93 results about "Probability inference" patented technology

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention provides a water conservancy grid parameter inversion method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source monitoring data of a target water conservancy project region, combining the multi-source monitoring data with a preset water conservancy grid physical mechanism model, and building an artificial intelligence probability inference frame for heterogeneous medium parameter inversion; and iteratively optimizing an implicit association relationship between permeability coefficient spatial distribution and hydraulic conductivity dynamic change in the water conservancy grid physical mechanism model, generating a parameter probability distribution set, and according to the parameter probability distribution set, combining with a historical seepage field evolution mode of the target water conservancy project area to obtain a historical seepage field evolution model of the target water conservancy project area. And the equivalent parameter value of each grid unit in the water conservancy grid physical mechanism model is dynamically corrected, a water conservancy grid parameter inversion result is generated, the precision and stability of water conservancy grid parameter inversion under complex working conditions are improved, and reliable support is provided for engineering safety and risk early warning of reservoirs and dams.

A spatial omics multi-modal fusion method under a single cell level comprises the following steps: extracting spatial morphological characteristics of differential expression genes and cell nucleuses from spatial transcriptome data, single cell sequencing data and histological images, and realizing field adaptation among different platforms by using a conditional variation auto-encoder. And based on a probability inference model, fusing spatial transcriptome expression, unicellular omics and morphological characteristics, and jointly inferring the type and gene expression level of each cell. A spatial cell network is constructed through a graph attention mechanism, and spatial diffusion and recognition of cell types in a full slice range are realized. In combination with a multi-omics enhancement module, undetected gene and protein expression is completed based on expression similarity, and prediction consistency is improved through spatial correction. According to the method, high-resolution reconstruction of single-cell multi-omics information in a three-dimensional space is realized, the information coverage and spatial resolution of spatial omics data are improved, and an efficient and low-cost solution is provided for spatial biology and precise medical research.

The invention provides a gastric cancer TNM staging automation method and system based on a large language model. The method comprises the steps that a medical data set related to gastric cancer of a patient is acquired; preprocessing the medical data set; inputting the preprocessed medical data set into a trained large language model; extracting examination conclusions in the preprocessed medical data set, splicing all diagnosis conclusions into a long text for input, and inserting special separators between different sources for distinguishing; performing feature extraction on the long text input to generate a feature vector; probability inference is carried out after suspicious remote transfer labeling in the feature vectors; carrying out conflict statement resolution and credibility judgment on the feature vectors, and then carrying out TNM stage classification; and outputting the TNM staging result of the patient. The method has the beneficial effects that full-process automatic processing from medical data set acquisition, preprocessing and feature extraction to final TNM stage classification is realized.

The invention belongs to the crossing field of traffic engineering and information technology, and discloses a highway continuous longitudinal slope section traffic safety knowledge graph construction method. According to the method, road section features, traffic flow, historical accidents and environmental data are integrated, and a multi-source data set is constructed through cleaning and standardization processing; defining a road section, an accident, an environment and a vehicle entity, and constructing a multi-level knowledge graph comprising a static topology layer, a dynamic attribute layer and a probabilistic reasoning layer; knowledge triples are extracted by using a natural language processing technology, and real-time risk assessment and early warning are realized by dynamically fusing association logic of gradient, environment and accident data in combination with a rule engine; a graph database is adopted to store the graph, and semantic query and dynamic updating are supported. The method breaks through the limitation of traditional single data source analysis, solves the problem of coupling risk assessment of dynamic environmental factors and static features under complex terrains through multi-source fusion and probabilistic reasoning mechanisms, and remarkably improves the traffic safety risk prediction precision.

The invention discloses a water environment pollution traceability system and method. The system comprises a multi-level monitoring module, a flow field reconstruction module, an inversion solving module, an intelligent identification module and a decision judgment module which are connected in sequence. The multi-level monitoring module acquires pollutant concentration and a three-dimensional flow field parameter time sequence in real time, and performs spatial interpolation and time filtering processing on flow field data; the flow field reconstruction module establishes a dynamic source-receptor response matrix according to the pollutant transmission track network; an inversion solving module calculates posterior probability distribution of the parameters; the intelligent identification module identifies the pollution source type through a pre-training classification model; and the decision judgment module comprehensively judges the effective pollution source according to the posterior probability confidence coefficient and the spatio-temporal evolution characteristics. According to the method, flow field reconstruction and probability inference technologies are fused, high-precision identification and parameter estimation of the dynamic pollution source of the water body are achieved, and the problems that in the prior art, mobile pollution sources cannot be effectively treated, and intermittent emission cannot be achieved are solved.

The invention discloses an intelligent substation virtual loop online transmission verification method based on GOOSE / SV closed loop test, and relates to the technical field of intelligent substation operation and maintenance. Through precise clock synchronization and an improved cross-correlation algorithm, in combination with wavelet noise reduction and spectral clustering analysis, nanosecond synchronization quality evaluation of GOOSE / SV signals is realized, and the hidden transmission risk discovery time is shortened from regular maintenance to real-time monitoring; a hierarchical hidden Markov model is adopted to analyze equipment-level to system-level behavior modes, real-time probabilistic reasoning is realized in combination with a dynamic Bayesian network and particle filtering, a multi-dimensional evaluation system is constructed, the reliability evaluation capability under complex working conditions is remarkably improved, an optimization scheme is generated based on network path characteristic analysis and bottleneck identification, and the reliability of the system is improved. Multi-scene closed-loop verification is carried out by means of a digital twin technology, safety and reliability of parameter optimization are ensured, and full-process intelligent operation and maintenance of the virtual circuit of the intelligent substation from state perception to optimization verification are realized.

The invention discloses a robot control method and system based on long-term and short-term human motion intentions, and relates to the technical field of robot control. According to the method, based on a time sequence trajectory prediction module of a Transform neural network, multi-step accurate prediction of a short-term trajectory of human motion is realized. And establishing a dynamic probability distribution model of a human long-term moving target by adopting an iterative Bayesian probabilistic reasoning method to identify a human final target point. And through real-time updating of the contact force and the weight, the period changing intention is calculated. Through the synergistic effect of short-term time sequence trajectory prediction, long-term intention probabilistic reasoning and haptic feedback, accurate prediction of a human short-term motion trajectory, robust recognition of a long-term target intention and dynamic response to intention sudden change are realized, and the prediction accuracy of the short-term trajectory is ensured while the prediction accuracy of the long-term target intention is improved. The robustness of long-term target recognition and intention sudden change response is remarkably improved, the defects of an existing human motion intention prediction method are overcome, and the intelligence and efficiency in a man-machine cooperation task are improved.

The invention provides a multi-parameter fusion filtering equipment health monitoring method and system. The method comprises the following steps: acquiring a filter membrane pressure harmonic component and a particulate matter concentration change rate, and aligning according to a time window to generate a characteristic matrix; calibrating a pressure component in a matrix through real-time data of a honeycomb array layout pressure sensor, and judging an effective blockage characteristic when the node detects that the pressure is abnormal; a health score and a blockage risk thermodynamic diagram are generated through probability inference in combination with the particulate matter concentration time sequence trend; and calculating a distribution deviation between the thermodynamic diagram and a real-time abnormal region, and dynamically adjusting a pressure harmonic weight coefficient and a topology verification threshold value, so that the thermodynamic diagram approaches actual distribution, and closed-loop monitoring is formed. According to the invention, dynamic sensing and closed-loop optimization of the blockage risk of the filter membrane are realized, and the multi-parameter fusion monitoring precision and the micro-blockage early warning timeliness are improved.

The invention discloses a multi-region aerosol disinfection and odor removal control method and system based on the Internet of Things, and the method comprises the steps: deploying a sensor network in multiple regions, synchronously collecting and carrying out the time-space correlation processing of multi-modal data including chemical, acoustic, thermal imaging and air flow field data; adopting a probabilistic reasoning model to estimate the probability that each area becomes a pollution source in real time, and generating a pollution source probability distribution map; on the basis of the distribution diagram and the air flow field data, the diffusion trend of pollutants is predicted through a diffusion prediction model, and a composite control strategy for treating a pollution source and intervening a pollutant diffusion path is generated; acquiring actual environment data fed back by the sensing network, comparing the actual environment data with a deviation generated by a prediction result, and performing parameter adjustment on the probabilistic reasoning model or the diffusion prediction model according to the deviation; according to the invention, the problems of response lag, no traceability and lack of predictive intervention capability in the prior art are solved, and adaptive control of pollution events is realized.

The invention relates to the technical field of hydrological prediction, and discloses a flood prediction method and system based on machine learning, and the method comprises the steps: constructing a multi-source data probability representation layer, and converting multi-source hydrological data into probability distribution representation through a variational auto-encoder; a Bayesian depth feature extraction layer is constructed, and a random weight neural network is adopted to extract hydrological features from data represented by probability distribution; constructing a causal structure learning layer, and learning a causal relationship between variables from hydrological features by using a causal discovery algorithm; a probability inference prediction layer is constructed, and prediction distribution of hydrological variables is generated through a Markov chain Monte Carlo method; an interpretable anti-fact analysis layer is constructed, and hypothesis analysis and decision evaluation of a flood scene are supported; according to the method, the transformation from determinacy prediction to probability prediction is realized, the interpretability is enhanced through causal learning and symbol probability hybrid reasoning, and reliable technical support is provided for flood control decision making.

The invention relates to the technical field of concrete detection, in particular to a concrete structure crack damage evaluation system based on acoustic emission sensing, which comprises a signal acquisition module, a characteristic discrete evolution module, a probability inference module, an energy gradient analysis module and a boundary defining module. According to the method, a multi-dimensional evolution set is constructed by extracting the amplitude and energy standard deviation of an acoustic emission signal in a continuous time window, the synchronous change trend of the multi-parameter standard deviation is analyzed by using a Bayesian probability model, and the non-uniform expansion posterior probability is calculated to lock a key signal set of dominant damage expansion. An energy fluctuation coefficient is calculated and a space sequence is generated by combining sensor space coordinates, and a fluctuation coefficient stable interval is identified according to a gradient attenuation rule of energy along with a distance, so that a physical boundary of a crack damage dynamic active region is quantitatively defined, and accurate evaluation of a non-uniform expansion state and an active range of a concrete crack is realized.

The invention relates to the technical field of stereoscopic vision, and discloses a synchronous speed visual matching method and system, electronic equipment and a storage medium, and the method comprises the steps: synchronously collecting a stereoscopic video sequence with a predefined frame rate; executing multi-target hybrid tracking and motion induction detection, and outputting target motion information including position and velocity vectors; extracting hierarchical motion features of the target from continuous multiple frames of the stereoscopic video sequence, and performing unified space-time coding; under geometric constraints of stereoscopic vision, scale cosine similarity, direction similarity and trajectory consistency measurement are calculated and serve as observation evidences to be input into the probabilistic reasoning model for fusion, and a posterior probability representing matching reliability is output; the weight distribution of the speed similarity and the direction similarity is adjusted according to the motion characteristics of the targets in the scene, and the stable corresponding matching relation between the left view target and the right view target is established. According to the method, high-time-resolution information can be utilized, motion features and geometric constraints can be effectively fused, and the method has self-adaptive capacity.

The invention relates to the technical field of chip control, in particular to a remote control system based on a Bluetooth micro-control chip, which comprises a trend identification module, a communication state judgment module, a power regulation module, a link activity inference module and a control instruction issuing module. According to the invention, the continuous trend of the signal intensity change in the Bluetooth connection process is deeply analyzed, so that the prospective judgment of the stability of the communication link is realized, the optimal transmitting power is dynamically matched according to the real-time quality of the link, the power consumption is actively reduced when the signal condition is good, and the reliability of the communication link is improved. When the signal fluctuates, the power is enhanced to guarantee the reliability of the connection, energy waste and connection interruption risks caused by a fixed power strategy are avoided, in addition, an objective basis for evaluating the link activeness is established by recording the frequency of the power regulation behavior, and the reliability of the link is improved. And a communication link which is really in an inactive state is accurately identified by combining probability inference, so that invalid connection can be released in time to save electric quantity.

The invention relates to a digital-intelligent automatic point alignment and full information verification method based on a power distribution terminal tester, which comprises the following steps of: acquiring electrical quantity signals and operating parameters of a terminal through the tester, and establishing communication connection through a digital-intelligent point alignment module; starting a three-remote check matrix model to perform matrix check on telemetering, remote signaling and remote control functions, calling an FFT harmonic synthesis telemetering calibration algorithm to dynamically calibrate a deviation value, introducing an abnormal attribution model based on a Bayesian network to perform probability reasoning on abnormal data, generating a report containing abnormal information and calibration suggestions, and uploading the report. The method improves the verification efficiency and overcomes the problem that the existing mode is tedious; checking accuracy is improved, and the problem that measurement deviation is difficult to correct is solved; abnormality processing capability is enhanced, defects of insufficient attribution capability are made up, stable operation of the power distribution terminal is guaranteed, and support is provided for power grid safety.

The invention discloses a route leakage detection method based on probabilistic reasoning and path scoring, and the method comprises the steps: obtaining an autonomous system path in a border gateway protocol route, and enabling the autonomous system path to comprise a plurality of autonomous system links, obtaining autonomous system business relationship probability data about the plurality of autonomous system links through an autonomous system business relationship probability inference algorithm, and obtaining autonomous system business relationship determination data about the plurality of autonomous system links through an autonomous system provider authorization object; the autonomous system business relation probability inference algorithm is set according to a valley-free rule, and the autonomous system business relation comprises provider-to-customer, customer-to-provider and peer-to-peer; according to the autonomous system business relationship probability data and the autonomous system business relationship determination data, calculating the score of the autonomous system path through a path score calculation formula; and comparing the score of the autonomous system path with a preset threshold value, and judging whether the border gateway protocol route of the autonomous system path is a leaked route or not.

The invention relates to the technical field of dust pollution treatment, in particular to a port dry bulk cargo storage yard electronic greenhouse closing system based on multi-dimensional full-time management and control. According to the system, a time reference is established by identifying an operation event, multi-source sensor data is mapped to a unified time axis, and space-time registration is realized by combining Bayesian probability inference; constructing a physical information neural network based on the registration data to predict a continuous dust concentration field; a virtual closed boundary is generated according to the concentration threshold value, and the closing effectiveness is evaluated; calibrating physical parameters by using a CFD model in combination with a parameter inversion method; and in the rolling prediction time domain, constructing an optimization model weighted by the spraying cost, the environmental protection penalty and the control cost, solving an optimal spraying control strategy, and feeding back an execution effect to the model to form closed-loop optimization. By means of the system, the raised dust of the storage yard can be precisely treated, and it can be guaranteed that the raised dust is sealed in the electronic greenhouse in an open-air scene.

A single-cell level spatial omics multi-modal fusion method, comprising: extracting differential expression genes and nuclear spatial morphological features from spatial transcriptome data, single-cell sequencing data and histological images, and realizing field adaptation between different platforms by using conditional variational autoencoder. Based on a probability inference model, spatial transcriptome expression, single-cell omics and morphological features are fused to jointly infer the type and gene expression level of each cell. A spatial cell network is constructed by a graph attention mechanism to realize the spatial diffusion and recognition of cell types in the whole slice range. Combined with a multi-omics enhancement module, the unmeasured gene and protein expression are completed based on expression similarity, and the prediction consistency is improved through spatial correction. The method realizes high-resolution reconstruction of single-cell multi-omics information in three-dimensional space, improves the information coverage and spatial resolution of spatial omics data, and provides an efficient and low-cost solution for spatial biology and precision medicine research.

The application relates to the technical field of unmanned aerial vehicle swarm detection, and discloses an unmanned aerial vehicle swarm detection method and system based on multilayer early warning, which comprises the following steps: collecting radar signal and photoelectric signal data and unmanned aerial vehicle speed, generating initial data by unified mapping according to preset state transition and observation matrix, making prediction-residual correction by combining preset noise parameters to obtain a dynamic trajectory, smoothing the trajectory, statistically analyzing the trajectory according to height granularity, adaptively clustering to form layered height intervals, identifying dense clusters according to distance radius and core points when the number of layers exceeds a threshold, extracting cluster centers and density gradients, constructing a probability inference structure according to the above and the speed, fusing prior formation, wind disturbance and delay, outputting a dynamic threat level, calling a pre-generated heat map to position a high-threat area when the threat exceeds a threshold, combining airspace safety constraints and speed to predict a future trajectory, updating a situation, adaptively configuring radar beams and coverage, and outputting real-time detection data. The method can realize reliable airspace management.

This invention provides a collision detection method and system for robotic arms based on Bayesian inference, relating to the technical fields of robot safety perception and human-computer interaction. The method includes: constructing a generalized momentum model containing unknown dynamic terms and external joint torques based on a joint spatial dynamics model of the robotic arm; performing probabilistic inference on the unknown dynamic terms using an integrated Bayesian neural network, outputting the predicted mean and variance of the unknown dynamic terms; constructing a data-driven adaptive momentum observer based on the predicted mean and variance to generate an estimated signal of the external joint torques; and performing collision detection based on sparse Bayesian inference on the estimated signal based on the sparsity characteristics of external collision perturbations to generate a collision determination signal. This method solves the technical problems of insufficient sensitivity and high false alarm rate in existing collision detection technologies, achieving improved accuracy in external torque estimation and collision detection sensitivity.

The invention provides an enterprise cash flow intelligent prediction and risk early warning method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining enterprise cash flow data; constructing a cash flow stability tensor according to the enterprise cash flow data; dividing the cash flow stability tensor into a committed cash flow tensor and a realization cash flow tensor; determining a state description vector according to the committed cash flow tensor and the implementation cash flow tensor; according to the state description vector, constructing a hidden semi-Markov model; performing state prediction on the enterprise cash flow through the hidden semi-Markov model to obtain enterprise cash flow state distribution; constructing a risk infection graph according to the enterprise cash flow state distribution; probability inference is carried out on the risk infection graph to obtain the risk probability of the enterprise cash flow; and carrying out risk early warning according to the risk probability.

The application provides a kind of based on artificial intelligence chemical substance fast identification method and system, belong to intelligent detection analysis technical field;The method includes the preprocessing and structural conversion processing of physical signal and social context information, extracts the physical evidence characteristics and social evidence characteristics of the sample to be measured;According to physical evidence characteristics and social evidence characteristics, through double drive hypothesis generation, the bayesian inference network containing candidate substance hypothesis node, physical evidence node and social evidence node is constructed;Solving the posterior probability value corresponding to each candidate substance hypothesis, according to the posterior probability value and evidence chain analysis result executes decision, generates and outputs chemical substance identification report.The application generates double drive hypothesis by fusing physical signal and social context information, and uses bayesian network to carry out probability inference, which significantly improves the accuracy and reliability of chemical substance identification in complex scene.

The invention relates to a seal use safety control method, system and device based on multi-source feature fusion and a medium. The method comprises the steps that a to-be-stamped paper surface image and a stamping candidate area are obtained, illumination normalization and geometric correction preprocessing are conducted on the to-be-stamped paper surface image, and a standardized stamping paper surface image is obtained; performing multi-source feature extraction on the standardized stamping paper surface image to obtain a feature set; performing space and channel alignment and fusion processing on the feature set to obtain fusion features; pixel-level decoding is carried out according to the fusion features to obtain a blank probability graph, and global blank probability inference is carried out according to the blank probability graph to obtain a blank determination score and a confidence coefficient thereof; based on a preset double-threshold mechanism, generating a control decision and audit record according to the blank probability graph, the blank judgment score and the confidence coefficient; the control decision is one of blocking, warning and permission. By adopting the method, high-precision white paper identification can be realized so as to realize printing compliance control.

The present application relates to the technical field of laser cladding manufacturing, and discloses a kind of laser cladding additive manufacturing light spot size adaptive control method.The method is according to the real-time monitoring data in laser cladding process, the dynamic characteristics of molten pool are multidimensional fusion processing, generate the space-time characteristic tensor containing temperature gradient distribution, molten pool topographic feature and spectral feature information;Again, the space-time characteristic tensor is input into deep probability inference model, and the abnormal state of cladding is probabilisticly characterized, and the light spot regulation parameter vector with confidence is output;Then, according to the vector, a three-dimensional energy field distribution model is constructed, and the energy field propagation path and evolution trend are predicted based on dynamic graph convolution network;Finally, for the prediction result, an adaptive light spot adjustment mechanism based on generative adversarial network is constructed, and a multi-level light spot adjustment threshold that changes dynamically with the cladding process is generated, to realize the adaptive control of light spot size and improve the cladding forming quality.

The invention relates to the technical field of stroke monitoring, and discloses a method and a system for detecting the stroke risk after waking of a home elder based on water and electricity information, and the method comprises the steps: synchronously obtaining household water, electricity and body data in a morning waking monitoring time period, and generating a morning waking water event sequence and an electricity load sequence through flow sequence form segmentation and electricity event steady-state analysis; morning awakening physiological indexes are formed according to the skin electricity, the heart rate and other data; determining a unified time reference according to a morning beginning behavior completion moment or a physiological composite sign, constructing an alignment time window, and mapping the three types of data to a discrete time grid; establishing double-hypothesis probability inference, respectively constructing water utilization, power utilization and body three-channel observation likelihoods, and performing joint fusion with a dependency relationship to obtain a current occurrence probability; and establishing a survival analysis model by using the time variation covariable in the aligned time window, and giving the occurrence probability of the future time period. The method does not depend on videos, and the judgment capability can be kept when the body data are missing.

The invention relates to the technical field of artificial intelligence, in particular to a student high-order thinking ability assessment method, device and equipment based on project-based learning behavior data, a storage medium and a computer program product. The method comprises the following steps: obtaining multi-source heterogeneous behavior data of a to-be-evaluated student in a project-based learning process to obtain a to-be-processed behavior data set; performing cleaning, standardization and cross-source alignment processing on the to-be-processed behavior data set, and performing fusion based on the aligned data to obtain unified behavior data; performing feature engineering processing based on the unified behavior data to extract a behavior feature set for representing high-order thinking related cognitive activities; based on a preset learning model, extracting behavior evidences from the behavior feature set, fusing the behavior evidences and performing probability inference; according to the incidence relation between the behavior evidence and the preset thinking index and the ability dimension and the probability inference result, the target assessment result is determined, and the accuracy of student high-order thinking ability assessment is improved.

The application discloses a kind of water transport ship traffic collaborative control methods based on deep learning, specifically includes: obtaining ship operation data set, time index alignment is carried out, and state vector sequence is generated;State vector sequence is encoded to feature, and deep boltzmann machine model is constructed;First conditional energy function, second conditional energy function and energy coupling matrix are established between model layer, and form hierarchical conditional energy function set;Hierarchical conditional energy function set is carried out to hidden variable probability inference, and hidden layer probability state vector sequence is generated;Parameter flow shape is divided to energy function parameter, and policy optimization is carried out under the constraint of confidence domain, and updated energy function parameter set is generated;Updated energy function parameter is carried out to energy calculation, and state probability distribution sequence is generated;State probability distribution sequence is carried out to strategy mapping, and ship traffic collaborative control strategy is generated.The application constructs hierarchical energy coupling model, realizes the collaborative control of multiple ships state.

An intelligent anti-theft electricity distribution system, method, electronic device and storage medium, the system comprises: a data acquisition module configured to acquire current data, power data and temperature data of a power distribution line; a data processing module configured to perform spatio-temporal alignment processing on the current data and the temperature data to obtain a current temperature data matrix, and construct a node current balance equation based on the current temperature data matrix and the power data; a confidence generation module configured to construct a Bayesian network based on a preset historical electricity theft case library, input a calculation result of the node current balance equation into the Bayesian network for time series probability inference calculation to obtain a electricity theft behavior confidence; an instruction generation module configured to match a preset risk level according to the electricity theft behavior confidence, and generate a risk disposal instruction according to the risk level; and an execution module configured to determine a target line to perform a target operation according to the risk disposal instruction. The technical solution provided by the present application can solve the technical problem of single detection dimension.

The invention relates to the technical field of account supervision and management, and discloses a chess and card game account transaction monitoring system which comprises the following steps: classifying asset transfer according to a business trigger source, and binding game information for game result type transaction as a traceability anchor point; a conservation verification gateway is arranged to carry out instant arbitration on game result type transactions, a traceable value balance which is adjusted only according to verified game net income and expenditure is established for an account, when point-to-point type transactions are processed, the transaction amount is compared with the balance to add a deterministic risk qualitative label, and the point-to-point type transactions are processed. According to the method and the system, credential management is realized at a data source, so that a monitoring system is converted from probabilistic inference on hybrid data into deterministic auditing based on a clear fund source, and therefore, monitoring resources are accurately focused on high-risk transactions, and supervision efficiency and auditing performance are improved.

The invention discloses a vulnerability probability graph automatic construction method, system and device based on service operation characteristics and a medium, and relates to the technical field of network security, and the method comprises the following steps: collecting service call chain data generated in a system operation process, endowing an edge with a weight representing a call frequency, constructing a graph structure, extracting multi-dimensional operation characteristics of each node in the graph, and constructing a vulnerability probability graph according to the multi-dimensional operation characteristics. The method comprises the steps of obtaining a graph structure, performing normalization processing to form a unified node semantic representation, performing context-aware information propagation and vulnerability probability inference based on the graph structure and the node semantic representation to obtain a vulnerability probability value of each node, screening high-risk nodes according to a preset risk threshold, extracting associated edges, and generating a vulnerability probability graph. According to the method, by introducing multi-dimensional operation feature extraction, a graph attention network propagation mechanism and a dynamic threshold screening strategy, the technical bottleneck of graph structure vulnerability detection is effectively solved, and the accuracy of vulnerability probability inference and the automation level of graph construction are remarkably improved.