420 results about "Analytical algorithm" patented technology

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention discloses an intelligent operation and maintenance fault early warning method for a rail transit variable-frequency power supply, and relates to the technical field of power electronics and intelligent operation and maintenance, and the method comprises the following steps: setting a multi-point synchronous collection device to carry out the high-precision real-time collection of voltage and current signals of a variable-frequency power supply system, and carrying out the digital coding processing of data; and performing frequency domain analysis on the acquired voltage and current signals by using a preset spectrum analysis algorithm, and extracting harmonic amplitudes and phase characteristic parameters of different orders. According to the invention, through high-precision synchronous acquisition and spectrum analysis, rapid and accurate identification of harmonic abnormity is realized; a machine learning model and dynamic threshold adjustment are fused, and the adaptability and intelligent recognition capability of the system to fault modes under multiple working conditions are enhanced; and a trend prediction and graphical early warning platform is introduced, so that the fault risk is pre-judged in advance and visually responded, and the operation and maintenance efficiency and the operation safety of the rail transit power supply system are remarkably improved.

The invention provides an AI-based nuclear power station high-risk operation compliance real-time monitoring method and system, and the method comprises the steps: processing human body joint point coordinate data of an operator through employing an AI dynamic contour modeling algorithm, and generating a limb movement track sequence; scanning the preset operation area to generate a relative motion vector between the special operation tool for the nuclear power station and the operator; detecting an action deviation mode of the operator based on the limb movement track sequence, and generating a detection result; fusing the relative motion vector and multi-channel video stream frame synchronization data obtained by applying an AI video analysis algorithm to generate a space intrusion relation between the nuclear power station special operation tool and the nuclear power station high-risk equipment; and when the action deviation mode exists in the detection result and the space intrusion relation exceeds a preset safety threshold, generating an operation compliance signal. According to the invention, real-time dynamic evaluation of human motion compliance and tool equipment safety distance in the high-risk operation process of the nuclear power station is realized, and the safety monitoring precision and real-time early warning capability of high-risk operation are improved.

Artificially intelligent systems and methods for financial coaching provide personalized, fiduciary-compliant financial guidance through advanced machine learning architectures with measurable performance criteria. The systems implement privacy-preserving processing pipelines that detect personally identifiable information using multi-layered pattern recognition including regular expressions for formatted data sequences, named entity recognition with confidence thresholds above 0.85, and contextual analysis algorithms. A multi-step artificial intelligence processing workflow includes automated language detection, emotional tone classification with confidence scoring, financial profile transformation using predefined templates, context-aware question rephrasing, and semantic similarity matching employing vector embeddings with financial domain vocabulary weighting applying multiplier values between 1.3-2.0. Specialized training methodologies expand datasets through mathematical transformation functions utilizing statistical standard deviations with incremental variations between 0.5-2.0. Mood-based escalation logic automatically transfers users to human advisors when emotional indicators exceed confidence thresholds above 0.8. The systems maintain response times below 5 seconds while providing regulatory compliance through curated content sources and predefined fiduciary instruction parameters.

The invention belongs to the technical field of energy consumption optimization, and particularly relates to a digital twin building energy consumption dynamic optimization system, which comprises a modeling module for acquiring physical, structural and equipment layout parameters of a building, and constructing a target building three-dimensional region segmentation model containing geometric transmission constraints by combining three-dimensional modeling and a grid segmentation algorithm; the control mapping module is used for collecting data such as regional people flow density and equipment energy consumption, and constructing multi-level association control mapping including people flow-rate first association mapping, rate-energy consumption second association mapping and comfort level score-based second association feedback mapping through an association analysis algorithm; the analogue simulation module constructs a generator and a discriminator through association mapping, performs synchronous confrontation simulation in combination with a three-dimensional region segmentation model, and solves an optimal energy consumption rate; and the feedback adjustment module compares actual operation with feedback data, corrects mapping parameters and optimizes algorithm weights, realizes building energy consumption dynamic optimization, and ensures that the overall energy consumption and comfort level score meet preset thresholds.

The invention discloses a forest carbon sink dynamic monitoring system based on multi-source data fusion, and relates to the technical field of carbon sink monitoring, and the system comprises a multi-source data collection and fusion module which collects forest-related multi-source data, and generates a standardized fusion data set; the carbon sink reserve estimation module is used for constructing a carbon sink reserve estimation model according to the fused data set, and calculating forest carbon sink reserves based on a model output result; the carbon sink reserve prediction module is used for predicting the forest carbon sink reserve at the next moment by using a time sequence analysis algorithm; the state change monitoring module is used for analyzing a carbon sink dynamic change trend based on forest carbon sink reserve prediction data and historical reserve data; and the early warning and decision support module is used for triggering an early warning signal of a corresponding level based on the abnormal change information. Through fusion of federated learning, ARIMA time sequence analysis, a sliding window and a linear regression technology, efficient and continuous tracking of a carbon sink reserve change trend is realized, and change abnormity can be identified in time.

The invention relates to the technical field of multi-modal behavior anomaly detection scheme design, in particular to a multi-modal behavior anomaly detection method and system under the condition that encrypted traffic is not decrypted. The method comprises the following steps: capturing a network encrypted traffic data packet in real time; on the premise that decryption is not carried out, multi-mode non-decryption features such as flow layer statistics, time sequence interaction, encryption handshake and context association are extracted in parallel; establishing a dynamic normal behavior baseline model of each feature based on historical data; the real-time features are compared with the baseline model, comprehensive judgment is carried out through a multi-mode correlation analysis algorithm, and an anomaly detection result is output; and when the abnormal condition is judged, the network control equipment is automatically linked for blocking. The method thoroughly gets rid of dependence on traffic decryption, realizes high-precision and self-adaptive detection and rapid automatic response to abnormal behaviors in encrypted traffic through multi-dimensional feature fusion and dynamic baseline technologies, and effectively solves the problem of failure of a traditional detection technology in an encrypted environment.

The invention relates to a historical building disease diagnosis method and device assisted by artificial intelligence, equipment and a medium. The method comprises the steps of obtaining a multi-angle image sequence of a historical building and performing preprocessing to obtain a standardized building structure image, performing edge feature extraction on the standardized image to identify dislocation feature points to generate an edge information graph, and segmenting a dislocation region through a region growing algorithm based on the edge information graph to generate an initial dislocation region distribution graph. Performing deformation degree and displacement calculation on each region in the distribution diagram to determine a dislocation severity level, comparing dislocation region changes in each period through a time sequence analysis algorithm by combining an image sequence to generate a dynamic change mode, and optimizing boundary division according to the severity level and the dynamic change mode to generate a diagnosis report; automatic identification and evolution tracking of historical building dislocation diseases are realized, the problems of high subjectivity, fuzzy boundary judgment and lack of disease evolution prediction of traditional manual investigation are solved, and the scientificity and long-term effectiveness of repair decision are improved.

The invention provides a method for detecting the quality of a foundation steel bar welded joint, and belongs to the technical field of steel bar welded joint quality detection.The method comprises the steps that a stress distribution matrix is obtained through ultrasonic detection equipment, and a fatigue distribution matrix is established in combination with a fatigue analysis algorithm; a stress fatigue correlation analysis method and a bipartite graph maximum matching algorithm are used to construct a stress fatigue correlation matrix, a damage evaluation deep learning model based on a Transform architecture is used to generate a damage distribution matrix, and a time sequence prediction algorithm is used to consider a fatigue center change effect to establish a damage expansion trend matrix. A damage prediction matrix is deduced through a damage prediction deep learning model of a bidirectional long-short-term memory network, and finally, a quality detection result matrix is comprehensively evaluated and output by utilizing a game model, a multi-objective optimization algorithm and a gating weighting function, so that the technical problem that the quality detection accuracy of the steel bar welded joint is not enough is solved.

The invention relates to the field of financial information management, and discloses a fund exchange information management system based on big data analysis, and the system comprises the steps: obtaining multi-source fund exchange data based on a data collection interface, carrying out the data cleaning and standardization processing, and constructing a fund information basic database; extracting fund exchange feature parameters from the fund information basic database through a preset feature extraction rule, and generating a fund feature data set; and based on the fund feature data set, mining a fund exchange association relationship by adopting an association analysis algorithm, and establishing a fund exchange relationship graph. According to the method, the association relationship among the transaction subjects is fully mined by constructing the fund exchange relationship graph, and the risk assessment is performed in combination with the fund exchange characteristic parameters and the relationship graph characteristics, so that the problem of inaccurate risk assessment caused by neglecting the association relationship of the transaction subjects in a traditional mode is solved, and the accuracy of risk level judgment is improved.

The invention discloses a PDF contract file identification method and system, a medium and a program product, and relates to the technical field of information identification, and the method comprises the steps: carrying out the image analysis of an obtained PDF contract file through employing a deep learning model, generating an editable text, and synchronously identifying the page layout of the document, outputting structured data including page numbers, text paragraphs and coordinate information; performing multi-dimensional feature matching on the text paragraphs according to an adaptive semantic analysis algorithm, a preset contract template library and a dynamic keyword library, and positioning contract core element information; performing entity relationship verification on the content of the contract core element information, correcting an extraction error through dependency syntactic analysis and semantic role labeling, and establishing a contract element data set containing a confidence coefficient weight; and performing serialized packaging on the verified contract element data set to generate a contract element list conforming to electronic signature authentication. According to the invention, the processing efficiency and recognition precision of the PDF contract file are improved.

The invention provides a high-precision numerical control machine tool operation state abnormity monitoring method and system, and relates to the technical field of numerical control machine tool operation state abnormity monitoring. The method comprises the steps that the displacement deviation of a feeding system, the oil supply pressure of a lubricating system, real-time temperature data of a driving motor stator and original signals during machining are collected; the three are preprocessed to obtain a final time sequence data set, and an original signal is processed to obtain a normalized smooth physical signal; then obtaining associated features by using a multi-parameter association analysis algorithm, and comparing the associated features with a preset dynamic reference feature library to judge the running state; if the operation state of the high-precision numerical control machine tool is abnormal, the fault tree diagnosis model is used for positioning an abnormal root part and process parameters, then a targeted solution is generated, full-process monitoring of the operation state of the high-precision numerical control machine tool can be completed, and data collection, preprocessing, correlation analysis, abnormality judgment, and root positioning and targeted solution generation during abnormality are achieved.

The invention discloses an ANTLR4-based multi-manufacturer network equipment configuration analysis method and system, and the method comprises the steps: carrying out the preprocessing of a configuration file; based on a machine learning algorithm of multi-dimensional feature analysis, automatic identification of configuration file manufacturer types is realized; based on the identification result of the manufacturer type, loading an ANTLR4 grammar parser corresponding to the manufacturer type; a lexical analyzer in ANTLR4 realizes lexical analysis on the configuration file through lexical rule definition, a Token classification system and an optimized analysis algorithm; a syntactic analyzer in ANTLR4 constructs an abstract syntax tree according to a lexical analysis result and a syntactic rule defined based on a context-independent grammar; traversing the abstract syntax tree, and extracting key configuration information; and converting the specific configuration format of each manufacturer into a unified standard model. According to the method and the system, network equipment configuration files of different manufacturers can be automatically identified and analyzed, and key configuration information is extracted and converted into a unified model.

The invention relates to a human body health state assessment system and assessment method based on multi-point acquisition, and the system comprises an event sensing module which is used for collecting an image data signal, a physiological signal and an environment data signal of a human body, and outputting an asynchronous pulse event flow when the signal change is detected; the pulse coding module is used for generating a space-time pulse sequence through a preset adaptive threshold coding technology; the neural network processing module is used for performing event driving processing by utilizing leakage integral distribution neurons in a preset pulse neural network according to the space-time pulse sequence to obtain a current health characteristic pulse mode; and the health state decoding module is used for obtaining a health state score and a risk classification result of the current human body through a preset pulse distribution rate analysis algorithm and a preset time sequence decoding technology according to the current health characteristic pulse mode. Therefore, the problems of high power consumption, high delay, low data transmission efficiency, low early pathological feature recognition accuracy and the like of a human health state evaluation system are solved.

The invention provides a high-precision road network topology model construction method based on multi-source data fusion, and belongs to the technical field of traffic transportation information processing, and the method comprises the steps: obtaining traffic industry management data, open source map data and map provider navigation data, carrying out the data fusion, and obtaining fusion data, constructing a topological network to perform validity and connectivity verification, and performing processing by using a fracture stitching algorithm to generate a road network topological structure; inheriting and standardizing attribute fields according to a road section space overlapping relation to obtain a structured attribute set, and optimizing by utilizing a space analysis algorithm and structural logic judgment to obtain an optimized road network topological structure; and combining the structured attribute set with the optimized road network topology structure to obtain a high-precision road network topology model. According to the method, the problem that a set of universal, efficient and iteratively-updated data fusion and topology modeling framework is lacked in existing high-precision road network topology model construction is solved.

The invention discloses an electronic file content anti-counterfeiting and tampering detection method adopting a locked signature, and belongs to the field of information security. According to the method, file content is automatically segmented according to semantics through a context coding self-adaptive segmentation algorithm, and each segment of content fingerprint is generated by applying a content embedding disturbance weighted digest algorithm. And constructing each segmented abstract into a directed relation chain by adopting an embedded relation chain transformation signature mechanism, and carrying out digital signature overall locking. During detection, the segmented abstract and the relation chain structure are compared again, and precise positioning and tracing of tiny tampering, segmented change and structure adjustment of the text content are achieved. And through a signature duration chain entropy analysis algorithm, performing comparison and evolution path analysis on previous version states, and outputting a detailed traceability report. According to the method, authenticity verification and tampering detection of the electronic file content are greatly improved, and the method is suitable for the fields of data anti-counterfeiting, compliance supervision, electronic evidence management and the like.

The invention provides an intelligent wire harness adaptive control method and system, and the method comprises the steps: S2, extracting a corresponding current range and a communication protocol from an output mode library according to an equipment type judgment result, and determining an initial output parameter; s4, executing the adjusted output parameters through a low-power-consumption communication module, obtaining equipment operation state data, and judging whether the real-time operation requirement is met or not; s6, after the pre-adjustment parameters are executed, equipment response data and energy consumption data are obtained, and whether system overload or power consumption exceeding exists or not is judged; s8, updating the equipment feature database and the output mode library according to the adjusted operation configuration, and obtaining updated collaborative performance indexes and energy consumption indexes; and S9, if the collaborative performance index and the energy consumption index do not reach the performance threshold, repeatedly executing the combined optimization process of the dynamic adjustment algorithm and the feature analysis algorithm. According to the invention, high-efficiency cooperation and energy consumption optimization of the wire harness and the intelligent equipment are realized.

The invention discloses a generator set anomaly detection method based on edge calculation. The method comprises the following steps: S1, generating multichannel working condition data of a standardized generator set; s2, deploying a lightweight long short-term memory network prediction model in an industrial edge computing node; s3, constructing a multi-mode residual error map; s4, performing generator set abnormity judgment on the local residual error sequence and outputting generator set abnormity candidate events; and S5, generating a generator set anomaly causal interpretation graph for the generator set anomaly candidate events by using an interpretive analysis algorithm, wherein the generator set anomaly causal interpretation graph reveals the contribution relationship of each working condition feature to the generator set anomaly confidence coefficient. According to the method, the traceability and operability of fault positioning are remarkably improved, and operation and maintenance personnel are assisted in accurately formulating a maintenance strategy.

The invention provides a method and device for determining familial defects of power equipment, and relates to the technical field of operation and maintenance of the power equipment. The main purpose of the technical scheme is to establish an objective and unified judgment standard so as to more accurately judge whether the electrical equipment has familial defects or not. According to the main technical scheme, the method comprises the steps of obtaining historical familial defect data and corresponding target features based on power equipment of a target category; based on the target feature, traversing a preset knowledge graph model to determine the occurrence frequency of the target feature, the preset knowledge graph model being an association relationship model constructed based on the operation data of the target power equipment and used for representing the defect event of the power equipment; based on the historical familial defect data, using a feature analysis algorithm to determine a weight coefficient of a target feature; and based on the occurrence frequency and the weight coefficient, calculating a suspected familial defect rate to determine that the target power equipment has the familial defect.

The invention discloses a photovoltaic module health state real-time monitoring method, system and device and a medium, and relates to the technical field of photovoltaic energy monitoring and intelligent operation and maintenance, and the method comprises the steps: collecting the health data of a photovoltaic module through a photovoltaic module working state sensor and an environment sensor, and transmitting the health data through a multi-network protocol; a feature importance evaluation method is adopted to construct a priority scheduling model to dynamically adjust the photovoltaic module health data acquisition frequency, anomaly detection is performed on the acquired photovoltaic module health data on an edge device, a graph convolutional network and a time sequence prediction model are fused to construct a cloud model, an anomaly detection result is input into the cloud model, and the cloud model is subjected to feature importance evaluation. Health trend analysis and state prediction are executed through the cloud model; early warning grading processing is carried out according to health trend analysis and state prediction results, a multi-level response mechanism is established through a correlation analysis algorithm, and a maintenance resource linkage process is triggered. According to the method, the real-time performance of key data acquisition and the data transmission efficiency are effectively improved through a priority scheduling scheme based on the information entropy weight.

The invention discloses a fidelity simulation method and system for a mine catastrophe scene, and relates to catastrophe simulation. The method comprises the steps of obtaining mine disaster historical data; performing statistical analysis and feature extraction on the historical data by adopting a multi-dimensional correlation analysis algorithm to obtain a main control element type set of the mine disaster and a weight coefficient of each main control element; setting an underground catastrophe scene reproduction scheme of each main control element type according to the main control element type set; catastrophe process monitoring data are collected in the target simulation area; establishing a mapping relation model between the main control elements and the disaster characteristic parameters through a machine learning algorithm; and generating an arrangement scheme of the underground catastrophe simulation device according to the mapping relation model. For the problem that mine catastrophe simulation lacks physical mechanism constraints in the prior art, residual errors of a gas diffusion equation, a heat conduction equation and a fluid dynamic equation are introduced into neural network training to serve as physical constraint loss items, and the generalization ability of simulation under different geological conditions and mining modes is improved.

The invention provides a multi-dimensional audit log driven system behavior association analysis method and system, and the method comprises the steps: obtaining multi-dimensional log data from terminal equipment, and constructing a four-dimensional tensor; constructing a behavior path function based on the four-dimensional tensor and a preset clustering analysis algorithm; based on the behavior path function, constructing an evolution credibility function, and obtaining a behavior path credibility score through the evolution credibility function; based on the behavior path credibility score, constructing a structure impedance function, and identifying an abnormal behavior path through the structure impedance function; and performing correlation analysis on each event node in the abnormal behavior path to obtain a system abnormal behavior. According to the method, dynamic changes of short-term behavior mutation and long-term behavior evolution of equipment are accurately captured to perform association analysis on abnormal behaviors, so that potential risks are effectively identified, and the operation stability of the equipment is improved.

The invention provides a logistics track spatial data acquisition method based on a three-dimensional model, and relates to the technical field of data processing, and the method comprises the steps: 1, collecting the dynamic position data of a logistics object in a three-dimensional space in real time through a multi-source sensor, the three-dimensional space coordinate data comprising horizontal direction coordinates and vertical direction coordinates; 2, inputting the dynamic position data into a pre-constructed three-dimensional environment model, and generating a preliminary three-dimensional track through a space registration algorithm; and step 3, performing real-time updating processing on the preliminary three-dimensional trajectory, dynamically correcting trajectory data through a time sequence analysis algorithm, fusing obstacle information in the three-dimensional environment model, optimizing the precision of the preliminary three-dimensional trajectory, and obtaining optimized three-dimensional trajectory data. According to the invention, high-precision acquisition, dynamic optimization and precise correction of the logistics track data are realized, the accuracy and environmental adaptability of the logistics track data are improved, reliable data support is provided for intelligent logistics management, and the logistics efficiency is improved.

The invention belongs to the technical field of mixing equipment, and particularly relates to a non-intrusive homogenizer operation process abnormity monitoring method and system, and the method comprises the steps: calculating the mechanical angular acceleration according to the real-time rotating speed of a motor shaft end; on the basis of a pre-constructed inverse dynamic model, according to the motor orthogonal axis current, the motor shaft end real-time rotating speed and the mechanical angular acceleration, a rheological resistance characteristic index is calculated; processing the rheological resistance characteristic index by using a singular spectrum analysis algorithm, and extracting a trend line of the rheological resistance characteristic index through construction of a trajectory matrix, singular value decomposition and diagonal averaging operation; the information entropy of the first-order difference sequence of the trend line is calculated to serve as the mixing stability entropy, and the mixing end point of the operation process of the homogenizer is judged according to the convergence condition of the mixing stability entropy relative to the reference entropy, so that anomaly monitoring of the operation process of the non-intrusive homogenizer is achieved. According to the invention, abnormity monitoring and end point determination of the mixing process of the homogenizer are realized.

The invention discloses a geothermal well recharge regulation and control method combined with a thermal breakthrough prediction model, and particularly relates to the field of geothermal energy development and utilization. According to the method, basic parameters of a thermal reservoir, operation parameters of a recharge system and water quality parameters of a recharge water source are collected and standardized to form a data set, so that a thermal breakthrough risk factor matrix is constructed; a thermal breakthrough prediction model is established by fusing a coupling analysis algorithm and an improved long-short-term memory neural network; after K-fold cross validation and dynamic error compensation algorithm correction, outputting a standard prediction parameter set and a thermal reservoir permeation partition; dividing a thermal breakthrough risk grade by combining the risk factor matrix and geothermal utilization requirements, setting a regulation and control threshold value, and establishing a correction parameter; a regulation and control unit is formed based on the risk level and the permeation partition, a targeted regulation and control scheme is formulated, dynamic matching of the recharge state and the scheme is achieved through real-time monitoring of the Internet of Things, the thermal breakthrough and reservoir blockage risks are reduced, and the recharge stability and sustainability are improved.

The invention relates to the field of enrichment and extraction of waste gold ores, in particular to an element distribution detection method for an enrichment and extraction process of low-grade waste gold ores. The method comprises the following steps: firstly, obtaining a standardized occurrence medium based on a collected sample, and calculating absolute mass of elements actually carried by the standardized occurrence medium; then, based on the absolute mass of the elements actually carried by the standardized occurrence medium, an element occurrence form distribution normalization algorithm and a distributed element recovery efficiency analysis algorithm are adopted respectively, and the real occurrence probability distribution and the mass flow direction proportion of the elements in the standardized occurrence medium are obtained; and finally, based on the real occurrence probability distribution and the mass flow direction proportion of the element in the standardized occurrence medium, obtaining the enrichment selectivity intensity of the element through an occurrence medium collaborative enrichment coupling algorithm. The technical problems that the element occurrence information in the low-grade gold ore waste rock is not clear, the element mass migration path in the output branch of the extraction process is not clear, and the enrichment rule is difficult to track are solved.

The invention discloses a chemical process anomaly type identification method based on a multi-dimensional causal association index, and belongs to the technical field of chemical process monitoring and fault diagnosis. The method comprises the following steps: firstly, carrying out offline modeling: acquiring normal working condition data, constructing a monitoring model by utilizing a trend slow feature analysis algorithm, and determining a statistic control limit; during online application, statistics of real-time data are calculated through a model, and an alarm is given and a type identification process is triggered if the statistics exceed a limit. A multi-dimensional causal association index (MCAI) is constructed by analyzing whether a quality variable deviates or not and further performing comprehensive analysis on a root variable causing an anomaly from four dimensions of a position (CPI) of the root variable in a process, an attribute (CAI) of the root variable in a causal network and an influence propagation capability (CPD) of the root variable. The problem that a traditional anomaly detection method cannot distinguish process faults from new working conditions is solved, false alarm can be effectively avoided, and a decision basis is provided for accurate maintenance.

The invention discloses a cold chain temperature control remote monitoring and early warning system and method driven by the Internet of Things, and the system comprises a monitoring terminal module which is disposed in a cold chain environment and is used for collecting temperature and environment state signals and generating original sensing data signals; the wireless communication module is connected with the monitoring terminal module, receives an original sensing data signal and packages the original sensing data signal into an Internet of Things transmission frame signal; the cloud platform processing module receives the Internet of Things transmission frame signal, analyzes and stores the data, and generates a temperature abnormity early warning signal through an integrated artificial intelligence analysis algorithm; the early warning service module receives the temperature abnormity early warning signal and generates a multi-stage early warning notification signal according to a predefined warning strategy; the terminal execution module is connected with the cloud platform processing module and the early warning service module. According to the cold chain temperature control remote monitoring and early warning system and method driven by the internet of things, the problems of temperature control information lag, inaccurate early warning and difficulty in tracing in the cold chain transportation process can be solved.

The invention relates to the technical field of API abnormal behavior detection, in particular to a multi-feature fusion API abnormal behavior detection method, system and device and a medium. The method comprises the following steps: collecting API traffic of a traffic probe monitoring system through the Internet or deploying a traffic probe in a service system, and obtaining an API request data packet and a reply data packet; performing time sequence feature analysis on the API request data packet and the reply data packet through a time sequence feature analysis algorithm to obtain a time sequence feature anomaly detection result; analyzing the content features based on a deep learning algorithm to obtain a content level anomaly detection result; analyzing results of the two dimensions are integrated, and whether API calling is abnormal or not is judged through an analysis algorithm. The completeness bottleneck of artificial feature engineering is broken through, the detection efficiency and accuracy are greatly improved, and more intelligent and more reliable technical support is provided for API security protection.

The invention discloses a carbon quota management method and system for carbon asset transaction, and relates to the field of data management, and the method comprises the steps: constructing a knowledge graph of enterprise and environment association, fusing multi-modal data through employing a cross-modal attention mechanism, and carrying out the industry classification of enterprises; determining a carbon emission risk threshold value of each industry by using a data envelope analysis algorithm, constructing a double-layer game framework, generating a technical progress coefficient sequence in combination with a multi-head self-attention mechanism, and determining a carbon quota interval; constructing a dynamic feature vector, simulating carbon emission evolution paths in different scenes through a Monte Carlo algorithm, and outputting a carbon emission accounting result; and predicting the supply and demand trend of the carbon quota, calculating supply and demand gaps under different scenes, comparing the supply and demand gaps with a preset threshold value, and triggering an adaptive adjustment mechanism. According to the invention, the carbon emission risk can be identified, industry technology evolution and market demand changes are dynamically captured, and the accuracy and flexibility of carbon quota management are improved.