184results about How to "Comprehensive collection" patented technology

The invention discloses an intelligent online monitoring device for a transformer, and relates to the technical field of transformers. The intelligent on-line monitoring device for the transformer comprises a front-end acquisition layer, a data transmission layer, a data processing and analysis layer, a cloud monitoring and management layer and an emergency response layer, through the deployment of the high-precision sensor network and the edge computing nodes, the system can accurately collect and process the operation data of the transformer in real time, and the monitoring accuracy and efficiency are improved. And secondly, the data transmission layer adopts a self-adaptive communication protocol and a data encryption technology, so that real-time and reliable transmission and security protection of the data are ensured, and meanwhile, traceability and tampering prevention of the data are realized by utilizing a block chain technology. In the data processing and analysis layer, the system realizes intelligent early warning and continuous optimization of transformer data through an integrated learning and deep learning model, and improves the accuracy and reliability of early warning. And the emergency response layer is arranged, so that the system can quickly respond to faults.

The invention relates to the technical field of college teaching, in particular to an intelligent multi-dimensional college teaching quality improvement and evaluation system, which comprises a data acquisition module, a data processing module, an intelligent analysis module, a strategy recommendation module and an effect evaluation module, and is characterized in that the data acquisition module is used for acquiring original data in a teaching process from multiple dimensions; the data processing module is used for processing the collected original data to form a standardized and structured data set; the intelligent analysis module is used for carrying out deep analysis on the processed data by applying an intelligent algorithm and carrying out multi-dimensional quantitative evaluation on the teaching quality; the strategy recommendation module is used for providing personalized teaching strategy recommendation for teachers based on the analysis and evaluation result in combination with a preset teaching quality improvement model; and the effect evaluation module is used for tracking and evaluating the teaching effect after the teaching strategy is implemented in real time. The system realizes comprehensive, objective and accurate evaluation and improvement of teaching quality of colleges and universities.

The invention relates to the technical field of automatic inspection, in particular to an automatic road surface inspection robot control method and equipment, and the method comprises the steps: collecting environment data and robot motion state data through integrating a plurality of sensors, carrying out the preprocessing, carrying out the data fusion through employing a Kalman filtering algorithm, and generating a comprehensive road surface state map; a disease area is accurately identified and positioned through a convolutional neural network, dynamic path planning and polling strategy optimization are performed through a reinforcement learning algorithm, adaptive control of a complex road surface environment is realized through a fuzzy control algorithm, and meanwhile, a targeted road surface maintenance suggestion and an emergency processing scheme are generated according to real-time polling data, so that the real-time polling of a road surface is realized. According to the method, the scientificity and timeliness of road surface maintenance and traffic management are effectively improved, the road surface diseases can be accurately recognized and the inspection path can be optimized in a complex environment, and the road surface inspection efficiency and the intelligent level of road maintenance are remarkably improved.

The invention relates to an intelligent behavior analysis and anomaly detection system for Internet of Things security, which is applied to the technical field of Internet of Things security, and comprises a data acquisition module, a data preprocessing module, a behavior modeling module and an anomaly detection module, the data preprocessing module cleans the data, the behavior modeling module generates a vector sequence of equipment behaviors through feature embedding and position coding, and the anomaly detection module analyzes time sequence association of the equipment behaviors by utilizing a self-attention mechanism and a multi-head attention mechanism and judges whether the equipment has abnormal behaviors or not. The cooperative defense module realizes cross-device cooperative defense through an edge detection unit and a cloud analysis unit, and the threat intelligence sharing module realizes sharing and updating of threat intelligence through a local intelligence database and an external intelligence interface. And the response module carries out automatic defense and notifies a system administrator through the automatic defense unit and the alarm unit when an abnormity is detected.

The invention relates to a roadbed compaction quality monitoring, management and maintenance method and system based on virtual and real index collaboration. The method comprises the following steps: step 1, establishing an indoor compaction test for reproducing on-site roadbed compaction conditions, and establishing a data set of the indoor test; 2, implementing a field test on roadbed pressure, and establishing a data set of the field test; 3, building a roadbed compaction quality prediction model based on virtual and real data collaborative training; and 4, carrying out construction management and control application on the roadbed compaction quality prediction model based on virtual and real index collaborative prediction. According to the method, the influence of roadbed filler parameters, road roller working parameters and external environment parameters of roadbed compaction on the compaction quality is fully considered, multiple virtual-real indexes cooperate with one another to establish an artificial neural network compaction quality prediction model of the roadbed compaction quality, and the model is optimized and improved while being migrated to engineering practical application. And real-time accurate monitoring, management and maintenance of the compaction quality are realized in the on-site roadbed compaction process.

The invention relates to the technical field of operation monitoring, in particular to an operation parameter monitoring and early warning method and system for medical beauty equipment. The method comprises the following steps: acquiring medical beauty equipment data; performing equipment action range analysis on the medical beauty equipment data to generate medical beauty equipment action range data; carrying out equipment monitoring parameter integration through a multi-source sensor based on the action range data of the medical beauty equipment, and generating multi-modal monitoring data of the standard medical beauty equipment; and performing long-term performance degradation trend monitoring on the multi-mode monitoring data of the standard medical beauty equipment to generate long-term performance degradation trend data of the key parts of the equipment. Through multi-modal data integration, long-term performance trend analysis, biomechanical modeling and intelligent early warning decision optimization, the reliability and the intelligent level of monitoring and early warning of the operating parameters of the medical beauty equipment are improved.

The invention relates to the technical field of medical apparatus and instruments, in particular to a scar rehabilitation therapeutic apparatus for realizing treatment based on AI evaluation analysis, which comprises a host, an AI evaluation system and a magnetic treatment component, and is characterized in that the host comprises a shell and an electric control system; the AI evaluation system comprises a data acquisition module and a treatment planning module; the data acquisition module is used for acquiring human body scar area information, the treatment planning module identifies data acquired by the data acquisition module, scar information is obtained after the data are identified and processed, and a scar rehabilitation treatment scheme is planned according to the scar information; the electric control system controls the magnetic treatment assembly to perform scar rehabilitation treatment according to the scar rehabilitation treatment scheme; the magnetic treatment assembly comprises a magnetic fixing device, a magnetic driving device and a magnetic patch; the magnetic patch is attached to a human body scar area, and the magnetic driving device executes a scar rehabilitation treatment scheme under the control of the electric control system, so that the magnetic patch rotates and moves.

The invention discloses an ophthalmology department vision examination data collection and analysis system, and relates to the technical field of ophthalmology department vision examination data collection and analysis. An examination data collection module is used for examining and collecting ametropia and eye movement behaviors of a current patient, and an ametropia basic correction unit is used for calculating and outputting correction lens degree JD; an eye movement capturing precision evaluation value Y is calculated and output by utilizing an eye movement capturing precision evaluation unit, a feedback adjustment coefficient T and an updated correction lens degree JDnew are sequentially calculated and output by utilizing an ametropia feedback adjustment correction unit, and a result display module is utilized to display the correction lens degree JDnew. The updated correction lens degree JDnew of current correction and next optometry is displayed to the current patient, correction degree calculation of ametropia is continuously optimized according to the accuracy change of eye movement behavior capture, then the correction effect is improved, meanwhile, a formed feedback cycle mechanism also enables the system to have the self-learning and optimization ability, and the system can be widely applied to the field of medical science, medical science and medical science. And individual differences and disease changes of patients can be continuously adapted.

The invention relates to the technical field of code peculiar smell detection and reconstruction recommendation systems, in particular to an incremental code peculiar smell detection and reconstruction recommendation system and a method thereof.The incremental code peculiar smell detection and reconstruction recommendation system comprises a code extraction and static analysis module used for extracting a to-be-analyzed code snippet from a target project and preprocessing the code snippet to form a to-be-analyzed file; the runtime information acquisition and dynamic analysis module is used for operating the file to be analyzed in the sandbox environment, acquiring and summarizing runtime information and forming a dynamic analysis report; the learning module is used for receiving a static analysis result and a dynamic analysis report, comprehensively considering static information and an incremental model updating report, constructing vector representation of code potential features and updating an incremental learning model; and the reconstruction recommendation module is used for scoring each code snippet based on the code feature vector and an incremental learning model, recommending a corresponding reconstruction rule, and providing a more accurate peculiar smell detection result by fusing static code features and dynamic operation information.

The invention provides a road inspection method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining a plurality of inspection points on a target road, determining a vehicle inspection position point according to each inspection point, the vehicle inspection position point being a position where an image collection device of an automatic driving vehicle can shoot the inspection point, the inspection points are in one-to-one correspondence with the vehicle inspection position points; adopting a heuristic search algorithm to calculate and obtain an inspection route at least according to all the vehicle inspection position points; and controlling the automatic driving vehicle to inspect the inspection points in sequence according to the inspection route to obtain corresponding inspection images, uploading all the inspection images to a back-end server to determine an inspection result, and inspecting the target road. The method has higher stability, can adapt to more extreme working environments and collect more stable detection data, and meanwhile, the automatic driving vehicle is provided with various abundant high-precision sensors, so that target defects and quality can be monitored more comprehensively.

The invention provides a capacitance detection method, system and device and a storage medium, and relates to the technical field of capacitance detection. The method comprises the following steps: acquiring a capacitance type and a parameter range of a measured capacitor, and matching with a preset resonance library according to the capacitance type and the parameter range to obtain a target resonance inductance parameter; constructing a target LC resonance test circuit based on the target resonance inductance parameter, the capacitor type and the parameter range, and measuring the tested capacitor based on the target LC resonance test circuit to obtain resonance response data; and analyzing the resonance response data to obtain a capacitance performance index. Through the scheme, the capacitor testing efficiency is improved.

The invention provides an intelligent dynamic reactive power compensation system and a control method thereof, and relates to the technical field of circuit reactive power compensation systems, and the system comprises a control system which is internally provided with a console and a main control chip, and is used for controlling the starting and stopping of the whole reactive power compensation system; the distribution device is in signal connection with the control system, and the distribution device is connected with an external cable; and the compensation system is connected with an external cable through the distribution device. According to the system and the method, through real-time data acquisition and an advanced control algorithm, the system can quickly respond to the change of a power grid, dynamically adjust SVG, SVC and a synchronous phase modifier, realize high-precision reactive compensation, improve the stability and the power supply quality of a power system, and predict the reactive power demand by utilizing model reasoning and real-time monitoring, so that the power supply quality of the power system is improved. And an optimal compensation scheme is generated, and a strategy is dynamically adjusted, so that over-compensation or under-compensation is avoided, and the system is ensured to be always in an optimal state.

The invention relates to the technical field of data monitoring, in particular to a concentrator data acquisition method and device, equipment and a medium. The method comprises the following steps: collecting monitoring data received by a concentrator; classifying the monitoring data into a plurality of groups of data according to the acquisition equipment, wherein each group of data corresponds to one acquisition equipment; determining a device type corresponding to the target data set, and inputting the target data set into a preset recognition model corresponding to the device type to obtain a recognition result about whether the target data set output by the preset recognition model is abnormal, the target data set being any one of multiple groups of data; if the identification result is that the target data group is abnormal, analyzing an abnormal reason; and generating a warning signal containing the abnormal reason, and sending the warning signal to terminal equipment corresponding to the related personnel. According to the invention, the data exception of the concentrator can be identified in time.

The invention discloses a health state self-diagnosis device and method of a compressed air foam fire extinguishing system, and belongs to the technical field of fire fighting, and the health state self-diagnosis device of the compressed air foam fire extinguishing system comprises a fire extinguishing device, a control device assembly and a collector assembly, a collector assembly and a control device assembly are both arranged in the fire extinguishing device, the collector assembly is responsible for monitoring key parameters of the fire extinguishing device in real time, the collector assembly is in circuit connection with the control device assembly, the control device assembly comprises a data collection system and a processing system, the data collection system is in circuit connection with the collector assembly, and the processing system is in circuit connection with the collector assembly. The data collection system is used for collecting real-time data of all elements of the detection device, the data collection system is connected with the processing system, and the processing system processes the collected data, monitors abnormal conditions of the device, gives an alarm in time, and can perform real-time feedback correction and risk assessment.

The invention relates to the technical field of yarn quality detection, and discloses an intelligent yarn quality recognition system and method.The system comprises a data capture layer, an image optimization layer, a model analysis layer and a user interaction layer; and through optimization processing of noise reduction, color space conversion, image segmentation and the like, a yarn image area needing to be analyzed is extracted. And constructing a defect detection model by adopting a deep learning algorithm, and carrying out defect identification and classification on the yarn image. And the user interaction layer is responsible for displaying a detection result and receiving a user operation instruction to carry out corresponding system operation or parameter adjustment. According to the method, the accuracy and efficiency of yarn quality detection are improved, the defect detection model is constructed by adopting the deep learning algorithm, whether the yarn image has defects and the specific types of the defects can be objectively and consistently judged, and the problem of detection inconsistency caused by subjective judgment in manual detection is avoided.

The invention discloses a prizing-imitating micro-plastic collecting robot and a multi-mode switching control method thereof, and relates to the technical field of underwater robotics.The prizing-imitating micro-plastic collecting robot comprises a fish head, a mouth opening and closing device, pectoral fins, a trunk, a filtering assembly, dorsal fins, a fish tail, tail fins, a fish tail driving assembly, a power source and a controller, and the fish head, the trunk and the fish tail are linearly and sequentially arranged from front to back; the mouth opening and closing device is arranged at the front end of the fish head. The two pectoral fins are symmetrically arranged on the left side and the right side of the fish head. Two water filtering mechanisms are symmetrically arranged on the left side and the right side of the fish head, a collecting bin and an equipment bin are sequentially arranged in the trunk from front to back, a rotary valve assembly is arranged on the front side of the collecting bin, a filtering assembly is located on the rear portion of the collecting bin, and the fish tail is of a three-section structure. The prizing-imitating micro-plastic collecting robot continuously collects micro-plastic in water through a set route, time and labor are saved, the robot can work in a deep water area, the collecting efficiency is high, the use cost is low, the robot is not affected by the environment and weather, safety is high, and the micro-plastic in the water area is comprehensively collected.

A computer-implemented multispectral imaging method is disclosed for analyzing a sample comprising a plurality of types of fluorescent labels, each of the plurality of types of fluorescent labels having a respective emission spectrum. The method comprises: receiving multi-channel image data, each channel in the multi-channel image data comprising image data derived from an unfiltered image of a sample and having a respective spectral content; for each channel: i) forming a vector for measuring quantum particle counts from the image data for the channel, the vector having an entry for each pixel in the image; ii) iteratively generating, for each pixel in the image, a vector of possible values having entries contributing to the unfiltered image by each of the plurality of types of fluorescent labels, and for each iteration, generating a vector of possible values for each pixel by multiplying the vector of possible values for each pixel by a hybrid matrix, the hybrid matrix defines a relationship between the unfiltered image and the multi-channel image data, calculates a vector of expected quantum particle counts, the vector having entries for each pixel in the image; and iii) selecting a vector of possible values for which, given a corresponding vector of expected quantum particle counts, a negative log-likelihood function describing the probability of the generated vector of measuring quantum particle counts is minimum; and for each of a plurality of types of fluorescent labels in the sample, constructing a corresponding data structure comprising image data, where, for each pixel, the data structure comprises an entry contributed by the fluorescent label type for a vector according to possible values for the pixel, such that each data structure is available to reconstruct an image of the sample, the spectral content thereof corresponds to a corresponding emission spectrum of the fluorescent label type for which the data structure is constructed.

The utility model discloses a tire burst device for automobile detection, and particularly relates to the technical field of tire burst devices, the tire burst device comprises a workbench, the rear part of the upper end of the workbench is fixedly connected with a feeding assembly, the middle part of the upper end of the workbench is slidably connected with a clamping device, and the right part of the clamping device is fixedly connected with a motor; a blasting assembly is slidably connected to the front portion of the upper end of the workbench. According to the tire burst device for automobile detection, the tire can be rolled to the lower portion of the clamping device in cooperation with the feeding assembly in the tire mounting process, the labor force of workers can be reduced, the carrying weight is reduced, similarly, the tires are rolled down through the feeding assembly in the tire discharging process, the carrying pressure of the workers is reduced, and the working efficiency is improved. In the experiment process, the device can be matched with a clamping device to drive the tire to rotate, the state of the tire in the driving process is simulated, the device can be matched with a motor to control the rotating speed of the tire, and therefore the state that the tire is exploded at different speeds is simulated, and the experiment range is expanded.

The invention relates to the field of area coverage control methods, and discloses a multi-agent system channel type area coverage control method based on arc length, which comprises the following steps: S1, target area division: dividing a channel type target area into a plurality of sequential sub-areas based on an arc length division method; s2, designing a distributed controller of a virtual leader agent, wherein the distributed controller of the virtual leader agent ensures that the arc length of a coverage area of each group of agents is in direct proportion to the number of the group of agents; and S3, designing an individual intelligent agent distributed controller, and enabling the intelligent agent to realize sequential team coverage control on the area S by combining the individual intelligent agent distributed controller with a distributed controller of a virtual leader intelligent agent. The distributed controller designed by the invention enables the intelligent agent to adjust the coverage area of the intelligent agent in real time according to the change of the actual environment.

The invention relates to the technical field of inhaul cable monitoring, in particular to a parallel steel wire inhaul cable broken wire monitoring method and monitoring system based on weak grating optical fibers. The method includes the steps that when an inhaul cable is manufactured, weak grating sensing optical fibers are installed in the inhaul cable, the weak grating sensing optical fibers are arranged along the whole length of the inhaul cable, and a plurality of strain grating measuring points are evenly arranged on the weak grating sensing optical fibers at intervals in the length direction; in the service stage of the inhaul cable, installing an optical fiber demodulator to be in signal connection with the weak grating sensing optical fiber in the inhaul cable; weak grating sensing optical fiber signal wavelength is collected in real time based on an optical fiber demodulator, and the broken wire condition in the inhaul cable is determined according to the relation between the weak grating sensing optical fiber signal wavelength and the number of broken wires in the inhaul cable. The method for monitoring the broken wire of the parallel steel wire inhaul cable based on the weak grating optical fiber is very simple, the breakage condition of the steel wire in the inhaul cable can be accurately monitored, interference of the external environment is avoided, the overall monitoring result is accurate, and the monitoring method is simple.

The utility model relates to the technical field of leftover material processing, and discloses leftover material processing equipment for a calender, which comprises a fixing frame, a fixing plate is fixedly mounted on the left side of the fixing frame, a motor is fixedly mounted at the top of the fixing plate and can enable a rotating shaft to rotate, and the rotating shaft is fixedly mounted at the output end of the motor and can rotate around the rotating shaft. According to the leftover material processing equipment for the calender, when leftover materials of rubber sheets are cut off by the cutter, the leftover materials are collected through the collecting box, then the reciprocating lead screw is driven to rotate through the connecting rod, the nut drives the L-shaped rod to move, and therefore the leftover materials are cut off by the cutter, and the leftover materials are collected through the collecting box. The moving rod drives the evacuating plate to comb the interior of the collecting box, the situation that leftover materials fall to one end of the collecting box and possibly fall to the ground after being stacked is prevented, in this way, operators do not need to clean the ground, only leftover materials in the collecting box need to be poured out, and the workload of the operators is relieved.

The invention discloses a distribution network overhead line partial discharge monitoring system and a distribution network overhead line partial discharge monitoring method, and relates to the technical field of power system monitoring, the system comprises a sensor module, a data acquisition module, a signal processing module and a central control module, and the monitoring accuracy and reliability are improved by fusing a plurality of innovative algorithms; the time-frequency analysis algorithm captures signal time-frequency features, the fuzzy logic algorithm processes uncertainty, the deep belief network algorithm extracts deep features, and the false alarm rate and the missing report rate are reduced by reasonably determining weight coefficients and fusing the results of the three algorithms. Signal information is enriched, various environments are adapted, the contribution degree of each sensor is dynamically adjusted by determining a weight coefficient, the anti-interference capability is improved, and signals are comprehensively and accurately collected.

The invention discloses an integrated power data safety monitoring and analysis system, which relates to the technical field of power data safety and comprises a data acquisition module, a safety monitoring module, a risk assessment module, an early warning decision module and an emergency response module. Advanced technologies and innovative algorithms are integrated, efficient cooperation of all the modules is achieved, comprehensive, accurate and pre-processing of data acquisition, precise anomaly detection of safety monitoring, scientific and accurate grading of risk assessment, timely and effective early warning decision making and rapid and appropriate emergency response are included, the innovative algorithms improve the accuracy and reliability of monitoring analysis, and the system is suitable for large-scale popularization and application. The method adapts to safety requirements of different scales of power systems and changes, can effectively cope with novel safety threats, reduces safety incident loss to the greatest extent, and guarantees stable operation of the power systems.

The invention discloses an unmanned workshop data processing system based on data analysis, particularly relates to the technical field of big data, and comprises a monitoring area determination module, a data acquisition module, a data preprocessing module, a data analysis module, a quality evaluation module, a judgment module and a man-machine interaction module. The monitoring area determination module is used for determining a single sub-time monitoring area and a single PCB monitoring area; the data acquisition module is used for acquiring a first unmanned workshop key information value; the data preprocessing module is used for obtaining a second unmanned workshop key information value; the data analysis module is used for performing data processing on the second unmanned workshop key information value to obtain a third unmanned workshop key information value; the quality evaluation module is used for constructing a quality evaluation model to obtain a PCB manufacturing comprehensive evaluation value; the judgment module judges whether the manufacturing of the PCB meets the standard or not; and the man-machine interaction module is used for providing a visual interface. The production efficiency of an unmanned workshop is effectively improved, and the qualified rate of PCBs is improved.

The invention discloses an industrial control asset risk identification method and system based on semi-supervised anomaly detection, and relates to the technical field of network security, and the method comprises the steps: obtaining related asset information through combining active scanning, passive scanning and log detection, and extracting asset feature flow based on the related asset information; performing flow anomaly detection on the asset characteristic flow by using a semi-supervised anomaly detection model, and updating asset data characteristics with abnormal flow; and performing feature traffic hash value matching and feature library comparison on the updated asset data features, then judging whether the asset information has a vulnerability risk, and judging the risk level of the asset. According to the method, an asset change monitoring function is added on the basis of risk asset identification, asset change can be sensed, and a data basis is provided for abnormal flow analysis; moreover, offline updating of the feature library of the risk asset identification system is realized, and the real-time performance and traceability of asset data are ensured.

The impurity collecting device comprises a treatment box, a water inlet pipe is installed on one side of the upper end of the treatment box, a sewage box is installed on one side of the upper end in the treatment box, a first button type valve is installed at the lower end of the sewage box, and a multi-stage rotary filtering mechanism is installed in the treatment box. And an impurity collecting mechanism is mounted in the treatment box. The sewage treatment device has the beneficial effects that multiple layers of filter boxes are arranged, each layer of filter box is provided with multiple filter boxes, the filter boxes can filter sewage discharged by the first button type valve or the second button type valve, when the filter boxes rotate, through gear transmission, the filter boxes can be turned over, a cleaning plate can also be rotated, and the cleaning effect is improved. By means of the device, impurities on the filter screen can be scraped off, then the impurities fall into a collecting area under the action of gravity, the impurities on the filter box can be cleaned through vibration, the impurities can be conveniently collected, participation of workers is not needed, and the labor intensity of the workers is reduced.

The utility model relates to the technical field of sewage sampling, in particular to a sampling device. Universal wheels are fixed to the four end corners of the bottom of the base, an angle adjusting structure is arranged at the position, close to the middle point, of the top of the base, a height adjusting structure is arranged at the top of the angle adjusting structure, and a lifting structure is arranged on one side of the bottom of the height adjusting structure. According to the sampling device provided by the utility model, through the design of the angle adjusting structure, the sampling range of the device is wide, and the actual condition in sewage is accurately reflected; through the design of a height adjusting structure and a lifting structure, it is ensured that the device can accurately obtain samples in water bodies with different depths, and possible missing of key data due to the fixed sampling depth is avoided; and through the design of the leaking stoppage structure, the sampling operation is convenient and efficient, the sampling operation efficiency is greatly improved, and the operation time and the labor cost are reduced.

The invention discloses a multi-mode mental health assessment system and method based on interface type emotional interaction, and relates to the technical field of medical care. The system comprises a data collection unit, an emotion recognition unit, a feature extraction unit, an evaluation unit, a feedback unit and a tracking unit. The method comprises the following steps: performing feature extraction on collected information through a feature extraction unit, constructing a relation between modals, fusing the relation and mutual influence among key parts, images, voices and texts in feature information, and paying attention to the relation of information in the modals and between the modals by a modal information interaction module; in the modal information interaction module, a multi-head dot product attention mechanism is used to accurately select features associated with another modal, and a feature fusion module is used to select parts containing emotion information in the interaction features for classification. The classification accuracy of the multi-modal data is higher than the classification accuracy of the single-modal data, so that the accuracy of mental health assessment is effectively improved.

The invention discloses a sleep environment parameter recommendation system based on sleep big data, which relates to the technical field of sleep big data and comprises a sleep monitoring system, a monitoring data analysis system, an environment equipment automatic regulation and control system and a sleep monitoring data feedback system. And the sleep monitoring system is used for monitoring the sleep process of the user and realizing data collection in the monitoring process, and the data collection is divided into body sleep data collection and environment data collection. The sleep environment parameter recommendation system based on the sleep big data can effectively collect the sleep physical state and the sleep environment of the user in a room, meanwhile, the system is further in interactive connection with all intelligent devices in the room, the sleep quality of the user is judged according to the collected sensing data, and the user experience is improved. The data is compared with an original sleep data model, and the sleep environment is automatically optimized, so that model parameters most suitable for sleep are obtained.