4634 results about "Finger print" patented technology

A method for quality inspection of an etching paste material based on a spectral response difference, including: identifying a position of density mutation boundary through spectral collection and density gradient analysis. Applying a specific frequency beam to obtain the resonance response parameter, generating stress field reconstruction data. Performing a reverse optical tracking to obtain a defect formation path, generating degradation prediction data. Establishing a self-organizing optical monitoring grid, to form a hierarchical spectral fingerprint library. Constructing a multi-point linked defect blocking network, to form an interconnected optical energy field. Monitoring a cooperative response to obtain network stability data, Identifying abnormal position coordinates based on a material quality grade and the network stability data, obtaining the abnormal position coordinates. Performing a phase adjustment to obtain a phase difference spectral set, conducting a phase comparison marking on the abnormal position coordinates, completing the quality inspection of the etching paste material.

The systems and methods disclosed herein monitor application (e.g., artificial intelligence (AI) model) operations using interactions between the application and a kernel. The systems and methods disclosed herein intercept, using a kernel interface, one or more function invocations transmitted from the application (e.g., an AI model without model modification). Event record(s) are generated for one or more functions to define process identifiers, resource interaction types, timestamps, and / or resource identifiers. Observed pattern(s) for the application are identified by comparing current event record(s) with previous record(s), and the identified observed pattern(s) are evaluated against reference pattern(s) to generate score(s). Data packet(s) that indicate observed pattern(s), corresponding score(s), and / or cryptographic digital fingerprint(s) of the one or more functions are generated. The data packet(s) are transmitted to distributed ledgers for immutable storage.

The invention relates to the technical field of cloth dyeing uniformity detection, in particular to a cloth dyeing uniformity detection method and system based on image analysis, and the method comprises the steps: building a three-dimensional optical response model according to the optical characteristics of a fluff cloth fiber material; calculating to obtain a villus shadow intensity distribution diagram according to the linear polarization degree image data, and calculating to obtain a theoretical artifact reflectivity distribution diagram through a three-dimensional optical response model according to the villus shadow intensity distribution diagram and the surface normal vector mapping data; extracting an actual measurement reflectivity distribution diagram from the hyperspectral image data, and performing differential operation on the actual measurement reflectivity distribution diagram and the theoretical artifact reflectivity distribution diagram to obtain a real dyeing component diagram; dividing villus unit grids on the real dyeing component graph, and calculating a spectral fingerprint variance value of pixels in each villus unit grid; and when the spectral fingerprint variance value exceeds a preset process tolerance threshold value, judging that the corresponding fluff unit grid has a real dyeing defect. And the identification precision of the dyeing defects of the fluff cloth is effectively improved.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable electrical performance detection method and system, and the method comprises the steps: synchronously collecting a broadband electromagnetic signal, a mechanical vibration signal and a temperature signal at a cable monitoring point; calculating a wavelet coherence coefficient between the signals through continuous wavelet transform, and constructing a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics; performing time slicing and high-order singular value decomposition on the tensor to obtain a time-varying core tensor sequence, mapping the time-varying core tensor sequence into a high-dimensional manifold curve, and generating a system state fingerprint by calculating local curvature distribution and topology invariants of the curve; inputting the fingerprints into a pre-trained defect prediction model, and directly outputting defect inoculation probability and evolution stage judgment; according to the method, the limitation that early weak defect detection is not sensitive in a traditional method is broken through, and early warning and accurate diagnosis of cable insulation latent defects are achieved.

The invention discloses a liver and gall lesion early screening system and method based on image fusion, and relates to the technical field of medical image processing and computer-aided diagnosis, and the method comprises the following steps: reconstructing a multi-modal image space-time coordinate system under a unified event time baseline, generating a respiratory displacement field and a magnetic sensitive pulse fingerprint, and constructing an artifact suspicion map; and performing anti-fact playback based on the artifact suspicion chart, performing frame-by-frame playback on the image acquisition sequence, quantifying artifact superposition tracks with consistent directions, and solidifying an artifact anchor point set. According to the method, space-time coordinates are constructed based on a unified event time baseline, a breathing displacement field and magnetic sensing pulse fingerprints are introduced, anti-fact playback, distortion kernel inference and residual decoupling are combined, artifact recognition and fusion intervention are achieved, and artifact closed-loop elimination is completed by judging threshold-driven fusion regulation and time reversal phase gating, so that the artifact recognition accuracy is improved. And the fused image authenticity is improved.

The invention provides a file uploading attack interception method based on semantic entropy enhancement, and aims at overcoming the defects of an existing file uploading security protection technology in the face of complex attacks. The method specifically comprises the steps that S1, file format analysis and content extraction are conducted, hidden scripts are mined through nested content recognition, and intermediate representation is generated through grammar cleaning and coding specifications; s2, constructing an abstract syntax tree and semantic entropy calculation, tracking a pollution chain, analyzing a high-risk function, identifying a high-entropy character string, modeling and controlling flow complexity, and generating a semantic entropy vector; s3, dynamic scoring and decision making are carried out, and accurate judgment is carried out in combination with white list perception, feature comparison, multi-modal model scoring, adaptive threshold and sandbox observation; and S4, carrying out real-time interception and feature synchronization, blocking malicious file landing, generating an attack log and synchronizing an attack fingerprint. The method takes the semantic entropy vector as a core, breaks through the limitation of static features, remarkably improves the recognition rate of complex attacks, reduces missed judgment, and guarantees the safety of Web applications.

The invention discloses a dynamic network access control method and system based on a zero-trust architecture, and the method comprises the steps: integrating equipment health degree evaluation through the triple dynamic binding of biological feature dynamic binding, equipment fingerprint salt value hash verification and environmental state perception, and constructing a real-time trust basis; dynamic risk quantification is realized based on a multi-source heterogeneous data fusion machine learning model, real-time upgrading and degrading self-adaptive adjustment of authority is realized through an AI driving strategy generation module according to a real-time risk score, a zero-trust sandbox limitation sensitive operation is triggered for high-risk access, and a minimum authority channel is started for low-risk access; performing fine-grained access control and intercepting an unauthorized request in real time by adopting an agent-free API gateway technology, and monitoring an operation behavior in combination with a block chain non-tampering storage access log and an anomaly detection algorithm; finally, a continuous self-adaptive evolutionary cycle is formed through a risk assessment-policy execution-abnormal feedback closed loop mechanism, and the static lag problem of traditional network access control is systematically solved.

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 discloses an application of an electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction, and the human body exposure fingerprint spectrum is composed of more than three of exposure markers of 2, 4, 6-tribromophenol, 2, 4, 5-trichlorophenol, 4-bromophenol, 1-hydroxypyrene and bisphenol A. The invention further discloses an application of the electronic waste pollution human body exposure fingerprint spectrum in exposure risk prediction. According to the method, a systematic method of urine pollutant exposure omics is adopted for the first time, comprehensive screening and characterization of pollutant exposure markers related to specific components and by-products are achieved in electronic waste exposure worker groups, and the method is the most systematic organic pollutant exposure research known in the field at present.

The invention discloses a ground engineering construction quality supervision digital delivery system, which relates to the technical field of engineering supervision, and comprises the steps of generation and multi-source data checking, activity record structuring and anti-verification before hiding, space-time reference unification and point cloud fusion playback, rule checking and differential rectification, and object-level marshalling delivery and asset authority management. According to the method, a unified data pool, activities, events and evidence chains are constructed through object codes to achieve time-space consistent association, image, point cloud and detection consistency check is conducted before hiding, blocking difference and unified reference fusion point cloud are generated, indexes support three-dimensional playback, rule base batch check is conducted, and issuing rectification is conducted. And the object-level delivery list and fingerprints support cross-platform identification and auditing to form a traceable link.

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 water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention relates to the technical field of electricity metering anomaly monitoring, and provides a distributed resource access-oriented lightweight electricity metering anomaly monitoring method, which comprises the following steps of: acquiring electrical data and environment associated data of distributed access points at an edge side, and constructing a fluctuation fingerprint feature vector; when it is detected that the fluctuation fingerprint feature vector meets a preset fluctuation triggering condition, based on Pearson correlation analysis between the electrical data and the environment associated data, performing decoupling and elimination on false anomalies caused by environmental factors to obtain a fluctuation fingerprint feature vector of non-environmental suspicious anomalies; based on historical normal operation data, constructing a normal working condition mode library by adopting a clustering analysis method, and determining a feature center and a dynamic safety radius of each normal working condition mode; and calculating the feature distance between the non-environmental suspicious abnormal fluctuation fingerprint feature vector and each normal working condition feature center at the edge side, and judging whether the electricity consumption metering is abnormal or not according to the relationship between the feature distance and the dynamic safety radius.

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

The invention discloses a multi-agent collaborative decision-making system and method based on knowledge Token, and the method comprises the following steps: S1, constructing a knowledge base architecture, executing the content Hash calculation, and generating an original fingerprint in an original fidelity layer; s2, extracting content, packaging the content into knowledge Token, and writing identification, semantics, source, authority, value, contract, association, traceability and health information; s3, activating a knowledge layer, constructing a vector index and mapping knowledge graph nodes; s4, unifying an index layer, matching task requirements, and screening and verifying candidate Tokens to obtain an authorization set; s5, the multiple agents generate local decisions and fuse values and semantics to form a collaborative result; and S6, binding a Token identifier after consistency verification, generating a collaborative decision and recording data consanguinity information. According to the method, multi-agent collaborative decision-making is realized through the knowledge Token and the knowledge graph, and the decision-making accuracy and traceability are improved.

The invention discloses an SIP protocol encryption malicious traffic detection method based on deep learning, and the method comprises the steps: obtaining traffic behavior characteristics, and carrying out the preprocessing of the traffic behavior characteristics, and obtaining a behavior characteristic matrix; behavior affiliation and pattern recognition are carried out on the behavior characteristic matrix to complete SIP behavior fingerprint modeling, and an SIP behavior fingerprint database is obtained; training a preset deep learning model by using the SIP behavior fingerprint database to obtain a malicious traffic detection model; and embedding the malicious traffic detection model into an actual network environment, and carrying out real-time malicious traffic detection. According to the invention, high-real-time malicious traffic detection is realized.

The invention provides an ultrasonic fingerprint chip and an integral clock generation method and device of the ultrasonic fingerprint chip. Appropriate fingerprint pixel units can be selected from a plurality of fingerprint pixel units to serve as first-class pixel units and second-class pixel units, and the first-class pixel units and the second-class pixel units are respectively connected with a first-class detection circuit and a second-class detection circuit. Wherein a first integrator in the first type of detection circuit is sequentially connected with a first comparator, a time delay regulator, a clock dominant frequency duplicator and a frequency dividing circuit; the frequency dividing circuit is connected with the second type detection circuit; a second integrator in the second type detection circuit is connected with an amplification structure; the amplification structure is also connected with a second comparator; and the second comparator is connected with the time delay regulator in the first-class detection circuit. The circuit can automatically generate and obtain the integral clock signal which is matched with the ultrasonic fingerprint echo signal and is high in precision and good in effect, so that the received ultrasonic fingerprint echo signal is precisely processed, and a target signal with high quality is obtained.

The invention discloses a method for testing compatibility of creative application software based on big data storage. The method comprises the following steps: acquiring environment and version information to generate fingerprints; similar evidence extraction failure feature clusters are recalled; evaluating a case risk sequence based on the cluster and evidence; a version difference event mapping graph is used for obtaining a seed; spreading the seeds to obtain an influence domain and generating an increment regression set; the duplicate removal execution cases are combined, and receipts and judgment are output; inference of a minimum cost explanation path is hit by the failure case atlas; obtaining a consensus root cause according to the explanation path, and generating complementary measurement and verification; and verifying the gating updating atlas and precipitating new evidence to drive self-updating. According to the method, incremental regression and root cause interpretation updating are realized by utilizing differential propagation and anti-fact verification closed loop of the environment and version fingerprints and the compatible relationship knowledge graph, and the method has the advantages of high efficiency, high coverage and high credibility.

The invention relates to the field of information security, and provides a symmetric encryption key secure transmission method and system. The method comprises the following steps: acquiring a unique fingerprint of equipment and a system parameter, and deriving the unique fingerprint of the equipment and the system parameter through a Hash algorithm to generate a seed key; deriving an initial symmetric key through a Hash algorithm based on the seed key, and performing recursive calculation in combination with key update parameters to obtain a session key; generating a plurality of random components, equally dividing the session key, and then carrying out XOR operation on the session key and each random component to obtain a key fragment set; and carrying out encryption verification on the session key and the plurality of key fragments in the key fragment set, generating verification values corresponding to the plurality of key fragments, and respectively transmitting the plurality of key fragments and the corresponding verification values through a plurality of different transmission paths. According to the invention, dynamic derivation and secure fragmentation transmission of the symmetric key without hardware protection and asymmetric encryption are realized, and the system security is improved.

The invention relates to the technical field of information, and provides a bulk commodity transaction system based on a block chain, and the system comprises a parameter and publishing module which uniformly receives and stores parameters; the unified examination and approval engine performs isomorphic arrangement and gray level updating through a configurable template and a state machine; consistency snapshots and recovery are paused / abnormally collected snapshots and monotonically replayed, and it is guaranteed that the sequence is consistent with a window; the security fund risk control uses DSL + fund DAG to drive freezing / unfreezing / deduction and account checking; and block chain evidence storage and auditing execute field-level hash and pedigree anchoring to realize verifiable traceability. According to the system, an end-to-end consistency and verifiability framework is constructed around the aspects of field-level fingerprinting, unified examination and approval, windowed stable sorting, consistency snapshot and monotone replay, fund event patterning, contract assembling and signature anchoring, settlement / final recalculation and pedigree uplink and offline verification; and under the conditions of abnormity, recovery and version evolution, each link keeps uniform caliber, stable sorting and approvable state.

The invention discloses a wide-area time traceability credible authentication method in a power heterogeneous network environment, and belongs to the technical field of power system informatization and automation. Comprising the following steps: acquiring various high-precision time source information in a power heterogeneous network environment, and acquiring an initial time reference of the power system time synchronization device; generating uniform reference time, and embedding a digital watermark containing time source information and credibility thereof for each timestamp by using the digital watermark embedding module; controlling the power system time synchronization device to sequentially generate time transmission fingerprints at all levels of network nodes; constructing a unique time fingerprint chain covering the whole link; packaging the time fingerprint of each time transmission node and the traceability information thereof, and performing uplink storage; precise prediction and optimal control of time transmission errors are realized; if the overall cumulative time error exceeds a range of + / -10 [mu] s, the predictive correction is maintained until the error is controlled within + / -10 [mu] s.

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 discloses an intelligent early warning and hierarchical linkage disposal method for hydrogen pipeline leakage, and relates to the technical field of hydrogen pipeline safety monitoring, and the method comprises the steps: building a pipeline health fingerprint reference library, deploying a five-dimensional cooperative sensing array, synchronously collecting, purifying and processing leakage and hydrogen embrittlement signals, and extracting core feature parameters; a dynamic algorithm is utilized to quantify hydrogen embrittlement risks, a leakage point is positioned, a comprehensive decision result containing information such as risk types and levels is output, differential grading disposal is started according to the decision result, emergency resources are deployed through a resource scheduling algorithm, a disposal plan is dynamically optimized through a closed-loop feedback mechanism, and efficient management of hydrogen pipeline safety is achieved.

The invention relates to the technical field of underwater static target object detection and processing, in particular to an unmanned cross-domain positioning and acoustic fingerprint processing method and system for an underwater static target. Comprising the following steps: performing wide-area scanning on a task sea area, and scheduling an AUV and unmanned aerial vehicle cluster to a target area after discovering a suspicious target; the unmanned aerial vehicle cluster receives an acoustic signal of the AUV and transmits the acoustic signal to the cooperative resolving center in combination with self-positioning data so as to provide accurate coordinate guidance for the AUV; the AUV sails to a target area, parallel processing map construction and accurate positioning, target identification and dual-mode fingerprint generation are carried out, and a target task package is packaged; the ROV receives and analyzes the task packet, autonomously plans a path and sails to a target area, a sonar is started to collect data and generate real-time feature fingerprints, and the real-time feature fingerprints are matched with fingerprints in the task packet; and after matching succeeds, a specific job task is autonomously executed. According to the invention, a complete automatic solution is provided for scenes such as deep and far sea detection, emergency salvage, pipeline maintenance and the like.

The invention discloses an LED chip service life rapid estimation method and device and a medium, and relates to the technical field of electrical digital data processing, and the method comprises the steps: designing a current pulse stress sequence for a calibration sample based on photoelectric thermal characteristic data, synchronously collecting photoelectric thermal response data before and after a pulse, and carrying out verification completion and mapping storage; generating a photoelectric thermal pulse response data set; according to the photoelectric thermal pulse response data set, photoelectric thermal change characteristics of the LED chip sensitive to the service life are extracted, and a photoelectric thermal pulse service life fingerprint of the LED chip is constructed; constructing a service life rapid estimation model, and predicting the service life of the LED chip according to the photoelectric thermal pulse service life fingerprint and the working condition information to generate the predicted service life of the LED chip; according to the method, the photoelectric thermal pulse life fingerprint of the LED chip is constructed, the photoelectric thermal evolution process under the pulse stress is compressed into a unified life characterization carrier, and a degradation feature basis capable of being directly called is provided for life modeling.

The invention provides a ship-based unified storage and management design data method, which comprises the following steps that: an operation end generates and sends an editing request with a node identifier, an editing type, a timestamp and a digital signature, and a WEB front end receives and verifies the legality of the request through an encryption channel; the storage service generates an intention lock based on the permission table and version number matching, and concurrent access control is achieved. Then, the WEB front end carries out content definition partitioning (CDC) and Hash fingerprint calculation on a design file stream, the storage service realizes partitioning duplicate removal and incremental storage according to a fingerprint matching result, and a Merkle tree is utilized to construct a file version integrity Hash root; on this basis, the system analyzes model differences, constructs a semantic difference chart, and calls a rule engine to perform automatic pre-check. According to the method, efficient storage, collaborative editing, security and credibility and hierarchical scheduling management of ship design data are realized, and the method has the technical advantages of high data consistency, high resource utilization rate, traceability auditing and the like.

The invention belongs to the technical field of environment monitoring, and discloses an ecological environment anomaly detection and early warning method based on artificial intelligence. The emission characteristic fingerprint database is constructed according to enterprise production process characteristics by acquiring multi-point pollutant concentration, meteorological parameters and enterprise space distribution data in a park. By analyzing the space-time diffusion trajectory of pollutants, identifying abnormal areas and calculating reverse traceability parameters, a space-time superposition inversion model of multi-source emission is constructed. A multi-enterprise contribution degree separation matrix is generated based on fingerprint matching degree weighted calculation, and accurate separation of emission contributions of all enterprises in the mixed pollution field is achieved. By monitoring pollutant concentration fluctuation characteristics in real time, contribution degree weight distribution is dynamically adjusted, and the accuracy of a monitoring result is ensured. And accurately determining a responsibility subject with excessive emission and generating an early warning signal. According to the invention, accurate identification and responsibility determination of the pollution source can be realized in a complex multi-source emission environment, and the accuracy and fairness of industrial park environment supervision are effectively improved.

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention discloses a battery failure reason prediction method and a computer storage medium, and the method comprises the steps: outputting EIS prediction data through a time sequence model according to the data in the charging and discharging process of a battery; according to EIS prediction or actual measurement data, relaxation time characteristic peak time and corresponding intensity are obtained; constructing a candidate circuit topology in the element library according to the characteristic peak time and intensity and EIS prediction or actual measurement data, and calculating and outputting an optimal equivalent circuit model character string and a parameter set; fusing relaxation time characteristic peak time and intensity, an optimal equivalent circuit model character string, a parameter set, a structural characteristic and a current / voltage derivative characteristic, splicing into a low-dimensional vector, and constructing an electrochemical state fingerprint; carrying out embedded representation on the electrochemical state fingerprints, and recognizing typical degradation state clusters through dimension reduction and clustering; and performing failure cause semantic attribution, and outputting a failure cause category and a prediction report. According to the invention, automatic identification and output of potential failure reasons of the battery are realized.

The invention discloses an HPLC and HRF dual-mode communication method and system for an intelligent power grid, and the method comprises the steps: enabling a source communication node to transmit power grid service data through an HPLC and HRF dual channel, and forming a dual-mode data package; after receiving the data, the target communication node demodulates and recovers service data from the HPLC and HRF signals, synchronously measures and calculates the two paths of signals, and extracts real-time cable fingerprints reflecting power line medium characteristics and real-time space fingerprints reflecting space wireless characteristics; further, performing similarity comparison on the real-time dual-mode fingerprint and a pre-stored source node reference dual-mode fingerprint; and finally, judging the security state of the communication link and the validity of the data in a combined manner according to whether the comparison result meets a credible threshold value or not and by combining the integrity verification of the service data, and executing subsequent network operation accordingly. According to the invention, the physical layer fingerprints are invisibly extracted in normal service communication, so that real-time link authentication without special detection signals is realized, and man-in-the-middle attack and equipment counterfeit are effectively defended.