152results about How to "Improve trust" patented technology

The embodiment of the invention provides a vehicle communication data processing method, a vehicle and a storage medium, and the method comprises the steps: obtaining target feature data and real-time state data of a target vehicle, the target feature data being used for representing multi-dimensional physical features associated with the target vehicle, and the real-time state data being used for representing real-time state data of the target vehicle; the real-time state data is used for representing an engine running state and an ignition state of the target vehicle; generating key seed data based on the target feature data and the real-time state data; a vehicle session key is generated according to the key seed data, and the vehicle session key is used for encrypting and decrypting the session data packet; and performing data communication of the target vehicle by using the vehicle session key. According to the method and the device, the technical problems of low security of a fixed key management scheme and great influence of an encryption authentication mechanism on real-time performance in related technologies are solved.

The invention discloses a large-model-driven urban railway newly-opened station flow prediction method, and relates to the technical field of urban railway traffic operation management. The technical problems of low prediction precision, poor interpretability of a traditional model and insufficient reasoning flexibility in a complex urban scene caused by lack of historical passenger flow data of a newly opened station of an urban rail are solved. The method comprises the following steps: firstly, collecting pre-processed passenger flow related multi-dimensional data, and then constructing a multi-dimensional reference system to retrieve a target station similar reference or extract a macroscopic index reference; judging a scene category through a rule engine, calling a corresponding template, and filling information to generate a structured cue word; inputting the cue word into the large language model, and outputting a content prediction value and a result of a natural language reasoning chain according to the reasoning thinking chain template; and finally, checking the result, and completing optimization of the method based on feedback. The method has a good application prospect in the technical field of urban rail transit operation management.

The application discloses an information distribution method and system based on a geographic fence and unified credit evaluation, and belongs to the technical field of big data processing. A user publishes business event information in a same account, and independently sets a geographic fence and a receiver credit threshold value; the system determines a second user currently located in the geographic fence and having a credit score not lower than the threshold value, and pushes the information; in response to an acceptance operation of the second user, an interaction relationship between the two parties is established; and in response to a mutual evaluation result after the interaction ends, credit scores of the two parties are dynamically adjusted. Through user-defined distribution conditions, platform implementable auditing and mutual evaluation credit systems, the application breaks the double barriers of traditional socialization and algorithm recommendation, realizes directional, efficient and credible distribution of information, and reduces consumption of network resources and server resources caused by invalid information transmission.

The application belongs to the technical field of advertisement pushing, and discloses an advertisement intelligent putting system and method based on specific user driving: demand expressions voluntarily submitted by users are collected, characteristic elements of each expression item are extracted, and a demand expression characteristic set is obtained; multi-layer condition discrimination is performed on the demand expression characteristic set, and uncoordinated expression items are removed, so that a preliminary putting demand set is obtained; situation derivation enhancement is performed, so that a complete putting demand set is obtained; core expression item data of adjacent demand sequences are extracted, and sequence mapping and fusion processing are performed, so that a persistent preference guidance group is obtained; peripheral expression item data is extracted, and an instant change coordination group is constructed; the peripheral expression items in the complete putting demand set are matched and adjusted, so that an adaptive putting advertisement set is obtained; and the precision marketing efficiency of advertisement intelligent putting is greatly improved.

The invention discloses an enteral nutrition information management method and system based on artificial intelligence, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: carrying out the forward reasoning of an intestinal function-clinical decision fusion feature vector through an artificial intelligence decision model, obtaining a decision probability distribution vector, and carrying out the optimization and integration in combination with a clinical constraint condition, generating a nutrition intervention parameter set; performing decision basis analysis on the nutrition intervention parameter set, calculating each feature contribution degree, mapping each feature contribution degree into a natural language description fragment, and generating an enteral nutrition AI decision traceability report; and performing clinical research and judgment on the nutrition intervention parameter set and the enteral nutrition AI decision traceability report to generate an enteral nutrition clinical decision execution scheme. According to the method, the contribution degree of each feature is calculated and mapped into the natural language description fragment, the enteral nutrition AI decision traceability report is generated, transparency and interpretability of an artificial intelligence decision process are realized, and trust and adoption efficiency of an intelligent result are enhanced.

This application relates to the field of electronic product interface technology, and more particularly to an interface structure, a motherboard, and an electronic product. The interface structure includes a terminal assembly and a switch assembly. The terminal assembly includes multiple strip-shaped metal lines, some of which have disconnection notches. The switch assembly includes a connecting terminal located at the disconnection notch and is used to slide between a first position and a second position along the length of the metal line. When the connecting terminal is in the first position, it conducts electricity through the metal line; when the connecting terminal is in the second position, at least one end of the connecting terminal is not connected to the metal line, and the metal line remains disconnected. This technical solution, through a mechanical structure, can achieve partial isolation of the metal lines, reducing the risk and cost of using the interface structure and improving its reliability and user trust.

This invention discloses a method for accurate heart rate calculation based on multi-source multimodal fusion, relating to the fields of biosignal processing and health monitoring technology. The method includes: simultaneously acquiring multi-source array data such as biosignals, environmental information, and motion status, and constructing a time-aligned dataset; preprocessing and extracting features from the data to obtain multi-dimensional features such as pulse peak interval, R-wave interval, environmental interference level, and exercise intensity level; identifying the current dynamic scene based on features and adaptively assigning heart rate calculation weights to photoelectric and electrocardiogram signals; calculating two preliminary heart rate values ​​in parallel and performing weighted fusion; combining individual feature calibration and a time-series trend model to perform anomaly judgment and correction on the preliminary heart rate values, and outputting the final heart rate value and confidence score. This method, through multi-source fusion and scene-adaptive weighting, achieves multi-scene adaptive, anti-interference, and personalized accurate heart rate monitoring.

The application discloses an anchor point guided clustering based data classification method, device and equipment, relates to the technical field of digital data processing, and comprises the following steps: obtaining an original data set to be classified, converting each sample data into a numerical vector, and constructing a data matrix; initializing a clustering center matrix and an anchor point matrix, and setting a fuzzy coefficient; obtaining the anchor point matrix and the clustering center matrix after joint iterative optimization; calculating the fuzzy membership degree of each sample vector; the category with the maximum fuzzy membership degree is taken as the final category label of the sample vector, and the classification results of all sample vectors are output. The application solves the problems that the existing fuzzy clustering method is sensitive to initial conditions, is easy to fall into a suboptimal solution, leads to unstable and inaccurate classification results, and cannot be directly solved by using a gradient descent algorithm, and is difficult to be applied to large-scale data sets, and realizes the enhancement of complex data classification precision and the applicability in different data scale scenes.

The application provides an industrial equipment fault diagnosis model construction method and related equipment, and relates to the technical field of industrial equipment fault diagnosis. The method comprises the following steps: obtaining a physical mechanism model of the industrial equipment; the physical mechanism model comprises a physical constraint equation; constructing a deep learning network; the deep learning network comprises a composite loss function constructed according to the physical constraint equation; collecting full-time series state data of the industrial equipment during operation; the full-time series state data comprises specified time series state data collected when the industrial equipment is in a healthy state; based on the full-time series state data, the deep learning network is trained for parameter optimization to obtain a final available industrial equipment fault diagnosis model. The method of the application aims to solve the problems of weak early fault identification ability, poor interpretability, lack of physical meaning and strong data dependence in the existing industrial equipment fault diagnosis technology, and effectively improves the intelligent level and operation and maintenance efficiency of the industrial equipment fault diagnosis.

This invention discloses a large-scale model defense method based on semantic logic. By employing semantic logic analysis and model learning techniques, it systematically identifies and blocks the confusion effect of bias, thereby reducing the impact of bias in pre-training data on the guidance process. It includes an interpretable component that provides insight into the alignment between the generated output and the desired direction, enhancing the interpretability of the guidance process, increasing model transparency and user trust. This method may reduce reliance on manual annotation and computational resources, thus improving resource efficiency. Through unbiased guidance representation, it improves the model's generalization ability across different tasks and domains, contrasting with the limited generalization capabilities of existing technologies, exhibiting high scalability and adaptability. It can be applied to different language models, providing in-depth understanding of the consistency between the generated output and the desired direction, thereby enhancing the interpretability of the steering process while mitigating the impact of bias.

The application discloses an employee performance automatic examination and evaluation management system, and relates to the technical field of intelligent performance evaluation.The system comprises a structured data collection module, a behavior data modeling module, an initial performance modeling module, a dynamic weight optimization module, a performance report generation module and a blockchain storage module.The structured data collection module is used to connect the human resource information system, the enterprise resource planning system, the customer relationship management system and the project management platform in the enterprise through a standardized interface, and to collect the structured performance-related data generated by the employees in the above systems in real time based on a business event triggering mechanism.The multi-source structured and unstructured data are fused to construct a collaborative relationship modeling mechanism based on a graph neural network and a dynamic weight optimization mechanism based on reinforcement learning, so that a comprehensive, objective and personalized evaluation of the employee performance is realized.Meanwhile, the blockchain storage technology is introduced to ensure that the whole examination process is traceable and tamper-proof.

The invention relates to the technical field of vehicle control, in particular to a vehicle control method and system based on the head posture of a driver, and the method comprises the following steps: obtaining first inertial data of the head of the driver, and synchronously collecting image data containing optical mark points on a head-mounted device; based on the image data, calculating to obtain a visual estimation posture of the head of the driver; based on the first inertial data, calculating to obtain an inertial estimation posture of the head of the driver; performing data fusion on the visual estimation attitude and the inertial estimation attitude to obtain six-degree-of-freedom attitude information of the target; acquiring head posture information and a driving instruction of a driver, and establishing a mapping rule between the head posture information and the driving instruction information; and according to a mapping rule, converting the target six-degree-of-freedom attitude information into a corresponding vehicle control instruction, and sending the vehicle control instruction to a vehicle execution mechanism to control vehicle actions, so that the vehicle driving safety of a driver can be improved.

The invention discloses an electric charge bill intelligent interpretation and optimization system based on multi-source data fusion and rag, and belongs to the technical field of energy consumption optimization. Comprising a multi-source data access and analysis module, an electricity price policy and charging rule knowledge base construction module, a multi-source data fusion and feature modeling module, a rate optimization inference module, a bill attribution analysis module, an intelligent explanation module, a front-end interaction display module and the like. Structured bill data, load time sequence features and unstructured electricity price policy document information are uniformly included in the same feature space, an electricity price policy knowledge base is used as a constraint source, and an explicit mapping relation from bottom layer data to upper layer rules is established. The rate optimization result strictly follows the current charging regulations in form and can be semantically attributed to specific terms, and a finer and more personalized rate and power utilization strategy scheme can be provided on the premise of ensuring compliance.

The utility model relates to a rear brake stroke guaranteeing tool, which is clamped on a vehicle handle, measures the swing displacement of a brake handle by taking the vehicle handle as a reference, and structurally comprises a clamping part which is connected with the vehicle handle in a clamping manner, and a measuring part which is positioned on a swing displacement path of the brake handle and measures the displacement of the brake handle. According to the utility model, the clamping part at the front end of the clamp is clamped on the handlebar of the electric vehicle, the scales on the left side and the right side are read, applicable brake requirements are set for different vehicle types, the brake is adjusted to the appointed scale position of the hand brake, and the rear brake stroke in the production and manufacturing process of the electric vehicle is consistent.

The application provides a medical question and answer method and system based on cooperative dual-source retrieval enhancement generation, which comprises the following steps: according to an input original medical question and candidate items, performing task self-adaptive query rewriting through a large language model to generate differential diagnosis keywords, entity enhanced queries and hypothetical medical abstracts; for the differential diagnosis keywords, performing an iterative network retrieval with a reflection mechanism to obtain network candidate evidence; for the entity enhanced queries and the hypothetical medical abstracts, performing a hybrid retrieval in a local medical knowledge base to obtain local candidate evidence; aggregating and deduplicating the network candidate evidence and the local candidate evidence, obtaining the aggregated heterogeneous evidence, performing deep semantic correlation scoring through a cross-encoder model, screening out a target evidence set, and splicing the original medical question as context input into a generative large language model to generate a final medical answer. The application improves the accuracy, timeliness and explainability of the medical question and answer system.

The application relates to a driving safety field model construction method, device, control method and system, the model construction method comprises the following steps: defining a dynamic field generated by a single obstacle, calculating the sum of the dynamic fields generated by all obstacles in a preset area around a vehicle as the risk potential energy of the vehicle; a mapping equation of the risk potential energy and the reciprocal of the collision time is established; based on historical driving trajectory data, the linear fitting integration between the risk potential energy and the reciprocal of the collision time is maximized as an optimization target, parameters in the mapping equation are iteratively searched and optimized, an optimal parameter set is obtained, and a driving safety field model is determined; and the risk threshold of the driving safety field model is updated according to real-time environmental parameters. The application establishes a linear mapping of the risk potential energy and the kinematic index, makes the risk assessment have a clear physical standard, solves the problem that model parameters are difficult to quantitatively determine by using a data driving means, and improves the modeling accuracy.

The application discloses a kind of multi-micronet robust game optimization scheduling method and system based on dynamic auction official algorithm, and relates to energy system scheduling and optimization technical field.The method constructs system physical model and double-layer robust optimization model based on Stackelberg master-slave game, upper layer maximizes system operator's profit to formulate price signal, lower layer maximizes the worst scenario income of multi-micronet alliance to optimize resource scheduling, combined with multiple constraint conditions, Stackelberg equilibrium is solved iteratively using dynamic auction official algorithm.The application converges to global optimal solution quickly through three-dimensional hybrid driving price updating mechanism, realizes system economic benefit maximization, operation robust and reliable, and has good explainability and practical application value.

The invention relates to the technical field of IPTV (Internet Protocol Television) value-added services, in particular to a method for preventing false ordering of an IPTV value-added service, which comprises an operator, an SP (Service Provider), a cashier desk, a payment plug-in, a user data interface and the IPTV, a user browses the value-added service provided by the SP through an IPTV set top box and initiatively initiates an ordering request based on the own demand, and when the SP receives the ordering request of the user, the SP sends the ordering request to the IPTV. A payment plug-in is called, the payment plug-in is a unique cashier entrance, real equipment of a user is recognized through technologies such as equipment fingerprints, the authenticity of a request source is ensured, the payment plug-in carries a cached unique equipment identifier, a background interface of a cashier is accessed, and the background of the cashier executes multiple verifications; according to the method, false violation ordering behaviors are accurately recognized and intercepted in time, violation ordering is blocked before ordering is completed, economic losses of users are avoided, bad debt losses of operators are reduced, the operators can intercept malicious violation ordering in advance, trust of the users to the operators can be increased, and user stickiness can be increased.

The utility model discloses a vending device for vacuum-packaged prefabricated dishes, which belongs to the technical field of vending machines and comprises a shell frame, and a food making component, a food delivery component, a machine maintenance window, a food outlet, a payment window and a food ordering screen are mounted in the shell frame. The food making assembly comprises a bag opener, a squeezer, a heater and a fetching clamp which are sequentially arranged from bottom to top, the food delivering assembly comprises a dinner plate storage box and a food delivering device, a plurality of dinner plates are stored in the dinner plate storage box, the food delivering device is provided with a fixed part and a sliding part, and the fixed part and the sliding part are connected with each other. The fixing part is installed on the side wall of the shell frame and located below the bag opener. According to the selling device for the vacuum-packaged prefabricated dishes, a plurality of functional assemblies such as food conveying, meal making and meal delivering are integrated, all the assemblies are matched with one another, automation of the whole process from storage, conveying, making to meal delivery of the prefabricated dishes is achieved, manual intervention is greatly reduced, and the manual maintenance frequency and cost are reduced.

The invention discloses a full-automatic long-term hot spring monitoring system, and relates to the technical field of environment monitoring and tourism facility management, and the system comprises a monitoring terminal cluster which is composed of a plurality of monitoring terminals disposed at different nodes of a hot spring area; each monitoring terminal is used for automatically collecting physical and chemical parameters of a water body at a node where the monitoring terminal is located; the cloud service platform is in communication connection with the monitoring terminal cluster and is used for receiving, storing, analyzing and visualizing the physical and chemical parameters; intelligent early warning and data management are carried out according to an analysis result; and the user access terminal is in communication connection with the cloud service platform and is used for receiving the intelligent early warning and providing access service for the physical and chemical parameters for a user. According to the invention, unmanned, intelligent and informationized hot spring monitoring is realized, and the operation efficiency, the service quality and the resource protection level are remarkably improved.

The invention relates to the technical field of man-machine emotion interaction, and discloses a social robot multi-mode emotion analysis intelligent accompanying system, which comprises a data acquisition module used for acquiring voice, image and text input data of a user; the multi-modal emotion recognition module is used for extracting emotion features of the collected voice, image and text data and generating corresponding voice emotion vectors, and the system has five core advantages that three-modal deep fusion is combined with conflict detection and composite emotion analysis, so that the emotion recognition accuracy and anti-interference performance are greatly improved; the multi-Agent collaborative reasoning is matched with a strategy knowledge base, and the co-situation quality of the complex scene is optimized; long-term memory and short-term memory are coordinated, and continuity and individuation of long-term accompanying are The side cloud collaborative architecture reduces the computing power consumption and improves the end side adaptability; the whole process privacy compliance design ensures data security, and lays a foundation for large-scale commercial use.

The application discloses an interactive form design method and system based on a language large model, and belongs to the technical field of human-computer interaction and form automatic generation. In view of the problems that a traditional form design tool is tedious to operate, highly depends on manual experience, and an existing method based on a large model has high training cost, weak interaction ability and difficulty in real-time collaboration, the following scheme is used for improvement: defining a JSON Schema specification of a form and constructing a visual form designer, integrating a language large model interaction entrance in an interface, dynamically assembling a request containing user instructions, a JSON Schema specification and existing form data according to whether the form is empty, and submitting the request to the language large model; receiving a response in a streaming manner, and real-time analyzing and progressively updating a form designer preview interface, so that a user can directly generate or modify a form through natural language instructions, and multi-round interaction and manual subsequent adjustment are supported. The method is mainly used for reducing a form design threshold, improving design efficiency and intelligent collaboration level.

A management system and an automated valet parking system are provided to make it easy for users to recognize when a request from a user has been transmitted to the automated valet parking system. The automated valet parking system includes a vehicle as the object of automated valet parking in a parking lot, and a terminal. The terminal includes at least one of a user terminal for the vehicle and an infrastructure terminal located in the parking lot. In response to a user's request related to automated valet parking of the vehicle, the vehicle performs a first response, and the terminal performs a second response.

The application relates to the information technology field and particularly discloses an online shopping mall platform intelligent management method and system, which comprises the following steps: S1, a data acquisition system is constructed, platform operation data, user behavior data and external market data are acquired in real time; S2, historical sales data are acquired, a Prophet time series model is used to analyze the historical sales data, future sales trends and inventory demands are predicted, and dynamic replenishment suggestions are generated; S3, inventory management is optimized through a dynamic programming algorithm, and the optimal inventory threshold is calculated in combination with the external market data, and automatic replenishment triggering is realized; S4, a user portrait is established according to the user behavior data, and individualized coupon strategies are generated based on a collaborative filtering algorithm; S5, work orders and customer service are dynamically matched according to a multi-attribute decision algorithm; and S6, warranty information is stored, and the non-tamperability and traceability of construction time, material batch and technician ID data are ensured, so that the operation efficiency and user experience of the shopping mall platform are improved.

The invention discloses a multicolor printing method and device, a storage medium and printing equipment. Consumable identification information of current printing consumables in the current printing equipment is read, target printing concentration corresponding to the current printing consumables is determined according to the consumable identification information, and the target printing concentration is used for being associated with target developing temperatures corresponding to all target display colors in the current printing consumables; determining a user printing interface associated with the current printing equipment, matching the printing concentration in the user printing interface to the target printing concentration, and displaying the printing concentration in the user printing interface; and generating a heating control instruction for displaying the multiple target display colors for the current printing consumable based on the target printing concentration, and driving a printing head of the current printing equipment according to the heating control instruction. The consumable identification information is automatically identified and matched with the target printing concentration, an accurate heating control instruction can be generated to drive the printing head, it is ensured that all colors can be accurately developed, a user interface is synchronously updated, and intelligent and high-reliability multi-color printing is achieved.

The invention discloses an endotoxin expert question-answering method and system based on a neural symbol mixed architecture. The method comprises the following steps: constructing a structured endotoxin knowledge graph; extracting a new entity and a new relationship from a new document through automatic processing, and dynamically fusing the new entity and the new relationship into the knowledge graph based on hybrid weighted calculation of semantic association strength and domain rules; receiving a natural language query of a user, and identifying a key entity and a core intention in the query by utilizing a large language model; and based on the key entity, searching a local knowledge sub-graph containing multi-hop neighbor nodes and an association relationship in the knowledge graph, after the sub-graph is subjected to symbol rule logic verification, serializing the sub-graph into a fact constraint context, inputting the fact constraint context into the large language model, and generating a natural language answer with evidence traceability information. According to the method, accurate, efficient and compliant intelligent question answering of endotoxin-related questions is realized through a neural symbol mixed architecture combining a knowledge graph and a large language model in combination with dynamic knowledge updating, logic conflict resolution and a multi-scene deployment scheme.

This invention belongs to the field of detection technology, specifically relating to a method for detecting the heavy metal content in food additives. The detection method includes the following steps: (1) sample pretreatment; (2) preparing a series of heavy metal standard solutions of different concentrations, testing their net strength using an ICP-MS instrument, and obtaining a standard working curve; (3) testing the net strength of the sample solution and blank solution using an ICP-MS instrument, and obtaining the concentration of heavy metals in the food additives by combining the standard working curves, and further obtaining the content of heavy metals in the food additives. The detection method of this invention can be used to detect the heavy metal arsenic in compound meat product thickeners. Through enzymatic hydrolysis-assisted extraction and ultrasonic synergistic extraction, gradient ultrasound combined with temperature control is used to further destroy the sample structure and improve the arsenic dissolution efficiency; EDTA and citric acid are used to complex metal ions, and multiple components synergistically reduce the fixation effect of other ions on arsenic. Through synergistic effect, the accuracy of the detection results is improved.

This invention provides an interpretability system for mortality prediction models, comprising a prediction model module, a global interpretation module, an individual interpretation module, and a visualization interface module. By introducing a dual interpretation mechanism of the global and individual interpretation modules, the SHAP analysis unit of the global interpretation module generates a global feature importance ranking and an individual feature contribution distribution visualization, demonstrating the importance ranking of multi-source features in predicting mortality risk, enabling users to establish a macro-level understanding of the key aspects of the mortality prediction model. The individual interpretation module generates personalized interpretation reports, indicating the quantitative contributions of key features and crucial characteristics that drive increases or decreases in mortality prediction values. Through this combination of macro-level understanding and micro-level attribution, the mortality prediction model's predictions are transformed from an incomprehensible "black box" into a verifiable chain of evidence, effectively solving the core pain point of traditional models being difficult to apply due to their lack of interpretability.

The present application relates to the field of medical information technology, in particular to a medical consumable intelligent evaluation system fusing evidence-based medicine and personalized analysis. The system comprises a data fusion module, an evidence-based evidence management module, a personalized analysis module, a decision interaction module and a closed-loop feedback tracing module. The data fusion module integrates internal and external data of the hospital to generate a structured data set; the evidence-based evidence management module quantitatively grades the evidence and constructs a dynamic evidence database; the personalized analysis module combines patient characteristics and preferences, and outputs individualized multi-dimensional analysis results through a patient-surgery scene-consumable matching model; the decision interaction module generates structured options based on the analysis results, supports doctor-patient collaborative decision-making and records the process; the closed-loop feedback tracing module collects real-world feedback data for calibrating and iteratively optimizing the matching model. The present application realizes intelligent, reasonable and personalized evaluation of medical consumables, and improves the scientificity, interpretability and auditability of the evaluation.

The invention relates to a Pareto multi-model collaborative evaluation-based wind-light-hydrogen storage intelligent regulation and control method and system. The method comprises the following steps of 1, collecting and storing multi-source heterogeneous data; step 2, cognitive diagnosis and anomaly detection based on a large language model; 3, performing digital twinning parallel simulation and multi-algorithm testing; step 4, performing multi-agent collaborative scoring and dynamic Pareto optimization; and step 5, performing Pareto feedback preferential selection and adaptive strategy switching. The system executes the wind-solar-hydrogen storage intelligent regulation and control method, and comprises a large model and a simulation platform on the cloud side, an energy management platform on the side and energy equipment on the end side. Multi-modal understanding and knowledge reasoning capability of a large model are applied to operation monitoring and algorithm decision making of the wind-light-hydrogen storage integrated system, and multi-target collaborative optimization scheduling of hydrogen production, energy storage, internet surfing and the like is achieved by constructing a digital twin simulation test environment and a hierarchical intelligent evaluation system.