113 results about "Intelligent environment" patented technology

The invention relates to the technical field of intelligent environment monitoring and early warning, and discloses an intelligent environment monitoring and early warning system based on multi-sensor fusion, which comprises an intelligent environment monitoring and early warning system. The intelligent environment monitoring and early warning system comprises an intelligent sensing layer, an edge computing layer, a network transmission layer, a data storage layer, an intelligent analysis layer, an application service layer and a system support layer. According to the intelligent environment monitoring and early warning system based on multi-sensor fusion, multi-source monitoring means such as a fixed monitoring station, mobile monitoring equipment, a wearable terminal and satellite remote sensing are integrated through the intelligent sensing layer, and a multi-sensor fusion technology is matched, so that the dimension limitation of a traditional single monitoring mode is broken through; environmental parameters (pollutant concentration, meteorological factors, ecological indexes and the like) can be captured in all directions, and multi-dimensional and high-precision data acquisition in a complex environment is realized.

The invention discloses an intelligent environment monitoring and regulation method for a marine ranch, and belongs to the field of environment monitoring, and the method comprises the steps: collecting water body parameters, employing a data fusion algorithm to generate a comprehensive environment state index, and solving the business problems of data dispersion, response lag and inaccurate adjustment in water body environment monitoring and regulation. A time sequence analysis model and an optimization algorithm are combined, potential anomalies are analyzed, a risk change trend is predicted, environment adjustment requirements are dynamically calculated, a precise regulation and control instruction set is generated and transmitted to equipment to be executed, and meanwhile, through deviation comparison between real-time feedback data and a preset threshold value, comprehensive indexes and regulation and control parameters are iteratively updated, so that environment stability is ensured. Through multi-level data processing and a self-adaptive adjustment mechanism, the timeliness and accuracy of water body environment regulation and control are remarkably improved, closed-loop management from monitoring to optimization is achieved, and efficient support is provided for intelligent decision making in a complex environment.

The invention is suitable for the field of environment monitoring, and provides a sampling information management system for intelligent environment monitoring, which comprises a user authority management and control module for realizing multi-level authority management based on an RBAC (Role Based Access Control) model; the equipment full life cycle management module monitors the equipment state in real time through the Internet of Things and carries out early warning calibration; the standard dynamic adaptation module intelligently associates a standard with a sampling scene, and synchronously authorizes updating; the sampling closed-loop management module supports double-end cooperation, offline operation and block chain evidence storage; the data interaction and analysis module integrates the data and intelligently predicts the data; the auditing and tracing module records whole-process operation and gives an early warning for abnormity; and the multi-terminal collaborative interaction module is adapted to multiple systems. The monitoring efficiency and the data quality are improved, and support is provided for environmental protection decision making.

The invention relates to the field of robot perception and intelligent man-machine interaction, an equipment platform and a perception module, a system is provided with an Intel RealSense D435i depth camera which is used for acquiring an RGB-D image sequence containing depth information in real time, and meanwhile, an advanced YOLOv8-Pose deep learning model is embedded, so that the depth information of the RGB-D image sequence is acquired in real time. High-precision detection, natural interaction, a gesture guidance mechanism, direction inference and target candidate aggregation, interactive target screening and judgment, a robustness enhancement strategy, deep deletion and noise robustness and an incomplete target completion mechanism of human upper body skeleton key points in a front scene are realized; the man-machine collaborative target object identification and positioning method based on depth vision, attitude estimation and semantic understanding is mainly realized. The technology can be suitable for service robots, intelligent assistant robots and other practical application scenes with high requirements for autonomous perception and natural interaction, and man-machine co-fusion and intelligent environment construction are further promoted.

The invention discloses a highway service area intelligent environment monitoring and management system based on the Internet of Things, and relates to the field of environment monitoring and management, and the system comprises an acquisition module which is used for uploading the carbon emission source data of each energy consumption unit of a target service area, synchronously acquiring the carbon concentration in the environment of the target service area and the associated meteorological parameters of the same time interval, and transmitting the acquired data to a cloud server; constructing a carbon footprint basic data set; the traceability module is used for receiving the carbon footprint basic data set, constructing a traceability link based on the data set, analyzing carbon emission key nodes, and identifying a carbon emission ratio and a generation path of each node; according to the method, carbon emission related data and associated meteorological information are collected, a complete carbon footprint data basis is constructed, carbon emission key links and formation paths are carded out through reasonable analysis, the emission reduction potential of each link is calculated in combination with the actual operation condition of a service area, and links needing to be regulated and controlled are determined according to double judgment standards.

The invention discloses an unmanned aerial vehicle GNSS-free relative positioning method and device and a medium, and relates to the technical field of unmanned aerial vehicle autonomous navigation and intelligent environment perception. According to the method, a traditional PnP pose estimation framework and dependence on the physical size of an identifier are abandoned, the relative geometric center position of the visual identifier in the image is extracted by using deep learning, and the real height reference provided by the millimeter wave radar and the real-time attitude information of the unmanned aerial vehicle are combined; and a scale-adaptive, analytic and non-iterative relative position calculation model is constructed, and high-precision estimation of the three-dimensional relative position of the unmanned aerial vehicle relative to the landing mark center is realized.

The invention provides a chip testing method based on an ATE platform, and relates to the technical field of chip manufacturing and testing. According to the method, multiple advanced test technologies and intelligent algorithms are creatively integrated, test environment parameters are accurately controlled through an intelligent environment regulation and control system, radio frequency and digital signals are efficiently processed in cooperation with a test engine, a test execution stream optimizes a high-speed interface test by using a chip built-in Loopback function and an adaptive algorithm, and the test efficiency is improved. The intelligent control and analysis system dynamically adjusts a test process and accurately predicts chip defects based on a federal learning framework, and realizes Chiplet interconnection verification and closed-loop data analysis feedback optimization of a test program at the same time. The chip testing efficiency and precision are effectively improved, the testing cost is reduced, dependence on expensive equipment is reduced, the method can be widely applied to various chip testing scenes, and remarkable economic benefits and technical advantages are brought to chip manufacturing enterprises.

The invention discloses a natural gas pipeline failure risk assessment system and method based on a multi-factor coupling model, and relates to the technical field of natural gas pipeline safety, and the system comprises an intelligent environment monitoring module, a dynamic technical parameter library, a block chain management assessment module, a coupling calculation engine, an enhanced visualization platform and a self-adaptive early warning module. According to the method, the risk coupling degree is calculated by adopting the risk coupling algorithm improved based on the nonlinear characteristic and the synergistic effect, so that risk coupling quantification is realized; the adaptive capacity of the natural gas pipeline evaluation system is improved through technical innovation integration, full-chain closed-loop management is formed through combination of technologies such as block chain evidence storage, machine learning defect prediction and three-dimensional GIS rendering, model parameters and early warning strategies can be dynamically adjusted according to real-time data, and conversion from passive response to active prevention is achieved.

The invention relates to the technical field of data processing and intelligent environment control, in particular to a sleep environment feedback control data processing method based on cognitive behavior therapy logic, which comprises the following steps: a reference model construction step: acquiring a non-contact multi-mode time sequence signal, and reconstructing a reference signal balance curve to construct a target signal manifold model; a simulation flow generation step of converting the preset interference characteristics into signal modulation operators and injecting the signal modulation operators into the model to generate a simulation signal flow; a double-track difference calculation step: executing double-track difference operation to respectively extract residual features representing system deviation and theoretical interference; a similarity judgment and control step: calculating the topological similarity of the residual features in a time-frequency domain, judging a deviation dominant type, generating a corresponding control instruction, and driving an execution mechanism to act; according to the method, psychological anxiety and environmental discomfort are effectively distinguished by using a double-track differential mechanism, and accurate attribution analysis of sleep interference sources is realized.

The invention discloses an environment gas leakage intelligent detection method based on big data analysis. The method comprises the following steps: collecting monitoring data and constructing a sensor network space-time diagram fused with meteorological conditions; predicting the background concentration of each node by using a sequence decomposition attention model; calculating a concentration residual error and constructing a space-time residual error map; extracting an abnormal feature vector through a physical information enhanced graph attention network; calculating an abnormal score in combination with the normal mode memory bank and generating a leakage source probability distribution diagram; when the conditions are met, calling a computational fluid dynamics model to simulate a theoretical concentration field; and determining a leakage event through spatial correlation analysis. According to the invention, the false alarm rate of gas leakage detection in an open environment is obviously reduced, and the leakage source positioning precision is improved.

The invention relates to the technical field of coal mine safety ventilation, in particular to a coal mine ventilation and exhaust device and a control system thereof. According to the technical scheme, the coal mine ventilation and exhaust device and the control system thereof comprise a ventilation pipeline outer cavity, a ventilation fan, an air volume induction module, a filtering device, a gas detection module, a control module, an air suction machine and a protection sieve plate, the ventilation fan is arranged in the ventilation pipeline outer cavity, and the air volume induction module is arranged on one side of the ventilation pipeline outer cavity; a filtering device is arranged on the other side of the outer cavity of the ventilating pipeline; according to the invention, deep integration of multi-source data and dynamic environment modeling are realized through the intelligent environment prediction module, the temporal-spatial resolution of environment monitoring and the comprehensiveness of risk assessment are remarkably improved, safety early warning is upgraded from passive threshold judgment to active coupling analysis, the false alarm rate is greatly reduced, potential risks are intervened in advance, and the safety of the mine environment is guaranteed.

The invention relates to the field of intelligent environment control, in particular to a method and system for controlling operation of air conditioning equipment and a storage medium. The method comprises the following steps: acquiring external sound; based on a voiceprint library of the air conditioning equipment, air conditioning equipment noise and environment noise are separated from the external sound in a frequency domain; setting, for each frequency in the spectrum of the external sound, a weight indicating a corresponding tolerance of noise at that frequency; on the basis of the set weight, whether the noise of the air conditioning equipment at the frequency affects the user experience or not is determined for each frequency; and when the noise of the air conditioning equipment at any frequency affects the user experience, operation of the air conditioning equipment is adjusted.

The invention provides an equipment identification method and system based on packet traffic semantic feature enhancement, and electronic equipment, and the method comprises the steps: converting the original Internet of Things traffic into a general packet-level traffic semantic feature which can be understood by a large language model, and then carrying out the fine adjustment of the large language model through the packet-level traffic semantic feature, and the large language model can automatically learn potential Internet of Things equipment traffic characteristics and execute equipment classification identification decisions. According to the technical scheme, accurate identification of the Internet of Things equipment is realized, a network administrator can grasp the type and state information of the access equipment in real time, access or abnormal behaviors of unauthorized equipment are effectively identified, safety protection measures are taken in time, and the overall safety of an intelligent environment system is guaranteed.

The invention relates to the technical field of container houses, and discloses an intelligent environment control system for a container house, a temperature acquisition module deploys temperature sensing nodes in a plurality of temperature control areas divided in the vertical direction of the container house, and obtains the internal air temperature value and the envelope surface temperature value of each temperature control area; the model construction module determines a thermal resistance characteristic coefficient according to the surface temperature value and the internal air temperature value of the enclosure structure, determines a ventilation heat exchange coefficient according to the ventilation flow value and the internal and external temperature difference, and constructs a dynamic thermal resistance model based on the thermal resistance characteristic coefficient and the ventilation heat exchange coefficient; a deviation analysis module performs deviation analysis on the internal air temperature value and a preset target temperature field to obtain a temperature field deviation degree, and calculates a temperature control demand level; the temperature control module generates a cooperative control sequence of variable frequency air conditioner power adjustment and ventilation device opening adjustment according to the temperature control requirement level and a preset energy consumption constraint condition, and the uniformity, comfort and energy efficiency of temperature control of the container house are improved.

The utility model relates to a kind of intelligent environment monitoring devices running on domestic operating system, including temperature and humidity sensor, noise sensor, network interface, remote signalling lightning protection module, smoke sensor, water leakage sensor, vibration sensor, infrared intrusion detector, PM2.5 sensor, audible and visual alarm, monitoring equipment, temperature and humidity sensor, noise sensor, network interface, remote signalling lightning protection module, smoke sensor, water leakage sensor, vibration sensor, infrared intrusion detector, PM2.5 sensor, monitoring equipment, audible and visual alarm are connected with host computer, host computer uses 32 industrial ARM embedded processor main chip and uses domestic embedded real-time operating system RT-Thread, can monitor UPS detailed parameter, through with UPS communication acquisition, real-time monitoring the operating parameter of UPS, and the data of acquisition is uploaded to monitoring system platform, can monitor UPS input voltage, output voltage, output current, load percentage, UPS temperature, frequency, battery pack voltage, alarm failure etc.

A front shell layer and a back shell layer of the portable electronic identification device are spliced into a preset identification shape, the portable electronic identification device can be detachably attached to an external object through a fixing part, an electronic device integrated control device, a multi-type sensor and a light-emitting module are arranged in the portable electronic identification device, and a mode starting instruction associated with an attachment is received. And activating the corresponding sensor to acquire data, and generating a light instruction to drive the light-emitting module to generate a corresponding light effect. According to the portable electronic identification device, cooperative work of intelligent environment perception and visual feedback can be realized, and the requirements of portability and multi-mode adaptation are met.

The application discloses a low-cost, high-precision and high-reliability method for unmanned ship intelligent environment perception by fusing image segmentation and millimeter wave radar, which can acquire the environment information of the route and the surrounding of the unmanned ship in real time during the driving of the unmanned ship, including the shoreline, the sailing ship, the navigation mark and the floating object, and provide decision support for the real-time route adjustment and obstacle avoidance of the unmanned ship. The method comprises four parts of the millimeter wave radar, the binocular camera, the image segmentation module and the information fusion module, the image segmentation module is responsible for reading the image data, executing the image segmentation algorithm, identifying the target and generating the minimum fitting rectangle of the target, the information fusion module is responsible for receiving the data of the radar and the image segmentation module, acquiring the polar coordinates of the fusion target, matching the target size and outputting the result to the controller.

The invention provides an intelligent environment-friendly industrial laser cutting robot, and belongs to the technical field of laser cutting robots. Comprising a machining table, a cutting mechanical arm and a waste recycling mechanism, and the waste recycling mechanism is composed of a fixed grid plate, a storage plate, a collecting groove, a sliding filter plate, a knocking assembly, a collecting cavity, a first collecting box, a second collecting box, a limiting groove, a limiting plate, a ball, a telescopic spring, a first check block, a second check block and a protective door. The storage plates are integrally formed by triangular blocks and symmetrically arranged on the two sides of the through hole of the grid plate, and the positioning precision of the plates is guaranteed. The inclined filter plate cooperates with the check block to achieve efficient classification of large, medium and small chippings. The knocking assembly drives the filter plate to slide through slight shock and cooperates with the telescopic spring to prevent blockage. The problems that in the prior art, the structure is complex, manufacturing and maintaining cost is high, energy consumption is large, scrap classification is insufficient, and positioning precision is low are solved.

The invention discloses an intelligent environment regulation and control method and system for concrete winter maintenance, and relates to the technical field of environment monitoring, the system comprises a control center, and the control center is connected with a building acquisition module, an intelligent processing module, a maintenance analysis module and a strategy execution module; the method comprises the following steps: constructing a virtual-real twin space, carrying out graph conversion on collected data, obtaining a concrete monitoring dynamic graph and an environment monitoring dynamic graph, carrying out curve extraction on the concrete monitoring dynamic graph, obtaining a local monitoring coefficient, carrying out anomaly capture, obtaining a raw material mutation monitoring section, and carrying out time checking on the environment monitoring dynamic graph according to the raw material mutation monitoring section. Obtaining an environment sudden change time period; performing optimization simulation through a virtual-real twin space based on the environment sudden change time period to obtain an optimization regulation and control strategy; parameters are accurately controlled, personal errors are reduced, the concrete quality is guaranteed, and the curing efficiency is improved.

The invention relates to the technical field of computer technology and garden management, and discloses an intelligent environment response type park scenic spot sightseeing system and method and an edge computing architecture.The system comprises a distributed environment monitoring network, an edge computing server and an AR visual interaction interface, the edge computing server integrates multi-modal data through a data fusion module, and the AR visual interaction interface is connected with the edge computing server; and dynamically planning and generating an optimal touring route which optimizes the environmental comfort, the path distance and the tourist congestion degree at the same time by using a reinforcement learning algorithm based on a multi-dimensional reward function, and carrying out real-time touring on the optimized route information and real-time environmental data by using an AR visual interaction interface. And the function of adaptively adjusting the display information according to the sight focus of the user is realized. According to the invention, through deep integration of environment, position and congestion degree data, dynamic adaptive adjustment of a touring route is realized, scientificity and real-time performance of navigation are effectively improved, and more intelligent, comfortable and efficient garden touring service is provided for tourists.

The utility model discloses a self-loading and unloading type hot-line work mobile storehouse, which comprises a mobile storehouse, and a storehouse door is arranged on one side of the mobile storehouse. The telescopic lifting mechanism is divided into two unit groups which are arranged at the two ends of the movable storeroom correspondingly and used for loading and unloading the movable storeroom from a compartment of a cargo carrying vehicle. The intelligent environmental control system comprises a master control unit, namely a control panel, arranged in the mobile warehouse, and a plurality of supporting facilities used for adjusting and optimizing the temperature, humidity and air quality in the mobile warehouse. When the movable storeroom needs to be unloaded, the hydraulic transverse rods are started through the controller, so that the hydraulic vertical rods on the two sides extend to the outer sides of the two sides of a compartment of a cargo carrying vehicle, then the hydraulic vertical rods are started, and the whole storeroom is lifted to a certain height relative to the ground; after a certain distance is formed between the bottom of the storehouse and the top of the carriage, the vehicle can drive away; on the contrary, the loading operation of the mobile storeroom can be completed.

The application relates to the technical field of gas insulated ring network cabinets, and discloses an intelligent environment-friendly gas insulated ring network cabinet, which solves the problem that when the leakage position of an environment-friendly gas tank is positioned, the environment-friendly gas cannot be smoothly captured by a gas monitoring sensitive device, and the positioning of the leakage position of the gas tank is difficult; the airflow generated when the gas leaks in the sealing cover drives the fan blade to deflect; the position of the connecting piece where the gas tank leaks is determined according to the position of the driven measurement assembly; the specific direction of the leakage position on the side of the connecting piece is determined; the later maintenance is facilitated; the detection is not affected by the physical or chemical properties of the environment-friendly gas stored in the gas tank; the detection effect is reliable; the tracer gas is also introduced into the rubber cover; when the gas leakage of the connecting piece assembly position is slow, the tracer gas flows into the gas measurement pipe and is detected by the gas detector, and the gas leakage can also be detected, and the safety and reliability of the gas tank leakage detection are further improved.

The utility model discloses an acquisition board for an intelligent environment quality monitor, and belongs to the field of environment monitoring equipment, and the acquisition board comprises a bottom plate, the top of the bottom plate is provided with a main controller, the top of the bottom plate is provided with a mainboard, the top of the mainboard is provided with a 485 chip, the top of the mainboard is provided with a W5500 chip, and the 485 chip and the W5500 chip are connected with a power supply. Six connecting interfaces are formed in the top of the bottom plate, and a high-speed SPI interface and a network port are formed in the outer side of the main controller; a W5500 chip is arranged to support 10 / 100Mbps high-speed Ethernet data transmission, so that the requirements of most network applications on the data transmission speed can be met, quick and stable data transmission can be ensured in application scenes such as real-time data acquisition, video monitoring data transmission, network communication and the like, and three 485 chips are expanded to be connected with six interfaces, so that the data transmission speed can be greatly improved. And simultaneous access of multiple devices is supported.

The invention belongs to the technical field of power distribution, and particularly relates to an intelligent environment-friendly ring main unit power distribution switch control system and method, which are used for forming closed-loop control through real-time acquisition, state evaluation and instruction execution based on a sensor deployment scheme, configuration parameters, equipment state data and calibration parameters output by an intelligent monitoring system deployment and calibration module. According to the method, differential regulation and control strategies can be adopted for different operation states, control parameters are dynamically optimized in combination with machine learning, and an equipment operation state evaluation result, instruction execution feedback information, a load transfer execution result and optimized control parameters are output; on the basis, by means of an intelligent monitoring system and a cooperative control mechanism, the defect that a traditional control process lacks intelligent cooperation is overcome, real-time sensing of the operation state and rapid recognition, response and disposal of faults are achieved, accurate positioning can be achieved without tedious manual troubleshooting, and the fault repairing time is remarkably shortened.

The invention discloses an EdgeManager framework and AIoT system resource management method and detection method based on Lyapunov optimization, the method comprises the first step of EdgeManager system model construction and the second step of dynamic resource management, and the dynamic resource management method comprises task dynamic allocation, communication and computing resource allocation, a HyDRL-MO hybrid optimization algorithm and Lyapunov optimization. And the Lyapunov optimization comprises queue stability processing based on the Lyapunov optimization and deep learning online resource management based on an EdgeManager framework. Through the EdgeManager framework provided by the invention, the resource management efficiency and reasoning performance in an edge intelligent environment can be remarkably improved, and effective support is provided for industrial automation, large-scale video monitoring and other application scenes with high QoS (Quality of Service) requirements.

This invention belongs to the field of computer vision and intelligent environmental perception technology, specifically relating to a method for anomaly detection and intensity estimation of sandstorms based on event cameras. It includes constructing and training an estimation model based on a multi-stream regression framework, and using the trained estimation model to predict sandstorm concentration. The estimation model includes an encoding module, a temporal distillation module, a cross-channel interaction module, and a regression prediction module. The encoding module includes three parallel color spectral encoders for red, blue, and green. This invention effectively overcomes the technical limitations of traditional air quality prediction methods based on RGB images, such as insufficient temporal resolution, limited dynamic information expression capabilities, and insufficient utilization of information from a single data source. It achieves high-precision estimation of air particulate matter concentration and maintains good robustness and adaptability even under complex environmental conditions.

The invention discloses an intelligent environment sensing convenient display support which comprises a base and a lifting mechanism, the lifting mechanism is mounted on the base, and the lifting mechanism is used for moving a display up and down; the system further comprises a monitoring unit, a light-emitting unit and a control unit, the monitoring unit monitors ambient brightness and sends the ambient brightness to the control unit, and the control unit adjusts the brightness and color of the light-emitting unit and the display according to the ambient brightness. The convenient display support with the intelligent environment perception function is integrated with the environment monitoring sensor, the environment illumination intensity can be monitored, the illumination intensity can be displayed in real time, the brightness, the color temperature and the working mode of the display are automatically adjusted according to the detection result, and excessive eye use caused by the fact that a user does not adapt to environment changes is reduced.

1. The name of the design product: intelligent environment monitor. 2. The use of the design product: the design product is used for air environment monitoring. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The application relates to the technical field of ring network cabinets, in particular to a modular intelligent environment-friendly gas ring network cabinet which comprises a base, a supporting hollow bottom plate and cabinet bodies and the like; the base is connected with the supporting hollow bottom plate, and the base is provided with a plurality of supporting pulleys; the supporting hollow bottom plate is provided with a plurality of cabinet bodies, every two left and right adjacent cabinet bodies are symmetrically connected, every two upper and lower adjacent cabinet bodies are in the same direction, every cabinet body is provided with an arc-shaped protrusion at the top, every cabinet body is provided with an arc-shaped groove at the bottom, the arc-shaped protrusions and the arc-shaped grooves of every two upper and lower adjacent cabinet bodies are mutually clamped, and every cabinet body is provided with an air slot. The second clamping plate and the first clamping plate groove are clamped, and the gas is extracted, so that every cabinet body is fixed and the assembling gap is secondarily sealed, and the moisture problem caused by the penetration of water vapor is avoided.