60 results about "Topological order" patented technology

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.

The invention provides an anti-fact fair data synthesis method and device based on causal reasoning, and aims to generate high-quality synthesis data meeting the fairness requirement by mining the causal relationship between observable features. The synthesis method comprises the following steps: extracting observable features, sensitive features and labels from original data, extracting potential features through a variational automatic codec, and constructing a causal relationship graph; designing a generator according to a topological sequence of the causal relationship graph, connecting a causal path, inputting the potential features and the related features into the generator in sequence, and constructing a data generation process conforming to a causal structure; introducing a discriminator to carry out adversarial training on a generation result and original data, and optimizing generator parameter distribution; finally, synthetic data meeting fairness requirements are generated. According to the method, effective regulation and control on the influence of sensitive characteristics and strict constraint on a causal structure are realized, the generated data has higher fairness and interpretability, and the method can be applied to the fields with higher fairness requirements, such as finance, medical treatment and education.

A computing device learns a directed acyclic graph (DAG). (A) A target variable is defined from variables based on a topological order vector and a first index. (B) Input variables are defined from the variables based on the topological order vector and a second index. (C) A machine learning model is trained with observation vectors using the target variable and the input variables. (D) The machine learning model is executed to compute a loss value. (E) The second index is incremented. (F) (B) through (E) are repeated a first plurality of times. (G) The first index is incremented. (H) (A) through (G) are repeated a second plurality of times. A parent set is determined for each variable based on a comparison between the loss value computed each repetition of (D). The parent set is output for each variable to describe the DAG that defines a hierarchical relationship between the variables.

A computing device learns a best topological order vector for a plurality of variables. (A) A topological order vector is defined. (B) A target variable and zero or more input variables are defined based on the topological order vector. (C) A machine learning model is trained with observation vectors using values of the target variable and the zero or more input variables. (D) The machine learning model is executed with second observation vectors using the values of the target variable and the zero or more input variables to compute a loss value. (E) (A) through (D) are repeated a plurality of times. Each topological order vector defined in (A) is unique in comparison to other topological order vectors defined in (A). The best topological order vector is determined based on a comparison between the loss values computed for each topological order vector in (D).

A computing device learns a directed acyclic graph for a plurality of variables. (A) A target variable and zero or more input variables are defined based on a predefined topological order vector and a first index. (B) A machine learning model is trained with observation vectors using the target variable and the input variables. (C) The machine learning model is executed using the observation vectors with the target variable and the input variables to compute a residual vector. (D) The first index is incremented. (E) (A) through (D) are repeated a first plurality of times. A parent set is determined for each variable by comparing the residual vector computed each repetition of (C) to other residual vectors computed on other repetitions of (C). The parent set is output for each variable to describe a directed acyclic graph that defines a hierarchical relationship between the variables.

A computing device learns a directed acyclic graph (DAG). (A) A target variable is defined from variables based on a topological order vector and a first index. (B) Input variables are defined from the variables based on the topological order vector and a second index. (C) A machine learning model is trained with observation vectors using the target variable and the input variables. (D) The machine learning model is executed to compute a loss value. (E) The second index is incremented. (F) (B) through (E) are repeated a first plurality of times. (G) The first index is incremented. (H) (A) through (G) are repeated a second plurality of times. A parent set is determined for each variable based on a comparison between the loss value computed each repetition of (D). The parent set is output for each variable to describe the DAG that defines a hierarchical relationship between the variables.

The invention relates to the technical field of artificial intelligence, in particular to an AI agent workflow implementation method and system, electronic equipment and a storage medium, the AI agent workflow implementation method is applied to an agent, and the method comprises the steps of determining a node topological sequence of a target workflow based on a node relationship of the target workflow; and according to the node topological sequence and in combination with the configuration information of each node of the target workflow, performing scheduling execution on each node of the target workflow, and generating a final execution result of the target workflow. According to the invention, standardization and automation of the complex workflow are realized by using the intelligent agent, the execution efficiency and accuracy of the workflow are improved, the operation cost is reduced, and the user experience and satisfaction are improved.

A ground state of a non-Abelian topological order is prepared using physical qubits that are logically organized onto a lattice formed of a plurality of sublattices. Physical qubits assigned to respective vertices of a respective sublattice in a first subset of sublattices are entangled with plaquettes of the respective sublattice. The plaquettes of the respective sublattice in the first subset are measured. Physical qubits assigned to respective vertices of a respective sublattice in a second subset of sublattices are entangled with plaquettes of the respective sublattice. The plaquettes of the respective sublattice in the second subset are measured. Based on the plaquette measurements, a controller determines whether any plaquettes are hosting Abelian topological order. Responsive to determining that a plaquette is hosting Abelian topological order, the controller causes performance of a feed-forward action on the plaquette to generate the ground state of the non-Abelian topological order.

The invention provides an electric power settlement visual configuration method and system based on node dragging. According to the method, a data type set and a computing node of power settlement are provided in a visual interface, and a node dragging operation is responded to analyze an operation rule expression and bind power transaction data; a unidirectional node connecting line is generated by dragging a connecting line between nodes by a user, a settlement dependence topology with a calculation node as a vertex and the connecting line as a directed edge is constructed, and a data reference path between the nodes is mapped; according to a node initialization calculation sequence of which the in-degree is zero in the topology, converting an expression of the binding relation into an executable script through a variable binding device; and driving the distributed framework to schedule tasks according to a topological sequence based on the calculation sequence, determining affected downstream nodes and data items in combination with topology when monitoring data change, performing incremental re-calculation according to the topological sequence, and outputting a settlement result. According to the invention, visual configuration and dynamic updating automation of power settlement are realized.

A system and method include learning a topological order of a plurality of variables in a directed acyclic graph based on real data, computing parameter estimate values corresponding to the real data, computing error values based on the real data, the topological order, and the parameter estimate values, generating simulated data from the parameter estimate values and the error values, such that simulated variables in the simulated data preserve a causal relationship between variables in the real data, and the simulated variables in the simulated data preserve a correlation relationship between the variables in the real data, and reorganizing and outputting the simulated data based on the topological order.

A system and method include generating a topological order of a DAG by creating residual series vectors, calculating normality statistic and MSE values for the residual series vectors, comparing the normality statistic values with a critical value, for each normality statistic value that is less than or equal to the critical value, adding a variable index to a temporary order list and the MSE value to an MSE list, counting a number of elements in the temporary order list, if the number of elements in the temporary order list is zero, updating an order list based on the normality statistic values or if the number of elements in the temporary order list is not zero, updating the order list based on at least one of the temporary order list or the MSE list, and outputting the order list as the topological order of the DAG.

In one aspect, a method for determining personalized search results with respect to mandatory constraints and qualitative preferences on search criteria representable via a DAG includes collecting input from users or software systems; transforming the inputs into the mandatory constraints and preferences on search criteria, wherein the search criteria is partially ordered and representable through the DAG; determining weights for the search criteria using a function, and associating the weights with the nodes of the DAG to determine a partial ordering of such weights equivalent to an order of the DAG nodes, where any node at level “k” has a weight “w” such that sum of all or any finite subset of the weights of the nodes at levels below “k” is always less than “w”, the level being defined by a topological order of the DAG; and determining search results that satisfy the mandatory constraints on the search criteria.

The present application relates to a modular lighting fixture and a controller therefor, a lighting effect signal generation method, and a lighting effect playing method. The lighting effect signal generation method comprises: acquiring a lighting effect application package, which comprises lighting effect control parameters corresponding to a plurality of layers; on the basis of a topological order relationship represented by a light-emission unit mapping table, converting the lighting effect control parameter of each layer into timing control data; and outputting the timing control data to each lamp block of the modular lighting fixture, so as to generate a light emission signal.

The invention discloses an assessment method and device for a large model generated text, a readable storage medium and a program product, and relates to the technical field of artificial intelligence, and the assessment method for the large model generated text comprises the steps: obtaining a user-defined assessment template, and analyzing a dependency relationship and an execution priority of each preset assessment dimension in the user-defined assessment template, obtaining a directed acyclic graph used for representing the topological sequence of each preset evaluation dimension; according to a topological sequence of each preset evaluation dimension in the directed acyclic graph, evaluating the to-be-evaluated large model generation text on each preset evaluation dimension to obtain a score corresponding to each preset evaluation dimension; and determining a text evaluation result by counting the score corresponding to each preset evaluation dimension. According to the technical scheme, multi-dimensional evaluation can be carried out on the large model generation text, the multi-aspect capability of the model can be measured more comprehensively and accurately, and the scene adaptation capability of the text evaluation scheme is improved.

A system and method include learning a topological order of a DAG by setting an initial index value of a first index, setting an initial score value of a score, setting an initial order list, computing an initial SSCP matrix, sweeping the initial SSCP matrix based on the first index, incrementing the first index to obtain an updated index value of the first index, determining an index value of a second index, computing an updated SSCP matrix, computing an updated score value as a sum of an initial score value and a value identified from the updated SSCP matrix, and computing an updated order list from an initial order list based on the updated index value of the first index and the index value of the second index.

The invention discloses a code abstract generation method and system based on hierarchical context awareness, and belongs to the field of natural language processing and software engineering. The method comprises the following steps: generating hierarchical annotations for a code warehouse based on a function call graph, firstly extracting a call relationship in the graph and eliminating a ring structure, and then classifying leaf nodes and generating annotations; a topological sequence from bottom to top is adopted in the core step, the importance score of a child node is calculated for each father node, child node annotations, class function descriptions and code bodies of the child nodes are fused, and a large language model is input to generate annotations till the whole graph is covered. The method is suitable for an automatic document generation scene of a complex software project, and through a technical path combining function call graph analysis, child node importance measurement and class function description, the accuracy and semantic consistency of code annotation are improved; the method can be widely applied to the fields of software maintenance, code understanding, intelligent development tools, automatic document generation of enterprise-level code warehouses and the like.

The application provides a causal discovery method and device fusing field knowledge of a large language model and a flow model, and relates to the technical fields of artificial intelligence and data mining. The method comprises the following steps: acquiring observation data and variable semantic information of a system to be analyzed; determining a causal topological order between variables by using a large language model; constructing a mask affine autoregressive flow model based on the causal topological order, and training the model by using the observation data; calculating a Jacobian matrix of the flow model with respect to input data, constructing a statistic based on the Jacobian matrix, and performing hypothesis testing; pruning according to a test result to obtain a candidate causal graph; inputting the candidate causal graph into the large language model, and performing secondary verification and pruning on candidate connections by using the variable semantic information to obtain a final sparse causal graph. The application introduces prior knowledge by using semantic information of the large language model, and fits data by using a more flexible function form of the flow model, thereby significantly improving the accuracy and robustness of causal discovery.

The present application provides an automated vulnerability handling method and system, wherein the method includes: obtaining multi-source heterogeneous data and temperature field distribution of the target system, semantically associating the vulnerability data with historical repair records, and combining the dynamic dependency chain to generate vulnerability propagation path characteristics; by matching the temperature field distribution with the thermodynamic pattern of known vulnerability attacks, identifying hardware load abnormality areas. Based on the dynamic graph attention mechanism, the vulnerability propagation path characteristics and hardware abnormality data are collaboratively analyzed to generate a patch deployment strategy that comprehensively considers repair priority and resource allocation. During execution, the topological order and data throughput of the patch distribution path are dynamically adjusted, and the resource occupancy ratio is optimized to ensure that the duration of cross-system service interruptions is controlled within the safety window. The present application improves the accuracy of vulnerability repair and system stability.

The invention discloses a calculation optimization method and device, a storage medium and a computer program product, and belongs to the technical field of computers. The method comprises the following steps: determining a topological sequence of a computational graph; determining a target operator node in the plurality of operator nodes and a target input node corresponding to the target operator node based on a calculation relationship represented by the topological sequence and the calculation graph, the target operator node being an operator node used for in-situ calculation in the calculation graph, the memory used by the output data of the target operator node is consistent with that used by the input data of the target input node; and executing a calculation task corresponding to the calculation graph based on the target operator node and the target input node. That is, according to the scheme, the nodes capable of performing in-situ calculation in the calculation graph can be automatically identified by the computer equipment. Compared with a method of manually assigning in-situ calculation, the scheme has the advantages that errors are not prone to occurring, efficiency is higher, most of even all nodes capable of conducting in-situ calculation can be found, and therefore the overall calculation performance can be greatly improved.

The application provides a causal discovery method and device fusing field knowledge of a large language model and a flow model, and relates to the technical fields of artificial intelligence and data mining. The method comprises the following steps: acquiring observation data and variable semantic information of a system to be analyzed; determining a causal topological order between variables by using a large language model; constructing a mask affine autoregressive flow model based on the causal topological order, and training the model by using the observation data; calculating a Jacobian matrix of the flow model with respect to input data, constructing a statistic based on the Jacobian matrix, and performing hypothesis testing; pruning according to a test result to obtain a candidate causal graph; inputting the candidate causal graph into the large language model, and performing secondary verification and pruning on candidate connections by using the variable semantic information to obtain a final sparse causal graph. The application introduces prior knowledge by using semantic information of the large language model, and fits data by using a more flexible function form of the flow model, thereby significantly improving the accuracy and robustness of causal discovery.

A system and method include generating a topological order of a DAG by creating residual series vectors, calculating normality statistic and MSE values for the residual series vectors, comparing the normality statistic values with a critical value, for each normality statistic value that is less than or equal to the critical value, adding a variable index to a temporary order list and the MSE value to an MSE list, counting a number of elements in the temporary order list, if the number of elements in the temporary order list is zero, updating an order list based on the normality statistic values or if the number of elements in the temporary order list is not zero, updating the order list based on at least one of the temporary order list or the MSE list, and outputting the order list as the topological order of the DAG.

The embodiment of the invention provides a data scheduling method, electronic equipment and a storage medium. The data scheduling method comprises the following steps: acquiring file access information of target equipment to a storage system; according to the file access information, predicting a probability that the target device accesses a target file in the storage system in a subsequent time slice, and obtaining a first predicted access probability of the target file; according to the first predicted access probability of the target file, determining respective second predicted access probabilities of a plurality of data blocks in the target file; obtaining a physical topology sequence of the target device to each storage node in the storage system; determining a target storage node of the data block according to the second predicted access probability of the data block and the physical topological sequence of each storage node; and caching the data block from the target storage node to the corresponding target equipment so as to improve the pre-caching hit rate of the file in the storage and calculation separation scene and improve the access efficiency of the file in the storage and calculation separation scene.

The invention belongs to the technical field of artificial intelligence and natural language processing, and particularly relates to a large model multi-hop tool calling training data construction method based on subgraph backtracking, which comprises the following steps: acquiring a large model callable tool to obtain a tool name, a tool description and input and output parameter information; constructing a tool graph which takes tools as nodes and dependencies between input and output of the tools as directed edges; sampling directed acyclic tool sub-graphs with different tool numbers from the constructed tool graph; traversing all dependent edges in the tool sub-graph according to a sub-graph topological sequence, and generating sub-task data corresponding to the dependency relationship by utilizing a first large language model based on tool information connected with each edge; synthesizing all the generated sub-tasks into a complete natural language query by using a second large language model; and verifying the synthesized data and the corresponding tool execution track to generate high-quality multi-hop tool learning data.

A processor-implemented method for generating a topological order using an artificial neural network (ANN) includes receiving a set of tasks to be performed; representing the tasks in a graph comprising a plurality of nodes connected by edges, each node corresponding to a task in the task set; assigning a scheduling priority to each node in the graph; selecting a next one of the potential next nodes according to a probability of each of the potential next nodes based on the assigned scheduling priority and a topology of the graph; a topological order of the tasks is generated by repeating the selection of the next node.

Systems and methods are presented for improved determination of cell correspondences between circuit designs. A method may include steps of accessing a source circuit design and a layout circuit design, accessing previously-determined corresponding hierarchical cells between the source circuit design and the layout circuit design, and determining additional corresponding hierarchical cells between the source circuit design and the layout circuit design, including by constructing a class-correspondence graph for the source circuit design and the layout circuit design, determining components from the class-correspondence graph, constructing a component-containment graph for the components of the class-correspondence, topologically sorting the component-containment graph, processing the components in a top-down topological order to determine the additional corresponding hierarchical cells between the source circuit design and the layout circuit design.

The application discloses a kind of vehicle-mounted controller firmware upgrade method, it is related to intelligent network connection automobile technical field, including, the application is accurately described the dependence between versions by introducing directed acyclic graph, provides clear topological order basis for upgrade process, fundamentally eliminates the system function conflict and disorder caused by upgrade sequence confusion;Utilize distributed consensus protocol to ensure that each controller reaches state coordination before upgrading, effectively improves the overall robustness of multi-controller cluster when facing network delay or individual node exception, avoids upgrade failure or version bifurcation caused by part of node not ready;Through group-level binding token and atomic switching mechanism, it is guaranteed that the controller group with strong dependence relationship can be updated as a unified transaction unit, significantly enhancing the security and reliability of cross-domain function linkage.

Non-Abelian anyons are created, braided, and fused using the physical qubits of a QCCD-based quantum processor. A controller controls operation of a confinement apparatus to cause a plurality of physical qubits to be confined by the confinement apparatus. At least some of the plurality of physical qubits are logically organized onto a lattice and have been prepared to provide a non-Abelian topological order ground state. The lattice is formed of a plurality of sublattices. The controller causes performance of an anyon creation gate to cause creation of a first pair of non-Abelian anyons on a sublattice of the plurality of sublattices; causes a path traversal gate sequence to be performed to cause at least a first anyon of the first pair of anyons to traverse a braiding path to form a closed loop on the sublattice; and determines a fusion channel formed by fusing the first pair of anyons.

A system obtains deterministic results of netlist transformation in a multi-processor parallel computing system. The system receives a netlist. The system determines a set of components representing components with assigned identifiers. The system determines identifiers for remaining components. The system visits a component of the remaining components of the netlist in a topological order starting from the set of components. The system assigns a new identifier to the visited component. The new identifier is generated based on properties of the visited component and a connection of the visited component with at least one component of the set of components. Once the component is assigned an identifier, the system adds the component to the set of components. The system repeats these steps to assign an identifier to remaining components that are not included in the set.

This application provides an automated code repository generation method, electronic device, and program product, relating to the field of software design and development. The method includes: acquiring functional specification information; generating corresponding functional subtrees based on the functional specification information; clustering nodes in the functional subtrees into multiple functional modules based on the cohesion principle, and generating a modular functional diagram; mapping the functional modules in the modular functional diagram to file structures, generating an enhanced file structure diagram; modeling data flows between functional modules and file sequence dependencies within functional modules based on the enhanced file structure diagram to generate a repository planning diagram; and generating code files using a test-driven development process based on the topological order determined by execution dependencies to form a code repository. This method enhances consistency assurance from high-level functional planning to low-level code implementation, improving the modular quality and maintainability of the code repository generation process.

This invention discloses a fund clearing method based on dynamically constructed directed acyclic graphs (DAGs). The method includes: receiving a transaction request containing transaction context information; selecting a clearing strategy based on this information and instantiating a clearing strategy DAG in memory; traversing and executing the rule templates corresponding to each node according to the topological order of the DAG; collecting atomic accounting instructions generated by all nodes during the traversal; and submitting the instructions to the core accounting system for execution after the traversal is complete. This invention, by deconstructing the clearing logic into a dynamically constructed DAG, achieves separation of business logic and execution engine, improving business flexibility and system performance. It can be used to handle complex transaction scenarios with high concurrency and changing rules, while simplifying auditing and reducing fund processing risks.