2904 results about "Intelligent equipment" patented technology

The invention provides an intelligent equipment fault diagnosis and reasoning method and system based on unsupervised learning. Belongs to the technical field of industrial equipment fault diagnosis and prediction. 2) unsupervised abnormal feature extraction and detection; (3) dynamic causal reasoning based on unsupervised learning; (4) a dynamic self-optimization inference engine; (5) intelligent fault reasoning and exploration; (6) enhancing the dynamic knowledge graph; and (7) fault prediction and decision support. Aiming at the problems that a large amount of annotated data is needed, causal analysis is lacked, and multi-source data fusion is difficult, non-annotated efficient detection and classification, fault mode diagnosis, prediction and decision support of equipment faults are realized through feature extraction of an unsupervised depth model, construction of a dynamic causal relationship, and fusion of a real-time self-optimization inference engine and a dynamic knowledge graph; therefore, the downtime and the maintenance cost are greatly reduced.

The invention discloses an equipment intelligent safeguard system based on an offline large model, and relates to the field of equipment management and safeguard, and the system comprises a collection module which collects an equipment surface defect image and a time sequence signal through an unmanned plane, collects a text log, processes the defect image, the time sequence signal and the text log, and generates a multi-modal data set; the feature fusion module is used for performing feature extraction on the multi-modal data set and fusing the three features to generate a multi-modal feature; the updating module is used for extracting a fault triple from the semantic vector, constructing a Bayesian causal graph according to the fault triple, dynamically updating node confidence and generating a knowledge graph; the maintenance strategy module is used for matching the multi-modal features with a knowledge graph and positioning a fault root cause; and the lightweight model deployment module is used for pre-training a lightweight model in an edge server, accelerating parameter aggregation by using a quantum annealing algorithm, adjusting and optimizing a global large model according to the aggregated parameters, and generating a strategy in combination with reinforcement learning.

The invention relates to an intelligent equipment access system and access method based on an AI large model, and relates to the field of artificial intelligence and Internet of Things. According to the technical scheme, a dynamically adaptive universal object model access framework is constructed, functional attributes, control instructions and data formats of intelligent devices of different brands and protocols are mapped into a standardized logic model, meanwhile, a multi-mode AI large model engine is combined, high-precision voice recognition, voiceprint feature detection and multi-level semantic intention analysis capabilities are integrated, and the intelligent devices of different brands and protocols can be obtained. And an intelligent interaction closed loop of end-cloud collaboration is formed. Semantic analysis adopts a mixed strategy, a natural language instruction is converted through function mapping, a local knowledge base is utilized to match user scene requirements, and external data is queried by means of cloud networking to optimize instruction logic. According to the scheme, the multi-device access and operation and maintenance cost is remarkably reduced, the semantic understanding accuracy and scene adaptability are improved, and the method is widely applied to the fields of smart home, smart medical treatment and industrial Internet of Things.

The invention belongs to the technical field of intelligent equipment fault diagnosis, and particularly relates to a motor fault real-time diagnosis method and system based on LSTM and random forest. The method comprises the steps that a vibration signal, a current signal and a temperature signal of a motor are collected and preprocessed; performing feature engineering, extracting time domain, frequency domain, time-frequency domain and cross-modal correlation features, and determining an optimal static feature subset through a hybrid screening strategy; constructing a hybrid fault prediction model comprising a random forest model and an LSTM sequential network model; fusing prediction results of the two models by adopting a dynamic credibility weighted fusion mechanism; and performing real-time decision and hierarchical feedback control based on a fusion result. According to the method, advantages of multi-source information and the model are fused, the classification precision of the lag type is remarkably improved, real-time fault diagnosis and active protection are realized, the equipment maintenance cost is reduced, and the method is suitable for motor health management of intelligent agricultural equipment such as mowers.

The invention relates to the technical field of intelligent lighting, and discloses a light emitting diode intelligent dimming system based on multispectral control, which comprises a multi-sensor data acquisition module, a data fusion processing unit, a user behavior prediction module, a dynamic spectrum adjustment module and the like. Environmental data are collected in real time through multiple sensors, user behaviors are predicted in combination with a machine learning algorithm, and personalized adjustment and dynamic response of illumination are achieved. The system can accurately adjust the color temperature, brightness and spectral distribution of the light source according to the changes of ambient light, temperature and humidity, human body activities and the like, and provides a more efficient and energy-saving illumination scheme. Meanwhile, through cooperative work of the Internet of Things technology and intelligent equipment, the comfort level of the whole environment is optimized. Particularly, the intelligent feedback and learning module of the system continuously optimizes the control strategy according to the use feedback of the user, the user experience and the adaptability of the system are further improved, and a comfortable and intelligent lighting environment is provided for the user.

The invention relates to the technical field of calibration test, and particularly discloses a strapdown inertial navigation automatic calibration test method of a temperature control biaxial rate position turntable. Comprising the following steps: S1, multi-dimensional error modeling and layered calibration architecture: constructing a comprehensive error model containing a sensor scale factor error, an installation non-orthogonal error, a zero offset error, a cross axis coupling error and a temperature related error; s2, a wide temperature range self-adaptive compensation mechanism: carrying out heat preservation at a preset temperature point and executing turntable positioning and rate movement; s3, performing automatic data processing and parameter estimation; and S4, performing real-time error correction and dynamic verification: performing real-time error correction based on the estimated parameters, and evaluating navigation performance indexes such as position, speed and attitude precision. Through technical innovation, high-precision, automatic and wide-temperature-range calibration of the strapdown inertial navigation system is realized, and the method is suitable for aerospace, intelligent equipment and other scenes with strict navigation precision requirements, and has remarkable engineering application value and market competitiveness.

The invention relates to the technical field of intelligent equipment evaluation and fault prediction, and discloses a dynamic equipment quality evaluation method based on multi-agent collaborative reinforcement learning, and the method comprises the steps: obtaining original data from equipment and a sensor, and carrying out the preprocessing; analyzing the preprocessed data, and identifying key factors influencing the quality of the equipment; constructing a plurality of expert models, and predicting and evaluating an analysis result; fusing the weight of each expert model to obtain a comprehensive evaluation result; and performing risk assessment on the comprehensive assessment result, extracting key influence factors and decision rules, and generating an assessment result. Efficient fusion of multi-source data and automatic identification of key factors are realized, and the accuracy and reliability of equipment quality evaluation are remarkably improved; a model fusion result is optimized through dynamic weight distribution, so that the prediction performance is improved; the problems that in the prior art, data fusion is difficult, key factor identification depends on experience, and the interpretability of an evaluation result is insufficient are solved.

The invention relates to the technical field of operation and maintenance control, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an intelligent equipment configuration changing method and device, equipment and a medium. Generating a preliminary equipment configuration instruction sequence based on the change intention information; rechecking the configuration instruction through the intelligent checking component, and generating a rechecked configuration instruction sequence; sending the rechecked configuration instruction to the target equipment and executing the rechecked configuration instruction; and verifying whether the equipment state meets the expectation, and if not, executing a rollback operation. Configuration accuracy is ensured through knowledge representation, configuration errors are automatically detected and corrected through intelligent verification, configuration efficiency and safety are improved through automatic issuing and state verification, abnormity is quickly recovered through automatic rollback, and reliability and stability of configuration change are improved.

The invention discloses an intelligent equipment management method and system based on digital twinning. The method comprises the following steps: constructing an initial digital twinning model based on a multi-source data fusion result; monitoring the operation state of the initial model in real time and collecting feedback data; based on difference analysis of feedback data and a fusion result, evaluating a model deviation degree; and iteratively correcting the parameters of the digital twinborn model according to the deviation degree to optimize the precision. The method can solve the problem of how to carry out iterative correction on the model precision according to the multi-source data fusion result so as to solve the problem of overlarge deviation of the digital twinborn model.

The invention relates to the technical field of data processing, in particular to a data fusion intelligent equipment linkage management and control system based on a large model, and the system comprises a data collection module which outputs standardized data carrying privacy grading identifiers; the data processing module outputs an optimization algorithm identifier and a collaborative strategy identifier which are synchronously bound; the privacy algorithm fusion scheduling module generates atomization calculation graph data and strategy audit logs fusing privacy operation; the multiple optimization resource module executes calculation graph data and drives a parallel simulation environment rehearsal resource scheme, and the double-closed-loop feedback module is activated when an arbiter continuously detects that deviation exceeds the standard for three times; the safety instruction generation module fuses the output result and the audit log to generate a digital signature equipment control instruction; and the double-closed-loop feedback module updates knowledge base weight parameters through a data feedback loop, and controls the feedback loop to adjust a resource allocation threshold in a grading manner according to simulation confidence, so that dynamic balance between resource efficiency and privacy intensity is realized.

The invention discloses an AI-based cultural tourism content personalized recommendation system, which relates to the technical field of tourism planning and comprises a user information acquisition module, a cultural tourism content database information storage module, a recommendation engine module, an emotion analysis module, a real-time recommendation updating module and a multi-channel display module. According to the method, basic information, tourism preference, historical behaviors, geographic positions and social media data of users are collected, personalized portraits are generated and accurately recommended, sentiment analysis, real-time positions and dynamic behaviors are combined, the recommended content is continuously optimized by the system to adapt to the changing requirements of the users, and the user experience is improved. Through multi-channel display, personalized labels and clustering analysis, the system provides customized tourism suggestions, supports collaborative filtering and content recommendation strategies, real-time feedback and a self-optimization mechanism to improve the precision, adapts to intelligent equipment, recommends tourism contents according to environmental changes, and improves the user experience.

The invention relates to the technical field of body-equipped intelligent robots and intelligent equipment, in particular to a self-adaptive flexible tightening body-equipped intelligent robot which comprises a body-equipped moving platform, the top of the body-equipped moving platform is provided with a body-equipped lifting mechanism, and the top of the body-equipped moving platform is provided with a body-equipped articulated arm module. And a tightening actuator and a clamping jaw actuator are arranged at the tail end of the outer part of the body articulated arm module. According to the self-adaptive flexible tightening intelligent robot and the tightening process method, sensing and self-adaptive adjustment of external environment changes are achieved through multi-module integration, and the tightening process method based on a multi-mode sensing system and an intelligent control system is constructed; vision, force sense and touch sense data are fused, real-time monitoring and dynamic adjustment of the tightening process are achieved through a machine learning algorithm, and the intelligent level and quality stability of tightening operation are effectively improved.

The invention relates to the technical field of intelligent equipment monitoring, and discloses a sandstone separator monitoring control method and system based on multi-source data processing. According to the method, equipment operation parameters, real-time image data and vibration sensing data of the sandstone separator are collected in real time, data preprocessing and feature extraction are carried out, a monitoring model based on a multi-modal deep learning framework is constructed, and accurate joint characterization of the equipment state is achieved. The system can dynamically generate a control instruction according to a monitoring result, adjust equipment operation parameters in time, and avoid production interruption and quality problems. Besides, the multi-modal data fusion technology and the self-adaptive PID control algorithm are adopted, so that the operation efficiency and stability of the sandstone separator are further improved, and powerful technical support is provided for the field of intelligent manufacturing.

The invention relates to the technical field of intelligent equipment autonomous control, and discloses an AIGA-driven intelligent equipment autonomous control and feedback adjustment method, which comprises the following steps of S1, collecting equipment body state parameters and external environment data in real time through a multi-mode sensor array; s2, combining the collected data with a preset user instruction and a real-time environment semantic analysis result; s3, disassembling the task target into a continuous action instruction set; s4, executing the action instruction set through the layered real-time control architecture; s5, monitoring an action execution effect based on the multi-modal sensor array; and S6, analyzing the relevance between the evaluation result and the action instruction through a causal inference engine. According to the method, an equipment action execution result is fed back to an intention generation layer in real time, a deviation source is positioned through a causal inference engine, a control strategy is dynamically adjusted, closed-loop iteration from one-way instruction execution to perception-decision-execution-verification is achieved, and the effect of self-adaptability in a dynamic scene is achieved.

The invention relates to the technical field of security management, in particular to an AI-based intelligent security remote inspection and control method, which comprises the steps of front-end intelligent equipment deployment, intelligent inspection execution, data processing and intelligent analysis and remote control and linkage response. Compared with the defects of incomplete information and high false alarm rate due to the fact that equipment appearance anomaly detection mainly depends on single-modal data in the prior art, the scheme adopts multi-modal data preprocessing and feature extraction, constructs a cross-modal alignment network architecture, dynamically fuses laser radar, RGB-D images and IMU data by using a self-attention mechanism, and improves the detection accuracy of the equipment appearance anomaly. According to the method, more accurate equipment appearance abnormity identification is realized by combining environment self-adaptive adjustment, and the model is compressed and deployed to an edge end through a knowledge distillation technology, so that real-time reasoning and analysis are realized, the intelligent level of security inspection and the accuracy of abnormity detection are remarkably improved, the false alarm rate is reduced, and the practicability and reliability of the system are enhanced.

The invention discloses a monitoring and early warning method and system based on active load prediction of a power distribution network, which breaks through the limitation of purely depending on historical data and simple statistical analysis, fully considers the complexity and uncertainty of load change influenced by various factors, and achieves the early warning of the load change through the combination of multiple models. The method can accurately classify and analyze the operation active load of the power distribution network through an active power analysis model, a decision tree model and the like, thereby improving the prediction precision. In the aspect of data processing, mass operation data generated by intelligent equipment in the power distribution network can be effectively integrated and mined by utilizing a vector machine model and a cloud large model server, an early warning mechanism is flexible, the method can adapt to different operation scenes and load characteristics, false alarm and missing alarm are avoided, and the method is suitable for popularization and application. According to the method, the power distribution network fault identification efficiency of the power distribution network power factors in different time periods is remarkably improved, and effective operation of the power distribution network is ensured.

The invention relates to the technical field of automatic control, in particular to an intelligent equipment management method and system, and the method comprises the following steps: obtaining a control cabinet log, extracting an equipment temperature load pressure sequence, comparing a set offset to generate a deviation label, collecting an operation time length and load change mapping early warning table, and generating an abnormal record; and extracting task configuration and equipment state re-marking alternating points to generate load configuration, resetting an instruction sequence to generate a scheduling sequence, extracting an instruction time-consuming marking interference section, and switching a standby link to generate an evasion list. According to the invention, by collecting the temperature, the load rate and the pressure sequence in the operation cycle of the equipment, the dynamic state identification capability is improved, the scheduling path is optimized, the resource contention is relieved, the delay path is marked in combination with the original response data, and the standby link is switched to avoid interference, so that the flexibility and stability of linkage scheduling are improved, and the task execution beat accuracy is enhanced; the instruction blocking and interruption risks are effectively reduced, and the operation toughness and response efficiency of a production line are improved.

The invention relates to the technical field of equipment intelligent diagnosis, in particular to a generator digital twinning diagnosis method based on temperature field online simulation, which comprises the following steps: respectively constructing a three-dimensional virtual model, a temperature field simulation model and an intelligent agent diagnosis model; the digital twin is in data connection with the physical entity; data alignment is achieved through a unified space-time coordinate system, a mapping relation is established between a vertex of the three-dimensional virtual model and a grid node in the temperature field simulation model, and a fault probability matrix output by the intelligent agent diagnosis model is matched to a three-dimensional grid of the three-dimensional virtual model through spatial interpolation; according to a fault probability output by the intelligent agent diagnosis model, correcting a boundary condition of the temperature field simulation model, triggering local grid adaptive encryption of the temperature field simulation model, and marking a fault position in real time through a three-dimensional virtual model; and the simulation result of the temperature field simulation model verifies the fault probability output by the intelligent agent diagnosis model. Through the method, the fault identification accuracy can be effectively improved.

The invention relates to the technical field of equipment fault diagnosis, in particular to an intelligent equipment fault diagnosis system based on a knowledge graph and deep learning, which comprises a data acquisition module, a knowledge graph construction module, a deep learning reasoning module, a diagnosis output module and a feedback optimization module. According to the system, real-time reflection of the equipment operation state is realized through multi-source heterogeneous data acquisition and knowledge graph dynamic modeling, and the fault recognition capability is improved in combination with a double-branch deep learning network and an attention mechanism. A diagnosis result is displayed in a graphical interface, and closed-loop optimization is supported. The device state change can be comprehensively captured, the complex working condition adaptability is enhanced, and the diagnosis accuracy and the intelligent level are improved.

The invention discloses a digital twinning system and method for displaying a ship spraying robot flow field through element universe, and belongs to the technical field of intelligent equipment. The digital twinning system comprises a physical entity layer, a data and tool layer, a digital twinning layer and a virtual application layer. The working method comprises the steps that actual parameters of the ship spraying robot are collected, and entity mapping is carried out; building of a virtual physical model of the ship spraying robot is completed; establishing a digital model of the spraying flow field of the ship spraying robot; building a virtual flow field simulation model based on a digital model; checking of ship spraying simulation results is completed through the simulation model, and simulation parameters are optimized; and completing entity improvement and reconstruction of the ship spraying robot. The visual display method for the spraying flow field of the ship spraying robot is constructed for the spraying operation scene in the dock by adopting a virtual simulation method based on the universe and the digital twinning technology. And the working efficiency is improved while the labor cost is reduced.

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

The invention discloses a complex water area garbage intelligent detection and collection method based on multi-modal perception, and the method comprises the steps: obtaining image perception data and semantic environment data of a complex water area; the method comprises the following steps: performing preprocessing, performing feature extraction and semantic coding, and then performing multi-modal feature alignment fusion, feature extraction and multi-task detection to obtain a garbage detection result, thereby effectively realizing deep fusion of semantic priori and visual features, and improving the accuracy and robustness of garbage recognition in a complex water area environment; then task importance evaluation is carried out to construct a garbage disposal priority queue, a path dynamic planning strategy oriented to energy consumption constraint and task priority collaborative optimization is constructed to carry out path generation, navigation energy consumption is reduced on the premise that task benefits are considered, and garbage detection and collection tasks in a complex water area are achieved; the system adapts to various intelligent devices such as unmanned ships, unmanned aerial vehicles and shore-based platforms, and has high autonomy, environmental adaptability and engineering deployability.

The invention discloses a digital twinning-based heterogeneous body-equipped intelligent equipment collaborative management system and method, and belongs to the technical field of digital twinning and body-equipped intelligent systems. The system comprises a digital twin platform, a communication module, a task scheduling module, a cooperative control module and an edge computing node. The digital twinborn platform constructs a high-fidelity three-dimensional model of a physical environment and digital twinborn bodies of the intelligent devices with the bodies; the communication module is used for realizing bidirectional data interaction between the platform and the intelligent equipment; the task scheduling module performs task allocation and path planning by adopting a reinforcement learning algorithm based on the global dynamic situation map; the cooperative control module models an equipment space-time relationship through a graph neural network, predicts conflicts in real time and generates a dynamic avoidance strategy; and the edge computing node carries out real-time preprocessing on the equipment sensing data. According to the method, global optimization scheduling and multi-device intelligent cooperation are realized, and the overall efficiency, robustness and adaptability of the system are remarkably improved.

The invention provides a target perception and avoidance decision-making method in visual navigation of intelligent equipment, and belongs to the technical field of intelligent decision-making. The method comprises the following steps: constructing a space-time diagram neural network, introducing semantic tag codes to define nodes and edges of the space-time diagram neural network, and predicting the trajectory of each obstacle through a regression network; an environment semantic label is defined, the environment semantic label and the predicted obstacle trajectory are coupled, and a semantic enhanced risk field is generated; and constructing a semantic road network map based on the risk field of semantic enhancement, generating a global path point sequence, and calling a dynamic step RRT * algorithm to avoid obstacles by taking the current global path point as a temporary target. According to the method, the risks of different areas can be evaluated more accurately, the risk value is adjusted according to the type and the position of the obstacle, the safety and the reliability in a complex environment are improved, and decision errors caused by environment changes are reduced.

The invention relates to the technical field of remote control, in particular to an intelligent device remote cooperative control method and system based on the Internet of Things, and the method comprises the following steps: monitoring and adjusting the CPU utilization rate and memory usage of an intelligent device based on an Internet of Things platform, optimizing the data collection and network resource distribution, and improving the intelligent device remote cooperative control efficiency. By analyzing equipment performance and a network load balance index, equipment connection priority ranking and task load management are realized, and task execution and connection frequency is dynamically adjusted, so that real-time task requirements are met, and system efficiency is improved. According to the method, the performance index of the equipment is monitored in real time, the data transmission interval is dynamically adjusted, the data processing speed is remarkably increased, resource waste is reduced, efficient use of network resources is ensured through real-time load analysis and optimization of network nodes, the problem of data delay caused by network congestion is avoided, and in the aspect of equipment connection management, the data transmission efficiency is improved. An intelligent priority decision mechanism allows the key device to quickly access the network in an emergency.

The invention provides a bulk cargo wharf intelligent equipment scheduling system and method, equipment and a storage medium. The scheduling system comprises a data acquisition module; an information analysis and strategy generation module; a joint scheduling calculation module; a scheduling execution module; an execution feedback module; a model updating module; and a rule induction module. According to the method, a closed-loop liberalization system fusing semantic understanding, strategy generation and scheduling execution is constructed, so that the problem that unstructured information cannot be effectively utilized by a scheduling system is solved; scheduling parameters can be accurately extracted from unstructured data, even scheduling characteristic parameters are directly extracted from the unstructured data and structured scheduling strategy suggestions are generated, the data threshold of system intelligent transformation is reduced, a scheduling model is driven together with the structured parameters, a feedback learning mechanism is introduced, and the scheduling efficiency is improved. And efficient, intelligent and self-learning management of equipment scheduling is realized, so that the system can adapt to complex and variable port operation environments, and has good expansibility and generalization ability.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention provides a real-time intervention method and system based on blood glucose prediction of a diabetic patient, and the method comprises the steps: obtaining blood glucose monitoring data and multi-dimensional life monitoring data based on blood glucose monitoring equipment and intelligent equipment, and carrying out the timestamp alignment of the multi-dimensional life monitoring data and the blood glucose monitoring data; inputting the aligned multi-dimensional life monitoring data and blood glucose monitoring data into a pre-trained blood glucose prediction model, outputting a first blood glucose level prediction value and a second blood glucose level prediction value, and generating a real-time intervention suggestion according to the first blood glucose level prediction value and the second blood glucose level prediction value; if the first blood glucose level predicted value or the second blood glucose level predicted value is lower than a threshold value, graded early warning is carried out, corresponding intelligent equipment is triggered to execute corresponding response measures, and the early warning mechanism can achieve early warning through a real-time, normalized and highly-personalized intervention strategy in combination with intelligent operation of the intelligent equipment. The life quality of a diabetic patient is obviously improved; and the blood sugar control is optimized.

The invention relates to the technical field of intelligent electric control systems, and discloses an intelligent electric control system and a control circuit board with the intelligent electric control system. The system comprises a signal acquisition module used for acquiring an input signal and judging a signal partition; the mode mapping module is used for constructing a mapping relation between the signal partition and the control partition; the parameter combination module is used for extracting key partitions and judging a parameter combination matching degree to obtain a bias combination; the state updating module is used for updating the mapping relation based on the deviation combination; and the control management module generates a control management library. The control circuit board integrates each module hardware circuit unit to realize signal transmission and data interaction. Accurate processing, dynamic mapping, optimal parameter combination, state real-time updating and scientific control management of the input signals are achieved, the intelligence, adaptability and reliability of the electronic control system are improved, and the system is suitable for electronic control scenes of various intelligent devices.

The invention belongs to the field of agricultural automation and intelligent equipment, and discloses a portable measuring method and device for pork intramuscular fat content. By screening the characteristic wave bands related to the fat content and selecting the corresponding narrow-band LEDs, the accuracy and effectiveness of the multispectral image are improved. And meanwhile, through multi-modal image fusion analysis, in combination with the RGB image, the black-and-white image and the multi-spectral image, the precision of target segmentation and content prediction is improved. And establishing an end-to-end content prediction model by adopting a mode of combining a CNN and an MLP network. Meanwhile, the FCNN network is used for processing the spectral data, the tedious spectral data preprocessing process is omitted, the advantages of the spectral data and the RGB image are fully played, and a new thought and method are provided for the field of spectral modeling. The technical scheme has the advantages of portability, high efficiency, accuracy and the like, provides powerful support for pork nutrition evaluation, and can provide reference for determination of the fat content of other livestock and poultry meat.