15 results about "Call tree" patented technology

This specification provides a technical solution for bounded model detection. Using the method and apparatus provided in this specification, the application or module to be verified is uniformly denoted as the module under test. First, the call tree constructed by each verification unit corresponding to the module under test is obtained. Then, each node's corresponding first verification indicator is detected. Based on the first verification indicator, a first type of node that needs independent verification is determined. Next, the call relationships in the call tree are used to verify the first type of node, and other nodes are inlined into their corresponding call scenarios for verification. This effectively reduces the repetitive verification of complex functions and improves verification efficiency.

The application discloses a program energy efficiency optimization method and system based on stage division and multi-stage cooperation, and the method comprises the following steps: obtaining a profiling call tree of a target program, and initializing a current level as a maximum level of the profiling call tree; extracting a node set of the current level, extracting effective nodes, extracting features, and determining a stage type of the effective nodes; adding a binary tuple formed by a node name of each effective node and the stage type into a selected stage set S; constructing a stage feature matrix for the selected stage set S of the profiling call tree, and predicting an optimal frequency combination of the target program by using a pre-trained multi-stage cooperative energy efficiency optimization model. The application aims to realize optimal processor frequency configuration of each stage of a program under the condition that the global performance constraint is met, and significantly optimize the total processor energy consumption of the program under the premise that the execution performance is not lost.

The application provides a software design document generation method, system, device and medium, the generation method comprises the following steps: obtaining source code, constructing a call tree reflecting the calling relationship between functions in the source code, and an independent root node not covered by the call tree; extracting the document segment of the independent root node, and in the direction from the leaf node to the root node of the call tree, taking each node in the call tree as the current node in turn, extracting the document segment and the function summary of each node of the call tree; and splicing the document segments of all nodes according to the call tree and the independent root node to generate the software design document of the source code. The application effectively breaks through the context window limit of the large language model and realizes the analysis of deep nesting and super large scale code.

The invention discloses a data analysis method and device for a transaction strategy and a storage medium, and relates to the technical field of data processing. According to the method, related variables, operation instructions corresponding to the related variables and calling relations among the related variables are determined according to declaration codes received by a transaction data visualization interface; generating a variable calling tree according to the related variable and the calling relation, and generating a target code based on the operation instruction and the variable calling tree; obtaining an execution result of the target code, and determining a transaction signal according to the execution result; and according to the execution result and the transaction signal, rendering and outputting a transaction data visualization view on a transaction data visualization interface. According to the method, the target code is automatically generated by analyzing the declaration code, a plurality of strategy indexes do not need to be manually written to generate a transaction signal and draw code logic of key information, the change of the strategy indexes can be quickly responded, and the time cost for quantifying the transaction strategy is reduced.

The invention discloses a core network-oriented network function interaction anomaly detection method and system, and belongs to the technical field of mobile communication network security, and the method comprises the steps: converting call tree data into a time sequence interaction sequence through logic anomaly detection, and inputting the time sequence interaction sequence into LSTM-AE after numerical coding and embedded mapping; the second layer generates high-order features and compresses the high-order features into global potential representation; the dual-layer LSTM decoder reconstructs a sequence through a fully connected layer based on the potential representation to determine logical anomalies such as call missing, redundancy, or illegal calls with a reconstruction error exceeding an error threshold. The method comprises the steps of analyzing pcap flow data of a core network control plane through frequency anomaly detection, extracting frame / IP / TCP multi-level feature vectors, inputting the frame / IP / TCP multi-level feature vectors into an LSTM model to obtain a predicted flow normal probability after data cleaning, feature selection and standardized preprocessing, and identifying low-frequency attacks such as low-speed DoS when the flow normal probability is lower than a flow threshold value; and first bit missing and low-frequency anomalies in core network logic anomalies can be effectively detected.

The application provides a ROM patching method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: patching a first to-be-modified function on a ROM by using a first method; the first to-be-modified function is located at an upper layer of a call tree; the first to-be-modified function is a function that needs to call a basic function; patching a second to-be-modified function on the ROM by using a second method; the first method and the second method are different; the second to-be-modified function is located at a bottom layer of the call tree; the second to-be-modified function is a basic function.

The application provides a kind of microservice system multi-source data based on unsupervised anomaly detection method, and the specific steps are four parts of microservice selection, multi-source feature sequence construction, anomaly detection and interpretation.Microservice selection, extract tracking data to build call tree, analyze fault propagation mode, and define the leaf node in call tree as fault sensitive instance corresponding to microservice.Multi-source feature sequence construction, analyze the correlation between indicators and tracking and different faults, correlate in time and realize serialization of features.Anomaly detection, use sliding window mechanism to divide time series features into continuous data blocks, judge data block state by encoding and reconstruction of data block.Anomaly interpretation, extract data block reconstruction distance, obtain microservice instance and corresponding features with larger anomaly probability.The application can be used for enterprise, cloud service provider to evaluate the running state of key business, application service and system in internal cloud environment, and provide protection for improving application service quality.

The embodiment of the present specification provides a method and device for vulnerability detection of a target application, which is a decentralized application based on a block chain and supports resource exchange and transfer based on several types of tokens. The above method comprises: firstly, recording a transaction execution track involving a smart contract in an application execution process to form a call tree, the call tree containing transaction nodes and event nodes. Then, based on the call tree, extracting token transfer events and mining the roles involved in each transaction to determine target transfer actions occurring between a user and the target application, and each target transfer action forms a token flow. Then, the token flows for each type of token are merged, and the cumulative transfer-in amount and the cumulative transfer-out amount are determined based on the merged token flows. According to the cumulative transfer-in amount and the cumulative transfer-out amount, it is determined whether there is a token leakage vulnerability.

The application discloses a code recognition method and device, a computing device and a computer storage medium, and the method comprises the following steps: acquiring the calling information of a target method during code execution; generating a topology graph containing the calling tree of the target method according to the calling information and the execution result of the target method; and recognizing the target method capable of being converted into concurrent execution in the code by using a preset recognition model according to the topology graph. The application can analyze the code capable of being executed in a serial-to-concurrent mode with a relatively high probability, and provides a reasonable and feasible recognition and analysis scheme for concurrent execution code which can be quickly implemented for a developer, so that the risk in the project implementation process is reduced, the performance of an online product is optimized, and the highlighted defects of the prior art, such as the high requirement for the experience and skills of the developer and the lack of professional analysis tools, are overcome.

The application discloses a kind of network function interaction abnormality detection methods and systems for core network, belong to mobile communication network security technical field, method is converted into time sequence interaction sequence by calling tree data through logic abnormality detection, after numerical coding and embedding mapping, input LSTM-AE: first layer LSTM encoder extracts preliminary hidden state, second layer generates high-order feature and compresses into global latent representation;Double-layer LSTM decoder reconstructs sequence based on latent representation through fully connected layer, to reconstruct error exceeds error threshold to determine missing call, redundant or illegal call and other logic abnormalities.Frequency abnormality detection analyzes core network control plane pcap traffic data, extracts frame / IP / TCP multi-level feature vector, after data cleaning, feature selection and standardization preprocessing, input LSTM model to obtain predicted traffic normal probability, when traffic normal probability is lower than traffic threshold, identify low-frequency attack such as slow DoS;It can effectively detect the first missing in core network logic abnormality and low-frequency anomaly.

The invention provides a program complexity analysis method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology or other related fields, and the method comprises the steps: generating a call tree of a to-be-analyzed program based on a call relationship among a plurality of call programs in the to-be-analyzed program, when any two calling programs in the multiple calling programs have a calling relationship, the first calling program is a father node of a second calling program having the calling relationship, and the second calling program is a child node of the first calling program; determining a plurality of leaf nodes and root nodes in the call tree, and determining the total complexity of the root nodes on the basis of a set membership between the plurality of leaf nodes and the root nodes; and taking the total complexity of the root nodes as the program complexity of the program to be analyzed. According to the method provided by the invention, the total complexity of the root nodes is determined by utilizing the father-son relationship between the leaf nodes and the root nodes in the call tree, and finally, the total complexity of the root nodes can reflect the overall complexity of the full call chain.

A system and method for Generative Artificial Intelligence (Gen AI) based calling workflow optimization is provided. Historic data associated with user is fetched from multiple data sources and voice commands previously provided as voice prompts over an IVR call tree by the user are fetched for generating master data sheet. Outbound call is generated in the form of Interactive Voice Response (IVR) tree for user by processing master data sheet. Responses provided over outbound call are converted to text in the form of query, via first bot type. Prompt is generated based on text in the form of query and other queries in IVR tree and generated prompt are provided as input to large language model. The large language model identifies category from master data sheet which corresponds to query based on prompt to generate reply to query. Reply to query is inserted in IVR tree in real-time.

The invention discloses a program energy efficiency optimization method and system based on stage division and multi-stage collaboration, and the method comprises the steps: obtaining a parsing call tree of a target program, and carrying out the initialization setting of a current hierarchy as the maximum hierarchy of the parsing call tree; extracting a node set of the current level, extracting effective nodes, extracting features and determining stage types of the effective nodes; adding a two-tuple formed by the node name and the stage type of each effective node into a selected stage set S; and constructing a stage feature matrix for the selected stage set S of the analysis call tree, and predicting the stage feature matrix by using a pre-trained multi-stage collaborative energy efficiency optimization model to obtain an optimal frequency combination of the target program. According to the method, the minimum processor energy consumption and the optimal processor frequency configuration meeting global performance constraints in each stage of the program are realized, and the total processor energy consumption of program operation is remarkably optimized on the premise of ensuring that the execution performance is not lost.

The application is a Node.js application data race detection method based on a happens-before relationship rule, belongs to the technical field of computer software, and can solve the problem that it is difficult to detect data races in Node.js applications. The application proposes a happens-before relationship (HB relationship) rule for Node.js applications, divides it into three categories for different steps, and proposes an HB relationship matching rule and a resource characteristic matching rule. First, the application uses dynamic program analysis to instrument the measured application program and collect and analyze running data, extracts resource access conditions and asynchronous call HB relationships between event processing functions, and constructs an asynchronous call tree. Second, the application applies a tree reachability detection algorithm to the asynchronous call tree to detect candidate data races. Third, the application uses the HB relationship matching rule and the resource characteristic matching rule to determine whether the candidate data race is a false positive. Finally, the application outputs a data race detection report after the measured application exits.

The invention relates to the technical field of network security, in particular to an anomaly detection method, system and device in a shared environment and a storage medium, and the method comprises the steps: collecting behavior data of a plurality of hosts in the shared environment, and converging the behavior data to construct a process call tree; selecting candidate processes meeting preset candidate conditions according to the process call tree, and aggregating corresponding application role domains according to the candidate processes; in each application role domain, process capability categories are divided, and a tail segment process chain is constructed according to the divided capability categories; and performing anomaly detection on the tail-segment process chain according to a preset judgment condition to obtain an anomaly detection result. The unknown attack behavior is detected under the environment of multi-service coexistence and multi-node deployment during shared operation, and the detection accuracy is improved at the same time.