418 results about "Expert system" patented technology

The invention relates to the technical field of agricultural intelligent detection, in particular to a fruit tree disease and pest detection method based on machine vision, which comprises the following steps: acquiring visible light, infrared light and ultraviolet light images of a fruit tree at multiple angles through a multispectral camera to obtain complete fruit tree surface information, and preprocessing the acquired images to obtain a fruit tree disease and pest detection result; comprising the steps of denoising, illumination correction and image alignment, extracting feature vectors, including the color, shape and size of scabs, pest distribution and other features, of pest and disease damage from preprocessed images through a convolutional neural network, fusing the pest and disease damage features with environmental data by using a multi-modal data fusion algorithm, and enhancing the accuracy of pest and disease damage classification. Classifying and identifying diseases and pests based on a support vector machine classification algorithm; according to the dynamically adjusted threshold value and suggestions provided by the expert system, a disease and pest diagnosis report is generated, and prevention and control measure suggestions are provided; according to the invention, efficient and accurate detection and management of fruit tree diseases and insect pests can be realized, and the intelligent level of agricultural management is improved.

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention discloses a power distribution equipment state monitoring system and monitoring method based on the Internet of Things technology, and relates to the technical field of power distribution equipment monitoring, the system comprises the following components: a sound acquisition module, a data transmission module, a data processing center and an early warning display module; by combining the Internet of Things technology, the expert system and the feature engineering technology, high-precision acquisition and analysis of operation sound signals of the power distribution equipment are realized, and by constructing the fault voiceprint feature library, the system can quickly recognize and match equipment fault features, so that early warning is given out at the initial stage of the fault, and the fault early warning efficiency is improved. In addition, due to the application of the cross-equipment associated fault prediction model and the fault propagation prediction model, the system can predict the propagation conditions of cascading faults and system-level faults, more comprehensive fault information is provided for operation and maintenance personnel, prevention measures can be taken in advance, and the fault monitoring accuracy and timeliness are improved. The influence of faults on a power distribution system is reduced.

The invention discloses a city updating intelligent expert system architecture and method based on a large language model, and relates to the technical field of city planning and construction. The knowledge base management module supports efficient retrieval and application of professional domain knowledge; the expert agent group comprises a user-defined core expert and a system dynamic supplement expert; the expert team collaborative decision-making module is used for executing multiple rounds of hierarchical collaborative decision-making processes; the meta-agent module is responsible for project feature analysis and expert team dynamic configuration; the auxiliary agent module comprises a host agent which is responsible for guiding the decision making process; the recorder agent generates a decision summary report; the verification agent is responsible for verifying and evaluating the professional ability of the expert agent; and the scoring agent evaluates the decision quality. According to the method, the problems of dispersed professional knowledge, low cross-domain expert cooperation efficiency, high decision-making cost and the like in the existing city updating decision-making process are solved, and the decision-making efficiency and quality of city updating early-stage planning are comprehensively improved.

The invention provides a robot cable manufacturing process optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting cable manufacturing data, constructing a digital twin model, and achieving the process flow simulation through a graph network; building a deep reinforcement learning environment by taking the manufacturing data and the simulation data as state input; analyzing a causal dependency relationship of process parameter adjustment; constructing an industrial knowledge graph expert system to generate an optimization strategy; and a process optimization closed loop is formed. According to the invention, self-adaptive optimization of the cable manufacturing process is realized, and the manufacturing efficiency and quality are improved.

The invention discloses a motor vehicle maintenance management service system, and relates to the technical field of motor vehicle maintenance management, and the system comprises a client information management module which builds data for storing client multi-information, supports multi-format import and export, intelligently recognizes and updates, and guarantees the information to be timely and accurate; the vehicle fault diagnosis module is provided with various interfaces and expert systems, analyzes and diagnoses faults based on big data, and gives reasons, suggestions and cost estimation; the maintenance work order management module supports work order full-process operation, automatically distributes personnel and updates the progress in real time for clients to inquire; the maintenance accessory management module is provided with an inventory database and can intelligently match accessories and provide position query navigation; and the maintenance personnel management module is used for storing personnel information and evaluating performance. According to the invention, management efficiency and accuracy can be improved, accessory management and cost are optimized, personnel quality and service quality are improved, customer experience and satisfaction are enhanced, powerful support can be provided for enterprise decision making, and intelligentization of the motor vehicle maintenance industry is promoted.

The invention provides a bearing fault identification method based on a dynamic generative adversarial network and expert feedback, and relates to the field of bearing fault diagnosis, and the method comprises the steps: generating a high-fidelity fault vibration signal through employing a condition generator and a triple discriminator generative adversarial network; verifying and generating sample quality through a 1D residual verification network and adding the sample quality into a training set; segmenting the vibration signals passing the test by using layered adaptive sampling, and keeping high-frequency impact characteristics in the vibration signals; a dynamic sparse attention mechanism is adopted to reduce unnecessary attention calculation and improve calculation efficiency, and different types of faults are accurately recognized in combination with a hybrid expert system classifier; and detecting the confidence of the diagnosis result, and triggering a feedback mechanism to regenerate a sample to complete autonomous iterative optimization when the confidence is low. According to the method, a generative adversarial network, a fault diagnosis model and a feedback mechanism are fused, accurate diagnosis of bearing faults is achieved through multi-level data enhancement and screening feedback, the diagnosis precision is continuously improved in continuous iteration, and the method is suitable for solving the problem that a traditional method is poor in performance under data scarcity and noise interference. The innovative closed-loop evolutionary logic of generation-diagnosis-feedback is provided, and the robustness and accuracy of fault recognition are remarkably improved.

An intelligent fault-in-rail analysis method based on multimodal fusion learning. Ultrasonic analysis data of the rail and high-definition rail surface images taken by line scan cameras are exploited in combination to detect and display internal and surface faults of the rail through different renditions using multimodal data. Rail DEtection TRansformer (R-DETR) technology or a You Only Look Once (YOLO) inspection machine-learning algorithm are employed in a deep convolutional neural network to analyze B-scan and rail surface image data to recognize rail line faults. After obtaining expressive data through data enhancement by computer processing, faults are recognized and pinpointed using the deep convolutional neural network learning algorithm. Analysis results are corrected with expert systems. The disclosed automatic ultrasonic fault-in-rail inspection solves the problem of slow detection and difficulties in tracking faults in everyday routine maintenance while also protecting the safety of rail operation and maintenance personnel.

The invention discloses a multi-mode electroencephalogram analysis model construction method and an online processing method and system. According to the method, multi-modal physiological signals such as electroencephalogram, electrocardio, skin electrical activity and eye movement are collected, and an integrated multi-modal signal collection device is used for preprocessing and feature extraction. A modal expert system based on an adaptive Transform architecture is adopted to replace a standard feed-forward network so as to enhance the feature processing capability. By means of the method, the features can be complemented under the condition of mode or data missing, the robustness, real-time performance and accuracy of the brain-computer interface technology in multi-mode data processing are improved, and the method is particularly suitable for the fields of emotion recognition, cognitive load monitoring, neural rehabilitation and the like.

The present application relates to the technical field of artificial intelligence, and in particular to a multi-modal human physiological data classification model and a training method therefor, a multi-modal human physiological data classification method, and a device, aiming to solve the classification problem in cases of missing multi-modal physiological data. The classification model of the present application comprises a multi‑headed self‑attention module, a normalization module, a fusion expert system and a decision-making module. The fusion expert system comprises four expert subsystems: an electroencephalogram expert subsystem, an electrocardiogram expert subsystem, an electrodermal activity expert subsystem and a multi-modal synchronization fusion expert subsystem. The multi-headed self-attention module is configured to perform feature extraction on multi-modal synchronization data; the normalization module is configured to generate normalized feature data on the basis of the extracted features; the four expert subsystems are configured to perform classification tasks on the basis of the normalized feature data corresponding to electroencephalogram data, electrocardiogram data, electrodermal activity data and multi-modal synchronization data, respectively; and the decision-making module is configured to calculate a final classification result on the basis of the four classification results. The present application can improve the accuracy of classification results in cases of missing multi-modal data.

The invention discloses a virtual teaching training method and system based on a large language model and a medium, and the method comprises the following steps: determining preset feature parameters of virtual students according to teaching levels and subject contents; constructing a large language model, generating virtual students based on the large language model according to preset feature parameters of the virtual students, and inputting multi-modal teaching information; the virtual students perform model training and learning on the multi-modal teaching information to generate learning state information and feedback information; the virtual teaching expert proposes a suggestion for dynamically adjusting and optimizing the teaching strategy based on the evaluation result; dynamic learning and interactive feedback are carried out by simulating virtual students with different priori knowledge levels and cognitive competence, and meanwhile, real-time evaluation and personalized improvement suggestions are carried out on the teaching process by utilizing the constructed virtual expert system, so that a user is helped to continuously optimize a teaching strategy, and the teaching quality of a specific subject is improved.

The invention discloses an unknown pollutant non-target identification method and system based on mass spectrum and a generative model, and is applied to the technical field of environmental monitoring and analytical chemistry. The method comprises the following steps: collecting mass spectrum data of a water body sample to be detected; preprocessing the original mass spectrum data, and outputting standardized features; inputting the standardized features into a pre-trained generative model to generate a plurality of candidate molecular formulas; using chemical and physical constraints to eliminate candidate molecular formulas which do not conform to rules; executing a rule-driven algorithm to obtain candidate pollutant molecular structures; carrying out comprehensive scoring and sorting on candidate pollutant molecular structures through chemical prior and environmental prior; and semi-quantitative or relatively quantitative concentration determination is carried out. According to the method, a rule-driven expert system and a data-driven generation model are combined, unknown pollutants which do not exist in a standard library can be effectively recognized and analyzed, and full-process automatic processing from original mass spectrum data to pollutant structures and concentrations is achieved.

The invention discloses a high-voltage power distribution station room dynamic environment and equipment state monitoring system and method. The system comprises a multi-dimensional data perception acquisition integration module, a heterogeneous data feature extraction and fusion transmission module, a pre-analysis module based on composite weight distribution, an association mapping module combined with historical data, a core diagnosis module based on an expert system rule base and a multi-modal visual output module. Multi-source data such as temperature and humidity, gas concentration, vibration, voltage and current are collected, after feature extraction and fusion, a data potential relation is mined by using composite weight distribution and historical data association, then equipment state evaluation and fault diagnosis are performed based on an expert system rule base, and finally, visual display and early warning output are performed in a multi-mode form. The system and the method can comprehensively monitor the environment and the equipment state of the power distribution station house, accurately diagnose faults, and effectively improve the operation safety and the operation and maintenance efficiency of the high-voltage power distribution station house.

The invention discloses an urban financial service full-link risk prevention and control management system, and belongs to the technical field of risk prevention and control. Comprising a multi-source heterogeneous data acquisition and treatment module, a dynamic risk portrait construction module, a real-time risk monitoring and early warning module, a risk disposal and decision support module, a cross-scene risk linkage analysis module, a compliance and privacy protection module and a system adaptive optimization module. Automation and manual cooperation of risk disposal are realized; a low-risk event automatically executes a preset action through a formula, so that the disposal efficiency is improved; medium and high risk events are pushed to an expert system, a disposal scheme is generated in combination with historical cases, and a block chain records an operation log to ensure transparency and traceability; the module balances machine decision and manual auditing boundaries, and the misjudgment risk is reduced.

The invention relates to the field of broadband alarm, in particular to a broadband user-based optical modem power failure and fiber break alarm real-time monitoring method, which comprises the following steps of: S1, monitoring and recording the online state, the light attenuation level, the flow information, the packet loss rate, the network delay and the log of an optical modem under OLT (Optical Line Terminal) equipment in real time; s2, automatically identifying an abnormal mode and a potential fault according to a model established by a machine learning algorithm and a deep learning algorithm; s3, automatically diagnosing problem reasons according to the collected data, and optimizing the knowledge base in combination with an expert system; s4, performing deep mining and analysis on historical fault data, and predicting a potential fault risk based on the historical data and a real-time monitoring result; and S5, continuously updating and optimizing a fault detection and diagnosis model through a self-learning algorithm model, and uniformly monitoring and managing all data acquisition, analysis and alarm modules. The fault detection accuracy and timeliness can be improved, and potential fault prediction and preventive maintenance are realized.

The invention discloses a wrist joint spasm assessment method and a wrist joint spasm assessment device based on multi-source data fusion. The rotating speed data, the rotating angular speed data and the angle change data of the wrist joint, the pressure change data of the whole palm, the acceleration change data of the palm, the torque change data of the wrist joint and the electromyographic change data of related muscles in the movement process are measured, and scoring is carried out in combination with an expert system. The evaluation of wrist spasm is realized by combining a multiple linear regression algorithm with a differential evolution algorithm; and based on the multi-source data fusion, establishing an evaluation model by using a Bi-LSTM network so as to realize spasm evaluation. The wrist serves as a spasm detection part, and compared with traditional spasm detection equipment, a more convenient data acquisition mode is achieved while the evaluation precision is guaranteed.

The invention relates to an optimization method of a hybrid expert system, computer equipment, a readable storage medium and a program product. A plurality of experts contained in the MOE are deployed in a plurality of artificial intelligence chips in groups, and the method comprises the following steps: carrying out routing calculation on an original input tensor to obtain a routing calculation result; determining input element grouping information corresponding to each expert based on expert index information and expert weight information in the routing calculation result; taking the expert dimension as a parallel dimension, executing rearrangement operation for the original input tensor in parallel based on the input element grouping information, taking a rearrangement result as input information of an expert, executing matrix multiplication and accumulation operation, and taking the expert dimension as the parallel dimension, and based on the input element grouping information, executing anti-rearrangement operation of the matrix multiplication and accumulation operation result in parallel to obtain a final operation result. By adopting the method, the MOE reasoning performance can be improved.

The invention discloses an unsupervised industrial defect detection method, system and device based on a multi-mode expert system. The unsupervised industrial defect detection method comprises the following steps: acquiring a label-free industrial defect image data set and a text cue word; inputting into a multi-modal expert system to obtain a corresponding prediction result; screening out a preliminary labeling result; filtering the preliminary labeling result based on a prior constraint to obtain a fine label; inputting the industrial defect image data set and the fine label into a self-supervised model for training, and outputting a pseudo label after training is completed; performing double filtering on the pseudo labels by adopting a dynamic threshold value and a prior constraint; inputting the industrial defect image data set and the pseudo label after the current training double filtering into the self-supervised model after the last training for re-training, outputting the pseudo label, and returning to execute the double filtering until the trained self-supervised model is obtained; and predicting an industrial defect image to be detected by adopting the trained self-supervised model to obtain a corresponding industrial defect detection result. And the detection accuracy and efficiency are improved.

The invention discloses an incomplete multi-mode crisis event detection method based on a memory pool and a mode perception expert system, and relates to the field of machine learning, and the method specifically comprises the steps: building a prompt strongly related to an event instance based on the memory pool, and complementing a missing mode through a large model; a CNN encoder, a ResNet encoder and a CLIP encoder are utilized to construct a double-flow encoder, and text and visual feature extraction is achieved; capturing fine-grained event information and clues; the distribution difference between a complemented sample and a complete sample is identified based on a modal perception expert system, and the quality of fusion representation is improved; and inputting the mixed features into the classification head to complete a crisis event detection task. According to the invention, through the prompt construction module based on the memory pool, a large model is introduced to realize careful completion of a missing mode, and the distribution difference between a completion sample and a complete sample is accurately identified based on a mode perception expert system, so that a multi-mode data missing scene can be effectively coped with, and the accuracy and timeliness of crisis event detection are improved.

The invention relates to the technical field of artificial intelligence auxiliary medical treatment, and provides an artificial intelligence-based marine first aid auxiliary diagnosis method and system. The method comprises the following steps: receiving a real-time multimedia data stream from an offshore first-aid site; carrying out conjoint analysis on the image and audio information, positioning key elements of the involved injured individuals and the surrounding environment, and obtaining an emergency scene description after enhanced analysis; extracting professional suggestions suitable for the current first-aid situation in combination with correction parameters of influences of specific variables of the marine environment on injuries and patients; activating a remote expert system, interacting with field first-aid personnel through two-way video communication, and generating personalized guidance information; and combining the personalized guidance information with the calibrated visual field of the first-aid personnel, and displaying decision support information provided by artificial intelligence in the visual field of the first-aid personnel. According to the technical scheme, the first-aid response speed and accuracy are improved, and it is ensured that the wounded patient obtains the most appropriate medical treatment in the first time.

The invention discloses an automatic report generation method based on cross-modal agent collaboration, and relates to the technical field of automatic document generation, and the method comprises the steps that a central scheduling engine coordinates an outline generation agent, a data query agent, a chart drawing agent and a data analysis agent; the central scheduling engine drives the outline generation agent to generate a structured framework of a report based on a user demand, and dynamically schedules each agent according to a task dependency relationship; the data query agent realizes data retrieval by fusing retrieval enhancement generation and a thinking chain technology; the chart drawing agent adaptively generates a chart according to the data type; the data analysis agent is combined with an expert system and a knowledge base to generate an analysis conclusion; wherein the outline generation agent, the data query agent, the chart drawing agent and the data analysis agent are coordinated in parallel under the coordination of the central scheduling engine, and exception handling and dynamic adjustment are realized through a cross-agent coordination mechanism.

The invention provides a parameter efficient fine tuning method based on a hybrid expert system, and an image segmentation method and device. The method comprises the following steps: based on an improved sensitivity constraint function and a low-rank matrix constraint function in a self-adaptive low-rank adaptation module; obtaining a target low-rank matrix corresponding to a to-be-trained model parameter of a to-be-trained network layer in the pre-trained segmentation model; obtaining an output result of the to-be-trained network layer according to the target low-rank matrix, a routing decision value of the hybrid expert system and the sample image; according to a segmentation loss value between an image segmentation result corresponding to an output result of the to-be-trained network layer and an image segmentation label of the sample image, and an equilibrium loss value corresponding to each expert module in the hybrid expert system, obtaining a target loss value of the pre-trained segmentation model; and according to the target loss value, performing parameter efficient fine tuning on the to-be-trained model parameter iteration to obtain an image segmentation model. According to the method, the robustness is improved, the inter-task interference is reduced, and the task processing performance is improved.

The invention discloses a method for intelligently designing a tunnel grouting scheme and an expert system. The method comprises the following steps: step 1, establishing a three-dimensional geological tunnel model; 2, grouting design parameters and the value range of the grouting design parameters are determined, a grouting design parameter set is generated randomly, and numerical simulation is conducted; 3, generating a simulation result parameter set, and combining the simulation result parameter set with the grouting design parameter set to form a data sample; various simulation result parameters in the data sample are set as input layer data, and various grouting design parameters are set as output layer data; 4, repeating the step 2 and the step 3, generating a data sample set and training a neural network prediction model; 5, establishing a plurality of independent neural network models according to different geology and excavation face characteristics, and integrating the models to an expert system; and step 6, inputting field parameters through an expert system, and intelligently generating a grouting scheme. The problems that an existing traditional method is low in efficiency, depends on manpower and lacks accuracy are solved.

The invention discloses a network traffic anomaly detection method and system based on a multi-modal coupling Mamba model and a mixed expert. The method comprises the following steps: collecting network traffic; cleaning the network flow data, and standardizing the data format; shunting the cleaned network traffic data based on quintuple (a source IP, a destination IP, a source port, a destination port and a protocol type) information; extracting data packet length, transmission direction and load byte information, constructing a data packet length sequence and a load byte sequence based on a time sequence view angle and an interaction view angle, and constructing a layered data packet interaction graph; the multi-mode flow representation is input into a coupling Mamba model, deep coupling and dynamic interaction are carried out among different modes, and high-level feature representation with higher identification capacity is generated; dynamically selecting and activating a plurality of expert sub-networks through a gating network by utilizing a hybrid expert system; and a final flow detection result is generated in combination with a result output by the MoE classifier and the original features, and confidence feedback is provided.

The invention belongs to the field of safety technology monitoring, and particularly relates to an electrical cabinet safety monitoring method and system. The temperature, the voltage and the current of the electrical cabinet are monitored in real time through the sensors, and data are transmitted to the information management system in the computer through the base station to be processed. An information processing system is arranged in the information management system, the information processing system has a preliminary fault judgment capability through an expert system, a model of the information processing system is trained through data transmitted by a sensor, and data belonging to faults complements the information processing system to serve as historical monitoring data. The real-time monitoring data is compared with historical monitoring data in a database so as to judge whether the electrical cabinet breaks down or not, and if the electrical cabinet breaks down, a buzzer of the calculator gives an alarm. In the practical application, the electrical cabinet is monitored in real time through the sensor, a worker is reminded of the failure of the electrical cabinet through the buzzer, the worker does not need to go to the electrical cabinet site to inspect, and manpower is greatly saved.

A model training method, a vehicle control method and a related device can be applied to the field of artificial intelligence, and the method comprises the following steps: obtaining road condition information of a target vehicle; according to the road condition information, target information is obtained through a first neural network model, and the target information is driving intention prediction of the target vehicle, prediction of a driving path or prediction of an interaction behavior of the target vehicle and the environment; and according to the target information, updating the first neural network model through an expert system or a result obtained by processing the road condition information and the label corresponding to the road condition information by the expert system. According to the method, the gradient for updating the AI model is guided and determined by utilizing the output of the expert system, which is equivalent to closed-loop training of the AI model, so that the precision of the AI model is improved.

Methods and an expert system for processing a plurality of inputs collected from sensors in an industrial environment are disclosed. A modular neural network, where the expert system uses one type of neural network for recognizing a pattern relating to at least one of: the sensors, components of the industrial environment and a different neural network for self-organizing a data collection activity in the industrial environment is disclosed. A data communication network configured to communicate at least a portion of the plurality of inputs collected from the sensors to storage device is also disclosed.

The embodiment of the present application provides a method, device and system for obtaining an optical network performance optimization solution and diagnosing faults. The method includes: collecting operation logs, performance indicators and alarm information of various types of equipment in the optical network according to a pre-set data collection frequency, as well as collecting optical network topology data, network-level performance data, equipment configuration information and computer room environment data; cleaning and preprocessing all collected data; inputting all processed data into a pre-trained performance optimization model, a pre-built expert system and / or a pre-trained fault prediction model, generating an optical network performance optimization solution through the performance optimization model, and predicting the potential fault type, possible fault occurrence time and fault location of the optical network through the expert system and / or the fault prediction model. The embodiment of the present application can not only obtain a better optical network performance optimization solution, but also improve the fault location speed and accuracy.

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning is disclosed. The system operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.

The invention discloses a fruit and vegetable disease and insect pest spectrum database dynamic evolution updating and diagnosis optimization method based on model confidence feedback driving. Comprising the following steps: acquiring spectral data; based on the disease diagnosis model, outputting a corresponding model confidence coefficient; executing a calibration module, and outputting a calibrated model confidence coefficient; based on a preset confidence model, judging whether the model confidence is not less than a threshold value of high model confidence; based on the expert system module, judging whether the corresponding model confidence is not less than a threshold value of low model confidence; if so, executing an expert reexamination process; if not, inputting an abnormal sample library; and obtaining the number of newly added expert samples, a low confidence ratio and an interval to the last training time, executing an incremental training module, and outputting an instruction for updating and optimizing the disease and pest spectrum database and the abnormal sample library. And a data updating and model optimization mechanism for cooperative work of experts and models is established, so that misjudgment accumulation caused by excessive confidence of the models is effectively prevented, and the reliability of entering a database is ensured.