108 results about "Finite state" patented technology

Provided are systems and methods for enhancing the output accuracy and consistency of sequence processing models, particularly when generating text that must conform to a specific formal language. Systems and methods can integrate finite-state or push-down automata with the decoding process to ensure that the generated sequences adhere to the desired syntax or grammar rules. A system can generate or otherwise leverage a finite-state or push-down automaton that encodes constraints associated with a particular formal language or syntax. At each decoding iteration of a sequence processing model, the decoding system can use the finite-state or push-down automaton to evaluate the token validity for each of a number of possible output tokens. The output of the sequence processing model can then be limited to or otherwise guided towards tokens that are indicated as valid by the finite-state or push-down automaton.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

The invention belongs to a specification mining technology of software engineering, and relates to a construction method of an approximate specification mining model and a software behavior verification system. The construction method comprises the following steps: generating a group of linear temporal logic LTL formulas for describing positive example time sequence attributes according to a positive example set; using an LTL formula to generate a potential negative example for evaluation and training; designing and constructing a neural network for analyzing the finite state automaton from the parameter assignment so as to simulate an acceptance behavior of the finite state automaton; and iteratively searching the neural network through a gradient descent algorithm until the maximum number of iterations is reached, and mining to obtain the finite state automaton. The neural network can simulate acceptance of the finite state automaton in the reasoning process, the finite state automaton can be explained at low cost, and the problem of search space explosion is solved.

The invention discloses a book-based subject knowledge system construction method and device, and the method comprises the steps: representing a directory as a directed tree, and dividing the directed tree into a skeleton module and a plurality of chapter modules; carrying out the association and expansion of a title through a large language model, and generating an abstract; knowledge concepts, subject terms and structural relations in titles in all the modules are extracted and subjected to double correction to obtain a plurality of knowledge system trees, and then a mother tree obtained after the skeleton module is processed and a sub-tree obtained by the chapter module are spliced; and finally, giving a plurality of knowledge trees obtained according to a plurality of groups of book directories in the same subject field, and combining the knowledge trees into a unified knowledge system tree based on a finite-state machine principle. According to the method, the knowledge system is constructed and decoupled into an extraction stage and a fusion stage based on LLM, the problem of a pain point of drifting between knowledge concepts and attributes when a simple chapter title is faced is solved, and strict control on semantic consistency and hierarchical rationality in a path merging process is ensured based on a state machine.

A system provides a language model that utilizes an optimized parser and operates at the byte- level in a context free grammar (CFG) and a tokenizer vocabulary. The CFG is constrained and automatically transformed to a byte-level grammar. A prediction mechanism is used to predict the allowed sequences of bytes that can appear after a given prefix. In some instances, only the next token is constrained so that candidates for the next token can be intersected with tokens in the tokenizer vocabulary. To achieve this, lattice parsing is performed using a finite state automaton (FSA) that is generated and then minimized.

The invention relates to the technical field of intelligent training and data analysis, in particular to an intelligent model construction method and system for student training data analysis. Comprising the following steps: constructing an SOP time sequence knowledge graph, obtaining a standard operation specification document and expert-level operation demonstration data, and constructing the SOP time sequence knowledge graph; real-time feature extraction: capturing a multi-modal data stream, and generating a real-time behavior feature vector; state recognition and path deduction: inputting the real-time behavior characteristics into an event classifier to recognize a key event, performing path deduction based on deterministic finite state automaton logic in an SOP time sequence knowledge graph, and determining and outputting a current state pointer; deviation analysis and judgment: extracting a compliance mask based on a current state, and injecting the compliance mask into a decoding process of the attention network; and performing feedback and adjustment to generate a corresponding deviation event signal. Through deep analysis, feedback is more targeted, and the accuracy and fineness of training evaluation are remarkably improved.

The invention discloses a multi-model cache management algorithm based on online learning, which comprises the following steps: S1, an SSD (Solid State Disk) controller receives an I / O (Input / Output) request sent by an upper-layer application and captures a request feature; s2, after a fixed number of pages are processed every time, the request features are aggregated to generate window-level indexes; s3, performing discretization processing, and mapping into a finite state space; s4, the Q table calculates the predicted value of each strategy in the basic strategy pool in the current state; s5, the online learning module dynamically selects a cache replacement strategy of the next period in the basic strategy pool through the predicted value; and S6, additionally setting a preferential expelling chain table in the cache, monitoring the busy degree of a rear-end flash memory chip in real time by an active write-back module, and asynchronously writing cold dirty pages in the preferential expelling chain table back to the flash memory. According to the method, the cache hit rate is improved through dynamic strategy switching; the active write-back module reduces dirty page replacement delay and prolongs the service life of the SSD; the total memory occupation of the Q table and the Xgboost model is small, and the method adapts to the resource limitation of the embedded controller.

The invention discloses a finite state inquiry method and system based on a large model and a knowledge graph, and relates to the field of business handling, when there is a business handling intention, a finite state machine sets an initial business state, and a large language model analyzes, inputs and extracts keywords and demands to determine a current state; the user naturally converses with the big language model, and the big model is guided to complete the process according to the knowledge graph and the current state; when the user interrupts questions, the large model answers the questions according to the knowledge graph, and then the finite-state machine records the state to help the user to seamlessly return and continue to handle the questions; according to the method, the problem of large model illusion is effectively solved, and the accuracy and stability of user intention judgment are improved; intelligent autonomous business handling without manual intervention is realized; the seamless recovery process after the user interrupts questions at any time is supported, and the flexibility and coherence of business handling are enhanced; comprehensive knowledge coverage is ensured by means of a knowledge graph, and user questions are accurately answered; and meanwhile, the dependence on manual business personnel is reduced, and the labor cost is reduced.

The invention provides a code-free SaaS application rapid development method and system based on data source driving, and the method comprises the steps: constructing a forward business process directed acyclic graph, defining compensation association and reality binding for a forward operation node of the forward business process directed acyclic graph, and generating a visual transaction model; analyzing the transaction model to generate a transaction step sequence, constructing a finite state set and a state transfer function based on the sequence, and packaging the finite state set and the state transfer function into an SAGA process state machine definition object; generating a global transaction ID when a transaction life cycle is started, constructing a transaction context object in combination with the state machine definition object, and serializing to generate an initial transaction context instance; and loading and deserializing the instance, starting a state-driven execution loop, terminating by iterating single-step state transition when the current state belongs to a termination state set, judging a final result of the transaction, and archiving an audit log. According to the method, transaction process modeling and formalized state driving execution are fused, and the method is suitable for SaaS application development of cross-heterogeneous data sources.

The invention relates to the technical field of robot autonomous decision-making, and discloses an underwater robot behavior decision-making method based on a behavior tree, which describes and controls the specific behavior of an underwater robot based on the behavior tree, organizes and manages complex behavior logic through a tree structure, and analyzes a specific task to determine the behavior of the underwater robot. And decomposing into behavior sub-trees, and combining control nodes and action nodes in the behavior sub-trees to complete a specific logic switching function. Compared with a traditional finite-state machine, the method has the advantages of modularization, expandability and high readability, the coupling degree between modules in the sub-tree is low, the sub-tree can be transplanted to multiple positions of the behavior tree, the problem that the workload of the reuse state of the finite-state machine is large is effectively solved, and the method has good practical application value.

A computer-implemented system that reduces and resists computationally generated fraudulent reviews by enforcing aspect-level decomposition with evidence requirements, multi-tier cryptographic verification, and deterministic trust scoring. Reviewer tiers (public, independent reviewer, independent professional reviewer) use verifiable credentials, zero-knowledge purchase proofs, and device or platform attestation. Evidence gates apply fixed, monotonic transformations; mandatory-evidence aspects yield zero contribution absent a required artifact. A finite-state dialog protocol with immutable timer fields enforces typed, aspect-bound objections. A Consumer Trust Board employing threshold signatures issues binding decisions affecting Consumer Trust Score computation with bounded retro-adjustment. A certification registry maintains credentials and sanctions that calibrate weighting. Non-instrumented submissions are badge-ineligible and weight-capped. Uniqueness keys prevent double counting. Dialog records are version-chained and batch-anchored via collision-resistant commitments. All computations use parameters from a versioned configuration to ensure deterministic reproducibility.

The present application provides an anomaly detection system for industrial control systems, encompassing data collection, data processing, anomaly detection, and response execution modules. It uniquely employs a strategy based on Finite State Machines (FSM), actively querying the data collection module to gather operational data from sensors and Programmable Logic Controllers (PLC). The data processing module standardizes and formats the data, while the anomaly detection module uses a predefined FSM model and adaptive learning mechanisms to enhance the accuracy of anomaly identification.

The invention discloses a quantum key distribution system and method. According to the system, a phase coherence monitoring mechanism is innovatively introduced, and early attack detection is realized through a dynamic consensus sampling protocol; a zero-trust relay network is constructed by adopting a wavefront fragmentation principle, and trust dependence on relay nodes is eliminated; self-adaptive safety monitoring is realized based on a finite state automaton, and a system safety strategy is dynamically adjusted. According to the method, the problems of resource consumption type attack, relay trust dependence, static protocol limitation and the like faced by an existing QKD system are solved through a strict mathematical framework, and long-distance quantum key distribution with efficient resources, endogenous security and elasticity and physical layer security guarantee is realized. Experiments show that compared with a traditional scheme, the detection delay of the system is reduced by about 38%, the resource waste is reduced by about 40%, and the availability of the system reaches 99.995% or above.

The invention provides a voice generation method and device, a computer readable storage medium and electronic equipment, and the method comprises the steps: obtaining an input text which represents a dialect text of a target small language; converting the input text into a standard text through a finite state conversion model; the standard text is analyzed through the target voice model, audio features of the standard text are obtained, target voice is generated according to the audio features, each group of data in the multiple groups of data comprises historical standard texts and historical audio features, the historical standard texts comprise general texts and special texts, and the historical audio features comprise general audio features. The general text represents the standard language text contained in various languages including the target small language, and the special text represents the standard language text only contained in the target small language. According to the method and the device, the problem that the voice corresponding to the complex small language cannot be accurately generated is solved, and the effect of accurately generating the voice corresponding to the small language is achieved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a computer network intrusion detection method and system based on artificial intelligence, and the method comprises the steps: constructing a finite state automaton to carry out the state jump compliance verification of a connection flow, intercepting the illegal flow, only sending the compliance flow into a feature engineering module, and extracting the multi-dimensional behavior features; inputting a deep neural network classification model to determine whether the behavior is an intrusion behavior; according to the technical scheme provided by the invention, the data input to the artificial intelligence model can be ensured to have complete compliance on the protocol level, so that the model is prevented from learning false feature association caused by protocol violation, and the antagonistic attack based on protocol state jump can be effectively resisted on the premise of not depending on adversarial training.

The invention belongs to the technical field of aviation safety information processing and intelligent retrieval, particularly relates to an aviation safety knowledge mixed retrieval system based on a creative distributed database, and solves the problem that the prior art cannot meet the requirements of aviation safety information processing accuracy, efficiency and reliability. The method comprises the following steps: firstly, depending on a Peng and Feiteng trust and creative server and an OceanBase distributed database, matching an integrated storage engine and a physical collaborative distribution module, and constructing a multi-type data fusion storage base; then realizing precise semantic retrieval, weak network off-line data management and increment synchronization through double-engine cooperation of an intelligent engine layer, fusion of a domain-specific embedded model and an RRF optimization algorithm and combination of a finite-state machine and a positive and negative counter algorithm; and the application service layer standardizes an API and a multi-tenant isolation strategy, and adapts to multi-terminal and third-party system integration. The accuracy, efficiency and reliability of aviation safety information processing are remarkably improved.

The invention discloses a hardware acceleration method and system for a ZSTD data compression algorithm based on an FPGA. Unified hardware implementation and collaborative optimization are carried out on three key links of LZ77 character matching, Huffman coding and finite state entropy coding in the ZSTD data compression algorithm. According to the system, a multi-channel parallel LZ77 character matching hardware architecture is adopted, and character matching is executed on continuous positions in a plurality of parallel windows at the same time. A modular design and a deep pipeline structure are adopted, and a buffer and pipeline mechanism is introduced among modules such as an LZ77 matching module, a Huffman coding module and a finite state entropy coding module. The 375MB / s compression throughput can be achieved under the 100MHz clock frequency, the compression speed is remarkably increased while the compression ratio is guaranteed, and the method is suitable for scenes with high requirements for compression performance of data centers, edge computing and embedded systems and has high practicability and popularization value.

A text segmentation and sensitive word detection method based on matrix multiplication, comprising the following steps: obtaining an original text string and a sensitive word library; constructing a deterministic finite state automaton tree diagram of sensitive words according to the sensitive word library; converting the original text string into a text character two-dimensional matrix and recording the length of the text character two-dimensional matrix; constructing a matching two-dimensional matrix according to a horizontal matching rule, a vertical matching rule, an oblique matching rule and an inverse oblique matching rule, the length of the matching two-dimensional matrix being the same as that of the text character two-dimensional matrix; performing dot multiplication processing on the text character two-dimensional matrix and the matching two-dimensional matrix to obtain a corresponding result matrix; generating a corresponding matching text string according to the result matrix, and matching with the deterministic finite state automaton tree diagram to determine whether there is a sensitive word. The application supports interval text character information detection, improves the sensitive word detection accuracy and detection efficiency.

The invention relates to a technique for implementing, controlling and automating a task or process on a blockchain such as, but not limited to, the Bitcoin blockchain. The invention is particularly suited for, but not limited to, automated execution of contracts such as smart contracts for financial agreements. However, other types of tasks and non-financial contracts can be implemented. The invention can be viewed as the implementation or incarnation of a state machine or DFA on a blockchain by using the unspent outputs of blockchain Transactions to represents the states of the machine, and spending of those outputs as the transition of the machine from one state to another. The invention provides a technical realisation and implementation of a mathematical model of computation conceived as an abstract machine that can be in one of a finite set of states, and can change from one state to another (transition) when a triggering event of a finite set (called input) occurs. The invention comprises compilation and codification techniques for the DFA implementation.

A method includes detecting an outage in a wireless communications network, acquiring specifications documents describing communication protocols between a plurality of network elements and a plurality of user equipment of the network, translating, using a generative artificial intelligence technique, the specifications documents into a first FSM that models expected events and corresponding actions from perspectives of the plurality of network elements and the plurality of user equipment, translating, using the generative artificial intelligence technique, a plurality of event logs of the network into a second FSM that models observed behaviors of the plurality of network elements and the plurality of user equipment, detecting, based on a comparison of the second FSM to the first FSM, a behavior of the observed behaviors that deviates from the communication protocols, and identifying a network element of the plurality of network elements that is associated with the behavior as a contributor to the outage.

The invention discloses a trusted space-based data annotation management and control method, system and device, and a medium. The method comprises the following steps: responding to a data labeling task request, determining a task type and labeling a task life cycle, dividing the task into a plurality of finite states, and binding a dynamic strategy set for each state; determining the computing power demand of each state according to the task type, and obtaining and configuring a corresponding resource template; when a user initiates a trusted spatial data loading request, obtaining user information and a loading demand, activating a resource template in an authentication state, and performing authentication according to a dynamic strategy set; after the authentication is passed, triggering a state transition event to enter a data loading state, and loading target data; after labeling is completed, a labeling result is obtained and subjected to quality inspection auditing; and if the audit is passed, outputting a result, and uploading the migration event and the annotation result for evidence storage. According to the method, the labeling work can be efficiently and safely completed according to the task, meanwhile, the integrity, traceability and safety of the data are effectively guaranteed, and the quality and efficiency of the labeling work are improved.

The present invention relates to a computer implemented decoding method that includes decoding a number of run-length encoded mask strings to obtain decoded mask bits for each of a number of scan channels. The method also includes applying the decoded mask bits to automatic test pattern generator (ATPG) scan response data of each scan channel to obtain a multiple input signature register (MISR) signature. Additionally the present invention also relates to an apparatus configured to carry out the method. The apparatus being implemented either with at least one memory and at least one processor or with a plurality of finite state machines and a plurality of logical AND gates.

This invention discloses a method for autonomous operation of humanoid robots based on ROS. It employs a decision-making and control framework that fuses hierarchical finite state machines with YOLOv8 vision to achieve continuous autonomous execution of multiple tasks in complex unstructured environments. The method includes: system initialization and standardized processing of multimodal perception data; target 3D localization fused with YOLOv8 and coordinate transformation; task scheduling and state transitions using hierarchical finite state machines; segmented navigation and motion planning of joint trajectories; and ROS command issuance and full-process closed-loop control. This invention addresses the rigidity of traditional script-based control by fusing hierarchical state machines with visual servoing. Combined with a full-process anomaly handling mechanism and multi-target scene processing rules, it can dynamically adapt to changes in complex unstructured environments, effectively avoiding problems such as state freezes and erroneous grasping.

The invention discloses a sliding input method, a sliding input device, sliding input equipment and a medium, which are used for a mobile terminal, are internally provided with an input keyboard, and comprise the following steps: responding to a sliding input operation on the input keyboard, generating an activated key sequence, and extracting sequence features based on coordinate information contained in the activated key sequence; and searching an initial candidate word list corresponding to the activation key sequence by adopting a finite state conversion machine, and screening and / or sorting according to sequence features to obtain a target candidate word list and displaying the target candidate word list. The sliding input method is not limited to specific languages and keyboard layouts, and can meet the sliding input requirements of the multi-language multi-keyboard layout mobile equipment under the language families such as the Euclidean language family, the sub-non-language family and the Kongsu language family. Meanwhile, the feature extraction method is simplified, the computing resource consumption of feature extraction can be greatly reduced, and the requirement of a user for highly matching candidate words is met while efficient computing is kept.

A system can include a plurality of hardware threads, one or more schedulers, and one or more execution pipelines. At least one hardware thread of the plurality of hardware threads can include one or more finite state machines. At least one finite state machine of the one or more finite state machines can be of a first type. The at least one finite state machine can process hazards on instructions. The at least one finite state machine can determine cycles in which the instructions are safe to issue to at least one of the one or more execution pipelines.

The present invention discloses a distributed fault-tolerant control system for a multi-spacecraft formation attitude system based on a fractional-order sliding mode, belonging to the field of automatic control. The method comprises: first, constructing a multi-spacecraft formation attitude system model, including a spacecraft mixed fault model and a multi-spacecraft formation flight attitude error system. Based on the constructed multi-spacecraft formation attitude system model, a distributed finite state observer is designed based on finite time theory, so that each observer can timely observe its own concentrated interference value and perform effective compensation. Finally, a new distributed collaborative fault-tolerant controller for the multi-spacecraft formation attitude system is designed based on fractional-order sliding mode theory, which can realize fault-tolerant control of the spacecraft formation attitude system, thereby ensuring the stability and reliability of the multi-spacecraft formation attitude system during mission execution.

The application belongs to the technical field of software dangerous behavior detection, and is a multi-level specification software dangerous behavior detection method, comprising the following steps: setting a positive and negative example set and a distance measurement function for quantifying the similarity degree of software behavior sequences, and dividing the positive example set into a plurality of positive example subsets; constructing a temporal logic specification weight tree as a triple consisting of a root node weight function, a left child formula depth weight function and a right child formula depth weight function; extracting positive examples from the positive example subsets to form a positive example reduced set; inquiring a large language model to obtain software dangerous behavior logic formulas and converting the software dangerous behavior logic formulas into equivalent finite state automata; sampling a negative example reduced set from the automata; inquiring the large language model based on the positive and negative example reduced sets multiple times to obtain formula fragments, dynamically updating the weight tree, and obtaining candidate formulas; and selecting the optimal candidate formula as a temporal logic specification to detect whether the software behavior sequence conforms to the temporal logic specification. The application alleviates the challenges of scarce dangerous behavior data and a too large dangerous behavior search space.

The invention relates to the technical field of data management, and particularly discloses a warehouse operation data management and control method, which comprises the following steps: constructing a multi-source heterogeneous data unified access layer, and carrying out standardization and metadata annotation; establishing a space-time alignment fusion mechanism based on the high-precision timestamp and the three-dimensional grid index; deploying a dynamic rule engine to realize millisecond-level self-adaptive updating of a service rule; respectively constructing a Markov chain, a finite-state machine and a hidden Markov model for three types of core entities of goods allocation, order and equipment to carry out state evolution prediction; and an abnormal risk index is calculated in combination with the prediction deviation and the rule conflict, and closed-loop verification is realized through end-to-end log comparison. According to the technical scheme, real-time consistency guarantee of storage data, operation traceability enhancement and prospective early warning of abnormal risks can be achieved, and fine operation of millisecond-level response and large-scale intelligent storage in a high-concurrency scene is supported.

An object-goal navigation framework for an autonomous mobile robot uses a dynamic Scene Graph (DSG) and a Relational Semantic Network (RSN) for semantic-guided object-goal navigation. The DSG is a hierarchical world representation generated from a prior spatial configuration of the environment. The RSN encodes relational semantic knowledge between objects and the regions or rooms in the environment. The object-goal navigation problem is then solved using a probabilistic planning framework with relational semantic knowledge. Using DSG and RSN, the global planning problem is formulated as a Markov decision process (MDP). The computed global planning policy directs the robot to visit a room or perform local searches. A finite state local controller is then used to execute the global planning policy and search for the target object.

The embodiment of the invention discloses an SQL (Structured Query Language) injection detection method, which comprises the following steps of: scanning an SQL statement word by word through a preset finite state automaton to obtain a machine lexical type of each lexical unit, constructing a lexical unit queue of the SQL statement on the basis of characters and lexical types of lexical units, and detecting the SQL (Structured Query Language). And obtaining a detection result through a pre-trained detection model for risk control. According to the method, an SQL statement is split through a state transition strategy of a finite state automaton to obtain lexical units, the problem that a regular matching mode cannot adapt to rapidly-changing attacks is avoided, meanwhile, due to the SQL injection principle, the mode for determining the lexical units and the types of the lexical units can effectively recognize zero-day attacks, and therefore the zero-day attacks can be effectively recognized. And the detection accuracy and efficiency of SQL injection are greatly improved.