1297 results about "Data needs" patented technology

The embodiment of the invention discloses an in-memory computing circuit based on a magnetic cache, and the circuit comprises at least one magnetic cache unit, at least one in-memory computing unit, and a timer. The magnetic cache unit in the at least one magnetic cache unit is used for caching data output by the corresponding in-memory computing unit as to-be-processed data within the corresponding data retention time; the timer is used for respectively setting data retention time for the at least one magnetic cache unit; and the in-memory computing unit in the at least one in-memory computing unit is used for extracting the data to be processed from the corresponding magnetic cache unit for calculation and outputting the computed data to other magnetic cache units. According to the embodiment of the invention, the invention achieves the flexible adjustment of the data retention time of the magnetic cache unit in various in-memory calculation scenes, and achieves the provision of a high-capacity cache for the data needed by in-memory computing under the lower power consumption.

The invention provides a key value cache data scheduling method and device and a large model reasoning method and device, and relates to the technical field of artificial intelligence. The method comprises the following steps: predicting target key value cache data required by a large model for reasoning a subsequent token by utilizing a prediction model; the subsequent tokens refer to tokens which are not reasoned by the large model; judging whether the target key value cache data needs to be scheduled or not; if scheduling is needed, generating a scheduling strategy; obtaining target key value cache data from the first storage space according to a scheduling strategy, and storing the target key value cache data to a second storage space; wherein the target key value cache data is used for enabling the large model to infer subsequent tokens. According to the method and the device, frequent access to the first storage space is reduced, and only the required target key value cache data is transmitted every time instead of full-amount key value cache data, so that occupation of bandwidth resources caused by transmission of the key value cache data is reduced.

The invention relates to a city planning checking method and system based on natural language processing, and the method comprises the steps: receiving a user request instruction, carrying out the analysis, and obtaining the checking intention information and target region information; retrieving and matching a corresponding region rule set based on the target region information, and generating a checking task list and a data demand list; obtaining multi-source heterogeneous data and preprocessing the multi-source heterogeneous data to obtain standardized check data and historical fusion features; executing a preset differentiation checking process according to the task type; generating a checking report based on the differentiated checking process result; in conclusion, according to the method, the user request is processed and analyzed through the natural language, the planning knowledge base is constructed, deterministic and fuzzy rule tasks are processed in combination with the differentiated checking process, cooperation of rule terms and case features is achieved, the problems that a traditional checking method is low in automation degree and insufficient in fuzzy term processing capacity are solved, and the efficiency of checking is improved. The method has the effects of improving the checking efficiency and enhancing the interpretability of decision suggestions.

The invention relates to a large model data protection method and device based on a trusted environment, equipment and a medium. In the scheme, after a server side obtains original large model data, the original large model data needs to be encrypted and signed, and then the original large model data and an encrypted information table are issued to a target application side; when the target application end calls the large model data, performing exception check according to a credible strategy; if the check is passed, performing signature verification on each encrypted data slice; if the signature verification succeeds, decrypting the encrypted slice data by using the encrypted information table to obtain original large model data; wherein the server side and the target application side are both in a trusted environment. It can be seen that the trusted environment and the key system are combined, and a dual protection mechanism for large model data is achieved; moreover, before the large model data is called, the operation environment of the target application end needs to be subjected to exception check, so that an attacker is prevented from acquiring or tampering the sensitive data, and all-around data protection is provided for the large model data.

The invention discloses a high-frequency power data acquisition adaptive frequency and priority processing method and device based on resource load feedback, and a storage medium, and belongs to the technical field of high-frequency power data processing. The method comprises the following steps: acquiring resource load information of a distribution network side intelligent terminal node in real time; according to the real-time resource load information, judging whether a preset sampling period of the power monitoring data needs to be adjusted, if so, dynamically correcting through a double-layer fast and slow ring adjusting mechanism to obtain a final sampling period, and otherwise, maintaining an original period; acquiring power monitoring data acquired by the node in the corresponding sampling period; and on the basis of a preset priority queue grading rule, a tube queue algorithm is utilized to adopt a differential transmission strategy for the data, so that priority processing of different levels of data is realized. The method does not need to depend on a prediction model, and realizes acquisition link elastic control and key data delay guarantee through real-time quantification of node loads, dual-time-domain closed-loop adjustment of a sampling period and hierarchical queue management messages.

The invention provides a data compression method and device, and relates to the technical field of data processing and data compression. The method comprises the following steps: acquiring to-be-compressed data, and acquiring a data category of the to-be-compressed data; determining a plurality of candidate compression strategies corresponding to the to-be-compressed data based on the data category; the importance degree of the to-be-compressed data is obtained, and the equipment state parameter of the current equipment is obtained; determining a target compression strategy and a compression level corresponding to the target compression strategy from the candidate compression strategies based on the equipment state parameters and the importance; and executing the target compression strategy on the to-be-compressed data according to the compression level to obtain compressed data. According to the method, the target compression strategy can be dynamically determined according to the data type, the data importance and the equipment state, high flexibility and adaptability are achieved, the data storage and processing efficiency is improved, good balance between the data quality and the storage space is ensured, and the method adapts to modern changeable equipment environments and data requirements.

The invention discloses a power data security transmission strategy dynamic optimization method based on gray measurement, which comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data in a power system, and generating a time sequence data set and a structure attribute data set; s2, performing privacy protection and noise addition on the time sequence data set and the structure attribute data set by applying a differential privacy mechanism; s3, performing evaluation by adopting a gray scale measurement and calculation method, calculating a gray scale weight and generating a transmission strategy; s4, according to the gray scale weight classification data, adjusting a transmission path and a time sequence; s5, dynamically adjusting the differential privacy noise intensity and the gray scale weight according to the network bandwidth, the transmission delay and the data requirement; and S6, continuously monitoring the transmission efficiency and the privacy effect, and adjusting a transmission strategy and a protection mechanism in real time according to feedback. According to the method, the dynamic collaborative optimization of privacy protection and scheduling efficiency of the power data in the transmission process is realized by fusing a differential privacy mechanism and a gray calculation method.

The invention discloses a data circulation trusted system and device based on a block chain and privacy calculation, and relates to the technical field of trusted data. Comprising a data supply end, a data demand end and a credible supervision end, the data supply end comprises a digital identity identification module which is used for generating a dynamic digital identity according to an enterprise unified social credit code and an ISO industry classification code and submitting the dynamic digital identity to a first block chain of the credible supervision end for authentication; the mixed desensitization module is used for carrying out first 3-bit plaintext reservation and hash replacement on the sensitive field by adopting an AES-256 algorithm, binding a UTC timestamp to generate a desensitization record and uploading the desensitization record to a first block chain; and the zero-knowledge proof generation module is used for converting the privacy calculation result into a zero-knowledge proof voucher and pushing verification parameters to a sandbox environment of the data demand end. According to the invention, the security and privacy of data in a circulation link and the credibility of transactions are guaranteed, and the problems of privacy leakage, uncredible transactions, difficult supervision and the like possibly existing in data circulation are solved.

Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems to comply with system requirements frameworks that indicate specific requirements on how the computing systems should handle certain data types. In response to detecting a change to one or more digital data requirements of a system requirements framework, the disclosed systems access a configuration profile of an entity and utilize a large language model to determine if data assets or data processing operations comply with the changes to the digital data requirements. In response to determining a configuration gap of the configuration profile based on detected changes to the digital data requirements, the disclosed systems utilize a large language model to generate tasks for correcting the configuration gap. The disclosed systems generate one or more tasks by modifying data assets, data processing operations, or other digital data associated with the entity.

The invention relates to the technical field of artificial intelligence chips, and discloses a data processing method and device, a processor and electronic equipment. Determining a first jump step length required for rearranging the block data based on the block size of the input data in the first dimension; secondly, performing jump reading on the block data according to a first jump step length, rearranging and loading read input elements into a thread private register so as to realize transposition storage of the block data in the thread private register, and mapping a thread direction on a second dimension so as to obtain a data basis of convolution correlation calculation; convolution correlation calculation is executed based on the transposed data in the thread private register and the weight data in the thread private register, repeated data reading in the calculation process is reduced during sliding window calculation, and an operator is converted into calculation performance bottleneck operation from memory access performance bottleneck operation.

The invention provides an intelligent test platform, which comprises a test demand analysis module used for carrying out demand analysis on input test demand information based on a demand analysis agent to obtain a plurality of test demand items; the test case generation module is used for calling a case generation agent to generate a plurality of test cases corresponding to a plurality of selected target test demand items in response to a selection operation on the plurality of test demand items; the test script generation module is used for calling a script generation agent to generate at least one test script corresponding to at least one checked target test case in response to check operation on the test cases; the test data demand analysis module is used for calling a data demand agent to perform demand analysis on the test case to obtain a plurality of test data demands; and the test data generation module is used for calling a data generation agent to generate test data corresponding to the test data demand. According to the invention, the test efficiency and the test reliability are improved.

The multi-source data automatic collection method based on the large language model comprises the steps that feature distribution of historically collected data is analyzed, and an optimal collection strategy is dynamically generated in combination with the state of a current data source; scheduling the acquisition task in real time to obtain a dynamically scheduled data stream; performing real-time anomaly detection and self-adaptive compensation on the dynamically scheduled data stream to obtain a self-fault-tolerant data stream; analyzing entity association and service logic of the self-fault-tolerant data stream, and predicting potential data demand points; automatically expanding a data acquisition range to obtain knowledge-enhanced data assets; identifying protocol features of the data transmission stream of the data assets, and analyzing the data by an analyzer based on protocol feature mapping to obtain protocol-independent standardized data; and performing semantic noise filtering and cross-modal cleaning to obtain semantic pure data. According to the invention, the defect that protocol adaptation and adaptive strategy optimization cannot be carried out in the existing multi-source data acquisition technology is overcome.

The invention discloses a large model corpus data interaction system and method based on a trusted execution environment, a terminal and a medium. The system comprises a data provider, a data demander and a trusted data space service platform. And the data provider is used for encrypting the corpus data, and performing preprocessing, directory sorting and packaging on the corpus data in the data provider connector to obtain the data capsule. And the data demander is used for submitting a calculation task in the data demander connector and obtaining corpus data of the data capsule for calculation after authorization. And the trusted data space service platform is used for storing the encrypted corpus data, controlling access of a data user, scheduling a calculation task, managing a key and generating an audit log. Through trusted execution environment hardware isolation and full-link encryption, it is ensured that corpus data only exists in a plaintext form in the trusted execution environment in the full life cycle from access to destruction, and unauthorized access and leakage are completely eradicated.

The invention discloses a vibration signal processing method based on self-adaptive wavelet packet and deep learning fusion, and belongs to the field of sewage plant equipment fault diagnosis. The method aims at solving the problems that traditional signal processing is poor in flexibility, the non-stationary signal processing capacity is weak, the deep learning data requirement is large, and the high-frequency weak feature capturing capacity is limited. According to the method, the high-frequency acceleration sensor is adopted, the vibration signals of the sewage plant equipment are accurately collected, the self-adaptive wavelet packet decomposition technology is applied, the primary function is dynamically selected, the number of decomposition layers is optimized, self-adaptive threshold noise reduction is achieved, and the signal processing quality is improved. Meanwhile, in combination with a one-dimensional convolutional neural network and a bidirectional LSTM model, local and global features of the signal are extracted respectively, and pre-processed data are formed through gating weighted fusion. According to the method, the signal-to-noise ratio and the weak fault detection rate are remarkably improved, feature redundancy and data requirements are reduced, the calculation efficiency and diagnosis accuracy are improved, the method is suitable for sewage plant equipment fault diagnosis, and the industrial applicability is enhanced.

The invention discloses an underground space mass water inrush accident process simulation method and an escape safety level evaluation method, and the method comprises the steps: building a three-dimensional model according to the building structure and equipment arrangement of an underground space; grid division is carried out on the three-dimensional model, and unit and node data needed by fluid dynamics simulation are generated and calculated; determining initial conditions and boundary conditions; establishing a multiphase flow model according to the initial condition and the boundary condition; inputting the unit and node data, the initial condition and the boundary condition into a multiphase flow model, and simulating the dynamic process of a large number of water inrush accidents in the underground space to obtain a simulation result; and determining emergency disposal measures according to the simulation result. By means of the scheme, the dynamic process analysis precision of a large number of sudden water inrush accidents in the underground space can be improved, and therefore the escape safety level of each area in the underground space can be scientifically and reasonably evaluated.

The invention discloses a city inspection data sharing method and device based on an unmanned aerial vehicle, and the method comprises the steps: firstly determining an inspection time period when the inspection data needs to be shared, and collecting the position, speed, signal and meteorological data set of the unmanned aerial vehicle in the time period according to a preset sampling frequency; after all the data sets are aligned through timestamps, features are extracted respectively to form multi-dimensional feature vectors; converting the multi-dimensional feature vector into a one-dimensional array, and generating a hash feature value through a hash algorithm in combination with a timestamp and a random number; extracting a master key and an authentication key from the hash feature value, generating an initialization vector based on the master key, binding the master key, the authentication key and a timestamp, and then distributing the three to a receiver; inspection data is divided into blocks, a number and a timestamp are added to each block, encryption is performed by using a master key and an initialization vector, an authentication tag is generated in combination with an authentication key, and a data packet is packaged and then transmitted. According to the invention, the problem of low security during routing inspection data sharing of the unmanned aerial vehicle in the prior art is solved.

The invention discloses a data circulation method and device in a trusted data space, equipment and a medium, and relates to the technical field of data security sharing, and the method comprises the steps: obtaining a node instance request sent by a data demander through a first connector node in the trusted data space; wherein the data demander accesses the trusted data space through the first connector node; responding to the node instance request to determine a data flow path between the first connector node and a second connector node in the trusted data space, so that the data provider transmits to-be-transmitted target data to the data demander through the data flow path; wherein the data provider accesses the trusted data space through the second connector node. According to the invention, secure transmission and sharing of data in a trusted data space are realized.

The invention provides a data optimization method and system based on self-adaptive multi-stage compression, and relates to the technical field of data processing, and the method comprises the steps: obtaining to-be-compressed data; preprocessing the to-be-compressed data to obtain preprocessed data; performing primary compression on the preprocessed data to obtain primary compressed data; according to the access frequency and the storage requirement of the first-level compressed data, judging whether the first-level compressed data needs to be subjected to second-level compression or not; if yes, entering the next step; otherwise, storing the first-level compressed data into a rapid storage medium; and carrying out secondary compression on the primary compressed data by adopting a progressive storage partitioning technology to obtain secondary compressed data, and storing the secondary compressed data into a low-speed storage medium. The adaptive multi-stage compression technology is adopted to ensure that a lightweight compression strategy is used for frequently accessed data, so that additional processing overhead for high-frequency accessed data is avoided, and deeper two-stage compression is performed on low-frequency accessed data to save storage space to the greatest extent.

A data regulation and control method, device, equipment and medium in an intelligent building data platform relates to the technical field of intelligent buildings, and comprises the steps of performing feature extraction on target multi-source data to generate a target feature vector, the target feature vector comprising a time sequence feature, an equipment state feature, an environment feature and an event feature; data demand prediction is carried out on the target feature vector based on a preset target demand prediction model to output a target data demand, and the target data demand comprises data demand occurrence time, a data demand scale and a data demand type; determining a target load balancing algorithm according to the target data demand and the real-time system load state; and performing data regulation and control through the target load balancing algorithm, the real-time system load state and the target data demand. Through the application, reasonable allocation and optimization of resources can be effectively realized, the user experience is improved, manual intervention is not needed, and the operation and maintenance cost is reduced.

According to the multifunctional monitoring method and system of the solar power generation system provided by the invention, the target multifunctional monitoring data needing to be processed and the important knowledge field information of the target needing to be processed are fused; the hidden knowledge field information of the target needing to be processed and the important knowledge field information of the target needing to be processed are synthesized, more knowledge field information of the target needing to be processed is extracted, and multifunctional monitoring data can be accurately determined. Therefore, the monitoring accuracy and reliability of the solar power generation system can be improved. Besides, through the target multifunctional monitoring data needing to be processed, the target needing to be processed is monitored, a solar power generation monitoring result is obtained, the monitoring accuracy is guaranteed, normal work of equipment can be guaranteed, and therefore the stability of the power generation amount is guaranteed.

The invention relates to the technical field of data processing, and discloses an automobile industrial database rapid management method and system based on virtual-real mapping. The method comprises the steps that a vehicle type mixed line recognizer recognizes the vehicle type switching state of a production line, and dynamic data of a vehicle type identifier, a process path and a resource demand are acquired; adjusting a partition strategy of the heterogeneous database according to the dynamic data, and generating a partition mapping table and an access weight matrix; binding the mapping table with the three-dimensional twin model through a time sequence association algorithm to form an automatically switched virtual-real mapping object pool; predicting a cross-vehicle-type data demand based on the object pool, and generating a preloading queue and a conflict avoidance strategy; and the vehicle type switching trigger processes the preloading queue to realize zero-delay data switching and consistency verification. The response speed and the data access efficiency of database management in the vehicle model line changing process are improved.

The embodiment of the invention provides a test case generation method and device, electronic equipment and a storage medium. The method comprises the steps that a requirement document and a technical document are analyzed according to the user story dimension, a scene set under the user story dimension is obtained, the scene set comprises multiple scene cases, each scene case comprises a set of data description information, the set of data description information is used for describing data details of multiple pieces of data needed by the scene case, and the data details of the multiple pieces of data are stored in the scene set; the data details of the data comprise interfaces corresponding to the data; generating a test data pair corresponding to the scene case according to the interface information of the interfaces corresponding to the data required by the scene case and the data description information included in the scene case; and generating a test case corresponding to the scene case based on the scene case and the test data pair corresponding to the scene case. Through the method, the test case can be automatically generated according to the demand document and the technical document corresponding to the demand document, and the generation efficiency and accuracy of the test case are greatly improved.

The invention provides a method and a device for generating a customized report of an SAAS (Software as a Service) system based on dynamic template configuration. The method comprises the following steps: acquiring a customized visual report template for realizing business data decoupling; editing the report template based on a preset editor, and generating a template configuration file; analyzing a structured marker in the template configuration file to generate a metadata description file; querying a business database through a unified data interface according to a data requirement defined by the metadata description file, and obtaining and assembling customized business data corresponding to the tenant ID; and at least based on the customized business data, controlling a rendering process through a dynamic position calculation algorithm, and dynamically filling the data to a corresponding position of the report template to generate a customized report. According to the method, the problems of code redundancy and difficulty in maintenance of the SAAS multi-tenant report are solved, zero code configuration is realized, and report generation efficiency and tenant personalized adaptation capability are improved.

The invention relates to a special equipment quality control digital governance data demand identification method based on a rooting theory. The method comprises the following steps: obtaining special equipment quality control related data; wherein the quality control related data of the special equipment comprises design specifications, manufacturing logs, inspection reports and operation and maintenance records; performing text analysis on the quality control related data of the special equipment based on a rooting theory three-level coding technology to generate a feature item list; extracting special equipment quality control demand keywords and keyword related vocabularies from the feature item list to obtain a keyword related vocabulary list; and classifying the keyword related vocabulary list, and aggregating different categories of keywords and related vocabularies to form a special equipment quality control data demand list. According to the invention, accurate identification and digital management of quality control data requirements of the special equipment are realized.

The invention provides an electric field analysis method. Firstly, a target model is generated through simulation modeling, continuous electric field distribution data is obtained, a first data set and a second data set are established, rasterized image feature extraction is performed on the first data set and the second data set through a graph neural network, corresponding first graph features and second graph features are obtained, and a cascade grid processing network is used to process the electric field distribution data. Firstly, a target power line is predicted according to the first graph feature, then the target power line is converted into a target electric field mode according to the second graph feature, and finally an electric field prediction result is generated and a preset analysis process is executed. According to the method, through the combination of the graph neural network and the cascade grid processing network, the continuity characteristic of electric field distribution is effectively reserved, and meanwhile, the noise problem caused by discretization is avoided. And through rasterized feature data processing, the data demand and the calculation complexity are greatly reduced, and the efficiency and the accuracy of modeling analysis are improved.

The embodiment of the invention provides a model service method and device and a storage medium. It is proposed to build a plurality of service instances deployed for the model service as a data transmission network. After the service instance receives the model calling request, whether model data needed by model loading exists in the local storage space or not can be inquired firstly, and if not, the needed model data can be remotely downloaded from other service instances on the basis of a data transmission network, and the downloaded model data is stored in the local storage space. Therefore, model data sharing can be realized based on the data transmission network established among the plurality of service instances, and the network transmission rate among the service instances in the data transmission network is far higher than the back-to-source downloading rate, so that even if the service instance does not have the model data required for loading locally, the service instance can not be loaded locally. Model data can also be downloaded remotely from other service instances. Therefore, the model loading efficiency can be effectively improved, so that the response efficiency to the model calling request is improved.

The invention relates to a private data sharing method based on a block chain and privacy security computing, and relates to the technical field of block chains, a data owner performs local encryption processing on to-be-shared private data, and generates encrypted data and identification information in combination with data desensitization and format standardization preprocessing operation; a smart contract is deployed through a block chain network, an abstract of encrypted data, an access control strategy and data index metadata are written into a block chain, a data demander initiates a joint calculation request through the smart contract according to a data index on the block chain and a demand of the data demander, identity verification and permission auditing are carried out, and after permission auditing is passed, the data demander sends the joint calculation request to the block chain. And performing conjoint analysis on the encrypted data, after the analysis is completed, automatically recording a calculation result abstract by the smart contract, safely returning a joint calculation result to a data demander, triggering a data traceability and automatic destruction mechanism according to the smart contract, and automatically clearing all intermediate data, keys and encrypted fragments.

The embodiment of the invention discloses a data space and a data interaction method. The data space comprises a plurality of modules, and an efficient intelligent data circulation system is constructed. The data market platform stores data resource description, and the data resource library is responsible for storing data governance information. And the AIAgent module converts query of a data demander into a structured request through a natural language processing technology, and performs data matching. And a matching result generates a data use protocol through the contract management module, so that the data use compliance is ensured. And the user interaction module is used for displaying and confirming protocol content and ensuring consensus of a data provider and a data demander. Once confirmed by the protocol, the data resource description will be delivered to the data demander through the AIAgent delivery engine. According to the invention, the data interaction and delivery efficiency is greatly improved, manual intervention is reduced, intelligent scheduling, development and utilization of data resources are realized, and data transaction circulation is effectively promoted.

The invention relates to the technical field of power systems, and discloses a power system node attribution and reasoning method, system and device and a medium, and the method comprises the steps: obtaining first device data of a target power system; performing first preprocessing on the first equipment data, and establishing a time-varying causal graph model according to a first preprocessing result; and solving the time-varying causal graph model to obtain a decision strategy. According to the method, the accuracy and efficiency of power system safety monitoring are improved, and faults can be quickly positioned and repaired. Through feature extraction of preprocessing, key information is extracted to support data requirements of a time-varying causal graph model. The introduced time-varying causal graph model considers the time factor and the causal relationship, so that the operation state of the system is reflected more accurately. A decision strategy obtained by solving the model ensures safe operation of the power system, and power failure and economic loss caused by faults are reduced.

The invention discloses a data classification and grading method and device based on rule engine and artificial intelligence fusion, and belongs to the technical field of Internet, and the method comprises the steps: obtaining source data needing to be classified and graded, and processing the source data into source data in a uniform format; training the extracted useful features to obtain a trained artificial intelligence model, and predicting the preprocessed source data through the trained artificial intelligence model to obtain a data classification and grading result output by the artificial intelligence model; the rule engine performs preliminary classification and grading on the preprocessed source data according to a set data classification and grading rule to obtain a preliminary classification and grading result; and fusing a data classification and grading result output by the artificial intelligence model with a preliminary classification and grading result output by the rule engine to generate a final accurate and reliable data classification and grading result. According to the invention, interaction between the user and the advertisement content through voice or gestures is supported, so that the attraction and effect of the advertisement are improved.