233 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 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 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 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 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.

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.

The application discloses a handshake system, method and device of a three-dimensional convolutional neural network, computer equipment and a medium. The method comprises the following steps: obtaining to-be-transported data, and writing the to-be-transported data into a cache as a cache data block in a preset size; writing the size of the cache data block into a register group according to a preset storage mode to obtain data block record information; when a data operation request is received, obtaining required data information from the data operation request, wherein the required data information comprises three-dimensional coordinates; determining whether the cache data block contains data corresponding to the required data information based on the three-dimensional coordinates and the data block record information; and if the cache data block contains the data corresponding to the required data information, generating a response signal indicating that there is valid data in the cache data block. The handshake efficiency is improved by using the application.

The application discloses a Lamoeba chip architecture and a runtime reconstruction mechanism method based on an asynchronous mechanism, which combines an asynchronous reconfigurable Lamoeba chip and a reconstruction algorithm mechanism with time as a cost. The asynchronous mechanism provides a hardware basis for reconfigurable computing. The Lamoeba chip adopts a communication architecture of an on-chip mesh NoC to perform data transmission and communication on a network. The network adopts a 2D-mesh topology structure. Different types of computing modules are mounted in the network. In combination with a microprocessor in a single-chip microcomputer, software programming and routing address configuration are performed to complete the calculation of corresponding algorithms. The reconstruction algorithm calculates the data operation time, routing time and arbitration time of different paths in the network to find the shortest time allocation mode for algorithm mapping and change the deployment of the computing modules in the algorithm to the hardware resources. The application has the advantages of flexible general computing, high special computing, low power consumption without a clock and the ability to avoid various problems caused by the clock in the synchronous circuit.

The application relates to a multi-stage flow water multi-path data operation and access control system, which comprises a multi-stage flow water processor group, a basic parameter configuration module, a storage-computation-access parameter configuration module, a storage-computation-access timing control training unit, a storage-computation-access scheduling controller, a CPU unit, an SOC bus, a parallel data moving module and a DDR storage unit; under the multi-stage flow water pipeline processing of multi-path input data, the data flow water processing training of storage, computation and access is carried out, the architecture configuration scheme of the multi-stage flow water processor group or processor core and the storage-computation-access timing control method of the multi-stage flow water processing are obtained through the training data operation, and the storage-computation-access processing of the multi-stage flow water is completed according to the architecture configuration scheme and the flow water processing control method obtained through the training; the system can dynamically adapt to different performances and customer demands in various application scenarios, reduces the number of SOC buses and memory units, improves the flow water processing access efficiency, improves the access and computation parallel degree efficiency, simplifies system design and saves cost.

The invention discloses an Internet of Things asset positioning system based on an identifier analysis system, relates to the technical field of network communication and positioning, and solves the problem that an existing system does not support accurate positioning and management in cross-enterprise and cross-region circulation and exchange processes of Internet of Things assets. The asset positioning system comprises an equipment communication end and an asset positioning end. The equipment communication end comprises an active identification carrier module and an information transmission module; the asset positioning end comprises an identification analysis docking module, a data analysis processing module, an asset positioning service module and a display early warning module; detecting and backing up the acquired real-time data of the equipment through an anomaly detection model, and positioning all the equipment through an asset positioning service module; acquiring real-time data of equipment and generating an equipment identification code through the active identification carrier module; the data operation load of the identification analysis server is reduced, the efficient and stable operation of the asset positioning system is ensured, and the safety and reliability of all equipment data in an enterprise are ensured.

The embodiment of the invention discloses a multi-data-type multiplier and a method for realizing multiplication of multiple types of data, relates to the technical field of digital integrated circuits, and is convenient for realizing the multiplier supporting data operation of multiple data types at lower cost, so that the area of the integrated circuit and power consumption resources are saved. The method comprises the following steps: basic multiplication units of at least two data types, wherein the basic multiplication units are used for realizing mantissa part or fixed-point number multiplication of floating-point numbers of predetermined data types through combinational operation; the control unit is used for selecting basic multiplication unit combinations of different data types according to the data types of the input matrix; the input matrix is decomposed into a plurality of parts, and the parts are respectively transmitted to corresponding basic multiplication units for operation; and performing shift and accumulation operation on the operation result of each part. The method is suitable for an integrated circuit design scene.

The present invention proposes a drainage pipe undulation detection method and detection robot. This method utilizes the pipeline detection robot to collect the travel distance and pitch angle changes at the lowest gravity point of each pipeline cross-section during its travel through the pipeline, forming a data record library for continuous feature points within the detection pipeline. The data visualization system calculates the data and simulates a simulation diagram of the pipeline's lowest gravity point, providing a basis for determining pipeline operation. The present invention uses visualization to equivalently simulate the undulation change diagram of the entire detection pipeline, providing a basis for determining the degree of influence of water gravity on the flow direction. At the same time, compared with pipeline simulation methods such as internal point placement of laser radar, the present invention is simpler to operate.

The present disclosure provides a vector processing circuit and a vector processing method. The vector processing circuit includes a vector register, a two-dimensional register, and an operation circuit. The vector register is configured to store first data. The two-dimensional register is configured to store second data. The operation circuit is configured to perform a first matrix multiplication operation on the first data and the second data. After performing the first matrix multiplication operation, the vector register is configured to store third data to replace the first data, and the operation circuit is configured to perform a second matrix multiplication operation on the third data and the second data. Thus, the data exchange and delay can be reduced.

This invention discloses an accelerated convolutional neural network operation system, computing device, and computer storage medium. The operation system includes a first static random access memory (SRAM), a global-local buffer, and a cluster of operation units. The first SRAM stores the data required for the entire convolution operation. The global-local buffer stores frequently accessed data during the convolution operation. The cluster of operation units includes multiple operation units for performing multiplication and accumulation operations. The global-local buffer includes: an input feature map buffer for storing input feature map data and providing data sequentially according to a predefined loop order; a weight buffer, which includes a second SRAM, a feature map temporary storage area, and a first loop engine; and a partial sum buffer for storing partial summation results, including a third SRAM and a second loop engine. This invention is applicable to convolutional neural networks.

The present invention relates to sports training and testing technology and discloses a water training integrated machine for dragon boat racing. The system comprises a dragon boat, a shore data processing device, and a water device. The water device is mounted on a blade of the dragon boat and includes a sensor, a data operation unit, and a communication transmitting module, sequentially connected. These components are powered by a power supply module. The data operation unit communicates with the shore data processing device via the communication transmitting module. The shore data processing device comprises a data processing module, a display module, and multiple communication receiving modules. The display module and communication receiving modules are electrically connected to the data processing module. This technical solution enables accurate measurement and real-time monitoring of stroke force during water sports training, thereby enhancing performance analysis and training efficiency.

The invention provides an alarm method for a chemical central control system and a related device, and belongs to the technical field of alarm. According to the method, operation intervention is carried out on a DCS computer according to an alarm prompt, the DCS computer transmits an operation intervention instruction to a DCS database, and the DCS database carries out data operation processing on the operation intervention instruction and then generates a control adjustment signal; adjusting the real-time chemical production operation data according to the control adjustment signal; and the adjusted real-time chemical production operation data is fed back to the DCS database again, and the DCS database carries out data operation processing on the adjusted real-time chemical production operation data again until the adjusted real-time chemical production operation data is recovered to the preset value of the important alarm in the DCS database. The problems that the importance level of alarm cannot be distinguished, and an operator cannot be effectively prompted to focus on key alarm information are solved.

The application discloses a digital receiver signal demodulation method, system, device and storage medium, and relates to the technical field of communication software radio. The method comprises the following steps: acquiring a complex signal, and judging the quadrant of the complex signal in a coordinate system; rotating the complex signal to the first quadrant of the coordinate system according to the absolute values of the real part and the imaginary part of the complex signal; determining the rotation angle and the iteration number of the complex signal; rotating the complex signal according to a specific rotation angle for multiple times until the last iteration result approaches the real axis, and outputting the complex signal and the phase after iteration; performing error compensation on the real part of the output complex signal to obtain the amplitude of the output complex signal, and restoring the phase after iteration according to the quadrant of the initial complex signal to obtain the phase of the output complex signal; and the method adopts a pipeline structure according to the iteration characteristics, increases the data throughput, and improves the data operation efficiency. Part of the operation is replaced by a lookup table, and resource consumption is saved.