1272 results about "Adaptive algorithm" patented technology

A multi-modal articulation system translates human gestures into control commands for digital and automation devices. The system integrates a camera module, which captures facial and hand movements, with an image processing unit that extracts facial features, such as eyebrow movement, lip curvature, eyelid motion, and eye trajectory. These extracted features are analyzed in real time by an articulation recognition module that compares them against a database to recognize specific gestures. The system tracks and processes air-drawn hand gestures using a gesture trajectory tracking unit, which converts the motions into digital representations and classifies them using deep learning algorithms. The recognized gestures are converted into machine-readable control signals and executed on various connected devices via a relay control unit. The system incorporates adaptive algorithms to improve accuracy, account for environmental conditions, and stabilize hand tremors, providing a gesture-based control interface for applications ranging from smart home systems to multimedia devices.

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention relates to the technical field of crustal stress field data processing, in particular to a crustal stress field three-dimensional dynamic inversion method based on a multi-scale adaptive algorithm. The method comprises the following steps: acquiring a geological data set of a target area; constructing a crustal stress field three-dimensional initial model based on the geological data set, and performing geologic body space division and mesh generation to obtain crustal stress field three-dimensional mesh model data; performing multi-scale region division on the crustal stress field three-dimensional grid model data, and establishing a multi-scale weighting function to obtain multi-scale partition mapping information; and constructing a cross-scale boundary adaptive transmission mechanism, and establishing a stress tensor continuity constraint model at a multi-scale partition boundary to obtain cross-scale stress boundary coupling data. Through a multi-scale adaptive algorithm and dynamic closed-loop optimization, high-precision, dynamic and continuous inversion of a crustal stress field in a complex geologic structure is realized.

The invention provides a self-adaptive impedance matching method and system of a microwave frequency band. The method comprises the following steps: performing initial impedance measurement on a plurality of microwave component ports to obtain a complex impedance parameter as a matching reference; in the operation process, a current impedance state and a reflection coefficient are collected through a reflection monitoring mechanism, and a dynamic impedance state vector is constructed; inputting the state information into an adaptive algorithm module, generating a matching parameter vector and configuring an adjustable device to realize real-time adjustment of a matching network; calculating a matching error according to the current reflection state and a target reference value, and driving parameters to be iteratively updated; after the matching precision requirement is met, the final parameters are loaded to the controller to complete actual configuration; meanwhile, the system periodically collects environmental parameters and matching effects, and dynamically judges whether rematching is needed or not so as to maintain long-term stable operation of the system. The method has the capabilities of automatic sensing, closed-loop optimization and environmental change adaptation, and can improve the matching efficiency and signal transmission quality of a microwave system under multi-band and high-dynamic working conditions.

The invention relates to a time sequence knowledge graph multi-hop reasoning method and system oriented to the legal field. The method comprises the following steps: establishing a dynamic mapping relationship through a semantic association technology; extracting life cycle states of the legal provisions, and introducing a legal conflict detection algorithm; setting a revocation influence factor; determining time embedding representation, and introducing clause conflict matrix elements to construct a time sequence knowledge graph; constructing a space-time coupled completion model based on the graph attention network and the long short-term memory network to perform path completion; determining three selected agents, performing reinforcement learning, designing a reward function for each agent, constructing a collaborative arbitration mechanism in combination with a dynamic priority strategy and a weight adaptive algorithm, and performing multi-hop reasoning. By collecting multi-source legal data, the authority, the real-time performance and the relevance of the data are ensured; according to the method, the path reasoning capability in a low-coverage scene can be improved, so that a complex legal knowledge multi-hop path reasoning task can be accurately and efficiently completed.

The invention discloses a battery energy storage safety management system based on a BMS, and relates to the technical field of battery energy storage safety management, the system comprises a data acquisition module, a data analysis processing module, an equalization strategy generation module, an equalization execution module and a safety monitoring early warning module; operation parameters of the battery pack and the single batteries are comprehensively acquired through the data acquisition module, the actual operation state of the battery pack is accurately sensed, health factors are calculated by comprehensively considering multiple factors, weight coefficients are dynamically adjusted, the characteristics of the single batteries are comprehensively considered, and the charging and discharging states of the battery pack are calculated according to the health factors, the charging and discharging stages and the environment temperature. The equalization strategy and current are determined through a self-adaptive algorithm, dynamic response to the running state of the battery pack and environment change is achieved, equalization operation is accurately executed through the equalization execution module, accurate equalization control is achieved, and through real-time monitoring of the safety monitoring and early warning module, safe running of the battery in different environments is guaranteed, the performance of the battery is improved, and the service life of the battery is prolonged.

The invention discloses a long-distance communication optical cable fault automatic detection and positioning system and method, and the method comprises the steps: collecting optical signal data in an optical fiber link in real time through the deployment of a multi-optical fiber sensor or a distributed optical fiber sensing technology; denoising processing is carried out on the collected data, abnormal fluctuation is analyzed by applying a self-adaptive algorithm, and the type of a fault which is about to occur is identified; classifying the faults by using a machine learning classification algorithm; time domain reflection and wavelength multiplexing technologies are combined, a composite algorithm is adopted to accurately position a fault point, and path optimization and error correction are carried out through sensing node data comprehensive analysis; an environment compensation algorithm is introduced, influences of environment factors on the optical signals are analyzed in real time, and errors are eliminated; and based on a fault positioning and analysis result, an AI algorithm is adopted to evaluate a fault influence range, a repair scheme is automatically generated, and priorities are scheduled for repair.

The invention provides a self-adaptive nonlinear image enhancement method and system for a low-light scene of a mobile terminal, and relates to the technical field of image enhancement, and the method comprises the steps: carrying out the image preprocessing and noise reduction, and carrying out the graying and noise suppression of an input color image through a local variance self-adaptive algorithm; adaptive down-sampling is carried out, and the down-sampling proportion is dynamically adjusted according to the image resolution and the content complexity, so that the processing efficiency is improved; brightness adaptive enhancement is carried out, and the overall brightness of the image is rapidly improved by adopting an Otsu method and a lookup table; contrast nonlinear enhancement: enhancing image details and contrast in combination with a Laplace operator and local mean adjustment; and color restoration: restoring the resolution through bilinear interpolation and performing weighted fusion to realize natural color reconstruction. And finally, a high-quality image of which the brightness, the contrast ratio and the color are remarkably improved is output. According to the invention, the recognition accuracy and processing efficiency of the low-illumination image are improved.

The invention discloses a temperature control system and method for a hot runner of an injection molding machine mold, and the method comprises the steps that the temperature control system collects the temperature data of each region of the hot runner in real time through a temperature sensor, and carries out the adjustment according to the data feedback; a built-in self-adaptive algorithm automatically adjusts a temperature control strategy according to different injection molding materials and production requirements, such as temperature upper limit, temperature lower limit and heating and cooling opportunities, and the production efficiency is optimized; when the system recognizes temperature fluctuation, a heating mode or a cooling mode is automatically switched, and the cooling system is dynamically adjusted for temperature balance; a plurality of temperature sensors are arranged in a key area of the mold for regional monitoring, and temperature control output is adjusted in real time; meanwhile, an embedded learning algorithm gradually optimizes a temperature control strategy according to historical production data, adjusts control parameters, and improves the production precision and efficiency. According to the temperature control system and the temperature control method for the mold hot runner of the injection molding machine, the problems of low temperature control precision, poor energy efficiency and slow response in the prior art are solved, and efficient and intelligent temperature control capability is provided.

The invention relates to the technical field of monitoring and early warning, and discloses an automatic monitoring and early warning method and system for a pump room. The method comprises the following steps: collecting hydraulic pulsation, cavitation acoustic emission and bearing temperature rise data through multiple sensors; a pump efficiency attenuation vector is obtained based on an environment adaptive algorithm optimization feature; predicting the health state of the equipment by using a hydraulic model; carrying out deep learning classification by adopting a fault propagation network; and four-stage grading early warning is realized by combining water inflow-pump efficiency coupling judgment. According to the method and the device, the technical problem that the gradual change fault and the multi-parameter coupling fault mode of the pump room equipment under the underground complex working condition cannot be accurately identified in the prior art is solved, and the accuracy of fault prediction and the timeliness of early warning of the underground drainage pump room are improved.

The invention relates to the technical field of computer science, in particular to a stamping prediction method based on a multi-machine learning model and an automatic compensation device.The stamping prediction method comprises the steps that firstly, a sheet microstructure and material characteristics are obtained through a transmission electron microscope and an atomic probe tomography technology, and then finite element simulation sampling is conducted; a data set is expanded by using an adaptive algorithm, multiple models are constructed and trained to obtain a final springback prediction model, an automatic compensation device is embedded to realize automatic compensation, and the automatic compensation device covers visual parameter input, embedded springback prediction, springback compensation calculation and an automatic control module. Parameters can be visually input, springback can be accurately predicted, a compensation value can be calculated, and the process can be adjusted. The method aims at solving the problems that in the high-strength plate stamping process, due to springback, the size and shape of a part deviate from the design requirement, and a traditional prediction and compensation technology is insufficient in precision and efficiency.

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention belongs to the technical field of liquid level measurement and industrial process control, and particularly relates to a radar liquid level meter system fusing an adaptive algorithm and multi-modal data. Comprising a data acquisition unit, a signal acquisition and preprocessing unit, a signal denoising and optimizing unit, a data fusion and anomaly detection unit, a self-adaptive calibration and drift compensation unit and a display and alarm terminal. The system has the advantages that the radar liquid level meter, the auxiliary sensor, signal processing, data fusion, anomaly detection, self-adaptive calibration, drift compensation and other technical means are organically combined, and the system can achieve high-precision and real-time monitoring of the liquid level under the complex working condition.

The invention provides an electric power and magnetic power dynamic joint detection method and system based on multivariable fusion, and relates to the technical field of power electronic equipment detection. According to the method, voltage, current and magnetic field sensors are arranged in a to-be-tested electromagnetic system, original physical quantity data are collected, and a multivariable time sequence data set is constructed; through preprocessing and feature extraction, a dynamic feature matrix related to electric power and magnetic power is generated; constructing a joint estimation model based on the matrix, and comparing the joint estimation model with a reference value to obtain a detection residual error; generating a state label according to the residual size and trend, and evaluating the estimation credibility in combination with a confidence interval; and a feedback mechanism is further constructed, model parameters and feature structures are optimized by adopting an adaptive algorithm, and online iteration and self-learning are realized. The method is high in detection precision, high in state recognition capability, good in adaptability and robustness, and suitable for energy flow monitoring and intelligent diagnosis of a complex electromagnetic system.

The invention relates to the technical field of oil filter control, in particular to an oil filter digital intelligent control system based on sensors, which comprises a fusion monitoring unit, an oil temperature control unit and a feedback regulation unit. The oil temperature control unit calculates the optimal heating rate and the target temperature range through a self-adaptive algorithm according to the environment and the initial temperature of oil, compares the optimal heating rate and the target temperature range with the actual oil temperature in the heating process, adjusts the heating power through pulse heating according to the result, judges the working load according to the specific heat capacity of oil and adjusts the frequency of a vacuum pump. And the feedback regulation unit evaluates the system stability based on the target operation frequency and the heating power of the vacuum pump, generates an optimization strategy and feeds back the optimization strategy to the oil temperature control unit, so that accurate oil temperature control is realized, the equipment stability and the energy utilization efficiency are improved, and efficient operation of the oil filter is guaranteed.

The invention provides a monitoring and early warning method and system for a natural protection area of wild animals. The method comprises the following steps: firstly, collecting a multi-modal ecological monitoring data stream containing animal activity track data, a habitat environment parameter sequence and a biological voiceprint signal set; then, dynamically matching the animal activity trajectory data with a reference threat mode to generate a first ecological association network feature; and extracting migration path fragments, combining with an environment parameter change gradient, and deducing second ecological association network features through the model. And then, performing cross-modal fusion on the two features to generate a multi-level threat coupling vector, constructing a map based on the biological voiceprint, performing overlay analysis on the map and the coupling vector, and calibrating and outputting a real-time threat level pedigree through an adaptive algorithm. And finally, according to the mapping relationship between the real-time threat level pedigree and a preset strategy library, activating a corresponding ecological protection intervention instruction set, realizing multi-modal data comprehensive analysis, accurate real-time monitoring and early warning and intelligent response, and effectively protecting the ecology of the natural reserve of wild animals.

The invention discloses a calibration system in an active channel of a waveguide array phased-array antenna, and relates to the technical field of calibration in the active channel. Comprising a calibration judgment module, a waveguide directional coupling module, an amplitude-phase detection module and a digital domain optimization module, the calibration judgment module realizes intelligent triggering calibration through real-time monitoring of a multi-dimensional environment and performance parameters, and ensures that a calibration process is started only when the system state is deteriorated; the waveguide directional coupling module ensures that the calibration signal is accurately injected into each channel, and realizes cross polarization suppression through a polarization grid filter; the amplitude-phase detection module realizes high-precision measurement of amplitude, phase, group delay and polarization parameters of feedback signals of each channel by using superheterodyne and IQ demodulation technologies; and the digital domain optimization module calculates and updates the complex compensation weight of each channel through real-time digital signal processing and a self-adaptive algorithm, and realizes comprehensive and accurate calibration in combination with temperature-frequency interpolation and nonlinear pre-distortion compensation.

The invention discloses a PCB detection method and device based on multi-modal fusion and an adaptive algorithm, and relates to the field of electronic manufacturing detection. The method comprises the following steps: acquiring optical imaging data by using an optical imaging unit, acquiring tomography data by using an X-ray unit, and acquiring three-dimensional point cloud data by using a laser unit; collecting a reference signal, and updating detection parameters of the optical imaging unit, the X-ray unit and the laser unit; performing feature level fusion to obtain multi-modal fusion data; and inputting the multi-modal fusion data into a detection model, and performing defect comparison in combination with a historical database to obtain a defect detection result of the PCB. According to the method, optical imaging, X-ray penetration and laser three-dimensional modeling technologies are combined, and full-dimensional defect detection of the surface, the interior and the three-dimensional structure of the PCB is covered, so that the defect accuracy is improved. Meanwhile, a traditional fixed parameter detection mode is broken through through a self-adaptive intelligent algorithm, dynamic parameter optimization and multi-scene self-adaption are achieved, and the detection efficiency is effectively improved.

The invention relates to the technical field of medical data analysis, in particular to an intelligent sphygmomanometer data analysis method based on a self-adaptive algorithm. The method comprises the following steps: receiving continuous blood pressure measurement data containing timestamps and synchronously acquired behavior data, deducing the getting-up time of a user, presetting and dynamically adjusting a morning rising blood pressure analysis window according to the getting-up time, and extracting a corresponding blood pressure section; performing anomaly elimination and interpolation completion on the extracted blood pressure data to form a complete and continuous time sequence, and calculating a change rate sequence and recursive quantitative characteristics; in combination with short-term and long-term historical data, respectively generating statistical characteristics, and dynamically updating the baseline reference value through an individual adaptive model; and further comparing the current blood pressure peak value and the rising rate with a reference baseline to identify a morning blood pressure abnormity event. The method can improve the individual suitability of the analysis window and the accuracy of blood pressure abnormity recognition, and is suitable for fine analysis and risk early warning of continuous blood pressure data.

Systems and methods implement algorithms for initializing medical devices, including by programming initial medical device settings. Further, systems and methods implement adaptive algorithms for changing medical device settings. Settings initialization algorithms can calculate the settings most likely to be applicable for the patient at-issue using databases of positive clinical outcomes and respective therapy settings for existing users. Settings adaptation algorithm are configured to update medical device therapy settings at one or more intervals including based on databases of positive clinical outcomes and respective therapy settings for existing users.

The invention discloses a lightweight MQTT protocol-based firmware upgrading method for a reconnected Internet of Things device in an off-network state, and relates to the technical field of Internet of Things. By collecting a device information set and a network information set, the reconnected Internet of Things device in the off-network state is upgraded based on the device information set and the network information set; constructing a dynamic block adjustment strategy for determining the size and downloading priority of the firmware blocks by using a self-adaptive algorithm; based on the equipment information set and the network information set, constructing an intelligent reconnection strategy for adjusting reconnection time by using a time sequence prediction model, and intelligently partitioning transmission firmware by using a dynamic block adjustment strategy so as to adjust the size and downloading sequence of transmission firmware blocks in real time; and an intelligent reconnection strategy is used to recover the connection when the network fluctuates and continue downloading, receiving and verifying the transmission firmware block, a verification information set is generated, and the integrity of the transmission firmware block is verified. And the continuity and the success rate of firmware upgrading are obviously improved.

The invention discloses an integrated multi-network integration satellite portable station, which belongs to the technical field of wireless communication networks, and comprises the following steps: a radio frequency module acquires satellite link signal quality and ad hoc network node distribution parameters, and generates a network original parameter set; the parameter processing module generates a network state feature vector through multi-dimensional feature extraction, and dynamically calculates a fusion weight value of the satellite and the ad hoc network based on a multi-target optimization model; the data receiving unit generates a multi-network modal scheduling instruction and a coding path according to the weight value, and receives production data and an emergency video stream; the data encapsulation unit encapsulates data in a mixed manner of framing compression and quantum key encryption; and the routing distribution module dynamically distributes the transmission proportion according to the weight. According to the invention, dynamic fusion weight calculation, a closed-loop power adaptive algorithm and a multi-protocol coding path selection mechanism are adopted, so that real-time optimal distribution of network resources, dynamic adaptation of transmitting power and efficient and safe transmission of mixed data streams can be realized.

The invention provides a multi-modal feature perception and meta reinforcement learning adaptive algorithm scheduling system and method, and the system comprises a multi-modal feature coding module which is used for carrying out the cross-modal feature alignment and fusion of input image data, physical signals and structured parameters; the deep reinforcement learning decision module is used for constructing a multi-objective optimization strategy by adopting a TD3 algorithm framework; the self-adaptive scheduling execution module is used for realizing strategy distillation and dynamic priority scheduling; and the multi-modal feature coding module comprises a dynamic graph convolutional network and a hierarchical cross attention mechanism. Through technical innovation, comprehensive breakthrough is achieved in the aspects of multi-modal data processing precision and efficiency, reinforcement learning scheduling stability and adaptability, engineering deployment lightweight and robustness and the like, the defects in a dynamic scene in the prior art are effectively overcome, and the intelligent level and the practical application value of an algorithm scheduling system are remarkably improved.

The invention relates to the technical field of classroom behavior analysis, and discloses a classroom behavior analysis system based on an adaptive algorithm, which comprises a detection module, a parameter extraction module, a feature integration module and a decision generation module. The detection module collects classroom multi-modal behavior data and identifies active student terminals; the parameter extraction module obtains a real-time posture vector, a voice spectrum feature, an interaction response time delay and a teaching content semantic tag of a target student; the feature integration module performs cross-modal fusion on the data to generate attitude, voice, interactive fusion features and semantic association features; and the decision generation module performs hierarchical association modeling and dynamic priority ranking through a multi-head attention mechanism, generates a behavior matching degree and determines a target analysis object. The system realizes multi-modal data fusion by means of algorithms such as space-time decomposition and dynamic density clustering, improves the accuracy and real-time performance of classroom behavior analysis, and provides support for teaching optimization.

The invention provides an intelligent online analysis and monitoring system and method for circuit board production, and the system comprises an image collection module, a multispectral imaging module, a three-dimensional scanning measurement module, a copper thickness test module, a data collection module, an intelligent analysis and recognition module, and a self-adaptive algorithm optimization module. The system is used for carrying out real-time image acquisition on each key station in the circuit board production process by adopting a high-resolution industrial camera. Through high-definition image acquisition, multispectral imaging, three-dimensional scanning and measurement, comprehensive data acquisition in the circuit board production process is realized, the problem of limited data acquisition range in the prior art is solved, accurate detection and classification of circuit board defects are realized by applying an intelligent analysis algorithm, and the production efficiency is improved. The accuracy and efficiency of defect detection are improved, automatic adjustment and optimization of algorithm parameters are achieved through the self-adaptive algorithm optimization module, it is ensured that the algorithm can continuously adapt to changes of the production environment, and the stability and reliability of the system are improved.

The invention relates to the technical field of edge computing, and discloses an edge computing intelligent terminal based on the Internet of Things technology, which comprises a data acquisition module, a network state monitoring module and the like. The data acquisition module can dynamically adjust the acquisition frequency and precision according to network and equipment parameters; the network state monitoring and evaluation module can accurately judge the network risk and timely switch the network to guarantee data transmission; and the equipment operation monitoring and state evaluation module can effectively maintain the stability of the equipment. And the data processing decision module optimizes the data processing flow by using a self-adaptive algorithm. Meanwhile, the system performance is improved by adjusting the weight coefficient according to the scene, and the security encryption system protects the data security comprehensively. The edge computing intelligent terminal effectively solves the existing technical problems, has remarkable effects in the aspects of improving data processing efficiency, guaranteeing equipment stability, enhancing safety, adapting to different scenes and the like, and is of great significance in promoting development of the Internet of Things industry.

The invention provides a vehicle path planning method based on an adaptive optimization algorithm, and relates to the technical field of adaptive algorithm application, and the method comprises the steps: 1, based on a current order demand, a vehicle resource and a preset constraint condition, obtaining a vehicle path planning result; an initial path scheme set meeting the requirements of a time window, the number of splitting times, vehicle type matching and a temperature zone is generated through a step-by-step progressive decision-making process; 2, starting a plurality of independent optimization links according to the initial path scheme set, and dynamically adjusting local path nodes by each link through a greedy strategy to generate an optimization candidate scheme set; and step 3, performing real-time evaluation on the optimized candidate scheme set, determining a final candidate scheme based on the cost saving rate, the demand satisfaction rate and the soft service quality index, and updating the final candidate scheme as the current reference scheme. According to the method, dynamic events are sensed in real time through an adaptive optimization algorithm, influences are evaluated, the path is intelligently adjusted, and the timeliness and accuracy of path planning are improved.

The invention discloses a furniture production informatization management system based on data analysis, and relates to the technical field of production management, and the system comprises a production plan optimization module, a raw material cutting optimization module and a quality prediction center module. The production plan optimization module collects data by deploying an RFID sensing network, establishes a multi-dimensional evaluation matrix to determine order priority, optimizes a production plan by using a mixed integer programming model and an improved algorithm, and generates a production scheduling scheme after digital twinborn verification; the raw material cutting optimization module analyzes order data to construct a raw material model, optimizes component layout by applying a self-adaptive algorithm, and generates a cutting scheme with process constraint and an excess material map in combination with stress analysis; through an intelligent production management means, the production efficiency, the raw material utilization rate and the product quality stability are remarkably improved, and remarkable economic benefits are brought to enterprises.

The invention relates to the technical field of automobile electronic testing, and discloses an integrated ECU power-on burning test monitoring system and method. The system comprises a power supply intelligent regulation and control module, a burning verification module, a test diagnosis module, an abnormity monitoring module and a feedback optimization module. The power supply intelligent regulation and control module realizes stable power supply output through a dynamic voltage frequency regulation algorithm and a temperature self-adaptive algorithm; the burning verification module is used for ensuring the burning integrity by using a block redundancy check and increment verification algorithm; the test diagnosis module diagnoses faults based on the multi-dimensional test data and the decision tree model; the abnormity monitoring module monitors abnormity in real time by using real-time streaming data processing and an isolated forest algorithm; and the feedback optimization module optimizes test diagnosis through incremental learning and a Bayesian optimization algorithm. According to the system and the method, comprehensive and accurate monitoring and optimization of the ECU are realized, and the performance and the reliability of the ECU are effectively improved.