825 results about "Real time transmission" patented technology

The invention discloses a device for measuring flowability of explosive modeling powder, which comprises a step-press-loading analogue unit, a timing and weighing unit and a computer control unit, wherein the step-press-loading analogue unit directly and indirectly drives a screw rod which extends into a blanking bin to rotate and perform reciprocating movements along an axial line and drives a pusher dog in the blanking bin to perform the reciprocating movements along an arc section through the combined action of a cylinder and a pneumatic reversal valve so as to ensure that the explosive modeling powder in the blanking bin falls into the timing and weighing unit, and the process more truly imitates an explosive conveying process of an explosive step-press process. The timing and weighing unit transmits a weighing value of the explosive modeling powder which falls in the timing and weighing unit to the computer control unit in real time, and the computer control unit calculates the instantaneous flow speed of the explosive modeling powder of a north side and generates a flow speed-time curve according to sampling data. The device can measure the flowability of the explosive modeling powder so as to provide an objective basis for choosing a proper explosive step-press process for the explosive modeling powder.

The invention discloses a concrete strength remote monitoring method and system suitable for a complex environment, and belongs to the technical field of civil engineering structure health monitoring. The remote monitoring method comprises the following steps: step 1, acquiring performance data of a concrete structure and related environmental factor data, and transmitting multi-source heterogeneous data to a data processing center in real time through a preset wireless communication protocol; step 2, constructing a five-dimensional tensor data structure, and realizing accurate mathematical expression of a complex coupling relationship between environmental factors and material characteristics through tensor decomposition; step 3, capturing nonlinear time-varying characteristics of concrete strength evolution; 4, quantifying the age effect through an intensity development rate index; step 5, based on the intensity development rate change trend, adaptively adjusting the data sampling frequency and monitoring the environmental condition fluctuation; and step 6, evaluating the safety state of the concrete structure in real time, and ensuring safe and reliable operation of the concrete structure in a complex environment.

The invention provides a rock-soil body multi-field coupling intelligent monitoring system comprising a multi-field sensing device configured to monitor parameters of a deformation field, a seepage field, a temperature field and a stress field of a rock-soil body; the edge calculation and transmission device is configured to perform preprocessing and real-time transmission on the multi-field sensing data; and the intelligent decision-making device is configured to analyze the preprocessed data based on a multi-field coupling model and generate disaster early warning information. According to the rock-soil body multi-field coupling monitoring system and method provided by the invention, synchronous monitoring of a deformation field, a seepage field, a temperature field and a stress field is realized through the multi-field sensing device; real-time data processing is performed in combination with an edge calculation and transmission device, and a dynamic early warning mechanism is established by using an intelligent decision-making device, so that the technical problems of multi-field data space-time correlation failure, insufficient multi-source data fusion precision and poor early warning mechanism adaptability are effectively solved, and the geological disaster early warning method has the remarkable advantages of improving the geological disaster early warning accuracy and timeliness.

The invention discloses a lithium battery full life cycle quality tracing system based on digital twinning, and relates to the technical field of lithium battery quality tracing, and the system comprises a physical entity layer which integrates multiple types of sensors and detection equipment to collect full-link data; the digital twinning modeling layer is used for constructing a multi-scale model to map a battery state; the data acquisition and transmission layer is used for realizing data real-time transmission preprocessing through an edge gateway and an encryption protocol; the data storage and management layer is used for establishing a data warehouse by using a distributed database and performing backup; the intelligent analysis and decision-making layer integrates an algorithm to realize anomaly detection, performance prediction and tracing; the visualization and interaction layer is used for providing a multi-terminal visualization and interaction function; and the block chain evidence storage layer constructs an alliance chain to ensure that the data cannot be tampered. According to the invention, full-life-cycle data connection and credible tracing of the lithium battery are realized, the quality problem positioning efficiency is improved, intelligent early warning and optimization capabilities are provided, the operation threshold is reduced, and industrial quality control and green development are facilitated.

The invention relates to the technical field of direct current motor servo drive control, and provides a Zynq AXI4 bus-based real-time transmission system and a control method thereof, and the system comprises a Zynq system on chip, a DDR3 common data area, an AXI4 bus interconnection architecture and a dynamic partition manager. By implementing the scheme, firstly, the multi-channel characteristic of the AXI4 bus is utilized, special transmission channels are distributed for different types of data, and parallel data interaction is realized; secondly, a dynamic partition manager is deployed at a PL end, a DDR3 public data area is dynamically divided into a plurality of partitions in real time according to needs, multi-task parallel is achieved, and the real-time performance of the system is improved; thirdly, the read-write operation on the DDR3 in each control period is split into a plurality of state machines through precise scheduling of the state machines, the state machines are combined into a state loop to be mounted to an AXI4 bus, it is ensured that key tasks are completed on time, and the certainty of the system is improved; fourthly, the size and the number of partitions in the DDR3 are dynamically adjusted through a dynamic partition manager, and the storage efficiency is improved.

The invention discloses an intelligent warehousing dynamic optimization system based on a digital twinborn technology. The system comprises the following steps: S1, creating a digital twinborn model: acquiring basic data of an entity warehouse and constructing an accurate digital model; s2, data collection and integration: deploying Internet of Things equipment and realizing real-time transmission and processing of data; s3, constructing an analysis algorithm: developing an advanced analysis algorithm and implementing a machine learning model; s4, dynamic optimization and adjustment: automatically adjusting a storage layout and optimizing a warehouse-in and warehouse-out process; and S5, prediction and early warning: analyzing and predicting potential problems and setting an alarm mechanism based on historical data. According to the invention, the real-time performance and accuracy of data are obviously improved, the environment monitoring capability is enhanced, the cargo safety is guaranteed, and the space utilization rate is greatly improved.

There are provided methods, systems and non-transitory storage mediums for providing personalized item recommendations to a user in real-time. Data related to items on a web resource and as user data comprising profiles, preferences, and past interactions with items of a plurality of users are received. Personalized strategies and constraints for a second set of items are received. An objective function representing optimization goals is also received. Using an inference engine, user behavior of a plurality users is inferred based on the received data. In real-time, user interactions of a user with the web resource is received, and personalized item recommendations are generated by a personalized recommendation engine for the user based on the user interaction, inferred user behavior, offer strategies, and the objective function. The personalized item recommendations, along with discount types and time periods, are transmitted to the user's device in real-time.

The invention discloses a safe operation system and a safe operation method of a space-ground integrated rescue vehicle, which are used for the rescue vehicle working in a complex disaster environment and ensure stable and reliable two-way communication connection between the rescue vehicle and a command center by establishing an intelligent switching mechanism of a low-orbit satellite main link and a ground network standby link. Real-time transmission of key instructions and field information is guaranteed, and a foundation is laid for safe operation. Advanced risk early warning is achieved through a space-ground cooperation mechanism, dynamic decision making and path planning are carried out by integrating satellite information and local sensor data, and the global adaptive capacity of the rescue vehicle to the complex disaster environment is improved. Satellite pre-sensing information is combined with a local decision, and a pre-control instruction is generated to act on a drive-by-wire chassis, so that the rescue vehicle can smoothly deal with hidden or developmental risks in advance, and the driving safety and stability are improved.

The invention provides a nut thread defect detection method and system and a medium, and the method comprises the steps: collecting a nut image based on a high-resolution camera, and carrying out the preprocessing of the nut image, and obtaining a preprocessed image; scanning the outer surface of the screw thread of the screw cap based on a laser scanner to obtain three-dimensional point cloud data of the screw cap; extracting image features based on the preprocessed image, generating image data, and performing fusion processing on the image data and the three-dimensional point cloud data to obtain fusion data; thread parameter identification is carried out through a deep learning algorithm based on the fusion data, and thread defect information is obtained; transmitting the thread defect information to a terminal in real time according to a preset mode; through combination of image processing, a three-dimensional laser scanning technology and a deep learning algorithm, automatic detection of screw cap threads is realized, and thread defects including the size, the shape, the number and the like of the threads can be rapidly and accurately identified, so that the detection efficiency and precision of a production line are improved.

The invention discloses a rail transit multi-network integration method based on a digital trunking technology, relates to the technical field of communication, and aims to solve the problem that a traditional narrowband trunking system cannot meet the requirements of voice and data comprehensive bearing and vehicle-ground large-bandwidth service real-time transmission. The method comprises the following steps: constructing a time division duplex long-term evolution broadband digital trunking unified bearing platform; an intermediate media access control layer fusion architecture is deployed, and seamless mobility management of the heterogeneous network is realized through unified authentication and multi-link aggregation; an intelligent vertical switching algorithm based on prediction and multi-criterion decision is introduced, a bidirectional long-short-term memory network model is utilized to predict a network state trend, and an improved entropy weight method and an analytic hierarchy process are combined to dynamically select an optimal access network according to service requirements. According to the invention, the network operation efficiency and the user experience can be obviously improved, and high reliability and high service quality of the rail transit communication system are ensured.

The invention discloses a remote monitoring system based on bird species identification and population number estimation, which comprises a front-end monitoring subsystem, a data transmission subsystem, a data analysis and processing subsystem and a user interaction subsystem, and is characterized in that the front-end monitoring subsystem is composed of a plurality of monitoring nodes; each monitoring node is provided with a high-definition camera, a sound sensor, a temperature and humidity sensor and an illumination sensor, the devices work cooperatively, and data of birds and the environment where the birds are located are collected in an omnibearing mode. According to the remote monitoring system based on bird type identification and population number estimation, manual intervention is greatly reduced through an automatic monitoring and analysis process, the monitoring efficiency is improved, the accuracy of bird type identification and population number estimation is remarkably improved based on an algorithm of deep learning and multi-modal data fusion, and the accuracy of bird type identification and population number estimation is improved. And real-time transmission and processing of data are realized through a remote monitoring system.

The invention discloses an Internet of Things main service platform data analysis cooperation method, and relates to the technical field of data analysis, and the method comprises the following steps: obtaining a to-be-analyzed terminal uploading data set, and detecting the real-time transmission time delay and data encryption level of each data segment in the terminal uploading data set; based on the historical data analysis collaborative log, extracting a tolerance loss coefficient between the real-time transmission time delay and the analysis fault-tolerant rate, and obtaining an analysis fault-tolerant deviation value caused by transmission time delay change according to the tolerance loss coefficient and the real-time transmission time delay; if the real-time transmission time delay of the current Internet of Things main service platform does not cause interference on the data encryption analysis algorithm, processing the data encryption grade and the analysis calculation power consumption to obtain a calculation loss coefficient, and performing calculation power compensation on the initial analysis calculation power distribution quantity according to the calculation loss coefficient, the data encryption grade and the analysis fault-tolerant deviation value; obtaining a first actual analytical calculation force; the method has the effect of improving the collaboration efficiency and stability of data analysis of the whole platform.

The invention discloses a multi-modal data fusion-based steel wire rope detection and real-time transmission method and system, and the method comprises the steps: starting a detection system, collecting a steel wire rope damage detection signal, collecting equipment position and operation state data, constructing an original data set, carrying out the preprocessing, building an incidence matrix, dividing a processing unit, and then extracting features, and forming an initial feature data set. And extracting damage features through a multi-branch network, strengthening the weight of a key region, fusing position and equipment state features through a multi-modal fusion module, generating fusion vectors, classifying and identifying, and outputting a structured detection result. And the acquisition equipment end performs grading processing on the data, distributes transmission channels according to priorities, dynamically adjusts parameters, adds integrity and time sequence identifiers, and the upper computer verifies the data integrity and triggers abnormal supplementary transmission. And the upper computer decodes the data, reconstructs the waveform, gives an alarm in real time in combination with preset parameters, and generates details of damage key information. Reliable technical support is provided for safe operation and maintenance of the steel wire rope in industrial scenes.

The invention discloses a shield cutter cutting load real-time monitoring and adjusting method and system. The method comprises the following steps: constructing a shield cutter distributed sensing network, and collecting strain, vibration and temperature of a cutter; a wireless transmission network is established, and the collected strain, vibration and temperature of the cutter are transmitted in real time; according to the strain, vibration and temperature, received in real time, of the cutter, cutting load parameters are obtained in a multi-source data fusion mode; based on the obtained cutting load parameters, stratum parameter identification is carried out in combination with a deep learning algorithm; in combination with the stratum parameters and the cutting load parameters, a wear state evaluation model based on fuzzy reasoning is established, and the tool wear state is evaluated; and designing a multi-objective optimization function according to the wear state of the cutter and the stratum parameters, solving the multi-objective optimization function, obtaining optimal shield tunneling machine operation parameters, and performing dynamic adjustment. The cutting load of each cutter in shield construction can be monitored in real time, and the cutter parameters can be adaptively adjusted.

The invention discloses a cross-platform advertisement and landing page intelligent matching system based on multi-dimensional user portraits, and relates to the technical field of advertisement marketing, and the system comprises a multi-dimensional data collection module which obtains user data in a cross-platform manner and carries out encryption transmission storage; the multi-dimensional user portrait construction module is used for clustering and analyzing behavior data, constructing a layered label system and realizing cross-platform ID unified mapping; the advertisement and landing page resource library module stores and manages multiple types of resources, double labels are labeled, and rapid calling is supported; the intelligent matching engine module is used for calculating the matching degree of portraits and resources and optimizing matching in combination with real-time behaviors and platform characteristics; the effect monitoring module is used for collecting core indexes, transmitting the core indexes in real time and marking abnormities; and the iterative optimization module is used for adjusting parameters by using reinforcement learning based on the monitoring data and updating a matching rule. According to the method, a data island is broken, the matching accuracy and the conversion effect of the advertisement and the landing page are improved, the utilization of marketing resources is optimized, and a win-win situation between a marketing party and a user is realized.

The invention discloses a voice data compression and index storage method based on a voiceprint template, and relates to the technical field of voice compression processing. The voice data compression and index storage method based on the voiceprint template comprises the following steps: S1, collecting and preprocessing acoustic state data, coded data and edge synchronization data, and constructing a standardized voice input data set; s2, evaluating the individual sound channel difference degree, and stripping non-universal components highly related to individuals in the voice; s3, analyzing the audio compression content, and constructing a structured total compression amount; s4, performing joint evaluation on real-time transmission regulation and control requirements of the compressed data, and dynamically adjusting an uploading priority and a continuous transmission strategy; and S5, constructing a multi-dimensional reverse index system at the cloud. The problems that for a high-noise far-field pickup scene, an existing compression method does not consider that individual sound channel characteristics are stripped from original signals, so that compression redundancy is high, and retrieval interference is large are solved.

The invention discloses a radar station lightning protection device online monitoring method and system based on the Internet of Things, and relates to the technical field of equipment online monitoring. According to the invention, sensors are deployed in each component of the lightning protection device, real-time transmission and integration of monitoring data are realized by using the Internet of Things technology, a health degree evaluation model based on artificial intelligence is constructed, and remote automatic monitoring and health degree prediction of the lightning protection device of the radar station in a remote area are realized; according to the invention, the health degree evaluation model is combined with the correction coefficient to analyze the assembly evaluation data, and the health degree of each component is accurately predicted, so that the lightning protection effect of the lightning protection device is determined; according to the technical scheme, the space-time limitation of traditional manual inspection is broken through, the problem that a lightning protection device of a radar station in a remote area is difficult to monitor is solved, and conversion from passive maintenance to active prediction is achieved; through real-time data acquisition and intelligent analysis, potential hidden dangers of components can be found in advance, so that workers can take measures in time, and the lightning stroke risk is reduced.

The invention relates to the technical field of power signals, in particular to a power line broadband carrier signal adaptive enhancement and noise reduction method and system. According to the method, the multi-dimensional interference characteristics of the power line transmission environment are acquired and the initial modulation parameters are pre-adjusted, so that the influence of environmental interference can be reduced at the initial stage of signal transmission, the signal transmission quality is improved, and the multi-path delay inequality of the aggregated signal and the channel attenuation coefficient are acquired in real time and are combined with dynamic channel adjustment, so that the signal transmission efficiency is improved. Signal components can be dynamically adjusted and optimized according to real-time changes of a power line channel, the adaptive capacity of a system is improved, signals can be optimized in real time by means of real-time transmission quality monitoring and optimization of enhanced carrier signals, the lagging influence of signal quality is avoided, and the system performance is improved. The method ensures that the power line communication system has good adaptive enhancement and noise reduction capabilities, improves the overall performance of the communication system, and can effectively overcome the influence of multipath effect, electromagnetic interference, attenuation and other factors on channel estimation in the prior art.

The invention discloses a multi-dimensional analysis house building health monitoring and early warning system and method, and relates to the technical field of monitoring and early warning. During operation of the system, various sensors are deployed to monitor the physical states of different materials in a building, collect surrounding environment data and transmit the data to a central processing unit in real time, and through a data fusion algorithm, real-time monitoring and early warning are achieved; the method comprises the following steps: integrating sensor data from different materials into a unified data set, preprocessing and normalizing the data, constructing a multi-material interaction mathematical model, calculating an effective stress sigma eff of a building structure, constructing a building overall health assessment model, and calculating a building health comprehensive index HI; according to the building health comprehensive index HI and the comparison result of the health index of each material and the safety threshold value, the health change of the building is detected in real time, an intelligent decision support system is provided, and building management personnel are helped to carry out long-term maintenance planning and resource optimization distribution.

The invention belongs to the technical field of power information transmission, and particularly relates to a power data real-time transmission optimization method based on a low earth orbit satellite. By matching the optimal transmission path in real time, it is ensured that data can be efficiently transmitted to a destination within a short time, meanwhile, waveform compression is carried out on the power monitoring data, the transmission data size is effectively reduced, the transmission burden and cost are reduced, and in the transmission process of the to-be-transmitted data, the transmission efficiency of the to-be-transmitted data is improved. In addition, real-time quality evaluation is carried out on a transmission link of the to-be-transmitted data packet, and the transmission link is flexibly adjusted based on an evaluation result, so that continuity and reliability of data transmission are guaranteed, and meanwhile, it can be ensured that the to-be-transmitted data packet with high priority can be transmitted in advance; according to the invention, the breakpoint cache mechanism can be intelligently started, the loss of the data packet to be transmitted is avoided, and the data packet is preferentially sent after the signal is recovered, so that the efficiency and stability of data transmission can be further improved.

The invention provides a numerical-digital accurate prediction method and system for artificial stratum freezing multi-physics field evolution. The method comprises the following steps: S1, constructing a numerical model of artificial stratum freezing engineering multi-physics fields; s2, a sensor collects multi-physical field data of an engineering field in real time and transmits the multi-physical field data to a data transmission server in real time; s3, carrying out simulation calculation on the numerical model, extracting a multi-physics field calculation result of the numerical model at the measurement position of the sensor at preset time, and comparing the multi-physics field calculation result with the multi-physics field data of the engineering site at the corresponding time; s4, judging whether the difference value between the multi-physics field calculation result and the multi-physics field data is smaller than an allowable value or not, if so, entering S5, and if not, adjusting parameters by an optimization algorithm, and returning to S3; s5, obtaining an optimized numerical model; s6, repeatedly executing the steps S3-S5 in a project proceeding process, and continuously updating and optimizing the numerical model; and S7, a result is extracted from the updated optimization numerical model, and the artificial stratum freezing multi-physics field evolution process is accurately predicted.

The invention relates to the technical field of artificial intelligence, and discloses a gobi desert surface temperature prediction method based on deep learning. According to the method, a system architecture in which an acquisition end, a remote sensing end, an edge computing terminal and a central server cooperatively operate is constructed, and fusion and real-time transmission of multi-source observation data are realized. By designing a space-time neural network model fused with physical constraints, combining an angle-guided convolutional neural network and a gated cycle unit structure, and introducing surface energy balance and Stefan-Boltzmann radiation consistency constraints, high-precision prediction and physical consistency modeling of surface temperature are realized. And a cross-scale reconstruction and missing detection complementation mechanism is adopted, so that the spatial continuity and resolution of a prediction result are improved. According to the method, high-precision, stable and self-adaptive prediction of the surface temperature of the gobi desert area is realized, and intelligent support is provided for meteorological monitoring, ecological evaluation and early warning decision making of the arid area.

The invention relates to the technical field of measurement signal transmission, and discloses a measurement signal real-time transmission system in air-ground cooperative localization, which is characterized in that an environment signal fingerprint spectrum and a modulation depth spectrum representing environment modulation intensity are generated offline, and in a real-time stage, a mobile terminal sends a detection signal and attitude data of the mobile terminal; and the positioning correlator performs self-adaptive pre-normalization on the physical characteristics of the received signal according to the attitude data and the modulation depth map, and performs mode matching with the fingerprint map to determine the position. According to the invention, the superposition influence of the attitude change of the mobile terminal and the environmental reflection on the signal can be effectively separated; the problem that different positioning results are generated at the same position due to different flight attitudes is avoided, the system has the capabilities of signal time sequence verification and atlas timeliness diagnosis, and the measurement reliability and the system adaptability in a complex real environment are improved.

The invention discloses an unmanned aerial vehicle power transmission line patrol method, which comprises the steps of S1, intelligent route planning, constructing a power transmission corridor digital twin model based on three-dimensional laser point cloud data, S2, autonomous flight control, adopting an STM32F4 series MCU as a bottom layer controller, and running a real-time operation system, S3, multi-modal data acquisition, adopting a multi-modal sensor matrix and a cooperative acquisition mechanism, and carrying out multi-modal data acquisition. S4, real-time transmission processing is carried out, and adaptive code rate control is adopted; and S5, intelligent analysis and decision making are carried out, and a deep learning module is adopted. According to the unmanned aerial vehicle power transmission line patrol method, key defects such as insulator damage and wire strand breakage in a power transmission line are recognized and analyzed in real time, image processing and target detection are directly completed at an equipment end through edge computing capability, and only high-definition images with suspected defects are screened and returned to a ground station or a data center; the transmission data volume and the communication load are greatly reduced, the mechanism effectively realizes the real-time preliminary screening of the line defects, and the inspection efficiency and the response speed are improved.

The invention discloses an in-vehicle network active bandwidth reservation type burst flow containing method. The method comprises the steps that 1, the injection time of ST flow to an in-vehicle network is calculated, and a gating list is configured; 2, residual bandwidth resources, RC flow available bandwidth and bandwidth reservation of any link are calculated; 3, calculating an injection time slot of RC to the vehicle-mounted network and configuring a gating list; 4, calculating the injection time of burst flow to the vehicle-mounted network and configuring a gating list; 5, the wakeup bandwidth amount of any link of the vehicle-mounted network is calculated; and 7, recalculating the injection time slot of the RC flow influenced by the burst flow to the vehicle-mounted network, and configuring a gating list. According to the method, the bandwidth is actively reserved when the ST flow and the RC flow are scheduled and registered, and the conflict flow is accommodated on the premise that the real-time transmission quality of the registered flow is not influenced, so that the real-time transmission quality requirement of the registered flow and the ultra-low delay requirement of burst flow transmission are ensured, and an intelligent driving system is enabled to operate normally so as to improve the driving safety and reliability of a vehicle.

The invention relates to the technical field of image data processing, in particular to a screen projection remote control method and system based on the gap NEXT. The method comprises the following steps: constructing a port mapping channel, and judging the type of current equipment; if the equipment type is a mobile phone, a preset screen projection tool is injected into a controlled end of the swan NEXT system, and screen picture content of the controlled end is extracted through the preset screen projection tool to form picture data; if the equipment type is a simulator, intercepting simulator operation frames according to a preset frame rate, and combining the simulator operation frames into a continuous image sequence to form picture data; performing video coding on the picture data, and transmitting the picture data to a control end in real time through a port mapping channel; and the control end receives, decodes, renders and displays to obtain a visual picture. By establishing an adaptive port mapping channel and a differentiated picture acquisition mechanism, cross-platform low-delay screen projection and high-precision remote control between the real machine and the simulator of the gap NEXT are realized.

The invention relates to the technical field of network communication, in particular to an intelligent visual edge gateway control method based on a 5G network. According to the method, real-time transmission and timely control of edge gateway information are achieved through the 5G network, meanwhile, the operation state of the edge gateway is visually and comprehensively mastered through the information, timely finding and processing of problems are facilitated, the visual quality and the data transmission quality are ensured along with channel communication quality evaluation and analysis of the 5G network, and the service life of the edge gateway is prolonged. The resource allocation is dynamically adjusted in an information progressive mode, the transmission timeliness of priority data is guaranteed during channel load fluctuation, the problem that a fixed scheduling strategy is poor in adaptability is solved, meanwhile, the operation state information of the edge gateway is further analyzed, and the service life of the edge gateway is prolonged. According to the method, reasonable fault management can be carried out according to hardware or software faults of the edge gateway, namely, the hardware faults and the software faults are analyzed, so that the faults of the edge gateway can be found and processed in time, manual intervention is reduced, and the reliability of the system is improved.

The invention relates to the technical field of granary monitoring, and discloses a granary storage environment adaptive supervision system and a control method.The system comprises an environment monitoring module, an intelligent analysis module, a supervision execution module and a cloud learning platform. Comprising temperature, humidity, carbon dioxide concentration and pest activity data, data preprocessing and feature extraction processing are carried out, accuracy and feature effectiveness of environment data are guaranteed, meanwhile, a granary environment original data set is generated in time through real-time transmission and preliminary verification, monitoring comprehensiveness and reliability are guaranteed, and a granary environment monitoring system is established. A high-quality data basis is provided for environment state evaluation; by comparing the granary environment characteristic data with the preset environment safety threshold value, the comprehensive environment state index is generated, and the safety state, the early warning state and the dangerous state are divided, so that the system can intelligently identify the environment abnormity level.

The invention provides a data recording method for a vehicle, which is executed by a second electronic control unit of the vehicle, and comprises the following steps: during operation of the vehicle, receiving to-be-recorded data related to an intelligent driving function from a first electronic control unit through the second electronic control unit of the vehicle in real time, the to-be-recorded data is non-duplicated data of data specified to be recorded in data recording requirements of the second electronic control unit, and the second electronic control unit has redundant power supply capability; and storing the received to-be-recorded data to the nonvolatile memory through the second electronic control unit in response to meeting a preset triggering condition. The invention further provides a data recording device for the vehicle and a computer program product. The intelligent driving data and the non-repetitive data of the EDR are transmitted to the second electronic control unit with redundant power supply in real time, so that bus overload can be avoided under the condition that an existing whole vehicle power supply framework is not changed, and the power-off storage requirement is met.

A numerical control cutting process digital twinning implementation method based on three-dimensional reconstruction comprises the steps that a digital twinning model and a physical logic twinning model of a numerical control machine tool and a geometric model of a workpiece and a cutter are constructed respectively, and real-time synchronization of a physical machine tool model and a digital twinning machine tool model is achieved by transmitting motion data and machine tool state data in real time; the cutting state of a workpiece and a cutter is calculated in real time through a cutting simulation algorithm, collision detection simulation is carried out, a workpiece model constructed based on a one-way line cluster model is converted into a standard STL triangular mesh model based on a three-dimensional reconstruction algorithm, and the triangular mesh model is mapped into a digital twinning monitoring scene through coordinate system conversion. According to the method, a high-precision digital twinborn model is constructed, real-time data synchronization and cutting process simulation are combined, and rapid conversion between a simulation data model and a display model is realized based on a composite block algorithm.