5739results about "Material testing goods" 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 discloses a wharf armor block quality detection method, and belongs to the technical field of concrete structure detection.The method comprises the steps that a surface model is constructed through a movable three-dimensional laser scanning device, and a high-risk defect area is recognized; performing encryption scanning and impact echo detection in the high-risk area by adopting a frequency-adjustable dual-mode flaw detection device to generate a three-dimensional defect distribution diagram; a core sample is drilled in the defect area for compressive strength testing, and the material performance is analyzed in combination with a non-contact infrared spectrum; and establishing a dynamic evaluation model, calculating a safety coefficient by integrating surface defects, internal defects and material discreteness data, and generating a three-dimensional visual report. The problems that a traditional detection method is low in efficiency, insufficient in internal defect positioning precision, one-sided in material performance evaluation and the like are solved, full-dimensional detection from the surface to the interior and from the macroscopic level to the microscopic level is achieved through a multi-source data fusion and closed-loop feedback mechanism, and the reliability of structural safety evaluation and the scientificity of maintenance decision are remarkably improved.

The invention discloses an asphalt pavement fatigue damage model calibration method based on AI and digital twinning, and relates to the technical field of damage detection. Compared with the prior art, the problems that traditional asphalt pavement design depends on empirical formulas and static parameters, material aging, environmental coupling and load uncertainty are difficult to dynamically reflect, and cross-scale correlation between microscopic interface behaviors and macroscopic structure performance is lacked are solved; the method comprises the following steps: collecting and preprocessing multi-source heterogeneous data in real time through a multi-modal data fusion and dynamic sensing system, simulating and accurately quantifying asphalt-aggregate interface binding energy and adhesion work in combination with molecular dynamics, and constructing a cross-scale damage evolution model; a physical information neural network is used for embedding an improved Paris formula, actually measured data and a physical rule are deeply fused, and the locality hypothesis of a traditional model is effectively corrected.

Systems, computer program products, and methods are described herein for building material data management. With reference to an example method, the method receives a first dataset formed of first data entries associated with a building material data source and generates a building material data entity based on the building material data source. The method includes outputting a representation of the building material data entity. The first dataset may be formed of data records (e.g., construction documents, user inputs, sensor data, or machines data), and the representation may be a visual representation may include notifications, visual presentations of the building material entity, etc.

The invention belongs to the technical field of concrete crack detection, and particularly discloses and provides a high-strength concrete construction crack detection system and method.The system comprises a multi-parameter cooperative sensing module, a construction crack recognition module, a crack risk analysis module and a crack early warning processing module. According to the invention, the temperature, humidity, strain and multispectral image data from a plasticity stage to a hardening stage are collected in real time through the multi-parameter cooperative sensing module, and crack characteristics are extracted by combining staged differential signal processing and a deep convolutional neural network, so that the detection precision of micro cracks and internal defects is remarkably improved. Meanwhile, multi-physics field data are fused, a crack evolution risk rate model is innovatively constructed, dynamic probability prediction before crack initiation is achieved, a grading disposal scheme is automatically triggered through risk grade division and a progressive early warning mechanism, drying shrinkage and temperature crack causes are effectively distinguished, the prevention and control efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. And full-life-cycle accurate monitoring of the high-strength concrete is supported.

The invention provides a material simulation test method and platform for mold flow analysis, and relates to the technical field of material simulation testing, and the method comprises the steps: carrying out the product demand analysis of a target test material, and obtaining the product demand information; performing injection molding analysis to obtain an injection molding control parameter threshold; obtaining an injection molding simulation test model; carrying out an injection molding simulation test, and carrying out image pre-acquisition to obtain a first pre-acquired image; defect feature analysis is carried out, attention area distribution is carried out, and a target attention area is obtained; an injection molding defect area image is obtained, and defect feature information is obtained; and feedback testing and parameter optimization are carried out, optimized injection molding control parameters are obtained, and injection molding control is carried out. The technical problems that in the prior art, injection molding analysis generally depends on a fixed mathematical model and limited technological parameters, and problems in the molding process cannot be recognized and adjusted in real time, so that the production efficiency and the product quality are low are solved.

The invention provides a civil construction monitoring intelligent sensor information detection method and system under signal mismatch, and the method comprises the steps: carrying out the time-frequency analysis of temperature and strain fundamental frequency components, selecting a wavelet basis function adaptive to the concrete setting and strength increasing process, carrying out the self-adaptive threshold processing of a wavelet coefficient, removing high-frequency noise, and carrying out the detection of the temperature and strain fundamental frequency components. Low-frequency signal characteristics reflecting concrete strength increase are reserved; performing feature extraction on the denoised temperature and strain signals by adopting a singular value decomposition algorithm, detecting internal defects of the concrete through abrupt change of a singular value spectrum, and if crack and hole defects exist, causing strain signal abrupt change by stress concentration; if it is detected that defects exist in the concrete, that is, spatial heterogeneity of temperature and stress distribution exists, temperature and strain monitoring data of multi-point arrangement and a defect detection result serve as boundary conditions and constraints, and a temperature field and a stress field in the whole concrete structure are obtained through numerical simulation.

The invention relates to a concrete temperature-stress two-parameter cooperative monitoring and early warning method and system, and belongs to the technical field of concrete structure health monitoring, and the method comprises the steps: activating a composite sensor network disposed at a preset position of a concrete structure, and executing a sensor self-calibration mode; the method comprises the following steps: synchronously acquiring temperature and stress original monitoring data of each node, processing based on an altitude air pressure compensation algorithm, and outputting a temperature gradient field matrix and a stress tensor sequence with aligned time domains; the method comprises the following steps: separately calculating a thermal stress component and an effective stress component, performing integral calculation on hydration reactivity, generating a multi-dimensional feature vector set, inputting a pre-constructed prediction model, executing time sequence evolution prediction, calculating a crack probability value, performing environment correction in combination with real-time environment parameters, outputting a risk level identifier, and matching a preset regulation and control strategy. And generating an equipment control instruction set and executing corresponding regulation and control actions. According to the invention, the accuracy and timeliness of crack risk prediction can be improved.

The invention relates to a concrete chloride ion gradient monitoring and durability enhancing method and system based on MXene, and solves the problems that a traditional drilling sampling method is high in operation process destructiveness, high in cost and low in detection efficiency, test results are remarkably influenced by environmental conditions, and field real-time performance and universal applicability are lacked. The method comprises the following steps: embedding a plurality of prefabricated sensing units containing MXene-Cl composite electrode layers and toughening protection layers into concrete at different depths according to a vertical gradient to form a monitoring network; potential signals are collected through a sensing unit, after correction, the potential signals are introduced into a Nernst equation for inversion to obtain the chloride ion concentration, and osmotic distribution imaging is achieved by combining a preset vertical and transverse gradient estimation method. The intercalation adsorption characteristic of MXene on chloride ions is utilized to capture and retard migration of the chloride ions to the reinforcing steel bar. The method has the advantages that chloride ions are monitored in real time, migration of the chloride ions to the steel bars is blocked, the concrete degradation state is evaluated in combination with multi-parameter data, and early warning and durability improvement are achieved.

The invention discloses an ARVR resin wafer process which comprises the following steps: preparing a to-be-detected resin wafer and a reference test piece, and carrying out positioning marking on a micro-nano structure area of the to-be-detected resin wafer to obtain a detection sample; performing multi-dimensional spectrum characterization on the detection sample and the reference test piece, determining a material non-uniform distribution area of the detection sample through comparative analysis, and obtaining molecular structure characterization data; carrying out stress field detection and dynamic response analysis on the material non-uniform distribution area to obtain mechanical property characterization data and high-risk defect points; carrying out microstructure and morphology analysis on the high-risk defect points to obtain defect feature data; and performing system evaluation according to the molecular structure characterization data, the mechanical property characterization data and the defect characteristic data to obtain a health degree evaluation result of the resin wafer. According to the technical scheme, multi-scale characteristic characterization can be carried out on the micro-nano structure area in the resin wafer for AR / VR, and systematic detection of the characteristics such as the molecular state, the interface stress and the phase change of the material is achieved.

A method for extracting a wear particle feature signal based on segmentation entropy is provided, including obtaining a raw signal to be processed by performing real-time data acquisition using a lubricating oil wear particle monitoring system; obtaining a preprocessed signal by performing low-pass filtering and harmonic interference suppression on the raw signal to be processed; dividing the preprocessed signal into a plurality of time domain sequence segments with a sliding window; calculating segmentation entropy corresponding to each time domain sequence segment, normalizing a segmentation entropy set to obtain normalized segmentation entropy; obtaining an adaptive threshold through curve fitting based on empirical cumulative distribution of normalized segmentation entropy, obtaining a plurality of non-zero discrete time domain signal segments by segmenting the preprocessed signal by the adaptive threshold; and obtaining final extraction results of the wear particle feature signal by excluding residual noise interference through target signal feature recognition indices.

Embodied within a realm of transformative innovation, a proactive Al-powered system emerges, seamlessly and autonomously managing the addition of chemical admixtures to control and adjust the composition of concrete during production and transport. The system comprises a concrete mixer tank, reservoirs for the chemical admixtures, a mechanism to dispense them as needed, sensors, and proactive Al-based control system, embodying the pinnacle of the proactive intelligent automation. Sensors track the concrete's properties and environmental conditions, and the proactive Al-based control system analyses the sensor data in real time to discern the precise type, quantity, and timing of chemical admixtures required to maintain the optimal properties of the concrete within the mixer tank, ensuring unwavering fidelity to the desired specifications. This autonomous Al-based system aims to produce concrete with minimal water addition, ensuring consistent concrete quality and reducing reliance on manual intervention.

The invention relates to the technical field of radio orientation, in particular to a measuring method capable of tracking and recording motion trail information of a vibrating rod, which comprises the following steps: arranging at least four UWB (Ultra Wideband) base stations, fixing UWB transmitters at the joint of the vibrating end of the vibrating rod and a rubber tube, and fixing IMU (Inertial Measurement Unit) sensors at the vibrating end of the vibrating rod; according to the data output by the UWB transmitter and the IMU sensor, the position and posture of the vibrating rod in the detected target mold are calculated through a positioning algorithm, and the motion trail of the vibrating rod is further calculated; and according to the vibrating time, the vibrating area and the vibrating area overlapping degree of the vibrating rod, the vibrating state of the concrete is analyzed. The UWB technology and the IMU technology are fused, so that high-precision positioning of the vibrating rod can still be kept under the ground where wireless signals such as GPS signals cannot be transmitted, and the vibrating quality can be conveniently detected and evaluated.

The invention discloses a rubber product quality detection method and system, and relates to the related technical field of rubber quality detection.The method comprises the steps that rubber products are conveyed to a target detection table, and data acquisition of the rubber products is executed; inputting the wave impedance distribution data and the surface heat distribution gradient field into a defect causal atlas channel to execute phase inversion fusion processing, establishing a cross-modal coupling phase tensor, and identifying deep defect response characteristics by solving a phase difference matrix; activating a pressure spectrum sensing unit to apply control pressure, reading a local mechanical response spectrum, sending the local mechanical response spectrum to a cross-modal calibration layer, and executing joint error authentication; and performing quality detection report according to a self-calibration defect mapping result. The technical problems that in the prior art, multi-modal sensing data fusion efficiency is low, deep defects are difficult to comprehensively recognize, and consequently the quality detection precision and reliability of rubber products are insufficient are solved, and the technical effects that cross-modal cooperative detection is achieved, and the precision and reliability of defect recognition results are improved are achieved.

Provided herein are methods and compositions for determining and reporting carbon dioxide sequestered and / or carbon dioxide avoided in operations involving concrete, including concrete raw material transport, concrete production, and concrete use.

The invention belongs to the technical field of roadbed engineering, and particularly relates to a construction uniformity evaluation method for a clay improved copper tailing roadbed. The method comprises the following specific steps: S1, determining the proportion of the clay improved copper tailing roadbed filler; s2, applying the blended clay improved copper tailings to roadbed construction; and S3, carrying out construction detection on the clay-improved copper tailing roadbed, calculating a mixing uniformity coefficient, and comparing the mixing uniformity coefficient with a uniformity coefficient in a laboratory to evaluate the construction uniformity of the clay-improved copper tailing roadbed. According to the method, after roadbed construction is completed, a working project of measuring the compactness through a field sand filling method is combined, the mixing uniformity coefficient is calculated with the deviation between the composition proportion of roadbed filler sampled through the sand filling method and a proportion design result as a parameter, and the construction quality can be effectively evaluated conveniently.

Methods, apparatus, and storage medium for providing real-time asphalt density and / or moisture content prediction with ground penetrating radar. A method includes using raw data from a ground penetrating radar sensing an asphalt concrete at a location; processing the raw data to obtain processed data; determining an asphalt density of the asphalt concrete at the location based on the processed data according to an asphalt density prediction model; determining a compaction status of the asphalt concrete at the location based on the asphalt density and a target compaction threshold; and outputting the compaction status of the asphalt concrete at the location to an operator for determining whether to continue or stop compacting the asphalt concrete at the location. In another method, moisture content for cold recycling and existing asphalt concrete pavement is predicted in real-time from using raw from data of ground penetrating radar mounted on a vehicle.

The invention relates to a roadbed compaction quality detection method and system based on multi-dimensional data, and the method comprises the steps: obtaining sensor data of a roller of a road roller in real time, and obtaining pavement images and positioning data of a plurality of continuous time points; when the road surface is flattened, the roadbed structure is gradually stable, so that the corresponding pressure data and vibration data are gradually stable. By analyzing the stability of the pressure data and the vibration data, the stability of the pressure data and the vibration data is determined. According to the method, vibration data, stable data and temperature data of a road section at a stable time point are extracted, and then a target data reference range corresponding to a corresponding temperature grade and a roadbed material is found from a pre-constructed data model, so that the target data reference range can verify the vibration data and the stable data at the stable time point; therefore, whether the road roller performs the road rolling operation in a reasonable range or not is judged. In addition, the road surface is detected by using the road surface image.

The invention relates to the field of concrete detection, and discloses a concrete structure health real-time monitoring system based on multi-source sensing fusion, and the system comprises a data collection unit which carries out the collection of the physical field data of a concrete structure through a sensor array, and obtains the multi-source sensing data; performing structure response difference analysis on the multi-source sensing data to obtain concrete structure response difference data; according to the response difference data of the concrete structure, performing spatial and temporal distribution density analysis on a microcrack propagation path to obtain microcrack propagation spatial and temporal distribution density data; a data evaluation unit; multi-scale damage evolution path simulation is carried out through the structural damage time-space correlation characteristic data to obtain multi-scale damage evolution path data, the sensor array is used for collecting multi-source sensing data, structural response difference analysis is carried out, tiny changes of a concrete structure can be accurately captured, potential structural problems can be found in time, and the construction efficiency is improved. And a high-quality data basis is provided for subsequent analysis.

The invention relates to the technical field of road engineering, in particular to a method for determining the optimal construction temperature of warm-mixing synchronous regeneration of waste SBS (styrene butadiene styrene) modified asphalt, which comprises the following steps: customizing a two-component synchronous regeneration agent for SBS modified asphalt with different aging degrees; warm-mixing synchronous regenerated SBS modified asphalt at different temperatures is prepared, and thermal insulation treatment is performed to simulate the actual construction process; testing the typical rheological property of the warm mixing synchronous regenerated SBS modified asphalt; drawing a warm mixing synchronous regeneration temperature-rheological property scatter diagram, and constructing a corresponding fitting equation; ideal temperature intervals of high-temperature rutting resistance, medium-temperature cracking resistance and low-temperature fracture resistance are divided, and the intersection of the three temperature intervals is used as the optimal warm mixing synchronous regeneration temperature. According to the method, the warm mixing synchronous regeneration temperature can be quickly and accurately determined, the synergistic interaction effect of the double-component synchronous regeneration agent-warm mixing agent is exerted, and meanwhile, the performance loss of the regenerated asphalt mixture caused by secondary aging due to too high heating temperature is avoided.

The invention relates to the field of construction temperature regulation and control, in particular to an intelligent temperature control method and device for fan foundation construction in an alpine region. The method comprises the following steps: collecting real-time concrete temperature monitoring parameters based on a distributed temperature sensor, carrying out time sequence temperature discrete trend analysis and spatial temperature fluctuation distribution evolution, and constructing a temperature fluctuation distribution evolution diagram; concrete surface environment parameters are extracted, heat exchange simulation processing is carried out on the temperature fluctuation distribution evolution diagram, multivariate coupling correlation analysis is carried out, and a temperature-environment quantitative relation model is constructed; and obtaining historical meteorological logs and meteorological environment data of the construction area, carrying out similar meteorological condition matching calculation, carrying out short-term meteorological trend prediction, and generating short-term meteorological trend prediction features. According to the dynamic temperature regulation and control requirements, safe construction of the fan foundation concrete in the high and cold environment is guaranteed, and the construction quality is improved.

The invention provides a power transmission and transformation project reinforced concrete corrosivity evaluation method and system, and relates to the technical field of civil engineering durability evaluation.The method comprises the steps that temperature data of a project location and the total volume, the water content and the pore volume of reinforced concrete are collected, and a freeze-thaw failure index formula is constructed; the product type is judged according to the measured sulfate concentration, different sulfate erosion index formulas are constructed in combination with related data, and the magnesium ion concentration is independently considered in the marine environment; constructing a chlorine salt erosion index formula according to the chloride ion concentration; extracting data from the research report to construct a carbonization erosion index formula; and giving a weight to each erosion type to establish a comprehensive corrosivity evaluation index formula, and judging the erosion degree according to a result. By means of the method, various erosion factors can be comprehensively considered, the erosion degree can be effectively judged according to the evaluation result, and a key basis and scientific guidance are provided for durability guarantee, protective measure making and maintenance decision making of the reinforced concrete structure of the power transmission and transformation project.

A method for estimating an elemental composition of a multiphase oilfield fluid includes obtaining a sample of a multiphase oilfield fluid; blending the sample with a viscosity modifying agent to transform the sample into a high viscosity paste; introducing the high viscosity paste into a chamber of an XRF apparatus; using the XRF apparatus to make an XRF measurement of the high viscosity paste; and evaluating the XRF measurement to estimate an elemental composition of the obtained multiphase oilfield fluid.

The invention discloses a transformer fault detection method based on gas in oil and related equipment, relates to the technical field of power equipment monitoring, and effectively solves the problem that the content difference of characteristic gas of the same fault type is large under different transformer oil temperatures due to the fluctuation of the transformer oil temperature in the traditional method by adopting the technical scheme. The detection system eliminates the influence of the oil temperature change on the gas content through oil temperature correction and standardization processing, so that the fault judgment of the transformer is more accurate and reliable. And meanwhile, the detection system adopts the characteristic gas ratio matrix to judge the fault type, so that misjudgment possibly caused by pure dependence on a content absolute value is avoided, and the accuracy and reliability of transformer fault diagnosis are improved.

The invention particularly relates to a method for predicting the service life of a boric acid solution corrosion concrete member in a nuclear power plant. The method comprises the following steps: determining boric acid solution accelerated corrosion concrete member test control parameters; based on the boric acid solution accelerated corrosion concrete member test control parameters, boric acid solution accelerated corrosion concrete member tests are carried out in groups; constructing a boric acid solution corrosion concrete member life prediction model based on boric acid solution accelerated corrosion concrete member test data; and predicting the boric acid solution corrosion concrete member service life based on the boric acid solution corrosion concrete member service life prediction model. According to the method, a model for predicting the service life of the concrete member corroded by the boric acid solution is established by considering the water-binder ratio of the concrete, the temperature, the concentration of the boric acid solution and the corrosion time on the basis of research test results of physical properties and macroscopic mechanical properties of the concrete member corroded by the boric acid solution or field actual measurement data; and the service life of the boric acid solution corroded concrete member is predicted.

The invention belongs to the technical field of hydroelectric generating set monitoring, and relates to a water turbine bearing temperature and lubricating oil state monitoring and fault diagnosis method and system. According to the method, triple-redundancy temperature measurement values of a water turbine bearing are adopted, multiple parameters are fused, redundancy design is adopted, sensor failure diagnosis is executed, a temperature-vibration-oil pressure combined feature space is constructed according to the triple-redundancy temperature measurement values and online oil quality parameters, a fault propagation map is established, and the fault propagation map is analyzed. And finally, based on the multi-level diagnosis model and in combination with the fault propagation atlas, composite fault mode recognition is realized, a fault classification result is obtained, and the composite fault mode recognition is realized based on the multi-level diagnosis model, so that the problems of low reliability, single monitoring dimension and insufficient fault recognition capability of a sensor in the prior art are effectively solved. And the equipment reliability and the operation safety are obviously improved.

The invention discloses a method for predicting the damage of a prestressed concrete cylinder pipe in a complex environment, and relates to the technical field of pipeline structure health monitoring, and the method comprises the steps: collecting multi-physical field data through deploying multiple types of sensors, transmitting the data to a central monitoring system in a wireless manner, carrying out the standardized preprocessing, and obtaining a multi-source fusion data set; a salt migration driving index dsa, a stress disturbance induction index yry and an electrochemical activity potential index che are constructed, a comprehensive corrosion risk index SCR is obtained, and corrosion behavior evaluation is carried out. And when the corrosion behavior is evaluated as a disturbance sign, a corrosion change prediction index CAR is further calculated according to time sequence evolution forming a corrosion risk index SCR, grading evaluation and transition identification of the corrosion state are realized, an evaluation result is stored in a database, and a corrosion risk evolution trend is presented through a visual chart. The method supports early warning and emergency response decision making, and has high adaptability and engineering practicability without adding hardware.

The invention relates to the technical field of concrete curing, in particular to a concrete crack risk prediction and curing system based on thermo-acoustic coupling indexes. According to the scheme, a temperature field and an acoustic emission signal are synchronously acquired through the multi-modal data acquisition module; a thermal diffusion shear index and an acoustic emission energy density are calculated by a multi-modal data fusion module, and are fused into a unified thermo-acoustic coupling factor to quantify a cross-scale risk; the structural constraint compensation module corrects the factor by using pre-stored constraint information to generate a final risk index; the self-adaptive maintenance decision-making module maps a dynamic maintenance strategy according to the indexes; and finally, the maintenance robot executes precise maintenance operation. The technical problems that macroscopic thermal stress and microcosmic damage signals cannot be evaluated in a unified mode, different-source data are difficult to fuse, and high-constraint area risk identification is inaccurate are solved, and continuous prediction and self-adaptive cooperative control of cracks from a potential stage to an initiation stage are achieved. And the accuracy and the intelligent level of early-age crack prevention and control of the concrete are remarkably improved.

The purpose of the present invention is to provide a technique for optically quantifying a compound of which a color does not change due to oxidative degradation.　Another aspect of the present invention provides a method for diagnosing oil characteristics, the method being characterized by: using an optical sensor having a light source and a detector that detects light emitted from the light source to acquire an absorption spectrum in a wavelength range of 800 nm to 3000 nm of an oil containing an additive having a maximum molar absorption coefficient value of 50 or less in the ultraviolet visible wavelength range of 250 nm to 800 nm; and inputting data based on the absorption spectrum into an estimation model implemented in an information processing device to predict the concentration of the additive.

The invention relates to the technical field of lubricant analysis, in particular to a gearbox lubricating oil product state intelligent recognition system and method, and the system comprises a lubricating oil state parameter collection module, a multi-dimensional feature fitting degree classification module, a spectrum pollutant dynamic detection module, a state change trend storage module and a lubricating oil abnormal deviation early warning module. According to the method, the lubricating oil state features are accurately extracted by constructing a multi-dimensional feature matrix and high-dimensional space mapping, the fitting degree weight and the data scale are combined to adjust, optimize and classify, the state recognition precision is improved, the pollution change is recognized through trend analysis in dynamic spectrum pollutant detection, and the monitoring sensitivity is improved. A data storage strategy driven by parameter change ensures the integrity of high-value data and reduces invalid storage, an offset calculation method is combined with data trend comparison to improve the recognition accuracy of an abnormal state, and misinformation and missing report are reduced.