299 results about "DataOperation" patented technology

The invention provides a safety monitoring method and system for a financial information service platform. Wherein a collaborative encryption transmission channel between a budgeting unit and an auditing department is constructed, a joint key is generated based on a multi-party computing protocol, and the ciphertext operation authority of a sensitive field is dynamically configured; performing ciphertext variance calculation and regression analysis on the encrypted budget execution record through the security operation node, generating a verifiable statistical result, binding the authority, and synchronously updating the sensitive field access frequency; a hierarchical security threat baseline is generated in combination with permission, frequency and historical query records, and cross-project high-frequency query and data operation deviation are associated; and when the query deviates from the baseline, calculating the risk confidence coefficient according to the deviation, triggering multi-party verification path adjustment and collaborative alarm, and dynamically limiting the ciphertext permission of the abnormal query. According to the technical scheme provided by the invention, cross-department data security sharing and dynamic risk collaborative prevention and control are realized.

The invention relates to a data operation method of a model, which is applied to the operation of an artificial intelligence (AI) model. In the method, based on an input tensor of an operation in an AI model during actual operation, a calculation graph corresponding to the operation in the AI model is compiled into a byte code instruction, and then the byte code instruction is interpreted and operated through a virtual machine which is pre-configured with a corresponding processing function, so that the operation in the AI model is executed. The execution of a traditional tedious compiling process is effectively avoided, and the running time of the AI model is shortened.

The invention relates to a data model construction and simulation method for mathematical data operation processing. The method comprises the steps that self-supervised learning is conducted on multi-modal data of a physical system corresponding to a mathematical data operation processing scene, and a unified embedding space is constructed; based on the unified embedding space, constructing a quantum-neural differential equation hybrid model; based on a digital twin interface, synchronizing a physical system and a simulation environment according to the quantum-neural differential equation hybrid model to obtain a simulation result; and based on a simulation result, dynamically adjusting model parameters of the quantum-neural differential equation hybrid model through reinforcement learning to obtain a corrected quantum-neural differential equation hybrid model. According to the method, precise modeling and efficient simulation of the physical system in a complex mathematical data operation processing scene and dynamic optimization of model parameters are achieved, the simulation precision and adaptability to the physical system are effectively improved, and the problems of multi-modal data fusion processing, complex physical system dynamic evolution simulation and the like can be better solved.

The invention relates to the technical field of power grid power supply balance regulation and control processing, and discloses a source network load storage energy flow intelligent regulation and control system and method based on cloud computing, and the system comprises a source network load storage operation state analysis module, a source network load storage energy flow regulation and control parameter analysis module, and a source network load storage energy flow regulation and control execution module. A power load supply-demand difference value of a power grid side of a target power supply area is accurately counted based on data operation, and power load regulation and control objects required by the power grid side of the target power supply area are intelligently screened in combination with an intelligent recognition algorithm and regulation and control feature information and identity feature information of the power load regulation and control objects of the target power supply area. Scientific and dynamic matching of a power grid side power load regulation and control object of a target power supply area is realized, the power energy flow regulation and control precision and efficiency of a power supply side, an energy storage side and a power grid side in source grid load storage are improved, and the operation safety and the power resource utilization efficiency of a power system are improved.

Along with rapid development of an artificial intelligence technology, calculation-intensive applications generally need a large number of calculation resources, but the calculation resources of mobile terminal equipment (such as vehicles) are often limited, so that the requirements of the applications are difficult to meet. In order to solve the challenge, the invention provides a deep reinforcement learning method based on a Rainbow DQN algorithm, and the deep reinforcement learning method is specially used for solving the problem of calculation unloading in the V2X (Vehicle-to-Everything, V2X). Under the condition of considering task characteristics and system resources, according to the capability differences of various computing units of cloud, edge and end in the aspects of data operation, analysis, decision making and the like, the method integrates multiple improved deep reinforcement learning methods, aims to solve the problem of insufficient computing capability of end vehicles, optimizes the time delay and energy consumption of the whole system, and improves the system performance. The problem of calculation unloading in an Internet of Vehicles scene is solved.

The invention discloses a server, and relates to the technical field of servers, the server comprises at least one mainboard and at least one backboard, each mainboard comprises a central processing unit, the central processing units are mutually independently arranged, each backboard is provided with at least one hard disk, each hard disk is based on a disk sequence of the hard disks, and each hard disk is provided with at least one hard disk. According to the technical scheme, the central processing unit is connected with the nearest central processing unit, so that the central processing units can independently operate and execute the functions of data storage and data operation, the problem of load imbalance existing in the multiple central processing units in the server can be avoided, and the operation efficiency in a multi-processor mode is improved.

The embodiment of the invention discloses a data processing device, a chip product, computer equipment and a data processing method. The data processing device comprises an instruction decoding module, a data moving module and a plurality of calculation units; wherein the instruction decoding module is used for receiving a calculation instruction based on open source instruction set architecture RISC-V extension; the data moving module is used for responding to the calculation instruction and determining a prediction configuration parameter to be used by a target calculation unit corresponding to the calculation instruction; the calculation unit is used for utilizing a target calculation unit corresponding to the calculation instruction to obtain configuration parameters to be used by the target calculation unit when the calculation instruction is executed; and if the configuration parameter hits the predicted configuration parameter, performing unit configuration by using the predicted configuration parameter so as to perform data operation processing.

The invention discloses a non-inductive cooperative regulation and control method and device for flexible loads in a building and a medium. The method relates to the field of power control, and solves the problem that a high-precision and quick-response flexible load regulation and control scheme is lacked. According to the method, the problem of space-time correlation adaptation of the equipment is solved by collecting the running state, environment and personnel distribution multi-source heterogeneous real-time data of the equipment, constructing the topological dynamic graph of the equipment, outputting nonlinear space-time correlation information and capturing the dynamic thermal-mechanical coupling relation between the equipment in real time. A personalized regulation and control instruction is generated by an independent decision-making unit to replace a fixed threshold value; coupling disturbance is predicted in combination with associated information, initial PID parameters are compensated, it is ensured that the temperature adjusting amplitude is converged to a user perception threshold value, and frequent starting and stopping of equipment are avoided. The independent decision-making unit and PID compensation are both based on local data operation, and the instruction response period is shortened; differentiated control instructions are converted according to equipment sensitivity, a high-sensitivity area is finely regulated and controlled to ensure comfort, and a low-sensitivity area preferentially responds to power grid peak clipping.

The invention provides a deep learning compilation optimization method based on active transfer learning, and belongs to the field of deep learning compilation, and the method comprises the following steps: S1, generating operator scheduling, and extracting features as training data; the operator scheduling is to transform the data operation process of operators on deployment hardware; s2, selectively labeling data by adopting an active transfer learning method and pre-training the performance prediction model; the output of the performance prediction model is the expected time required for the operator to execute on the deployed hardware; s3, inputting an operator to be compiled, and performing automatic compiling by using the pre-trained performance prediction model; according to the method, an active transfer learning method is utilized to optimize the automatic compilation process of deep learning, so that the cross-hardware portability of a deep learning compiler is improved, and the deployment cost of a deep learning model on different hardware platforms is reduced.

The invention discloses a data operation coding platform throughout the full life cycle of rail transit engineering, which is used in the technical field of constructional engineering and comprises a design module used for selecting and managing design-level components in a design stage and inputting coding information related to design; the construction module is used for selecting and managing construction-level components in the construction stage and inputting coding information related to construction; the operation module is used for selecting and managing maintenance-level components in an operation stage and inputting code information related to maintenance; the operation module is used for operating the coding information input in the design module, the construction module and the operation module to generate a coding system; and the transmission module is used for transmitting related data among the design, construction and operation stages. According to the invention, each component is coded and calculated, so that the data can be freely combined and transmitted, and the identification degree, standardization, automation and sharing of the data are greatly improved.

The invention belongs to the field of weighing of belt conveyors, and provides a mixed metering device of a belt conveyor, which combines weight and volume. The device is composed of a weight sensing system, a volume sensing system and a data comprehensive processing system, the weight sensing system obtains the instantaneous weight # imgabs0 # of a belt conveyor measuring area, the volume sensing system obtains the instantaneous volume # imgabs1 # of the belt conveyor measuring area, the instantaneous weight # imgabs3 # of materials is obtained by multiplying the instantaneous volume # imgabs1 # by the material density # imgabs2 #, and the instantaneous weight # imgabs0 # and the instantaneous volume # imgabs1 # of the belt conveyor measuring area are obtained. The data comprehensive processing system is responsible for data operation of the weight sensing system and the volume sensing system, data fusion between the weight sensing system and the volume sensing system is completed at the same time, # imgabs4 and # imgabs5 are fused to obtain the final instantaneous material weight # imgabs6, a volume metering module can be used for achieving compensation of a weight metering result, and the volume metering module can be used for measuring the instantaneous material weight # imgabs6. When the weight metering unit is verified and maintained, weight missing data can be supplemented.

The invention discloses a data processing method and device, equipment and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: when a target tensor to be subjected to target arithmetical operation is obtained, determining a storage position span of each dimension in the target tensor, and at least according to a first target dimension with the maximum storage position span, obtaining a target arithmetical operation of the target tensor; and segmenting the target tensor into a plurality of sub-tensors. Thus, it can be guaranteed that the element distribution areas of all the sub-tensors are relatively concentrated, so that when equipment such as a graphics processor executing target data operation obtains the values of all the elements in the sub-tensors, the distributed sub-tensors do not need to be read from multiple storage areas, the data reading efficiency can be improved, the operation efficiency can be improved, and the user experience is improved. The problem that the bottleneck exists in the operation efficiency improvement in the related technology is solved.

The invention discloses an on-chain enterprise risk identification method and system based on a credit chain large model. The system comprises a data preprocessing module, a model matching module, a weight parameter loading module, a data operation reasoning module, a risk prediction output module and the like. Large model training and optimization are carried out by integrating industry, region, type and other multi-dimensional data sets, a credit chain vertical type general model and a special model are formed, and the method can adapt to credit and risk prediction evaluation, monitoring and early warning of a single enterprise or an integrated enterprise in different supply chain and industry chain scenes. A more comprehensive, accurate and real-time solution is provided for enterprise management and industry supervision, and remarkable beneficial effects and advantages are achieved.

The invention discloses a privacy enhanced access control method for an industrial internet of things. The method comprises the following steps: step 1, generating and registering a key; step 2, data encryption and storage; step 3, access control and verification; and step 4, data calculation and decryption. According to the method, the security of a private key is protected through the uniqueness and non-replicability of a physical unclonable function (PUF), encryption state data operation is realized in combination with Paillier homomorphic encryption, and distributed access control and auditing are performed by using a block chain smart contract, so that security storage, access management and privacy protection of device data are realized, the security and efficiency of a system are effectively improved, and the security of the system is improved. The problems of security and privacy protection in the prior art are solved, and a more perfect security solution is provided for an industrial Internet of Things environment.

The invention discloses a floating-point operation unit of sine and cosine functions, a floating-point operation acceleration method, equipment and a medium, and relates to the technical field of microprocessors, and the floating-point operation unit comprises an instruction fetching module which is used for obtaining a floating-point data operation demand, selecting a target multiply-add instruction based on the floating-point data operation demand, and sending the target multiply-add instruction to a decoding module; the decoding module is used for analyzing and extracting a table look-up index control signal and a multiply-add component switching signal in the target multiply-add instruction; the table look-up module is used for accessing the coefficient table according to the table look-up index control signal and reading the corresponding target Taylor expansion coefficient; the dynamic switching module is used for switching a data operation path according to the multiply-add component switching signal; and the SIMD multiplication and addition component is used for calling a corresponding data operation path to carry out floating point data operation on the target Taylor expansion coefficient and a source operand in the floating point data operation requirement, and outputting an operation result in a corresponding output format. Hardware is reused, multiple sets of independent circuits are not needed, dynamic bit width adjustment is supported, and the chip area is reduced.

The invention provides a computing device, system and method, a chip, equipment and a medium. The computing device comprises a reading circuit, a computing circuit, a storage circuit, a control circuit and a plurality of first registers. The at least one first register is configured with task configuration information of the tasks, and the control circuit reads register information corresponding to the tasks one by one according to a preset mode; enabling a first register identified by a currently read register information to output first configuration information to a reading circuit, output second configuration information to a computing circuit, output third configuration information to a storage circuit, and controlling the reading circuit to perform time-sharing data reading according to the first configuration information of each task, and controlling the calculation circuit to perform data operation in a time-sharing manner according to the second configuration information of each task, and controlling the storage circuit to perform data storage in a time-sharing manner according to the third configuration information of each task, so that the resource utilization rate and the calculation efficiency of the calculation device can be improved.

A data operation method for a model, and a related apparatus, which are applied to the running of an artificial intelligence (AI) model. In the method, on the basis of an input tensor of an operation in an AI model during actual running, a computation graph corresponding to the operation in the AI model is compiled into a bytecode instruction, and the bytecode instruction is then interpreted and run by means of a virtual machine for which a corresponding processing function is pre-configured, such that the operation in the AI model is executed, thereby effectively avoiding the execution of a traditional tedious compiling process, and shortening the running duration of the AI model.

The invention discloses a processor and a method for solving processor data conflict and low-power-consumption execution. When instructions are dispatched, each long instruction is recorded into an OITF table item, and each long instruction is written back, the instruction is removed from the OITF table item; the long instruction which is dispatched but not written back is stored in the table item in the OITF; when each instruction is dispatched, the target information of the instruction is compared with the target information in each table item in the OITF so as to judge whether the instruction has a data conflict with a long instruction which is dispatched and not written back or not, when the data conflict is generated, an assembly line is blocked at a dispatching point, and after the data conflict is relieved, dispatching is continued, and in addition, the data conflict between the instruction and the long instruction is eliminated. And when the dispatched instruction is executed, data operation of different types of instructions is carried out through the shared data operation sub-module. The method can effectively solve the data conflict generated in the operation of the processor, and reduces the power consumption and area of the processor in the execution stage.

The invention discloses a distributed energy generation power prediction method and system based on time-frequency characteristics. The method comprises the following steps: collecting historical generation power time sequence data of distributed energy nodes needing to be predicted; historical power generation power time sequence data are input into an encoder, a self-attention layer of the encoder extracts dependency features of the sequence, and an STFT layer of the encoder extracts time-frequency features of the sequence; then adding the historical power generation power time sequence data, the sequence dependence feature extracted by the self-attention layer of the encoder and the sequence time frequency feature extracted by the STFT layer of the encoder, and obtaining an output encoding result of the encoder through a feed-forward layer; and finally, based on an output encoding result of the encoder, predicting a generated power sequence of the distributed energy nodes by using a decoder. According to the method, a self-attention mechanism and STFT are utilized to effectively capture the dependency relationship and the time-frequency characteristics between a large amount of distributed energy historical power generation data for a long time, sparse operation is introduced, the data operation amount is effectively reduced, and the accuracy of sequence prediction is improved.

The invention discloses a radial basis function boundary optimization and multivariate quadratic quasi-interpolation combined data fitting method, which comprises the following steps of: firstly, selecting m data points at two ends from a given data set, fitting boundary data by using Gaussian radial basis function interpolation, and adjusting function characteristics through shape parameters; and fitting all data including the boundary data by using a multivariate quadratic quasi-interpolation method, calculating a weight function of the data and the corrected data, and combining a boundary fitting function to obtain a final result. The method provided by the invention solves the problems that the traditional interpolation calculation is complex, the numerical value is unstable and the boundary processing precision is low, the calculation complexity is low, and the numerical value is stable and reliable; the application effect in the fields of mechanical design, fluid dynamics simulation and the like is remarkable, compared with traditional polynomial fitting, the boundary fitting error is reduced by at least two orders of magnitude, and the calculation efficiency is greatly improved.

The present invention discloses a method and system for identifying abnormal disturbances in optical cables, the identification method comprising: collecting the backscattered Rayleigh scattering beat frequency signal of the optical cable; segmenting the backscattered Rayleigh scattering beat frequency signal, and processing it to obtain an amplitude grayscale image and a phase differential time domain signal; based on a pre-constructed recognition model, sequentially identifying the amplitude grayscale image and the phase differential time domain signal to be identified, and obtaining a disturbance identification result. The present invention can solve the problem of a large amount of data caused by a long sensor line in an actual optical cable monitoring scenario, reduce unnecessary data calculation and processing, and greatly improve the recognition efficiency and real-time performance.

The invention relates to the field of low-altitude anti-null data processing, in particular to a large-data-volume data processing method based on multi-level cache. According to the invention, each functional module is independently bound with a CPU core, after the previous functional module finishes processing, result data is stored in a cache and a current writing position is recorded, and the next functional module regularly reads the buffered data and records a current reading position; each function module independently uses the bound CPU to carry out data operation, and the processing delays among the function modules are not superposed, so that the data processing real-time performance is improved.

The embodiment of the invention discloses an electric leakage detection method, device and equipment, a medium and a program product. Comprising the steps of obtaining a to-be-processed signal in an electric leakage judgment period; according to the to-be-processed signal, waveform peak points in the electric leakage determination period and peak left edge points corresponding to the waveform peak points are determined; determining every two adjacent peak left edge points as a peak left edge point group, and counting the total sampling number and the trough sampling number corresponding to each peak left edge point group; according to each total sampling number, each trough sampling number, a preset correction wave period sampling number lower limit value, a preset correction wave period sampling number upper limit value and a preset trough sampling number threshold value, determining a leakage waveform from correction waveforms corresponding to each peak left edge point group; and determining an electric leakage detection result according to the distribution condition of each electric leakage waveform in the electric leakage judgment period. The complexity and the data operand of a circuit required by electric leakage detection are reduced, and the stability and the universality of electric leakage detection are improved.

The embodiment of the invention provides a storage scheduling method, a processing unit, a system on chip, computing equipment, a computer storage medium and a computer program product. The storage scheduling method is applied to a processing unit, the processing unit comprises a plurality of kernels, each kernel executes data operation of a task flow of an operation type of the kernel by adopting multi-level storage configuration of the processing unit, and the method comprises the following steps: determining estimated operation duration of each operation task in the task flow of each kernel, obtaining an estimated operation duration of each operation task of the plurality of kernels; according to the estimated operation duration of each operation task of the plurality of kernels, determining each to-be-scheduled task of each task flow of the plurality of kernels in the current scheduling time period; and scheduling each to-be-scheduled task into the multi-level storage configuration, so that a previous operation task belonging to the same task flow as the to-be-scheduled task is operated when the access duration lower limit of the storage unit scheduled by each to-be-scheduled task is reached.

1. Name of the product in this design: Edge Computing Server (RL-3000H4 4U). 2. Purpose of this design: It is used for data computing and processing, and can have functions such as computing, storage and AI acceleration. It is suitable for smart cities, industrial internet, smart retail, smart manufacturing, vehicle networking, cloud computing, 5G and other scenarios. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model. 5. The bottom surface of the product is a part that is not easily seen or cannot be seen during use, so the bottom view is omitted; it is already shown in the perspective view, so the top view is omitted.

The embodiment of the invention discloses a privacy intersection method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving first privacy intersection data sent by a first participant; receiving second to-be-privately-exchanged data fed back by the first participant; processing the second to-be-used privacy intersection data based on the second parameter factor to obtain to-be-used privacy intersection data corresponding to the current participant; and based on the first privacy intersection data and the to-be-used privacy intersection data, determining target common data shared by the current participant and the first participant. According to the technical scheme, the problems of complicated data operation process, poor universality and low accuracy of the privacy intersection method based on the elliptic curve are solved, the data operation amount of privacy intersection is reduced, the universality of the privacy intersection method based on the elliptic curve is enhanced, and the privacy intersection efficiency and accuracy are improved.

The invention relates to a multi-expansibility intelligent service unit based on an S5000C + 310P architecture, and belongs to the technical field of computers. The intelligent service unit comprises a VPX case, an S5000C general-purpose computing module, a 310P intelligent module, a network switching module, a power module, a timing system module and a bus bottom plate, wherein the S5000C general-purpose computing module and the 310P intelligent module are interconnected through PCIe (Peripheral Component Interconnect Express). According to the method, the S5000C general computing power and the 310P AI computing power are fused, so that multi-task parallel processing is realized, the data operation and AI reasoning efficiency is remarkably improved, the processing delay is reduced, and the intensive computing scene requirements are met; through the modular integrated design, disassembly, assembly, debugging and later expansion and upgrade are facilitated, the operation and maintenance workload and cost are reduced, and the deployment flexibility is improved.

This invention discloses a distributed disaster recovery drill data optimization and early warning method based on a data warehouse. The method includes: deploying a real-time data warehouse based on Iceberg data lake technology on a central server, using Flink and Spark streaming data to manage data online and offline, and calculating process pool occupancy in real time; training a central early warning model in a distributed manner. When the process pool occupancy falls below a set value, it prioritizes cross-regional private disaster recovery drill data calculations, enabling real-time tracking and capture of memory anomalies and early warning based on the central early warning model; submitting model training results from various locations to a central server to update a headquarters drill result database, storing the drill results after each training session, and storing private data in the real-time data warehouse. This method can effectively overcome model bias and reduce computational complexity.

The application provides a graph data processing method, system, device and medium, comprising processing a plurality of to-be-processed graph data in a training data set to obtain node features and edge features; generating a node feature matrix and a resistance variation array according to the node features and the edge features; generating first transition features according to a graph neural network, the node feature matrix and the resistance variation array; inputting the first transition features into one or more graph convolution layers in the graph neural network for weighted calculation to output second transition features; inputting the second transition features into a readout network previously constructed in the graph neural network to generate result features; adjusting weight parameters of the graph neural network according to the result features and a Hamiltonian loss function, realizing training of the graph neural network to obtain a target neural network; and generating target result features according to node features, edge features and the target graph neural network of target graph data. The graph data operation time is reduced, and the processing speed is accelerated.

The present invention relates to the field of detection management, and specifically to a data transmission system and method for a detector based on a cloud platform, including: a slide processing module, a detector module, a cloud communication module, a signal filtering module, and an information management module. The slide processing module is used to extract the supernatant of the sample and make a detection slide. The detector module is used to construct a detection channel and generate a detection signal. The cloud communication module is used to synchronize the cloud data link in real time. The signal filtering module is used to filter out clutter and perform sample clustering. The information management module is used to determine the concentration of the substance to be detected and verify the sample number. The present invention can avoid deviations in detection results caused by uneven or contaminated samples, reduce sample confusion and detection errors, screen out optical deviations in the pulse detection process, improve the detection accuracy, shorten the time required for local data operation, improve the detection efficiency, and save the detection time.