335 results about "Graph algorithms" patented technology

The invention discloses a privacy enhanced intelligent search method and system based on multi-round iteration. The method comprises the following steps: performing hierarchical semantic analysis on a query input by a user; splitting the complex query into sub-queries based on a task dependency graph algorithm; according to the sub-query, retrieving an evidence fragment from the multi-source data, constructing a semantic element coverage matrix to detect a knowledge gap, and if an uncovered element exists, generating a supplementary sub-query for iterative completion until a preset termination condition is met; integrating cross-modal data through a federated learning technology, and generating a structured knowledge graph fragment in combination with semantic vector alignment and an evidence fusion algorithm; performing dynamic desensitization processing on the retrieval result; and a closed-loop iterative updating mechanism is formed based on a user explicit and implicit feedback optimization retrieval strategy. The problems of traditional intelligent search in the aspects of semantic understanding depth, complex problem reasoning, search result accuracy and integrity, user privacy security and the like are effectively solved.

The invention relates to the technical field of robots, and discloses an SLAM navigation-based inspection robot inspection track self-correction method, which comprises the following steps: S1, acquiring environmental data through a 2D laser radar, an attitude sensor and an odometer sensor, and generating an initial environmental map and a navigation path by using an SLAM mapping algorithm; s2, fusing the laser radar pose data, the odometer pose data and the pose sensor pose data to obtain real-time pose information, comparing the current pose with the target path pose, and calculating a path error; s3, a robot motion instruction is adjusted through a compensation control algorithm based on the path error, and the path deviation is corrected; s4, continuously monitoring path errors and correcting the path errors in real time; and S5, storing the corrected track as a navigation path file. According to the invention, by introducing the robot remote controller, complex functions such as mapping, navigation, navigation point marking, path storage, motion control and the like are modularized, and the operation process is greatly simplified.

The invention discloses an inspection robot and an inspection method thereof, and relates to the technical field of robots, and the inspection robot comprises a remote control center which is in communication connection with an environment perception and mapping module, an intelligent movement and obstacle avoidance control module, and an intelligent analysis and user interaction module; the environment sensing and mapping module integrates a laser radar, an ultrasonic sensor and an RGB-D camera, and realizes multi-dimensional environment data acquisition; the intelligent movement and obstacle avoidance control module adopts an SLAM algorithm to position and map, adopts an RRT algorithm to plan a path, adopts a CNN algorithm to recognize an obstacle, and adjusts movement through a PID controller and a kinematic model to realize intelligent obstacle avoidance; the intelligent analysis and user interaction module reduces delay through edge calculation, provides real-time data processing and a graphical user interface and displays the state and operation guide of the robot. On the whole, by integrating various sensors and advanced algorithms, efficient and intelligent inspection operation is realized, and the inspection efficiency and safety are improved.

The invention belongs to the field of multi-modal data processing, and particularly relates to a multi-modal fusion analysis method and system for test data, and the method achieves the intelligent analysis of real-time multi-dimensional demand data through constructing a bidirectional multi-modal constraint generation model and fusing multi-modal reasoning generation, forward logic constraint and reverse causal constraint sub-models. Wherein the forward logic constraint sub-model constructs a forward constraint hierarchical reasoning association node network through entity-relation extraction and a graph algorithm based on a test standard criterion and a historical constraint sequence, and generates a forward logic constraint set in combination with a depth index algorithm; the reverse causal constraint sub-model constructs a combined conflict value through modal reasoning accuracy, performs conflict tracing and adjustment in combination with a cross-modal conflict threshold, and generates a reverse causal constraint set; the two constraint sets are fused through a multi-modal reasoning generation sub-model, a target generation demand text meeting the confidence coefficient requirement is finally generated, and intelligent analysis of clinical test data is achieved.

The invention relates to the technical field of smart communities, and discloses a smart community intelligent security scheduling management method based on deep learning, which comprises the following steps: acquiring video monitoring data and access control record data of a plurality of communities, cleaning the data, unifying the format and content of the data by adopting a data standardization technology, and storing the unified data into a database; the method comprises the steps of obtaining standardized community security and protection data, mining a personnel flow mode and a vehicle track mode among different communities by adopting a graph algorithm analysis technology on the basis of a community relation network to obtain a cross-community personnel flow mode and a vehicle track mode, and obtaining a cross-community security and protection result according to analysis results of the cross-community personnel flow mode and the vehicle track mode. And an anomaly detection algorithm is adopted to identify potential safety hazards, and if an abnormal condition is detected, early warning information is generated. According to the invention, deep correlation analysis of cross-community security data is realized, and the intelligent level of community security management is effectively improved.

The invention discloses a credit loan fraud data identification method and system based on artificial intelligence and a graph algorithm, and relates to the technical field of credit loan risk control. User information and behavior data are collected from multiple sources, a relation network graph is constructed after cleaning, dense communities are identified, cross-community intermediary nodes are positioned, community portraits are generated, behavior patterns are quantified, and similar groups are clustered. According to the method, a complex fraud mode is accurately recognized, hidden association is mined through a graph algorithm, the recognition accuracy is improved in combination with community portraits and behavior clustering, the dynamic adaptability is high, real-time feedback and incremental learning ensure that novel fraud is rapidly coped with, and the method is suitable for popularization and application. A high-interpretability report meets compliance requirements, user trust is improved, large-scale data is efficiently processed, modular design adapts to different credit and loan scenes, cost is reduced through automatic decision, the omission ratio is reduced, fraud loss is directly reduced, and the rate of return on investment is high.

The invention provides a robust factor graph integrated navigation method based on adaptive MCMC (Markov Chain Monte Carlo), and in an INS / GNSS (Inertial Navigation Satellite System / Global Navigation Satellite System) integrated navigation system, factors such as multipath effect and non-line-of-sight can greatly reduce the positioning accuracy of satellites, so that the effect of the whole integrated navigation system is influenced. In urban traffic, many canyon environments greatly influence the positioning precision of vehicles, so that a combined navigation method with robust performance and estimation precision needs to be researched. The invention provides a robust factor graph integrated navigation method based on the adaptive MCMC. The method comprises the following steps: firstly, introducing adaptive MCMC into a factor graph optimization framework based on a prior stage and a posterior stage so as to accurately estimate GNSS time variable measurement noise covariance; secondly, introducing an innovation x2 detection algorithm to detect and eliminate gross errors; the method can effectively estimate GNSS time-varying variable measurement noise while reducing gross error interference. Compared with a robust adaptive factor graph algorithm based on M estimation and a robust factor graph combined navigation algorithm based on variational Bayes, the method has the advantage that the positioning precision is obviously improved.

The invention provides a low-airspace public route construction method, device and equipment, and relates to the technical field of aircraft airspace route planning. The method comprises the following steps: determining a plurality of target candidate nodes corresponding to a city based on ground traffic topological characteristics of the city corresponding to a low airspace and spatial distribution characteristics of low-risk ground features; performing triangulation on the plurality of target candidate nodes to obtain an initial edge set; wherein the initial edge set comprises a plurality of initial edges; performing sparse processing on the plurality of initial edges based on a greedy tensor graph algorithm, and determining a plurality of target edges from the plurality of initial edges; wherein the length of the shortest path between the two nodes of the target edge is greater than the greedy tensor coefficient times of the Euclidean distance between the two nodes; and constructing a low-airspace public route based on the plurality of target edges. By adopting the technical scheme provided by the invention, the utilization efficiency and safety of the low airspace can be effectively improved by constructing the unified low airspace public route.

The invention relates to the technical field of knowledge graph construction, in particular to an intelligent customer relationship knowledge graph construction method, which comprises the following steps of: firstly, acquiring customer data from a multi-source data source; preprocessing the collected data, and protecting sensitive information by adopting a differential privacy technology; defining a mode of a knowledge graph, identifying an entity by adopting a BERT model, extracting a cross-domain relationship in combination with a graph neural network, and constructing a preliminary customer relationship knowledge graph; designing an incremental updating algorithm, and updating the knowledge graph in real time or regularly; based on the constructed customer relationship knowledge graph, a Cypher query language and a graph algorithm are applied to mine customer relationships; performing deep mining on the customer relationship by using a graph algorithm, and identifying key customers, potential customer groups and association relationships among the customers; in this way, the technical problems that in the prior art, when the knowledge graph is constructed, the data processing efficiency is low, relation extraction is inaccurate, and the knowledge graph is not updated in time are solved.

The invention belongs to the field of big data, and relates to a data consanguinity analysis visualization method, which comprises the following steps of: obtaining metadata of target data from a plurality of data sources, and determining a metadata type and a metadata attribute of the metadata; obtaining a blood relationship model of the target data; based on the blood relationship model, the metadata type and the metadata attribute, performing data blood relationship analysis on the target data by adopting a preset graph algorithm to obtain a blood relationship analysis result of the target data; and based on the blood relationship analysis result, establishing a blood relationship graph of the target data, and displaying the blood relationship graph in a preset interactive interface. The invention further provides a device, equipment and a medium. In addition, the invention also relates to a block chain technology, and the metadata can be stored in a block chain. The method can be applied to the fields of financial insurance, medical health management and the like, and the blood relationship graph of the data can be efficiently and accurately established and displayed.

Described are examples for creating a graph model for data obtained from multiple data sources and stored in a common data format. A selection of columns of data stored in the common data format that are to be associated with one another in the graph model can be obtained. Based on the selection, a graph model can be created including metadata that defines a list of vertices corresponding to at least a portion of the columns and a list of edges between two or more of the vertices identifying a relationship between corresponding columns. A graph query or graph algorithm can be performed over the graph model of the data as stored in the common data format without requiring copying of the data to a separate graph database.

The invention discloses a special intelligent control box for an intelligent street lamp, relates to the technical field of industrial automation, and solves the problems that in an existing intelligent street lamp control method, parameter adjustment under multivariable coupling is difficult, and response time is uncertain and priority is reversed due to non-preemptive scheduling. Comprising a multivariable mapping module, a decoupling control module, a real-time multi-task scheduling module and a priority dynamic evaluation module, according to the scheme, the nonlinear coupling model is constructed through the multivariable mapping module, and precise control is realized by using the decoupling control module; the real-time multi-task scheduling module adopts an elastic time slice allocation mechanism to optimize resource allocation; the priority dynamic evaluation module monitors and eliminates priority inversion through a resource allocation graph algorithm; according to the invention, the control capability of the intelligent street lamp on a complex process is greatly improved, and the system response speed, production efficiency and stability are remarkably improved.

The invention provides an otoscope image processing system for establishing database model analysis based on deep learning, and relates to the technical field of image processing, the otoscope image processing system comprises an acquisition layer, a processing layer, an extraction layer, a modeling layer and an analysis layer, the processing layer adopts an AI image expansion algorithm to carry out fusion splicing and interference elimination processing on acquired images, and the extraction layer adopts a mixed feature extraction method to extract color, texture and shape features of the images processed by the processing layer and introduce MPEG-7 descriptors to express the features; according to the method, otoscope images of a patient are collected, features and feature values of the images are extracted, a focus global shared image library is constructed through deep learning modeling, the focus global shared image library is compared with the otoscope images of the patient for training, and corresponding focuses are determined, so that different ear diseases can be automatically identified and diagnosed by learning and analyzing a large amount of ear image data, and the diagnosis accuracy is improved. And more accurate and timely diagnosis information is provided for doctors.

The present invention provides a method for evaluating the effects of communication confrontation based on a knowledge graph, which relates to the field of communication confrontation. The present invention first constructs a knowledge graph of the communication network, converts nodes, communication relationships, and interference relationships into a graph structure, and represents the connection and interference between nodes through the edges in the graph. Then, the node connection relationship is obtained through graph query, the impact of interference and communication quality is calculated, and the shortest path between nodes is calculated using a graph algorithm to achieve an evaluation of the communication effect. The present invention aims to provide a more comprehensive and accurate evaluation of the effects of communication confrontation from the perspective of complex communication networking by analyzing the connection relationship and information transmission path between communication nodes. Through this method, the effect of communication confrontation can be evaluated from the global perspective of the communication network, thereby improving the comprehensiveness and accuracy of the evaluation.

The invention discloses a large-scale vector approximate nearest neighbor search heterogeneous system based on a GPU-NVMe framework, which is characterized in that the system is based on a direct GPU-NVMe framework, and a hybrid index combining a clustering algorithm and a graph algorithm is designed. According to the hybrid index, vectors with good clustering are efficiently processed by utilizing the parallel processing capacity of the GPU, and meanwhile, the search accuracy of noise vectors is improved by taking a graph index as a compensation mechanism. According to the method, collaborative optimization of hardware and software is fully considered, vector index data deployment is implemented at an NVMe driving level, meanwhile, CPU and GPU hardware are cooperatively utilized to carry out a gigabit-level vector retrieval calculation process, and an extensible large-scale retrieval system on a direct GPU-NVMe architecture is constructed. Compared with the prior art, the method has the advantages that the performance bottleneck in the gigabit-level vector data search is effectively solved, the search throughput and accuracy are remarkably improved, and the search delay is reduced.

The invention relates to the technical field of water supply network monitoring, and discloses a water supply network leakage monitoring and positioning system and method based on big data analysis, and the method comprises the steps: obtaining continuous audio stream data and network topological graph data collected by a distributed sensor, and calculating background baseline parameters; performing multi-scale wavelet transform decomposition on the current audio frame, calculating an energy entropy feature and generating a single-frame abnormal score; applying a cumulative sum control chart algorithm to output a cumulative abnormal index; generating an enhanced anomaly score through a graph attention network; and performing leakage judgment and positioning based on the abnormal scores of the multiple sensors. The technical problem that weak leakage signal detection sensitivity is low is solved.

The invention relates to the technical field of derivative variable calculation, and discloses a derivative variable calculation method and system based on a graph algorithm, and the method comprises the steps: analyzing derivative variable definition and calculation logic through employing a variable SQL syntax tree analysis technology, taking an analyzed variable as a node, determining a variable dependency relationship as an edge according to a processing task, and carrying out the calculation of the derivative variable; constructing a directed acyclic graph and storing the directed acyclic graph in a graph database; the nodes store variable metadata, execution priorities and calculation information, and store calculation task metadata and calculation information; according to node priorities, finding out high-priority variable nodes, querying all in-degree edges of the high-priority variable nodes, forming one or more subtrees, forming high-priority execution subtrees, performing topological sorting on the execution subtrees, and performing execution by an execution engine; the method aims to solve the problems of low calculation efficiency and insufficient utilization of calculation resources in the prior art.

According to the knowledge base-oriented interactive causal inference support method and system provided by the invention, a user causal analysis intention is precisely limited through interactive dialogue, a multi-dimensional intention is precisely captured, a fuzzy causal problem is converted into a computable analysis parameter, and ambiguity is remarkably reduced; multiple strategies such as an explicit causal declaration mode, time sequence correlation, statistics of significant correlation and comparative difference analysis are integrated to collect causal evidence, the four strategies are executed in parallel, multiple evidence sources of causal inference are covered, and limitation of a single method is avoided; and the query efficiency is optimized in combination with a special index and a graph algorithm, and finally evidence is presented in a structured and interactive mode and confidence is clearly distinguished, so that the response efficiency is greatly improved. The system solves the problems of fuzzy intention, single evidence and low efficiency in traditional causal inference, is suitable for the fields of industrial fault analysis, medical diagnosis, social phenomenon research and the like, and provides scientific and transparent causal hypothesis generation support for users.

The invention discloses an intelligent connection layout system for an interactive flow chart, and the system comprises an automatic obstacle avoidance connection module which is used for carrying out the path planning of each node layer, and taking each node layer as an obstacle to obtain a flow connection line; the flow layout optimization module is used for carrying out layout optimization on each node graph layer and the corresponding flow connecting line according to a force-oriented graph algorithm; according to a genetic algorithm, adjusting cross connecting lines and overlapping connecting lines between the node layers; the interactive adjustment module is used for generating a corresponding Bezier connection curve according to the moving path drawn by the user; adjusting a corresponding flow connecting line according to the moving position of each node graph layer; and the intelligent recommendation module is used for recommending the flow chart connecting lines of the personalized layout style according to the plurality of historical flow connecting lines of the user and the corresponding operation records. According to the method, the flow chart connecting line layout is intelligently generated, optimized and adjusted, so that the production efficiency of the flow chart and the user interaction experience are improved.

The invention discloses a topology anti-error risk management and control method and system, and relates to the technical field of risk management and control, and the method comprises the following steps: obtaining a power grid topology structure chart, and employing a graph algorithm to code power grid nodes in the topology structure chart, and obtaining a node feature vector; constructing a truth-supplementing parameter group based on the measurement missing node identification result and the node level, and performing random disturbance on the truth-supplementing parameter group based on a preset first constraint condition to obtain a plurality of truth-supplementing schemes; repairing the node feature vectors of the measurement missing nodes by adopting a truth-supplementing scheme to obtain a node repairing feature vector set, and performing similarity analysis on the node repairing feature vector set and a preset risk scene; and calculating an optimal similarity threshold value corresponding to the authentic scheme based on a similarity analysis result, constructing an optimal pairing group, and applying the optimal pairing group to actual anti-error risk identification, so that the problem that the accuracy of power grid risk management and control is reduced due to the lack of a dynamic adaptation mechanism between the repaired node features and an actual risk scene in the face of node measurement deficiency is solved.

The invention relates to the technical field of data processing, in particular to a vibration compaction forming energy transfer efficiency evaluation method, which comprises the following steps: firstly, integrating multi-source data, and generating a semantic enhancement data set through a self-adaptive semantic annotation algorithm; then, node relation extraction is carried out based on the semantic enhancement data set, and a knowledge graph model is constructed and dynamically maintained by using a first graph algorithm so as to structurally represent nodes and relations in the compaction process; based on the knowledge graph model, a parameter influence result is evaluated by a second graph algorithm, and a risk prediction result is generated in combination with a time sequence prediction algorithm; then, a reward function is defined according to a parameter influence result and a risk prediction result, and an optimal vibration compaction parameter combination is determined; and finally, generating a parameter recommendation report and collecting user feedback to iteratively optimize the knowledge graph. According to the method, the accuracy of vibration compaction parameter selection can be improved, and the energy transmission efficiency and the engineering quality are optimized.

The present application discloses a method and system for optimizing the scheduling of flood storage and detention areas, which involves reservoir scheduling and data processing technology, including: determining the flood storage and detention areas contained in the target basin and dividing them into multiple levels; using the Voronoi diagram algorithm to divide the flood storage and detention areas into adaptive grids of a set size; determining the grid data covered by any adaptive grid at a set time interval, and determining the corresponding grid attributes based on the grid data; constructing a flooding cost model for each adaptive grid; using an improved NSGA‑III algorithm to solve an optimization model with the goals of minimizing the total flooding cost, minimizing the maximum value of the super-safe flow of the river channel, and minimizing the water level deviation of the reservoir group; and outputting the optimal flood diversion control plan that meets the current moment according to the solution results. The method of the present application realizes quantitative decision-making on the scheduling of flood storage and detention areas through refined grid segmentation and dynamic scheduling cost evaluation.

The invention relates to a knowledge graph construction and retrieval optimization method based on water affair data, and the method comprises the steps: taking the unified knowledge expression of a water affair object and an event thereof as a target, determining a water affair business field and business range, and constructing a water affair knowledge graph conceptual model covering a static knowledge ontology and a dynamic event ontology; performing knowledge extraction on the obtained multi-source heterogeneous water affair data, converting the data into a unified triple form, and constructing a data layer of a water affair knowledge graph; and on the basis of the data layer, node, path and region query is performed on the knowledge graph by adopting a graph algorithm, and semantic enhancement retrieval and reasoning are performed in combination with a large language model. By constructing the water affair knowledge graph and introducing knowledge extraction and retrieval enhancement capability of a large language model, dynamic modeling and semantic reasoning of water affair objects, events and relationships thereof are realized, high-risk area identification, emergency path planning and emergency early warning are supported, and the knowledge utilization rate and intelligent decision level in the water affair field are improved.

The invention belongs to the technical field of industrial robot control, and provides an error compensation method and system for an industrial robot, and the method comprises the steps: collecting a composite sensing signal and a pre-tightening force of the industrial robot, and constructing a contribution degree matrix; an error transfer link is extracted, a space-time correlation function is constructed based on the link, and a pretightening force attenuation dominant area is determined through a dynamic cloud atlas algorithm; gradient change features and attenuation trend coefficients of the attenuation dominant region are extracted, a to-be-compensated region is screened, and the saturation compensation risk of the execution mechanism is judged; and if the saturation risk exists, screening pretightening force sensitive joints to construct a compensation distribution model, outputting a segmented compensation demand quantity, and dynamically calibrating the segmented compensation quantity based on a real-time compensation result. The system comprises a contribution analysis module, an attenuation positioning module, a saturation analysis module and a compensation calibration module. The method is beneficial to positioning an error source dominated by pretightening force attenuation, and is beneficial to improving compensation precision and stability.

The invention provides a security group implementation method based on a Paas cloud platform and a method for optimizing network topological structure management, and the method comprises the steps: collecting and processing network traffic data, and forming a data set containing normal traffic and malicious traffic; analyzing and predicting a network security threat in real time through an LSTM network based on the data set; normal traffic and malicious traffic are detected in combination with an SVM and a random forest algorithm; and network topology management is optimized by using a graph algorithm and a distributed computing method. According to the invention, the deep learning algorithm is adopted to carry out real-time threat analysis and prediction, so that the detection and defense capabilities for zero-day vulnerabilities and advanced threats are improved; the SVM and the random forest algorithm are adopted to classify and identify normal and malicious traffic, so that the accuracy and the response speed of the security group are improved; and network management and resource allocation are optimized by adopting a graph algorithm and a distributed computing method, so that the controllability and security of network security are improved.

The invention discloses a power grid regulation capability evaluation method, system and equipment based on a large industrial load, and a medium, and belongs to the technical field of power management, and the method comprises the steps: collecting power data, carrying out the preprocessing, and generating a load multi-source data set; constructing a load prediction model, extracting time sequence features in the load multi-source data set, and dynamically fusing sequences in the time sequence features to generate a load prediction value; nodes, edges and attributes are defined, a load prediction value is evaluated, and a risk knowledge graph is generated; through a traditional graph algorithm, node importance scores are calculated and normalized, load risk scores are generated, risk threshold values are set, and load risk levels are marked; setting an adjustment rule, and adjusting the load prediction value; based on the load risk level and the time-of-use electricity price in the load multi-source data set, enterprise compensation cost and upstream and downstream economic losses are calculated, and an economic influence quantification table is generated. According to the method, dynamic coupling quantification and positioning of the power grid regulation risk are realized by constructing the risk knowledge graph.

The invention relates to the technical field of water demand prediction, in particular to a crop water demand prediction method and system based on the agricultural Internet of Things, and is applied to accurate moisture management in complex soil structures such as terraces. And in combination with the dynamic weight of interlayer moisture migration, the moisture absorption matrix is updated in real time. Through time sequence sliding and cosine similarity calculation, a moisture retention time matrix is dynamically corrected, and accurate distribution of soil moisture is ensured. A graph theory algorithm is introduced to construct a directed graph model of a water flow path, an interference matrix is generated in combination with binary masks, matching of water absorption and water retention time is optimized, and the irrigation efficiency and the water resource utilization rate are improved. The irrigation strategy can be dynamically adjusted according to the moisture state between the soil layers, moisture waste is avoided, and the accuracy of crop growth moisture supply is improved.

The invention discloses a multi-dimensional graph series-parallel and visualization method for telecommunication network fraud cases based on a graph database. According to the method, intelligent series-parallel analysis across time-space cases is realized by constructing a knowledge graph containing various entities such as cases, personnel, accounts and equipment and deeply fusing a hard association matching algorithm and a soft similarity graph algorithm. The system has the functions of white list filtering, time sequence behavior analysis, NLP information extraction and the like, and can effectively identify criminal gangs, track fund flow directions and mine crime modes. And finally, dynamic visual display is carried out through a plurality of interactive views such as a force-oriented graph, a time axis and a mulberry-based graph, and clue intelligent pushing and report automatic generation are supported. According to the method, the case association mining depth, the serial-parallel analysis efficiency and the interpretability of a complex network are remarkably improved, and an efficient technical support is provided for the public security organization to fight against telecommunication network fraud.

The invention provides a multi-feature factor matching and interrupt processing method for storm tracking. The method comprises the following steps: calculating storm features required by tracking; carrying out discretization processing and coding on the storm features, and constructing a sparse matrix; constructing an auto-encoder model, performing data reconstruction on the sparse matrix based on the auto-encoder model, and calculating main features of the single storm; on the basis of the main features, calculating the feature vector distance between the previous and later time secondary storms, and constructing a feature space distance matrix; a bipartite graph algorithm is adopted, the incidence relation of the previous and later time-order storms is obtained based on the feature space distance matrix, and a storm matching result is constructed; identifying an interrupted storm based on a storm matching result and carrying out secondary matching; and constructing a storm trajectory based on a secondary matching result and removing a repeated storm trajectory. According to the method, the movement track of the storm can be accurately tracked from formation to extinction of the storm, reliable data support is provided for meteorological monitoring and forecasting, and the timeliness and accuracy of severe convective weather early warning are improved.

The invention discloses an intelligent engineering software development process risk quantitative evaluation system, which belongs to the technical field of risk quantitative evaluation and comprises a data acquisition module, a data processing module, a dynamic risk knowledge graph module, a risk identification module, a risk quantitative evaluation module, a risk monitoring and early warning module, a risk decision module and a feedback optimization module. According to the method, the dynamic risk knowledge graph is constructed, so that the risk change in the software development process can be updated and reflected in real time. The relation network between entities is explored through the graph algorithm, potential risk events and risk factors can be found easily, the accuracy and timeliness of risk identification are improved, potential risk points in the software development process can be accurately identified by combining preprocessed data and the dynamic risk knowledge graph, and the risk identification efficiency is improved. Quantitative assessment of the risk is realized by defining the risk index and calculating the risk score.