2703 results about "Data needs" patented technology

The invention discloses a deep learning-based intelligent identification method and system for rock and soil mechanical parameters, and aims to accurately identify the elastic modulus, strength parameter and permeability coefficient of rock and soil and support the decision and design of engineering projects under complex geological conditions. And starting from data demand analysis, determining a parameter identification target and planning a field test layout. A basic data set is constructed by comprehensively collecting and integrating multi-source data including laboratory and field test data, remote sensing information and the like. The data is pre-processed and analyzed to remove noise and explore associations between the data. The method comprises the following core steps: constructing a rock-soil mechanical model by using a deep learning algorithm, and realizing high-precision inversion of parameters by defining and optimizing an objective function. And verifying an inversion result and carrying out uncertainty analysis. According to the method, an analysis result is fed back to engineering design adjustment and decision support, and scheme optimization is achieved. The model and strategy are dynamically adjusted through continuous monitoring and real-time data, and safety and high efficiency of engineering implementation are guaranteed.

The invention relates to the technical field of computer data backup, in particular to a cloud-based data backup system and method, and the system comprises a data load monitoring and partitioning module which constructs a feature set of computer backup data based on storage nodes in a cloud environment, recognizes a data set with similar features, and carries out data backup according to the difference degree between feature parameters; and judging whether the feature set of the computer backup data needs to be re-divided or not to obtain partition data of the computer backup data. According to the invention, through real-time monitoring and clustering analysis of the data features, the backup process is more intelligent and efficient. The dynamic adjustment of the feature set enables data with different access frequencies and importance to automatically obtain corresponding priorities. According to the method, the backup of the key data can be preferentially completed in the peak period, the risk that the key data is delayed to be processed in the case of resource shortage is reduced, meanwhile, the idle period is fully utilized to process the low-priority data, and the resource utilization efficiency is optimized.

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 discloses a method for rendering a large-scale point cloud at a web end, and belongs to the technical field of large-scale point cloud rendering, and the method comprises the steps: S1, carrying out the data preprocessing of point cloud data, and carrying out the octree division of the whole point cloud; s2, on the basis of octree division, performing Lod hierarchical data division on leaf nodes of the whole octree; s3, data which needs to be rendered currently are screened out from the octree through view cone cutting; s4, high-level data and low-level data are separated from the screened data needing to be rendered, the low-level data are used for real-time rendering, and the high-level data are used for non-real-time rendering; and S5, combining the real-time rendering result and the non-real-time rendering result, and outputting all rendering results. According to the method, a similar double-buffer structure is used, and large-scale point clouds can be rendered in a real-time and non-real-time mixed mode at a web end.

The invention discloses an industrial internet data processing method based on big data, which comprises the steps of designing a visual data processing flow arrangement tool, allowing a user to flexibly define logic and steps of data processing, and realizing common data processing operation through a predefined data processing operator library in the flow arrangement process. A user is allowed to select a required data processing component through dragging operation through a visual configuration interface, and corresponding data types and processing conditions are set to form a complete data processing flow chart; acquiring data type information configured by a user, performing primary condition filtering judgment on the data according to the information, and screening out original data meeting conditions; judging whether data needs to be dynamically calculated along with time or not according to whether conditions are changed or not; a real-time data processing engine is introduced, and data are collected, cleaned, converted and analyzed in real time; through a visual interface, NLP interaction, real-time processing and intelligent scheduling, the flexibility of data processing is improved.

The invention relates to the technical field of CIM (common information model) platforms, in particular to a multi-element and multi-scale data management method and system based on a CIM basic platform, and relates to data acquisition, access and classification in a service interface, incremental acquisition and offline copying mode. Data needs to be translated to conform to spatial, elevation and temporal references of a CIM platform, unify geocoding and spatial expression, and semantic-based place name address association. And the BIM and the CIM are fused through a knowledge graph. And performing three-dimensional data grading processing, and constructing CIM1 to CIM5 grade data. Government affair data are subjected to graph falling through a unified address library. Overall data governance covers data aggregation, classified governance, two-dimensional and three-dimensional fusion, BIM and CIM fusion technology and government affair data association, classification and level-to-level management of data is realized, different data combination application is supported, and CIM business application and government affair process auxiliary approval and decision making are assisted.

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 an input rewriting method and device of an intelligent question answering system, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring dialogue data input by a user for the intelligent question-answering system, and judging whether the dialogue data needs to be rewritten or not; generating a plurality of candidate rewriting results by adopting a preset rewriting model, a predefined rewriting template and a preset rewriting rule under the condition of determining that the dialogue data needs to be subjected to type rewriting processing; inputting the plurality of candidate rewriting results into a pre-trained rewriting scoring model to obtain rewriting scores corresponding to the plurality of candidate rewriting results; and according to the rewriting score, determining a target rewriting result from the plurality of candidate rewriting results, and inputting the target rewriting result into the intelligent question and answer system. By adopting the method, the answer generation accuracy of the intelligent question-answering system can be improved.

The invention discloses a blockchain and homomorphic encryption-based power data privacy protection method and system, and belongs to the technical field of power data privacy protection, and the method comprises the steps that a data provider encrypts power data by using Paillier homomorphic encryption, uploads a ciphertext hash value to a blockchain, carries out the digital signature of a ciphertext, divides a key, and carries out the encryption of the key; enabling each data demander to receive the ciphertext, the signature and the key fragment; and the data demander performs cooperative decryption after performing data source and integrity verification to obtain a plaintext. Through distributed storage of a key sharing scheme, the security of the key is remarkably improved, single-point faults are avoided, the fault tolerance of the whole system is improved, and the flexibility of data transaction is optimized; for a mass data transaction process, the encryption and decryption efficiency is improved, the data processing speed and performance are improved, the real-time performance of data transaction is ensured, the resource consumption is reduced, and the user experience is improved.

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 relates to the technical field of data processing, in particular to a report generation method and device based on a large model and electronic device.According to the report generation method and device based on the large model, report generation information input by a user is obtained, a target data model needing to be queried and query conditions are accurately positioned, and an SQL query statement is automatically generated through the large model debugged through a business model; the target data model is directly queried, the data required by the report is quickly obtained, and after the user instruction is received, the large model immediately generates the target report, so that the instantaneity requirement of the service is met, and the report can be ensured to flexibly adapt to the data analysis requirement of a new service scene; and the intelligence level and the service adaptability of report generation are integrally improved. According to the report generation method based on the large model, flexible and changeable data requirements of services can be quickly responded, the data obtaining and processing efficiency is improved, the data analysis requirements of new service scenes are effectively met, and therefore the data report generation problem in the prior art is solved.

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 relates to the technical field of network communication and route management, in particular to an intelligent router flow management method for a home scene, which comprises the following steps that: a home router collects network activity data of equipment, including data flow size, connection frequency and equipment type, performs cleaning and formatting processing, and stores the data flow size, the connection frequency and the equipment type; formatted device behavior data is generated. According to the method for analyzing the behavior data of the home network equipment based on the long and short-term memory network, the prediction precision of the periodicity and dependency of user behaviors is improved, and the allocation of bandwidth resources is more prospective and dynamic. Specifically, resources can be automatically adjusted according to predicted data requirements, and particularly in the peak period of requirements, it is ensured that key applications such as video conferences and online education preferentially obtain necessary network resources. According to the flow management technology, the overall operation efficiency of the network is improved by optimizing bandwidth utilization and reducing network congestion.

The invention relates to a tensor decomposition personalized federated learning-based network-photovoltaic climbing cooperative attack identification method and system, and a storage medium. The method comprises the steps of S1, obtaining a false data injection attack model aiming at photovoltaic climbing; s2, data needing to be transmitted are compressed in a tensor decomposition mode, the information amount needed by communication is reduced, and meanwhile the compression rate is defined; s3, obtaining a tensor local model and a personalized model which are adaptive to tensor decomposition, and combining tensor decomposition and federated learning training to obtain a personalized model training strategy and a tensor local model training strategy; and S4, improving a global model aggregation mode of traditional federated learning to obtain two global model aggregation modes of an aggregation factor matrix and an aggregation vector group. The method can achieve the accurate recognition of the false data injection attack in the photovoltaic climbing process on the premise of reducing the data transmission requirements.

The invention discloses a box-type substation monitoring data processing method and system, and relates to the technical field of monitoring data processing, and the method comprises the steps: determining the type of monitoring data, constructing a monitoring data set and an abnormal fault set, and determining the correlation between the monitoring data set and the abnormal fault set; the method comprises the following steps: calculating a correlation index between monitoring data and an abnormal type, and a weight of each associated monitoring data in the abnormal type; a periodic monitoring strategy is adopted, data changes are captured in real time, abnormal values are screened out, and data fluctuation interference is eliminated through analysis; aiming at the quantity of initial monitoring data, adjusting and executing an anomaly detection strategy: comprehensively considering all anomaly types associated with the data when the data is single, and predicting a future anomaly risk by utilizing buffer time; when multiple data exist, completely matched anomaly judgment is preferentially carried out, and then whether anomaly judgment of single data needs to be carried out or not is judged; comprehensive and real-time monitoring and efficient abnormity early warning of the operation state of the box-type substation are realized, and the safe and stable operation capability of the substation is improved.

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 relates to the technical field of data processing, in particular to a financial data synchronization method and system, computer equipment and a storage medium, and the method comprises the following steps: based on input financial data, performing formatting and data cleaning, preprocessing the data, analyzing fund flow data, and performing consistency verification on the fund data; inconsistent data are identified and recorded, and a consistency verification result is generated. According to the method, by analyzing the data and extracting the incremental data needing to be synchronized, the synchronization efficiency is improved, real-time updating of the data is ensured, prediction of synchronization task time is carried out in combination with calculation resource requirements and real-time system loads, the synchronization priority is calculated, the reasonability of resource use is improved, the synchronized data is comprehensively verified, and the synchronization efficiency is improved. Data missing, repetition and inconsistency are found and corrected in time, the accuracy and consistency of financial data are ensured, the efficiency of financial data synchronization is improved, accurate and timely transmission and updating of the data are ensured, and the financial management process is optimized.

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.

The invention provides a deep learning-based bridge structure erosion degree monitoring method, which comprises the following steps of: for bridge steel samples of different types and parts, characterizing grain boundary structures and grain orientation microstructure characteristics by utilizing an electron back scattering diffraction method, and determining typical microstructure types of crystals; performing preprocessing and feature engineering processing on the obtained large sample data set, extracting chloride ion concentration and microstructure features, and constructing training data of an intercrystalline corrosion prediction model; according to the change rule of the chloride ion concentration and the typical microstructure type of the crystal, a bridge steel intergranular corrosion prediction model is constructed, and training and verification are carried out on a large sample data set; a wireless sensor network is arranged in a bridge steel service environment, chloride ion concentration and temperature and humidity environment parameters are collected in real time, and data needed by model input are obtained through data preprocessing and feature extraction.

The invention discloses a unit combination scheduling method based on a constraint space-time diagram neural network and a related device. The method comprises the steps of collecting bus load data and power system load data of a power grid at each moment every day; establishing a unit combination problem by taking each day as a unit, and solving to obtain corresponding optimal unit power under constraint conditions; using a sliding window to divide the bus load and the corresponding optimal unit power according to time periods; based on the expanded training data set, taking the divided bus load as input data, taking the corresponding optimal unit power as a data label, and training a pre-established space-time diagram neural network model; and dividing bus load data needing to be predicted according to time periods, and inputting the trained time-space diagram neural network model to obtain a unit combination scheduling scheme. According to the method, the solving precision of a unit commitment scheme based on data driving is improved, the time consumption and the calculation amount for calling a solver are reduced, a linear constraint layer and a post-processing method are constructed, and the reliability of a prediction result is perfected.

The invention provides a data processing method, a switching node and a related system, which are applied to a distributed computing system comprising a plurality of computing nodes and at least one first switching node, and each first switching node is connected with at least one computing node in the plurality of computing nodes. After receiving control information of at least one computing node connected with the first switching node, the first switching node sends a data reading request to the at least one computing node according to the control information; and the first switching node sends a calculation result to the at least one calculation node after obtaining the calculation result obtained by calculation according to the data read according to the data reading request. In the distributed computing system, when a plurality of computing nodes need to carry out set communication, an exchange node obtains data needing to be aggregated from the computing nodes, carries out data aggregation, and then sends an aggregated result to the computing nodes, so that the amount of data needing to be transmitted and the number of times of synchronization when data aggregation is carried out through set communication can be reduced; the calculation efficiency is improved.

A system includes a neural network architecture. It a new type of neural network able to process statically mapped as well as temporally sequenced information with much better power utilization, data requirements and operational efficiencies. Unlike prior artificial neural network approaches, the present invention includes uniquely defined sets of relationships. The unique use of non-linear input-output mapping functions combined with a time-variant pilot function, and a deterministically bounded, fully-differentiable, nonlinear resonance field subsystem allows the present invention to be readily deployed to work with virtually any neural network architecture / implementation including photonic, opto-acoustic or other variants. This dramatically reduces the size and complexity of virtually any neural network architecture because it offloads what would otherwise need to be done in the form of back / forward propagation trained weights and biases to much simpler, more scalable differentiable input / output mapping functions.

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 field of data security, and discloses a solid state disk data destruction system, which comprises a multi-stage encryption and erasure module used for performing multi-layer encryption on data when the data is written and executing inverse algorithm processing for multiple times when the data needs to be destructed so as to ensure that the data is unrecoverable; the transient voltage impact module is used for applying transient high voltage to the Nand chip of the solid state disk; the partition sensitivity identification module is used for dividing the storage space into a plurality of partitions and marking data sensitivity in partition metadata; the asynchronous monitoring module is used for monitoring an access mode and command input of the solid state disk in real time and triggering corresponding destruction operation; and the central control unit is used for scheduling and managing the work of the modules and determining specific data destruction measures according to the security events and the partition sensitivity identifiers. The invention allows data to be managed through a user-specified data sensitivity level and automatic anomaly detection of the system, ensuring that the data is properly protected according to its sensitivity.

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 discloses a transmission resource allocation method, system and device for a wireless body area network, and a storage medium, and the key points of the technical scheme are as follows: selecting a communication channel; communication connection with the sensor nodes is established through the communication channels, node states reported by the sensor nodes are received, and the node states comprise data types, emergency degrees, buffer area states and data demand quantities; calculating the comprehensive priority of each sensor node according to the data type, the emergency degree and the buffer area state; according to the comprehensive priority and the data demand quantity of each sensor node, calculating to obtain the time slot quantity of each sensor node; and determining a time slot allocation parameter of each sensor node according to the number of the time slots, and adjusting the transmitting power of the to-be-adjusted sensor node according to the energy consumption state of the to-be-adjusted sensor node. Transmission resources can be dynamically allocated, and the resource utilization rate and the network stability are improved.

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 invention provides an AI edge calculation method and device, electronic equipment and a storage medium. The AI edge calculation method comprises the steps that an edge server obtains a first model set corresponding to a processing service type of the edge server; selecting a corresponding first business processing terminal according to the task plan and the data demand of the business processing terminal; obtaining terminal attributes and service request data; generating a second model set by using the terminal attribute data, a model adjustment algorithm and the first model set; determining a business processing party; when it is determined that the service processing party is the first service processing terminal, selecting a second model from the second model set and sending the second model to the terminal; acquiring the current environment and operation data of the terminal, and sending the data and the control instruction to the terminal to control the terminal to adjust the second model according to the data and a preset algorithm to generate a third model; and processing business data by using the model, and executing corresponding operation. Through the scheme, the performance of edge calculation can be improved, and more efficient and more intelligent business processing can be realized.