294 results about "Environmental sensing" patented technology

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

Techniques for operating an environmental sensing system are described. In an example, a cloud-based server system receives a first indication that a first type of air pollutant is present within a first structure from a first plurality of indoor air quality (IAQ) sensing devices positioned within the first structure. A second structure within a predefined distance to the first structure is then identified by the server system. The server system then determines that the first type of air pollutant is not present within the second structure. The server system then causes a second plurality of IAQ sensing devices positioned within the second structure to change an operating mode from a normal sensitivity mode to a high-sensitivity mode.

The invention provides an emergency disaster dynamic decision-making method and system based on a multi-modal AI large model, and the method comprises the steps: firstly obtaining a real-time disaster monitoring data set of a target disaster area, covering disaster image collection, environment sensing, historical response records and geographical distribution topological data, carrying out the feature extraction of the real-time disaster monitoring data set, and carrying out the feature extraction of the real-time disaster monitoring data set; then calling a pre-trained multi-modal dynamic decision-making model, analyzing the disaster scene matching degree of the multi-modal disaster feature set, generating a matching degree score and disaster response optimization strategy set, and screening a target optimization strategy based on the matching degree score, and generating an emergency decision instruction set and sending the emergency decision instruction set to a disaster response terminal, finally obtaining decision execution feedback data of the disaster response terminal, and performing incremental parameter optimization processing on the multi-modal dynamic decision model to realize more accurate and effective emergency disaster dynamic decision.

The invention relates to a lithium battery charging system, and relates to the technical field of battery charging, the lithium battery charging system comprises a charging module, a detection module and a data processing module, and also comprises a battery health assessment module used for collecting battery cycle index, internal resistance and capacity attenuation parameters, and constructing a battery health state assessment model; the power grid interaction module is used for acquiring real-time electricity price data and a power grid load state; the environment sensing module comprises an environment temperature and humidity sensor and is used for collecting environment temperature and humidity parameters; and the AI prediction module is constructed based on a deep learning network and is used for predicting a battery state change trend. According to the invention, the battery health evaluation module is arranged to collect parameters such as cycle index, internal resistance and capacity fading rate to construct the SOH evaluation model, the problem of single dimension of battery health state evaluation in the prior art is solved, the aging degree of the battery can be accurately identified, and the risk of overcharge and undercharge of the aged battery due to improper power adaptation is avoided.

The invention relates to the technical field of carbon emission monitoring and environment perception, and discloses a user-side carbon emission real-time monitoring and tracing system, which comprises a multi-source data integration module, which is used for acquiring microenvironment parameters related to carbon emission in real time; the microenvironment feature analysis module is used for analyzing the feature parameters of the traffic road section through a microenvironment perception algorithm and establishing a microenvironment feature library; the environment relation modeling module is used for constructing a vehicle performance and microenvironment relation model and calculating an environment adjustment coefficient; the dynamic carbon emission calculation module is used for generating a carbon emission estimated value; the abnormal recognition and early warning module is used for recognizing an abnormal emission mode under a specific microenvironment combination and constructing an early warning mechanism; the carbon emission map building module is used for building an urban microenvironment carbon emission influence map; the carbon footprint analysis and report module generates a refined carbon footprint report through a microenvironment factor contribution degree quantitative decomposition algorithm; according to the method, through microenvironment influence decomposition and visual display, a carbon emission result has good interpretability.

The present invention belongs to the field of multi-equipment safety management and control for personnel and equipment clusters at an underground coal mine driving working face, and solves the problem of safety hazards caused by human-machine collaborative operations as the mechanization level of underground equipment increases. Provided are a system and method for safety monitoring and early warning of human-machine collaborative operation at a working face. The system comprises a safety management and control system for mobile equipment at a working face, which is used for performing in real time position, orientation and environmental sensing of individual equipment and behavior analysis of drivers; working-face collaborative intelligent wearable devices, which are used for environmental sensing of operating personnel and individual equipment and acquiring in real time the motion postures of the operating personnel; and a working face output control subsystem, which is used for identification and management of operating personnel entry and exit, position monitoring of personnel and individual equipment, operating personnel behavior identification and monitoring and working environment monitoring, and generating corresponding alarm instruction information. The present invention can realize human-equipment and equipment-equipment safety management and control during multi-equipment collaborative operation at a driving working face.

The invention provides a regional ecological environment quality evaluation method and system based on remote sensing data, and relates to the technical field of environment remote sensing and ecological monitoring, and the method comprises the steps: extracting red light and near-infrared reflectivity at the peak of a growing season by using Landsat8 satellite data, and calculating a mixed vegetation index; synchronously measuring vegetation indexes of pure vegetation and bare soil on site to obtain a vegetation coverage rate, and obtaining water transparency, suspended solid concentration and chlorophyll a concentration through on-site measurement on the basis of green light, red light, blue light and near-infrared reflectivity in a heavy rainfall period and a dry season; the method comprises the following steps: constructing a linear regression model of reflectivity and water quality parameters through a least square method, calculating a water quality index, constructing an extreme weather influence factor based on ten-year extreme weather data, and finally fusing a vegetation coverage rate, water quality, the extreme weather influence factor, annual precipitation, annual average temperature, optimal regional vegetation temperature and historical rainfall extremum. And constructing an ecological quality index, and dividing ecological environment quality grades.

The invention relates to the technical field of building construction early warning, and particularly discloses a building construction temporary facility early warning management and control system based on the Internet, environment data of a construction site are collected in real time through a multi-dimensional environment sensing equipment array, and a surface dust accumulation distribution image of a windproof dust suppression net is synchronously obtained through an image collection device; performing multi-source environmental parameter fusion modeling according to the environmental data to obtain a construction site environmental parameter fusion model; performing dynamic efficiency evaluation on the dust suppression capability of the windproof dust suppression net to obtain a dust suppression efficiency evaluation coefficient of the windproof dust suppression net; and when the dust suppression efficiency evaluation coefficient is lower than a preset threshold value, an atomization spraying system is triggered automatically, and a corresponding spraying instruction is generated automatically, so that the atomization spraying system is controlled. Through real-time monitoring, dynamic evaluation and automatic control, the use efficiency of the windproof dust suppression net can be effectively improved, raised dust emission of a construction site is reduced, and environment-friendly compliance of construction activities is ensured.

The invention provides a water chlorophyll a concentration inversion method and system based on multi-modal data and a lightweight model, and relates to the technical field of water environment remote sensing evaluation. The method comprises the following steps: firstly, acquiring a Gaofeng No.5 satellite remote sensing image, a sentinel No.3 satellite image and ground actual measurement data, and completing image preprocessing and water body pixel extraction; constructing a hyperspectral index and an aquatic vegetation index, and fusing the hyperspectral index and the aquatic vegetation index with the water body temperature, the pH environmental factors and the spectral reflectivity to form a multi-dimensional feature sample set; a core feature subset is obtained through random forest and XGBoost coupling feature selection, and a lightweight student model is trained based on knowledge distillation; and constructing a to-be-predicted feature sample for the to-be-predicted time phase image and the environment factor, inputting the to-be-predicted feature sample into the lightweight student model to obtain a chlorophyll a concentration predicted value, and generating a spatial distribution map and a quality control map layer. According to the invention, high-precision, low-redundancy and efficient deployment chlorophyll a concentration inversion is realized.

The invention relates to the technical field of mining local fan air duct air volume self-adaptive adjusting equipment, in particular to a mining local fan air duct air volume self-adaptive adjusting device. According to the technical scheme, the self-adaptive adjusting device for the air volume of the air duct of the mining local fan comprises a fan body, a radiator, an installation support, an electric adjusting air door, an air storage bin, the air duct, an environment detection module, an air speed detection module and a control module, the radiator is arranged on one side of the fan body, and the installation support is arranged on the bottom face of the fan body; an electric adjusting air door is arranged on one side of the ventilator main body; the environment sensing module is arranged, triple redundancy design is adopted, when the gas concentration is abnormal, whether the gas concentration is a spontaneous combustion symptom or not can be discriminated in combination with temperature data, or the coal rock collapse risk is pre-judged through the dust concentration change, and the safety interlocking response time is shortened.

The invention provides a method and a system for evaluating the health degree of audio and video conference equipment, which comprehensively utilize a QFD thought and AHP and AI methods to construct an equipment fault model and a fault risk transfer model, solve the field problem of fault influence dependency analysis among an equipment principle, a real-time operation state and a historical operation state in equipment fault detection, and improve the reliability of the equipment fault detection. The evaluation method comprises the following steps: scanning a basic state in real time and detecting an environment in real time; verifying the integrity of the signal link; carrying out equipment performance standard test; the influence of the hardware loss on functionality is evaluated; performing actual use state testing, including limiting condition testing and fault injection testing; and performing health degree modeling and grading, and establishing a multi-dimensional comprehensive evaluation model. According to the method, a traditional regular maintenance mode is exceeded through multi-dimensional dynamic modeling and a predictive intervention mechanism, real-time environment sensing and hardware loss modeling are fused, a fault window can be pre-judged, the equipment operation fault rate is reduced, and preventive maintenance is achieved.

The invention discloses a multi-probe environment sensing device for environment monitoring, and relates to the technical field of environment monitoring, the multi-probe environment sensing device comprises a fixed seat and a protection assembly, the middle part of the fixed seat is provided with a lifting assembly, the outer side of the middle part of the lifting assembly is provided with a shell, one side of the shell is provided with an air inlet, and the other side of the shell is provided with an air outlet; an air inlet is formed in one side of the shell, an air outlet is formed in the other side of the shell, the protection assembly is arranged outside the shell and comprises a servo motor, the servo motor is arranged on the outer side of the shell, and a driving gear is fixed to the end of an output shaft of the servo motor. The servo motor drives the gear set to drive the fan blade shaft to rotate, external air is forcibly sucked and evenly conveyed to the sensor, local concentration deviation is effectively eliminated, the monitoring accuracy is improved, meanwhile, the synchronous gear is used for linkage with the filter cover to rotate, the contact position of the filter face is continuously changed, and the filtering effect is improved. Meanwhile, the self-cleaning effect is achieved through cooperation with air outlet airflow back flushing, and the monitoring precision and stability of the sensor are improved through the design.

The invention relates to an on-line monitoring device and system based on an alternating current arrester. The on-line monitoring device comprises a basic monitoring unit, an environment sensing unit, a relay unit, a control unit and a calibration unit. The basic monitoring unit comprises a Rogowski coil 1 for monitoring the leakage current of the lightning arrester and a Rogowski coil 2 for monitoring the impact current. And the relay unit is controlled by the control unit and controls the working states of the Rogowski coil I and the Rogowski coil II. The environment sensing unit is used for monitoring environment parameters around the lightning arrester, the control unit judges environment parameter error coefficients according to the environment parameters and then feeds back the environment parameter error coefficients to the calibration unit, and the calibration unit forcibly corrects monitoring results. The measuring range can be automatically switched, high-precision monitoring is achieved through the first Rogowski coil in the case of low current, the second Rogowski coil is automatically switched to conduct wide-range monitoring in the case of impact current, the two Rogowski coils do not affect each other, and the measuring device has the advantages of being low in energy consumption and high in precision. And the calibration unit forcibly corrects the monitoring result, so that the detection precision of the device at low temperature is improved.

The invention discloses a satellite remote sensing atmospheric pollution gas concentration inversion method and system based on DOAS, and belongs to the technical field of atmospheric environment remote sensing monitoring. The method comprises the following steps: acquiring solar back scattering spectrum data observed by a satellite load and preprocessing the solar back scattering spectrum data; performing spectrum correction by using a high-precision instrument response function; in a selected fitting window, constructing a fitting model comprising a target gas differential absorption cross section, a Ring effect, Mie scattering and multiple scattering contributions; carrying out spectrum fitting by adopting a nonlinear least square algorithm, and carrying out inversion to obtain a differential batter column concentration (DSCD) of the target gas; in conjunction with an Atmospheric Mass Factor (AMF), the DSCD is converted to a Vertical Column Concentration (VCD). The system comprises a data acquisition and preprocessing module, a spectrum fine correction module, a DOAS spectrum fitting module, a dynamic AMF calculation module and a column concentration inversion and output module, and optionally comprises a data assimilation and traceability analysis module. According to the satellite remote sensing atmospheric pollution gas concentration inversion method and system based on the DOAS, the parameterized multiple scattering correction items are introduced, a dynamic AMF calculation scheme is developed, and the inversion precision and reliability under the complex atmospheric condition are remarkably improved.

The invention discloses an ecological environment remote sensing data analysis method, and belongs to the technical field of ecological environment. The data analysis method comprises the following steps: collecting remote sensing image data of a monitored water area, and carrying out atmospheric correction and geometric correction on the remote sensing image data; utilizing a co-occurrence matrix method to extract water body spectral texture features so as to identify spectral texture feature wavebands related to water body turbidity, chlorophyll content and dissolved oxygen; constructing a water quality parameter inversion model, training and verifying the model by using field sampling data, and optimizing the model; regularly acquiring new remote sensing image data of a monitoring area, inputting the new remote sensing image data into the optimized water quality parameter inversion model, and monitoring spatial and temporal distribution changes of water pollution parameters; determining a key area according to an inversion result, and obtaining high-temporal-spatial-resolution supplementary data of the key area by using the unmanned aerial vehicle; and collecting meteorological and hydrological station observation data, and comprehensively analyzing the source and flow direction of pollutants in combination with the inverted water quality parameter spatial and temporal distribution information.

The invention provides an air purification method and device based on big data, electronic equipment and a storage medium, and relates to the technical field of data processing. The air purification method based on the big data comprises the steps that indoor air data detected by a plurality of air purifiers and environment sensing data detected by a plurality of associated sensors are obtained, and the actual air quality change trend is determined according to the indoor air data and the environment sensing data; according to the actual air quality change trend, the initial air pollutant concentration, the highest pollutant concentration and the air quality change attribute are determined, and the overall air quality in the current environment is determined according to the initial air pollutant concentration, the highest pollutant concentration and the air quality change attribute; according to the actual air quality change trend, the indoor air data and the environment sensing data, a pollution source and a corresponding pollution source type are determined; and according to the air influence data, the overall air quality, the pollution source and the pollution source type in the preset distance range, a target purification mode is determined.

The invention relates to the cross technical field of intelligent traffic and heat management, and discloses an airplane air-conditioned vehicle anti-overheating protection system based on a radiation refrigeration coating, comprising: a multilayer composite structure module, the multilayer composite structure module comprising a radiation refrigeration coating, a spectrum regulation and control structure layer and a heat conduction structure layer with adjustable heat conductivity; the environment sensing module is used for collecting the solar irradiation intensity and the temperature state outside the airplane air conditioner vehicle; the control module is used for generating a control instruction for adjusting the spectrum regulation and control structure layer and the heat conduction structure layer; the response execution module is used for outputting a control instruction according to the control module; and the state feedback module is used for monitoring the thermal physical state of the structure after adjustment in real time. A multi-layer composite heat management scheme based on a radiation refrigeration coating and a spectrum regulation and control structure is adopted, and effective reflection of external heat radiation and optimized dissipation of internal heat are achieved by intelligently and dynamically adjusting a heat flow path and radiation emission.

The invention discloses a Chla monitoring method based on visible-near infrared spectrum and machine learning, which is a modeling method for performing chlorophyll a concentration parameter inversion by using visible-near infrared hyperspectral data, and combines primary screening of spectral characteristic wave bands, training sample expansion based on GAN, spectral characteristic wave band fine screening based on CARS and a regression modeling technology. The problems of high dimension of hyperspectral data and insufficient samples are solved, the overall Chl-a modeling precision is improved, and the method is suitable for water eutrophication monitoring, marine ecological assessment and environment remote sensing application.

The invention relates to the technical field of data processing, and discloses a high-speed rail lighting fault prediction method and system, and the system comprises a multi-dimensional collection module and an intelligent prediction module. According to the high-speed rail lighting fault prediction method and system, a multi-dimensional acquisition module is connected with a high-speed rail lighting system and a sensing device, management data of all lamps, operation data of all electrical circuits and environment sensing data of all carriages are obtained and classified to form a data set, an intelligent prediction module analyzes the use state of each lamp, and the intelligent prediction module is used for predicting the lighting fault of the high-speed rail. A corresponding fatigue coefficient is generated, then a fixed monitoring period, a temperature threshold value, a humidity threshold value, a frequency threshold value and an amplitude threshold value are set, the fluctuation trend of each electrical circuit and the environment change trend of each carriage are analyzed, a corresponding fluctuation coefficient and an environment data set are generated, and the multi-dimensional prevention capability is high. The risk of lamp fatigue damage, the risk of electrical circuit fault and the risk of lamp fault damage are evaluated, corresponding management suggestions are output, and the intelligent prediction management efficiency is high.

The invention discloses a blasting seismic source excitation control method and system based on environmental factors and electronic equipment, and relates to the technical field of seismic exploration. The method comprises the following steps: constructing a blasting performance model and loading the blasting performance model to a core calculation unit; a sensor is deployed in the blasting area, an environment sensing array is obtained, and a wireless communication link between the environment sensing array and the core computing unit is constructed; after the environment sensing array returns real-time environment factors, the core calculation unit starts the blasting performance model for analysis and outputs a control adjustment value; evaluating the adjustment value and optimizing and correcting based on an evaluation result to obtain a real-time blasting parameter; and the real-time blasting parameters are adopted to carry out focus excitation adjustment control. The technical problem that the blasting effect is not ideal due to the fact that blasting source excitation control cannot adapt to complex environment changes in real time in the prior art is solved, and the technical effect of improving the stability and adaptability of the blasting effect is achieved by obtaining environment factors in real time based on the environment sensing array and conducting intelligent adjustment in combination with the blasting performance model.

The invention provides a water quality parameter inversion method and system based on spectrum correction and electronic equipment, and relates to the technical field of water environment remote sensing. According to the method, the water quality parameter and the water body reflectivity value of the surface feature spectrometer are measured synchronously when the remote sensing image passes through the scene, spectrum correction is carried out on the remote sensing image by using actually measured data as a reference, and the accuracy of water quality parameter inversion is improved. Firstly, water quality parameter and water body reflectivity value measurement of a surface feature spectrometer are synchronously carried out innovatively when a remote sensing image passes through a scene, so that the consistency of measured data and the remote sensing image in time and space is ensured, and a reliable basis is provided for subsequent spectrum correction and water quality parameter inversion modeling. Secondly, a spectrum correction model is established based on the synchronously acquired measured data, the spectrum of the remote sensing image can be adjusted according to the measured data, and the reflectivity deviation caused by atmospheric correction errors and sensor calibration errors is reduced, so that the accuracy of the spectrum data is improved.

A digital-physical twin system and method for environmental process modeling and forecasting are disclosed. The system includes a digital twin server configured to receive environmental data from a real-world environment, including hyperspectral and spectroscopic imaging, simulate environmental process transitions using a predictive model based on the environmental data, and generate parameters for a physical experiment designed to validate or refine the predictive model. A physical twin device, comprising a scaled and instrumented representation of the real-world environment, is configured to execute the physical experiment under controlled conditions. Experimental data is returned to the digital twin to iteratively refine the predictive model in a closed-loop learning cycle using self-supervised and reinforcement learning. The system supports spatially-spectrally selective experimentation, including fluorescence spectroscopy, to enhance environmental sensing. This architecture enables scalable, sample-efficient modeling of processes such as post-wildfire hydrology, vegetation regrowth, and soil change.

The invention discloses a pollution source-based comprehensive risk assessment and monitoring system for plateau aviation occupational health, and relates to the technical field of risk assessment. The system comprises a pollution source identification module, an individual exposure evaluation module, a physiological state analysis module, a comprehensive evaluation prediction module and a health intervention suggestion module. The technical key points are as follows: according to the scheme, multi-parameter fusion monitoring points are deployed, a three-dimensional pollution map is constructed, and a plateau height and meteorological correction function is introduced, so that the overall scheme realizes spatial distribution modeling and comprehensive strength quantification of pollution sources in complex environments such as plateau airports; the problem of inaccurate pollution identification caused by traditional static data and experience judgment is solved, a high-precision and dynamically-updated pollution data basis is provided for subsequent exposure evaluation, the real-time sensing ability of the system to environmental risks is improved, and the accuracy and intelligence of the system in an environmental sensing layer are embodied.

The invention discloses a multi-modal classroom evaluation system based on a large model, and belongs to the technical field of multi-modal data processing, and the system comprises a multi-modal data collection module which is used for synchronously collecting visual data, voice data and environment sensing data; the distraction evaluation module is used for identifying whether student behaviors are abnormal or not according to a pre-trained large model; the multi-modal verification module is used for calculating the behavior confidence coefficient by utilizing the visual data, the voice data and the environment data when the behavior is abnormal; and the behavior decision module is used for judging whether misjudgment exists or not according to the magnitude of the behavior confidence. According to the invention, misjudgment is eliminated through multi-modal verification, and the accuracy and reliability of classroom monitoring are improved.

The invention provides a tone tuning method and a tone tuning system with an environment sensing artificial intelligence (AI) function, and a terminal device. The tuning method comprises the following steps: analyzing an acquired space sound wave signal, and generating a space characteristic parameter; the collected input audio signals are analyzed, audio characteristic parameters are generated, and corresponding noise types are output; matching the spatial feature parameters to corresponding spatial types, and determining scene types according to the spatial types and noise types; and obtaining a tuning parameter corresponding to the scene type, and outputting an adjusted audio signal. According to the method, the physical space type and the voice noise type are combined to judge the scene type at the moment, and the high misjudgment rate, the rigid tuning strategy and the insufficient dynamic adaptability caused by single-mode judgment are avoided.

Provided is an environmental sensing method based on model evolution, and the method performs a channel estimation task under the current communication system to obtain channel response data, thereby realizing environmental sensing. Firstly, an interaction mechanism between electromagnetic waves and environmental objective is divided into reflection and transmission. Subsequently, a mathematical model of channel response and environmental objective is constructed, and an environmental sensing problem is modeled as a compressed sensing optimization problem. Lastly, the present disclosure initiates from a baseline model and enables iteration and evolution of the model by solving the objective to solve the compressed sensing optimization problem, ultimately achieving environmental sensing. Compared with the existing environmental sensing reconstruction methods, the environmental sensing method based on model evolution herein closely resembles real electromagnetic wave propagation characteristics and requires less prior environmental information.

The invention relates to the technical field of biomedical engineering, and discloses a blood oxygen saturation degree detection method which comprises the following steps: S1, a multi-wavelength light source is utilized to irradiate a target area, and the light source comprises light with different wavelengths in a visible spectrum range; s2, collecting intensity information of light reflected or transmitted by the tissue; s3, constructing an oxyhemoglobin saturation model according to the received light intensity ratios under different wavelengths, and further provides an oxyhemoglobin saturation detection device which comprises a multi-wavelength light source module used for emitting light with different wavelengths in a visible spectrum range; and the optical sensor array is used for collecting the intensity information of the light reflected or transmitted by the skin tissue. By means of the multi-wavelength light source, machine learning optimization, personalized calibration and the environment sensing module, the accuracy and stability of blood oxygen measurement are remarkably improved, different individual differences and complex environment conditions can be adapted, and accurate blood oxygen saturation monitoring is ensured.

The invention discloses an intelligent management and control system for a digital flow of port production operation, and particularly relates to the field of data management, and the system comprises a port order information extraction module, an operation plan generation module, a port departure operation perception module, an operation risk analysis module, a position recognition module, an abnormal meteorological analysis module, and an operation load prediction module. According to the method, the operation plan is produced, time state sensing and departure ship monitoring are carried out based on the operation plan, and then the operation risk is analyzed, so that departure is flexibly adjusted, position deviation and meteorological abnormity are sensed through the positioning equipment and the environment sensing terminal after departure, and the working efficiency is improved. When the position of the ship deviates due to stormy waves, the weather and deviation relevance is quantified, and the subsequent operation plan is optimized accordingly, so that the future production operation difference caused when the port operation is greatly influenced by external factors such as weather, tide and channel conditions is avoided, and the operation and transportation efficiency can be improved on the premise of ensuring normal production operation.

The invention provides a data acquisition and analysis system for grain processing, a processing device and a processing method, and relates to the technical field of grain processing. The data acquisition and analysis system comprises an image acquisition module, a light source module, a component sensing module, a specification adaptation module, a data processing module and a man-machine interaction module; the appearance details of processed products are captured through high-definition images, internal components are analyzed through spectral analysis, temperature, humidity and dust are monitored through environmental sensing, dominant and hidden defects such as cracks and excessive moisture can be recognized at the same time, the detection effect is improved, different products can be automatically recognized, corresponding detection standards can be called, the parameter debugging time during production changing is effectively shortened, and the production efficiency is improved. According to the full-process automatic processing device, the requirements for multi-variety and small-batch production are met, in addition, through the full-process automatic processing device, manual intervention is greatly reduced, the labor cost of a production line is effectively reduced, the counting precision is improved, quantitative packaging errors are avoided, and therefore the intelligent level and the production efficiency of grain processing are effectively improved.