440 results about "Pattern matching" patented technology

The invention relates to the field of energy consumption analysis, and discloses an AI-based energy consumption data analysis and prediction system, which comprises the steps of collecting environmental parameters and running states of equipment, dynamically identifying the current system working condition by using a working condition identification algorithm combining incremental clustering and historical mode matching, and predicting the energy consumption data. Collected data is divided according to time, space and working condition dimensions, multi-scale features are extracted, normalization parameters can be dynamically adjusted along with changes of working conditions, a drift index is calculated through comparison of a drift threshold value and historical distribution, an optimal normalization updating strategy is selected according to the drift index, the normalization parameters are dynamically updated, and energy consumption trend prediction is conducted through a statistical model. A prediction result is combined with a working condition label to carry out weighted correction, error analysis and deviation detection are carried out in combination with a drift index, working condition prediction and historical error data, and an analysis result is fed back to a working condition sensing module, a feature adaptive module and a normalization control module. The method has the advantage of improving the stability and reliability in a dynamic environment.

The invention provides a cross-protocol identification data intelligent analysis middleware method and system. The method comprises the following steps: taking multiple industrial communication protocol identifiers in advance to form a metadata set, and constructing a protocol feature element model library containing a non-standard protocol feature template; a programmable logic device is introduced, physical layer signal waveform characteristics are matched, and corresponding communication protocol stacks are dynamically switched at a hardware level according to a matching result; receiving a communication data stream through an electric domain processing unit of the programmable logic device, and generating a protocol analysis logic sequence in combination with a state transition rule of the meta-model library and a mode matching rule of the protocol stack; performing combined operation on the structured feature template and the analysis logic sequence to generate an executable cross-protocol data analysis rule; and when unknown protocol characteristics are detected, triggering dynamic reconstruction of the template, so that the analysis rule is adapted to a newly added protocol. According to the technical scheme provided by the invention, the protocol compatibility development cost in an industrial multi-protocol scene is remarkably reduced.

The invention relates to the technical field of shot action recognition, and discloses a shot throwing action recognition method based on a single attitude sensor. The method comprises the following steps: acquiring a single attitude sensor original data stream in a shot throwing process, and extracting a three-axis acceleration sequence and a three-axis angular velocity sequence from the single attitude sensor original data stream; performing motion event segmentation processing on the two sequences, and generating a key action time period mark set comprising a preparation stage, a sliding step stage, a power generation stage and a release stage; based on the mark set, calling an attitude feature solution algorithm to perform spatial trajectory reconstruction on the original data stream to obtain a key attitude frame set containing a joint angle change curve and a centroid displacement trajectory; and inputting the key attitude frame set into an action recognition model for attitude mode matching, and outputting a shot throwing action classification result which comprises an action deviation parameter generated based on a standard action template library and a key frame correction identifier. According to the method, key attitude information is reconstructed, and support is provided for shot throwing action analysis and improvement.

A computer-implemented system for multidimensional generative value assessment and applicant selection, consisting of: a data collection unit configured to electronically receive applicant data consisting of structured academic records, work experience records, digital documentation, and unstructured narrative responses generated from generative self-assessment instruments and contextual interviews; a feature extraction unit coupled to the data acquisition unit, configured to apply computer-assisted text processing, semantic analysis, and token-level attribute identification to transform narrative responses and structured data into multidimensional feature vectors that represent generative indicators of innovation, mentoring, collaborative performance, resilience, social contribution, ethical consistency, and predicted institutional impact; a weighting calculation unit configured to assign weight values ​​to the extracted feature vectors based on a digital generative profile definition matrix that includes dimensions, sub-criteria, indicators, documentation requirements and importance coefficients, with the weighting being distributed across the generative dimensions defined in the digital matrix and configurable according to the institutional context; a quantitative rating unit configured to calculate a generative rating score by aggregating weighted feature vectors derived from self-assessment inputs, interview-based ratings, document analyses, and authenticity predictions, with the aggregation including normalization, nonlinearity correction, conflict handling, and artifact frequency balancing to obtain a consolidated score; a proof verification unit configured to electronically validate referenced digital evidence by performing content extraction, metadata verification, pattern matching, and cross-document correlation to determine authenticity, credibility, and contextual relevance with respect to the calculated feature vectors; a classification determination unit configured to assign a classification level to an applicant by comparing the generative assessment score with a set of system-defined calculation thresholds, including at least a lower threshold, a middle threshold and an upper threshold, the classification levels representing different generative maturity states and determining subsequent eligibility for selection; a decision generation unit configured to produce a digital output data set that includes classification level, feature aggregation summaries, evidence validation results, and recommended organizational actions, wherein the decision generation unit encodes the data set in a digitally signed, tamper-proof format and stores it on a non-volatile storage medium; and A system control unit acts as an operational interface to all other units and is configured to orchestrate data flow, scheduling, process state transitions, and event logging to ensure verifiable traceability, consistency, and auditability of the evaluation and selection processes.

The invention provides a time-varying graph neural network traffic flow prediction method based on a dynamic memory bank, and belongs to the technical field of traffic prediction. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a memory enhancement layer and a prediction output layer. The data embedding layer preprocesses a traffic flow sequence, associates a collaborative coding time sequence mode with a road network, and synchronously constructs a dynamic graph structure; the space-time coding layer is subjected to space-time stream decoupling extraction, a space branch models multi-scale space dependence through a time delay graph convolution module and a space Mama module, and a time branch extracts multi-granularity time features through a hierarchical time sequence sensing module and a time Mama module; the memory enhancement layer performs pattern matching and reconstruction on the space-time fusion features by means of a dynamic memory bank; and the prediction output layer generates a prediction result by taking the GCRN as a decoder. According to the method, the space-time dependence of the traffic situation is accurately captured, the prediction curve is highly fit with the true value, and the high-precision prediction of the traffic flow is realized.

The invention discloses an on-line monitoring and fault early warning system for the running state of a dynamic compression-shear testing machine, belongs to the technical field of fault diagnosis, and aims to solve the problems of poor adaptability to multiple motion modes, lagging fault early warning and fuzzy fault positioning in the prior art. According to the system, core collaborative link fault sensitive point monitoring parameters are matched according to a current motion mode, a dynamic threshold value is generated to construct a fault judgment threshold value system, real-time data subjected to cyclic division are collected and loaded to generate a time sequence data set, a reference candidate range state is judged based on the threshold value, and a fault judgment reference library is constructed; and executing deviation analysis prediction trend through the reference library, generating a compensation instruction, identifying potential faults in combination with a threshold value, an actual value and a prediction value, calculating a link fault probability, and completing diagnosis. According to the invention, monitoring adaptability and accuracy can be improved, compensation in advance and accurate fault early warning are realized, and stable operation and test accuracy of equipment are guaranteed.

The invention discloses an API-oriented dynamic fuzz testing and vulnerability detection method, and belongs to the technical field of software security testing. The method comprises the following steps: extracting a dependency relationship, an input parameter, an output response and context state data of API calling, generating an initial API dependency graph, dynamically updating by capturing API state change in real time, forming an API state graph, and executing boundary-oriented variation based on parameter constraint characteristics, so as to obtain an API state graph; generating a variation test parameter, calling an API (Application Program Interface) of the variation test parameter to monitor a process memory behavior and response metadata, and generating a multi-dimensional abnormal signal; and performing mode matching on the abnormal signal and the vulnerability feature knowledge base, outputting a vulnerability type label and generating a path tracing report. According to the method, a context state sensing dynamic graph modeling technology is adopted, and a boundary-oriented intelligent variation strategy and multi-source abnormal behavior collaborative analysis are combined, so that precise vulnerability triggering, intelligent vulnerability judgment and complex scene coverage can be realized.

The invention relates to the technical field of industrial equipment state monitoring and fault diagnosis, in particular to a trend fault prediction method based on cooperation of a dynamic mode and a threshold value. According to the method, a theoretical prediction interval dynamically changing along with a load is generated in real time by establishing nonlinear mapping between working conditions and key parameters, parameter drift interference caused by working condition fluctuation is effectively eliminated, a real-time health baseline of equipment is quantified in combination with maintenance records, and the width of an early warning threshold value is cooperatively adjusted according to feature similarity and the health level. Self-adaptive monitoring of different aging stages of the whole life cycle is realized, mode matching is performed by utilizing multi-dimensional feature vectors and fusing physical field information, deviation severity and form similarity are comprehensively evaluated through fuzzy reasoning, abnormity is locked in advance according to high feature goodness of fit when a numerical value does not seriously exceed a limit, and a real-time monitoring result is obtained. Early weak symptoms are accurately captured, abnormal sources are output, and the diagnosis precision under variable working conditions is remarkably improved.

The invention discloses an electric meter historical data restoration method based on a multi-dimensional incidence relation, and relates to the technical field of electric power data management and intelligent restoration, the accuracy and traceability of data restoration are realized by establishing a multi-source exception type library and an element time-varying degradation model, and the accuracy and traceability of data restoration are improved for multi-source data conflicts. The method comprises the following steps: pre-defining a plurality of abnormal mode combinations and corresponding repair strategies, positioning a fault source through mode matching of a real-time index and an abnormal type library, selecting reference data in combination with a historical average deviation rate of a target ammeter and an associated ammeter, avoiding deviation caused by single judgment, and for nonlinear degradation, firstly determining fault elements of a shunt and an ADC chip, a resistance degradation model and a conversion error model are constructed, a theoretical correction value is calculated, a result is optimized through a machine learning model trained by historical normal data, meanwhile, original readings, model parameters and calculation bases are stored in an associated mode, a traceable log is formed, and the repair accuracy is guaranteed.

The invention relates to the technical field of energy data management and control, and discloses a smart park energy data dynamic management and control system. The energy data acquisition module of the system acquires and preprocesses the operation data flow in the park in real time. The energy feature analysis module performs multi-dimensional feature extraction on the data stream, and identifies feature vectors such as peak load and steady-state operation. The event trigger engine generates an energy management event signal based on conditions such as feature vector and threshold comparison, timing pattern matching and the like. The parameter node identification module analyzes an event signal time sequence and positions key nodes such as equipment start and stop and load sudden change. The offset calculation module calculates the offset of the energy parameter relative to the historical reference for the key node. The similar parameter derivation module derives a migratable parameter set containing general control parameters and scene adaptive parameters according to the offset and a historical database. And the dynamic regulation and control module performs real-time adjustment such as power distribution and equipment scheduling on park energy distribution by using the set.

The invention provides an intelligent question answering method and system based on a traditional Chinese medicine classical meta-term engine. The method comprises the following steps: identifying and extracting core traditional Chinese medicine terms and a user query intention in a user question; the core traditional Chinese medicine terms are input into a pre-constructed traditional Chinese medicine classical meta-term engine to be processed, normalized mapping and semantic association extension of the terms are completed, and the engine is constructed based on a classification-term-word list-semantic CTVS association framework. Comprising a traditional Chinese medicine classical book term ancient and modern double-classification system C layer, a meta-term extraction and standardization T layer, a dynamic word list V layer and a semantic association network S layer. Carrying out graph pattern matching and reasoning in the S layer, traversing and integrating a plurality of deep association paths by taking classical element terms and corresponding modern clinical terms as double center nodes, and retrieving answer information associated with the query intention; and carrying out integration sorting and structured assembly on the answer information to generate a structured answer. Intelligent questions and answers can be performed on traditional Chinese medicine related questions of the user.

The invention relates to the technical field of fault detection, and discloses a big data-based machine room equipment fault diagnosis method and system, and the method comprises the steps: collecting load information data streams generated by all equipment in a machine room in real time, and carrying out the filtering, and obtaining a filtered information sequence; performing grouping processing on the filtered information sequence by using a K-means clustering algorithm to obtain a dynamic change mode group, calculating a key statistical index, and obtaining an abnormal feature set; if the abnormal feature set exceeds a preset variance threshold, classifying the abnormal feature set and determining a fault mode to obtain a fault mode category set; matching the fault mode category set with a preset fault mode database to obtain a fault mode matching result, and generating a corresponding risk assessment report to obtain a risk assessment report; and adjusting the operation parameters of the machine room equipment to obtain the equipment operation parameters. According to the method, equipment faults can be diagnosed in time.

The invention discloses an e-commerce data cleaning method and device, equipment and a medium, and relates to the technical field of data processing, and the method comprises the steps: obtaining the data information of various e-commerce customer data sources, recognizing the data type and the connection configuration of the e-commerce customer data sources, and converting the various data sources into target data according to a standardized interface and the connection configuration; determining a cleaning strategy based on the target data evaluation result, the data context and the historical cleaning effect through a reinforcement learning mechanism, and cleaning the target data based on the cleaning strategy; business meanings of fields in the cleaned data and the initial e-commerce user data are determined through semantic analysis, and a mapping relation between the initial e-commerce user data and the cleaned data is established according to the business meanings and a pattern matching algorithm; and converting the format of the cleaned data into a format meeting the data processing requirement of the e-commerce data processing system through the mapping relationship, so that the e-commerce data processing system correspondingly processes the cleaned data. And intelligent cleaning of the data is realized.

The invention relates to a self-adaptive evaluation and precise learning path generation system based on a knowledge graph. The method comprises the following steps: constructing a standard knowledge graph and an error region knowledge graph; performing misunderstanding mode matching based on the learning interaction data of the students, updating the misunderstanding knowledge graph of the students, and generating a personal misunderstanding knowledge graph of the students; setting a graph conversion operation from the student personal misunderstanding knowledge graph to a standard knowledge graph, and determining a target graph conversion operation sequence through a search algorithm by taking the student personal misunderstanding knowledge graph as an initial state and taking a sub-graph corresponding to a learning target in the standard knowledge graph as a target state; the target image conversion operation sequence is a personalized learning path. The problems that cognitive errors are difficult to eradicate and learning paths are poor in adaptability can be effectively solved, and meanwhile by means of scientific cost evaluation and an intelligent search algorithm, it is ensured that the generated learning paths conform to student individual cognitive characteristics and have high efficiency and feasibility.

The invention relates to the technical field of coal mine ventilation safety, and discloses a coal mine ventilation tunnel monitoring method and device, electronic equipment and a storage medium. According to the method, real-time airflow parameters and dust concentration data of a ventilation roadway are collected through a distributed sensor array, and an original monitoring data set is formed. And carrying out multi-dimensional feature extraction on the data set to obtain a comprehensive feature set containing an airflow velocity fluctuation feature, a dust distribution gradient feature and a temperature change trend feature. And constructing a ventilation tunnel three-dimensional dynamic model based on the comprehensive feature set, and mapping airflow and dust data to model space coordinates. And generating a ventilation resistance coefficient matrix according to the model, performing mode matching in combination with a historical blockage case library, and identifying coordinates of a potential blockage area. And starting the mobile verification device to carry out close-range scanning on the potential area to obtain high-precision point cloud data and infrared thermal imaging data. According to the invention, the intelligent level and emergency response capability of coal mine ventilation safety management can be improved.

The invention relates to the technical field of new energy power grid coordination control, and discloses a wind storage combined frequency modulation control method based on model prediction control and deep learning. The method comprises the following steps of: driving a high-frequency updated system feature library, constructing dynamic digital mapping of a wind storage system, performing multi-level scene evolution and collaborative optimization according to the dynamic digital mapping, and generating a control instruction. And the control instruction is verified and decomposed and then issued to physical equipment for execution. And synchronously acquiring system response data, performing frequency domain feature extraction and mode matching, and triggering closed-loop self-adaptive adjustment of dynamic digital mapping according to the frequency domain feature extraction and the mode matching. According to the method, online dynamic updating of the control model and accurate correction for the frequency domain characteristics of the system are realized, so that the model can closely track the change of an actual object, and the control precision and the dynamic response quality of wind storage combined frequency modulation are improved.

The invention provides a fine configuration method and system based on a strategy mode, and the method comprises the steps: defining themes used for abstracting entrance traffic, order distribution and other events through a configuration system, and configuring a basic data source and a data processing rule for each theme, wherein the data processing rule supports a user to carry out code development implementation in a basic class of each service line according to service requirements; the system automatically stores theme data to middleware, matches a corresponding theme through a strategy mode after receiving a user request, performs secondary data processing according to a configuration rule and returns a result; the technical effects of flexible configuration mode, quasi-real-time index calculation, support of multi-source data access, high-concurrency scene adaptation and good expansibility in flow fine management are realized, and the service operation demand change can be quickly responded.

The invention relates to the technical field of water pollution monitoring and big data analysis, and discloses a water pollution early warning method and system based on big data, and the method comprises the steps: collecting water and electricity use data and water quality monitoring data, and carrying out the standardized fusion, and obtaining an integrated data set; performing abnormal clustering analysis and multi-dimensional parameter relevance verification according to the integrated data set to obtain an abnormal linkage distribution result; performing historical behavior pattern matching and identity recognition based on the abnormal linkage distribution result to obtain hidden abnormal recognition data; performing reference coordinate retrieval and cross validation according to the hidden anomaly identification data, and determining specific position information of a potential pollution source; and carrying out pollution diffusion simulation on the position information, determining distribution information of an affected area and generating early warning notification data. According to the method, hidden pollution discharge behaviors can be effectively identified, and accurate positioning of pollution sources and dynamic evaluation of diffusion risks are realized.

The invention discloses a medical sensitive data four-layer intelligent dynamic identification system, and relates to the technical field of medical data safety processing, the medical sensitive data four-layer intelligent dynamic identification system comprises a data input module, a rule identification engine, a pattern matching engine, a machine learning engine, a strategy execution engine and a data output module, and the medical sensitive data four-layer intelligent dynamic identification system is characterized by further comprising an intelligent scheduler; the output end of the data input module is in communication connection with the input end of the intelligent scheduler. According to the medical sensitive data four-layer intelligent dynamic identification system, by introducing the intelligent scheduler and a dynamic judgment mechanism, the problem that a multi-layer identification architecture process is rigid in the prior art is effectively solved. The system can intelligently decide a processing path according to data characteristics and real-time identification confidence, and prevents simple data from undergoing redundant deep analysis, thereby optimizing calculation resource allocation, reducing system processing delay, and improving overall throughput and response efficiency.

The invention relates to the technical field of water conservancy project automation and industrial control, in particular to a water conservancy equipment collaborative operation system based on an industrial control system, which comprises a programmable input mapping and logic matching unit, a data processing unit and a data processing unit, the system carries out binary bit mode matching on the operation result and the feature data block in the memory; the logical operation and addressing unit is used for calculating control lag time and generating a structured digital control sequence data stack; the sequential control and instruction addressing unit is used for generating a cooperative control instruction set containing sequential logic; the output mapping and checking unit is used for reading real-time feedback, and if deviation exists, an interruption compensation program is triggered to correct an action curve of an executing mechanism; the storage rewriting and loop optimization unit is used for correcting transmission delay parameters of the model and realizing closed-loop optimization; according to the method, the problem of model distortion caused by physical property drift is solved, and stable full-life-cycle operation is ensured.

The invention discloses a vehicle information security identification method and device based on a behavior knowledge graph. The method comprises the following steps: determining the behavior knowledge graph; receiving a node exception event association graph from the vehicle end, wherein the node exception event association graph is determined by the vehicle end through an entity associated with the exception index; adding a suspicious edge connection relationship in the abnormal event association graph as a preset low-confidence abnormal relationship into the behavior knowledge graph to obtain an updated behavior knowledge graph; and based on the updated behavior knowledge graph, analyzing the connectivity of the relationship between the nodes by adopting a graph reasoning algorithm and a preset recognition rule, and recognizing a target attack chain matched with or similar to the known attack mode. Therefore, on the basis of preset recognition rule analysis and a graph reasoning algorithm, intelligent reasoning and automatic judgment are carried out on complex attacks matched with or similar to the known attack mode instead of simple threshold comparison, and the complex attacks implemented across different entities or functional subsystems can be effectively recognized.

The invention relates to the technical field of intelligent monitoring and fault diagnosis of concrete mechanical equipment, and discloses a fault detection method and system for a concrete mixing device. The method comprises the following steps: acquiring multi-source sensing data during operation of a device to construct a multi-dimensional data set; performing fluctuation feature extraction on the power data to obtain a fluctuation feature vector; preliminarily screening abnormal data through dynamic threshold comparison; performing time sequence persistence analysis on the abnormal data to obtain a refining abnormal set; performing mode matching verification on the refining anomaly set and the fault feature library to generate a fault confirmation result; and generating and outputting graded alarm information according to a confirmation result. According to the invention, through combination of multi-dimensional data fusion, dynamic threshold screening and persistent trend analysis, accurate identification and early warning of early persistent faults of the concrete mixing device under complex working conditions are realized.

According to embodiments, a memory system includes a data management circuit, a memory, a write control circuit, and a data determination circuit. The data management circuit manages data, which is received from a host on a first data size basis, on a second data size basis. The second data size is greater than the first data size. The data determination circuit determines whether or not data of the second data size received by the write control circuit matches a data pattern set in advance. In a case where first data received by the write control circuit matches the data pattern, the data management circuit sets a flag indicating that the first data matches the data pattern in an entry of a management table corresponding to the first data, and the write control circuit discards the first data without writing the first data to the memory.

The invention provides an AI-driven data multivariate fusion and flexible interaction method, which relates to the technical field of artificial intelligence, and comprises the following steps: firstly, analyzing multivariate data source interaction demand description, and generating a data interaction rule set containing data source association, format adaptation and permission interaction rules; constructing a data fusion mode library containing static, dynamic and incremental fusion modes based on the data interaction rule set, calling the pre-training interaction decision model to perform mode matching to obtain an association matching result containing mode adaptation degree and data source interaction priority information, determining a fusion path sequence and an interaction triggering condition according to the association matching result, and performing data fusion on the fusion path sequence and the interaction triggering condition. And generating a data interaction instruction set containing an interaction instruction identifier, and sending the data interaction instruction set to a data processing node to start a fusion interaction process, thereby realizing efficient and flexible fusion interaction of multivariate data.

The invention discloses an MES-based industrial production data intelligent analysis and management system, and belongs to the field of industrial production data analysis. The system comprises a central module used for synchronously acquiring a time sequence of man-machine interaction event data and machine tool sensor data; the evaluation unit is used for calculating a cognitive load index and a processing load entropy; and the resonance risk assessment module is used for calculating a comprehensive risk level. Wherein the calculation of the comprehensive risk level depends on an instantaneous risk item based on a cognitive load index and a processing load entropy and a mode matching item based on form similarity between a time sequence and a predefined out-of-step mode template. According to the method, man-machine is regarded as a dynamic coupling system for analysis, predictive intervention on potential processing failure risks can be achieved, and the stability and robustness of the production process are improved.

The invention discloses a standardized work management system for a standard system revision project, and the system comprises a heterogeneous data fusion module which captures the multi-modal process data of each stage in real time, and converts the multi-modal process data into a process state vector sequence; a time sequence constraint graph rule base is built in the compliance graph calculation module, a process state graph with time sequence attributes is constructed, and the multi-dimensional compliance deviation degree is calculated through graph mode matching; the strategy generation and conflict resolution module is used for generating a candidate set with a weight adjustment instruction through a learnable strategy priority ranking model based on the deviation degree and resolving logic conflicts; and the trusted execution and feedback closed loop module atomizes an execution instruction and carries out uplink evidence storage, and collects project response data to update rule base parameters and model weights online. According to the method, automatic and accurate compliance evaluation is realized through graph calculation, self-evolution of the system is realized through intelligent strategy generation and credible execution feedback, and the management efficiency, compliance and intelligent level are comprehensively improved.

The invention discloses a vehicle control compensation method and device based on a cross-system coupling relation, and relates to the technical field of vehicles. The method comprises the steps that in the vehicle running process, multi-source running data related to a steering system, a driving system, a braking system and vehicle body postures are collected in real time; inputting the multi-source operation data into the reference health model to obtain a predicted behavior signal of the vehicle in a health state; comparing the predicted behavior signal with a corresponding actually collected behavior signal, calculating a coupling deviation between the predicted behavior signal and the behavior signal, and extracting a spatial-temporal characteristic of the coupling deviation; performing trend analysis on the coupling deviation based on the spatial-temporal characteristics of the coupling deviation, and identifying a recessive progressive degradation mode of the vehicle in combination with a preset threshold and a mode matching algorithm; and when the degradation trend is recognized, a control instruction is generated to perform control compensation on the vehicle. According to the invention, the problem of low timeliness and accuracy of a vehicle control compensation mode in the prior art is solved.

The invention discloses a service exception early warning method based on log semantic recognition and pattern matching. According to the method, original logs are collected and preprocessed to obtain a standardized log stream, a suspected abnormal log set is screened out through an abnormal feature rule base containing keywords, regularities and frequency rules, a large language model is input to analyze semantics, and a structured result is output. And the result is stored in the exception knowledge base and indexed, and the new log and the database are subjected to semantic matching, so that the accuracy of the service exception early warning is improved, and the problem of low accuracy of the service exception early warning caused by low interpretability of the exception detection result in the prior art is solved.

The invention relates to the technical field of virtualization orchestration, in particular to an industrial control virtualization orchestration system and method, and the system comprises a task extraction module, a node behavior monitoring module, a stability evaluation module, an orchestration load adjustment module and a control task migration module. According to the method, the scheduling cycle closed-loop feature recognition is performed by collecting the control task scheduling mapping table and the execution instruction queue and combining the cycle control mark and the interrupt mark, so that an orchestratable task set can be effectively recognized, the behavior success rate is calculated according to parameters such as node response delay and task completion proportion, and node stability screening is performed; stability grade division of control nodes is achieved, a scheduling priority list is formed by setting a release threshold value, then the task migration opportunity is judged based on task residence time and IO completion conditions, migration targets are accurately selected in a behavior pattern matching mode, and the stability of task scheduling, the precision of resource allocation and the adaptability of control logic are remarkably improved.

The invention provides a multi-scene target detection and behavior recognition method and system based on deep learning, and relates to the technical field of software development behavior analysis, and the method comprises the steps: obtaining a code editing operation sequence, an interface interaction event sequence and a file access record sequence in a software development environment, and forming a multi-modal development behavior data flow; performing development scene identification on the multi-modal development behavior data flow to obtain a scene state identifier; constructing a semantic perception feature extractor, performing syntax structure analysis and semantic dependency modeling on the code editing operation sequence, extracting an abstract syntax tree structure and a program execution path diagram, and combining to obtain code structured features; performing code mode matching and abnormal mode positioning to obtain a target code segment detection result; extracting operation time sequence features and carrying out cross-modal feature alignment on the operation time sequence features and the code structured features to obtain a unified behavior vector; and performing time sequence dependence reasoning and behavior pattern classification to obtain a behavior recognition result.