1361 results about "Parameter analysis" patented technology

The invention relates to a battery life prediction and health assessment system based on BMS expansion parameter analysis, and the system fuses deformation quantity and electrochemical parameters, and remarkably improves the modeling precision and life prediction capability of a battery health state. The acquisition module obtains battery expansion characteristics and operation parameters through a sensor or a modeling mode, and constructs a multi-dimensional time sequence data set; the processing module performs normalization and feature reconstruction on the data to form a health track sample for learning; the prediction module outputs residual life and degradation trend scores based on a deep regression model, and introduces an expansion feature sensitivity adjustment mechanism; the evaluation module evaluates the health level of the battery according to the prediction result and a threshold rule and generates a report; the regulation and control module dynamically adjusts BMS control parameters according to an evaluation result, and optimizes energy efficiency and a safety strategy; the method breaks through the dependence of a traditional BMS system on electrochemical characteristics, realizes more comprehensive and accurate intelligent sensing and life management of the battery health state, and is suitable for various BMS architecture scenes.

The invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

The invention relates to the technical field of industrial automatic detection, in particular to an intelligent electromagnetic valve fault detection method and system based on multi-parameter analysis, and the method comprises the following steps: S1, synchronously collecting the dynamic current waveform, vibration spectrum information and environment temperature data; s2, extracting a current characteristic value and vibration energy distribution, and calculating a temperature drift amount; s3, the current characteristic value, the vibration energy distribution and the temperature drift amount are input into a multi-parameter incidence matrix, and a coupling deviation coefficient representing the coupling relation between parameters is generated; s4, calculating a comprehensive health index of the electromagnetic valve based on the coupling deviation coefficient; and S5, matching the comprehensive health index with a preset fault feature library, and outputting a corresponding fault type and a positioning result. According to the method, by fusing the multi-source characteristic parameters and introducing the health index evaluation mechanism, accurate recognition and positioning of the electromagnetic valve faults are achieved, and the detection accuracy and the response efficiency are improved.

The invention relates to the technical field of fire control management, in particular to a fire rescue intelligent management method and system based on a multi-mode AI large model. The method comprises the following steps: acquiring real-time environment monitoring data of a fire scene; carrying out environmental parameter analysis on the fire scene real-time environmental monitoring data, and carrying out gas change trend prediction to generate a scene gas parameter change trend; acquiring a fire scene thermal imaging image; performing temperature distribution visual identification and continuous frame temperature change rate calculation according to the fire scene thermal imaging image so as to generate a fire source area temperature change rate; performing multi-time ignition trend prediction according to the temperature change rate of the fire source area and the on-site gas parameter change trend so as to generate a multi-time ignition trend prediction map; and identifying a to-be-rescued area point based on the fire scene thermal imaging image. According to the invention, real-time and accurate distribution of on-site fire rescue resources is realized, and the rescue management efficiency and safety are improved.

The invention relates to the technical field of smart park management, in particular to an analysis monitoring method and system based on a smart park operation state, and the method comprises the following steps: obtaining park equipment operation data parameters and associated environment parameters, analyzing the equipment operation difference, recognizing the equipment operation state, and predicting the state change trend. And calculating an abnormal event propagation path and rate, determining a response priority, constructing early warning information, and obtaining a smart park monitoring record. According to the method, the accuracy of park equipment state identification is improved by combining the fluctuation analysis of the equipment operation data and the environmental parameters, early warning of abnormal states is realized by combining the time sequence correlation analysis and equipment state prediction, the influence of equipment faults on park operation is reduced, and the accuracy of park equipment state identification is improved through the topological structure and propagation probability calculation. The method achieves the prediction of an abnormal event propagation path, enhances the accuracy and timeliness of emergency response, improves the real-time monitoring and fault prediction capability, and guarantees the efficient operation and safety of a park.

The invention discloses an AI-based bridge construction intelligent monitoring method, particularly relates to the field of data analysis, and comprises the steps of multi-modal sensing network construction, cross-domain characteristic parameter extraction, comprehensive state evaluation modeling and intelligent decision and regulation. According to the method, deformation, corrosion, vibration and other multi-physical field data are synchronously collected through a multi-modal sensing network, a cross-scale feature extraction algorithm is adopted to construct creep-corrosion collaborative factors and other composite indexes, the limitation of traditional single-dimensional parameter analysis is broken through, a dynamic evaluation model is fused with weighted integral and a nonlinear function, and the dynamic evaluation accuracy is improved. Precise early warning of risks such as bridge pier instability and cantilever cracking is achieved, the multi-field coupling effect quantification capacity is remarkably improved, an intelligent joint control mechanism automatically triggers instructions such as jacking reinforcement and damping tuning through a three-level emergency response system, a monitoring-analysis-regulation closed-loop system is constructed, the hysteresis of manual decision is effectively eliminated, and the safety and reliability of the system are improved. And all-weather self-adaptive safety guarantee is provided for bridge construction.

The invention relates to an adaptive control method and device for a supplementary light source of an industrial camera, and the method comprises the steps: obtaining scene perception data of the industrial camera, carrying out the real-time classification and environment parameter analysis of a shooting scene based on the scene perception data, and obtaining scene feature information; performing image quality evaluation on the scene feature information, and comparing with a preset image quality standard to obtain an image quality matching degree; performing control parameter analysis based on the image quality matching degree and the scene feature information to obtain an initial light supplementing control parameter; acquiring image data of the industrial camera, and performing brightness distribution and detail feature analysis on the image data to obtain an image analysis result; performing dynamic light source simulation on the scene feature information based on an image analysis result to obtain a corresponding dynamic supplementary light source parameter; and performing comprehensive processing on the initial light supplement control parameter, the image analysis result and the dynamic light supplement source parameter to obtain a light supplement source adjustment strategy. The problem that a traditional fixed light supplementing method is difficult to adapt to a complex environment can be effectively solved.

The invention relates to the technical field of motor control, in particular to a variable frequency motor dynamic adaptive control method and system based on multi-parameter analysis. The method comprises the following steps: collecting a multi-dimensional electrical operation monitoring parameter flow of a variable frequency motor, carrying out multi-parameter nonlinear correlation analysis and parameter topological correlation reconstruction, and constructing a multi-dimensional feature topological correlation network; a variable frequency motor multi-working-condition travel history database is obtained, multi-working-condition disturbance transient response analysis is carried out, multi-modal behavior prediction evolution is carried out based on the multi-dimensional feature topological association network, and a dynamic multi-modal behavior prediction engine is constructed; and acquiring multi-part electromagnetic sensing parameters of the motor, and performing abnormal electromagnetic intensity fluctuation detection and abnormal part power loss calculation to obtain abnormal part power loss characteristics. The self-adaptive real-time parameter motor control is realized, the energy efficiency of the motor is improved, the maintenance frequency and cost are reduced, and the full-life-cycle optimal control of the motor is realized.

The invention relates to the field of converter station equipment parameter analysis, and provides a converter station equipment parameter analysis method and system based on multi-parameter fusion monitoring, and the method comprises the following steps: collecting multi-parameter characteristic data of converter station equipment; performing streaming processing and abnormal mode identification on the multi-parameter characteristic data to obtain equipment abnormity early warning information of the converter station equipment; constructing a time-varying graph neural network model based on the multi-parameter feature data, obtaining a space-time dependency relationship among parameters through a causal reasoning method, and dynamically adjusting the feature weight of the time-varying graph neural network model in combination with a reinforcement learning algorithm to obtain a running state evaluation model; and inputting the real-time state feature sequence into the operation state evaluation model to obtain an equipment operation state evaluation result, and visually displaying the equipment operation state evaluation result and the abnormal early warning information to obtain a comprehensive operation state evaluation report of the converter station equipment. According to the invention, real-time monitoring and evaluation of the operation state of the converter station equipment are realized.

The invention relates to the technical field of remote sensing surveying and mapping, in particular to an ecological environment surveying and mapping system and method based on remote sensing, and the system comprises a remote sensing image acquisition module, a preprocessing module, an environmental factor analysis module, an ecological environment parameter analysis module, a data fusion module and an output module. The remote sensing image acquisition module, the preprocessing module, the environmental factor analysis module, the ecological environment parameter analysis module, the data fusion module and the output module are connected in sequence; the preprocessing module comprises a radiation correction unit, a geometric correction unit and a noise removal unit; the data fusion module comprises a multi-source data fusion unit and a precision evaluation unit, and the output module comprises a visualization unit and a report generation unit. According to the method, accurate and reliable ecological environment data are provided by utilizing fusion of remote sensing images and environment data in combination with precision evaluation, and decision makers are helped to better understand and cope with ecological environment changes through visual display and report generation.

The invention discloses a 3D intelligent simulation generation method for clothing styles based on a model library, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the entity relation extraction and semantic mapping of multi-source clothing data, constructing a cross-modal knowledge graph, and extracting a physical constraint rule of the cross-modal knowledge graph; performing association reasoning and cross validation on the physical constraint rule and the version library to obtain a basic version template set; inputting the basic model template set into an intelligent clothing model, performing structured feature decoupling by the parameter analysis layer, dynamically adjusting a topological structure by the geometric generation layer, and generating 3D simulated clothing; and carrying out digital modeling on the basic model template set through a digital twinning algorithm, constructing a digital twinning body, carrying out physical simulation and optimization verification on the 3D simulation clothing by using the digital twinning body, and outputting a clothing style 3D simulation scheme. According to the invention, by constructing the pattern library and the intelligent garment model, the efficiency, accuracy and individuation level of garment 3D simulation generation are improved.

The embodiment of the invention relates to the technical field of visual inspection, and discloses a surface defect detection method based on machine vision, which comprises the following steps: performing parameter analysis on an image quality parameter corresponding to a casting surface image to determine an image accuracy index of the casting surface image; if the image precision index of the casting surface image is not less than a precision index threshold; if the area characteristic parameters are smaller than a preset area standard characteristic threshold value, defect detection is conducted on the corresponding casting area image, and if the area characteristic parameters are not smaller than the preset area standard characteristic threshold value, the corresponding casting area image is marked as a to-be-detected defect image; and comparing the regional defect parameter with a surface defect standard index preset in a casting detection library. According to the surface defect detection method based on machine vision in the embodiment of the invention, the casting surface image is divided into a plurality of areas, and feature extraction is performed on each area, so that the surface condition of the casting can be analyzed more meticulously, and the detection accuracy is improved.

The invention relates to the technical field of power equipment load management, in particular to a station electric equipment steady-state feature extraction system based on load awareness, and the system comprises a load data processing module which obtains load data based on the operation state of power equipment, analyzes the interaction effect between each type of load data, extracts the correlation features between parameters, and outputs the correlation features between the parameters; and identifying periodic characteristics and abnormal fluctuation of the load data to obtain a standard load data set. According to the method, more accurate and comprehensive steady-state feature extraction of the power equipment can be realized through multi-dimensional parameter analysis and processing of the load data. Meanwhile, the load data interaction effect is calculated, the incidence relation between the parameters can be better recognized and analyzed, the data consistency is ensured, and the feature extraction accuracy is improved. In data quantile analysis, data subsets of different quantiles are constructed, parameter characteristics under the steady-state condition are extracted, and subtle differences of equipment under different steady-state conditions can be captured.

The invention provides a performance optimization and regulation method for a high-temperature sintering furnace, and belongs to the technical field of intelligent regulation, and the performance optimization and regulation method comprises the following steps: arranging sensors in each heating area of the high-temperature sintering furnace, and monitoring the heating areas in real time; constructing a heating area operation state database, comparing the collected real-time data with a preset sintering process parameter range, and analyzing to obtain a performance analysis set of each heating area; establishing a three-dimensional heat conduction model, simulating heat flow distribution of each heating area under different heating powers and temperature settings, determining a thermal coupling effect of each heating area in different stages, and determining a heat transfer coefficient between adjacent heating areas; and an initial strategy of the high-temperature sintering furnace is determined, the initial strategy is optimized based on the auxiliary parameters of all the heating areas, and all the heating areas of the high-temperature sintering furnace are regulated and controlled according to the optimization strategy. The problems of inaccurate temperature control, single parameter analysis, insufficient thermal coupling effect processing and the like in the traditional regulation and control technology are effectively solved.

The invention discloses a multi-parameter fusion analysis method for fault diagnosis of an automobile shock absorber, and relates to the technical field of shock absorber analys.The method comprises the steps that a vehicle key point position set is obtained, and a sensor set is arranged to obtain a vehicle work sensing module; a vehicle working multi-sensing data flow is monitored and obtained, and a vibration damper working characteristic parameter set is obtained; optimizing to obtain a target data fusion algorithm, and obtaining working state fusion characteristic parameters of the shock absorber; carrying out matching degree calculation on the fusion characteristic parameters of the working state of the shock absorber; and carrying out fault judgment analysis on the dynamic matching degree of the shock absorber, determining fault diagnosis information of the shock absorber, and carrying out fault feedback optimization. The technical problems that the condition of the shock absorber is difficult to comprehensively and accurately detect and the fault part cannot be accurately and rapidly diagnosed and positioned in the existing shock absorber fault diagnosis parameter analysis, so that the comprehensiveness and reliability of the fault diagnosis of the automobile shock absorber are insufficient are solved, and the technical effects of improving the fault diagnosis efficiency and accuracy and the performance of the shock absorber are achieved.

The invention provides a railway anomaly monitoring method and system based on unmanned aerial vehicle routing inspection, and relates to the field of data processing, the method comprises the following steps: obtaining railway routing inspection data collected by an unmanned aerial vehicle, extracting a monitoring instruction in the railway routing inspection data, carrying out parameter analysis on the monitoring instruction, and obtaining an anomaly monitoring parameter set corresponding to the monitoring instruction; performing spatio-temporal feature extraction processing on the plurality of spatio-temporal frame sequences, generating a spatial feature vector and a time feature vector of each spatio-temporal frame sequence, screening out at least one abnormal region from the plurality of spatio-temporal frame sequences based on a dynamic matching relationship between the abnormal monitoring parameter set and the spatial feature vector and the time feature vector, and performing abnormal region extraction processing on the abnormal region. And calling an anomaly identification model to perform conjoint analysis on the spatial-temporal characteristics of the abnormal region, generating a railway anomaly monitoring result, and mapping the railway anomaly monitoring result to a physical coordinate system of the railway line to generate an anomaly positioning report. According to the invention, the accuracy and intelligent decision of railway inspection anomaly detection can be improved.

The embodiment of the invention relates to the technical field of ground surveying and mapping data modeling, in particular to a ground surveying and mapping driven space-time evolution modeling method and system.The method comprises the steps that firstly, multi-dimensional correlation features of elevations, coverage types and deformation data in at least three continuous periods are integrated; the limitation of single time point or single parameter analysis is broken through, and the nonlinear space-time coupling rule in earth surface evolution can be accurately captured; secondly, a spatial-temporal model construction method based on dynamic parameter optimization effectively fuses geographic spatial correlation features and coverage evolution trends, so that the model has adaptive characterization capability for a complex geographic process; in addition, by establishing a closed-loop feedback mechanism of prediction results and model parameters, real-time iterative optimization of the model parameters is realized, and long-term prediction stability in different geographical scenes is remarkably improved.

The invention relates to the technical field of copper wire manufacturing control, in particular to a copper wire multi-pass rolling and annealing cooperative control method and system based on an intelligent network, and the method comprises the steps: setting a process control node according to an industrial control process, building a cooperative communication network based on the process control node, and collecting a multi-mode industrial control signal; setting a data transmission processing protocol, establishing an industrial control sensing platform, integrating multi-mode industrial control signals, and converting the multi-mode industrial control signals according to process positions to generate process operation parameters; analyzing non-linear correlation between process steps, and calculating process adjustment parameters by combining the copper wire performance indexes; according to the data transmission processing protocol, the process operation parameters are transmitted to a cooperative control center and compared with the process adjustment parameters for analysis, and a parameter cooperative control instruction is generated; and the parameter cooperative control instruction is fed back to the rolling control node and the annealing temperature control node based on the cooperative communication network for parameter correction. According to the invention, the problems of low process synergy efficiency and insufficient signal processing capability in copper wire manufacturing are effectively solved.

The invention provides an early-stage automatic identification method and system for acute kidney injury related to cardiac surgery, and relates to the technical field of physiological parameter detection. The method comprises the following steps: acquiring average arterial pressure, cardiac blood discharge volume, central venous pressure and blood oxygen saturation, regularly recording and extracting data of adjacent time points, calculating a variation range, screening a mutation region, judging whether a reference range is exceeded or not, analyzing a multi-parameter synchronous variation condition, and marking a hemodynamics abnormal state. According to the method, abnormal changes of hemodynamics and urine metabolism are accurately recognized through real-time monitoring and multi-parameter analysis, the urine flow rate and the metabolite excretion rate are combined, misjudgment of a single index is avoided, the fineness of renal function change analysis is improved, the high-risk time period is accurately positioned through time cross comparison, the diagnosis accuracy is improved, and the diagnosis accuracy is improved. Trend analysis and risk grade division enable the change of the illness state to be more visual, and a real-time early warning system effectively monitors the fluctuation trend, optimizes clinical management and improves the timeliness of medical response.

The invention relates to the technical field of water pump fault diagnosis, discloses a condensate pump shafting vibration fault diagnosis method and device based on multi-parameter analysis and a medium, and solves the problems of sensor signal distortion and noise interference of a condensate pump under high-temperature and high-humidity working conditions through multi-source data fusion and environment adaptive compensation. Dynamic time-frequency domain feature extraction and an improved lightweight residual network are adopted, and efficient decoupling and accurate recognition of multi-fault concurrent features are achieved; the fuzzy inference rule base and the shafting fluctuation propagation model are combined, the fault positioning precision and the early fault detection rate are remarkably improved, scientific and reliable technical support is provided for diagnosis and maintenance of the shafting vibration fault of the condensate pump, and safe and stable operation of factory equipment is effectively guaranteed.

The invention discloses a high-precision charging pile measurement data processing system and method based on artificial intelligence, and relates to the technical field of charging intelligent management, and the method comprises the steps: analyzing and marking the equipment matching degree between equipment operation parameters in equipment operation data and a charging pile; acquiring historical environment monitoring records of the charging pile, acquiring abnormal equipment operation parameters from the abnormal equipment operation data, and analyzing data relevance between environment monitoring indexes in the historical environment monitoring records and the abnormal equipment operation parameters in the charging pile; acquiring a historical data measurement record of the charging pile, establishing a charging fault prediction model of the charging pile, acquiring a historical environment monitoring record, acquiring associated environment data, and optimizing the charging fault prediction model; and monitoring the charging state of the charging pile in the current period, performing fault prediction on the charging pile by using the charging fault prediction model, and managing the charging of the charging pile.

The invention discloses a CAD automatic modeling method and system based on parameter driving. The method comprises the steps that model parameter information needing modeling is obtained; generating a CAD (Computer Aided Design) model on the basis of parameter analysis, geometric construction and surface recognition schemes; performing reverse reconstruction of the three-dimensional CAD model based on topology analysis, geometric feature extraction, pattern recognition and parameter relation inference; modeling, optimization and feedback evolution are carried out based on model analysis, feature learning and generation of parametric modeling rules; and according to the obtained data information, CAD automatic modeling based on parameter driving is completed. According to the method, CAD automatic modeling based on parameter driving is achieved, the reliability is higher, the accuracy is better, and the efficiency is higher.

The invention relates to the technical field of fire-fighting engineering, and discloses a real-time fireproof monitoring data transmission system for fire-fighting engineering construction, which comprises a fire parameter analysis module, an environmental data analysis module, a fire source positioning module, a model construction module and a safety database. The fire parameter analysis module generates a temperature change record table through gridding temperature measurement nodes, estimates the smoke diffusion rate, and evaluates the thermal stress and deformation risk of the building structure; the environment data analysis module associates the air velocity with the smoke diffusion rate and corrects the parameter weight of the evaluation model; the fire source positioning module generates a fire source three-dimensional positioning map by using multispectral imaging equipment; the model construction module constructs a dynamic fire assessment model according to multi-dimensional data and outputs a comprehensive threat level; the security database stores building material parameters, historical case characteristics, and dynamic threshold rules. According to the system, through multi-module cooperation and multi-data fusion, the accuracy and timeliness of fire-fighting monitoring are improved, and the system is suitable for safety management of fire-fighting engineering.

The invention belongs to the technical field of task allocation and progress tracking, and particularly discloses and provides a detection task allocation and progress tracking method based on a big data management system, which comprises the following steps: firstly, calculating the matching degree of task and processing end multi-aspect information through a weighted fusion algorithm, and selecting a processing end with the highest matching degree as a main node; judging whether a constraint processing task is met or not, collecting related data during task execution, performing coupling analysis to obtain a progress standard-reaching rate, marking the processing end of which the standard-reaching rate is smaller than a set value as abnormal, and calculating a secondary allocation abnormal processing end task through a matching degree in combination with the progress and residual resource information of the processing end; according to the method, the parameters of the to-be-detected task and the processing end are synthesized through the weighted fusion algorithm, the matching degree including calculation, storage, time matching degree and task type correlation degree is analyzed, global optimal matching of the task and the processing end in the aspects of computing power, storage, task types and adaptability is accurately evaluated, and the problem that the task and the processing end are not matched is solved.

The embodiment of the invention provides a real-time defect detection method and device based on carbon fiber winding and a medium, and relates to the technical field of carbon fiber laying layer detection.The method comprises the steps that real-time environment data of carbon fiber winding are obtained, and multi-mode cooperative detection is conducted on the real-time environment data of carbon fiber winding, obtaining a carbon fiber real-time defect data set; performing multi-mode space-time synchronous calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronous data; performing strategy-level fusion on the synchronous data of the carbon fiber defects to obtain winding state preliminary judgment data; based on the winding state preliminary judgment data, multi-modal weight update data are determined through carbon fiber winding process stage analysis; and according to the modal weight updating data, determining the real-time winding defect of the carbon fiber through carbon fiber defect type analysis. According to the method, the technical problem of poor anti-interference capability in a single parameter analysis process in carbon fiber winding detection is solved.

According to the remote sensing image green roof intelligent identification method and system based on the improved Mask RCNN algorithm, the feature extraction capability of the algorithm is enhanced by introducing an attention mechanism, and the edge segmentation precision of a target is improved by constructing a joint loss function, so that precise detection of a green roof in a complex built environment is realized. According to the invention, the morphological parameter analysis module is synchronously integrated, geometric feature parameters such as the area, the perimeter and the shape index of the green roof can be automatically extracted, and integrated processing of green roof identification and feature parameter extraction is realized. Compared with a traditional method, the method can effectively solve the problems that target examples are difficult to distinguish, and environmental adaptability is poor, and provides a reliable technical means for green roof monitoring and ecological environment benefit evaluation.

The invention provides a waste glass activity rapid comprehensive evaluation method based on dissolution kinetics analysis. According to the method, the silicon ion dissolution characteristic of the waste glass in an alkaline environment is measured, the particle size effect and kinetic parameter analysis are combined, the corresponding relation with the 28-day compressive strength activity index is established, and rapid evaluation of the activity of the waste glass is achieved. The method comprises the steps of dissolution test, kinetic analysis and activity evaluation. Research finds that a power function relationship exists between the reaction rate constant and the particle size, and good linear correlation exists between the dissolution characteristic and the activity index. According to the invention, a complete evaluation system of particle size-reaction rate-activity index is established, the goal of predicting the 28-day activity index from the 24-hour dissolution characteristic is realized, and the error between the prediction result and the measured value is less than 2%. The method is short in evaluation period and accurate and reliable in result, and has important engineering application value.

The invention discloses a pigsty environment regulation and control optimization method and system, and belongs to the technical field of breeding environment management. The system comprises an intelligent sensing module, an environment analysis module, a parameter optimization module and a control management module. The intelligent sensing module is used for collecting pig house environment parameters and live pig physiological parameters through a multi-mode sensor; the environment analysis module is used for constructing a behavior recognition model, analyzing behavior characteristics based on physiological parameters and dividing abnormal regions; the parameter optimization module is used for establishing a physiological environment control model and setting an optimal optimization scheme for each abnormal region; and the control management module is used for generating an environment control instruction according to the optimal optimization scheme and controlling equipment execution. The pig house environment and the pig physiological parameters are collected and intelligently analyzed through the multi-mode sensor, automatic and precise environment optimization management is achieved, and the comfort level and the health level of the pigs are improved.

The invention relates to the technical field of the Internet of Things, and discloses a wireless sensor network environment monitoring system and a safety protection method based on the Internet of Things, and the system method comprises the steps: collecting sensing node environment data to generate a multi-dimensional feature matrix, and constructing a safety protection strategy set; setting threat parameter types, and establishing a security threat association model by using historical data; extracting real-time attack features and flow parameters, and generating a dynamic protection instruction set and an optimization scheme in combination with the model; and reconstructing a network node control mechanism to generate a target protection scheme, and dynamically and synchronously calibrating an instruction time sequence. The system comprises a node data acquisition module, a security policy generation module, a threat parameter analysis module, a threat modeling module, a risk assessment module, a protection mechanism reconstruction module and a time sequence synchronization module. Through multi-dimensional feature analysis, dynamic threat modeling, adaptive risk assessment and time sequence encryption synchronization, the security protection capability of the wireless sensor network in a complex environment is improved, and the method is suitable for security protection requirements in the field of Internet of Things environment monitoring.

The invention relates to the technical field of industrial equipment management, in particular to an intelligent power equipment inspection system based on artificial intelligence, which comprises a fault mode generation module, a fault detection module, a fault classification module, an inspection feature optimization module and an anomaly prediction and evaluation module. According to the method, the comprehensiveness of equipment state sensing is optimized by collecting the multi-dimensional operation parameters of the power equipment and analyzing the signal amplitude distribution and frequency domain change, the potential fault features are accurately recognized by combining the dynamic feature range and the signal distribution deviation matching, the signal anomaly detection accuracy is improved, and the fault detection accuracy is improved. The method comprises the following steps: extracting differentiated characteristic values of current and temperature signals, optimizing fault type classification, improving the positioning capability of a problem root, dynamically extracting and matching a signal trend, generating an inspection parameter matrix, realizing global dynamic monitoring of an operation state, and predicting a model based on a signal change rate and a time node range. The real-time performance and precision of abnormal trend prediction are improved, and the reliability of equipment operation and maintenance is enhanced.