970 results about "Smart surveillance" patented technology

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention relates to the technical field of intelligent monitoring, in particular to a multi-thread low-power-consumption intelligent monitoring system based on an AI processor. The method has the advantages that aiming at the problems of unbalanced computing power and power consumption, high multi-task processing delay and strong hardware dependence in the prior art, the NPU module of the RK3588 processor is combined with the INT8 quantitative model, so that the power consumption is lower than 10W under the 6TOPS computing power; a dynamic multi-thread scheduling mechanism is designed, parallel processing of more than eight paths of video streams is supported through binding of a priority queue and an NPU core, and end-to-end delay is compressed to be within 200 ms; a zero-copy video stream architecture is constructed, data transfer is eliminated through memory mapping, and preprocessing time consumption is reduced by 90%; an energy efficiency control module is integrated, the NPU voltage frequency is dynamically adjusted according to the load, and the energy efficiency ratio reaches 0.83 TOPS / W; space-time alignment of multi-model reasoning results is realized by adopting a frame ID synchronization technology, and the mismatching rate is lower than 0.1%.

The invention belongs to the technical field of image processing, and particularly relates to an intelligent campus monitoring method for preventing campus bullying, which comprises the following steps: acquiring audio and video data of campus monitoring, preprocessing at an edge end and screening out suspected bullying segments; the method comprises the following steps: calculating an interaction anomaly index reflecting limb conflict non-equivalence by analyzing spatial and temporal changes of human body key points in a video; vocabulary extraction and analysis are carried out on the audio, and a semantic bullying index for evaluating speech threats is obtained; according to the signal-to-noise ratio of the field environment, dynamic weighting is carried out on the two indexes, and the two indexes are fused into a more accurate comprehensive bullying index; the system carries out graded early warning and accurate positioning on the bullying event based on the comprehensive index, and immediately pushes an alarm containing a position, a target and a real-time picture to a related person in charge, so that rapid and accurate intelligent identification and intervention on the bullying behavior are realized.

The invention relates to the technical field of industrial process control, and discloses a denitration intelligent monitoring method and system based on data analysis and a storage medium. The method comprises the following steps: performing working condition identification by using an improved K-means algorithm to obtain stable operation, fire change preparation, fire change proceeding and fire change recovery modes; matching a segmented control strategy according to an identification result, and carrying out weighted fusion on feedforward and predictive control in a fire exchange preparation stage to generate a differentiated liquid ammonia regulation instruction; the disturbance feed-forward compensation amount is calculated in combination with the combustion state of the kiln, the adjusting instruction is restrained, and a liquid ammonia control instruction is obtained; the execution effect is monitored in real time through standardized deviation detection, manual intervention is switched in case of abnormity, and control parameters are fed back and updated in case of normal. The problems that NOx control lags behind and liquid ammonia consumption is not economical under the fire exchange working condition are solved, and the control precision and the self-adaptive capacity of the denitration system are improved.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention discloses a motion recognition method and system based on a redox photoelectric memristor, a terminal and a storage medium, and the method comprises the steps: selecting classical motions based on a human body motion data set, extracting time sequence data, and coding the time sequence data into an optical pulse sequence; constructing a reservoir array composed of a plurality of photoelectric memristors, and expanding an optical pulse sequence signal into a high-dimensional state vector; constructing a supervised training model, solving a weight matrix, and constructing a memristive cross array; and outputting an action classification result through simulation domain operation based on the output current multi-path light current signals in combination with the memristor cross array. According to the method, the motion features can be directly fed into a rear-end classification network for action recognition without depending on a complex digital feature extraction algorithm, so that the transmission and processing overhead of redundant data is fundamentally eliminated; a high-efficiency, low-delay and high-robustness hardware solution is provided for real-time and anti-noise motion recognition in scenes such as intelligent monitoring and man-machine interaction.

The invention discloses an internet-based pharmaceutical equipment intelligent monitoring and process optimization method and system, and relates to the technical field of pharmaceutical equipment process optimization, and the method comprises the steps: recognizing the potential non-uniform characteristics of an upstream process, predicting the dynamic change trend of the upstream process, and then evaluating the influence on the temperature, pressure and flow through combining with a neural network model, thereby achieving the intelligent monitoring and process optimization of the upstream process. And dynamically adjusting downstream parameters. And when the deviation exceeds a threshold value, fusing the state data of the equipment to judge the deviation of the stirring speed, inquiring a formula compatible database to generate an optimized downstream process parameter set, and verifying the batch consistency. And the real-time monitoring cycle automatically updates data, activates the real-time monitoring cycle, automatically updates the upstream process and the downstream process when dynamic change occurs, and ensures stable output of pharmaceutical batch quality. Real-time cooperative monitoring of all procedures in pharmaceutical production is achieved through the internet technology, downstream process parameters are automatically adjusted according to dynamic changes of the upstream procedures, and production efficiency and quality stability are improved.

The invention relates to the field of intelligent breeding, aims to solve the problems that traditional cattle breeding is inaccurate in monitoring and early warning, extensive in feeding control and insufficient in environment control linkage, and provides a fine management method and system. The method comprises the steps of system initialization and individual binding, multi-dimensional data acquisition, physiological state accurate early warning, dynamic adaptive feeding, environment linkage regulation and control and data statistics optimization. The system correspondingly comprises an individual identification and data acquisition module, an improved physiological state monitoring and early warning module, a dynamic adaptive feeding control module, an environment regulation and control module, a data storage and analysis module and a system operation and maintenance module. Multi-dimensional data are collected through the intelligent neck ring, an individual adaptation algorithm is adopted for early warning of the health state, an exclusive feeding scheme is generated, and the breeding environment is regulated and controlled in a linkage mode. The method and the system improve the health rate and the breeding efficiency of cattle herds, reduce feed waste, and are suitable for large-scale cattle breeding fine management scenes.

The invention provides a multi-source heterogeneous data processing method in the field of medical apparatuses and instruments based on a multi-modal large model, and relates to the field of intelligent monitoring of medical apparatuses and instruments, and the method comprises the steps: collecting apparatus images, audio and parameter data, inputting the data into the multi-modal large model, and fusing to generate multi-dimensional feature representation; constructing a component operation mode library for state matching; analyzing a state evolution chain to identify an abnormal propagation path and locate a fault; calculating task migration cost to generate a scheduling scheme; and lossless conversion of the system is realized. The medical instrument fault diagnosis accuracy is effectively improved, the system interruption risk is reduced, and the clinical application safety is guaranteed.

The invention relates to the technical field of communication production management, and discloses a communication equipment production intelligent management system based on machine learning. The system comprises a production data acquisition module, a feature engineering construction module, a dynamic clustering analysis module, an anomaly detection engine module and a production decision optimization module. The production data acquisition module acquires multi-source sensor data in real time and converts the multi-source sensor data into a standardized sequence with a unified timestamp; the feature engineering module extracts a time domain statistical feature, a frequency domain energy feature and an equipment state association feature to generate a high-dimensional feature vector set; the dynamic clustering module adopts an incremental algorithm to divide clusters online; the anomaly detection module establishes a multi-level Gaussian mixture model based on a clustering label, and quantifies an anomaly probability through a mahalanobis distance; and the production decision module integrates the results to generate an equipment maintenance priority sequence and a production takt adjustment instruction. According to the system, intelligent monitoring and dynamic optimization of the whole production process of the communication equipment are realized, and the real-time change requirement of a complex production environment is met.

The invention relates to the technical field of intelligent monitoring of engineering hoisting, in particular to an intelligent monitoring system for an overweight segmented hoisting process based on BIM (Building Information Modeling) and force feedback, which comprises a data synchronization module, a state fusion module, a BIM matching module, a track optimization module and a simulation monitoring module. The system obtains a hoisting state through multi-source sensing data, matches a BIM fine model, and generates a theoretical motion track. And the trajectory optimization module generates a force feedback deviation field by comparing expected force feedback data with actual force feedback data, reversely corrects a theoretical trajectory, and realizes dynamic path optimization based on a mechanical state. And the simulation monitoring module drives virtual simulation according to the optimized trajectory, updates the posture of the BIM model in real time, dynamically calculates an interference area, performs risk assessment, and automatically generates and issues a control instruction. According to the scheme, the response precision and the active safety control capability of the hoisting process to the physical state change are improved.

The invention discloses a risk control strategy-based decision engine and method, and relates to the technical field of financial risk control, and the decision engine comprises a strategy warehouse module, a mapping service module, an execution engine module, a data management module, a version rollback module, a security scale module, and a monitoring alarm module. According to the invention, function decoupling is realized through collaborative design of the seven modules, and the modules interact through a unified interface, support strategy dynamic loading, gray release, distributed parallel execution, version governance and security compliance capabilities, and ensure high cohesion and low coupling of the system; through unique strategy version management, intelligent mapping and gray release, plug-in parallel execution, multi-version consistency view, block chain audit rollback, security compliance management and control, intelligent monitoring and other technical innovations, a complex, dynamic and high-concurrency risk control business scene is effectively supported; the security, the flexibility, the expandability and the auditing performance of the decision engine are remarkably improved, and the technical advancement and the industry creativity are high.

The invention discloses a multi-dimensional time sequence data compression and rapid retrieval method and system, and relates to the technical field of large data compression retrieval. The multi-dimensional time sequence data compression and rapid retrieval method comprises the following steps: S1, collecting and preprocessing multi-source data in a monitoring abstract video stream, and constructing a standardized time sequence behavior data set; s2, analyzing behavior characteristics of frame segments in the sliding window, and dynamically adjusting anchor point labeling and compression strategies; s3, evaluating the coverage integrity of anchor point information in a compression section, and driving generation of an index path; s4, comprehensively evaluating the path behavior association strength and dynamically adjusting a loading decision; and S5, verifying the matching integrity of the behavior anchor point field and the jump pointer, and guaranteeing the localizability and jump stability of the event in the compression structure. The problems that in an intelligent monitoring scheme, video abstract compression is not bound with behavior semantics, a fine-grained positioning mechanism based on behavior labels is lacked, and key action segments cannot be directly positioned during user playback are solved.

The invention discloses an intelligent monitoring and early warning system and method for project progress and cost, and relates to the technical field of constructional engineering management. The streaming data fusion module adopts an Apache Flink engine and applies a dynamic time warping algorithm to carry out time alignment on multi-source asynchronous data to realize semantic unification; the space-time diagram neural network prediction module constructs a space-time association diagram, integrates global features such as weather and supply chain fluctuation, and adaptively learns an influence weight by using a graph attention mechanism; the self-adaptive early warning module adopts a Bayesian online learning framework, an early warning threshold value is dynamically adjusted according to a historical false alarm rate and a missing report rate, the method comprises the steps of data acquisition, fusion, mapping, joint prediction and self-adaptive early warning, the problem of data islands is solved, complex space-time association is accurately captured through a space-time diagram neural network, and real-time early warning is achieved. Joint prediction of progress and cost risk is realized, false report and missing report are reduced, and real-time performance, accuracy and decision-making efficiency of engineering management and control are improved.

The invention provides a fault intelligent monitoring and rapid handling method and device for a wind power plant fan. The method comprises the steps of obtaining multi-dimensional operation state data of a target fan; preprocessing the multi-dimensional operation state data at an edge computing node to generate a high-dimensional feature vector; inputting the high-dimensional feature vector into a first intelligent analysis model deployed at a cloud end to output real-time fault early warning information and component fault positioning information for a target fan, and automatically matching and generating disposal information from a preset disposal scheme library; and after the disposal operation is executed based on the disposal information, automatically collecting operation data after the target fan is restarted, and comparing the operation data with a preset verification reference to generate a quantized disposal effect verification result. According to the method, the diagnosis and prediction dual intelligent model is deployed, so that real-time early warning and accurate component positioning can be performed on the current fault, the future fault probability can be predicted, and the operation and maintenance strategy is converted from passive response to active planning.

The invention relates to the technical field of data analysis and processing, in particular to an automatic data standardization management method based on a large language model, a medium and equipment, and the method comprises the following steps: a data receiving step: receiving data from various data sources; a data source analysis step: identifying field semantics and standardization requirements; a standardized rule generation step: based on the semantic understanding result of the large language model, automatically generating a standardized rule suitable for a corresponding data source; a standardization rule application and optimization step: performing application and dynamic optimization; an intelligent monitoring and verification step of data quality: performing real-time quality detection of the data by using a large language model; and a data restoration and suggestion step: automatically deducing a correct value or giving a restoration suggestion when the data does not meet the standardization requirement. According to the method, the problems of non-uniform standards, high manual rule maintenance cost and lack of dynamic adaptivity caused by inconsistent formats and semantics of the multi-source data are solved.

The invention discloses a steel ladle hoisting safety monitoring and early warning method, system and equipment and a medium. The method comprises the following steps: acquiring double-side images in a steel ladle hoisting process; inputting the bilateral images into a multi-target detection model, and outputting bounding boxes, category labels and confidence coefficients of the plurality of key targets; obtaining a local cutting image of each key target according to the bilateral images and the bounding box, the category label and the confidence of each key target, and generating a structured text in combination with the bounding box position, the relative pixel distance and the category label of each key target; inputting the local cutting image, the structured text and the prompt word template of each key target into a multi-modal big language model, and outputting a behavior label, a risk level, a reason, a recommendation action and an instruction state of the current steel ladle hoisting scene through multi-modal reasoning; early warning is conducted according to the recommended action, and the steel ladle hoisting equipment is controlled according to the instruction state. According to the invention, intelligent monitoring and linkage early warning in the steel ladle hoisting process can be realized.

The invention discloses a BIM-based building construction monitoring method, and the method comprises the steps: arranging a data collection device at a construction site, and obtaining multi-source data, such as structural members, construction machinery, spatial positions of operators, construction states, safety indexes and environmental parameters; automatically mapping multi-source data to a BIM three-dimensional model through an adaptive coordinate transformation and semantic recognition algorithm, matching the multi-source data with construction plan nodes, and generating a BIM construction state model reflecting a field state in real time; the system automatically compares the model with a construction plan model, identifies a state and calculates deviations such as progress, position, size, quality and safety; the method comprises the following steps: displaying deviation parts and potential safety hazards in a BIM interface in a highlight manner, superposing rectification suggestions, issuing a construction adjustment instruction confirmed by a manager to a field terminal, and storing monitoring, comparison, early warning and processing records in a database according to a time sequence to form a digital file; and real-time intelligent monitoring and visual management of construction progress, quality and safety are realized.

The invention relates to a double-spectrum fused intelligent monitoring imaging method and system, and the method comprises the following steps: firstly, synchronously collecting a visible light image and an infrared image of a monitoring scene, and calibrating the pixel coordinates to generate an aligned double-spectrum map; then, extracting the texture features of the visible light image and the temperature gradient of the infrared image from the aligned bispectrogram, and respectively obtaining a texture feature map and a temperature gradient map; feature validity judgment is carried out based on the two images, a bispectrum effective area image is identified, complementary areas are marked, and complementary effective marks are formed; then, carrying out weight assignment on the marks, and constructing a pixel weight table; and finally, fusion calculation and dynamic range compression are performed on the texture feature map and the temperature gradient map by using the weight table, so that a monitoring fusion imaging map is generated, and the technical problem of detail loss caused by sudden illumination change and weak temperature difference due to the lack of adaptive perception capability for scene content in the existing fusion method is solved.

The invention relates to the technical field of anti-interference of intelligent monitoring data acquisition of a new energy power station, in particular to a self-adaptive anti-interference system for data acquisition of an intelligent auxiliary monitoring system. A windowing judgment mechanism is formed based on cross-node stage consistency; thirdly, defining participation paths of the data units with different stage attributes in cross-node association analysis, and aligning and screening multi-node data through data consistency management; when the stage consistency window meets a release condition, controlled stage regression processing is performed on the data unit in the unstable stage, so that the data acquisition node gradually enters a unified stage attribute; according to the method, the instantaneous abnormal processing of the interference influence is converted into staged and structured management, and the continuity, consistency and reliability of data acquisition and analysis in a complex power monitoring scene are greatly improved.

The embodiment of the invention provides an intelligent monitoring method and system based on multi-source heterogeneous data fusion. The intelligent monitoring method based on multi-source heterogeneous data fusion comprises the steps that production data sets in different production scenes are collected and stored in a classified mode; mining association features of equipment operation behavior records and material circulation track information through semantic association analysis, and extracting influence features of production environment perception data in combination with environmental factor mapping to obtain multi-source qualitative features; generating a node relation chain according to the business process association relation, and configuring a matching algorithm of qualitative scene features; screening feature subsets conforming to association rules, and combining and associating the feature subsets to obtain a scene feature combination; creating a scene exception reasoning model by using the scene feature combination and performing exception feature evolution analysis to obtain an exception reasoning result; and the abnormal propagation path is traced, the root cause node is positioned, and the abnormal monitoring label is generated, so that the accuracy of abnormal root cause positioning and the scene adaptability are improved.

The invention discloses an automatic monitoring method for an industrial solid waste treatment process, and the method comprises the steps: synchronously collecting a temperature distribution matrix, a gas concentration spectrogram, a kiln body vibration spectrum and a material rheological image in the solid waste treatment process, constructing a multi-source heterogeneous data set, and carrying out the time-space alignment preprocessing; based on a preset solid waste component feature code, calling a process parameter reference value, and performing self-adaptive correction in combination with the material rheological image features to generate a personalized reference set; fusing the multi-modal monitoring data to generate a solid waste reaction process three-dimensional state model, extracting core feature parameters, comparing the core feature parameters with a personalized reference set, and screening abnormal features; inputting the abnormal features into a pre-trained abnormal diagnosis model, and when an abnormal probability value exceeds a preset threshold value, triggering early warning and generating an abnormal identifier; according to the method and the system, intelligent monitoring and regulation of the industrial solid waste treatment process are realized, and the stability, the safety and the treatment efficiency of the treatment process are comprehensively improved.

The invention relates to the technical field of data reasoning, and discloses an intelligent monitoring and optimizing method and system for energy consumption in the solid waste treatment process, and the method comprises the steps: cleaning noisy points of energy consumption data during solid waste treatment, and obtaining standard energy consumption data of solid waste treatment; extracting characteristic indexes of time sequence characteristics in the standardized energy consumption data; according to the characteristic indexes, the energy consumption abnormal time period and the high energy consumption link of the target solid waste treatment process are recognized; according to the operation state data of the high-energy-consumption link, state association is carried out on the energy consumption abnormal time period, and an energy efficiency association map of the solid waste treatment process is obtained; according to the energy efficiency correlation map, performing energy consumption optimization on the target solid waste treatment process to obtain an optimized regulation and control strategy of solid waste treatment; performing energy level supervision on the target solid waste treatment process according to the optimized regulation and control strategy; according to the invention, the efficiency of intelligent monitoring and optimization of energy consumption in the solid waste treatment process can be improved.

The invention provides a parking lot safety operation intelligent monitoring and self-adaptive scheduling management platform based on cloud edge cooperation AI, and the platform comprises a data analysis and alarm module which carries out the analysis of the behaviors of a driver and the surrounding environment of a vehicle based on an AI model disposed on a parking lot intelligent terminal, and carries out the instant alarm when an abnormal driving behavior or a safety risk exists; the global analysis index determination module is used for uploading the behavior recognition result obtained through analysis and the time series data of the environmental risk information to the cloud platform, and aggregating and mining the time series data of each parking lot vehicle in the space region based on the cloud platform to obtain the global analysis index of the working efficiency and the safety situation of the parking lot vehicle; and the scheduling management module dynamically generates a task scheduling instruction and a path planning instruction of each parking lot based on the global analysis index, and issues the task scheduling instruction and the path planning instruction to the parking lot intelligent terminal for cooperative management of operation behaviors. The operation safety and the operation efficiency are remarkably improved, and human errors and resource waste are reduced.

The invention relates to the technical field of electric power, in particular to a primary and secondary fusion environment-friendly ring main unit intelligent monitoring system and method, and the system comprises a data collection module which is used for collecting multi-mode monitoring data of a ring main unit to obtain an original monitoring data set; the data processing module is used for performing standardization processing on the original monitoring data set to obtain a standardized multi-modal monitoring data set; the intelligent monitoring module is used for performing state evaluation based on the standardized multi-mode monitoring data set to obtain an equipment state monitoring evaluation result; the digital twinning module is used for constructing a digital twinning model and performing digital twinning analysis on the equipment state monitoring and evaluation result through the digital twinning model to obtain a health index and a fault risk prediction result; the strategy generation module is used for generating an operation and maintenance strategy set based on the health index and the fault risk prediction result; and the strategy execution module is used for executing operation and maintenance operation according to the operation and maintenance strategy set and recording an operation and maintenance execution effect.

The invention belongs to the technical field of industrial control, and discloses a thermal power plant coal conveying system intelligent monitoring system based on multi-source perception and edge calculation. Comprising the steps of collecting multi-modal sensing data through a heterogeneous sensor array, performing deep collaborative extraction on cross-modal features such as vision, acoustics, vibration, temperature and current by using a physical constraint correlation model, and dynamically identifying an equipment abnormal mode according to the cross-modal features; and a hierarchical fusion analysis architecture is introduced, so that multi-level confidence guarantee is provided for fault diagnosis. Through a simulation deduction mechanism driven by digital twinning, system response can be pre-evaluated before a control strategy is executed, a scheme with the optimal comprehensive performance is optimized, and the reliability of a control decision is ensured; an increment correction mechanism of state deviation real-time feedback is also realized, and control deviation caused by model mismatch is avoided; and each abnormal symptom of the coal conveying link can be accurately captured and adaptively handled, so that the system safety and the operation and maintenance intelligent level are remarkably improved.

The invention discloses a multi-camera collaborative pedestrian and vehicle cross-view tracking method and system, and relates to the technical field of intelligent monitoring. The method comprises the following steps: carrying out video stream acquisition on a monitoring area to obtain a multi-view video data set; multi-target detection of pedestrians and vehicles is carried out, tracking is carried out according to a plurality of detection targets, and a local tracking trajectory set of a plurality of cameras is generated; carrying out collaborative analysis on the plurality of cameras, constructing a multi-camera collaborative strategy to carry out cross-view trajectory matching on the local tracking trajectory set, and generating a global tracking trajectory set; and updating target states of the plurality of detection targets according to the global tracking trajectory set, and generating a multi-target cross-view tracking result of the pedestrian and the vehicle. The technical problem that in the prior art, information of different visual angles is difficult to integrate effectively in a multi-camera scene, so that target loss and track discontinuity easily occur in cross-camera tracking of pedestrians and vehicles is solved, and the technical effect of improving the continuity and accuracy of cross-camera tracking is achieved.

The invention discloses an open source software supply chain security intelligent monitoring and risk assessment method, relates to the technical field of software supply chain security, and solves the problem that only existence of indirect dependence can be identified generally in the prior art, but whether a vulnerability code can be called on an actual execution path cannot be accurately judged. According to the method, through dependence locking and Hash fingerprint embedding during construction, cryptographic-level accurate traceability of a component version is realized, and version ambiguity is thoroughly eliminated; in combination with deep interception of a JVM probe for method calling and class loading events during operation, a loading source and a call stack context of each class in a real execution path can be captured, bidirectional association mapping is performed on dynamic behavior data and a static dependency tree, and whether a vulnerability component is located on an executable call chain or not is accurately identified. Compared with the defect that coarse-grained warning is carried out only through version matching in a traditional tool, the method has the advantage that by means of dynamic-static data fusion, it is avoided that a large number of unavailable indirect dependency vulnerabilities are misjudged to be high in risk.