376 results about "False positive rate" patented technology

The invention relates to the technical field of side slope geological disaster monitoring, and discloses a side slope long-term monitoring early warning and threshold value dynamic adjustment method, which comprises the following steps: S1, deploying a plurality of sensors on a side slope; s2, preprocessing the collected original data; s3, setting an early warning threshold value for the key feature index; s4, dynamically evaluating and predicting the stable state of the slope in real time; s5, dynamically correcting and optimizing the initial early warning threshold value set in the step S3; s6, when the key characteristic index value exceeds the newest early warning threshold value, early warning is triggered; and S7, generating early warning information of different levels. The method solves the problems of tedious configuration of slope monitoring early warning threshold values and false alarm and missing alarm, achieves the adjustment and configuration of a unified threshold value through an efficient early warning trigger judgment disposal and threshold value dynamic adjustment mechanism, effectively improves the alarm success rate and early warning disposal efficiency, remarkably reduces the false alarm rate / missing alarm rate of slope early warning, and improves the safety of slope early warning. And the interference of the early warning system on residents is greatly reduced.

The invention relates to the technical field of industrial carbon emission monitoring, and particularly discloses an industrial carbon emission visual monitoring method and system based on digital twinning, and the method comprises the following steps: S1, carrying out the collaborative collection and preprocessing of carbon emission data; s2, dynamically updating the hierarchical digital twin model; s3, intelligent early warning and accurate tracing are carried out; and S4, virtual-real linkage visual interaction is carried out. According to the invention, by establishing a multi-factor driven hierarchical iteration mechanism, hierarchical updating and closed-loop verification of parameters-structures are realized, by establishing a space-time correlation-process coupling-mode matching three-dimensional fusion early warning rule, the false alarm rate and the missing report rate are reduced, a visual interaction system of virtual-real linkage is built, the decision-making efficiency is improved, and the decision-making efficiency is improved. And an accurate and efficient carbon emission control technical support is provided for multi-process industrial scenes such as metallurgy and chemical engineering.

The invention discloses a quick data traceability method and system based on multi-source log analysis, and relates to a data processing system or method specially suitable for administrative, commercial, financial, management, supervision or prediction.The method comprises the steps that firstly, the system responds to a service instruction to generate a transaction receipt containing a unique serial number and service attribute parameters; and establishing an association relationship between the two records and generating a to-be-confirmed record and a timestamp. And sending a service message containing the service attribute parameters to a receiving end. When an ownership right reverse search request of a receiving end is received, the system calculates an effective time interval based on current time and a preset time delay threshold value, and screens out a target to-be-confirmed record which is matched with a service attribute parameter and generates a timestamp in the interval. And finally, extracting the unique serial number from the target record, generating a confirmation receipt and sending the confirmation receipt to a receiving end system. The method is used for reducing the calculation overhead and false alarm rate of accurately positioning specific abnormal transactions in massive logs by a system, and improving the confidence and timeliness of an automatic traceability result.

The invention provides an aircraft system key parameter anomaly detection method and system based on an auto-encoder, and belongs to the field of aviation fault analysis. The method comprises the following steps: S1, preprocessing key parameters of an aircraft system, and dividing the key parameters into a training set, a verification set and a test set; s2, constructing abnormal data for simulating different forms of key parameter anomalies of the aircraft system; s3, implanting a plurality of anomalies; s4, constructing a composite loss function and training the auto-encoder; and S5, determining an exception threshold and performing exception detection, adaptively determining an exception judgment threshold based on a reconstruction residual statistical result of the verification set sample, and performing exception detection on the test set sample according to the exception judgment threshold. According to the method, the accuracy and robustness of unknown anomaly detection can be remarkably improved, the false alarm rate caused by noise and slight disturbance is reduced, and the method is suitable for high-dimensional aircraft system key parameter monitoring and anomaly early warning of aircraft environment control, flight control, hydraulic and other key systems.

The invention discloses an intelligent detection method and system for a flexible circuit board based on machine vision, and relates to the field of circuit board detection.The method comprises the steps that firstly, through a global coarse registration step, rigid transformation such as translation, rotation and zooming of the whole to-be-detected FPC image is rapidly corrected, and preliminary alignment with a standard template is achieved; then, local fine registration is introduced for nonlinear deformation such as local stretching and wrinkles caused by the characteristics of the FPC material, so that the fine and non-uniform local dislocation is accurately sensed and compensated, and nearly pixel-level accurate matching between the to-be-detected image and the template is realized. On the basis of high-precision alignment, differential attention deficit segmentation is carried out, so that false differences caused by inaccurate deformation registration can be effectively eliminated, real defects can be accurately identified, the false alarm rate is remarkably reduced, and the reliability and practicability of a detection scheme are greatly improved.

The invention belongs to the technical field of numerical control machine tool machining, and particularly relates to a numerical control boring and milling tool cutting force intelligent monitoring method and system, and the method comprises the steps: obtaining a real-time main force, a real-time transverse force, a real-time longitudinal force sequence, a real-time main shaft rotating speed, a signal sampling frequency and a basic statistical threshold value in a machining task of numerical control boring and milling equipment; obtaining characteristic force; calculating a historical mean value at the current moment, and recording the historical mean value as a historical mean value; calculating a current historical deviation, and determining the width of a sliding window; calculating an instantaneous deviation; obtaining the change amplitude of the characteristic force at the current moment, the severity of the change trend deviating from the historical normal state, and the maximum value of the change amplitude and the severity to obtain a risk index; and comparing whether the risk index is abnormal or not according to the basic statistical threshold, and generating a processing process digital file. The technical problems of high false alarm rate and poor reliability caused by incapability of adapting to normal wear of the cutter in the prior art are solved.

The invention discloses a power load event detection algorithm based on an adaptive window model, and belongs to the field of power detection, and the algorithm comprises the steps: collecting power load total power time sequence data, carrying out the preprocessing, carrying out the rough detection of the preprocessed data, positioning all suspected event points, constructing a composite feature vector, and screening the suspected event points. And eliminating misinformation and repeated event points, and outputting a final power load event detection result. According to the method, the missing detection rate and the false detection rate are greatly reduced, the high recall rate is achieved through the dynamic threshold value and the improved CUSUM algorithm in the coarse detection stage, the load event detection accuracy is improved through composite feature vector and mahalanobis distance screening in the fine detection stage, the false detection rate is reduced, and the method can adapt to different types of loads and different sampling frequency data.

The embodiment of the invention relates to the technical field of information, and discloses an interface verification and test method and device based on a large model, a medium and a product, and the method comprises the steps: obtaining a to-be-tested interface document, analyzing the interface document through a first large model, constructing a test request parameter based on a request parameter definition, acquiring a real-time response message by calling a real interface; performing semantic comparison on a response example in the document structure information and the real-time response message by using the second large model to obtain a corrected interface document; comparing the parameter constraint condition in the corrected interface document with the verification logic in the business logic code snippet by using a third model to generate a logic verification passing result; and on the basis of the corrected interface document and a logic verification passing result, a Mock rule and an automatic test case script are generated by utilizing the fourth model, so that the maintenance cost and the false alarm rate of the automatic test script are reduced, and the automatic test efficiency is improved.

The invention discloses an abnormal Transform model-based electricity larceny detection method and device and a storage medium, and relates to the field of electric power system electricity larceny detection, and the abnormal Transform model-based electricity larceny detection method comprises the following steps: capturing complex correlation among different time points in sequence data by using a self-attention mechanism of a Transform model; the characteristics of the normal time point and the abnormal time point are effectively distinguished by learning the relevance difference of each time point, the accuracy of electricity stealing user identification is improved, and the misjudgment rate is reduced. The method comprises the following steps: S1, obtaining user multi-dimensional power consumption time series data, preprocessing the data, and constructing a sliding window sample; s2, constructing an electricity larceny detection model based on an Angle-Attention mechanism, wherein the electricity larceny detection model comprises a prior correlation branch and a sequence correlation branch; s3, quantifying the time dependence deviation of the power consumption behavior by taking the relevance difference as an anomaly judgment standard; s4, training the model by using a minimax optimization strategy, and amplifying the relevance difference between normal electricity utilization and electricity stealing electricity utilization; and S5, performing electricity larceny risk prediction and model performance evaluation through the joint score of the relevance difference and the reconstruction error.

The invention discloses a power grid data management intelligent checking method and system based on four-dimensional cross validation. The method comprises the following steps: acquiring data from each service system of a power grid and preprocessing to generate a to-be-verified data set; performing value, rule, relation and attribute dimension verification on the to-be-verified data set in sequence; summarizing four-dimensional verification results of value-rule-relationship-attribute and dividing anomaly levels to generate an anomaly diagnosis report; and based on feedback data of an abnormal diagnosis result and an actual rectification result in the report, mining an association rule to expand a rule base, dynamically adjusting the weight of the rule in the rule base, and detecting and eliminating a failure rule. According to the method, full-dimension anomaly recognition can be achieved, complex scenes can be covered, the false alarm rate is effectively reduced, design rules are self-optimized, a rule base can be autonomously evolved, manual intervention does not need to be completely relied on, the rule updating period is greatly shortened, and dynamic changes of power grid services and data can be rapidly responded.

The invention relates to the field of cutter detection, in particular to a numerical control machine tool cutter wear identification method and system, and the method comprises the steps: obtaining a cutter image, and extracting a main body surface region of a cutter; calculating the micro texture entropy energy of the main body surface area, wherein the micro texture entropy energy is used for representing the texture complexity of the tool surface; calculating a macroscopic defect index of the surface area of the main body, wherein the macroscopic defect index is used for representing the geometric deviation degree of the edge contour of the cutter; calculating an environment interference coefficient based on the highlight area proportion and gray level distribution characteristics of the main body surface area; determining an adaptive fusion weight of the microscopic features based on the environmental interference coefficient, and performing weighted fusion on the microscopic texture entropy energy and the macroscopic defect index according to the adaptive fusion weight to obtain a wear state value of the tool; and identifying whether the cutter is worn or not by using the wear state value. According to the invention, the false alarm rate in the tool wear identification process is reduced, and the accuracy of the identification result is improved.

The invention discloses a mail anti-leakage detection system and method based on a large language model, and relates to the technical field of information security, and the system comprises a collection module which is used for obtaining a to-be-detected target mail, and the to-be-detected target mail comprises a mail header, a mail body, an attachment and related metadata; the preprocessing module is used for preprocessing the target mail; the large model analysis module is used for performing deep semantic analysis, including semantic understanding, sensitive entity recognition, communication intention recognition and context association analysis, on the preprocessed target mail based on a large language model; the risk assessment module is used for determining the risk level of the target mail based on the large model analysis result; and the processing execution module is used for executing corresponding processing operation according to the risk level of the target mail and the processing instruction. According to the method, the recognition accuracy of the mail anti-leakage system on complex semantic sensitive information is improved, the detection capability on multiple types of attachments is enhanced, dynamic risk assessment is realized, and the false alarm rate and the missing report rate are reduced.

The invention relates to the technical field of steam turbine shafting vibration monitoring, and discloses a steam turbine shafting vibration intelligent diagnosis and edge early warning method based on multi-source data fusion, and the method comprises the following steps: deploying multiple types of sensors at key measurement points of a steam turbine shafting, and constructing a monitoring network with complementary spatial dimensions; according to the method, multiple types of sensors are deployed at key measuring points to collect multi-source data, time sequence and frequency domain features are extracted, feature layer fusion is carried out, the weight is dynamically adjusted in combination with real-time working conditions, the limitation of a single data source is broken through, and the shafting state is comprehensively reflected; local real-time diagnosis is realized by deploying a lightweight model through edge nodes, and meanwhile, the model is updated by incremental learning to improve the generalization ability of complex working conditions, so that the problem of high cloud processing delay is solved, and the real-time early warning requirement is met; the false alarm rate is reduced by combining a dynamic threshold adjustment mechanism with equipment health baseline and real-time working condition deviation correction, and performing graded early warning on the overrun amplitude and confidence of a reference vibration predicted value.

The invention relates to the technical field of databases, in particular to a database circulation track auditing method based on multi-source data fusion. The method comprises the following steps: collecting multi-source data of different logs, preprocessing to obtain preprocessed multi-source data, extracting key features of the preprocessed multi-source data, and serially connecting the key features according to a time sequence to generate a database circulation track; collecting operation behavior data of a user, calculating a mean value and a standard deviation corresponding to the operation behavior data to determine a normal range and generate an initial baseline, and designing an automatic trigger updating mechanism to dynamically update the initial baseline to generate a dynamic baseline; and calculating the deviation degree between the actual value of each operation behavior dimension and the dynamic baseline, carrying out weighted summation to calculate a total risk value, comparing the total risk value with a risk threshold, and classifying the total risk value into low risk, middle risk and high risk. According to the invention, the false alarm rate can be reduced, and multiple times of missing report of few hidden anomalies can be avoided; the auditing resources can be focused on the high risk preferentially, manual intervention of low-risk abnormity is reduced, and compared with single-dimension grading, the auditing efficiency is improved.

The invention relates to a cross-language software vulnerability detection method and device, and the method comprises the steps: carrying out the analysis of a Joern static analysis pair, carrying out the integration and semantic enhancement of an abstract syntax tree, a control flow graph and a data dependence graph, and obtaining a cross-warehouse heterogeneous code graph; obtaining cross-language intermediate representation based on a compiler framework; after the cross-language intermediate representation and the cross-warehouse heterogeneous code graph are modeled, weighted fusion is carried out through a gated cross attention mechanism, and a multi-modal data set is obtained; carrying out migration training on the multi-modal cross-language vulnerability detection model, and carrying out vulnerability detection on cross-language software to obtain a detection result; through multi-modal data fusion and modeling, in combination with cross-language intermediate representation and a cross-warehouse heterogeneous code graph, the defects of a traditional method in the aspects of cross-language generalization ability and context reasoning ability are effectively overcome; the method has the advantages that the generalization ability of cross-language vulnerability detection is improved, the false alarm rate and the missing report rate are reduced, and the comprehensive utilization effect of global structure information is enhanced.

Systems and processes are disclosed for a multi-level quantum-based vertically classified entropy exploratory analytics tool designed to improve speed, accuracy, and scalability in anomaly detection and data analysis. The tool employs a dynamic algorithm selector for adaptive algorithm choice, a quantum encoder for precise data encoding, and a multi-level splitter and aggregator for efficient data segmentation and result integration. It includes a classification executor for accurate decision-making, an exploratory data analyzer for uncovering hidden patterns, and a multi-dimensional data processor for handling complex data sets. A qubit selector optimizes quantum resource allocation. The tool combines classical and quantum computing methods, enhancing robustness and versatility. This system significantly reduces false positive rates and improves processing efficiency, addressing the limitations of classical methods in handling large-scale, multi-dimensional data sets. The invention is particularly valuable for applications requiring rapid and precise data analysis, such as finance, cybersecurity, and scientific research.

The invention provides an elastic monitoring alarm method and system based on multistage cooperative verification. The method comprises the step of remarkably improving the alarm accuracy through a three-level verification mechanism. The first-stage verification adopts intelligent anomaly detection, compares a current index with a historical baseline, and identifies a preliminary anomaly; in the second-stage verification, association index analysis is introduced, and pseudo anomalies caused by normal business fluctuation are filtered; and the third-stage verification performs cross-customer collaborative analysis to distinguish individual anomaly from global events. In addition, fault root causes are automatically positioned through dependency link analysis, and structured alarm information containing causes, confidence coefficients and processing suggestions is generated. And a closed-loop learning mechanism is established, operation and maintenance feedback is continuously collected, and alarm parameters are automatically adjusted by adopting Bayesian optimization. According to the method, the false alarm rate is reduced, the root cause positioning accuracy is improved, the average repair time is shortened, and the IT operation and maintenance efficiency is remarkably improved.

The invention discloses an ALB sounding point cloud filtering and recovering method for complex terrains such as a submarine abrupt slope, belongs to the technical field of data processing, and solves the problems that the I-class misjudgment rate is high, topographic features are smoothed or weakened and effective point clouds at the submarine abrupt slope are easily filtered out excessively in an existing method. The method comprises the steps of performing sea surface elevation estimation and separation processing on original point cloud, processing underwater point cloud based on a self-adaptive morphology water body filtering method, introducing a recovery mechanism based on hyperboloid fitting, recovering abrupt slope area point cloud misjudged as a water body noisy point through residual analysis, and obtaining a water body noisy point. According to the method, a fine processing strategy of filtering first and then recovering is adopted, and water body noisy points are accurately removed through self-adaptive morphological filtering in the earlier stage in combination with grid reference surface and spatial multi-dimensional binary tree index neighborhood analysis. A recovery mechanism based on hyperboloid fitting is introduced, and the abrupt slope area point cloud misjudged as the noisy point is recovered through residual analysis. And the misjudgment rate in a submarine abrupt slope area is effectively reduced.

The invention provides a vulnerability early warning method and system based on asset fingerprint driving. The method comprises the following steps: acquiring vulnerability information data from a plurality of vulnerability information sources; asset fingerprint information of assets in the target system is collected; based on semantic analysis, version comparison and a fingerprint matching algorithm, performing fine-grained association matching on the vulnerability information data and asset fingerprint information, and identifying target assets affected by vulnerabilities; performing influence range analysis, risk level evaluation and repair priority ranking on the target assets to generate a vulnerability influence analysis report; based on the vulnerability impact analysis report, sending an early warning notification to a related person in charge, and automatically generating an electronic work order; the processing state of the electronic work order is tracked, the repair progress is recorded, and full-life-cycle closed-loop management of vulnerabilities from recognition to repair is achieved. The missing report rate and the false report rate are effectively reduced, and compared with manual matching, the full-automatic mode can form a complete closed loop, and the early warning efficiency is improved.

The invention relates to an electric meter box temperature rise abnormity detection method and detection system based on multi-terminal aggregation in the technical field of power distribution safety monitoring. The electric meter box temperature rise anomaly detection method comprises the following steps: establishing a single-end baseline model of an ith terminal about temperature-current; acquiring electric power parameters of I electricity meters in the electricity meter box and terminal temperatures Tt, i corresponding to I terminals, and obtaining a steady-state temperature rise baseline temperature at the moment t in the kth detection period based on a single-end baseline model; according to the method, through aggregation analysis and correlation modeling of multi-terminal data in the electric meter box, the local anomaly and the coupling effect between groups can be identified, the accuracy and robustness of temperature rise detection are remarkably improved, meanwhile, a three-layer constraint judgment system is constructed, and the accuracy and robustness of temperature rise detection are improved. Through a hierarchical judgment mechanism of statistical constraint, physical constraint and group constraint, data statistical characteristics and physical correlation characteristics are comprehensively considered, false alarm and missing alarm caused by single-factor fluctuation can be effectively reduced, and the false alarm rate is reduced.

The invention provides a postoperative patient supervision system for neural interventional therapy, and relates to the technical field of medical big data and artificial intelligence, and the system comprises a multi-modal data collection center, an interference feature decoupling unit, a trust capital quantification unit, a game strategy arbitration unit and a supervision execution unit. The multi-modal data acquisition center is configured to call time sequence monitoring data of a monitoring object; the trust capital quantification unit performs trust loss evaluation analysis on historical interaction feedback data; and the game strategy arbitration unit is configured to perform comparative analysis on the current clinical trust capital index and a preset trust threshold. According to the system, time-frequency domain matching is carried out on the residual error sequence and behavior state marking data, it is ensured that the system only carries out risk evolution prediction on neurogenic hemodynamic changes, and therefore the false alarm rate caused by external interference in a complex postoperative monitoring environment is remarkably reduced, and pure pathological feature components are extracted.

The invention relates to a malicious software detection method based on multi-feature fusion and interpretability analysis. According to the technical scheme, static features are extracted through'macroscopic-microscopic 'double paths, and a dynamic behavior knowledge graph is constructed in combination with sandbox monitoring, stain analysis and semantic abstraction and converted into feature vectors through graph embedding; multi-modal feature deep interaction and accurate classification are realized through a double-end cross attention-triple fusion-deep classification architecture; a hierarchical interpretable framework is constructed based on cross-modal causal alignment, case reasoning and anti-fact analysis, and a complete decision evidence chain is generated. The method has the advantages that comprehensive representation of the form, semantics and intention of malicious software is achieved, the detection precision and robustness are remarkably improved, meanwhile, the'black box 'dilemma of the model is solved, credible explanation is provided for security analysis, the method is suitable for complex network threat detection scenes, and it is verified through experiments that the method has good application prospects. According to the method, the detection rate of the Windows malicious software is remarkably increased, and the false alarm rate is effectively reduced.

The invention provides an intelligent underground space ground surface settlement remote sensing identification and evaluation method, and aims to solve the problems of low monitoring efficiency, insufficient precision and lack of comprehensive risk evaluation in the prior art. Multi-scale analysis space-time filtering is adopted to suppress the atmospheric phase; then constructing an entropy weight-principal component analysis fusion model to realize multi-source feature collaborative fusion, and applying a convolutional neural network (MSA-CNN) introduced with a multi-scale space attention module to intelligently identify a settlement area and optimize a boundary; and finally, quantifying a risk level based on an improved entropy weight-fuzzy comprehensive evaluation model, carrying out trend prediction and early warning by adopting an attention-enhanced LSTM model, and integrating a result to a WebGIS platform. According to the invention, full-process automatic monitoring is realized, the false alarm rate is reduced by more than 20%, and scientific decision support is provided for safety management of underground engineering.

The invention discloses an abnormal traffic detection method for an electric power communication network, relates to the technical field of traffic anomaly detection, and solves the technical problems that time sequence feature extraction lacks scene adaptation and traceability and grading treatment are insufficient in pertinence. The method adapts to flow fluctuation characteristics of peak / valley power consumption, rejects invalid bytes, standardizes unstructured load data and time sequence characteristics for an electric power protocol frame structure, more fits an actual scene of electric power communication, provides a reliable data basis for subsequent anomaly recognition, adopts a hard threshold and elastic threshold dual-judgment mechanism, and improves the accuracy and reliability of the power protocol frame structure. By combining multi-feature cooperative verification and continuous window verification, clear anomaly exceeding a baseline is covered, suspicious traffic which does not exceed the baseline but has abrupt change and fluctuation anomaly is captured, instantaneous interference is eliminated by relieving trend judgment, and the false alarm rate and the missing report rate are reduced.

This application provides a method, program product, electronic device, and storage medium for attributing anomalies in business metrics, applied in the field of business processing technology. The method includes: receiving a business question regarding anomalies in business metrics; analyzing the business question to determine core parameters for attribution analysis; retrieving business data and / or non-business data associated with the business metrics based on the core parameters to form an analysis context; and generating a target attribution conclusion for the business metric anomaly by reasoning through multiple collaborative agents and a large model based on the analysis context. The method provided in this application can improve the efficiency of attributing anomalies in business metrics, reduce the false positive rate of attributing anomalies in business metrics, and improve the coverage of attribution of anomalies in business metrics.

This invention provides a germline mutation detection method and system with a low false positive rate. The system is computer-executed and includes: first, performing a PCR repeat cluster consistency test on sequence alignment files generated from high-throughput sequencing reads, down-regulating the base count weights of inconsistent sites within the cluster; then, based on the sample-specific background error baseline, calculating the variation confidence index of each genomic site using an empirical Bayesian framework to obtain candidate single nucleotide variants (SNPs); obtaining candidate insertion / deletion variants through read clustering and physical verification of insertion fragment lengths; subsequently, performing a dual-engine cross-feedback iteration on the two candidate types until convergence, integrating and filtering, and outputting a structured mutation detection report. This invention significantly reduces the false positive rate of both SNPs and insertion / deletion variants while maintaining sensitivity, and improves the detection capability of complex insertion / deletion variants.

The application provides a closed loop process monitoring method based on improved dynamic latent variable analysis, and relates to the technical field of industrial process monitoring, and specifically comprises the following steps: collecting a section of sensor measurement data under normal working conditions of an industrial process as training data; calculating the neighborhood weight matrix of input data and output data respectively; establishing an improved dynamic latent variable analysis model to obtain the weight matrix and load matrix of input and output; constructing the feature matrix of input and output, calculating the covariance matrix and the control limit of the reduced rank Mahalanobis distance index; collecting test data, calculating the feature vector of input and output by using the projection direction matrix, calculating the reduced rank Mahalanobis distance index, and comparing with the control limit to judge whether a fault occurs or not. The technical scheme of the application overcomes the problem in the prior art that the detection method based on the open loop assumption design is difficult to effectively distinguish process faults, interference changes and normal adjustment behaviors of the controller, thereby causing the problems of missed report or false positive rate increase.

The invention discloses an intelligent micro-grid network attack detection method and system based on a block chain, wavelet transform and a support vector machine, a medium and a processor, and relates to the field of power grid network attack detection. The method aims at solving the problems that normal and attack exception are difficult to distinguish, the missing and false detection rate is high, and data are tampered easily in a traditional method. The method comprises the following steps: collecting and preprocessing DC micro-grid data; decomposing into high and low frequency components through wavelet transform, and extracting amplitude, frequency and energy related characteristic parameters; constructing and training a support vector machine model for real-time attack detection; and after detecting full-process data hash processing, uploading the data to the block chain, and storing evidence based on a PoA consensus mechanism. According to the method, attack features are accurately captured through wavelet transformation, high-accuracy classification is realized in combination with the SVM, the block chain guarantees data credibility, a micro-grid dynamic scene can be quickly responded, and the network security protection capability is improved.

Provided is a method for screening for an aptamer by sequencing. The method comprises: performing sequencing on a plurality of candidate aptamers linked to the surface of a solid-phase carrier to obta

The present application relates to the technical field of mine safety monitoring and geophysical exploration, in particular to a mine water disaster intelligent alarm system responding to multi-dimensional physical field parameter anomaly, comprising: a full-space data acquisition module: obtaining monitoring area observation data and calling background geological physical model; an ideal benchmark reconstruction module: constructing an ideal physical field benchmark under an anomaly-free state; a double-difference extraction module: calculating observation residuals of observation data relative to the benchmark and theoretical residuals of simulation responses relative to the benchmark; an inversion coupling verification module: driving the theoretical residuals to approximate the observation residuals and extracting parameter sensitivity characteristics; an intelligent alarm decision module: analyzing sensitivity characteristics and convergence state; determining as entity fluid anomaly characteristics to generate an alarm instruction; determining as non-target anomaly interference characteristics to execute signal suppression processing; the present application greatly reduces the false positive rate of mine water disaster detection.