11 results about "Deterministic algorithm" patented technology

The invention provides a security authentication method and device based on quantum random numbers and electronic equipment, and the method comprises the steps: obtaining quantum noise through a quantum random number generator, the quantum noise at least comprising a photon polarization state and vacuum fluctuation; performing analog-to-digital conversion on the quantum noise, and generating a true random number sequence according to the converted quantum noise to obtain a first target sequence; when a security authentication request is received, intercepting the first target sequence according to the type of the security authentication request to obtain a second target sequence; and sending the second target sequence to the target terminal to complete security authentication. The method solves the problem that in the prior art, random number generation depends on a deterministic algorithm, and the seed of the random number generation can be reversely analyzed, so that security authentication has the risk of being decoded.

The invention discloses a reservoir facies model construction method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a well logging interpretation result of each drilling well in a target area, and generating a well network model; based on the well network model, generating a first model corresponding to the target area through a deterministic algorithm; generating a second model under constraint control of the first model by using a randomness algorithm; according to the well logging interpretation result of each target drilling well, a sand-to-ground ratio threshold value is determined, and the sand-to-ground ratio threshold value is the proportion of the reservoir stratum in each drilling well in each drilling well; and adjusting the second model according to a sand-to-ground ratio threshold value to obtain a target reservoir facies model. According to the technical scheme, the second model is generated through the determinacy and randomness algorithm, so that the generated second model can be closer to the real situation of the target area; the second model is adjusted according to the sand-to-ground ratio threshold value, the target reservoir phase model is obtained, and the accuracy of a final result is guaranteed.

The invention discloses an infinite storage database system and method based on function calculation mapping, and belongs to the technical field of database storage. The core of the system is that a physical storage demand is converted into a calculation demand through a unique mapping relation from a data value to a composite function, and infinite logic storage in a fixed physical space is realized. The system comprises a data mapping layer, a function calculation layer and a physical storage layer. When data is written, a data value is mapped to a unique composite function through a deterministic algorithm, and only an identifier of the function instead of original data is stored; and during data query, performing reverse calculation through the function identifier to obtain an original data value. The composite function is a predefined mathematical transformation function, and the one-to-one correspondence relation between the input data value and the output function identifier is ensured. According to the invention, through an innovative architecture of replacing computing power with storage, the limitation of physical storage space is broken through, and the infinite data storage capability is realized under the limited storage capacity.

Embodiments include systems and methods for searching, tracing, and tracking Monero transaction chains. An exemplary method includes receiving, at a user interface using a plurality of Monero transaction menus, Monero transaction identification data for searching, tracing, and tracking Monero transaction chains. The method also includes determining, at the server computing device, an input of the Monero transaction identification data and an output of the Monero transaction identification data. The method also includes analyzing, at the server computing device, the input of the Monero transaction identification data determining a ring signature of the input of the Monero transaction identification data, the ring signature may include a plurality of decoys and a spendable and valid input. The method also includes generating, with a processor, a user interface displaying the plurality of decoys and the spendable and valid input.

A minimal cluster of G+1 disks with G same-size cells has protection group members distributed in accordance with a deterministic algorithm such that the cell at disk X, cell Y is assigned to group (X+Y−1) modulo M, where M is group size (G) plus 1 and one of the groups is reserved as spare capacity for recovering from drive failure. The cluster can be granularly expanded and eventually split into a non-flex cluster and a flex cluster that begins with the member distribution pattern of the deterministic algorithm. Relocations of members during expansion and split are achieved in part by submatrix transposition and reordering of disks.

The application discloses a bus passenger alighting station inference method based on a travel chain and an integrated learning, and comprises the following steps: collecting bus multi-source data; performing data cleaning and preprocessing on the multi-source data; fusing the multi-source data and judging a passenger boarding station according to a station arrival time window; performing preliminary inference on the alighting station based on a deterministic algorithm; establishing a deterministic algorithm to infer the passenger alighting station according to a travel chain assumption and passenger travel history; performing supplementary inference on the alighting station based on the integrated learning; introducing built environment and bus line external information for a journey that cannot be inferred by the deterministic algorithm; and establishing a multi-classification model based on an integrated learning two-layer stacking framework to further infer the passenger alighting station. The application realizes non-aggregated, station-level single-ticket bus passenger alighting station inference based on bus multi-source big data, can obtain accurate bus passenger flow space-time distribution characteristics, and provides a reference for bus operation organization optimization.

The application discloses a layered decoupling deterministic idempotent identification generation method and system. The method physically separates the session access stage and the service identification generation stage: the session access stage generates a session access certificate based on non-service attributes to prevent replay attacks; the service identification generation stage only constructs a service unique key based on service parameters and generates an idempotent handle through a deterministic algorithm; a minute boundary double window atomic query and TTL guarantee threshold checking mechanism are adopted to provide a clock drift tolerance of ±30 seconds; and offline verification capability is realized through reverse verification of the server. The application eliminates the long-term bidirectional mapping index of the service key to the identification, and the storage overhead can be reduced by more than 70%, the generation delay can be optimized to less than 1ms, and 100,000 + high-concurrency scenarios per second are supported; 100% cross-node consistency, zero-downtime configuration hot update, financial-grade security salt value rotation and cross-system offline verification capability are provided, and the application can be widely applied to distributed payment, e-commerce transaction and other scenarios.

This disclosure relates to a method and system for sanitization of sensitive data. The method includes analyzing sensitive data present within a page of an application based on a deterministic algorithm. Further, the method includes classifying the sensitive data into a high risk sensitive data and a low risk sensitive data based on a ML classification algorithm. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a destructive sanitization on each of the high risk sensitive data. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a non-destructive sanitization on each of the high risk sensitive data.

The embodiment of the invention discloses a method for determining an initial solution, and the method comprises the steps: collecting a better preset solution of a preset mathematical programming model in one or more historical business scenes in advance through a mode such as an uncertainty algorithm; according to the method, deterministic optimal initial solutions corresponding to mathematical programming models in different historical business scenes are provided, and the situation that uncertainty of solving results caused by an uncertainty algorithm and other modes is exposed to a user is avoided. Therefore, in the actual application process, the preset solution corresponding to the preset mathematical programming model of which the similarity with the first mathematical programming model meets the specified condition can be efficiently and quickly obtained to serve as one or more target initial solutions of the first mathematical programming model; therefore, a reproducible and relatively appropriate initial solution required by the first mathematical programming model corresponding to the current service can be provided for a user, so that the solving efficiency of the mathematical programming model is effectively improved, and the reproducibility requirement of the user is met.

Disclosed in the embodiments of the present application is a method for determining an initial solution. In the method, preferable preset solutions of a preset mathematical programming model in one or more historical service scenarios can be pre-collected by means of, for example, an uncertainty algorithm, so as to provide deterministic preferable initial solutions corresponding to the mathematical programming model in different historical service scenarios, thereby preventing the uncertainty of solving results caused by the uncertainty algorithm, etc., from being exposed to a user. In this way, in an actual application process, the preset solutions corresponding to the preset mathematical programming model, the similarity between which and a first mathematical programming model meets a specified condition, can be efficiently and quickly obtained and can be used as one or more target initial solutions of the first mathematical programming model, such that the user can be provided with a reproducible and relatively appropriate initial solution required by the first mathematical programming model corresponding to a current service, thereby effectively improving the solving efficiency of the mathematical programming model and meeting the reproducibility requirements of the user.

A minimal cluster of G+1 disks with G same-size cells has protection group members distributed in accordance with a deterministic algorithm such that the cell at disk X, cell Y is assigned to group (X+Y−1) modulo M, where M is group size (G) plus 1 and one of the groups is reserved as spare capacity for recovering from drive failure. The cluster can be granularly expanded and eventually split into a non-flex cluster and a flex cluster that begins with the member distribution pattern of the deterministic algorithm. Relocations of members during expansion and split are achieved in part by submatrix transposition and reordering of disks.