331results about How to "Real-time acquisition" patented technology

The invention provides an intelligent runoff pollution monitoring and response method and system, and the method comprises the steps: arranging a plurality of monitoring points in a runoff region, collecting water quality, hydrological and meteorological data in real time through a sensor, transmitting the data to a data processing center through a wireless network, carrying out the preprocessing, storage and analysis, and calculating the comprehensive pollution index of each monitoring point. And comparing with a set index threshold value. When the comprehensive pollution index exceeds a threshold value, the system identifies and evaluates key water quality parameters so as to determine a reasonable parameter threshold value; once data of a monitoring point exceeds a preset pollution threshold value, an alarm mechanism is triggered, then a pipe network water flow model and a geographic information system are combined to reversely track a pollution path, accurately position a pollution source and provide a basis for pollution control, and the process comprises the steps of screening suspected pollution source nodes, calculating a propagation path probability and preferentially checking a high-probability path. The traced pollution source or path probability is lower than a preset value.

The invention relates to the technical field of data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

The invention relates to the technical field of welding material performance testing, and discloses an acoustic-optical-electric synergistic welding material fatigue performance testing method and system, and the method comprises the steps: synchronously collecting a first multi-mode signal data set of a welding material in a fatigue testing process; carrying out fusion processing to obtain a fusion feature vector; performing time sequence feature extraction to obtain time sequence comprehensive representation; constructing and training an initial fatigue performance multi-task learning model comprising a shared feature extractor and a task-specific sub-network based on time sequence comprehensive representation; implementing domain adaptation and transfer learning on the fatigue performance multi-task learning model to obtain a target fatigue performance multi-task learning model; acquiring a second multi-mode signal data set in real time and inputting the second multi-mode signal data set into the target fatigue performance multi-task learning model to obtain a welding material fatigue performance test result, so that acoustic, optical and electric multi-mode signals can be effectively integrated, and high-precision, multi-task and migratable welding material performance test is realized.

The invention discloses a data monitoring and processing method based on dam stability, and relates to the technical field of dam monitoring, and the method comprises the following steps: arranging sensors: arranging high-precision sensors, such as a strain gauge, a displacement meter and the like, at key positions according to the structural characteristics of a dam; comprehensively analyzing the structure of the dam; according to the method, the high-precision sensors are arranged at key positions of the dam, real-time acquisition of dam stability parameters including strain, displacement, pressure, temperature and the like is achieved, the real-time data acquisition mechanism remarkably improves the monitoring frequency and continuity, the real-time performance and the accuracy of data are ensured, and compared with traditional regular manual inspection, the real-time monitoring accuracy is improved. According to the invention, more accurate and continuous monitoring data can be provided, so that the stability state of the dam can be evaluated more effectively.

The invention discloses a robot grinding real-time defect detection and self-adaptive correction system based on deep learning. The problem that in the prior art, real-time detection, defect correction and accurate path optimization are difficult to achieve in the grinding process is solved. The image, the three-dimensional point cloud and the moment data are fused through a multi-modal sensing system, and data fusion processing is performed by adopting a time synchronization mechanism, so that high-precision defect detection is realized. And the self-adaptive correction module dynamically adjusts the pressure, the speed and the angle of the grinding tool according to real-time feedback by using a reinforcement learning algorithm, so that accurate grinding for different workpiece surfaces is ensured. The system is further combined with closed-loop feedback control, the grinding path is continuously optimized, and the consistency and stability of the grinding quality are improved. Compared with the prior art, efficient and automatic defect detection and self-adaptive correction can be achieved on the complex and irregular workpiece surface, and the production efficiency and the surface treatment quality are remarkably improved.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention discloses an AI-based linkage response and control system, belongs to the technical field of safety monitoring, and aims at solving the problems of comprehensive and real-time acquisition, accurate analysis, timely early warning and decision making of environmental data so as to realize early warning and quick response of a fire disaster. The system comprises a data acquisition module, an AI analysis module, an AI identification module, an AI decision module and an execution terminal robot. The data acquisition module is used for acquiring environmental data of a monitoring sensor and a monitoring camera in a target scene; the AI analysis module is used for processing and analyzing the environmental data acquired by the monitoring sensor and outputting a sensor abnormal area; the AI identification module is used for analyzing an environment video stream acquired by the monitoring camera in real time and outputting a camera abnormal area; the AI decision module is used for further evaluating the abnormal region and dividing abnormal levels; and the execution terminal robot is used for performing real-time response and control on the abnormal area in the control room according to the abnormal level.

The invention discloses an automatic trajectory planning and navigation system of a traditional Chinese medicinal material excavating machine, relates to the technical field of traditional Chinese medicinal material excavating, and aims to solve the problems that the efficiency is low during current angelica sinensis excavating, and angelica sinensis plants are different in size due to different growth environments and characteristics, so that the excavating efficiency is low. During digging, the root hair and stem leaves of the angelica sinensis are easily damaged. Comprising a planting area calibration module used for dividing an angelica sinensis planting area and planting point positions in combination with terrain, an image acquisition module used for acquiring growth images of stems and leaves of angelica sinensis in real time, a planting environment data acquisition module used for acquiring planting environment parameters in real time, and a root hair three-dimensional distribution analysis module used for analyzing the root hair distribution condition of angelica sinensis. According to the method, the digging strategy aiming at the maturity is formulated, so that the digging efficiency and quality of the angelica sinensis are remarkably improved, damage to root hairs, stems and leaves of the angelica sinensis due to improper manual operation is avoided, and the technical problems that the efficiency is low and the angelica sinensis is easily damaged are solved.

The invention relates to the technical field of industrial equipment management, and discloses an equipment management method for a 3D digital factory, and the method comprises the following steps: sensing the operation parameters of a network collection device through the Internet of Things; processing the operation parameters by utilizing a cloud computing or edge computing technology; dynamically generating an equipment health state based on the processed data; the equipment management system of the 3D digital factory comprises an Internet of Things sensing network used for collecting operation parameters of the equipment; the data processing module is used for carrying out cleaning, noise reduction, dynamic modeling and anomaly detection on equipment operation parameters; and the equipment health state generation module is used for generating the health state of the equipment. According to the invention, through the Internet of Things sensing network, dynamic modeling, intelligent scheduling and full-life-cycle management technology, the effects of equipment state real-time monitoring, accurate maintenance, energy efficiency optimization and comprehensive asset management are realized.

The invention discloses an intelligent campus big data acquisition and management system based on cloud computing, and relates to the technical field of intelligent campus construction and big data processing, and the system comprises the following components: a big data acquisition module, a cloud computing resource pool, a dynamic resource scheduling engine, an intelligent load balancer, and a visual monitoring and management platform. The system realizes real-time and comprehensive acquisition of multi-source heterogeneous data in the campus through the big data acquisition module by using the Internet of Things (IoT) technology, a sensor network and an API interface, can effectively remove repeated, wrong and missing data through the data cleaning and preprocessing process, improves the data quality, and in addition, improves the data processing efficiency. The visual monitoring and management platform provides a visual monitoring interface, and supports an administrator to perform remote configuration, task scheduling and resource optimization operation, so that the efficiency of data management and operation and maintenance is greatly improved.

The invention relates to the technical field of power dispatching, in particular to a power demand real-time sensing and responding system and method based on Internet of Things driving, electronic equipment and a computer readable storage medium, and the method comprises the steps: collecting power utilization power and power utilization time data at a user side, and carrying out the preprocessing of the data; constructing an electricity utilization power curve, and extracting electricity utilization characteristics; using a K-means clustering algorithm to carry out clustering analysis on the user power consumption characteristics, and dividing the users into k power consumption categories; a power consumption demand prediction model is constructed by training a long short-term memory (LSTM) network model, and the future power consumption power curve trend is predicted; and establishing a power utilization scheduling response mechanism based on the prediction result and the clustering-divided power utilization behavior categories in combination with a charging and discharging scheduling strategy of the power energy storage equipment, thereby realizing flexible scheduling response to the power utilization behaviors in different time periods in the future. According to the method, the power consumption management precision and efficiency can be improved, personalized power consumption requirements are met, and energy utilization is optimized.

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 industrial automation, in particular to an industrial equipment self-diagnosis system based on algorithm model prediction, and the system comprises a model building module which comprises a historical fault model; the data acquisition module divides a plurality of to-be-detected point locations and acquires and converts vibration signals; the period judgment module performs periodic judgment on the real-time vibration graph, determines that the vibration period is abnormal according to the judgment result and gives an alarm, or analyzes the abnormal condition of the point location; the data analysis module adjusts a preset frequency based on the point location abnormal conditions, or determines an overall abnormal condition based on a plurality of point location abnormal conditions so as to determine a real-time fault degree, and performs fault early warning on a comparison result of the real-time fault degree and a preset fault degree, or adjusts a preset interval; the model comparison module inputs a plurality of pieces of real-time vibration data into a historical fault model and calculates a fault degree difference value; the model calibration module calibrates the historical fault model based on the fault degree difference. According to the invention, the accuracy of the model fault prediction result is improved.

The invention belongs to the technical field of railway transportation safety, provides a railway contact net carrier cable tension online monitoring and early warning method and system, and aims at solving the technical problems that in the prior art, capturing is not comprehensive, and different environments are difficult to adapt. Acquiring a tension data stream by using a context awareness technique; analyzing the tension data flow by using a hidden Markov model to obtain a preliminary analysis result, processing by using a neural network, generating an abnormal mode recognition result, performing risk assessment by using a Bayesian network algorithm and a digital twin technology to obtain a preliminary fault estimation probability, and predicting a target fault estimation probability in combination with multi-context sensing data; a self-adaptive control algorithm is used for generating an adjusted alarm threshold value, when the monitored tension value exceeds the adjusted alarm threshold value, early warning information is generated in time, and processing is carried out through the block chain technology to obtain target early warning information; according to the technical scheme provided by the invention, the monitoring and early warning capabilities of the railway overhead line system carrier cable are comprehensively improved.

The invention provides an artificial intelligence land biodiversity real-time on-line monitoring evaluation-risk early warning-risk elimination method and system, and the method comprises the steps: collecting and monitoring biological information, geographic and phenological information, environment quality information and other information, carrying out the data comparison and analysis, carrying out the data evaluation and influence factor analysis, and carrying out the risk diagnosis and early warning. A risk elimination method; the system sequentially comprises a data acquisition and monitoring module, a data analysis and diversity evaluation module, a risk diagnosis and early warning module, a risk elimination module and the like. Scientific basis and technical support are provided for land biodiversity protection, management and decision making, and the land biodiversity protection strength is enhanced.

The invention relates to a rock shear failure whole process prediction method based on acoustic emission monitoring, which comprises the following steps: firstly, collecting a rock sample, processing the rock sample into a plurality of rock test pieces, manufacturing an acoustic emission mold, secondly, installing an acoustic emission test platform, carrying out multiple acoustic emission tests of the rock shear failure whole process by using the acoustic emission test platform, and recording data of the multiple acoustic emission tests, the method comprises the following steps: selecting and calculating calculation parameters of a rock shear failure whole-process prediction model based on experimental data, finally constructing the rock shear failure whole-process prediction model based on an XGBoost algorithm, inputting the calculation parameters of the rock shear failure whole-process prediction method into the model, and outputting a rock shear failure prediction result. According to the method, the stability of the rock mass can be continuously monitored in real time, different stages in the direct shear process of the rock are predicted in a classified mode, and the safety and reliability of rock mass engineering such as tunnel engineering can be improved.

The invention provides a mine monitoring and early warning system and method based on microseismic data acquisition, and particularly relates to the technical field of mine early warning, and the mine monitoring and early warning system based on microseismic data acquisition mainly comprises an earth surface monitoring assembly, an internal monitoring assembly and a control assembly, surface data information, environment data information, positioning data information, strain information and micro-seismic information can be collected in real time, three-dimensional visualization of real-time changes of a mine is achieved, mine safety comprehensive evaluation indexes are obtained in real time according to the strain information and the micro-seismic information, comprehensive judgment is conducted by combining the strain information and the micro-seismic information, and the safety of the mine is improved. According to the invention, all-around monitoring coverage from the ground to the underground is realized, the monitoring capability of the whole condition of a mine is improved, the real-time combination of mine surface monitoring and rock stratum monitoring is effectively realized, the comprehensive evaluation index of mine safety is comprehensively judged, and the monitoring efficiency, timeliness and accuracy of coal mine safety are improved.

The invention discloses a self-adaptive music synthesis sleep aiding method and system based on physiological feedback. The method comprises the following steps: acquiring physiological data of a user; preprocessing the physiological data; evaluating the sleep state of the user based on the preprocessed physiological data; defining a music parameter space according to the sleep state; optimizing music parameters based on the sleep state and the music parameter space; generating and playing music according to the optimized music parameters; while music is played, physiological reactions of the user are continuously monitored, music parameters are adjusted in real time based on the physiological reactions, and feedback circulation is formed; analyzing a long-term sleep pattern of the user based on the data obtained in the feedback loop; according to the long-term sleep mode, updating a music parameter space and an optimization strategy; security monitoring is executed, and intervention is carried out when an abnormal condition is detected; according to the invention, a plurality of key indexes such as sleep time, deep sleep proportion, sleep quality and the like are obviously improved.

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 provides a cable tunnel access control remote control method and system based on digital twinning. The system comprises a digital twinning module, a field monitoring module, an intelligent analysis module and an access control control module, wherein the digital twin module is used for generating a monitoring model of a cable tunnel; the on-site monitoring module is arranged in a cable tunnel site and is used for acquiring on-site monitoring data of the cable tunnel and transmitting the acquired on-site monitoring data back to the digital twin module; the intelligent analysis module is used for carrying out environment safety analysis and personnel safety analysis according to the field monitoring data in the monitoring model, and carrying out conjoint analysis according to an obtained environment safety analysis result and an obtained personnel safety analysis result to obtain a control strategy of a corresponding access control node; and the access control module is used for performing corresponding remote control on the access control system of the cable tunnel site according to the obtained control strategy of each access control node. According to the invention, the safety and reliability of cable tunnel construction and daily management can be improved.

The invention discloses a long-distance high-pressure water delivery pipeline water hammer and leakage monitoring and positioning test device, which comprises a water tank, a water outlet side experiment unit, a water return side main path experiment unit and a water return side branch experiment unit, and is characterized in that the water outlet side experiment unit and the water return side branch experiment unit are communicated with the water return side main path experiment unit; the water outlet side experiment unit, the water return side main path experiment unit and the water return side branch path experiment unit are all communicated with the water tank. The device solves the problem that tiny leakage is difficult to monitor in the high-pressure water delivery process in the existing related test experiment technology, and the problems that the water hammer experiment signal precision acquisition is insufficient and the water hammer experiment causes impact damage to equipment. The invention further discloses a using method of the long-distance high-pressure water conveying pipeline water hammer and leakage monitoring and positioning testing device.

The invention relates to the technical field of material storage management, in particular to a BIM-based construction site material monitoring management method and system. The method comprises the following steps: obtaining a construction project drawing; performing drawing information extraction on the construction project drawing to obtain construction project material information data and construction project structure data; performing construction project three-dimensional modeling on the construction project material information data and the construction project structure data to generate construction project three-dimensional modeling data; obtaining total construction material data; and carrying out material information modeling on the total construction material data by using a BIM technology to generate total construction material modeling data. According to the invention, accurate three-dimensional modeling, intelligent marking, regionalized storage and real-time allocation of the construction site materials are realized through the BIM technology, and the accuracy of construction site material monitoring management and the emergency response real-time performance are improved.

The invention discloses a photovoltaic power generation system optimization management system based on intelligent scheduling, and relates to the technical field of renewable energy sources and intelligent power grids. The system comprises the following components: a distributed cooperative control strategy, an information sharing and transaction platform supported by a block chain technology, an intelligent scheduling and optimization engine, intelligent power grid deep integration and a user interaction and visualization interface. Through an innovative distributed cooperative control strategy, comprehensive operation data of a photovoltaic power generation system, a wind energy system, an energy storage system and a micro-grid peripheral energy system can be accurately collected in real time, and an energy supply and demand trend is analyzed and predicted based on big data; therefore, the system can automatically adjust the power generation plan, the energy storage strategy and the load distribution of each system, and the optimal operation of the whole energy system is realized.

The invention discloses a water and soil conservation monitoring system and method based on the Internet of Things technology, and relates to the technical field of water and soil conservation monitoring. A data acquisition module of the Internet of Things technology is utilized to judge small, medium and large drainage basins according to the drainage basin length of a monitoring area; then real-time data of soil humidity, drainage basin correlation and water and soil conservation measures are collected in real time from a monitoring area, a calculation and evaluation module is used for respectively inputting, sequentially calculating and outputting a soil erosion rate TQ, a drainage basin monitoring area evaluation value LY and a water and soil conservation measure adjustment value SBF, and the water and soil conservation measure adjustment value SBF is calculated and output on the basis of the water and soil conservation measure adjustment value SBF; according to the method, the accuracy of monitoring and the pertinence to small, medium and large drainage basins are ensured, a closed-loop feedback mechanism is formed, an innovative monitoring method is realized, and the beneficial effects provide more scientific, comprehensive and effective support for water and soil conservation work.

The invention discloses a method and system for flexibly formulating a product carbon footprint model based on LCA, and relates to the technical field of environmental sciences, and the method comprises the steps: determining a carbon footprint evaluation demand, and constructing an evaluation framework according to the demand; dividing the LCA data into data sets according to life cycle stages; collecting carbon emission data in a life cycle stage in real time; constructing an LSTM model for predicting the carbon emission trends of the product in different life cycle stages; and carrying out carbon footprint accounting, hotspot analysis, sensitivity analysis and uncertainty evaluation based on the LSTM model. According to the method for flexibly formulating the product carbon footprint model based on the LCA provided by the invention, flexible carbon footprint evaluation is realized through a modularized data platform and an adjustable system boundary; by combining the Internet of Things and the AI algorithm, the data acquisition accuracy and analysis efficiency are improved, manual intervention is reduced, carbon emission trend prediction and multi-dimensional analysis are realized, and the evaluation precision and decision support capability are improved.

The invention discloses an intelligent agriculture platform construction method based on big data analysis and a visual model, and the method comprises the steps: arranging a sensor network, and monitoring the key environment parameters of a farmland in real time; using the unmanned aerial vehicle to shoot crop images; according to the shot crop image, identifying a part with pest and disease damage characteristics in the crop image; deducing the age of the crop leaf according to the shot crop image; a cloud platform is built, farmland related data is displayed in an integrated mode through the cloud platform, and decision suggestions are provided based on big data analysis according to all data input into the cloud platform. The system integrates multiple functions such as farmland data display, unmanned aerial vehicle fixed-point automatic cruise, disease and pest recognition and early warning, agricultural question and answer assistant and the like, and provides one-stop intelligent agricultural management service for farmers. The platform provides scientific guidance suggestions for agricultural production based on big data analysis, and assists in improving yield and quality.

The invention discloses a fault detection method and system for a coal conveying programmed control system of a thermoelectric company, and relates to the technical field of coal mine fully-mechanized mining, and the method comprises the steps: building a fault intelligent diagnosis model based on historical fault data; collecting field data in real time, and judging whether a fault occurs or not through the fault intelligent diagnosis model; and executing a corresponding strategy according to the type and degree of the fault. According to the method, multi-dimensional feature analysis is carried out according to multiple groups of sensors, comprehensive analysis of coal level height data is realized by extracting basic features, statistical features, time domain features, frequency domain features and other features, and the accuracy of fault detection is remarkably improved. The deep learning network is utilized to train and classify the feature data, so that the fault types of blockage, coal interruption and the like can be identified more accurately, and misinformation and missing report are reduced. The coal level height data is collected in real time, features are rapidly extracted, and the running state of the coal drop pipe is monitored in real time.

The invention discloses a data analysis system for early diagnosis of digestive tract diseases, relates to the field of medical health, and constructs a multi-level digestive tract disease risk assessment model through machine learning and deep learning technologies. In the preliminary evaluation stage, oral cavity gas data in the standard data set C is trained by using a support vector machine (SVM), a preliminary digestive tract risk score D1 is generated, and the preliminary digestive tract risk score D1 is compared with a preset preliminary risk threshold value T1 to judge whether a patient has a digestive tract disease risk or not. And when the score D1 exceeds the initial threshold value T1, the system automatically starts a deep learning model such as an LSTM network to carry out more accurate secondary evaluation, and calculates and outputs a deep digestive tract risk score D2. By extracting big data and analyzing medical record data, the model is further optimized, a secondary risk threshold T2 is set, and finally, real-time examination prompts such as gastroscope or blood examination are sent to a doctor or an automatic system through linkage of the system and a diagnosis and treatment server.

The invention relates to the technical field of computers, and discloses a host firewall software strategy collection and analysis method. The method comprises the steps of S1, deploying acquisition agents in a distributed manner, and acquiring policy data of a host firewall; s2, carrying out standardized analysis and storage on the collected data; s3, uploading the standardized strategy data to a central management node through an incremental synchronization mechanism; s4, constructing a rule dependency graph based on the rule data at the central management node; and S5, performing conflict detection and redundancy analysis on the host firewall rule by using the rule dependency graph. Priority conflict, coverage conflict and mutual exclusion conflict detection is performed on the rule set through the rule dependency graph, the priority conflict detection ensures the execution logic consistency of the rule, the coverage conflict detection reduces the redundant execution of the rule, and the mutual exclusion conflict detection avoids the rule combination of logic contradictions. And through systematic conflict detection, the execution accuracy and reliability of the rule set are remarkably improved.