557 results about "Collection analysis" patented technology

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention provides an intelligent station multi-source heterogeneous data acquisition and remote collaborative analysis method and system, and relates to the technical field of heterogeneous data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the standardization processing, constructing a station real-time adaptive three-dimensional model through a self-calibration laser scanning technology, and carrying out the semantic mapping of the model and a BIM model; constructing a self-evolution feature library based on a digital twinborn model, and carrying out space-time correlation analysis by adopting a graph convolutional neural network; a remote cooperation platform is established to realize real-time cooperation of multiple experts, and a diagnosis process is recorded through a block chain technology. According to the invention, efficient acquisition, analysis and remote collaborative diagnosis of the data of the smart station are realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention relates to the technical field of user behavior analysis, and discloses a user behavior intelligent analysis and management system based on a big data technology. The system comprises a user behavior data acquisition module for acquiring behavior data in a multi-dimensional scene; the behavior feature intelligent recognition module is used for extracting features by using a deep neural network and a time sequence analysis algorithm and generating a map; the behavior pattern dynamic analysis module is used for analyzing pattern changes through dynamic clustering and a hidden Markov model; the abnormal behavior autonomous detection module is used for detecting anomalies based on the multi-dimensional anomaly score and an adaptive threshold value; and the behavior management intelligent optimization module is used for optimizing a management strategy by utilizing a reinforcement learning algorithm. In addition, a behavior data archiving module is further arranged to guarantee safe storage of data. The system can comprehensively collect and analyze user behavior data, accurately detect abnormity, intelligently optimize a management strategy, improve user experience, system safety and operation efficiency, and have wide application value in multiple fields.

The invention relates to the technical field of information management, in particular to an information scheduling scenarized service agent system. The system comprises an information scheduling acquisition and analysis module, a scene classification and service matching module, a service scheme instruction generation module and a service intelligent execution module, user behavior data, environment perception data and business system data corresponding to information scheduling can be obtained, and an original information set is generated; the original information set is preprocessed, an information scheduling scene classifier is constructed, the service matching degree is calculated in combination with the real-time service resource state, and a candidate service scheme set is generated; performing optimization sorting on the candidate service scheme set to generate an optimal service scheme, and disassembling the optimal service scheme into an executable service instruction sequence; receiving a service instruction sequence and generating a service evaluation report; and if the display does not reach the preset service quality threshold value, triggering a dynamic adjustment mechanism to realize self-iteration upgrading of the information scheduling scenarized service. According to the invention, intelligent scheduling and service of information in a complex scene can be realized.

The invention discloses a wind power blade defect data automatic acquisition and analysis method based on an unmanned aerial vehicle, and particularly relates to the technical field of data acquisition and analysis. The method comprises the following steps: collecting blade surface heat distribution data, locally applying a heat source to a blade, collecting a temperature attenuation curve, calculating a heat diffusivity gradient difference value and a temperature attenuation time index, integrating a multi-parameter space, and classifying a heat distribution abnormal region into a normal region and a real defect region in combination with a deep learning model; cross validation is carried out through optical images and ultrasonic data, so that the detection accuracy is improved; dynamically adjusting a sensitivity threshold value and model parameters of the thermal imager according to a classification result, and generating a detection report; through data feedback iterative optimization model and task planning, the problems of misjudgment and missed judgment of the thermal infrared imager in complex material distribution are effectively solved, the accuracy, reliability and automation level of wind power blade defect detection are improved, and important support is provided for efficient wind power operation and maintenance.

The invention provides a water supply data real-time collection and analysis method, device and equipment and a medium, and the method comprises the steps: obtaining multi-source monitoring data of a water supply network, and carrying out the space-time alignment and spatial interpolation of the multi-source monitoring data, and obtaining a water supply data set; according to the topological structure of the water supply network, the water supply data set is mapped into a dynamic topological graph, and the dynamic topological graph comprises node attributes, edge attributes, real-time pressure and flow data; according to the dynamic topological graph, calculating stability parameters of each node based on a fluid mechanics principle; the dynamic topological graph and the stability parameters are input into a pre-trained machine learning model, a system balance degree quantitative index is generated, and the system balance degree quantitative index is used for representing the operation stability of the water supply pipe network. By adopting the method, the overall balance state of the water supply system can be accurately quantified to realize early risk warning.

The invention discloses a power grid data acquisition and analysis system based on big data, particularly relates to the technical field of power network monitoring, and is used for solving the problem of key feature loss caused by cross-hierarchy data semantic association missing of an existing hierarchical aggregation mechanism. Performing standardized filling and time label layered alignment processing on the original data through the equipment acquisition module; the feature extraction module screens an abnormal waveform fragment and a steady-state parameter offset based on the equipment type and the real-time fluctuation amplitude; the semantic association module generates a space-time association weight in combination with the regional physical topological relation and the reactive circulation path sensitivity; the feature clustering module reconstructs a region-level feature aggregation packet through kernel density estimation and spatial weighted fusion; the cross-layer analysis module dynamically corrects the aggregation weight based on the current flow direction and the transient energy distribution; and the decision generation module generates an equipment positioning instruction and load scheduling strategy combination through alarm template matching, and finally realizes accurate positioning of fault equipment and generation of a scheduling strategy.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention provides an acquisition and analysis method and system for 3D printing tool clamp data based on big data, and relates to the technical field of 3D printing and intelligent manufacturing, and the method comprises the steps: obtaining a clamp historical design scheme, machining parameters and sensor data, carrying out the feature extraction and data cleaning, and forming structured data; then, the design intention is analyzed based on a BERT-CNN model, and a clamp applicability classification database is constructed in combination with the classification model; and then, generating a lightweight clamp structure by adopting a generative adversarial network and topological optimization, and optimizing the topological structure of the lightweight clamp structure through an optimization model. Thirdly, simulation analysis is carried out, stress, deformation and thermal stress are evaluated, errors are optimized based on simulation parameters, and optimized clamp design data are obtained; and finally, the clamp is manufactured, stress data are collected in real time, and machining process parameters are optimized through dynamic analysis. According to the method, clamp design and manufacturing are optimized through data driving, and design efficiency and clamp performance are improved.

The invention relates to the technical field of charging device detection, and discloses a charging device intelligent detection system and method based on dynamic load simulation. The system comprises a load configuration module, a charging parameter acquisition module, a load fluctuation calculation module, an abnormity identification module, a central processing unit and the like. The load configuration module configures dynamic load parameters according to the type of the charging device and generates a load simulation rule; the charging parameter acquisition module acquires an operation parameter set according to a rule; the load fluctuation calculation module generates a load fluctuation curve; the anomaly identification module calculates an anomaly fluctuation index; and the central processing unit fuses and processes the detection dimension parameters to generate state evaluation parameters. In addition, the system is further provided with a fault tracing module and a storage module. The system can simulate actual load change, collect and analyze parameters in multiple dimensions, realize fault intelligent diagnosis and traceability, can be widely applied to detection of various charging devices, improves the detection accuracy, reliability and efficiency, and reduces the detection cost.

The invention provides a mutual inductor fault diagnosis method based on multi-sensor data fusion and transfer learning, and the method comprises the following steps: S1, installing a plurality of high-precision sensors on the secondary side of a voltage transformer to be subjected to fault diagnosis based on the voltage transformer to be subjected to fault diagnosis; s2, on the basis of the step S1, performing data acquisition and processing on data acquired by various high-precision sensors through a DHDAS dynamic signal acquisition instrument; and S3, based on the step S2, carrying out fault diagnosis on the voltage transformer through a PC (Personal Computer) carrying acquisition analysis software on the DHDAS dynamic signal acquisition instrument. According to the method, multi-mode signals such as voltage, current and temperature are synchronously collected, time-frequency domain features are extracted through continuous wavelet transform, the dynamic characteristics of interlayer short circuit faults can be more comprehensively represented, meanwhile, by combining transfer learning and a deep neural network, the intelligent diagnosis model can remarkably improve the classification performance under the condition that sample data is small, and the classification efficiency is improved. And the limitation of a single-mode signal diagnosis method is effectively overcome.

The invention discloses an information acquisition and analysis method, device and equipment, a medium and a product, and relates to the technical field of cloud computing. According to the scheme, through the gateway with a multi-protocol identification function and the dynamic flow processing platform, the language model calling request transmitted by multiple data sources is monitored, unified management of LLM calling data is realized, and multiple protocols are adapted; when a language model calling request is received, forwarding the language model calling request to a language model service cluster, generating corresponding question and answer data, and obtaining user behavior data generated by a target data source according to the question and answer data, so as to carry out full-link multi-dimensional analysis based on the question and answer data and the corresponding user behavior data; the analysis process not only pays attention to the call request and response, but also considers the corresponding user behavior, so that the generated call analysis result is more accurate, and the credibility of LLM application effect analysis is improved.

The invention relates to the technical field of production management in the injection molding industry, and discloses a method and system for automatically generating a production scheduling plan based on production in the injection molding industry, and the method comprises the steps: obtaining a production order, an equipment state and material inventory data, generating original production parameter data, and deploying a distributed production scheduling network; collecting and analyzing multi-stage equipment load data and equipment state data, and generating corresponding influence factors; order priority data are collected, and production task areas are divided; and optimizing and calculating a production scheduling plan based on the task area data, integrating the multi-factor data to generate comprehensive production scheduling data, and further generating and outputting a production scheduling instruction. The system comprises a production scheduling network deployment module, an equipment load analysis module, an equipment state analysis module, a production task area analysis module and a production scheduling instruction generation module. Through multi-data integration and scientific modeling, the intelligent precision of injection molding production scheduling is realized, the equipment management efficiency, the order delivery capability and the overall production efficiency are improved, and the system is suitable for automatic production scheduling management in the injection molding industry.

The invention relates to the field of industrial automation, in particular to an intelligent dispatching industrial robot material sorting system. The system integrates various sensors such as a visual sensor and a laser radar sensor, and high-precision material identification is realized by applying multi-source information fusion and deep learning; a sorting path is dynamically planned through reinforcement learning, operation of multiple robots is coordinated according to a distributed collaborative optimization algorithm, and the motion stability is improved through self-adaptive control; and by matching with an intelligent monitoring management platform, data acquisition, analysis and early warning diagnosis are realized. Through practical application, the system effectively improves the material recognition accuracy and sorting efficiency, enhances the multi-robot cooperative ability and motion stability, and meets the efficient and accurate sorting requirements of modern industry.

The present invention belongs to the field of environmental meteorological monitoring and relates to data analysis technology, which is used to solve the problem that the prior art cannot combine multi-source data for comprehensive collection and analysis, resulting in the inability to accurately evaluate the overall state of environmental meteorology. Specifically, it is an environmental meteorological intelligent monitoring system based on multi-source data collection and analysis, including a data collection module, a data analysis module, a monitoring and analysis module, a necessary analysis module, an optimization analysis module and a database; the data collection module is used to collect environmental meteorological data; the data analysis module is used to perform weight analysis on environmental meteorological data; the present invention can monitor and analyze environmental meteorological data, combine the monitored values ​​of environmental meteorological parameters and weight coefficients for comprehensive analysis and calculation to obtain an evaluation coefficient, evaluate the overall environmental meteorological state within the monitoring period through the evaluation coefficient, and provide timely feedback and early warning when the overall environmental meteorological state is abnormal.

The invention relates to the technical field of chip scheduling and optimization, in particular to a neural network-based main control chip task scheduling and dynamic performance optimization method, which comprises the steps of data acquisition and feature engineering, neural network model design, simulation environment training, model compression and deployment preparation, real-time state monitoring, dynamic decision reasoning, scheduling strategy execution and performance optimization. The data acquisition and feature engineering comprises the following steps: S1, hardware index acquisition; analyzing task attributes (calculation-intensive / IO-intensive), a dependency relationship (DAG), deadline (Deadline) and a resource demand (CPU / GPU occupancy rate); collecting data during chip operation through a performance counter (IPC, cache hit rate and branch prediction error rate), a temperature sensor and a power consumption monitoring unit (PMU); the neural network scheduler can achieve the energy efficiency ratio which is 20%-40% higher than that of a traditional method (such as a CFS scheduler), meanwhile, the neural network scheduler adapts to sudden load changes, and the practicability and the application range of a main control chip are wider.

The invention relates to the technical field of artificial intelligence and biomedicine, and provides a multi-agent diagnosis planning device and method based on a consultation thinking process. The multi-agent diagnosis planning device comprises a diagnosis planning agent, a pathological section analysis agent, an in-hospital information aggregation agent, an information search agent, a knowledge base search agent, an evaluation agent, an arbitration agent and a risk assessment agent. Each agent realizes data interaction through a dynamic priority message bus, and the evaluation agent and the arbitration agent form a progressive verification closed loop: after the evaluation agent outputs a question evidence chain, the arbitration agent triggers diagnosis correction only when question items are greater than 3 items, and otherwise, final diagnosis is output based on a preset rule. According to the invention, the thinking process of multidisciplinary expert collaboration in clinical consultation is simulated by constructing a multi-agent collaborative diagnosis framework, and the whole process intelligence from medical data acquisition and analysis to diagnosis decision is realized.

The invention discloses an electric power data acquisition and analysis method and system based on an electric power complex working environment, and relates to the technical field of intelligent electric power systems, and the method comprises the steps: collecting data through a first network, and carrying out the primary processing through a first algorithm; performing anomaly detection through a first method, and performing deep analysis through cloud service; and a cloud analysis result is fed back to an edge side through a control signal for collaborative optimization. According to the method, the possibility of misjudgment and missed judgment is reduced, the robustness of anomaly detection is enhanced, continuous monitoring of the operation state of the power system is ensured, a scientific basis is provided for timely taking remedial measures, closed-loop optimization of cloud-side collaboration is realized, and the method is suitable for large-scale popularization and application. The advantages of the cloud computing capability and the real-time response capability of the edge equipment are fully exerted, a global view angle is provided for optimization regulation and control of the power system, and the operation efficiency and reliability are improved.

The invention belongs to the technical field of photovoltaic power station equipment diagnosis, and discloses a distributed photovoltaic power station equipment diagnosis method based on big data analysis, and the method comprises the steps: S1, data collection: collecting data transmitted by a sensor on each piece of equipment of a distributed photovoltaic power station, and receiving the operation data of the equipment in real time; s2, data preprocessing: preprocessing the collected data; s3, feature extraction: extracting feature parameters capable of reflecting the running state of the equipment from the preprocessed data through data analysis and data mining; s4, model establishment: constructing an equipment fault diagnosis model based on historical fault data and corresponding characteristic parameters; and S5, fault diagnosis: inputting data acquired in real time into the fault diagnosis model, completing state judgment of each device of the distributed photovoltaic power station, and identifying potential faults. According to the invention, the operation data of the photovoltaic power station is collected, analyzed and processed, so that intelligent diagnosis and maintenance of equipment are realized.

The invention relates to the technical field of concrete construction monitoring, and discloses a non-embedded concrete workability detection method based on deep learning, which comprises the following steps: shooting an image of concrete flowing in a mixer truck chute by using an image acquisition device, analyzing image data, and determining the workability of the concrete in combination with concrete mix proportion data, hydration reaction time and mixer truck chute angle. Therefore, the viscosity value and the slump of the concrete are output. According to the method, a computer vision algorithm, deep learning and machine learning are combined to finish capturing the characteristics of concrete flowing out of the mixer truck step by step, and machine learning is performed based on all captured characteristic values so as to output the concrete slump. An algorithm involved in the scheme supports an edge computing device carrying a network communication module to complete data acquisition and analysis, the communication module is utilized to transmit the acquired and analyzed data to a cloud server, and a commercial mixing station is accurately assisted to make a concrete mix proportion adjustment decision according to different engineering actual conditions.

The invention belongs to the technical field of atmospheric environment monitoring, and particularly relates to an atmospheric pollution channel transmission data analysis system which comprises an acquisition and analysis end and a visual display end which are in communication connection. The acquisition and analysis end comprises a multi-source heterogeneous data acquisition and integration unit, a data preprocessing unit, a pollution channel transmission identification unit, a transmission process analysis unit, a pollution source tracking unit and a dynamic prediction unit; according to the invention, a full-chain analysis framework from multi-source data acquisition and processing, pollution channel dynamic identification to source analysis and prediction and early warning is constructed, the identification precision and timeliness of a pollution transmission path are significantly improved, and a key supervision park in a monitoring area is determined through pollution management and control decision analysis. The key supervision parks are continuously monitored, and the atmospheric pollution emission management performance of the corresponding key supervision parks is judged, so that the targeted industrial park management is realized, and the pollution of each industrial park to the atmospheric environment is reduced.

The invention relates to a heat supply credible intelligent control method and system based on full trust creativity, and the method comprises the steps: collecting heat supply parameters in real time through Internet of Things equipment, and carrying out the preprocessing of the heat supply parameters through an edge calculation node, and obtaining the preprocessing data; performing thermal load prediction on the pre-processed data through a bidirectional LSTM neural network model based on a full trust creation platform, and outputting a thermal load prediction curve in a future time period; generating a dynamic scheduling instruction based on the thermal load prediction curve in combination with a real-time equipment state and an energy consumption optimization strategy; and the dynamic scheduling instruction is issued to the edge computing node, the operation parameters of the heat source, the pipe network and the user side are automatically adjusted, and when it is detected that the equipment is abnormal, standby equipment is automatically switched or a heat supply path is adjusted, and unmanned regulation and control of the heat supply system are achieved. According to the application, edge computing, the Internet of Things, trusted transmission and industrial control safety capabilities are integrated, so that full-flow coverage of heat supply scene data acquisition and analysis is realized, and the unattended scheduling capability of heat supply operation is improved.

The invention provides a method capable of comprehensively evaluating the intelligent degree of household intelligent home equipment, and the method comprises the steps: reasonably selecting architecture parameters capable of reflecting the intelligent degree; data required by architecture parameters can be effectively obtained by collecting and analyzing multi-dimensional data such as categories, quantity and scores of household intelligent household equipment and App application use conditions, and meanwhile, intelligent degree scores required by model training are set in a reasonable mode, so that the intelligent degree of the intelligent household equipment is improved. And finally, a linear model is adopted to construct a scoring model, so that the development difficulty can be effectively reduced, the evaluation accuracy can be improved, and a convenient and effective evaluation means is provided for subsequent users, operators and the like. According to the method, whether the user uses the intelligent equipment or not can be judged through the App accessed by the user, a relatively reasonable score is made for a family of which the equipment model cannot be captured according to the App, and two sets of rating standards are further adopted, so that the matching between the score and the rating can be effectively ensured, and the reliability of an output result is improved.

The invention discloses a hierarchical acquisition and analysis method and system for attribute information of a cultural relic building, and belongs to the field of electronic data analysis. According to the method, the acquired historical stored data of the cultural relic building is imported into a danger assessment strategy for cultural relic building danger assessment, maintenance judgment is performed, quantitative assessment is performed based on the damage condition of the cultural relic building, and whether the building needs to be maintained is judged according to a quantitative assessment result, so that the safety of the cultural relic building is improved; meanwhile, structure maintenance analysis is carried out according to structure composition importance and structure composition dangerousness before maintenance, and then accurate judgment of maintenance steps is carried out.

The invention discloses a laboratory instrument data automatic acquisition and analysis method and system, and relates to the technical field of laboratory instrument acquisition and analysis, and the method comprises the steps: obtaining sample identification information, automatically sorting and distributing the sample identification information to different analyzers, uploading detection data to a central database in real time, and associating a sample number, a detection timestamp and an instrument number; the method comprises the following steps: constructing a multi-dimensional feature space based on historical detection data, performing sub-cluster division by adopting a density clustering algorithm, establishing a cross-index logic constraint relationship in combination with an expert rule base, mapping real-time detection data to the multi-dimensional feature space, judging a data exception type according to sub-cluster affiliation and the logic constraint relationship, and when judging that the data is exceptional, judging that the data is abnormal. And triggering a secondary detection process, automatically dispatching a mechanical arm to send the sample back to a specified instrument to execute differentiated reinspection operation, and generating a comprehensive report containing detection process tracing. According to the invention, the automation level of detection is improved, the labor cost is reduced, the detection quality and efficiency are improved, and the laboratory detection work efficiency is improved.

The invention discloses a vacuum combined preloading intelligent regulation and control method and system for coastal soft foundation treatment, and the method comprises the steps: cleaning a construction area, laying a drainage sand cushion layer and a plastic drainage plate, constructing a vacuum system and a monitoring network, carrying out preloading according to a layering grading and tide adaptation principle, and starting a vacuum control unit. Data is collected and analyzed in real time to trigger a regulation and control instruction, a vacuum degree compensation value and supplementary loading operation are adjusted according to the instruction, secondary strengthening is conducted on an area with the strength not reaching the design, and finally unloading and leveling are conducted according to the standard; the system comprises a data acquisition module, an edge calculation module, an intelligent control module, a stacking execution module and a vacuum regulation and control module, and the modules are connected through wired or wireless communication links to realize data interaction and instruction transmission; according to the method, the problems that the vacuum degree is unstable and regulation and control are lagged in the coastal tidal environment in a traditional method are solved, and the soft foundation treatment efficiency and safety are improved.

The invention provides a municipal drainage pipeline cleaning device and method.The municipal drainage pipeline cleaning device comprises a movable jet cleaning device and an image collecting and analyzing system, the movable jet cleaning device is used for cleaning a pipeline, and the image collecting and analyzing system is arranged at the head of the movable jet cleaning device; the system is used for collecting image data in a pipeline while cleaning the pipeline, and analyzing and generating a three-dimensional model in the pipeline so as to realize visual inspection of internal defects of the pipeline. The problem is solved through a movable jet cleaning device and an image acquisition and analysis system, the movable jet cleaning device cleans a pipeline through a walking mechanism, a high-pressure water pump and a spray head assembly, meanwhile, the image acquisition and analysis system at the head of the movable jet cleaning device conducts synchronous data acquisition, and after an image acquisition unit acquires an image, the image is transmitted to a computer; image preprocessing is carried out through the image processing unit, a three-dimensional model in the pipeline is reconstructed through the three-dimensional modeling unit by using the computer vision technology, and high-precision detection and evaluation of the internal state of the pipeline are synchronously completed in the cleaning process.

The invention provides a fracture deformation-seepage cross-scale visualization experiment device and a test method, and relates to the technical field of hydraulic rock mechanics and measurement. Comprising a macroscopic observation assembly, a microscopic observation assembly, a microscopic seepage model, a temperature-stress-seepage loading system, a monochromatic coaxial light source and a data acquisition and analysis system, the microscopic seepage model is used for placing a fractured rock mass; the temperature-stress-seepage loading system is used for injecting fluid with fluorescent microspheres into the microcosmic seepage model, applying load, regulating and controlling temperature and collecting data; the data acquisition and analysis system is used for processing data and images. By arranging the macroscopic observation assembly and the microcosmic observation assembly, double-scale synchronous observation is achieved, the whole process of fracture internal fluid interface evolution and rock mass deformation can be obtained in real time under the multi-physics field coupling conditions of temperature, stress, seepage and the like, visual correlation of deformation, flow and stress is achieved, and the measurement accuracy is improved. And an experimental basis is provided for establishing a fracture multi-field coupling mechanism model.

The invention relates to the technical field of welding fault analysis, in particular to a rail welding process information intelligent collection and analysis method based on the Internet of Things technology, and the method comprises the steps: obtaining the data of each welding parameter in the rail welding process through an Internet of Things intelligent sensor group; according to the discrete degree of all modal component data in each welding parameter before each collection moment, the instantaneous interference complexity of each welding parameter at each collection moment is obtained in combination with the average level of the similarity degree between the modal components of the welding parameters, and then the credibility weight of each welding parameter at each collection moment is obtained; the method comprises the following steps: analyzing the interference removing singularity of each acquisition moment in the track welding process to obtain the interference removing singularity of each acquisition moment in the track welding process, analyzing the correlation between the interference removing singularity and each welding parameter in the track welding process to obtain the correlation fault significance of each acquisition moment in the track welding process, and judging the fault condition of the track welding equipment based on the correlation fault significance. The accuracy of intelligent analysis in the track welding process can be improved.

The invention, which relates to the health management field, discloses a health management service system comprising a data acquisition module, an analysis and evaluation module, a health management scheme making module and a health monitoring and early warning module. The data acquisition module is used for acquiring data of the medical detection equipment and the wearable health monitoring equipment and acquiring environmental data; the analysis and evaluation module is used for carrying out deep analysis and evaluation on the collected data; and the health management scheme making module is used for making a health management scheme according to the analyzed and evaluated result. The limitation that a traditional health management mode only depends on single data and lacks multi-dimensional comprehensive analysis is overcome, comprehensive and accurate control over personal or group health conditions is achieved by integrating data from multiple sources and covering medical detection, wearable device monitoring, environment data and the like, and the health management system is suitable for being popularized and applied. Real personalized and precise health management services can be provided, occurrence and development of diseases are effectively prevented and controlled, and life quality is improved.