295 results about "Tree generation" patented technology

The invention belongs to the technical field of computers, and particularly relates to a zero-code agent construction platform. The system comprises a module database, a semantic analysis and intention decomposition unit, a task flow tree generation unit and an agent construction unit, the semantic analysis and intention decomposition unit is used for performing image language analysis on a natural language instruction input by a user, decomposing the natural language instruction into a plurality of intention units with a sequence, and constructing an intention vector; the task flow tree generation unit is used for generating a plurality of corresponding task flow trees according to the intention vector; and the agent construction unit is used for analyzing all the task flow trees, finding out an optimal task flow tree, calling all target function modules in the task flow tree and completing the construction of the agent. According to the method, the precision of semantic analysis, the flexibility of task logic generation and the automation degree of agent construction are remarkably improved.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

Example methods, devices, and computer program products are provided. An example method includes receiving, by a third process, a first render tree generated by a first process. The first render tree is configured to draw an interface associated with the first process and a first display region. The method includes generating, by the third process, a first interface based on the first render tree. The first interface comprises the interface associated with the first process, on the first display region. The method includes receiving, by a fourth process, a second render tree generated by a second process. The second render tree is configured to draw an interface associated with the second process, on the first display region. The method includes generating, by the fourth process, a second interface based on the second render tree. The second interface comprises the interface associated with the second process, on a second display region.

The embodiment of the invention provides a map plotting method and device based on a large language model, equipment and a medium, and relates to the technical field of map plotting based on the large language model, and the method comprises the steps: obtaining raster image data and vector road data, so as to build a geographic space reference coordinate system; collecting a positioning information text corpus based on natural language description; building a spatial semantic processing model based on a self-attention mechanism according to the geographic space reference coordinate system and the positioning information text corpus; receiving a mixed instruction input by a user, and converting the mixed instruction into a structured instruction tree; generating a coordinate set according to the structured instruction tree, and generating a coordinate set scheme after verifying the coordinate set; plotting the coordinate set scheme into an interactive map based on a spatial semantic processing model; a user is supported to input a mixed data instruction, complex semantics in a natural language can be analyzed, map drawing is automatically carried out, and map drawing efficiency is improved.

The invention provides a sparse matrix distributed solution method, computer equipment and a storage medium, which are used for simulation of a circuit and an electromagnetic field, and the method comprises the following steps: generating a super node based on an elimination tree of a matrix generated in a simulation process; constructing an assembly tree according to the elimination tree and the super nodes; constructing a wavefront matrix according to the rows and columns of the matrix corresponding to the super nodes; traversing the assembly tree layer by layer, and carrying out parallel calculation on complete assembly blocks and incomplete assembly blocks of the wavefront matrix on the same layer of the assembly tree; according to the incomplete assembly blocks of each layer, the complete assembly blocks of the next layer of the assembly tree are updated until the last layer of the assembly tree is reached; and ending the traversal of the assembly tree, and solving an equation LUx = b by using the complete assembly blocks of all the wavefront matrixes. According to the scheme, the solving speed can be greatly improved, memory occupation is reduced, and simulation of a circuit and an electromagnetic field is facilitated.

The invention discloses a wind-light load multi-stage scene tree generation method based on source load uncertainty feature extraction, and the method comprises the steps: extracting wind-light load scene features based on a constructed stacked sparse auto-encoder model, and obtaining a wind-light load scene feature set; clustering the wind-light load scene feature set based on a density peak value improved affinity propagation algorithm to obtain a wind-light load typical day curve as a root node of the scene tree; generating a wind-light load scene tree year by year on the basis of considering different load growth modes according to root nodes of the scene tree; a scene tree is reduced through a scene tree reduction method based on the Sinkhorn distance, so that the scale of the scene tree is reduced. The wind and light load multi-stage scene tree generated by the method can reflect the time sequence information of the new energy output and load, characterize the randomness and volatility of the new energy output and load, can reflect the uncertainty of the long-term increase of the new energy output and load, and is of great significance to the planning and construction of a power grid.

The invention relates to the technical field of digital asset management, in particular to a block chain-based carbon asset full-life-cycle traceability and digital asset management platform. According to the platform, multiple data modeling and intelligent contract mechanisms are fused, and credible management of carbon assets in the whole process from generation, accounting, registration, transaction to auditing is achieved. The method comprises the following steps: collecting operation data of a forestry carbon sink project, constructing a structured Hash fingerprint, generating index root Hash by adopting a Merkle tree, and calculating a carbon sink amount as a basic data source of carbon assets; accounting the carbon sink credibility based on a Bayesian network model and eliminating abnormal data; carrying out anti-fact intervention analysis by utilizing a causal map, and screening abnormal assets with low causal consistency; on-chain matchmaking transaction of the carbon assets and the users is completed through a clustering and greedy matching mechanism; a life cycle auditing module is combined to evaluate responsibility contribution of key behavior variables to ESG indexes, and quantifiable, traceable and verifiable green asset full life cycle management is achieved.

A security policy matching method based on IP slices mainly comprises the steps of IP address slicing, policy association, policy tree generation, policy fast matching and the like, the method adopts a virtual address thought, an address object is sliced into an address unit, the corresponding relation between the address unit and a policy is reflected by utilizing a bit, and then an ordered AVL policy tree is constructed, so that the security policy matching efficiency is improved. And efficient matching between the source IP and the destination IP of the message and the security policy is realized. According to the method, when the number of strategies is configured more, the influence on the performance is smaller, and the expansibility is high; and meanwhile, the support for the ipv4 and the ipv6 is relatively good, the occupied memory is relatively small, and the hardware requirements of low-end equipment are met.

The invention discloses an intelligent fault tree generation method and system based on joint simulation and dynamic causal reasoning, relates to the technical field of fault trees, and solves the problems that the manual construction efficiency of a complex system fault tree is low, the hidden failure path omission ratio is high, and a model and operation and maintenance data are disjointed. The method comprises the following steps: constructing a multi-physics field coupled system joint simulation model; capturing a propagation path of the fault among the functional units in real time, and generating a fault propagation map; constructing a minimum cut set based on Monte Carlo simulation and Bayesian network reasoning, and automatically adapting an AND / OR logic gate to generate a fault tree; and dynamically updating the knowledge graph in combination with an online learning technology. The method has a good application prospect in the field of health management of equipment with high safety requirements.

The invention provides a model reasoning method, system and device and electronic equipment, and is applied to the technical field of computers.The method comprises the steps that a target question is input into a pre-trained abstract tree generation model, an abstract tree which is generated by the abstract tree generation model and comprises multiple levels of abstracts is obtained, and the multiple levels of abstracts comprise multiple leaf node abstracts; the target problem and the multiple leaf node abstracts are spliced and then input into an end-side large model, and a key value KV matrix is calculated through the end-side large model; and respectively combining the target problem with each leaf node abstract in the plurality of leaf node abstracts to form a multi-section reasoning context, inputting the multi-section reasoning context into an end-side large model, and performing parallel reasoning on the multi-section reasoning context by combining the end-side large model with a model intermediate result to obtain a target reasoning result. The model reasoning method improves the model reasoning efficiency.

The invention provides a decision tree generation method, a message processing method, equipment and a storage medium. The method comprises the following steps: detecting whether a range field exists in a received to-be-processed rule set or not; if the range field exists in the rule set to be processed, converting the range field into a mask form; for the to-be-cut target node, determining a first rule copy rate corresponding to a bit cutting mode; judging whether the first rule copy rate is greater than or equal to a preset threshold; if yes, determining a corresponding second rule copy rate when the geometric space cutting mode is adopted to cut based on the range field; and according to the first rule replication rate and the second rule replication rate, determining a target cutting mode suitable for the target node to cut the corresponding target node into two sub-nodes, and according to a cutting result, generating a decision tree corresponding to the to-be-processed rule set. According to the method, a proper cutting mode is adaptively selected to construct the decision tree for the rule set containing various fields, so that the tree construction speed of the rule set is improved, and the consumption of storage resources is reduced.

The invention relates to the technical field of enterprise data processing, and discloses an intelligent analysis system for enterprise operation data based on artificial intelligence. The system comprises an operation data acquisition module, an operation state analysis module, a decision tree generation module, a data compensation module and an execution parameter generation module. The operation data acquisition module acquires enterprise finance, supply chain logistics and market dynamic data in real time, integrates and cleans multi-source heterogeneous data, and generates an operation state data set; the operation state analysis module identifies a deviation degree between the key business index and a preset threshold value, and calculates a multi-dimensional abnormal value by means of a dynamic weighting strategy to obtain an abnormal feature set; a decision-making tree generation module constructs a multi-layer decision-making path according to the multi-layer decision-making path, matches an optimal branch in combination with a historical case library, and generates a visual decision-making tree; the data compensation module analyzes the influence of missing data and automatically supplements an external data source to generate a compensation parameter set; and the execution parameter generation module analyzes node priorities, quantifies resource configuration and time window parameters, and generates a strategy execution parameter packet.

The invention discloses an artificial intelligence algorithm-based e-commerce product recommendation method and system, and relates to the technical field of product recommendation. Comprising the following steps: constructing a user-behavior path flow tree by adopting a multi-criterion splitting mechanism, and generating a node path set; respectively slicing and recombining the user portrait set and the commodity portrait set, and carrying out cross combination; constructing a behavior cluster atlas to which the user belongs, projecting cross commodity nodes in the node path set and the candidate cross mapping set into an anti-cross atlas structure among behavior clusters, and extracting interaction points as a candidate recommendation set; and performing weight regression calculation on each node of the path of the user-behavior path flow tree by utilizing path probability density mapping corresponding to each cross commodity node in the candidate recommendation set, and generating a commodity recommendation sequence according to the maximum path passing probability. According to the method, the recommendation process with the behavior chain criterion and the coupling map mapping is constructed, so that the user experience is improved, and the conversion efficiency and the content distribution capability of the platform are enhanced.

The invention discloses a front-end Service code automatic generation method and system, and relates to the technical field of computers.The method comprises the steps that in response to a code generation demand of a user, the code generation demand is analyzed through a preset demand analysis model, and a demand analysis result is obtained; according to the demand analysis result, matching in a preset code syntax tree library to obtain a target code syntax tree; performing parameter optimization on the target code syntax tree to obtain an optimized target code syntax tree; generating an initial code in combination with the optimized target code syntax tree and YApi interface data; optimizing the initial code through a preset code optimization mechanism to obtain an updated code, and outputting the updated code at a front-end window; according to the method, the demand association graph and the interface calling chain are constructed according to the code generation demand, the highly matched syntax tree is screened out from the code syntax library, the corresponding code is generated, code optimization is performed, and the quality and reliability of the generated code are improved.

The invention discloses a fault tree generation method and device, a storage medium and an electronic device, and relates to the field of intelligent device.The fault tree generation method comprises the steps that a knowledge base corresponding to a fault processing file of target equipment is determined, and the knowledge base comprises a database and a vector library; the database is used for storing a plurality of slice knowledge corresponding to the fault processing file in a classified manner, each slice knowledge corresponds to one type of fault type, and the vector library is used for storing vector information corresponding to each type of fault type; processing the knowledge base through a preset large model to obtain slice questions corresponding to different slices in the knowledge base and slice answers corresponding to the slice questions; and constructing question and answer pairs based on the slice questions and the slice answers, and generating a target fault tree corresponding to the fault processing file based on a progressive relationship between different question and answer pairs. The problems that the process of drawing a plurality of fault trees is complex and the consumed time cost is high are solved, the drawing workload of the fault trees is reduced, and the drawing time cost is shortened.

The invention relates to the field of data processing technologies and financial science and technology, and provides an HQL statement detection method which comprises the steps that an HQL statement to be detected is analyzed to generate an abstract syntax tree and optimized, and an execution plan is generated according to the optimized syntax tree; performing operation step decomposition on the execution plan according to task process stages, and generating a performance analysis syntax tree corresponding to the HQL statement by taking operation steps obtained after decomposition as syntax tree nodes; querying a database according to a query object contained in the operation steps in the performance analysis syntax tree to obtain a related database table of the HQL statement, and calculating data of the related database table and data of the operation steps of the performance analysis syntax tree to obtain numerical values of a plurality of preset data indexes of the HQL statement; and calculating the score of the HQL statement according to the numerical value of the preset data index and a preset data index scoring algorithm, and generating a detection report of the HQL statement. According to the HQL statement detection method, the quality of the HQL statement can be automatically evaluated according to a unified rule.

Provided are a method and device for parsing a database query statement, and a storage medium. A query statement for a database table to be queried is obtained, and an empty database table corresponding to the database table to be queried is created; the query statement is executed in the empty database table, and a parser installed in a database is called to parse the query statement to obtain a syntax tree, and an execution plan is generated based on the syntax tree for execution in the empty database table; and the syntax tree is obtained from the parser in an execution process of the query statement, and a syntax tree serialization result is generated and output as a parsing result of the query statement.

The invention relates to a development tree inferring method and device based on a biological big language model and a medium, and the method is based on a phylogenetic tree generation and optimization model to jointly optimize the topological structure and branch length of a phylogenetic tree, so that the phylogenetic tree is directly inferred from an input genome sequence. The training process comprises the following steps: extracting embedded features from genome sequence data through a pre-training language model; based on the extracted embedded features, constructing an initial phylogenetic tree by using a tree structure generation algorithm, and introducing a topological structure of a potential variable representation tree; iteratively optimizing the topological structure and the branch length of the phylogenetic tree by using a topological learning component and a branch length learning component; and guiding gradient descent of the phylogenetic tree generation and optimization model through a scoring function to complete model training. Compared with the prior art, the method has the advantages of improving inference precision and robustness and the like.

The invention discloses a database query method, device and system and a medium, and the method comprises the steps: receiving a database query statement, analyzing the query statement to generate an execution plan of a tree structure, segmenting the execution plan, and obtaining a plurality of execution plan subtrees; matching the plurality of execution plan sub-trees with the intermediate result set in the cache, and determining whether intermediate result set sub-trees equal to the execution plan sub-trees exist in the intermediate result set or not; if an intermediate result set sub-tree equal to the execution plan sub-tree exists in the cache, replacing the successfully matched execution plan sub-tree with the equal intermediate result set sub-tree, and generating a replacement execution plan; and performing scheduling execution according to the replacement execution plan to obtain a corresponding database query result. The subtrees in the execution plan are matched and replaced through the intermediate result set in the cache, so that the intermediate result set in the cache can be directly used in subsequent execution operators, resource consumption and execution time are reduced, and database query performance is improved.

The invention discloses an RISC-V architecture built-in function conversion method and device and a storage medium, and the method comprises the steps: analyzing a source code file, and obtaining an operator function contained in the source code file; converting the operator function into a syntax tree, and traversing the syntax tree to obtain child nodes of the syntax tree; when the child node is the loop statement node, judging whether the loop step length of the child node is the same as a preset step length or not; generating a second syntax tree based on the basic loop step length sub-tree, expanding a scalar in the code block into an assignment statement of a vector, and identifying the assignment statement as a vector-scalar instruction optimization vector expansion statement; searching a statement to be subjected to vector-scalar instruction optimization from the following text; when the statement to be subjected to vector-scalar instruction optimization is found, the assignment statement subjected to vector expansion is converted into a common assignment statement, the vector instruction of the original instruction set is replaced with the vector instruction of the target instruction set according to the mapping vector instruction mapping table, and the vector instruction of the target instruction set is replaced with the corresponding vector-scalar instruction.

The invention discloses a government affair task dynamic distribution and multi-level auditing method and system, belongs to the technical field of government affair systems, and aims to solve the technical problem of how to realize a government affair task management method of task dynamic disassembly, hierarchical intelligent auditing and multi-source data deep fusion. Comprising the following steps: disassembling a government affair task by adopting a heterogeneous task tree generation method to form multiple levels of subtasks; the subtasks are distributed to an execution end based on the optimal distribution path; after receiving the subtasks, the execution end executes data acquisition and data submission operations; performing multi-level auditing on the data passing the basic logic verification according to a preset multi-level auditing rule, and executing rejection correction if any link in the multi-level auditing process is rejected; the data are stored in a global database, and data tables in the global database are associated through unique identification fields; and associating fields existing in the global library through an intelligent filling engine, and matching filling contents according to an acquisition scene to realize data updating and multiplexing.

Detecting an anomalous configurator of a plurality of managed entities. A plurality of configuration trees, each representing configuration parameters at a corresponding managed entity, include annotations that identify a configurator that configured each parameter. For a particular configurator, a plurality of subtrees is generated from the plurality of configuration trees. A set of weighted edit distances are calculated from the plurality of subtrees, each representing a degree of difference between a different pair of subtrees. A distance matrix is populated with the set of weighted edit distances, and the distance matrix is used to identify anomalous subtree(s) within the plurality of subtrees for the particular configurator. In embodiments, a configuration corresponding to an anomalous subtree considered to have been anomalously applied by the particular configurator. Data that identifies at least one managed entity associated with the anomalous subtree, and the particular configurator is stored.

Systems, apparatuses and methods provide technology that receives a first operator prompt associated with a character in a computing application, and generates, with a first machine learning model during an offline process, first responses based on the first prompt, where the first responses are dialog of the character. The technology stores the first responses into a dialog tree during the offline process, receives a second prompt associated with an end user of the computing application, generates, with the first machine learning model during the offline process, second responses to the second prompt and based on the first responses stored in the dialog tree, where the second responses are dialog of the end user, and adds the second responses to the dialog tree during the offline process.

The invention provides a skeleton binding three-dimensional model generation method and device, and the method comprises the steps: obtaining a to-be-bound three-dimensional model, converting the to-be-bound three-dimensional model into point cloud data, and extracting the global features and local features of the point cloud data; inputting the global features and the local features of the point cloud data into an autoregression skeleton tree generation model to obtain continuous coordinates of a skeleton, and performing discretization and decoding based on the continuous coordinates of the skeleton to obtain a skeleton tree structure conforming to a hierarchical relationship; extracting vertex features from the point cloud data through a point cloud feature extraction network, extracting skeleton features of a skeleton tree through a skeleton encoder, performing skeleton point cross attention calculation based on the skeleton features and the vertex features, and generating skin weights corresponding to the vertexes; the skin weights and the skeleton tree structure are subjected to skeleton binding, a three-dimensional model of skeleton binding is generated, and the precision of skeleton binding is remarkably improved through the skeleton tree conforming to the topological structure and the high-quality skin weights.

The invention provides a front-end code evaluation method and system, electronic equipment and a program product, and the method comprises the steps: obtaining a front-end code, carrying out the compiling and rendering of the front-end code, and obtaining a generated front-end page and a target front-end page; performing code extraction processing on the generated front-end page and the target front-end page, and performing redundant node cleaning and code structure tree generation processing on extracted markup language codes to obtain a generated code tree and a target code tree; performing tree structure and element content error calculation processing on the generated code tree and the target code tree to obtain a content structure error; performing visual structure error calculation processing on the generated front-end page and the target front-end page to obtain a visual structure error; and according to the content structure error and the visual structure error, determining and obtaining an evaluation result for generating the front-end code. The scheme can improve the generation accuracy of the front-end code, and can be widely applied to the technical field of artificial intelligence.

The invention relates to an aerospace equipment fault tree generation method based on a large model, and belongs to the technical field of aerospace reliability. By analyzing multi-source heterogeneous information, a current process step is quickly positioned, and on this basis, a user is assisted in obtaining related information such as operation detailed rules, disposal plans, design mechanisms, historical faults and matters needing attention, so that information pushing and knowledge accompanying are realized. The technology supports on-line monitoring, trend prediction and abnormity early warning of real-time data, and fault attribution and auxiliary troubleshooting are realized by acquiring troubleshooting knowledge and related data and assisting fault phenomenon description input by a user when abnormity occurs; the problems that the multi-source heterogeneous data is difficult to effectively integrate and utilize, the text data search efficiency is low, the quality is poor and the troubleshooting knowledge potential tapping is insufficient are solved.

The invention provides a method for generating a component tree by assembling a picture specification based on a large language model. The method for generating the component tree through the assembly picture specification based on the large language model comprises the steps that non-component legends in the assembly picture specification are recognized and separated, and component information is reserved; the large language model arranges the assembly pictures into a step tree according to the small sample prompt, and the step tree displays assembly steps through a tree-shaped structure; and combining the component information and the step tree to generate a component tree which displays the premise and subsequent relationship between the components through a tree structure. According to the method, the problem of difficulty in extracting components used for understanding the assembly sequence in the assembly picture specification is effectively solved, the component information used in each step in the specification and the mutual dependency relationship between the steps are effectively extracted, and the method has important significance on machine assembly.

To generate a summary that holds causal relationship for a plurality of images.SOLUTION: A summarization apparatus includes: a rhetorical structure tree generation unit configured to generate a rhetorical structure tree for a plurality of images regarding a certain event related in time series; an event relation generation unit configured to generate a tree structure that represents a relationship between events related to the images, using the images as nodes, based on the rhetorical structure tree; and a summary generation unit configured to extract subtrees from the tree structure so as to satisfy the constraints for the summary of the images.SELECTED DRAWING: Figure 2

The invention provides a road digital construction management quality cost single-core structure tree generation method, which comprises the following steps: 1, constructing a hierarchical structure tree: decomposing an engineering project according to the hierarchy of a unit project, a branch project, a component and a subentry project / sub-subentry project, and constructing a quality cost single-core structure tree; 2, defining a coding rule: formulating a uniform coding rule, and distributing a unique code for each hierarchical project; 3, realizing mode conversion: when a quality management service is carried out, converting the quality cost single-core structure tree into a quality inspection evaluation structure tree; when a cost management service is carried out, the quality cost single-core structure tree is converted into a metering machine account structure tree; and 4, establishing data association: associating the quality inspection result of the subitem project / sub-subitem project through the component code, and determining whether metering can be carried out according to the quality inspection result, thereby realizing metering and quality inspection data sharing. According to the method, the problem that a dual-core structure tree of a traditional quality inspection evaluation structure tree and a metering machine account structure tree is difficult to automatically map can be solved.

The invention relates to the technical field of natural language processing and artificial intelligence, and discloses a large-model complex instruction-oriented fine-grained intention recognition method, which comprises the following steps of: constructing a candidate analysis forest of an input instruction, and executing entity alignment of each node and a domain knowledge graph; performing semantic compatibility verification on the candidate dependency trees by utilizing entity relationships in the atlas, and screening an optimal analytic tree in combination with syntactic probability and knowledge consistency scores; if the analytic tree meeting the threshold value does not exist, a semantic conflict edge is positioned, and candidate mounting points are searched by using a map neighborhood relation to reconstruct a dependency structure; and finally, generating standardized data containing a structured analytic path based on the optimal analytic tree, and performing fine adjustment on the large model. According to the method, logic constraint and dynamic repair are carried out on the syntactic structure by introducing the knowledge graph, analysis errors caused by multiple modification or structural ambiguity in a complex instruction are effectively solved, and the accuracy and robustness of intention recognition of a large model in the vertical field are improved.