69 results about "Enumerated type" patented technology

The invention relates to a data query method and device based on a natural language and a medium, and belongs to the technical field of data analysis. Receiving a natural language query input by a user; constructing task planning guide information based on the natural language query, and inputting the task planning guide information into the large language model to identify a target task type corresponding to the natural language query; when the target task type is a data query type task, determining a query target index corresponding to the natural language query from an index library; on the basis of a DSL dimension normal form corresponding to the query target index, dimension fields related to semantics of the natural language query and matched enumeration values are recognized from the multiple dimension fields and enumeration value sets; and constructing a structured intermediate representation, generating an executable code corresponding to the natural language query, and executing the executable code to obtain a corresponding structured query result from a data source. According to the method and the device, the accuracy and the interpretability are considered, and meanwhile, the SQL generation flexibility is improved.

Systems and methods are disclosed comprising techniques for record-to-event conversion, such as retrieving at least one alphanumeric record associated with a monitored digital communication transmitted among two or more users, generating a time-enumerated data structure that stores an event entry set for the monitored digital communication, selectively identifying at least one discrete event for the monitored digital communication, generating one or more relevance scores for the at least one discrete event, identifying at least one valid discrete event from the at least one discrete event, generating an event attribute set for the at least one valid discrete event, updating the normalized event attribute set for a new event entry within the event entry set of the time-enumerated data structure, and transmitting the updated time-enumerated data structure within an elapsed duration after retrieving the at least one alphanumeric record.

The invention provides a standard adaptive execution method and equipment based on data standard management, and belongs to the technical field of data management. The method comprises the following steps: in response to a standardization request for a service system from a user, generating a corresponding mapping relation library based on a preset root library and a source system field; wherein the mapping relation library at least comprises a mapping relation, established on the basis of a preset semantic matching model, between the source system field and the standard field. And converting each source data value of the business system according to the mapping relation library, and executing real-time verification based on a preset multi-level value domain enumeration constraint before the source data values are written into the target library, so as to intercept conversion abnormal data according to a verification result and generate an abnormal snapshot. And when it is determined that the data standard attribute change event exists or the abnormal snapshots meeting the preset conditions exist, determining a corresponding update strategy type based on the change content of the data standard version change or the root cause classification result of each abnormal snapshot, so as to perform standardized execution according to a corresponding update strategy.

A computer-implemented method for a machine learning based rules compiler, the method comprising receiving machine learning generated ripple down rules, the ripple down rules comprising exception rules for tags in a tag set, the exception rules comprising tag string comparisons. The method further comprises compiling the ripple down rules into optimized computer code. Compiling the rules into computer code further comprises generating an enumeration statement for an enumeration containing the tag set, translating the exception rules for each tag in the tag set into if-else statements for a respective tag, including replacing the tag string comparisons with the enumeration; and generating a switch case statement for a current tag, the switch case statement having cases corresponding the tags in the tag set and including the if-else statements for the respective tag. The optimized computer code comprises the enumeration statement and the switch case statement.

The present invention relates to the technical field of data security, and in particular to an adaptive masking method and system, and a device and a medium. The method comprises: firstly, acquiring a keyword of a current file to be masked of a sender user; then, on the basis of the keyword, performing addition, deletion and modification on a current sensitive word library to obtain a new sensitive word library; and finally, generating a regular expression on the basis of the new sensitive word library, determining the positions of sensitive words on the basis of the regular expression, so as to obtain the sensitive words, and performing masking to obtain a masked file. Therefore, masking of a plurality of types of data is realized, and data types before and after masking remain unchanged, thereby ensuring the readability of a masked file while guaranteeing the security of masked data, and further increasing the masking speed; on the basis of different roles of receiver and sender users, the masking intensity is adaptively selected, thereby preventing important information from being leaked to untrusted persons, and overcoming the defect of an existing masking algorithm failing to resist collusion attacks and brute-force enumeration attacks; and multiple threads are used to concurrently process sensitive word retrieval and sensitive-word masking operations, thereby greatly increasing the masking speed.

The invention provides a retrieval enhancement mode linking method and device based on multi-round iteration, relates to the technical field of natural languages, and can accurately select a first candidate table and a first table element related to input content by combining the input content, key information of the input content, description information of tables in a database, description information of fields and enumeration values. And the accuracy of the first candidate table and the first table element is improved. The first candidate table and the first table element are further screened by utilizing the large language model, so that the semantic comprehension capability of the large language model can be fully utilized, and the accuracy of the second candidate table and the second table element is further improved. Through a table relation directed graph of a database, a retrieval enhancement generation technology is utilized in a multi-round iteration mode, the complex incidence relation between tables is fully considered, the range of a mode link result is gradually expanded, it is ensured that the finally obtained mode link result can completely cover input content, and the recall rate and the accuracy rate of mode link are increased.

The invention provides an enumeration conversion method and device, electronic equipment and a storage medium, and the method comprises the steps: creating a preset enumeration converter when an object relation mapping frame is initialized, and covering an enumeration type converter built in the object relation mapping frame through the preset enumeration converter; in response to the data query instruction, sending a structured query statement to the target database through the object relation mapping framework; receiving a target result set returned by the target database, and when the enumeration type field exists in the target result set, calling a preset enumeration converter to perform value conversion on the enumeration type field; and if it is determined that the enumeration type field cannot be converted to obtain the target enumeration value, returning the preset enumeration value. Compared with the prior art, the fault-tolerant logic of the enumeration converter is preset, and the preset enumeration value is returned when conversion fails, so that the system is prevented from crashing due to an unknown value, and the program stability is improved; by returning the preset value, system failure caused by incompatibility of enumeration values can be avoided.

The invention provides a data processing method and related equipment. The data processing equipment firstly obtains a first security word in a to-be-processed code text and a non-hard-coded code text set; and the data processing device filters the to-be-processed code text based on the first security word. And the data processing equipment identifies non-security words in the filtered code text to be processed. According to the method, the removal of the security text can be assisted in a mode of simplifying the code text through the security word, that is, the to-be-processed code text is simplified from the perspective of the security text, sensitive information in the code text is not changed, and missed judgment can be reduced. And the enumeration difficulty caused by non-security word recognition by using the keywords of the non-security words in the prior art can be reduced.

Systems and methods are disclosed comprising techniques for record-to-event conversion, such as retrieving at least one alphanumeric record associated with a monitored digital communication transmitted among two or more users, generating a time-enumerated data structure that stores an event entry set for the monitored digital communication, selectively identifying at least one discrete event for the monitored digital communication, generating one or more relevance scores for the at least one discrete event, identifying at least one valid discrete event from the at least one discrete event, generating an event attribute set for the at least one valid discrete event, updating the normalized event attribute set for a new event entry within the event entry set of the time-enumerated data structure, and transmitting the updated time-enumerated data structure within an elapsed duration after retrieving the at least one alphanumeric record.

The application provides a compression parameter analysis calculation method and system of an LZ77 compression algorithm, comprising judging whether an LZ77 code of compressed data stream adopts a flag bit method or an escape code method; for the flag bit method, using obtained flag bit B, bit number D of distance code and bit number L of length code to decompress data stream S to obtain compressed data stream Q, and determining that the bit number of distance code is L; for the escape code method, analyzing distance code with distance K and extension code; in response to K>L, generating special compressible data with distance 1-4 to inject an LZ77 compressor to obtain compressed data, and calculating distance escape code and extension code with distance 1-4. The method calculates LZ77 compression parameters according to enumeration, so as to realize data decompression, solve the problem of decompression of a black box compressor, and has great innovation and practical significance in the application of data analysis.

Disclosed in the present invention are a dynamic maximal clique enumeration device and method based on an FPGA with an HBM, the method including: the HBM stores a dynamic edge flow, a complete graph adjacency matrix, and candidate cliques; a matrix computing unit updates the complete graph adjacency matrix based on the dynamic edge flow, transmits the updated complete graph adjacency matrix to the HBM for storage, and determines header nodes, of which the corresponding candidate clique needs to be updated; a sequence computing unit constructs, according to the updated complete graph adjacency matrix and each header node to be updated, the sorted data set for reconstructing candidate cliques by data block sequencing; and an update computing unit executes, in parallel, an update task of the candidate clique corresponding to each header node to be updated based on the sorted data set, transmits the updated candidate cliques to the HBM for storage, and transmits the updated candidate cliques to the PC host to extract maximal cliques by means of a filtering operation. The present invention supports the computation of pipelined-type incremental maximal clique, thereby improving the overall computing efficiency of the task.

The application discloses a high-efficiency learning index construction method and system for accelerating query in a read-only scenario. The method first assumes that a distributed simplified model construction strategy processes a data set, divides data keys into groups of k, takes the first key of each group as a representative key, trains by using a PLA algorithm, and calculates an upper limit of a segment number; then a two-dimensional dynamic programming array is defined; the PLA algorithm is used to fit from the first representative key, and if successful, initialization cost information and space size are obtained, and if failed, a distributed remapping model strategy is used; then from the initial segment number to the upper limit, the outer layer enumerates the segment number, the inner layer enumerates the segment point, the minimum space cost is calculated and updated, and the result is saved; finally, the model information is taken from the rear to the front according to the calculated segment number, and the optimal segmentation strategy is obtained by integration. The application effectively constrains the error of the read-only learning index, reduces the query time of the index, and has the advantages of small index space occupation, low index height and small prediction error.

The invention relates to the technical field of database information management, discloses a big language model driven ODS layer full-process data resource evaluation modeling system, method and device, a storage medium and a program product, and is used for solving the technical problems of low modeling efficiency and poor effect in the prior art. Comprising a data input module used for capturing a table structure and table metadata of a newly-added business table corresponding to a target iteration version in real time; the modeling evaluation module is used for calling a large language model to fuse historical evaluation records and resource classification rules, evaluating data resources contained in the newly-added service table and outputting a resource classification result and a modeling evaluation conclusion; the data modeling module is used for calling a large language model to generate field description for each field contained in the newly added business table when modeling needs to be executed, and analyzing business source codes related to enumeration fields to generate field description of the enumeration fields when the fields are enumeration fields; and based on the table structure, the table metadata and the field description, generating a data resource model on the ODS layer.

The present disclosure provides a code automatic generation method, which can be applied to the field of artificial intelligence or other technical fields. The method comprises: in response to a request of a target user for initiating code generation, obtaining configuration parameters input by the target user; loading a pre-configured strategy component template, and generating an enumeration class and a strategy interface according to the configuration parameters and the strategy component template; generating at least one strategy implementation class by using the strategy component template based on enumeration values of the enumeration class, wherein the strategy implementation class is used to implement the strategy interface; and integrating the enumeration class, the strategy interface and the strategy implementation class to form a strategy mode code set. The present disclosure also provides a code automatic generation device, equipment, storage medium and program product.

The invention relates to the technical field of computer software, in particular to a method for generating an error view of an application program, electronic equipment and a storage medium. The method comprises the steps that an enumeration class is created, the enumeration class comprises a plurality of enumeration values, and an enumeration error type corresponding to each enumeration value is defined; defining an empty state view class, wherein configuration parameters of enumeration error types are packaged in the empty state view class; calling and executing a binding function to receive an error parameter, judging whether an enumeration value consistent with the error parameter exists or not, if so, judging that a current error type corresponding to the error parameter belongs to an enumeration error type, loading a corresponding configuration parameter from an empty state view class according to the current error type, and obtaining an empty state view root layout; a null-state view is generated based on the null-state view root layout and added to an interface for display to a user. The development efficiency can be effectively improved, and the development cost can be effectively reduced.

The invention discloses a metadata knowledge collection method, a data processing method, devices and equipment. The method comprises the following steps: inputting a historical structured query language (SQL) statement in a first input box of a first interface; in response to an input operation on the first input box, a historical SQL statement is received and analyzed, key information used for representing metadata knowledge is obtained, the key information can comprise a field and value information of the field, and the value information of the field is a data value of the field. According to the method and the device, the historical SQL statements can be touched in many scenes, so that the historical SQL statements are easy to obtain. Therefore, the metadata knowledge, namely the fields and the value information of the fields, is obtained by analyzing the historical SQL statements. Metadata knowledge can contain enumeration values and semantics thereof, subsequent operations such as data processing (such as data query) can be carried out in combination with the metadata knowledge, user underlying business data does not need to be contacted, the security is relatively high, the calculated amount is relatively small, and the accuracy of data query is improved.

Enterprise software relies on numerous types and instances of mathematical and logical calculations for proper functioning, which are formally specified in spreadsheets, known as Calculation Specification (CS) sheets, that provide illustrations of the inputs and outputs of the calculation, and are expressed in inputs and formulae applied on them to get values for outputs. The values for such inputs are retrieved by executing ad hoc queries on respective application's database. Present disclosure provides systems / methods to synthesize code using CS sheets by formulating a Programming By Example (PBE) problem for each input involved in the calculation. A query synthesis technique is then implemented that uses natural language processing and enumerative program synthesis techniques to infer queries corresponding to the PBE problem. These queries are filtered based on whether their output is consistent with expected output in PBE. Finally, the synthesized queries and calculation formulae are translated into a desired target programming language.

The application provides a data analysis method and device, electronic equipment and storage medium; the method comprises the following steps: for each obtained string, calling a preset first enumeration class to analyze all instructions in the string; calling a preset second enumeration class to analyze all data codes in the string; performing syntax analysis on the string to determine the execution logic between each instruction and each data code; determining the data source pointed to by the data code according to the field in the data code, and determining the data structure of the data source; and enabling each instruction to call the data code according to the execution logic, loading data according to the data structure pointed to by the data code, and obtaining target data. Therefore, the target data can be loaded across data sources and data types.

The present invention relates to the technical field of big data mining, and discloses a method for mining big data association rules with time series constraints, including: S1: Processing the data stream to be mined by using a time sliding window to form a database; S2: Scanning the database, calculating the value of each element in the database, deleting the elements with a value less than a predetermined value, and sorting the remaining elements according to the value size; S3: Scanning the database again, adjusting the order of the elements in the database, and the order of the elements after adjustment is the sorting order of the elements based on the value size; S4: Constructing an initial element value list, and constructing a K-set value list in an iterative manner; S5: Constructing a set enumeration tree, pruning the set enumeration tree by using a value strategy method, and the finally enumerated set of element sequences is the association rule of the database.

Example embodiments facilitate efficient comparison operations of tree structures, resulting in comparison metrics (e.g., similarity or distance metrics or scores) used enhance software systems, such as search algorithms, code optimization software, enterprise database applications, and so on. Trees to be compared are converted into sets, i.e., serialized using a novel enumeration method. Metric functions can then be efficiently applied to the sets to facilitate the comparison operations. In an illustrative embodiment, subtrees of larger trees can be compared individually, pairwise, where the comparison results of the subtree comparisons can be selectively weighted and summed to yield an aggregated comparison metric that is tailored for a specific application or comparison priority.

Systems, computer instructions and computer-implemented methods are disclosed for implementing space- and time-efficient enumerations. An instance of an enumeration class may be created with a constant, plurality of enumerations. A plurality of objects corresponding to the respective enumerations may be stored in memory along with a lookup table indexed by respective ordinal values of the plurality of enumerations, the lookup table including respective references to the stored objects of the instantiated enumeration class. A reference to an enumeration may be stored in a memory location by storing an ordinal value of the enumeration. A determination may then be made to convert a stored ordinal value to a reference to an object, and responsive to the determination, the ordinal value may be loaded and used as an index into the lookup table to obtain the reference to the object corresponding to the enumeration.

The present invention discloses a method and a storage medium for improving the readability of register model modeling code. A method for improving the readability of register model modeling code includes the following steps: creating a UVM register model, deriving from the field base class of the UVM register model to generate a subclass; expanding or adding a series of interface methods in the subclass to support method calls of enumerated type values; creating an enumerated type variable corresponding to the register field of the preset UVM register model; using the subclass to replace the field base class to reconstruct the example register; and using the interface method of the subclass to perform read and write access to the register field. The present invention can improve the coding method for the readability of register fields, avoid repeatedly referring to the design documents, save time for design and verification developers, and improve their work efficiency.

Described are techniques for automated least privilege role definition. The techniques include enumerating access patterns including a subject, a verb, an object location, and an object type of respective Application Programming Interface (API) calls in source code associated with a binary program. For a first function call in the source code, the technique is configured to generate a first object type based on an API definition associated with the function call, a first verb based on an API-to-verb matching database, a first subject based on a program-to-subject mapping database and the binary program, and a first object location based on the API definition associated with the function call. The technique further includes generating a Role Based Access Control (RBAC) role definition based on the enumerated access patterns.

The application relates to a nuclear power service data standardization method, device and computer equipment. The method comprises the following steps: determining data elements used by different service domains; sequentially performing deduplication and merging processing on the data elements of different service domains to obtain target data elements; constructing a data dictionary according to the target data elements, and configuring identification type attributes, representation type attributes and description type attributes of the target data elements in the data dictionary; for the target data elements with value domains that can be enumerated in the representation type attributes, summarizing the values of the target data elements to obtain a summary result, and generating a code set according to the summary result; and standardizing the nuclear power service data to be standardized according to the data dictionary and / or the code set. The method can effectively improve the standardization processing efficiency of data.

The invention discloses a longitudinal federated learning-oriented data value evaluation method and apparatus, belongs to the technical field of data processing, and solves the technical problems of difficult data value quantification, high privacy disclosure risk and possible return of false results by a third-party server in longitudinal federated learning. According to the method, an intersection cardinal number calculation task list is constructed by enumerating eigenvalue combinations of all parties; deriving a random number of subtasks for the task and assigning randomization parameters; synchronously distributing task information to a data provider; when executing the subtask, generating an encrypted pseudo sample and an adversarial sample and sending to a third party; performing complete unless non-negative and consistency triple verification on a returned result; and aggregating subtask results passing verification to calculate a data value. The method is mainly used for safely and efficiently quantifying the contribution value of each data party in the longitudinal federated learning on the premise of protecting the privacy of the original data, and promoting the marketization circulation of data elements.

The invention relates to a multi-component collaborative operation-based control method for ALD equipment, which comprises the following steps of: splitting the equipment into a plurality of components according to functions, newly establishing a corresponding functional block for each component, and respectively adding a plurality of actions for each functional block; declaring an enumeration data type; a variable is declarated in each function block; compiling action logic of each action; and calling each function block and the action of each function block in the main program according to the action logic of the equipment. The method has the advantages that the method is reasonable in design, equipment with a complex structure is split into a plurality of components, the action of each component is decomposed, and the action of each component is called according to the action logic of the equipment, so that the program structure is clear, the calling is convenient and flexible, and the program is convenient to maintain and update.

The invention discloses a method and a system for converting an RSML-e model into a Luter synchronous data stream language. The method is used for converting the RSML-e model into a Luter related type, a Boolean expression, an and-or table, a variable, a macro and a function. Type conversion processes undefined values by creating enumeration and structures. The Boolean expression conversion comprises truth value judgment, logical negation, pre-state reference and equivalence judgment. And constructing sub-expressions according to columns in the and-or table conversion, and using logic or connection. The variables are converted into a Luster node, and the ordinary variables and the input variables are used for processing initial values and conditions respectively. The macro and function are converted into nodes with input and return values, and the contained logic is converted into a Boolean expression. In the whole process, the mapping from the RSML-e logic to the Luter data stream is realized, so that the security attribute of the system can be verified by using a model detection tool, and the security of the system is improved.

The present disclosure provides a data testing method and device, electronic equipment and storage medium, relates to the field of artificial intelligence, further relates to the field of computer deep learning, and aims to at least solve the technical problem of low data testing efficiency in the prior art. The specific implementation scheme is: obtaining a plurality of operators and a plurality of operators corresponding to a first data type and a second data type, wherein the first data type is used to represent the input data type of the plurality of operators, and the second data type is used to represent the output data type of the plurality of operators; classifying the plurality of operators based on the first data type and the second data type respectively to obtain a classification result; performing enumeration connection on the plurality of operators based on the classification result to obtain a connection result; and testing the plurality of operators based on the connection result to obtain a test result, wherein the test result is used to indicate whether an abnormality occurs in the execution process of the plurality of operators.

According to the data query method and system based on the large model and the program product, enumeration type and aging type parameters are accurately recognized by calling the large model through a tool, and the parameter classification accuracy is ensured; the search module retrieves the service information dictionary based on enumeration type parameters and sorts the service information dictionary to obtain a candidate dictionary item set, and the comprehensiveness and accuracy of service matching are guaranteed; the time information conversion module converts the time efficiency type parameters into time description prompt information, and the standardization problem of time fuzzy description is solved; the input parameter generation module integrates multiple types of information to construct query request input parameters, and the target database is called in combination with the request module, so that efficient conversion from original query of a user to standardized database query is realized, the accuracy and efficiency of data query are remarkably improved, and the adaptation gap between natural language requirements and database structured query is effectively bridged.

The application discloses a conversion method and system from RSML ‑e model to Luster synchronous data flow language. ‑e The method converts RSML ‑e model into Luster, which involves type conversion, Boolean expression conversion, and-or table conversion, variable conversion, macro conversion and function conversion. Type conversion handles undefined values by creating enumerations and structs. Boolean expression conversion includes truth judgment, logical negation, previous state reference and equality judgment. and-or table conversion constructs sub-expressions by columns and connects them with logical or. Variable conversion is converted into Luster nodes, and ordinary variables and input variables are processed with initial values and conditions respectively. Macro and function conversion are converted into nodes with input and return values, and the contained logic is converted into Boolean expressions. The overall process realizes the mapping of RSML ‑e logic to Luster data flow, so that the safety properties of the system can be verified using model checking tools, improving system security.