14 results about "Probability assignment" patented technology

The invention relates to the technical field of product reliability evaluation, in particular to a product reliability evaluation method and system based on multi-information measurement fusion. The method comprises the following steps: acquiring an unknown parameter of life distribution adapted to an evidence source, generating a parameter interval for the unknown parameter, and generating basic probability distribution associated with the parameter interval based on the evidence source; calculating correlation coefficients between the evidence sources based on the basic probability distribution, and generating importance weights according to a plurality of correlation coefficients of the evidence sources and other evidence sources; calculating the information entropy of the evidence source, and obtaining a credibility weight through normalization based on the information entropy; carrying out normalization on an operation result of the importance weight and the credibility weight to obtain an evidence weight; taking the evidence weight as a weight coefficient of basic probability distribution, and obtaining comprehensive probability distribution through evidence fusion; and carrying out parameter estimation on unknown parameters based on comprehensive probability distribution, and substituting an estimation result into a reliability function to carry out reliability evaluation, so that reliability evaluation under fault-free data is realized.

An automated functional fault localization method for software product lines integrates techniques such as Spectrum-Based Fault Localization (SBFL), machine learning algorithms, data mining, and information theory. By utilizing code blocks as the detection granularity, it performs probability allocation from three perspectives: prediction, actual execution, and correlation. The suspiciousness values of code blocks are then computed using uncertainty reasoning algorithms, enabling the rapid identification of suspicious statements. Building on this, a more precise assessment of these statements is achieved by evaluating at three levels of granularity: global, local, and code block. This ultimately results in more efficient and accurate fault localization. The code blocks directly correspond to internal feature interactions, significantly enhancing the efficiency of searching for these interactions. For program statements with a higher likelihood of causing software errors, this method allows for quick identification and localization without manual intervention, thereby facilitating the maintenance of software product line systems.

The invention provides a task allocation method and device, equipment, a storage medium and a program product, relates to the technical field of data processing, and can dynamically adjust a task scheduling strategy according to the task load and the state of a proxy object so as to improve the resource utilization efficiency. The method comprises the following steps: obtaining the total quantity of tasks and the quantity of proxy objects, and determining the performance index of each proxy object; and based on the total task quantity and the proxy quantity, a plurality of resource allocation schemes are initialized and generated, and each scheme allocates different task quantities to the proxy objects. And calculating the fitness score of each scheme according to the performance index, and selecting the scheme with the highest score as a target resource allocation scheme. And according to the performance index of each proxy object, determining the scheduling weight of the proxy object in the target scheme. And for each to-be-scheduled task, calculating a selection probability based on the scheduling weight of each current proxy object, and allocating the proxy objects according to the probability. And after distribution, updating the scheduling weight of the proxy object, and repeatedly executing the process until all tasks are distributed.

The application discloses a content detection method and device, equipment, a storage medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring user input content to be detected; detecting the user input content by at least two target detection engines in M detection engines, obtaining a determination detection result output by each target detection engine and a first confidence of the determination detection result; determining probability distribution information of detection results of different categories of each target detection engine according to the confidence of each target detection engine and the first confidence of the determination detection result; determining target confidence of the detection results of different categories of the at least two target detection engines according to the probability distribution information and a conflict factor; and the target confidence is used for determining a detection result of the user input content. In this way, the problem that the content detection result is inaccurate can be solved.

Disclosed herein are system, method, and computer program product embodiments for determining a probability of a device at risk. The device may be associated with a plurality of security parameters. For a security parameter, the device can be in multiple states. A probability value corresponding to a security parameter can indicate the security parameter being in a state among the multiple states. A probabilistic graphical model may be used to represent dependences of the plurality of security parameters. A device security risk prediction module may determine a probability of the device at risk based on the probabilistic graphical model and the probability assignments to the plurality of nodes of the probabilistic graphical model, and further determine a user action instruction to be provided to a user of the device based on the probability of the device at risk.

The invention discloses a process parameter decision-making system and method based on multi-source information fusion, and relates to the technical field of conflict detection.The process parameter decision-making method comprises the steps that multi-source heterogeneous data in the process production process is collected, and feature extraction and uncertainty quantification are conducted on the preprocessed multi-source heterogeneous data; constructing a process knowledge graph, calculating distances and conflict entropies among different evidences, calculating evidence credibility weights by using the distances among the evidences, and correcting each evidence by using the credibility weights; carrying out evidence fusion by using an evidence theory to obtain a probability distribution function of comprehensive evidence; determining a current process state and confidence according to the fused evidence, extracting different process parameter adjustment schemes associated with the current process state from the knowledge graph, and selecting an optimal process parameter adjustment scheme by using a neural network algorithm; and executing the optimal process parameter adjustment scheme, collecting multi-source heterogeneous data of the next production cycle, and judging whether the optimal process parameter adjustment scheme is effective or not.

This invention discloses a decision-level contextual information inconsistency elimination system and method, relating to the field of artificial intelligence technology. Its features include: a human behavior data acquisition module, a human behavior data preprocessing module, a bidirectional long short-term memory human behavior recognition network module, an inconsistency detection module, an initial basic probability allocation mapping module, an evidence set conflict degree division module, an adaptive credibility assessment module, and a human behavior information application module, connected sequentially. The human behavior data acquisition module includes an inertial motion sensing unit and a physiological signal monitoring unit. The technical problem this invention aims to solve is to provide a decision-level contextual information inconsistency elimination system and method to achieve effective fusion of conflicting evidence, fully utilize the complementarity of multi-source information, and improve the reliability and accuracy of decision-making in complex scenarios.

The invention provides a reinforcement learning-oriented multi-track cross-state award attribution method, and belongs to the field of reinforcement learning and graph neural networks. According to the method, in each training process, track data generated by environment interaction are collected by adopting a traditional reinforcement learning method, and strategy loss and value function loss are calculated according to an original mode; meanwhile, a global state transition diagram is constructed based on the trajectory data, state attribution probability distribution is calculated by using a graph neural network, each trajectory final reward is allocated to a state node according to an attribution probability, and a reward attribution signal of each state node is obtained; a loss function, a value function, and a reward signal are modified based on the obtained reward attribution signal. Through the integration mode, the method not only can make full use of the advantages of information sharing and cross-state reward attribution of multiple tracks, but also can be seamlessly embedded into a mainstream reinforcement learning training framework, and can significantly improve the training effect and strategy performance of reinforcement learning in a sparse and delayed reward environment.

The invention discloses a training method of a file fragment classification model and a file fragment classification method, and relates to the field of file fragment classification, and the method comprises the steps: collecting different types of original file data, and carrying out the random sampling of data blocks of the different types of original file data at a fixed byte length; converting each data block into a corresponding initial token sequence; converting each initial token sequence into a target token sequence; and pre-training a Transform model based on each target token sequence after dynamic masking, the dynamic masking comprising: randomly selecting byte tokens with a preset proportion in the target token sequence as target byte tokens, and performing different masking operations on each target byte token based on probability allocation, the different mask operations comprise replacing the target byte token with the fourth function token, randomly replacing the target byte token with other byte tokens and keeping the target byte token unchanged. According to the method and the device, the generalization ability of the Transform model can be improved.

The application relates to the field of automatic driving, and particularly discloses a method for predicting takeover success probability and evaluating intervention effect under human-machine co-driving conditions. The method comprises the following steps: step 1, establishing a takeover success sample: determining the optimal takeover budget time and the takeover success rate cumulative curve of a driver, and constructing a mapping parameter set; step 2, predicting the takeover fitness of the driver: optimizing parameters and constructing a real-time takeover state vector T, calculating the Mahalanobis distance D M (S) of the real-time takeover state vector and a perfect takeover state vector D M (F), calculating the basic trust probability assignment m(s) of perfect takeover and the basic trust probability assignment m(f) of a failed takeover, and setting the decision threshold of the trust degree assignment as epsilon, and predicting that the driver can successfully take over when m(s)-m(f) > epsilon. The method for predicting takeover success probability and evaluating intervention effect under human-machine co-driving conditions can improve prediction accuracy and efficiency, and reduce takeover risks.

The invention discloses a day-ahead multi-time scale probability scheduling method for a power system based on joint chance constraint. The method comprises the following steps: 1, acquiring day-ahead predicted output data of a renewable power supply and a probability distribution parameter of output of the renewable power supply; 2, establishing a multi-time scale electric power system day-ahead scheduling model, and introducing a joint opportunity constraint into the model to carry out risk control on the system; 3, equally dividing the total risk probability, carrying out probability allocation on a single opportunity constraint, and solving an optimization problem to obtain an initial solution; 4, iteratively adjusting opportunity constraint probability distribution by adopting a projection gradient method so as to reduce the conservative property of a solution; 5, solving the optimization problem again based on the updated opportunity constraint probability distribution scheme, and evaluating the overall risk of the system; and 6, if the system risk level converges to a target value, outputting a final scheduling scheme, otherwise, continuing iteration. According to the method, the optimal allocation of the chance constraint risk probability can be realized, so that a low-conservative scheduling scheme meeting the system risk level can be obtained.

Systems and methods for progressively releasing features used in conjunction with a service in a computing environment are disclosed. The computing environment includes users and resources. A method includes providing access to the service to the plurality of users and providing a particular feature to be used in conjunction with the service. The method can additionally include maintaining a directory of the entities of the computing environment. The entities can include the users and / or the resources. The directory of the entities organizes the entities into groups. The example methods can also include assigning an access value associated with the particular feature to a particular group of the groups. The access value can indicates an assigned probability that the feature will be enabled with respect to a particular entity included in the particular group. The assigned probability can advantageously be greater than zero and less than one.

This invention discloses a keyword generation method based on category guidance and alternating meta-tuning. Addressing the probability imbalance caused by diverse domain distributions, the method first constructs a contrastive learning retrieval system that integrates category information, and then selects a high-quality support set to construct a pseudo-task. Next, the generation model parameters are decoupled into a high-sensitivity weighted sub-network and a backbone network. Then, alternating meta-tuning is performed: a three-step loop of simultaneous updates to both networks, updating the weighted sub-networks separately with a fixed backbone, and updating the backbone using new weights. Finally, a smoothing factor is introduced to update the retrieval system in reverse. This invention effectively calibrates the probability allocation between replication and generation through parameter decoupling and category guidance, improving the generation accuracy of keywords in both present and missing categories, as well as the model's generalization ability.

This invention relates to a method, system, and medium for pipeline routing on offshore platforms based on ant colony optimization algorithm, belonging to the field of intelligent optimization algorithm technology. The method includes the following steps: S11: Environment Modeling: Discretizing the three-dimensional space of the offshore platform into a mesh model, marking obstacle areas and feasible areas, and calculating the energy values ​​of mesh nodes; S12: Parameter Initialization: Constructing a heuristic information matrix based on pipeline inlet / outlet coordinates and equipment parameters. The matrix determines the node transition probability through a direction probability allocation strategy and a distance-sensitive function; S13: Path Optimization: Generating candidate paths through dynamic smoothing search, local pheromone adaptive adjustment, and collaborative niche search, and updating the pheromone; S14: Constraint Verification and Result Output: Verifying whether the path meets engineering constraints, and outputting the three-dimensional coordinates, length, and number of bends of the optimal pipeline path after iterative optimization. The method of this invention achieves efficient, safe, and economical pipeline routing on offshore platforms.