436 results about "Fingerprint database" patented technology

The invention particularly relates to a water pollution intelligent traceability system based on a three-dimensional fluorescence spectrum, which relates to the technical field of environmental monitoring, and comprises a water sample collection and three-dimensional fluorescence data acquisition module, a data preprocessing and PARAFAC component decomposition module, a multi-source pollution characteristic fingerprint database module and an intelligent comparison and pollution source analysis module. According to the method, standard acquisition and preprocessing are adopted, and the original data quality is ensured by means of scattering rejection, instrument correction and the like; pure fluorescence components are decomposed through a PARAFAC algorithm, and feature extraction reliability is guaranteed through triple verification. In the comparison link, spectrum similarity, concentration proportion difference and space-time hydrological constraint multi-round screening are adopted, and irrelevant pollution sources are removed; and then the contribution proportion is quantified, and Monte Carlo simulation, water quality and specific pollutant cross validation are carried out, so that the crossing from suspected tracing to quantitative responsibility investigation is realized.

The invention relates to the technical field of power plant data processing, in particular to a virtual power plant integrated management system, which comprises a data acquisition module for acquiring voltage fluctuation characteristics and power climbing rate parameters of energy equipment in real time; the equipment fingerprint database construction module generates an equipment fingerprint code comprising a dynamic response time-lag coefficient and an adjustment margin quantized value; the dynamic clustering module is used for dividing simulation sub-models of regional topology constraints based on the geographic position of the equipment and a power regulation threshold value; the multi-time scale prediction module is used for generating source load prediction data by fusing meteorological parameters through a space-time diagram convolutional network; the digital twin simulation module outputs an energy storage charging and discharging threshold value and a demand response priority strategy; and the cross-domain collaboration module adopts a double-chain block chain architecture to realize security verification and data integrity binding of federated learning feature parameters. According to the invention, through equipment characteristic digital modeling, regional co-simulation and multi-energy flow coupling regulation and control, the resource aggregation precision and response real-time performance in a new energy strong fluctuation scene are improved.

The invention discloses an environment-adaptive transformer oil sample Raman spectrum detection method and related equipment, and relates to the field of optical sensing systems. The method comprises the following steps: collecting oil sample Raman spectrums and environmental parameters in multiple operation scenes, and constructing a multi-scene spectrum characteristic model and a standard fingerprint database; pre-processing and denoising parameters are adaptively set based on the environmental perception vector, and baseline correction and joint denoising are carried out on the original spectrum; scene discrimination is carried out by fusing the characteristics of peak position, peak height, peak width, integral area and the like, a scene-related component standard spectrum dictionary is generated, and the concentration and confidence of each target component are obtained by adopting constrained spectral line unmixing and quantitative calibration; and driving the fingerprint database and the model to update in combination with quality control indexes such as spectral shape relevancy and residual errors and a drift detection result. The system is composed of a Raman spectrum acquisition module, an environment monitoring module and a data processing module, and can improve the robustness and quantitative precision of Raman detection of transformer oil in a complex environment.

The invention belongs to the technical field of fault early warning, and discloses a heater fault early warning method and system based on current analysis. Comprising the steps that transient current signals of all heating wire loops in the copper bush heater are collected in real time, harmonic characteristic parameters are extracted, and a dynamic current fingerprint database and a multi-dimensional harmonic characteristic spectrum are generated respectively; dynamically calculating the contact impedance between each heating wire and the copper sleeve groove according to the dynamic current fingerprint database and the multi-dimensional harmonic characteristic spectrum; the current distribution path of each heating wire in the copper sleeve groove is restored in real time, the abnormal contact form of each heating wire is identified, the falling risk probability and falling risk strength of each heating wire are dynamically predicted by fusing the falling precursor signals of each heating wire detected in real time, and a grading early warning strategy report is generated in real time; according to the invention, fault early warning can be converted into preventive maintenance from post-maintenance, the intelligent level of operation and maintenance of the heater is improved, and the continuity and safety of production are guaranteed.

The invention discloses a data security monitoring and early warning method based on the Internet of Things, and the method comprises the steps: obtaining the real-time operation characteristics of target Internet of Things equipment, and constructing an equipment behavior fingerprint database based on the real-time operation characteristics; constructing a quintuple ontology model based on the equipment behavior fingerprint database, mapping the general tactical framework to an Internet of Things equipment operation scene according to the quintuple ontology model, and generating a cross-domain tactical association rule database; analyzing the multi-source interaction data according to the cross-domain tactical association rule base, and constructing an attack path atlas according to the obtained multi-source interaction features; inputting the attack path atlas into a pre-constructed graph neural network model to obtain a multi-stage attack chain, identifying a persistent threat attack chain in the multi-stage attack chain, and calculating a confidence score of the persistent threat attack chain; and performing anomaly detection on the original network traffic data based on the confidence score, and generating an anonymized early warning analysis report. According to the method provided by the invention, the anomaly detection accuracy of the Internet of Things data is improved.

The invention discloses a high-precision positioning method based on combination of a Beidou satellite and 5G-Mesh, and relates to the technical field of satellite positioning, and the method comprises the steps: receiving a navigation signal of the Beidou satellite and a communication signal of a 5G-Mesh network in real time; the topological structure change of the 5G-Mesh network is monitored in real time, and the connection relation between the nodes is dynamically adjusted in combination with network performance indexes; the receiver performs pseudo-range measurement on the signals of the visible satellites; a navigation message is demodulated from the received satellite signal, and the accurate position of each satellite at the current moment is calculated by using a satellite orbit calculation model; constructing a signal intensity fingerprint database; outputting a reference position having the highest similarity with the current signal strength characteristic to carry out error modeling on the Beidou satellite signal and the 5G-Mesh network signal; and fusing the two kinds of position information by adopting a weighted fusion algorithm. By fusing the technical advantages of the two positioning technologies, the positioning precision and reliability are improved, and the requirement for high-precision positioning in a complex scene is met.

The invention relates to the technical field of injection molding production process monitoring, and discloses an injection molding production monitoring method, device and system.The injection molding production monitoring method comprises the steps that multi-source sensing data is collected, and pressure time sequence data and material spectrum data in the injection molding process are collected; extracting pressure waveform features to obtain a complete pressure waveform feature set; performing material characteristic analysis, and forming a material characteristic fingerprint in combination with the pressure waveform characteristics and the spectral data; material batch similarity calculation: constructing a material batch fingerprint database, and calculating the similarity between the new batch of materials and the historical batch of materials; process parameter optimization: continuously improving the accuracy of parameter optimization through multi-objective tradeoff optimization and parameter verification and iterative optimization; quality problem root causes are analyzed, and material problems and process parameter problems are accurately distinguished; through fusion of pressure waveform analysis and a material characteristic monitoring technology, comprehensive monitoring and intelligent regulation and control of an injection molding production process are realized.

The invention provides a chemical ship cabin intelligent coating leakage positioning and early warning control method and system, and relates to the technical field of intelligent monitoring, and the method comprises the steps: collecting data through a multi-class sensor array, constructing a parameter distribution map, extracting features through a convolutional neural network, carrying out the time sequence analysis through a long and short term memory network, and carrying out the early warning. The variational auto-encoder constructs a leakage feature fingerprint database, collects data in real time, then carries out feature extraction and fusion processing, matches the data with the fingerprint database, determines a leakage position and a leakage rate, divides a monitoring area, and executes prevention and control. The leakage monitoring precision and the response speed can be improved, and the chemical accident risk is reduced.

The invention discloses a method for tracing polluted water quality of a water outlet of a park based on Bayesian reasoning, and belongs to the technical field of data processing and management systems.The method comprises the steps that production management data of enterprises in the park is acquired to generate enterprise pollution feature vectors, and the enterprise pollution feature vectors are associated with enterprise geographic coordinates to construct a dynamic pollution fingerprint database; and acquiring water quality parameters of a water outlet monitoring point in real time, inputting the water quality parameters into the Bayesian probability model, calculating to obtain basic pollution probability distribution, calling the dynamic pollution fingerprint database and the pipe network topology coefficient to carry out space-time correction, generating a traceability probability matrix, and determining and outputting a target pollution enterprise list. According to the invention, production management data of an enterprise, real-time water quality monitoring data of a water outlet and pipe network physical constraint conditions are deeply fused, a probabilistic reasoning model is adopted for intelligent decision making, accurate and efficient management and positioning of a pollution source can be realized, compliance evidences are automatically generated, and the method is suitable for large-scale popularization and application. Therefore, the efficiency and the intelligent level of park environment management are improved.

The invention discloses a solid waste-based zinc chloride preparation process control method and system, and relates to the technical field of zinc chloride preparation, and the method comprises the following steps: obtaining a unified time base line, establishing a bubble phase reference field, collecting a sound-induced echo sequence, a micro-pressure waveform sequence and a light scattering sequence of a reaction liquid under the bubble phase reference field, and calculating the solid waste-based zinc chloride preparation process. Fusing to generate a bubble density space-time matrix; and performing reversible time-frequency rearrangement based on the bubble density space-time matrix, decomposing to obtain different frequency band distributions, extracting a narrow-band resident bubble group, calculating critical volume distribution and collapse precursor indexes, and constructing a dynamic risk fingerprint database. According to the method, a unified time base line and a bubble phase reference field are constructed, the bubble state is perceived in real time, collapse forebodes are recognized, a dynamic risk fingerprint database is established, directional intervention and reverse energy channel collapse nucleus extinguishing are achieved in combination with an energy aggregation node, finally closed-loop regulation is constructed through calibration write-back, the safety and stability of zinc chloride preparation are improved, and the method is suitable for large-scale popularization and application. And efficient utilization and clean production of solid waste are promoted.

The invention relates to an open source component multi-modal dependency risk tracing method and device, and the method comprises the steps: employing a mode of combining static analysis and dynamic analysis to analyze a component dependency relationship of software, and combining AST analysis and construction script analysis; function-level features are extracted, a binary fingerprint database is constructed, the similarity between different versions is analyzed through LSH, behavior patterns in binary codes are analyzed, and hidden dependencies or malicious code injection is detected; the version updating history of the dependent component is analyzed and monitored by using a time sequence, and the vulnerability security of the component is evaluated in combination with attack graph analysis; high-risk components on the path are calculated, potential supply chain attack points are identified, and risk points are subjected to cross validation; a time sequence diagram database is used for recording the component dependency relationship, and time backtracking query is supported. According to the method, a multi-mode dependency analysis method is adopted, potential dependency risks are rapidly identified, and potential supply chain attack risks are timely warned.

The invention relates to a human body sensing detection method and system based on machine learning, and the method comprises the steps: extracting target micro-motion change, space trajectory and time sequence features through the collection and high-precision marking of original time-space behavior data of millimeter wave, infrared, pressure and sound multi-type sensors in combination with preprocessing, noise removal and normalization standards, and carrying out the detection of a human body. A human body space-time behavior fingerprint database is constructed in an off-line or on-line state through a machine learning algorithm, and regional difference behavior characteristic models are respectively established for different space regions and installation materials. And automatically triggering multiple rounds of redundancy detection and learning optimization. The scheme reduces the false alarm rate of the system and supports continuous evolution of the model.

The invention relates to the technical field of distribution network unmanned aerial vehicle autonomous tour-inspection navigation, in particular to a distribution network unmanned aerial vehicle tour-inspection autonomous navigation method based on GPS information and visual information, which comprises the following steps: acquiring a GPS and smoothing projection coordinates, constructing a Thiessen region random index, optimizing a route and acquiring a flight image, evaluating a risk and adjusting exposure parameters, and generating a light environment value. According to the method, an environment model is constructed by fusing GPS information, Gaussian projection is adopted to eliminate earth curvature errors, moving average filtering is adopted to suppress signal noise, Thiessen polygon is utilized to quantify routing inspection point and obstacle distribution, a tornado optimization algorithm is combined to generate an optimal route, the shortest path without repetition and obstacle avoidance are realized, and obstacles are identified based on a YOLOv12 model. A risk avoiding strategy is made according to the risk value, a route is dynamically optimized, a light environment fingerprint database is constructed, illumination change is predicted in combination with LSTM, aperture shutter sensitivity and white balance are adjusted in a self-adaptive mode, image brightness and definition are optimized, and inspection efficiency and data reliability are improved.

The invention discloses a visible light indoor adaptive positioning method fusing self-attention and a width learning network. The method specifically comprises the following steps: step 1, collecting pose information of received signal strength RSS and PD at different indoor space reference points; step 2, constructing a multi-dimensional feature fingerprint database containing reference point coordinates, RSS values and pose information; step 3, constructing an SA-BLS positioning model and carrying out training; 4, testing the positioning model trained in the step 3; and step 5, inputting the RSS and PD pose data at the target point to be positioned into the tested positioning model, calculating the coordinates of the target position, and outputting a positioning result. According to the invention, the problem that the existing positioning method is poor in precision due to the fact that the receiving end of the existing visible light positioning is in a fixed pose and the pose of the receiving end is dynamic and the RSS obviously fluctuates in practical application in the prior art is solved.

The invention relates to the technical field of wireless positioning, and provides a high-speed rail user positioning method and system based on trajectory compensation, and the method comprises the steps: recognizing high-speed rail users, carrying out the data preprocessing, constructing high-speed rail grids, and generating a signal fingerprint database; preliminarily screening out a plurality of candidate grids according to RSRP and TA signal features reported by high-speed rail users in real time; based on the service cell switching sequence, a hidden Markov model or particle filtering is adopted for motion trail modeling, and the advancing direction and speed of a train are recognized; introducing a multi-source information fusion and grid compensation strategy; and selecting a final positioning result from the candidate grids in a weighted voting mode, and outputting grid IDs and latitude and longitude coordinates. Through multi-source data fusion, track compensation and intelligent decision making, the positioning precision and reliability of the high-speed rail user in a complex mobile environment are remarkably improved, and the method has important technical popularization value.

The invention relates to the technical field of data processing, and discloses an advertisement putting state real-time monitoring method and system. The method comprises the following steps: collecting an advertisement putting event stream, and extracting an instantaneous index and a fluctuation rate; counting historical abnormal conduction time delay and intensity, and constructing a conduction matrix; when single-point abnormity is detected, downstream abnormity is predicted according to the conduction matrix, the deviation degree is calculated, and an abnormal conduction chain is searched; conducting chain features are extracted to form abnormal fingerprints, fingerprint database similarity is matched, high similarity is directly confirmed, and low similarity is injected into probe flow verification. By constructing the real-time monitoring method based on the abnormal conduction matrix, the problems that in the prior art, the conduction relation between indexes cannot be quantified, the downstream abnormal moment cannot be predicted, the false report and missing report rate is high, and the abnormal confirmation response is slow are solved, and the accuracy, the real-time performance and the intelligent level of advertisement putting abnormal detection are improved.

The invention provides an antibacterial ceramic glaze sintering process optimization and intelligent temperature control method and system. According to the method, the molecular vibration characteristics of the antibacterial agent at different temperatures are dynamically obtained, and a thermal decomposition dynamic fingerprint database is constructed according to the attenuation law of the molecular vibration characteristics; meanwhile, a three-dimensional temperature field of the glaze layer is reconstructed based on a coupling relation between sound wave propagation parameters and glaze thermal expansion. And carrying out transfer learning on the fingerprint database and historical process data, predicting a glaze thermal stability attenuation parameter, and calculating a temperature gradient and molecular chain fracture compensation coefficient. And fusing the temperature field and the attenuation parameter, and dynamically regulating and controlling the sintering temperature curve and preserving heat by combining the compensation coefficient. The adjusted parameters generate closed-loop feedback by verifying the matching degree of the glaze microstructure and the antibacterial indexes, the compensation coefficient is corrected, and it is ensured that the antibacterial performance of the glaze is stabilized within the process allowance. The temperature control precision and the antibacterial performance stability of antibacterial ceramic glaze sintering are improved.

The invention relates to the technical field of intelligent monitoring of power distribution systems, and discloses an intelligent digital electric box power utilization monitoring management system based on the Internet of Things. The system comprises a data acquisition module, a data processing and analysis module, an intelligent control module, an Internet of Things communication module and a user interaction and management module. The data acquisition module acquires electrical and environmental parameters in real time; the data processing and analyzing module constructs an electricity utilization fingerprint database of the equipment, calculates a real-time threshold value through a dynamic threshold value engine, and realizes equipment-level anomaly recognition in combination with an anomaly detection engine; the intelligent control module executes a control action according to a detection result; the Internet of Things communication module realizes two-way data transmission; and the user interaction and management module provides a visual operation interface. According to the invention, through a dynamic threshold mechanism, equipment fingerprint identification and multi-level early warning control, equipment-level hidden faults are accurately identified, and the power utilization safety and the intelligent management level are improved.

The invention belongs to the technical field of network security, and particularly relates to a threat intelligence automatic extension method based on network surveying and mapping, which comprises the steps of automatically collecting and preprocessing multi-source threat intelligence by a large model, extracting key network identification information, generating standardized network asset characteristics, and obtaining a multi-dimensional asset characteristic matrix based on a network space surveying and mapping technology. Constructing a network asset fingerprint database, carrying out correlation verification, generating a set of verifiable network asset fingerprints, carrying out feature vectorization on the verifiable network asset fingerprints, carrying out homologous asset analysis through a weighted clustering algorithm, and outputting a threat entity topological graph comprising an IP address cluster, a domain name correlation group and a C2 infrastructure. According to the method, potential threats which are difficult to cover by traditional manual analysis can be automatically identified, an integrated multi-source threat intelligence platform and a network surveying and mapping tool are supported, diversified intelligence formats and feature requirements can be processed, the automation degree is high, threat paths can be rapidly expanded, and response time can be shortened.

The invention discloses a partial discharge signal multi-source cooperative detection method and system based on time-frequency fusion analysis, and relates to the technical field of multi-source cooperative detection, and the method comprises the following steps: obtaining partial discharge original signals of an electric signal channel and an ultrahigh frequency channel, respectively carrying out the time-frequency transformation, and generating a multi-source time-frequency characteristic spectrum; extracting high-frequency noise modes of different channels under a non-partial discharge condition based on the multi-source time-frequency characteristic spectrum, and constructing a cross-source interference fingerprint database; based on the cross-source interference fingerprint database, carrying out frequency-band-by-band difference mapping calculation on actually acquired multi-source time-frequency characteristics to generate a difference characteristic matrix; and introducing reverse consistency constraint into the differential feature matrix, carrying out weighted fusion to obtain multi-source coupling time-frequency feature mapping, and outputting a detection result of the partial discharge signal. According to the method, the multi-source time-frequency characteristic spectrum is constructed to carry out band-by-band differential mapping on the actual signal, so that cross-source noise interference is inhibited, and the accuracy and robustness of partial discharge detection in a strong noise environment are improved.

The invention relates to a circuit board defect identification method and system based on multi-dimensional image data, and belongs to the technical field of data identification processing, and the method comprises the following steps: obtaining synchronous image data of a circuit board to be detected in a plurality of imaging modes; performing space-spectrum joint registration on each modal image to generate a multi-dimensional image cube with a unified coordinate system and pixel alignment; inputting the multi-dimensional image cube into a pre-trained multi-branch heterogeneous fusion neural network; generating a pixel-level defect probability graph by utilizing a defect sensing context decoder, and performing geometric constraint optimization on the probability graph by combining prior information of a circuit board design layout; outputting defect types, positions and confidence coefficients, and establishing an interpretable defect fingerprint database according to the multi-dimensional response characteristics of the defects; the method has the beneficial effects that false defect signals generated by image noise and circuit board surface texture interference can be effectively inhibited, the omission ratio and the false detection ratio are greatly reduced, and pixel-level accurate defect positioning is realized.

The invention discloses a mobile phone lease risk intelligent early warning system based on multi-source data fusion, and relates to the technical field of mobile phone lease. Comprising a data acquisition module, an equipment fingerprint database module, a user credit assessment module, an equipment state monitoring module, a market environment analysis module, a cross-modal association analysis module, a three-dimensional risk assessment module, an early warning decision center and a dynamic confrontation training module. According to the mobile phone lease risk intelligent early warning system based on multi-source data fusion, omnibearing information including the user dimension, the equipment dimension and the market dimension is collected from multi-source data, a three-dimensional evaluation system of user credit, the equipment state and the market environment is established, and especially for the user dimension data, the risk of mobile phone lease is evaluated. According to the method, static data such as historical credit investigation and consumption records are collected, unstructured data such as real-time behavior tracks, social relations and APP use habits of users are also collected, risk factors of multiple dimensions are comprehensively considered, and a comprehensive and accurate risk assessment result is provided.

The invention discloses a similar weather retrieval method based on physical alignment fingerprints, and the method comprises the steps: obtaining a constructed fingerprint database and the time-space sequence data of a target meteorological field corresponding to the to-be-queried weather, and enabling the fingerprint database to be constructed through fingerprints generated by the time-space sequence data of the meteorological field corresponding to a plurality of historical weathers; performing physical consistent double-view contrast learning on the time-space sequence data of the target meteorological field to generate a first physical enhancement double-view result corresponding to a scalar field and / or a second physical enhancement double-view result corresponding to a vector field; inputting the first physical enhancement double-view result and / or the second physical enhancement double-view result into a trained fingerprint generation model to obtain a query fingerprint; retrieving a plurality of fingerprints similar to the query fingerprint in a fingerprint database to obtain a fingerprint retrieval result; and finding out a plurality of corresponding target historical weathers according to the fingerprint retrieval result, and taking the plurality of target historical weathers as similar weather retrieval results. The accuracy of similar weather retrieval can be improved.

The invention belongs to the technical field of marine environment monitoring and protection, and discloses an unmanned aerial vehicle multi-mode marine environment real-time monitoring system based on deep learning, and the system comprises the steps: extracting an instantaneous velocity vector based on an ocean current trajectory; a sea surface flow velocity field is generated, a calibration pile fingerprint database is constructed, and three-dimensional grid parameters are set; secondly, correcting the thickness of a vertical layer of the three-dimensional grid, constructing a tide compensation floating coordinate system, and generating a multi-modal sensor collaborative acquisition instruction set; secondly, performing hard synchronization on multi-modal data, generating a fusion data cube, predicting pollutant concentration increase through LSTM (Long Short Term Memory), and generating a high-risk area list; thirdly, obtaining the battery health state of the unmanned aerial vehicle, limiting the flight attitude, generating an optimized path, and predicting a residual endurance curve; and finally, dynamically allocating unmanned aerial vehicle-subarea mapping through an auction algorithm, verifying bidding by using a block chain, and finely adjusting LSTM parameters through an energy consumption data reverse calibration model and anomaly detection to form closed-loop optimization.

The invention discloses an intelligent diagnosis method for analyzing line hidden dangers based on dynamic electrical fingerprint characteristics, and belongs to the technical field of power line state monitoring and fault prediction. According to the method, a dynamic electrical fingerprint is constructed through high-frequency acquisition of line voltage and current instantaneous value signals and extraction of multi-dimensional features such as a time domain, a frequency domain and a time-frequency domain; establishing an electrical fingerprint database and a health model of different load working conditions in a line health state; during on-line monitoring, through three-level analysis of deviation comparison, state classification and trend prediction, graded early warning of line hidden dangers is realized, and abnormal feature items are output for auxiliary diagnosis. According to the invention, subtle changes of electrical parameters can be captured, the problems of low sensitivity, weak anti-interference capability and incapability of early warning in the prior art are solved, early recognition and advanced prediction of latent hidden dangers are realized, and the safety and reliability of a power system are significantly improved.

The invention discloses a webpage similar structure identification method and device, and relates to the technical field of computer data processing, and the method comprises the following steps: storing webpage data in a byte array form; sequentially traversing each byte in the byte array, determining an initial position of each HTML tag of the webpage in the byte array, and writing a byte data value of the initial position of each HTML tag and a byte data value corresponding to an HTML tag name into a hash function; calling a hash function to generate hash abstracts, and taking a set of the hash abstracts as a webpage structure ID; judging the similarity between the two web pages by comparing the web page structure IDs of the two web pages; or identifying the Web application associated with the target webpage by comparing the matching degree between the target webpage structure ID and the corresponding webpage structure ID stored in the Web application fingerprint database. The structural features of the web pages are efficiently and robustly extracted, the unique ID is generated, and the pages with similar structures in massive web pages are quickly identified.

The invention relates to the technical field of fault section identification, and particularly discloses a fault section identification method and system for a multi-branch hybrid power transmission line system, and the method comprises the steps: collecting multi-modal parameters, carrying out the dynamic topology modeling, outputting an enhanced feature matrix and a dynamic impedance thermodynamic diagram of a multi-branch hybrid power transmission line, performing spatial-temporal feature decoupling and credibility evaluation to obtain a decoupling feature vector group and a feature credibility label, performing intelligent path traceability and conflict resolution on the multi-branch hybrid power transmission line, constructing multi-source decision fusion and anti-interference verification, labeling coordinates of a core fault section and verifying a credibility certificate, and performing knowledge distillation and closed-loop self-learning. A typical fault fingerprint database is established, and whether model reconstruction is triggered or not is judged. According to the method, the problems of long consumed time, high cost, low efficiency and prolonged power failure time of traditional power transmission line fault section identification are solved, the fault positioning time can be effectively shortened, and the difficulty of troubleshooting is reduced.

The invention discloses an electric power protection measurement and control terminal information security protection method, system and device and a medium, and the method comprises the steps: building an environment fingerprint database through collecting the physical quantity of a power grid, carrying out the threat level evaluation, and adjusting a security strategy; based on the environment fingerprint database and the threat level evaluation result, classifying the transmission data, deploying quantum key distribution nodes in the transformer substation, and providing differentiated secure transmission for different types of transmission data; and in combination with the environment fingerprint database and the differentiated secure transmission data, executing cognitive security analysis, establishing an anomaly detection model, and obtaining threat traceability visual positioning. According to the method, the dynamic environment fingerprint database is constructed by collecting multi-dimensional physical quantities and is bound with the digital certificate to form a dual authentication mechanism, so that the defect that a traditional scheme depends on static cryptography is overcome, real-time perception and adaptive response to a complex and changeable electromagnetic environment are realized, and the security of the system is improved. And the environmental adaptability and the safety protection capability of the system are obviously improved.

The invention discloses a cucumber 1K liquid phase breeding chip and a special single nucleotide polymorphism variation site thereof. The 1K liquid phase breeding chip provided by the invention comprises a probe combination for detecting 1121 single nucleotide polymorphism variation sites in a cucumber genome, and the position information of a probe corresponding to each single nucleotide polymorphism variation site in a cucumber reference genome is shown in a table 3 in the specification. Single nucleotide polymorphism variation sites involved in the liquid phase breeding chip provided by the invention have the advantages of high polymorphism, good repeatability, stable and reliable marking, convenient statistics and the like, and a DNA fingerprint database for identifying the authenticity of cucumber varieties can be established based on the liquid phase breeding chip. The method can be used for cucumber variety authenticity, early breeding and identification of cucumber varieties and identification of cucumber seed resources, provides technical support for enrichment of cucumber germplasm resources, breeding of new varieties and protection of the new varieties, and has a wide application prospect.

The invention provides an indoor fingerprint positioning method based on area fitting, which relates to the field of fingerprint positioning, and comprises the following steps: dividing a plurality of triangular areas, and carrying out signal intensity fitting on each triangular area by adopting a signal area fitting method to obtain a plurality of signal intensities in one-to-one correspondence with virtual reference points; calculating to obtain an obstacle area through an obstacle identification algorithm, calculating to obtain an AP fingerprint map with a passable area according to the obstacle area, and establishing a retrieval fingerprint database corresponding to the AP fingerprint map; selecting a passable area with the minimum distance in the retrieval fingerprint database, and matching a reference point in the AP fingerprint map to realize positioning; by adopting the method, the signal intensity fitting of the whole indoor area can be carried out only by measuring the sparse reference points. A double-layer fingerprint database is constructed, positioning errors can be remarkably reduced, the number of fingerprint database data is greatly reduced, and therefore the online matching duration is shortened.