926 results about "Cloud database" patented technology

The invention relates to the technical field of agricultural data security, and discloses an agricultural data security sharing method based on federated learning, through cooperative operation of a user terminal module, a data classification module, a federated learning module, an agricultural cloud database and a security protection module, a user terminal encrypts, uploads, decrypts and feeds back agricultural data through an edge computing device; the data classification module performs multi-level label labeling on data of planting industry, animal husbandry and the like by using a differential privacy technology; the federated learning module is combined with homomorphic encryption to realize local model training and parameter aggregation; the agricultural cloud database stores encrypted knowledge assets by using an IPFS technology; the security protection module integrates block chain evidence storage and a dynamic access control mechanism, the workflow comprises data encryption uploading, privacy protection classification, federated model training, solution generation and security feedback, and by adopting a federated normal form of a data immobile model, the security protection module can effectively protect the security of the original data on the premise of ensuring that the original data is not out of the domain. And value mining and safe sharing of cross-regional agricultural data are realized.

The invention belongs to the technical field of water quality monitoring, and particularly relates to a construction method and device of a turbidity testing system based on a photoelectric detector. Optical signal data and multi-wavelength optical signal data are obtained; performing intelligent switching and spectrum matching according to the signal data, determining a target wavelength, and obtaining characteristic parameters related to turbidity by combining a scattering and transmission combined measurement principle; measuring different standard solutions according to the characteristic parameters to obtain corresponding characteristic parameters, constructing a turbidity characteristic parameter data set, and establishing a characteristic ratio turbidity calibration model according to the data set; measuring and calculating the turbidity according to the characteristic ratio turbidity calibration model in combination with a dynamic temperature compensation algorithm, and meanwhile, performing temperature compensation; and transmitting the parameter information to a cloud database for storage, performing hierarchical processing, performing regular intelligent self-inspection at the same time, and feeding back abnormal data when an abnormality occurs. Therefore, the problems of insufficient range coverage, weak automatic calibration capability, susceptibility to temperature drift and the like in the prior art are solved.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an accident scene generation method based on a scene knowledge graph and considering accident causes. Comprising the following steps: step 1, modeling a scene knowledge graph; 2, modeling the scene graph time sequence prediction model; 3, modeling the time sequence causal inference model; step 4, modeling the scene graph time sequence decision generation model so as to generate an accident scene; according to the method, the accident scene database with high authenticity, high diversity and accident cause consistency can be constructed under the condition that the accident scene sample data size is limited, the number of accident scenes of the same type in the automatic driving algorithm closed-loop self-evolution cloud database is efficiently expanded, the constructed scene database is used for training the automatic driving algorithm, and the accuracy of the automatic driving algorithm is improved. The adaptive capacity of the self-driving automobile to scenes with the same type of accidents can be effectively enhanced, and safe and reliable operation of the self-driving automobile in the real world is guaranteed.

The invention discloses a cold chain warehouse unmanned aerial vehicle obstacle avoidance and path planning checking method and system based on AI intelligence, and belongs to the field of intelligent obstacle avoidance, and the method comprises the steps: arranging different types of sensors used for sensing the environment change of a cold chain warehouse in the cold chain warehouse, the edge gateway integrates different types of sensors, preprocesses the collected data, and sends the processed data to a cloud database; constructing a digital twin model of the target cold chain warehouse and an intelligent inventory path planning algorithm in the cloud database; the cloud sends a path scheme output by the intelligent inventory path planning algorithm to the unmanned aerial vehicle terminal and the digital twin model at the same time; the unmanned aerial vehicle terminal completes a checking task according to the path scheme, and the digital twin model is updated in real time according to the path scheme and data fed back by the unmanned aerial vehicle terminal. According to the invention, it can be ensured that the unmanned aerial vehicle efficiently and safely performs inventory tasks in a complex cold chain warehouse environment.

Aspects of the disclosure are directed to integrating one or more large language models (LLMs) into a cloud database platform, such as a data warehouse. Users of the cloud database platform can provide queries to instruct one or more LLMs to perform generative natural language processing tasks by manipulating or generating text directly in the cloud database platform with a table valued function. Users can provide input to register or generate one or more LLMs of the cloud database platform for performing the natural language processing tasks. Integrating LLMs into the cloud database platform can improve processing capabilities of the LLMs and save computing resources, as specialized LLMs or application-specific API may no longer be necessary.

The invention provides a fan full life cycle archive management system and a management method thereof, and the system comprises an Internet of Things data collection module which is installed on a key part of a wind turbine generator set and collects vibration, temperature and stress data; the full-life-cycle digital archive management module is used for recording full-process information of the fan from design to operation and maintenance based on a cloud database and a block chain technology, and ensuring data integrity and traceability; the AI predictive maintenance analysis module analyzes data and predicts faults by using deep learning and machine learning algorithms; the intelligent scheduling and maintenance decision support module is used for generating an optimal maintenance plan in combination with external factors; and the responsibility tracing and process optimization module is used for recording maintenance details to optimize the process and spare part management. The management method comprises the steps of data acquisition, storage, analysis, maintenance plan making, task execution and recording, effect evaluation, process optimization and the like. Through cooperative work of all the modules and implementation of the system method, accurate and efficient full-life-cycle management of the draught fan is achieved.

The invention discloses an intelligent equipment remote monitoring and management system based on the Internet of Things, which relates to the technical field of equipment monitoring and management and comprises a multi-dimensional intelligent cooperation module, a self-adaptive dynamic optimization module and a real-time intelligent decision module based on edge calculation. The system is characterized in that the multi-dimensional intelligent cooperation module and the adaptive dynamic optimization module share data through a cloud database, and an optimization result of the adaptive dynamic optimization module is provided for the real-time intelligent decision module based on edge calculation. According to the invention, by designing the multi-dimensional intelligent cooperation module and the group intelligence optimization module based on equipment group behavior analysis, the functions of data sharing cooperation and load balancing resource optimization among equipment are realized, and the problems of equipment operation data isolation and incapability of information sharing cooperation response in a traditional system are solved; the cooperation and information sharing capability of the system is improved, the resource and load balance is optimized, and the overall stability and reliability of the system are improved.

The invention discloses a big and small model cooperation-based power equipment fault knowledge graph construction method, which comprises the following steps of: preprocessing a power equipment fault document, storing image information into a cloud database, and screening high-quality text information from a big language model; summarizing different text information by using a large language model to generate an initial ontology framework; performing pre-labeling on text information by using a large language model, obtaining a training data set in combination with rule matching scripts and experts, optimizing a traditional deep learning small model based on data characteristics to obtain entities, and eliminating error entities based on a hierarchical verification strategy; utilizing a large language model to carry out relation extraction on association degrees between entities and different entity relations; and performing entity alignment by using the large language model, the SBERT model, a depth-first search algorithm and context information to form a final power equipment fault knowledge graph. According to the method, the problems of high labor cost consumption and knowledge redundancy of the knowledge graph in the existing power equipment fault knowledge graph construction process are solved.

The invention discloses an insurance clause knowledge base self-maintenance method, system and device based on a large model, and relates to the technical field of data processing. The method comprises the following steps: obtaining an associated segmentation term fragment set; carrying out vector conversion, and storing the vector into a cloud database to obtain a vectorized cloud knowledge base; after the vectorized cloud knowledge base receives a user retrieval request, performing knowledge base clause priority adjustment through a secondary retrieval mechanism to obtain an updated vector cloud knowledge base; responding to a user retrieval request based on the updated vector cloud knowledge base to obtain a clause fragment retrieval result; and inputting the Prompt construction result and the clause fragment retrieval result into a large language model constructed based on an RAG enhanced generation technology to perform retrieval enhanced generation, and obtaining a target retrieval interpretation analysis result. The technical problems that in the prior art, an insurance clause knowledge base is low in maintenance efficiency and poor in retrieval accuracy are solved, and the technical effect of improving the self-maintenance efficiency and the retrieval accuracy of the insurance clause knowledge base is achieved.

The embodiment of the invention relates to the field of data management, and discloses a semiconductor process formula parameter safety management method and related products, and the method comprises the steps: obtaining semiconductor process formula parameters on target production equipment, and storing the semiconductor process formula parameters in a cloud database, deleting the technological formula parameters on the target production equipment; configuring a data security area in the cloud database to perform authority control including data access, data transmission and / or data editing on the stored process formula parameters; and before the semiconductor is processed by the production equipment, downloading the corresponding process formula parameters in the cloud database to the production equipment, and deleting the process formula parameters on the production equipment after the processing is completed. According to the scheme, the key process formula can be prevented from falling to the ground, and core process leakage is prevented.

The invention relates to the technical field of unmanned aerial vehicle intrusion detection, in particular to an unmanned aerial vehicle intrusion detection system based on personnel intrusion alarm, which comprises a user authentication module, an area setting module, an unmanned aerial vehicle control module, a shooting control module, an intrusion trend judgment module, an intrusion identification module, a display terminal, a server and a cloud database. According to the invention, a user can flexibly set a monitoring area after authentication, generates an unmanned aerial vehicle control signal, dispatches an unmanned aerial vehicle in a target aircraft nest base station, accurately determines a dispatchable unmanned aerial vehicle, inputs task data information to the dispatchable unmanned aerial vehicle, photographs personnel in the monitoring area through the dispatchable unmanned aerial vehicle, generates an image set, and sends the image set to the target aircraft nest base station. And then an intrusion trend value is calculated according to the image sets to determine whether a face recognition instruction is triggered or not, and identity recognition judgment is performed according to the face recognition instruction to determine whether an alarm signal is generated or not, so that omnibearing and high-efficiency monitoring of a monitoring area is realized, and the accuracy and reliability of safety monitoring are remarkably improved.

The invention discloses an unconventional feed quality evaluation grading method and system, and particularly relates to the technical field of feed quality evaluation. Conventional nutrients, functional nutrients and harmful substance components are comprehensively analyzed through a high-precision detection technology; constructing a multi-factor synergistic toxicity evaluation model, quantifying the synergistic effect of the various harmful substances based on the toxicity equivalent factors and a matrix interaction analysis method, and outputting a synergistic toxicity index; the overall quality of feed samples is evaluated by combining detection results and toxicity indexes, the feed samples are classified into high-quality, qualified and unqualified grades, targeted application suggestions and risk control measures are provided, a cloud database is constructed to record evaluation data, and an Internet of Things sensor is used for monitoring supply chain quality fluctuation in real time, automatically identifying abnormities and triggering early warning. Dynamic management of feed quality is achieved, scientificity and accuracy of quality evaluation of unconventional feed are effectively improved, potential safety hazards are reduced, and safe popularization and efficient application of the system in the breeding industry are promoted.

The invention relates to the field of environmental noise detection, and provides an environmental noise detection device and method, and the method comprises the following steps: collecting an environmental noise signal, and carrying out the preprocessing of the environmental noise signal, and obtaining a to-be-detected audio; adjusting a gain compensation coefficient according to the energy intensity of the to-be-tested audio to obtain a gain multi-source noise signal; separating the gain multi-source noise signal to obtain a plurality of separated noise signals, extracting key characteristics of the separated noise signals, and classifying the plurality of separated noise signals based on the key characteristics to obtain different types of classified noise signals; performing feature extraction and time series data analysis on the classified noise signals to obtain abnormal noise data, identifying the abnormal noise data to obtain a noise abnormal level, and adopting a corresponding emergency solution; the detection data and the early warning record are stored in a cloud database; a visualization result is generated based on the stored data. According to the invention, efficient detection, classification, anomaly identification and data management of environmental noise are realized.

The invention provides an energy-saving evaluation and operation strategy optimization method for a central air-conditioning system. The method comprises the following steps: firstly, building an Internet of Things platform based on the central air-conditioning system, and monitoring and collecting operation parameters of various equipment in the system in real time; unifying formats and preprocessing operation parameters, uploading the operation parameters to a cloud database for storage, and dividing the operation parameters according to categories; performing mapping modeling on the central air-conditioning system in the energy analysis platform, and performing parameter configuration at the same time; then, the divided data in the cloud database are connected according to evaluation categories, and meanwhile, an extensible formula expert database is introduced to evaluate the running state of the central air conditioning system based on an empirical algorithm, and abnormal strategy reminding is conducted; and prompting to adjust the running state of the equipment according to the strategy. According to the method, the problem of strategy missing is effectively solved through an optimization strategy of the extensible expert experience library; and through analysis and verification of seven functions, integrated, refined and systematic analysis of energy consumption and optimization strategy improvement and advanced management are realized.

The invention discloses a non-residual knowledge graph construction method and system based on multiple modes, and relates to the technical field of data processing, and the method comprises the steps: building a cloud database; establishing a first knowledge graph; configuring a trust channel, performing independent data authentication on entities in the first knowledge graph, and establishing a data authentication result; activating the entity similar authentication network to perform entity association calculation, generating an authentication calling instruction, performing auxiliary authentication of an authentication failure result, and generating an auxiliary authentication result; establishing a second knowledge graph, embedding entities and relationships of the second knowledge graph into a low-dimensional vector space, updating a relationship weight of the second knowledge graph, and establishing a third knowledge graph; and generating activation representation according to the third knowledge graph, and performing digital display and interaction. The technical problem that in the prior art, a non-abandoned digital protection and display method cannot comprehensively reflect non-abandoned richness is solved, and the technical effect of improving the expression ability and the display effect of the non-abandoned knowledge graph is achieved.

The invention belongs to the technical field of child growth and development evaluation, and particularly relates to a child development dynamic evaluation method and system based on multi-source data fusion. According to the method, the growth and development state of the child can be preliminarily judged through the growth and development sample curve generated through fusion, the second index data of the child are obtained through the non-intrusive acquisition equipment, and the acquisition frequency is adjusted based on the initial state type, so that the comprehensiveness and accuracy of the data are ensured, unnecessary frequent acquisition is avoided, and the accuracy of the data is improved. Processing the second index data through a pre-trained convolutional neural network model to generate individual growth fingerprints, screening out historical fingerprint data with high similarity with the individual growth fingerprints from a cloud database, constructing a personalized growth corridor, providing a personalized referable trajectory range for growth and development of children, and improving the growth and development efficiency of the children. And on the basis, whether the growth and development of the children are abnormal is judged, so that the growth and development conditions of the children are accurately evaluated.

The invention discloses a vehicle fault positioning and rescue scheduling platform and method based on GPS track acquisition, and relates to the field of vehicle fault positioning and rescue scheduling, and the method comprises the steps: uploading vehicle GPS and OBD data obtained by a vehicle-mounted terminal to a cloud database through employing a mobile network technology; according to the vehicle GPS and OBD data in the cloud database, the vehicle fault type is predicted and judged, and the position coordinate information of the vehicle fault is obtained; based on an expert scoring method, processing response levels of different vehicle fault types are set; accessing a high-precision digital map API (Application Program Interface), and marking schedulable rescue points and fixed GPS (Global Positioning System) coordinate data in a digital map; and establishing a vehicle fault rescue scheduling model, and carrying out comprehensive evaluation and scheduling on surrounding schedulable rescue points. The method has the advantages of effectively predicting the state and fault of the vehicle, efficiently responding to the processing of the fault of the vehicle, and preferentially scheduling and screening the schedulable rescue points.

The invention relates to the technical field of shared tray management, and discloses a shared tray management method and system, and the method comprises the steps: S101, dynamically adjusting the transmitting power through an intelligent reader-writer according to the material size and the stacking height, so as to optimize the read-write distance; s102, based on the system access request, evaluating and distributing encryption levels and permission settings of different levels in real time; s103, collecting and recording RFID tag information on the tray to a cloud database for data management; s104, monitoring the running state of the system and feeding back an adjusting instruction to ensure continuous and stable tray data processing; through the scheme of the embodiment of the invention, the read-write distance of the RFID tag can be regulated and controlled to solve the problem of information identification stability under different material sizes and stacking heights, and the access authority and encryption level of the system can be regulated and controlled to solve the problem of tray data security.

The invention discloses an eye fundus image recognition method, system and device based on deep learning, the efficiency and precision of eye fundus disease recognition are improved through a portable handheld terminal and a cloud database, the portable handheld terminal comprises an eye fundus image feature acquisition module, an eye fundus image recognition and judgment device and a man-machine interaction module, wherein the eye fundus image recognition and judgment device comprises a feature engineering module, an image recognition module and a visual auxiliary fault module, the feature engineering module is responsible for data expansion, enhancement and normalization, the image recognition module performs image classification prediction by constructing a deep learning model, and model construction, parameter initialization, model training and pruning optimization are included. The visual auxiliary diagnosis module assists doctors in diagnosis by generating a thermodynamic diagram. Remote storage and analysis of data can be realized, the diagnosis efficiency is improved, the visual loss risk is reduced, and the system is particularly suitable for resource-limited regions.

The invention relates to the technical field of energy storage battery control, in particular to a high-reliability low-attenuation energy storage battery management system based on intelligent control, and the system comprises a data collection module which is used for collecting battery temperature, environment temperature, SOC (state of charge), SOH (state of health) and external mechanical pressure information; the model construction module is used for constructing a battery attenuation risk dynamic coupling evaluation model and generating a battery safety and attenuation risk dynamic evaluation diagram; the self-adaptive adjustment module adopts a cross-dimension multi-stage cooperative control strategy algorithm, temperature compensation current control and SOH grading current limiting linkage are combined, and SOC dynamic charging optimization and mechanical pressure early warning are coupled; and the evaluation report module adjusts control parameters through a dynamic optimization algorithm, generates a battery health state evaluation report, synchronously uploads the battery health state evaluation report to a cloud database, and establishes a full-life-cycle battery attenuation risk file. Therefore, the problems that in the prior art, a risk assessment model is large in limitation, a control strategy lacks collaboration, and attenuation risk prevention and control lags are solved.

The invention relates to the technical field of tomato disease control, in particular to a method for detecting tomato root rot based on volatilization group information. The method comprises the four steps of volatile gas collection, data analysis, intelligent diagnosis and result display, and cloud database and model updating is assisted. The method comprises the following steps: firstly, obtaining tomato root volatile compounds through high-sensitivity sampling equipment, and analyzing volatile components by utilizing a GC-IMS technology to obtain lt; component, concentration gt; the method comprises the following steps: extracting data, generating a feature vector, combining generation and verification of an auditing identifier to ensure the compliance and integrity of the extracted data, accurately judging a disease grade based on a classification model, combining a health score and a classification result, providing prevention and treatment suggestions, and presenting a diagnosis result in a visual mode; and the cloud database continuously accumulates historical data, optimizes the classification model, and improves the system adaptability and the detection effect. According to the invention, early-stage accurate diagnosis and intelligent management of tomato root rot are realized, and the agricultural disease detection efficiency is improved.

The invention relates to the technical field of remote monitoring, in particular to a new energy vehicle battery remote monitoring method and system based on cloud computing, and the method comprises the following steps: extracting the temperature data of a plurality of monitoring points through a vehicle-mounted temperature sensor based on the data of a new energy vehicle battery, recording the current and voltage information of a BMS (Battery Management System), and arranging the data according to a time sequence and storing the data in a cloud database to obtain battery operation monitoring data. According to the invention, by accurately collecting the operation data of the new energy vehicle battery and performing time sequence storage, data management with finer granularity is realized, the battery state is ensured to be comprehensively monitored, the abnormal temperature detection precision is improved, the quantum key generation is utilized to generate and distribute keys, the data transmission security is enhanced, and the data transmission efficiency is improved through quantum encryption and remote identity authentication. Data integrity and privacy are guaranteed, cooling and charging strategies are optimized, temperature control accuracy is improved, the service life of the battery is prolonged, energy consumption is reduced, and new energy vehicle battery management is more efficient, safer and more intelligent.

The invention relates to the technical field of optical detection and image processing, in particular to a mobile phone lens window glass defect detection method. Comprising the following steps: acquiring a high-resolution image of a window glass sample by imaging equipment by adopting a multispectral imaging technology; inputting the acquired image into a pre-trained deep learning model for preprocessing; determining whether defects exist or not according to an output result of the deep learning model, marking the positions and types of the defects, and generating a detection report containing defect geometric parameters; if the defect exists, the severity of the defect is judged according to preset standards and geometric parameters of the defect, and the standards comprise the size, the shape, the depth and the position of the defect; and the detection result and the image data are stored in a cloud database to support subsequent quality analysis and process improvement. By combining the multispectral imaging technology, the deep learning model and the fuzzy logic system, the quality detection level of the window glass is comprehensively improved.

The invention discloses an intelligent construction method and system for continuous rigid frame bridge closure based on digital twinning, and the intelligent construction method and system for continuous rigid frame bridge closure based on digital twinning realize intelligent, few-person and safe construction operation in the closure construction process by constructing the intelligent construction system for continuous rigid frame bridge closure. The system comprises closure intelligent construction equipment, a centralized control platform, a cloud database, an intelligent control algorithm and a remote digital twinborn control platform. The method mainly comprises the following steps: constructing a digital twin system, arranging intelligent equipment, monitoring and analyzing environment data and displacement data of a closure section, performing closure section pushing intelligent control, and performing concrete pouring counterweight double-target intelligent regulation and control. Data monitoring of a closure section before closure is carried out through an intelligent construction system, real-time intelligent control over closure pushing and concrete pouring counterweight load adjustment is carried out, accurate data collection and bridge body stress balance in the closure process are ensured, and the closure quality and the intelligent construction level of the continuous rigid frame bridge are improved.

The invention discloses a riverway ecology real-time treatment method, and belongs to the field of riverway treatment, and the treatment method specifically comprises the following steps: S1, constructing a cross-platform data collection system, deploying a multi-source sensor, monitoring environmental parameters in real time, and storing the collected data in a cloud database; s2, analyzing the collected data, identifying water area pollution characteristics, evaluating the ecological health condition of a river channel and identifying main problems; according to the method, hardware resource requirements are remarkably reduced, model calculation efficiency is improved, key ecological indexes can be accurately evaluated, model prediction precision is guaranteed, and model deployment and adaptability are accelerated; the change of the ecological state of the river channel can be responded in real time, the problem of low efficiency or failure caused by a fixed strategy of a traditional treatment method is avoided, dependence on external resources is reduced, the maintenance cost is reduced, uncertainty and deviation in human decision making are reduced, and the treatment efficiency is improved.

The invention discloses an intelligent electronic stethoscope system based on wireless transmission and a data processing method thereof, and belongs to the technical field of electronic stethoscopes, the intelligent electronic stethoscope system comprises a heart sound acquisition module, a signal conditioning module, a power supply module, a wireless transmission module, an upper computer module and a database module; the microprocessor module serves as a core control unit, and the system can collect heart sound signals of a patient in real time and conduct amplification, filtering and spectrum analysis processing on the heart sound signals. The processed heart sound data is transmitted to a mobile device or a computer in real time through a wireless transmission module, so that doctors and users can check heart sound waveforms and related data conveniently; the system also supports the uploading of the data to a cloud database for remote diagnosis and query; the method not only can meet the requirements of modern medical treatment for high efficiency and convenience, but also can play an important role in improving the diagnosis precision and the remote diagnosis capability and reducing the infection risk.

A system for a multi-tenant API framework in scalable cloud-based database solutions, which includes the following: an API layer module that enables access to database services for multiple tenants; a tenant management module configured to handle tenant authentication, authorization, and role-based access control; a data partitioning module for partitioning tenant-specific data into isolated database structures; a resource allocation module for the dynamic allocation of computing resources to individual tenants based on usage patterns; a security module configured to protect tenant data through encryption and secure data transmission; A monitoring and analysis module for real-time monitoring of tenant activities and system performance.

The present invention discloses a multi-task machining process on-line monitoring method and system based on cloud-edge collaboration, comprising multi-source sensing terminals, edge computing platforms and a cloud server. Each multi-source sensing terminal uses sensing equipment to obtain multi-source heterogeneous signals such as spindle vibration signals and acoustic emission signals generated in a machining process and transmit the signals to a controller of each edge computing platform; the edge computing platform is used for periodically collecting and processing the signals, synchronously monitoring cutting vibration, tool wear, tool breakage and machining surface quality in combination with intelligent prediction models, and uploading monitoring data to the cloud server; the cloud server is used for storing the monitoring data of each edge computing platform into the cloud database, and updating the intelligent prediction models regularly with global data to improve the generalization ability of the models.

The invention discloses a diabetic patient data management method and system based on big data. The method comprises the steps that basic information and stable blood glucose data of a patient are collected, similar cases are matched through a cloud database to obtain influence factors, and a personalized data collection scheme is generated; customizing a user terminal and establishing a data management container, adaptively acquiring blood glucose and influence factor data, calculating a change degree through a sliding window algorithm and performing early warning; and dynamically updating influence factor weights by adopting a time-varying weight model, and generating an intervention strategy according to the weights. The system comprises a cloud server, a comprehensive management terminal, a user terminal and other modules, and realizes data acquisition-analysis-intervention closed-loop management. According to the scheme, through big data analysis and the dynamic weight model, personalized accurate management of the diabetic patient is realized, the acquisition strategy and the intervention scheme can be adjusted according to individual differences, and the risk early warning accuracy and the management efficiency are improved.

The invention relates to the field of service management, and discloses a big data analysis-based online exhibition service management system and method, which uploads collected and recorded exhibition information data to a cloud database for processing, and analyzes the exhibition information data during an exhibition to perform traffic analysis, thereby improving the service management efficiency. And then the matching degree of the exhibitors and the booths is analyzed based on the flow analysis result, and the booth matching degree and the audience preference degree are comprehensively analyzed in combination with the analysis result of the browsing paths and the interactive behaviors of the audiences, so that the booths are subjected to priority ranking, and intelligent recommendation is performed to the exhibitors. The effect evaluation module evaluates the recommendation effect of the booth, and dynamically adjusts the booth sequence in real time according to the evaluation result, thereby facilitating the continuous optimization of the recommendation effect of the booth, improving the accuracy, reliability and recommendation efficiency of the booth recommendation, and improving the recommendation efficiency of the booth. Therefore, automation and intelligentization of booth recommendation and distribution are realized more comprehensively.