1088 results about "Data Adjustments" patented technology

The invention relates to a digital integrated quality management system based on multi-source data fusion, and belongs to the technical field of industrial internet and quality management. A data acquisition layer of the system obtains real-time and static multi-source heterogeneous data through a multi-source adapter; the data processing layer is used for cleaning, converting and standardizing the acquired data; the intelligent analysis layer performs deep analysis and prediction on the data by using an adaptive quality prediction model, an anomaly detection module and a root cause analysis engine; the application service layer displays a quality trend and an anomaly detection result through a visual billboard, and provides credible tracing and collaborative decision-making functions; and the feedback closed layer adjusts system processing logic according to the decision support data to form closed-loop quality control. According to the method, real-time fusion and efficient utilization of multi-source data are realized through a dynamic routing technology, an adaptive quality prediction model and a block chain evidence storage mechanism, and the intelligent level and decision-making efficiency of quality management are remarkably improved.

The invention belongs to the technical field of stirring control, and particularly relates to an intelligent speed regulation stirring control method and system and computer equipment, and the method comprises the steps: determining a target control value of stirring control according to the requirements of a stirring technology; various sensors are installed on the stirring equipment, it is ensured that key parameters in the stirring process are accurately collected in real time, the data collection frequency is set, and it is ensured that dynamic changes in the stirring process are captured; filtering processing is carried out on the collected sensor data, noise interference is removed, the sensor data is corrected, and the accuracy of the data is ensured; key features in the stirring process of the combustion furnace are extracted through a data analysis method, and a mathematical model of the stirring process is established for prediction and control; a closed-loop control system is established, the stirring speed of the combustion furnace is adjusted according to data monitored in real time, the stability and accuracy of the stirring process are ensured through an intelligent algorithm and real-time parameter monitoring, insufficient stirring or excessive stirring is avoided, and therefore the product quality and the production efficiency are improved.

The invention discloses an electromechanical installation engineering automatic debugging method and system based on intelligent control, and the method comprises the steps: enabling an intelligent control system to monitor the energy consumption of equipment in real time, optimizing an operation mode, adjusting the working state according to data, and enabling the equipment to work cooperatively through the intelligent control system; deploying temperature and humidity, pressure, current and voltage sensors for stable wiring, and configuring parameters by adopting an intelligent debugging tool; based on the preliminary data, optimizing the system by applying an adaptive control algorithm, adjusting a strategy, fusing multi-dimensional data, and identifying performance bottleneck and abnormality; the centralized monitoring platform monitors the equipment state in real time and sends out an early warning signal; after multiple rounds of debugging, the energy-saving effect and performance are evaluated, a control algorithm is optimized, and optimization is continuously carried out according to historical data and environmental changes; according to the electromechanical installation engineering automatic debugging method and system based on intelligent control, the adaptive control algorithm and the machine learning technology are introduced, and the strategy is dynamically optimized according to the equipment operation state, the environment change and the historical data.

Methods, systems, and devices for leveraging automotive and wearable-based data are described. For example, a system may include a wearable device, a user device associated with the wearable device, and a vehicle. The system may determine when a user is within a proximity of the vehicle based on communications between the vehicle and the wearable device, between the vehicle and the user device, or both. The system may retrieve physiological data measurement from the user via the wearable device based on determining the user is positioned in a proximity of the vehicle, such that one or more operational parameters of the vehicle may be adjusted based on the physiological data. Additionally, or alternatively, the system may receive telemetry data from one more sensors of the vehicle and may provide feedback to the user based on the telemetry data satisfying one or more trigger conditions.

The invention discloses a converged communication system and method for emergency command and dispatch, and the method comprises the following steps: S1, constructing a converged communication network environment, and configuring a self-adaptive network switching module; s2, collecting and analyzing task demand data in an emergency scene, and generating a task demand prediction matrix; s3, generating a task scheduling rule; s4, acquiring current network state data, and generating task scheduling constraint conditions; s5, constructing and training a multi-agent reinforcement learning model; s6, optimizing a communication path decision strategy and a task scheduling rule by adopting a self-adaptive multi-objective optimization algorithm; s7, calculating an optimal communication path, and executing intelligent adaptive network switching; and S8, collecting task execution feedback data, and adjusting training parameters of the multi-agent reinforcement learning model. According to the method, multi-agent reinforcement learning and dynamic entropy regulation and control optimization are combined, emergency communication task scheduling and path optimization are achieved, and the method has the advantages of being high in adaptability, high in communication stability and excellent in task execution efficiency.

The invention discloses a method and a system for adjusting process parameters based on historical data, and a storage medium. The method comprises the following steps: constructing an equipment pool and a standard process parameter pool based on production equipment combinations and process parameters corresponding to production of different injection molding products; the production equipment combination comprises an injection molding machine and a plurality of auxiliary equipment; according to the injection molding product type and the key parameters of the injection molding machine to be adjusted, multiple sets of production equipment combinations consistent with the key parameters are selected from the equipment pool to serve as alternatives, and one set of production equipment combinations is recommended to serve as the optimal production equipment combination; and matching the process parameters of the optimal production equipment combination for producing the injection molding product of the type from a standard process parameter pool, and updating the process parameters to the injection molding machine to be adjusted and the auxiliary equipment. The parameter adjusting method does not need complex means such as model building, corresponding equipment and parameters are matched according to historical data, and the parameter adjusting method is simple, rapid and suitable for the field of injection molding.

The invention discloses a bridge structure optimization method based on BIM and a finite element method, and relates to the technical field of bridge structure optimization. According to the method, the finite element analysis model can be more accurately established, and the design precision is improved. Through finite element analysis and a digital simulation technology, structure response data is rapidly obtained, and design optimization is accelerated; the optimization of the final scheme is ensured by a multi-iteration optimization strategy, the extreme load response is predicted by introducing a machine learning algorithm, a high-risk area is identified in advance, and the overall performance is improved; based on a multi-scale analysis method, the durability and safety of the structure are comprehensively evaluated, weak links are identified, and long-term performance is guaranteed; meanwhile, the influence of environmental factors on material characteristics is considered, adjustment is performed according to climate data, the design scheme is closer to the actual environment, and durability is enhanced.

A machine learning-based fraud detection system (100) for financial transactions, comprising the following: a data collection and preprocessing module configured to capture transaction data from financial institutions, payment gateways and bank modules, and to perform data cleansing, normalization and format conversion; a module for creating user profiles and behavioral analyses, configured to create dynamic user profiles based on historical transaction patterns, spending habits, device usage, and geographic location; an anomaly detection and risk assessment module configured to use machine learning algorithms to detect deviations from normal transaction patterns and assign a dynamic risk rating to each transaction; a fraud detection module configured to classify transactions as legitimate or fraudulent based on supervised and unsupervised learning techniques, including decision trees, support vector machines (SVMs), and deep learning models; a rule-based and hybrid fraud detection module configured to integrate predefined rule-based fraud detection mechanisms with AI-driven anomaly detection to improve fraud detection accuracy; a real-time fraud detection and warning module configured to monitor transactions in real time and generate security alerts via SMS, email or mobile app when suspicious activity is detected; a transaction verification and multi-factor authentication (MFA) module configured to implement adaptive authentication measures, including biometric verification, one-time passwords (OTP), and challenge-response authentication for high-risk transactions; a self-learning and model-updating module configured to dynamically update fraud detection models using reinforcement learning and federated learning techniques; a data encryption and security module configured to protect transaction data using encryption techniques, including AES-256 and RSA, to prevent unauthorized access and data breaches; and A dashboard and reporting module configured to provide real-time analytics, visualizations, and fraud detection reports for financial institutions to monitor, track, and mitigate fraudulent activity.

The invention provides a software ecological migration method and system based on intelligent analysis. Wherein a compatibility evaluation database is deployed in a target architecture, an instruction set mapping rule is preset, and migration risk data is generated through semantic verification; dynamically allocating resources through a heterogeneous virtualization scheduling engine, calling a hardware acceleration unit translation defect instruction, and generating virtualization equipment simulation parameters based on the migration risk data; deploying protocol conversion middleware, dynamically analyzing and reconstructing a network protocol data packet, and combining instruction execution delay data to adjust analysis granularity; in cross-architecture transmission, instruction conversion defects are injected into control logic, timing sequence errors are corrected through clock offset compensation, and corrected data are transmitted back to update instruction mapping rules. According to the technical scheme provided by the invention, the data leakage risk is reduced, and zero shutdown in the migration process is ensured.

The invention provides a data processing system for cooperative operation of an intelligent combined fleet, and relates to the technical field of data processing, and the system comprises an acquisition module which is used for acquiring basic state data of a ship; the prediction module is used for generating state trend data in combination with the historical motion trail and the near-time data sequence, and the state trend data comprises a speed change rate and a course angle change trend in a future short time period; the sensing fusion module is used for receiving the state trend data of other collaborative ships, aligning the state trend data with the state trend data of the ship, and performing synchronous calculation to generate fleet collaborative prediction data; the adjusting module is used for recognizing an included angle error between the current ship thrust applying direction and the future resultant force direction of the target structure and calculating the attitude adjusting amount; the control output module is used for mapping into a thrust adjusting instruction and sending an execution instruction to a power system of the ship; according to the invention, autonomy and accuracy of fleet collaborative operation are improved.

The invention discloses a real-time behavior monitoring and dynamic optimization system for an advertisement putting strategy, and relates to the technical field of advertisement putting and user behavior analysis. A real-time multi-source user behavior data acquisition module acquires data; the distributed storage and indexing module supports fragmented storage, capacity expansion and multi-dimensional indexing; the behavior feature extraction and analysis module extracts features and identifies a time sequence rule; the putting effect evaluation module compares data through an index system and positions an effect short board; the user portrait dynamic updating module adjusts the label weight according to the latest data; the strategy optimization decision module optimizes parameters in combination with the portrait and the evaluation result; the execution scheduling module issues an instruction and monitors a state; the historical data mining and model training module is used for mining rules and iteratively training the model; and the visual interaction configuration module provides an interface and supports parameter configuration and data, effect and optimization suggestion display. According to the invention, full-scene real-time monitoring and cross-platform identity association of user behaviors are realized, and support is provided for refined advertisement marketing.

The invention relates to a Bluetooth connection method and system, and the method comprises the steps: obtaining Bluetooth identifiers of an earphone and a source device, and extracting a multi-connection strategy from a database; and calculating equipment positions, signal intensities and interference factors among all strategy steps, executing a three-dimensional verification matrix, comparing protocol units, and monitoring a frequency hopping sequence to realize dynamic sequence adjustment. Combining the signal intensity, the success rate and the interference risk to calculate a comprehensive weight, generating an optimized sequence, then dividing multiple subsequences for cross validation, and utilizing historical data to adjust the interference weight and reconstruct a step sequence. Collecting intensity mean values and success rate variances of multiple rounds of tests to construct a normalized fitness function, and selecting an optimal scheme after scoring each strategy. Through a multi-dimensional parameter fusion and iteration verification mechanism, traditional single-path limitation is broken through, and finally, Bluetooth connection is completed by adopting a highest score strategy, so that the problem of equipment interconnection in a complex electromagnetic environment is effectively solved.

The invention relates to the technical field of safety early warning, and discloses a safety early warning method and system based on multi-modal data fusion and a storage medium. The method comprises the following steps: deploying data acquisition equipment in a target environment, acquiring multi-modal data, and preprocessing the multi-modal data; respectively extracting features of each modal data; calculating correlation among different modal features, calculating an attention weight based on a correlation score, and introducing a gating mechanism to adjust the attention weight in combination with historical data; multiplying the adjusted attention weight by each modal data to obtain weighted modal data, and splicing to obtain fused modal data; and generating a training set and a test set based on the fusion modal data, training a safety early warning model, and outputting an early warning result through the test set. And calculating a prediction error according to the early warning result, and updating parameters of the safety early warning model according to the prediction error. The accuracy and reliability of the safety early warning model can be improved, and the false alarm rate is effectively reduced.

The invention discloses an integrated water quality monitoring and processing system for aquaculture, and the system comprises a data collection module which is used for obtaining dissolved oxygen, ammonia nitrogen, pH value, temperature and flow data from a plurality of spatial distributed sensing nodes; the data preprocessing module is used for completing noise removal, correction and time sequence synchronization; the water quality state estimation module is used for outputting real-time multi-point water quality states based on a distributed unscented Kalman filtering algorithm; the space-time fluid modeling module is used for constructing a Lagrange model to extract a flow velocity field and a diffusion coefficient; the scheduling optimization module is used for generating an aeration and dosing scheme by adopting an improved multi-target Pareto optimization algorithm based on a space-time sensitive boundary; the execution module is used for controlling an air blower, an aeration pipe and a dosing pump to complete oxygenation and dosing operations; and the self-adaptive updating module is used for collecting feedback data to adjust filtering gain and optimizing parameters so as to realize closed-loop control of the system.

The invention discloses a training sample data generation method and device, a readable storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: inputting a seed topic and a cue word of a programming language processing task into a sample data generation model; and modifying the seed topic in the modification range boundary condition by using the cue word based on the same algorithm thinking, and generating a test case set covering the constraint condition of the new topic according to the type of the new topic and the case generation condition. Answering the new question passing the verification, and outputting an answer passing the verification of the test case set; taking the new question, the test case set and the answer as training sample data; and adjusting and modifying the range boundary condition and / or the cue word and / or the use case generation condition, and generating new training sample data by utilizing the new question. The problem that training data generated by related technologies cannot meet model training requirements can be solved, and high-quality training sample data can be efficiently generated for programming language processing tasks.

A system for generating a projection structure using a graphical user interface, wherein the system comprises: a display device configured to display a graphical user interface (GUI); a computing device comprising a memory; and a processor, wherein the memory contains instructions configuring the processor to: receive a plurality of datasets, wherein each dataset comprises a plurality of parameters; generate a first projection structure as a function of the datasets; display the first projection structure through the GUI; receive natural language query data corresponding to the first projection structure using a chatbot interface; generate response data as a function of the query data; map, as a function of a look-up table, each categorization to a database entry index; obtain response data from the database; adjust the parameters as a function of the response data; generate a second projection structure; and display it in the GUI.

The invention relates to a gas pipeline safety monitoring system and method based on data analysis, and relates to the technical field of gas pipeline safety, and the system comprises a data acquisition module which is used for collecting the pressure, temperature and flow in a gas pipeline and the gas concentration of the surrounding environment; the abnormity identification module is used for analyzing the pipeline data of the sensor in real time and identifying the abnormal condition of the gas pipeline through an abnormity detection discrimination function; the model building module is used for analyzing and predicting future data of the gas pipeline and building a safety monitoring model based on historical data and real-time data of the gas pipeline; the data adjusting module is used for adjusting the pipeline data of the gas pipeline after the gas pipeline is detected to be abnormal or the potential risk is predicted; the model training module is used for obtaining an optimized safety monitoring model; and the safety monitoring module is used for carrying out safety monitoring on the gas pipeline according to the optimized safety monitoring model. The system can improve the accuracy and efficiency of gas pipeline safety monitoring.

The invention relates to the technical field of ship control, in particular to a fishing boat control system based on data analysis, which comprises a traction load analysis module, a fluctuation condition division module, an attitude disturbance estimation module, a propulsion power regulation module and a rudder angle correction output module. According to the method, by analyzing the traction force load sequence value and the moment arm length of the fishing net traction point, calculating the left and right side offset torque and marking the action direction, the attitude adjustment precision of the ship is optimized, the operation sea condition level is adjusted according to data, and the ship can be subjected to more reasonable attitude estimation and adjustment under the dynamic working condition; the wind pressure change rate and the pitch angle rate are combined to judge the direction resultant force trend, and the navigation attitude is adjusted according to the sea condition grade, so that the ship can more accurately resist the influence of sea surface storm waves and maintain the course stability, and the rudder angle is corrected by adjusting the propulsion power in real time, limiting the adjustment amplitude of the propulsion power and correcting the rudder angle according to the real-time data of the navigation attitude. And the propulsive efficiency and the navigation precision are optimized.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention relates to the technical field of mine equipment monitoring and maintenance, and discloses a mine equipment state monitoring and maintenance method based on the Internet of Things, which comprises the following steps: S1, multi-source data acquisition: synchronously acquiring equipment operation parameters and environmental parameters through vibration, temperature and pressure sensors and environmental sensors deployed on mine equipment; s2, edge calculation processing: performing filtering and anomaly detection on the sensor data at a mine edge gateway to generate a compressed data packet; s3, online learning and model dynamic updating; s4, new equipment is accessed; s5, maintenance decision and resource scheduling; and S6, performing closed-loop feedback optimization. High-error historical sub-trees are dynamically eliminated, new sub-trees are generated, model parameters are adjusted in combination with maintenance feedback data, on the basis that historical effective knowledge is reserved, equipment dynamic features are extracted, accurate matching of prediction results and maintenance strategies is ensured, and the self-learning ability and maintenance decision reliability of the model are remarkably improved.

The invention relates to the technical field of parameter control, and discloses a vibrating screen parameter self-adaptive control method and system based on machine learning. The method comprises the following steps: collecting coal characteristic parameters through an image device and a sensor; calculating a load coefficient and a difficulty index according to the parameters, and adjusting the parameters of the dual-motor vibration exciter to control vibration; the spring pre-tightening force and the screen surface inclination angle are adjusted according to vibration feedback, and the screening track is optimized; and the feeding speed and the baffle height are adjusted based on screening efficiency data, and self-adaptive control is achieved. The technical problems that traditional vibrating screen fixed parameter control cannot adapt to multi-coal-type changes, a vibrating screen control system lacks coal material characteristic real-time sensing and analyzing capacity, and a precise mapping and dynamic optimization mechanism is lacked between vibrating parameters and screening efficiency are solved.

A system for AI-supported cash flow management and forecasting, consisting of: a data acquisition unit configured to retrieve transaction data from multiple financial data sources, the data acquisition unit incorporating encrypted API connections and secure data transfer protocols to ensure data security; a data preprocessing unit that is operationally linked to the data acquisition unit and includes a data cleansing module for removing anomalies, duplicates and missing values, as well as a normalization engine for standardizing data formats, thereby ensuring a uniform data structure for processing; a predictive analytics unit configured to receive processed data from the data preprocessing unit, wherein the predictive analytics unit comprises an ensemble of machine learning models, including recurrent neural networks and long short-term memory networks, each model being optimized for different prediction horizons; an adaptive learning component embedded in the predictive analytics unit and configured to regularly retrain the machine learning models based on updated historical data and feedback on financial performance to ensure the accuracy of predictions through iterative improvement; a decision support unit that is operationally coupled with the predictive analytics unit and configured to provide real-time recommendations for cash allocation and liquidity adjustments based on forecasted cash flow patterns; a liquidity warning module configured to continuously monitor cash balances, compare them with pre-configured liquidity thresholds, and generate alerts when balances approach critical limits, thus enabling proactive management of cash flow risks; a visualization module configured to present insights into cash flow, trend forecasts, and liquidity recommendations in an interactive graphical format to facilitate user evaluation and strategic decision-making; and a network interface integrated into the system, configured to connect to external data sources for dynamic updates.

The invention discloses a chip-level GaAs etching temperature control method, which comprises the following steps: detecting the upper surface temperature of a GaAs wafer in the etching process in real time to obtain first temperature data; and according to a partition structure in the electrostatic chuck and the first temperature data, determining a to-be-adjusted electrostatic chuck partition and determining a second temperature of the upper surface of the carried GaAs wafer corresponding to the to-be-adjusted electrostatic chuck partition. And on the basis of the second temperature data and the target temperature, power adjustment is carried out on the heating unit of the to-be-adjusted electrostatic chuck partition through the partition temperature PID control module, and the second temperature data is adjusted to the target temperature. According to the invention, the power of the local heating unit is adjusted according to the wafer surface temperature data obtained in real time by using the partition temperature PID control module, and the temperature of each area is ensured to be uniform and stable. The control coefficient of the PID controller is optimized in an auxiliary mode through the neural network, and the self-adaptive capacity and the control precision of the system are improved.

The invention discloses an acousto-optic fusion bird repelling efficiency evaluation method and system, and the method comprises the steps: enabling a bird repelling device to recognize a target through an AI image, and enabling the bird repelling device to repel the target through the sound and light capability; collecting data and synchronously transmitting the data to a data analysis module; the behavior state of the birds is automatically analyzed based on image recognition and video processing technologies; comprehensively evaluating the bird repelling effect by using the efficiency evaluation index; according to historical data, a bird repelling strategy is optimized through an efficiency evaluation result; intelligent learning is introduced, and a bird repelling strategy is automatically adjusted; the system generates a regular efficiency evaluation report and feeds back data to the user; the system comprises a data acquisition module, a behavior identification and analysis module, an efficiency evaluation module and a strategy optimization module. According to the method, quantitative evaluation is carried out on the bird repelling effect, and the efficiency performance of bird repelling equipment is clarified; the effectiveness of a bird repelling strategy can be analyzed according to behaviors of birds; the bird repelling strategy is adjusted and optimized according to the data, and the bird repelling efficiency is greatly improved.

The invention relates to the technical field of robot teaching-free welding, and particularly discloses a robot teaching-free welding system based on visual localization and a welding method.The robot teaching-free welding system comprises a PLC, a power module, a wireless communication module and a visual localization coordinate conversion module. The path planning optimization module directly generates a welding path according to a visual positioning result, tedious operation is reduced, the problem of poor dependence and adaptability on pre-stored data is solved, the visual acquisition module and the positioning module work cooperatively, workpiece position and posture information is updated in real time, and the positioning precision is improved. The teaching-free intelligent decision-making and optimization module is combined with real-time data to adjust a path and parameters, the image processing and path planning optimization module generates a proper path according to image information for a complex workpiece, the sensor fusion module fuses vision and other sensor data, the adaptability of the system to the complex workpiece is enhanced, and the welding quality is guaranteed.

The invention provides a spatio-temporal data fusion-based power distribution network load prediction method and system, and relates to the technical field of power distribution network load prediction, and the method comprises the steps: collecting related data of a power distribution network, carrying out the wavelet transform decomposition of historical load data, obtaining a load feature matrix, constructing a hierarchical graph convolution network based on topological structure data, and extracting topological correlation features; and generating a spatial-temporal feature tensor through tensor decomposition fusion, training a depth probability prediction model adopting a variational auto-encoder structure, and adjusting prediction probability distribution in combination with environmental data. According to the method, the prediction precision is improved, a complex space-time dependency relationship can be captured, and reliable uncertainty quantization is provided.

The present application relates to a BIM-based hanging basket design method, computer device and computer-readable storage medium, wherein the design method comprises following steps: a parameter acquisition step; a hanging basket modularization step: decomposing structures of hanging baskets into a plurality of design modules; a selection step; a calculation step: calculating parameters of the design modules using a structural mechanics calculation method and / or a finite element calculation method; preliminary BIM model building step: building models of the design modules and a preliminary BIM model of the hanging basket; an optimization iteration step: converting the preliminary BIM model of the hanging basket into a finite element model for calculation, and feeding calculation results back to the preliminary BIM model for structural adjustment, repeatedly performing iterative calculations to obtain optimized hanging basket parameter data; final BIM model acquisition step: the BIM model of the hanging basket adjusted according to the optimized hanging basket parameter data is a final BIM model of the hanging basket. The design method enables autonomous calculation and design of the hanging basket with good versatility.

The invention discloses a power data security transmission strategy dynamic optimization method based on gray measurement, which comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data in a power system, and generating a time sequence data set and a structure attribute data set; s2, performing privacy protection and noise addition on the time sequence data set and the structure attribute data set by applying a differential privacy mechanism; s3, performing evaluation by adopting a gray scale measurement and calculation method, calculating a gray scale weight and generating a transmission strategy; s4, according to the gray scale weight classification data, adjusting a transmission path and a time sequence; s5, dynamically adjusting the differential privacy noise intensity and the gray scale weight according to the network bandwidth, the transmission delay and the data requirement; and S6, continuously monitoring the transmission efficiency and the privacy effect, and adjusting a transmission strategy and a protection mechanism in real time according to feedback. According to the method, the dynamic collaborative optimization of privacy protection and scheduling efficiency of the power data in the transmission process is realized by fusing a differential privacy mechanism and a gray calculation method.

A method includes obtaining a first track associated with an object. A first set of parameters is generated based on the first track. Measurement data are obtained from one or more sensors. A first set of features are extracted from the measurement data. Based on the first set of parameters and the first set of features, a second set of parameters are generated by a machine learning model. The second set of parameters represent an adjustment to the first set of parameters. Based on the second set of parameters, the first track is adjusted to generate a second track associated with the object. The second track is provided to an autonomous vehicle control system for autonomous control of a vehicle.

The invention provides a sea-land integrated measurement system and method based on multi-sensor fusion, and relates to the technical field of data processing. The system comprises a data acquisition module used for acquiring measurement data of a target area through various types of sensors and generating original data including spatial position information and measurement attribute information; the space-time synchronization module is used for adjusting the data generated by each sensor to a unified time reference and space reference coordinate system; the data fusion module is used for generating three-dimensional topographic data of the target area; the task distribution module is used for dividing the target area into a plurality of task areas according to the distribution characteristics of the three-dimensional topographic data and generating corresponding operation instructions; and the collaborative operation module is used for coordinating measurement behaviors of the operation platform in different task areas. According to the technical scheme, the precision and efficiency of the sea-land integrated measurement system in the aspects of multi-sensor data fusion and multi-platform collaborative operation can be improved.