transforming static websites into artificial intelligence (AI)-enabled interactive multi-modal conversational platforms. The system comprises a computing device having a processor for receiving user queries as text or speech input through an input module cooperating with a speech-to-text module. A natural language processing (NLP) module interprets intent, classifies user context, and retrieves grounded information from multiple webpages. A persona adaptation module dynamically modifies vocabulary, tone, and avatar representation across roles such as sales assistant, recruiter, educator, healthcare professional, etc. A response generator module produces structured natural language output, transmitted to a text-to-speech synthesis module and an avatar generation module to render synchronized lifelike video responses. An output rendering module displays multi-modal responses include text, audio, and video, thereby enabling direct navigation and escalation beyond limitations of conventional static websites.

The invention discloses a flexible robot motion control system and method based on a visual language action model, and the system comprises visual sensors which are disposed at a plurality of joints of a flexible robot, and are used for observing the multi-joint sensing information of the flexible robot; the remote voice input unit is installed on the flexible robot body and used for inputting a voice instruction of remote operation to the flexible robot; the motion control module based on the VLA framework is mounted in the flexible robot and used for controlling the flexible robot to act based on a visual language action model according to the multi-joint sensing information and the voice instruction; through the design of the intelligent control system, the complexity of the system is remarkably reduced, the control precision and flexibility are optimized, the coordination control problem of complex body segment units is solved, high-performance autonomous motion control is achieved, the system cost is reduced, the adaptability of the robot in diversified environments is enhanced, and the robot can be widely deployed in complex application scenes.

The invention relates to a computer-implemented method for data processing of a vehicle system (22) of a vehicle (10), comprising the steps: - Capture of a voice input (S10) by a microphone (26) coupled to or integrated into the vehicle system (22); - Conversion of voice input into a text message (S12) by the vehicle system (22); - Transferring the text message (S14) to a computing unit (12); - Generating a response using a machine learning algorithm executed on the computing unit (12), comprising: - Extracting a task list (S16) from the text message comprising at least one task, and - Obtaining information and / or generating commands (S18) for the vehicle system (22) according to the tasks in the task list; - Transmitting the response (S20) to the vehicle system (22); and - Execution of the commands contained in the response by the vehicle system (22) and / or generation of a speech output (S22, 40) from the response to read out the information contained in the response.

The invention relates to the field of artificial intelligence medical application, and discloses a lightweight intelligent traditional Chinese medicine inquiry system and a construction method thereof, and the system comprises a multi-dialect adaptive speech recognition module, a traditional Chinese medicine intelligent dialogue large language model module, a natural speech synthesis module, and a continuous learning mechanism module. The multi-dialect adaptive speech recognition module is used for converting dialect speech input of a patient into a standard text; the traditional Chinese medicine intelligent dialogue big language model module is the core of the system and is used for carrying out natural language understanding, dialectical reasoning and inquiry dialogue generation, and the natural speech synthesis module is used for converting a text response generated by the system into speech output; and the continuous learning mechanism module realizes continuous optimization of the large language model through incremental learning architecture and clinical feedback integration. According to the method, while the professional traditional Chinese medicine diagnosis capability is maintained, the calculation complexity is remarkably reduced, and the universality and sustainable development capability of system application are improved.

The invention belongs to the field of automobile evaluation, and provides an automobile live risk evaluation system and method based on a large model, and the method comprises the steps: receiving voice input information of an evaluator in a vehicle on-site inspection process; extracting component state information of the target vehicle based on the structured check item text; acquiring historical maintenance record data of the target vehicle, and performing association fusion on the historical maintenance record data and the component state information; based on a large language model, performing joint reasoning on a currently input inspection label and the historical maintenance record, constructing a causal chain related to an accident risk, and generating at least one subsequent to-be-inspected item; generating voice guide content and feeding back the voice guide content to the evaluator so as to prompt the evaluator to continue to check the target item of the target vehicle; gradually perfecting the risk check path of the target vehicle until the risk chain closed loop is completed; and calling an estimation formula to calculate a residual estimation value of the target vehicle, and outputting a structured risk assessment report.