6055 results about "Abnormality detection" patented technology

The invention discloses a battery pack thermal runaway risk identification and early warning system and method, and relates to the related field of battery risk early warning, and the method comprises the steps: collecting an operation parameter data set of a battery pack in real time through a multi-mode sensor network, carrying out the hybrid anomaly detection of the battery pack, obtaining a real-time anomaly detection result, and carrying out the thermal state prediction of the battery pack, and constructing a thermal state evolution trend chart to perform thermal runaway risk assessment, determining a thermal runaway risk level, triggering a multi-level early warning signal according to the thermal runaway risk level, and generating a thermal runaway suppression strategy. The technical problems that in existing battery thermal runaway early warning, due to monitoring limitation of a single sensor, the false alarm rate is high, response lags behind, and battery aging cannot be dynamically adapted are solved, and the technical effects that the thermal runaway early symptom detection rate is increased through multi-mode sensing, and the early warning time is shortened are achieved.

The invention relates to the technical field of smart home, and discloses an automatic coordinated regulation and control system and method for smart home equipment, and the system integrates a plurality of functional modules such as equipment access, environment perception and user behavior analysis. The equipment access module supports access of multiple protocols and endows the equipment with a unique identifier; the environment sensing module collects environment parameters in real time and compares the environment parameters with thresholds. The user behavior analysis module generates a user scene preference model by means of an algorithm, the cooperation strategy generation module synthesizes multiple factors to formulate a regulation and control strategy, and the dynamic priority management module allocates weights according to equipment and scene conditions. And the equipment control module executes the instruction and feeds back the state, and the abnormity detection module monitors the equipment state and triggers a strategy regeneration mechanism when abnormity occurs. The data storage module securely stores various data, and the user interaction module efficiently analyzes instructions. According to the invention, many problems of an existing smart home system are solved, automatic coordinated regulation and control of equipment can be realized, and user interaction experience is optimized.

The invention relates to the technical field of artificial intelligence, and discloses an anomaly detection emergency processing system and method based on artificial intelligence, and the system comprises a data collection module; a data preprocessing module; an anomaly detection module; an emergency decision module; an emergency execution module; a real-time monitoring and state feedback module; a multi-mode communication and coordination module; a man-machine interaction and visualization module; and a knowledge updating and model iteration module. The method is reasonable in design, the accuracy and timeliness of anomaly detection are remarkably improved through a multi-source heterogeneous data fusion and dynamic threshold adjustment technology, and the model robustness is enhanced in combination with incremental learning and an adversarial training mechanism; the intelligent decision-making module realizes multi-objective optimization processing by relying on a knowledge graph and a digital twinborn pre-judgment risk; redundant fault-tolerant execution and distributed consistency guarantee ensure high reliability of the system, and a man-machine cooperation mechanism considers both automation efficiency and manual intervention accuracy.

The invention discloses a distributed slope monitoring system based on edge cloud collaborative intelligent adaptive decision. The distributed slope monitoring system comprises a plurality of intelligent sensing unit ISU nodes deployed at key positions of a slope and a data processing and intelligent analysis unit, each intelligent sensing unit ISU node is used for transmitting data to an edge gateway in an ad hoc network wireless mode or directly uploading the data to a cloud platform and carrying out slope monitoring based on a local adaptive monitoring strategy; the data processing and intelligent analysis unit comprises an edge intelligent module, a cloud gateway and an edge gateway; the edge gateway serves as a middle layer and is used for protocol conversion, data aggregation, temporary storage and preliminary analysis; the cloud gateway is used for providing calculation and storage resources, training a more complex AI model based on historical and real-time data, and performing pattern recognition, prediction analysis and anomaly detection tasks; the edge intelligent module comprises a plurality of edge computing nodes and is internally provided with a lightweight AI reasoning unit, and the edge intelligent module is arranged on the edge side and used for implementing edge intelligent processing.

The invention discloses a power semiconductor device aging on-line diagnosis method, which relates to the field of power electronics, and comprises the following steps: integrating an intelligent sensor and an aging test circuit, and establishing a multi-physics field coupling simulation model; based on the multi-physics field coupling simulation model, evaluating the aging state of the current device to obtain the health index of the device; based on the multi-dimensional data matrix, constructing an anomaly detection model, and outputting an anomaly detection result; predicting the failure probability and the remaining service life of the semiconductor device according to an abnormal detection result; generating a maintenance suggestion based on the predicted failure probability and remaining useful life of the semiconductor device; according to the method, the maintenance suggestion is generated according to the prediction result, the user is guided to take preventive maintenance measures in time, the accuracy of aging state evaluation is improved, the timeliness of fault early warning is enhanced, and economic losses and technical risks caused by sudden faults are reduced.

The invention discloses a power distribution network tower abnormity monitoring method and system, and relates to the technical field of tower monitoring, and the method comprises the steps: arranging a multi-mode sensor on a power distribution network tower, and collecting the state data of the tower; constructing a power distribution network tower digital twinborn model, inputting tower state data, and predicting the future state of the tower; performing regular inspection on the tower by using an unmanned aerial vehicle, and collecting dynamic three-dimensional data of the tower; and the tower state data, the tower future state prediction result and the tower dynamic three-dimensional data are fused, and tower abnormity is monitored and early warning is performed. The invention also discloses a method. Through the application of the multi-source data fusion technology, the accuracy and reliability of anomaly detection are effectively improved, professional guidance is provided for maintenance personnel based on a grading early warning mechanism of a comprehensive anomaly index, the fault processing efficiency is improved, the accuracy and timeliness of tower anomaly monitoring are remarkably improved, and the safety and reliability of the tower are improved. And predictive maintenance and intelligent management are also realized, and the safety and reliability of the power distribution network are greatly enhanced.

The invention provides a textile woven structure anomaly detection method, which comprises the following steps: acquiring real-time tension monitoring data of warps and wefts in a jacquard textile weaving process, and establishing a tension distribution model in a normal weaving state according to the real-time tension monitoring data; if the classification result is in an abnormal state, spraying a pre-prepared chemical reagent on the surface of the abnormal position of the jacquard textile, and collecting spectral data of the surface of the textile after spraying; performing feature extraction and classification on the spectral data, and obtaining a comprehensive anomaly detection result by comparing a classification result with a tension position anomaly detection result; according to the comprehensive anomaly detection result, an abnormal area is accurately positioned and marked, and a sample in the abnormal area is cut and conveyed to a sample analysis device; and carrying out microstructure analysis on the cut sample, extracting fiber arrangement characteristics, judging the abnormal degree of fiber arrangement, and obtaining a microstructure abnormality evaluation result.

The invention discloses an intelligent inspection monitoring method and system based on electronic vision, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: collecting multi-mode inspection data, and carrying out the preprocessing of the data; target identification classification and anomaly detection are carried out based on a convolutional neural network; and optimizing the inspection path by adopting a graph theory model and a shortest path algorithm. According to the intelligent inspection monitoring method based on electronic vision, the image obtained in the inspection process is analyzed through the convolutional neural network, the fault detection precision and the equipment classification accuracy are improved, the flexibility and the accuracy of anomaly detection are greatly improved through the auto-encoder model, and the detection accuracy is improved. The equipment position is regarded as the node of the graph, and the optimal inspection path is calculated by adopting the graph theory model, so that the inspection efficiency is remarkably improved, the inspection scheduling is optimized, the inspection coverage rate is improved, and better effects are achieved in the aspects of fault identification accuracy, inspection efficiency and adaptability.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

The invention relates to the technical field of motor anomaly detection, in particular to a motor data anomaly detection method based on the Internet of Things. The method comprises the following steps: acquiring motor operation data, evaluating motor operation demand overload and load response conditions, and detecting bearing kinetic energy chain coupling recession and motor structure connection coordination decline trends, so as to determine dynamic magnetic field unbalance conditions in motor operation; based on the dynamic magnetic field unbalance condition, estimating the electromagnetic interference risk and the frequency oscillation growth trend of the motor, monitoring the error accumulation condition, and detecting the abnormal state of the motor; performing optimal configuration processing on motor operation parameters in combination with an abnormal detection result; according to the invention, the motor operation is more stable and efficient through the motor data anomaly detection.

The invention relates to the technical field of anomaly detection, and discloses an anomaly analysis method and system based on blasting data, and the method comprises the steps: collecting the blasting data of each monitoring node, dividing the monitoring node into a plurality of regions, carrying out the statistics of the anomaly density of each region in a preset historical period, and calculating the anomaly density of each region; according to the abnormal density, dynamically setting a region weight of an auto-encoder training sample; reconstructing the current round of data by using the weighted trained auto-encoder, calculating the residual error of each node, and extracting the activation path information of the node of which the residual error is higher than a reconstruction error preliminary screening threshold value; constructing a graph structure model between the monitoring nodes, and calculating an abnormal propagation path according to the correlation between the nodes; and matching the activation path with the propagation path, and determining the abnormal state of the target node. And the accuracy, the stability and the intelligent feedback capability of blasting data anomaly analysis are remarkably improved.

The invention discloses a power distribution room anomaly detection system based on cloud side-end cooperation, and belongs to the technical field of intelligent power grids. In order to solve the problems of high network bandwidth pressure, insufficient edge computing capability, low anomaly detection accuracy, difficulty in multi-source data fusion and the like caused by the adoption of an end-cloud direct connection architecture in an existing power distribution room monitoring system, the system comprises: a data acquisition layer configured with various heterogeneous sensors to acquire operating parameters and environmental data in real time; the edge storage and calculation layer carries out local real-time processing, anomaly detection, model training and visual display, an anomaly detection module of the edge storage and calculation layer carries out research and judgment on real-time data to generate early warning information, and a prediction and detection linkage module monitors an anomaly probability trend and adjusts a sampling frequency; the edge gateway realizes protocol conversion and data forwarding; and the cloud decision-making layer aggregates multi-edge node data, optimizes a global model through federal learning, and issues and updates a local model. The system is used for improving the accuracy, real-time performance and reliability of anomaly detection of the power distribution room, reducing the operation and maintenance cost and realizing intelligent operation and maintenance.

The invention provides an intelligent network connection automobile abnormity detection system and method based on big data analysis, and the method comprises the steps: obtaining metadata in the communication between a vehicle CAN bus and a cloud, the metadata comprising a protocol type and a load feature, carrying out the analysis of the load feature according to the protocol type, and extracting a key field of a data package; constructing a load feature matrix according to the key field, and normalizing the heterogeneous data to obtain a feature data set in a uniform format; a mahalanobis distance algorithm is adopted to calculate the distance between each data point in the feature data set and a preset base line, and if the distance exceeds a preset threshold value, abnormal traffic is judged, and an abnormal traffic mark list is generated; fingerprint features of a transmission link are obtained from the 5G network slices, the fingerprint features of different slices are subjected to standardization processing, and a cross-slice consistent fingerprint feature data set is generated; therefore, security threats in a complex Internet of Vehicles environment can be effectively handled, and the accuracy and reliability of abnormal traffic detection are improved.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to a data-driven unmanned aerial vehicle flight control system real-time defect prediction system and method, and provides the following scheme that the system comprises a data acquisition module, a sensor verification module, a defect prediction module, a credibility analysis module and a flight control optimization module. And the sensor verification module identifies sensor data exceptions and distinguishes fault types through data consistency, time sequence exception detection and environment correlation analysis. And the defect prediction module analyzes the verified data by using machine learning and predicts potential faults. And the credibility analysis module verifies the prediction result and generates a credibility score. And the flight control optimization module selects an optimization control strategy according to the credibility score, adjusts flight parameters or executes safe operations such as return flight and landing. According to the method, the fault identification and prediction capability of the unmanned aerial vehicle in a complex environment is improved, and the flight safety and the system reliability are enhanced.

The invention discloses a power distribution area equipment monitoring intelligent analysis method, and relates to the technical field of power system equipment monitoring, and the method comprises the steps: collecting and preprocessing the real-time operation data of power distribution area equipment; performing feature extraction on the real-time operation data by using a deep learning model to obtain an operation state feature vector; performing similarity calculation on the running state feature vector and a normal running state feature vector in a historical database to obtain a preliminary abnormal degree quantitative index; performing equipment state evaluation and classification according to the preliminary abnormal degree quantitative index to obtain an equipment state evaluation result; and taking corresponding measures according to an equipment state evaluation result, automatically generating a fault diagnosis report and pushing the fault diagnosis report to a maintenance terminal. The multi-parameter monitoring and deep learning feature extraction technology adopted by the invention improves the accuracy and comprehensiveness of anomaly detection, and can identify some potential fault features which are difficult to find by a traditional method.

The invention discloses a smart factory management system and method based on MES, and relates to the technical field of smart manufacturing, and the method comprises the steps: calculating a health degree score based on equipment historical maintenance records, and generating a time-space correlation multi-dimensional analysis data set containing an equipment topological structure and health degree parameters in combination with a digital twin network; performing spatial-temporal feature coupling and topological weight dynamic adjustment on the spatial-temporal correlation multi-dimensional analysis data set through a dynamic topological analysis algorithm, and generating a high-risk equipment list and an anomaly control instruction set; based on the high-risk equipment list, constructing a standardized multi-dimensional abnormal feature vector, and generating an aging weighted danger level signal through an entropy weight method; according to the method, space-time alignment of equipment operation state data and physical topology data is realized through a multi-rate Kalman filtering algorithm and an iterative nearest point algorithm, a high-fidelity digital twin network is constructed in combination with a dynamic graph convolutional network, and accurate anomaly detection and health degree evaluation are supported.

The invention relates to the technical field of quality detection, in particular to a product quality control method and system based on machine vision, and the method comprises the following steps: obtaining product surface image data, calculating the gray gradient value of each pixel, extracting the gray gradient change rate, recording the gradient amplitude and direction information, and generating product surface gradient data. According to the method, through pixel-level gray scale gradient calculation, the product local feature expression ability is improved, multi-scale gradient change trend analysis is combined, the accurate recognition ability of a product defect area is improved, through texture direction angle calculation and vector field construction, the direction change anomaly detection reliability is enhanced, and the direction change anomaly detection accuracy is improved based on the combination of a direction deviation accumulated value and an abrupt change threshold value. Effective identification of a structure sudden change area is ensured, adjustment is carried out for curvature continuity abnormal points, defect boundary fitting precision is optimized, defect area internal gradient distribution and boundary feature comparative analysis are carried out, accurate classification of defect types is realized, and stability and adaptability of automatic product quality detection are ensured.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention relates to the technical field of image analysis, in particular to a calcium carbonate master batch wind shear forming detection system based on machine vision. A calcium carbonate master batch wind shear forming detection system based on machine vision comprises an edge pixel collection module, a boundary trend recognition module, a distortion track repair module, an internal texture analysis module and a forming anomaly detection module. According to the method, the gray difference and gradient angle of continuous pixels are calculated, an edge distribution diagram is constructed, boundary detail expression is enhanced, the pixel arrangement direction and gray change analysis are combined, continuous edge line segments are extracted, the boundary recognition precision is improved, a closed contour is repaired according to line segment connection offset, and structural integrity is ensured; the method comprises the following steps: calculating a gray change rate in an internal region block, drawing a texture fluctuation map, accurately marking a sudden change region, realizing abnormal particle identification, fusing pixel-level gray features and region structure analysis, and improving the precision and integrity of form perception and defect identification.

The invention discloses a semiconductor packaging test optimization method and system, and relates to the field of packaging testing, and the method comprises the steps: collecting and preprocessing a multi-dimensional abnormal signal, generating a standard data set, carrying out the feature extraction of data in the standard data set, obtaining a standard feature vector, and inputting the standard feature vector into a constructed abnormality detection model, the method comprises the steps of obtaining an abnormal signal report, distributing a test item for a chip through the abnormal signal report and a predefined mapping rule, generating a structure map and an electrical map, inputting the structure map and the electrical map into a multi-mode Transform model, outputting a fusion map, calculating a test weight according to the fusion map, and generating a test weight map and a region priority list. According to the invention, the testing efficiency, the micro defect detection precision and the boundary failure prediction of advanced packaging are improved.

The invention belongs to the field of electricity consumption information acquisition, and provides an anomaly detection method and system for redundancy elimination perception multi-dimensional electricity consumption data fusion, and the method comprises the steps: obtaining historical electricity consumption data, carrying out the preprocessing, and obtaining a time sequence feature vector of the historical electricity consumption data through a trained convolution recursion auto-encoder; based on the preprocessed historical power consumption data, a space mapping function is adopted to obtain a basic feature vector; based on the preprocessed historical power consumption data, obtaining an incidence relation feature vector by adopting a trained graph convolutional neural network model; fusing the time sequence feature vector, the basic feature vector and the incidence relation feature vector by adopting a multi-head attention mechanism to obtain a fused representation vector; filtering redundant information in the fusion representation vector by using the trained redundancy elimination fusion model to obtain a comprehensive representation vector of the time sequence power consumption data; and performing anomaly detection according to the comprehensive representation vector of the time sequence power consumption data to obtain a historical power consumption data evaluation result.

The invention relates to the technical field of industrial control security and the technical field of computers, and discloses an industrial control time series data anomaly detection method, which comprises the following steps: effectively integrating a novel auto-encoder, a generative adversarial network and a self-supervised comparative learning network, and modeling long-term dependence by using an improved long-short-term memory network, extracting short-term features by combining causal convolution and expansion convolution of a time sequence convolutional network, and accurately positioning key features in a time sequence by adopting a self-attention mechanism in the last layer. A dynamic double-optimization strategy of a generative adversarial network and a self-supervised comparative learning network is introduced to optimize parameters of the novel auto-encoder, various types of pollution data can be effectively identified, the generative adversarial network can further learn time sequence features and generate diversified abnormal sequences, the detection precision is remarkably improved, and the method is suitable for large-scale popularization and application. Therefore, the accuracy of anomaly detection is improved, and the adaptability to a complex industrial control environment is enhanced.

The present invention relates to the field of disaster early warning technology, specifically to a method for early warning and forecasting of geological disasters by satellite remote sensing monitoring, which includes the following steps: based on ground remote sensing images, edge information of images is extracted frame by frame, pixel difference values ​​and spatial displacement information are identified, and the surface area where changes occur is marked to obtain basic anomaly detection results. In the present invention, by analyzing the ground remote sensing images frame by frame to extract edge information and compare the pixel differences between the previous and next frames, the slight changes in the surface can be monitored more finely, and by matching and analyzing historical data, potential anomaly types can be accurately identified, and targeted early warnings can be given for different types of geological disasters. By performing time series analysis on the trends of abnormal areas, the development trend and potential impact of disasters can be predicted, providing data support for disaster prevention and mitigation, improving the response speed and accuracy of the early warning system, and reducing the losses caused by untimely disaster response.

The invention provides an anomaly detection method and device based on adaptive multi-scale feature modeling, and relates to the technical field of data anomaly detection. The method combines time component decomposition and a gating network, and allocates suitable expert networks for data on different time scales. In an expert layer, a deep convolutional network and a cross-time-slice attention mechanism are utilized to mine mutual relations in different time periods and among the different time periods in a time sequence. Then, combining a graph structure, and modeling spatial interaction characteristics among variables; and finally, learning the potential representation of the data to generate reconstruction data, and calculating a reconstruction error and the KL divergence of a potential space to carry out model training, thereby carrying out efficient anomaly detection under an unsupervised condition. According to the method, the precision of anomaly detection in the industrial control system is improved, a more flexible and accurate anomaly detection tool is provided for practical application, and the method adapts to complex dynamic changes of time sequence data in the industrial control system under the condition that no label data exists.

The invention relates to the field of heat exchanger anomaly detection and fault diagnosis, and discloses a heat exchanger anomaly detection and quantitative diagnosis method, system, medium and equipment under multiple unstable working conditions, and the method comprises the steps: collecting data of key parts of a heat exchanger, and preprocessing the data, the data including online monitoring data and offline data; according to the preprocessed data, fluctuation working condition sensitive indexes are constructed, the fluctuation working condition sensitive indexes comprise leakage fault indexes and scaling fault indexes, and follow-up characteristic analysis indexes of the online monitoring data are obtained through the fluctuation working condition sensitive indexes so as to construct an unsteady-state multi-working-condition anomaly detection model; leakage fault quantitative diagnosis and scaling fault quantitative diagnosis are carried out according to the abnormal detection model under the unsteady-state multiple working conditions. Abnormality detection of the heat exchanger under multiple unstable working conditions can be achieved, and the accuracy of abnormality detection is improved.

The invention discloses a voiceprint-based power transformation equipment anomaly detection method and device, relates to the field of power transformation equipment monitoring, and can solve the technical problem of low accuracy of existing voiceprint-based power transformation equipment anomaly detection. The voiceprint-based power transformation equipment anomaly detection method comprises the steps of obtaining historical voiceprint feature data of a plurality of power transformation equipment, and based on a two-dimensional convolutional network, obtaining a plurality of historical voiceprint feature data of the plurality of power transformation equipment; extracting a local time-frequency feature of the historical voiceprint feature data to obtain a first voiceprint feature; performing feature extraction on the first voiceprint feature based on a bidirectional recurrent neural network to obtain a second voiceprint feature; according to the second voiceprint feature, multiple times of iterative training is carried out on a preset initial voiceprint detection model, joint optimization is carried out on model parameters based on comparative learning and perceptron classification during iteration, training is completed until the model parameters meet preset values, and a voiceprint detection model is obtained; and obtaining real-time voiceprint features of the to-be-detected power transformation equipment, and inputting the real-time voiceprint features into the voiceprint detection model to obtain an anomaly detection result of the to-be-detected power transformation equipment.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention relates to a digital twinning-based production line simulation method and related equipment, and the method comprises the following steps: carrying out the historical operation data mining of a target production line, and obtaining the historical time sequence operation data; performing dynamic logic simulation construction on the target production line based on the mapping space digital topological structure and the historical time sequence operation data to obtain a production line operation logic model; analyzing whether an abnormal position exists in the production line operation logic model or not through a preset anomaly detection algorithm, and if yes, labeling the abnormal position to obtain an abnormal labeling position; wherein the abnormal labeling position is a logic conflict point position and a non-optimal configuration area; and performing flow reconstruction on the production line operation logic model based on the abnormal labeling position to obtain a target virtual three-dimensional model, thereby solving the technical problems that the mapping relation between the physical entity and the digital model of the production line is not accurate enough and the actual operation state of the production line is difficult to reflect truly.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention discloses a medical equipment remote control system based on Internet of Things data, relates to the technical field of equipment remote control, and realizes efficient safety protection of medical equipment remote control through four links of multi-source data acquisition and behavior baseline construction, real-time anomaly detection, risk grading evaluation and linkage processing, and adaptive safety strategy execution. Firstly, an operation log is collected and cleaned, a multi-dimensional label is added, and a baseline model M is constructed to judge the deviation degree; then, deviation degree calculation is carried out on the instructions in real time, suspicious instructions are screened, and a structured suspicious instruction set is formed; in the risk grading stage, performing multi-factor grading on suspicious instructions and screening medium-high risk objects; and finally, according to the risk vector, calculating the priority of a strategy and matching a predefined interval, implementing security protection through measures such as multi-factor authentication, secondary approval or temporary freezing, and continuously writing back and updating a disposal result to form a closed loop to improve the overall security management and control level.