47 results about "Cloud data management" patented technology

The invention discloses an artificial intelligence self-adaptive education system based on big data, and relates to the field of intelligent education, the artificial intelligence self-adaptive education system comprises an education resource management module, a student data management module, a self-adaptive learning recommendation module, an auxiliary teaching decision module and a security reinforcement module, and a cloud data management library is used for classifying and integrating education resources. The method comprises the steps of establishing a knowledge graph, performing knowledge point classification on resources by constructing the knowledge graph, performing accurate matching on personal portraits of students and the knowledge graph through an incremental self-adaptive matching model, dynamically adjusting recommended learning resources in real time, analyzing collective learning problems through a big data intelligent analysis model, and generating collective teaching suggestions. A three-layer security firewall is adopted to ensure the security of the system; the problem that an existing self-adaptive education system neglects the learning progress and weak links of classes or groups is solved, and intelligent management, personalized learning recommendation and collective problem analysis of education resources can be achieved.

The invention relates to shipbuilding flatness inspection equipment and a use method thereof, and belongs to the technical field of shipbuilding flatness inspection. The shipbuilding flatness inspection equipment comprises a magnetic adsorption type mobile robot platform, a high-precision vision module, an edge computing unit and a cloud data management platform; the magnetic adsorption type mobile robot platform is provided with a crawler-type walking mechanism and a self-adaptive vacuum adsorption unit. According to the invention, efficient and high-precision whole ship flatness intelligent detection can be realized through multi-sensor fusion and a self-adaptive robot platform, the system is suitable for real-time quality monitoring in ship body section construction, deck assembly and fitting-out stages, local fine scanning can be executed along a welding seam area in a ship body section detection scene, and the detection accuracy is improved. A welding deformation out-of-tolerance area is synchronously marked, a non-key plane area can be switched to a global rapid scanning mode, and in the deck assembly stage, a cloud platform can integrate data in real time and assist in carrying out an assembly dislocation correction scheme.

The invention discloses a road guardrail real-time monitoring device and system based on wireless networking, and relates to the technical field of road facility monitoring, the road guardrail real-time monitoring device comprises a cloud data management platform, the cloud data management platform is in communication connection with a guardrail monitoring node module, an edge computing gateway module, an abnormal state alarm module and an operation and maintenance management module, the modules are in electric signal connection; the guardrail monitoring node module is used for planning monitoring nodes in an area to be monitored and deploying guardrail monitors at the monitoring nodes so as to acquire guardrail state data of the monitoring nodes in real time. Through the wireless networking technology, the road guardrail state is monitored in real time, and tiny deformation, abnormal vibration and inclination angle change of the guardrail can be captured in time, so that the real-time performance and accuracy of traffic safety monitoring are improved, and compared with traditional passive inspection, the active sensing mode has the advantages that the traffic safety is improved, and the traffic safety is improved. The labor cost and the leak detection risk are greatly reduced, and a powerful guarantee is provided for road traffic safety.

The present invention provides a photovoltaic power generation data acquisition system and method; the data acquisition system includes a photovoltaic power generation unit, a photovoltaic power generation data acquisition unit, a central data processing terminal, a cloud change operation unit, a malicious tampering identification unit, a cloud data storage repository, a power generation power prediction unit, a power quantity centralized scheduling unit, a scheduling task adjustment unit, and a power generation anomaly monitoring unit; by adding the cloud change operation unit, the present invention realizes cloud data management and processing of the power generation data stored in the photovoltaic power generation, and improves the management security and efficiency through identity verification processing before management. By adding the scheduling task adjustment unit, the present invention realizes the distribution processing of the power quantity of the photovoltaic power generation to substations at various locations, and adaptively changes the power distribution values of each substation.

The invention belongs to the technical field of small animal mental disease diagnosis, and provides a neuroimaging-based small animal mental disease diagnosis system, which comprises an image data acquisition and preprocessing module for performing multi-modal imaging on a small animal by using high-precision neuroimaging equipment, acquiring neuroimaging data, preprocessing the acquired image data, and acquiring neuroimaging data; comprising the steps of denoising, registration, segmentation and the like so as to improve data quality; a feature extraction and analysis module; according to the invention, the resolution and the signal-to-noise ratio of the image data are improved through the multi-modal neural imaging technology, and a reliable data basis is provided for accurate diagnosis; image features are automatically extracted and analyzed through a deep learning algorithm, human intervention is reduced, and the accuracy and efficiency of diagnosis are improved; a personalized diagnosis model is constructed, individual differences of small animals are considered, and more accurate diagnosis is realized; through introduction of the cloud data management platform, centralized storage, sharing and cooperative analysis of data are realized.

The invention relates to the field of cloud data management, in particular to a cloud game data management system. The system comprises a plurality of cloud game devices which are deployed in a plurality of edge cloud nodes, comprise at least one first cloud game device and are used for updating a first game in a readable and writable first cloud disk, and the first cloud disk is only used for storing data information of the first game; the distributed block storage module comprises a plurality of block storage units, the block storage units are respectively deployed in a plurality of edge cloud nodes, the first block storage unit is used for generating a second cloud disk under the condition that the updating of the first cloud disk is finished, and synchronizing the second cloud disk to the second edge cloud node; enabling a second block storage unit in the second edge cloud node to generate a third cloud disk which is the same as the second cloud disk and is used for running the first game; the first block storage unit is a unit of a first edge cloud node, the first edge cloud node is a node where the first cloud game device is located, and the second cloud disk stores snapshot data of the first cloud disk.

The application discloses a kind of light security communication method and system for agricultural perception node.The method includes that 256-bit static master key is preset in the bottom layer security storage area of perception node and heterogeneous convergence gateway, and the unique hardware serial number is read as node identification;Synchronous acquisition environmental data and visual feature data, splice into long payload plaintext;Derive dynamic session key, use dynamic session key to drive stream cipher to encrypt long payload plaintext, static master key drives block cipher to encrypt short header, and encapsulates into data frame;Data frame is sent through wireless channel, and heterogeneous convergence gateway receives and reversely decrypts short header to extract dynamic anti-fake factor, and replay attack verification is carried out;After verification, dynamic session key is reconstructed to restore data and uploaded to cloud data management server.The application also discloses a system using the above light security communication method for agricultural perception node, realizes the high security node legitimacy authentication and anti-replay attack under limited hardware computing power.

The invention discloses a ship-shore cloud intelligent platform, a ship intelligent management method and a storage medium. The ship-shore cloud intelligent platform comprises a ship end edge processing module, a cloud intelligent analysis module, a cloud data management module and a dynamic transmission control module. The ship end edge processing module is used for collecting data of a ship sensor, carrying out edge side preprocessing on the data and then sending the data; the cloud intelligent analysis module is used for receiving the preprocessed data, establishing a domain model based on multi-dimensional relevance of the data, and calculating and generating a state result of each functional module; the cloud data management module is used for carrying out relevance storage and management on the data based on the domain model; and the dynamic transmission control module is used for dynamically adjusting the data sampling strategy of the ship end edge processing module according to the real-time data flow. According to the invention, the problems of function abnormity and insufficient function real-time performance caused by satellite signal interruption and high propagation time delay in the existing ship intelligent platform are solved.

The invention discloses a pet personalized meal whole-process service system and method, and relates to the technical field of intelligent pet services, and the system comprises a cloud data and service module and an intelligent cooking execution module. The cloud data and service module is used for establishing and managing pet structured archives, generating quantitative recipes through a catering algorithm based on archive data, associating an e-commerce system to carry out clean vegetable commodity matching and order process management, and storing full-process data, and the full-process data comprises archive updating, cooking feedback and order recording. According to the pet personalized meal whole-process service system and method provided by the invention, through integration of intelligent hardware, cloud data management and e-commerce service, comprehensive personalization and scientization of pet feeding are realized, the problem of unbalanced nutrition in traditional pet feeding is effectively solved, and the pet can be fed according to the specific health condition and nutritional requirements of the pet. A nutrition formula is dynamically generated and adjusted, and pet health is promoted.

The invention relates to the technical field of civil engineering detection equipment, in particular to a wireless crack width measurement method and system based on image recognition, and the system is composed of a detection end, a handheld terminal and a cloud server. The detection end can collect a crack image and transmit position information; the handheld terminal realizes image preprocessing, crack identification and width calculation through an AI processor; the cloud server constructs a distributed storage system and is internally provided with a crack prediction model; according to measurement, crack data are collected through a detection end, an AI processor of a handheld terminal executes Gaussian filtering, CNN recognition and Canny edge detection to calculate the width, and a cloud server predicts the crack trend through an LSTM + SVR model. Through multi-module integration, a depth AI algorithm and cloud data management, the problems of low measurement precision, insufficient intelligence, difficulty in data management and the like are solved, high-precision and intelligent crack detection and prediction are realized, and the data management efficiency and safety are improved.

The invention belongs to the technical field of high-voltage cable monitoring, and relates to a high-voltage cable metal sheath grounding state diagnosis method and system fusing multiple parameters. And a complete'digital twinning 'closed loop fed back comprises physical world perception, network transmission, cloud data management, multi-parameter fusion, intelligent diagnosis decision, visual presentation, artificial feedback and model self-optimization. A data island is broken through multi-parameter fusion, a leap from'static threshold alarm 'to'dynamic intelligent prediction' is realized through adaptive learning, and finally a predictive maintenance system capable of continuously accumulating knowledge and improving accuracy is formed.

The invention specifically relates to the technical field of aircraft control and navigation, and discloses an intelligent unmanned aircraft trajectory control and navigation system based on multi-source data fusion, which comprises an airborne data acquisition module, a ground cloud data management module, a flight action recognition module, an energy consumption decomposition calculation module, a trajectory calculation calibration module and a trajectory correction control module, flight associated data, flight meteorological data and flight plan data are fused through an airborne data acquisition module and a ground cloud data management module, a flight associated data set is constructed, the trajectory length is calculated in a backstepping manner in an environment without GNSS signals, a high-precision trajectory measurement result of a region without GNSS signals is continuously output, an associated energy consumption coefficient is corrected, and a high-precision trajectory measurement result of a region without GNSS signals is obtained. It is ensured that the mapping relation between the level flight equivalent energy consumption and the flight distance is accurate and reliable, real-time feedback is provided for autonomous navigation and path control of the unmanned aerial vehicle, and the adaptability of the system in a complex environment is remarkably improved.

The invention relates to the technical field of cloud data management, in particular to an emergency data sharing method based on a cloud platform, which comprises the following steps: acquiring cloud emergency data distribution characteristics, access authority and transmission path state, associating three types of data to evaluate overall state identification node connection, analyzing transmission load and bandwidth to evaluate reliability, and sharing the data. And comparing the instruction with the node state, adjusting transmission correction feedback, monitoring a transmission track and load change analysis stability, and outputting a shared path maintenance state label. According to the invention, association verification is carried out by acquiring cloud emergency treatment data distribution characteristics and access levels, so that cross-node data state perception and complete mapping are realized, the transmission direction and rate are automatically adjusted according to load change and network stability, and balanced transmission and reliable output under multi-node cooperation are ensured; and the transmission track change is continuously monitored to keep the sharing period stable, so that the data flow is kept continuous, safe and efficient in a complex cloud environment, and the emergency data sharing real-time performance and consistency are improved.

Methods, systems, devices, and computer-readable media used by a cloud data management system for collecting data from accounts hosted by a cloud-based data warehousing system on different cloud platforms or in different cloud regions of a cloud platform. Collection of data in the multi-cloud platform and / or multi-cloud region environments may be facilitated by the on-demand creation of one or more data collection accounts. Based on the collected data, one or more data warehouse resources not managed by the cloud data management system may be discovered, configured, and onboarded to the cloud data management system.

The present application relates to a kind of ship construction flatness inspection equipment and its use method, belong to ship flatness inspection technical field, including magnetic adsorption type mobile robot platform, magnetic adsorption type mobile robot platform, high-precision vision module, edge computing unit, cloud data management platform, magnetic adsorption type mobile robot platform is provided with track type walking mechanism and adaptive vacuum adsorption unit.In the present application, efficient, high-precision full-ship flatness intelligent detection can be realized by multi-sensor fusion and adaptive robot platform, suitable for real-time quality monitoring of hull block construction, deck assembly and outfitting stage, can perform local fine scanning along the weld area in the hull block detection scene, simultaneously mark the welding deformation out-of-tolerance area, can be switched to global fast scanning mode for non-key plane area, in deck assembly stage, cloud platform can integrate data in real time, assist in carrying out assembly misalignment correction scheme.

This application provides a method for point cloud data management and rapid Web identification and visualization, relating to the field of point cloud data processing technology. The method involves establishing a point cloud database; performing point cloud similarity clustering on the point cloud database, and then performing adaptive denoising processing based on the similarity clustering results to establish a denoised point cloud set; establishing a scale-space pyramid, using the similarity clustering results to call and match the scale-space pyramid, and reconstructing a position multi-scale model based on the call matching results; performing feature extraction on the denoised point cloud set using the position multi-scale model to establish a tunnel structure feature set; and using the tunnel structure feature set to perform tunnel visualization fitting and create a Web visualization model. This application solves the technical problem in the prior art of low Web visualization quality due to the large amount of tunnel point cloud data and the presence of noise and redundant information, significantly improving the processing efficiency and rendering performance accuracy of tunnel point cloud data on the Web.

The invention relates to the technical field of cloud data management, and particularly discloses a resource scheduling management system and method based on a cloud platform, and the method comprises the steps: obtaining heterogeneous resources from cloud platform resources, carrying out the information collection of the heterogeneous resources through a heterogeneous data interface, carrying out the same-type division of the heterogeneous resources, and obtaining heterogeneous resource nodes, the method comprises the steps of obtaining historical resource scheduling data of equipment, performing resource scheduling feature extraction and time sequence analysis, constructing a resource scheduling feature vector, performing similarity matching, determining equipment scheduling heterogeneous resource nodes, establishing a dynamic load prediction algorithm, performing fitting analysis on a load trend in combination with a sliding window, predicting load change and generating a scheduling early warning signal. And in combination with a resource priority strategy, a resource allocation scheme is obtained, task migration is performed on high-load heterogeneous resource nodes, and the migration tasks are allocated to the same type of heterogeneous resource nodes with low loads, so that optimization of resource allocation is facilitated, and the resource utilization rate of the cloud platform is improved.

The present invention discloses a method for managing point cloud data of highlighted buildings based on multispectral images, which relates to the technical field of building point cloud data management. The method for managing point cloud data of highlighted buildings based on multispectral images includes the following steps: collecting predefined spectral images of highlighted buildings by using an unmanned aerial vehicle; performing a first comparative analysis on the multispectral point cloud segmentation data of highlighted buildings; performing a first evaluation analysis on the multispectral point cloud segmentation data of highlighted buildings; performing a second evaluation analysis on the multispectral point cloud segmentation data of highlighted buildings; and performing a third point cloud data management process according to the results of the third comparative analysis. Through the regulation of the first and second evaluation analyses and hierarchical comparative analyses, the present invention achieves the effect of improving the accuracy of collecting point cloud data of highlighted buildings in multispectral images at night, and solves the problem of low accuracy of collecting point cloud data of highlighted buildings in multispectral images at night in the prior art.

The application discloses a kind of ship-shore cloud intelligent platform, ship intelligent management method and storage medium, ship-shore cloud intelligent platform includes ship end edge processing module, cloud intelligent analysis module, cloud data management module, dynamic transmission control module;Ship end edge processing module is used to collect the data of ship sensor, and the data is sent after edge side pretreatment;Cloud intelligent analysis module is used to receive the data after pretreatment, and the field model is established based on the multidimensional correlation of data, and the state result of each functional module is calculated and generated;Cloud data management module is used to store and manage the correlation of data based on the field model;Dynamic transmission control module is used to dynamically adjust the data sampling strategy of the ship end edge processing module according to real-time data flow.The application solves the problem of abnormal function, insufficient real-time function caused by high satellite signal interruption and propagation delay in the existing ship intelligent platform.

The invention relates to an intelligent garbage treatment system and a treatment method thereof.The treatment system comprises a garbage can body and intelligent garbage management equipment communicating with the garbage can body; the processing method comprises the following steps: step 1, establishing a cloud data management platform for unified management of kitchen garbage; 2, the collected kitchen garbage is sorted, and the separated kitchen garbage is screened into organic garbage and inorganic impurities; and step 3, putting the collected kitchen garbage to be treated into a storage pool, and carrying out water cleaning, retting and dehydration. The method has the superior technical effects that a large amount of organic garbage in the sorted organic kitchen garbage is subjected to biological hydrolysis, so that slurry formed by biological hydrolysis can be used as a recyclable biomass carbon source, and the resource recycling rate is increased. The intelligent garbage treatment system is provided with the garbage recognition module, the type of garbage can be accurately recognized, and the garbage sorting efficiency is effectively improved.

The invention discloses an elevator Internet of Things abnormity diagnosis data management method and system. The method comprises the following steps: collecting heterogeneous time sequence data of an elevator and carrying out time alignment; performing real-time anomaly detection, data cleaning and adaptive compression on the edge computing node; uploading the processed data to a cloud, extracting and intelligently fusing multi-source features, and forming a unified feature vector; outputting an anomaly type, a severity level and a fault root cause chain by using the dynamic anomaly diagnosis model; and based on historical data and feature vectors, predicting the remaining service life of the key component through a fusion model, and generating a maintenance strategy by using a reinforcement learning optimization engine. The system comprises a physical sensing layer, a data acquisition and edge processing layer, a cloud data management and intelligent analysis layer and an application service layer. According to the elevator abnormal diagnosis system and method, full-process intelligent management from real-time monitoring and accurate diagnosis to predictive maintenance is achieved, and the accuracy, timeliness and operation and maintenance efficiency of elevator abnormal diagnosis are remarkably improved.

The embodiment of the invention provides a data processing method and device, electronic equipment and a storage medium, and relates to the technical field of Internet. The data processing method comprises the steps of obtaining a resource processing application sent by a data demand terminal; updating a first use record of target resource data in the cloud data platform based on the resource processing operation; if an update completion message for the first use record of the target resource data sent by the cloud data platform is received, sending an update notification to a private cloud data management platform, so that the private cloud data management platform updates a second use record of the target resource data in the private cloud data platform; if the preset verification condition is met, reading a first use record of each piece of resource data in the cloud data platform, and reading a second use record of resource data in the private cloud data platform; and matching the first use record and the second use record of the same resource data to obtain a data verification result. The accuracy of resource data use can be ensured, and resource abuse is prevented.

The embodiment of the invention belongs to the technical field of cloud computing, and particularly relates to a data server distribution method, a cloud data management platform and a computing device cluster, and the method comprises the steps that a management server receives an IP query request and obtains data transmission time delay corresponding to each data server, the data transmission time delay is used for indicating the time delay when the data server sends data to the terminal. And the management server determines a first data server corresponding to the data transmission time delay in the plurality of data servers, and sends a first IP address corresponding to the first data server to the domain name server so as to instruct the domain name server to transfer the first IP address to the terminal, and the terminal requests data from the first data server according to the first IP address. By adopting the application, the operation cost of the management server can be reduced, and the efficiency of transmitting data from the data server to the terminal can be improved.

The invention relates to an electrocardiogram monitoring system and method based on a twelve-lead system, and relates to the technical field of remote medical information processing, and the electrocardiogram monitoring system comprises an electrocardiogram signal acquisition terminal, a wireless communication transmission network, a cloud data management analysis platform and a remote monitoring interaction terminal, an analog front end and master control communication separated framework is adopted in the electrocardiosignal collecting terminal, a Wilson central electric end is synthesized through a resistance network to collect high-fidelity signals, a transmission network supports multi-mode self-adaptive switching, a partition storage and breakpoint resume strategy containing an abnormal event write-protection mechanism is started when the network is disconnected, data integrity is guaranteed, and the reliability of the electrocardiosignal collecting terminal is improved. The cloud platform generates diagnosis data by using a Demix superposition technology and an ST-T feature analysis algorithm, the remote terminal provides waveform rendering, electronic caliper gauge measurement and manual correction feedback functions, and the system solves the problems of easy signal interference, weak network data loss and low automatic diagnosis accuracy in remote monitoring.

The convenient intelligent walking rehabilitation robot comprises a walking aid device and a safety vest, the walking aid device comprises a frame, the frame is composed of a bottom frame, a U-shaped pipe, a rear support and a fixing support, front wheels are arranged on the lower portion of the bottom frame, driving motors are arranged on the side faces of the front wheels, and rear wheels are arranged on the side faces of the rear support. The gait dynamic state is obtained in real time through the shank IMU, the rehabilitation individuation precision is improved, vision and inertia sensing fusion is combined, the training state can be dynamically judged, data closed-loop feedback is formed, the mainboard controller is provided with a 5G / Wi-Fi / Bluetooth module, remote monitoring, cloud data management and emergency alarm are achieved, the equipment structure is simple and convenient, home scene arrangement is easy, and the system is suitable for popularization and application. And the system has the functions of voice control and obstacle recognition, so that the operation convenience and the use safety are improved.

The invention discloses an intelligent education cloud data management method and system based on modular design, and the method comprises the following steps: S1, building a standardized metadata model based on the multi-source heterogeneity of education data, and dynamically adapting to an acquisition module; s2, deploying independent functional unit modules on the cloud platform, constructing a reconfigurable assembly line through a standardized interface between the modules, automatically matching a processing path according to a data type, and outputting an incremental data set with a uniform format; s3, based on the access permission tree associated with the role, creating a logic storage partition bound with the teaching role; s4, the analysis demand is disassembled into micro-service computing nodes, and a multi-dimensional education analysis result is generated; s5, deploying an independent security monitoring module, and generating a periodic compliance audit report; according to the method, a module cascade feedback and security audit double-circulation mechanism is constructed, the problem of system module splitting is solved, a whole is formed under an interface framework, and the privacy protection strength, the scheduling efficiency and the behavior tracing reliability are improved.

The invention discloses a micro low-speed electric vehicle safety early warning system based on the Internet of Things, and belongs to the crossing field of intelligent traffic and the Internet of Things technology. The system comprises a physiological state recognition system, an edge computing node, a gateway, a cloud data management platform and an emergency help seeking terminal. The physiological state recognition system collects original physiological state signals of a driver through a steering wheel contact type sensor, a seat pressure sensor array and a vehicle-mounted camera. The edge computing node performs mean filtering noise reduction, feature extraction and decision tree classification processing based on information gain on the signal; and after the processing result is analyzed by the platform, the emergency help-seeking terminal is controlled to send out a help-seeking signal in a dangerous state. Through multi-dimensional biological feature fusion analysis, the problems that an existing system is incomplete in early warning and low in reliability are solved, the early warning reliability is improved, and the system is suitable for miniature low-speed electric vehicle scenes such as elderly scooters and community patrol cars.

The invention discloses a cloud collaborative data supervision system and method applied to power equipment, and relates to the technical field of cloud data management. Power equipment data are collected through a cloud, the collected original data are standardized to obtain operation characteristics, and after the cloud issues an instruction, the same operation characteristics of the power equipment are recorded in a unified manner; performing preliminary alarm according to the difference of the operation characteristics, calling historical characteristic data, classifying the operation characteristic abnormity of the power equipment, analyzing the abnormal association between the operation characteristics of the power equipment, performing secondary analysis on the operation characteristics of the power equipment, and sending a chain abnormity alarm and a key attention prompt to a manager through the cloud. According to the method, the stability of the power equipment management is enhanced by capturing the temperature change rate of each key stage, dynamically adjusting the temperature parameters in a predictive manner and integrating an associated monitoring and risk early warning mechanism.

The invention relates to the technical field of electronic stethoscopes, in particular to intelligent auscultation equipment for auscultation of internal arteriovenous fistula, which comprises an auscultation head body, one end of the auscultation head body is provided with a shell, and an inner cavity of the shell is fixedly connected with a host. The internal fistula assessment system has the advantages that the high-sensitivity sensor, the noise reduction technology, the visual interface and cloud data management are achieved, and the objectivity and efficiency of internal fistula assessment are improved; through the cooperative use of the auscultation head body, the shell, the host, the sensing module, the integrated noise reduction processing module, the AI analysis module, the signal transmission module, the alarm module, the data storage module, the operation interface and the USB interface, internal fistula sound can be rapidly collected, real-time accurate analysis is conducted through the AI technology, the efficient noise reduction technology is adopted, and the noise reduction effect is good. Interference of external noise on sound signals is effectively prevented, it is guaranteed that collected sound is clear, and doctors can make accurate judgment and processing conveniently.

The invention relates to the technical field of medical instruments, in particular to a thrombus detection quantification and early warning system and method in the CRRT process, and the system comprises an infrared camera shooting function module, an artificial intelligence learning module, a thrombus detection alarm module and a cloud data management platform. The infrared camera module adopts a 760nm-1500nm multispectral infrared camera, continuously shoots CRRT in-vitro pipeline blood at a frame rate of more than or equal to 30fps, and eliminates temperature interference in combination with a constant temperature compensation technology; the artificial intelligence module realizes nano-scale thrombus recognition through thrombus sample training based on a convolutional neural network and an attention mechanism; and the detection alarm module realizes thrombus feature analysis, blood coagulation time prediction and multi-stage alarm through three-dimensional volume calculation and an LSTM time sequence model, and generates a personalized anticoagulation scheme. According to the system, early-stage accurate detection and real-time early warning of thrombus are achieved through multi-technology fusion, and clinical verification shows that the CRRT treatment safety can be remarkably improved.