67 results about "Adaptive sensing" patented technology

The invention belongs to the technical field of image recognition, and provides a target tracking method and system based on AI vision, and the method comprises the following steps: collecting original video frames; environment adaptive image enhancement; performing multi-target detection and multi-modal feature extraction; estimating local optical flow motion; performing multi-target trajectory association; carrying out shielding processing and re-identification; outputting a track and analyzing a result; according to the method, a physical-deep learning cascade defogging model is set, light / dense fog processing paths are dynamically switched through a dark channel mean value, atmospheric scattering physical prior and U-Net residual error correction are fused, an environment self-adaptive sensing architecture is provided, the failure bottleneck of a traditional single model under sudden change fog concentration is broken through, and the real-time performance of the system is improved. According to the method, an apparent-motion-geometry ternary coupling trajectory cognition system is constructed, a dynamic cost matrix and a feature cache pool are designed, the ID switching problem caused by similar target aggregation and long-time shielding is solved, and the accuracy of target tracking in the shielding environment is improved.

The invention relates to the technical field of target tracking, in particular to a photoelectric tracking algorithm and system for self-adaptive target tracking, and the system comprises an image collection module, a cross-spectrum self-adaptive sensing module, an image processing and target recognition module, a self-adaptive tracking algorithm module and a servo control module, and constructs a closed-loop feedback link to achieve the synchronous optimization of parameters in a whole link. The algorithm comprises the steps of image acquisition and adjustment, target detection, parameter optimization, servo driving and feedback. The system can accurately estimate a high-speed turning target, is adaptive to complex environments such as low illumination / haze and the like, completes sheltered target recapture within 0.5 s, has servo compensation precision of + / -0.1 degree, is suitable for airport bird monitoring, port unmanned aerial vehicle tracking and highway vehicle monitoring scenes, and significantly improves tracking stability and practical value.

The invention relates to a DRL-based AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance method in a partially observable environment, which comprises the following steps of: firstly, executing an AUV path planning and obstacle avoidance task under the condition that marine environment information is partially observable; secondly, constructing an underwater environment model and an AUV kinetic model, and constructing a dynamic and static separated dual-channel adaptive sensing model through local environment information acquired by detection equipment; then, path planning is modeled as a Markov decision process, and a multi-objective optimization reward function is designed for collaborative optimization; finally, an improved maximum entropy SAC algorithm is combined with a priority experience playback mechanism to train a strategy network, a gliding mode is dynamically triggered through a physical constraint layer, attitude angle and thrust output is corrected in real time, and the safety boundary condition is monitored in real time. According to the method, the real-time performance of path planning, the dynamic and static obstacle avoidance success rate and the ocean current utilization rate under the complex sea condition can be remarkably improved, and a high-robustness autonomous navigation solution is provided for ocean exploration.

The invention discloses a self-adaptive sensing anti-shake regulation and control method and system for a spinal surgery auxiliary mechanical arm, and relates to the technical field of spinal surgery, and the method comprises the steps: collecting multi-dimensional data of a mechanical arm tail end actuator through a multi-dimensional sensor group, and transmitting the multi-dimensional data to an AeroStead space-level stability calculation platform; the platform carries out filtering processing on the data, and vibration of the mechanical arm and disturbance components caused by external interference are separated; constructing a spine mechanical arm rigid-flexible coupling dynamic disturbance observation and compensation model by using the disturbance component to observe disturbance and generate a compensation amount; calling a spinal operation area three-dimensional reconstruction and dynamic obstacle avoidance mixed density network model, and fusing different images to generate an operation area three-dimensional dynamic model and an obstacle avoidance adjustment signal; a mechanical arm motion control instruction is generated through a force sense position cooperation self-adaptive control algorithm; and the mechanical arm is driven to move for closed-loop regulation and control. According to the method, through fusion of multiple models and algorithms, the disturbance compensation precision, the operation area modeling accuracy and the control collaboration are improved, and the operation safety is guaranteed.

The invention provides an inertial sensing range and sensitivity adjusting method, which belongs to the technical field of parameter adjustment, and comprises the following steps: after an item opening instruction is obtained, obtaining basic information of a moving body, and calling an adaptive sensing range according to the basic information; different detection nodes are set in the whole process before a project is started, the feedback degree of a moving body is recorded in sequence when execution is carried out to each node, values of corresponding positions of an internal curve are increased or decreased according to the feedback degree, and corresponding sensitivity is dynamically set according to the internal curve. The feedback speed and external performance of the moving body are judged in the project execution process, and the follow-up content and links of the project are adjusted in combination with the action model library till the project is completed or closed. The inertial sensing range and sensitivity adjusting method provided by the invention can carry out specific dynamic adjustment according to the state of the moving body and the actual situation, has strong pertinence and ensures smooth completion of a project.

The invention relates to the technical field of intelligent control of engineering machinery, and provides a self-adaptive perception control method and system for dual-radar cooperative scheduling. According to the method, the first rotary radar and the second rotary radar are respectively installed on the rotatable support; obstacle distribution data of a tower crane hoisting operation area are obtained in real time through the first rotating radar and the second rotating radar, and real-time stage information of hoisting operation is obtained in real time through the operation state sensing module. Calculating a sensing dead angle position according to the real-time stage information; dynamically generating a first task instruction and a second task instruction according to the priority level, the sensing dead angle position and the obstacle distribution data of the target monitoring area, and controlling the first rotary radar and the second rotary radar to adjust the rotation angle and the sensing parameter, so that the first rotary radar performs emphasized sensing on the target monitoring area with high priority, and the second rotary radar performs emphasized sensing on the target monitoring area with high priority. And the second rotary radar performs compensation sensing on areas corresponding to the sensing blind area and the sensing dead angle of the first rotary radar.

The invention relates to the technical field of artificial intelligence, in particular to a self-adaptive sensing strategy switching method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-dimensional sensing data collected by a plurality of sensing modes on a robot; calculating the reliability score of each sensing mode at the current moment according to the multi-dimensional sensing data and the historical performance of each sensing mode; and determining a role label of each sensing mode according to the reliability score and the current task of the robot, determining a target sensing strategy of the robot under the current task according to the role label, and switching the sensing strategy of the robot into the target sensing strategy. Therefore, the problems that dynamic adjustment cannot be performed according to the current environment and the sensing effect is poor due to the adoption of a fixed mode combination in related technologies are solved.

The present disclosure relates to a multidimensional adaptive sensing system and method for event detection with improved accuracy. The system dynamically adjusts, using baseline and compound models, a triggering conditional threshold of each event based on real-time variations in environmental parameters and sensor health. The adaptive system allows for fine-tuning of sensitivity to environmental changes, seasonal variations, and sensor decay, thereby reducing false positives and negatives. Installation flexibility is achieved by selecting and deploying a subset of models tailored to available data at the sensor's location, ensuring optimal operation regardless of specific environmental conditions or sensor configurations. The adaptive sensing system is used in environmental control systems for dynamic routine management, sensor health management, a triage system for complex event analysis, and multifaceted compliance assurance solution. Further, the adaptive sensing system is used for automated configuration, model deployment, and maintenance by leveraging edge computing units and a central control unit.

The invention discloses a multi-parameter shallow gas detection method which comprises the following steps: S1, self-adaptive sensing network layout and multi-parameter synchronous acquisition: monitoring nodes synchronously acquire underground electrical parameters, soil gas composition and concentration, soil mechanics and pore pressure parameters and an environmental noise background value; accessing a hydro-meteorological data stream of the target area in real time; s2, multi-source interference signal identification and data purification: obtaining a purified multi-parameter data sequence reflecting real geological anomalies; s3, water-gas-force coupling dynamic modeling and migration simulation: outputting a shallow gas migration risk thermodynamic diagram; s4, intelligent diagnosis based on transfer learning and causal discovery: performing time sequence analysis on the purified multi-parameter data sequence, and identifying a gas reservoir core area, a dominant migration channel and dominant control factors influencing gas aggregation and migration; and S5, dynamic risk assessment and graded early warning: quantitatively calculating the risk probability.

The invention discloses an automatic alarm system of an air cushion type belt conveyor. The automatic alarm system comprises a sensing layer, a control layer and an execution layer. The sensing layer constructs a multi-dimensional sensing network through an air film thickness sensor, an air pressure sensor, a belt state sensor, an air chamber airflow audio analysis module, a motor power fluctuation detection module and a distributed optical fiber sensing module. The control layer adopts an edge-cloud computing collaborative architecture, an edge computing node is responsible for real-time data preprocessing, the fog computing layer runs a digital twin model to complete data fusion and state simulation, and a cloud computing center stores historical data and optimizes an algorithm and is provided with a three-level early warning and adaptive sensing mechanism. The execution layer executes parameter adjustment, sound-light alarm or emergency shutdown according to instructions of the control layer. Comprehensive monitoring, intelligent diagnosis and automatic regulation and control of the operation state of the air cushion type belt conveyor are achieved, and the operation reliability and the maintenance efficiency of equipment are remarkably improved.

The invention provides an integrated efficient reaction clarification system based on multi-sensor data fusion, and relates to the technical field of clarification systems. The self-adaptive sensing module is used for preprocessing the real-time operation parameters; the deep fusion processing module is used for generating trend pre-judgment data through the prediction model; the self-optimization control module is used for calculating and generating an initial equipment regulation and control instruction and optimizing the initial equipment regulation and control instruction; and the intelligent cooperative processing module is used for secondary optimization of the water quality regulation and control parameters. By means of the regression prediction model, risks are pre-judged in advance, and problem pre-prevention and control are achieved; through parameter adaptation, secondary optimization and accurate instruction transmission according to functional areas, all devices are pushed to operate cooperatively, the operation stability of the devices and the rationality of medicament consumption are effectively improved, manual intervention is greatly reduced, and the intelligence, high efficiency and reliability of industrial wastewater treatment are comprehensively enhanced.

The invention discloses a TPU film for robot electronic skin and a preparation method of the TPU film, and relates to the technical field of flexible electronic materials, the film takes TPU introduced with Diels-Alder dynamic reversible bonds as a matrix, a conductive filler is formed by dopamine-modified silver nanowires and MXene, the concentration of the conductive filler is in continuous or quasi-continuous gradient decrease, and the conductive filler is a conductive filler formed by silver nanowires and MXene. An integrated network with self-repairing and environmental stability is formed and is used as an electrode; phase change microcapsules and triboelectric nanofibers are compounded in a low-conductivity contact area to form an intelligent response layer which is self-adaptive in sensing threshold and capable of generating power by friction. Through one-step multi-nozzle electrostatic spinning forming, the problem that self-repairing, signal stability, environmental adaptability and energy independence in electronic skin are difficult to consider at the same time is solved.

The present disclosure relates to a multidimensional adaptive sensing system and method for event detection with improved accuracy. The system dynamically adjusts, using baseline and compound models, a triggering conditional threshold of each event based on real-time variations in environmental parameters and sensor health. The adaptive system allows for fine-tuning of sensitivity to environmental changes, seasonal variations, and sensor decay, thereby reducing false positives and negatives. Installation flexibility is achieved by selecting and deploying a subset of models tailored to available data at the sensor's location, ensuring optimal operation regardless of specific environmental conditions or sensor configurations. The adaptive sensing system is used in environmental control systems for dynamic routine management, sensor health management, a triage system for complex event analysis, and multifaceted compliance assurance solution. Further, the adaptive sensing system is used for automated configuration, model deployment, and maintenance by leveraging edge computing units and a central control unit.

The invention relates to the technical field of coal mine safety monitoring and intelligent early warning, and discloses an edge computing driven coal mine underground multi-parameter self-adaptive sensing and anomaly recognition sensor network, which comprises a plurality of mining intrinsic safety type intelligent sensor nodes for forming a distributed monitoring network, a multi-parameter acquisition module, a data processing module and a data processing module, various monitoring parameters of the underground coal mine are collected in real time; the edge calculation module is deployed with a lightweight long and short term relation-improved gating cycle unit time sequence prediction model, performs adaptive drift correction on a monitoring parameter background value to dynamically generate a threshold range, and generates an abnormal initial judgment result; the cooperative communication module is used for performing data interaction with adjacent nodes after the abnormal initial judgment result is generated, and generating a space-time cooperative verification result; and the exception decision module is used for generating a multi-parameter fusion judgment result according to the exception initial judgment result and the time-space cooperation verification result and outputting graded early warning. According to the method, the accuracy, the real-time performance and the engineering landing performance of coal mine underground dynamic disaster precursor identification are remarkably improved.

The invention relates to the field of grounding technology and electrical measurement, and discloses a grounding electrode current diffusion characteristic and environment correlation analysis method, which comprises the following steps: acquiring a ground electricity state vector representing soil electrochemical characteristics; constructing and calibrating a state phase space evolution model for describing time-space evolution of the vector; an adaptive dynamic analysis closed loop is executed, a state vector at the next moment and uncertainty are predicted through the model, an adaptive sensing protocol is generated based on the uncertainty to guide new data collection, a prediction result is corrected through new data, and a corrected state vector is obtained; and finally, based on the continuous corrected state vector, analyzing the causal association between the environment and the state, reconstructing three-dimensional complex conductivity distribution, and determining current diffusion characteristics such as grounding impedance through numerical calculation. According to the method, the limitation of static evaluation in the prior art is solved, and the dynamic prediction of the performance of the grounding system and the physical tracing of the abnormal cause are realized.

The invention provides a state monitoring system and method for a contact assembly of a transformer substation, and relates to the technical field of electrical variable measurement. The system comprises a multi-source data acquisition module used for synchronously acquiring multi-dimensional operation parameters of a contact assembly and performing integration based on a timestamp synchronization mechanism; the identification and weight dynamic configuration module is used for monitoring working condition data of the contact assembly in real time and judging the current working condition state of the contact assembly so as to obtain a dynamic weight coefficient of the current working condition state; the multi-parameter fusion evaluation module is used for normalizing the integrated multi-dimensional operation parameters and calculating a contact health state index by adopting a weighted fusion algorithm in combination with the dynamic weight coefficient of the current working condition state; and the state decision and early warning output module is used for judging the health state of the contact assembly according to the contact health state index and carrying out real-time display and early warning. According to the invention, comprehensive, accurate and adaptive sensing of the health state of the contact assembly can be realized.

The invention relates to the technical field of cold chain transportation environment control, and discloses a temperature and humidity control system for cold chain transportation, and the system comprises an environment modeling module which is used for constructing a heat conduction model of a cold chain space based on a random field theory and a finite element method, and outputting a stiffness matrix containing random parameters; the self-adaptive sensing network module is used for receiving the characteristic information of the stiffness matrix, dynamically optimizing sensor distribution positions, collecting multi-node temperature and humidity data and adding space-time labels; and the distributed data processing module is used for receiving the sensing data. According to the invention, through random field theoretical modeling and parameter-state joint estimation, the temperature field prediction precision is significantly improved and the system environment adaptability is enhanced; a distributed sensing network architecture and a heterogeneous calculation acceleration technology are combined, so that the cooperative control with optimal energy efficiency is realized while millisecond-level real-time response is ensured, and the performance bottleneck of a traditional cold chain temperature control system is comprehensively broken through.

The invention relates to the technical field of fault early warning, and discloses a CFB boiler operation safety domain fault early warning method based on multi-source data fusion, and the method comprises the steps: obtaining CFB boiler multi-source monitoring data, and generating a multi-dimensional spatio-temporal data matrix; analyzing the multi-dimensional spatio-temporal data matrix by using multi-resolution wavelet transform to generate a multi-scale feature tensor; analyzing the multi-scale feature tensor based on a tensor network, calculating a cross-scale coupling weight matrix according to a physical constraint equation, and extracting a cross-scale fault association mode vector through tensor contraction operation; performing similarity matching on the cross-scale fault association mode vector and a fault semantic prototype library to generate hierarchical fault situation information including a summary layer, a detail layer and a depth analysis layer; outputting a self-adaptive layered fault situation awareness result; according to the method, intelligent self-adaptive sensing of complex multi-scale fault situations is realized, the integrity of fault information is ensured, and the man-machine interaction efficiency is improved.

An energy-efficient adaptive 3D sensing system. The adaptive 3D sensing system includes one or more cameras and one or more projectors. The adaptive 3D sensing system captures images of a real-world scene using the one or more cameras and computes depth estimates and depth estimate confidence values for pixels of the images. The adaptive 3D sensing system computes an attention mask based on the one or more depth estimate confidence values and commands the one or more projectors to send a distributed laser beam into one or more areas of the real-world scene based on the attention mask. The adaptive 3D sensing system captures 3D sensing image data of the one or more areas of the real-world scene and generates 3D sensing data for the real-world scene based on the 3D sensing image data.

The invention relates to a dynamic self-adaptive communication, sensing and calculation integrated method for an air-space-ground integrated network. The method comprises the following steps: S1, constructing an air-space-ground integrated network model fusing a low earth orbit satellite, an unmanned aerial vehicle and ground users / edge nodes; s2, establishing a cross-layer communication-perception-calculation integrated joint optimization model, and respectively forming two modes of binary unloading and partial unloading according to whether a task can be segmented or not; s3, for a binary unloading mode, realizing distributed solution and generating an unloading decision and a beam / power / bandwidth / computing resource scheme by using a dynamic adaptive prediction alternating direction multiplier (DAP-ADMM); and S4, aiming at a partial unloading mode, adopting a weighted minimum mean square error alternative optimization method, introducing a receiving equalizer, error weight and other auxiliary variables, executing block coordinate descent iteration, and outputting an unloading proportion, power distribution and a beam forming result. According to the DA-ISCC architecture and the optimization algorithm thereof provided by the invention, cross-domain calculation unloading with low time delay, high throughput and low overhead can be realized in a high-dynamic space-air-ground integrated network environment, and the DA-ISCC architecture and the optimization algorithm thereof can be widely applied to emergency communication, low-altitude logistics, ocean monitoring and other scenes.

The invention discloses an intelligent power distribution network electric energy quality disturbance self-adaptive sensing method, device and system. The intelligent power distribution network electric energy quality disturbance self-adaptive sensing method comprises the steps of performing denoising processing on an obtained intelligent power distribution network electric energy quality disturbance signal; analyzing the de-noised electric energy quality disturbance signal of the intelligent power distribution network, calculating a hierarchical weighted permutation entropy considering a multi-phase grouping-Teager operator as a disturbance feature, and generating a disturbance feature vector; and inputting the disturbance feature vector into a pre-trained disturbance recognition model to obtain a disturbance recognition result. According to the method, the hierarchical weighted permutation entropy considering the multi-phase grouping-Teager operator is calculated as the disturbance characteristic according to the difference of characteristic information included between different types of disturbance signals, the disturbance characteristic vector is formed by selecting the disturbance characteristic with the large difference, disturbance identification is completed, and the identification efficiency and accuracy can be greatly improved.

The invention provides an intraoperative dynamic positioning system and method based on a magnetic sensor array and a composite magnetic field, and the system comprises a composite excitation handle which is internally and non-coaxially integrated with a static magnetic source and a dynamic magnetic source, and generates a non-axisymmetric static and dynamic composite magnetic field; the self-adaptive sensing array is provided with a plurality of magnetic sensing nodes and is used for synchronously acquiring magnetic field signals; and the navigation processing terminal is used for processing the signal and resolving the pose. According to the method, static magnetic and dynamic magnetic components are obtained through synchronous acquisition and frequency domain decoupling; the method comprises the following steps: fitting the position and direction of a handle by utilizing a static magnetic component, and solving a rotation angle around a shaft by taking the position and direction as initial values and combining a dynamic magnetic component to realize full six-degree-of-freedom positioning; and finally, obtaining the navigation pose of the instrument tip through filtering mapping. According to the invention, full-attitude, high-precision and strong-interference-resistance intraoperative dynamic positioning is realized, and the problems of precision degradation and visual lag of traditional electromagnetic positioning in a metal environment are solved.

The invention provides a self-adaptive control method based on multi-cycle waterway pressure characteristics and related equipment thereof. The self-adaptive control method comprises the following steps: acquiring target waterway pressure data in a plurality of preset detection cycles; extracting a characteristic value of the target waterway pressure data, and determining a current waterway pressure grade; according to the waterway pressure grade, the target rotating speed of a corresponding water pump motor is matched; and controlling the current rotating speed of the water pump motor according to the target rotating speed. Statistics and trend analysis are carried out on multi-cycle water pressure data through the steps of the method, self-adaptive sensing and response control over waterway load changes are achieved, and therefore the intelligent degree of a control system and the operation efficiency of a water pump are improved.

The invention relates to the technical field of underwater target detection, and discloses an underwater environment real-time lightweight target detection method and a readable storage medium, and the method comprises the steps: improving a standard YOLOv11n model, employing a C3-CPMSFA module, fusing global context information and multi-scale local features, and combining with a partial convolution strategy to achieve efficient feature extraction; feature processing is expanded to a frequency domain by using a C2SFA module, space-frequency joint adaptive sensing is realized by using a dynamic frequency domain mask, and back scattering noise is accurately filtered out; and semantic noise reduction and small target enhancement are realized through multi-scale feature progressive fusion and depth separable convolution by using an auxiliary head. According to the method, the accuracy and robustness of underwater small target detection are remarkably improved while light weight and real-time performance are kept.

Some aspects of the present disclosure provide an adaptive sensing method that enables separate power control parameter configuration for uplink (UL) communication transmission or sidelink (SL) communication transmission and sensing transmission. When a signal or a channel is used for both communication and sensing, two sets of parameters may be configured, a first set of parameters for UL or SL communication and a second set of parameters for sensing. Some aspects of the present disclosure provide a distributed power control method that involves slow power ramping of a transmitted signal used for sensing. The distributed power control method may also include interference avoidance. Some aspects of the present disclosure provide a centralized power control method that involves, a centralized network side device, such as a base station, sending a signaling to adapt UE power to mitigate interference between UEs when interference is detected.

The invention discloses an embedded control method for a direct-cooling refrigerator, the refrigerator is provided with a defrosting sensor located near an evaporator, and the embedded control method comprises the following steps: acquiring real-time temperature data of the defrosting sensor; based on a preset heat exchange model, the temperature data of the single defrosting sensor are used for synchronously estimating the internal temperature of a refrigerating chamber and the internal temperature of a freezing chamber; according to the deviation between the estimated temperature of the refrigerating chamber and the refrigerating set point, starting and stopping of a compressor are controlled; the start-stop period of the compressor is monitored and analyzed, and the current environment temperature interval is dynamically calculated according to the change trend of the stop duration; and when the calculated environment temperature is continuously lower than the preset low-temperature threshold value, the forced operation mode is started. According to the invention, through a single defrosting sensor and in combination with a series of intelligent algorithms for cooperative work, accurate estimation of the temperature of the double temperature zones, adaptive sensing of the environment temperature, self-diagnosis of the state of the sensor and optimization of the operation energy efficiency are realized.

The invention relates to the technical field of coal mine disaster prevention and control, in particular to an oil type gas-gas composite disaster intelligent sensing and early warning prevention and control system which comprises a self-adaptive sensing module, a multi-modal data fusion module, a lightweight intelligent early warning module, a hierarchical linkage regulation and control module and a centralized intelligent management and control platform. The method comprises the following steps: dynamically deploying multiple types of sensors through an adaptive algorithm, collecting multi-source data such as gas concentration, microseism and stress, fusing through an attention mechanism, inputting into an LSTM and random forest mixed model to realize disaster trend prediction, dividing early warning levels in combination with a dynamic threshold value, and then executing graded regulation and control such as extraction regulation, ventilation control and emergency power failure according to the levels. And a mine emergency system is linked to form a full closed loop of perception, fusion, early warning, regulation and control and emergency. According to the invention, the comprehensiveness and accuracy of composite disaster monitoring are improved, the false alarm and missing alarm rate is reduced, the early warning response time is shortened, and the mine safety guarantee capability is remarkably improved.

The invention discloses a driving distraction behavior monitoring system and method based on multi-scale neural adaptation and bidirectional feature fusion, the driving distraction behavior monitoring system comprises a front view camera, a cabin camera and a data processing unit connected with the cameras, the system extracts features through a real-time target detection model RT-DETR based on a Transform architecture, and the real-time target detection model RT-DETR is used for monitoring the driving distraction behavior. A bidirectional feature fusion module CGFM is guided through a context to accurately identify a high-risk area; enhancing fine-grained features of the eyes and the head in the cockpit picture by using a multi-scale guiding neural adapter Mona; a coverage debt concept and a multi-stage adaptive sensing mechanism are introduced, the problems of low detection precision, high omission ratio and poor real-time performance in the prior art are solved, and accurate and real-time detection and hierarchical intervention of distraction behaviors are realized. On a public State-Farm data set, the Precision, Recall, F1-Score and mAP50 of the method and the system respectively reach 0.9528, 0.9618, 0.9562 and 0.9708, and the method and the system can stably operate on a common vehicle-mounted CPU (Central Processing Unit) with the dominant frequency greater than or equal to 1.8 GHz at 22FPS, so that the driving safety and the user experience are remarkably improved.

The invention relates to the technical field of intelligent manufacturing equipment industry, and discloses a precise flow control oil brushing method based on digital instruction driving topological optimization roller body, which comprises the following steps: in a single contact period, performing combined execution of a forward angular displacement pulse for applying an oil film and a reverse withdrawing angular displacement pulse in an oil film non-separation window; the high-frequency component of the output torque of the motor is used for adjusting the pulse in real time to adapt to the surface appearance of a workpiece, the stirring sequence is executed in the machining interval period to maintain the homogeneity of oil components, and the internal logic of the digital instruction is reconstructed, so that the restriction of the physical form of an execution tool on the process flexibility is avoided; according to the invention, the smooth roller body with a simple form obtains a programmable subtraction control dimension, bidirectional engraving can be carried out on an oil film, self-adaptive sensing of the surface morphology of a workpiece is realized by using an endogenous signal of the motor, and the contradiction between the process and sanitary safety is solved.

This application relates to the field of information technology and discloses a multi-source heterogeneous data fusion and analysis platform for smart cities. It includes a data acquisition end error calibration module, a transmission link integrity assurance module, a multi-source data fusion and cleaning module, and a dynamic timeliness management module. Each module achieves data flow through a unified data interaction protocol. The acquisition end is used to collect data from traffic cameras and environmental monitoring sensors. The data acquisition end error calibration module is used to calibrate errors generated by the data collected by the acquisition end. The acquisition end error calibration module also includes an environmental adaptive sensing unit and a device health assessment unit. Through the synergistic effect of the environmental adaptive sensing unit and the device health assessment unit, data reliability is improved from the source. The environmental adaptive sensing unit uses a pure hardware electrical signal transmission link to achieve real-time response to changes in visibility and illumination.