84 results about "Numerical range" patented technology

The invention relates to a meter state adaptive estimation method supporting data incomplete completion. The method comprises the following steps: acquiring continuous measurement data of a meter under a unified time reference to form an original measurement data sequence; generating a state parameter set for describing the current working state based on the original measurement data sequence; dividing an original measurement data sequence into a plurality of data windows, and predicting an expected measurement value in each window according to the variation amplitude and trend direction of collected data in adjacent time periods; comparing the predicted measurement value with an actual measurement value, and identifying positions with difference values exceeding a set threshold value to form an incomplete position index set; aiming at the incomplete position index set, in combination with the change trend and continuity of adjacent data, searching a complementation value in a preset reasonable numerical value interval, and generating a complemented measurement data sequence; according to the invention, the integrity and accuracy of the meter data under the condition of loss or abnormity are improved.

The embodiment of the invention provides a model reinforcement learning method, device and equipment. The scheme comprises the following steps: in a sampling stage, using an inference engine and adopting a to-be-trained target model to generate an output sequence for an input sequence under a first strategy parameter, recording a first probability value of each lexical element in the output sequence, and calculating a dominant value of each lexical element, in a training stage, after a training engine is used for forward propagation to obtain a second probability value of each lexical element generated by a target model under a first strategy parameter, a first ratio of the second probability value to the first probability value of each lexical element can be calculated, and then the lexical elements with the first ratios within a preset numerical range are screened out to participate in calculation of a target function; and optimizing the target function to update the parameters of the target model.

The invention relates to a self-adaptive micro-liquid adding time intelligent optimization method, and belongs to the technical field of industrial process control. The method comprises the following steps: dynamically switching a rotating speed control mode according to a numerical range of an added target quantity, and calculating and outputting a target rotating speed instruction; pipeline pressure is monitored in real time, the pressure state is judged according to a graded pressure threshold system, a graded regulation and control strategy is executed, and a rotating speed fine adjustment instruction is generated; a time adjustment model is constructed based on the residual addition amount and the estimated actual flow, the addition time is dynamically estimated, and replanning of the target time and the rotating speed is triggered when the parameters are abnormal; and after adding is completed every time, operation data are automatically collected, mismatching items of the control parameters and the working conditions are analyzed and recognized through deviation traceability, and the rotating speed control parameters and the pressure regulation and control parameters are updated in a self-adaptive mode. According to the invention, intelligent optimization of the adding process is realized, and the adding precision and the system adaptability are effectively improved.

The invention provides a self-adaptive interval type-2 fuzzy logic enhanced intelligent manufacturing control method, which comprises the steps that a fuzzy set influencing a target system is predicted through a particle swarm optimization algorithm, the fuzzy set comprises one or more dimensions, and each dimension determines a numerical range capable of generating corresponding influence through an upper membership function and a lower membership function; activating a corresponding fuzzy reasoning rule in response to the obtained fuzzy set output by the particle swarm optimization algorithm, performing fuzzy reasoning based on upper and lower membership functions in the fuzzy set, and obtaining a reasoning set; and performing defuzzification on the reasoning set to obtain a control instruction, and controlling the target system to perform intelligent manufacturing based on the control instruction. According to the method, the capability of representing and processing uncertainty in the intelligent manufacturing control process can be improved, so that a more accurate decision instruction is provided to improve the product quality and the production performance.

The invention discloses a communication trace data compression and storage method and system based on parallel program characteristics. The method comprises the steps that MPI calling is intercepted through a reloading MPI function for a target parallel program, and communication trace data is collected; for each piece of communication trace data, if the data is the communication trace data during first calling, recording the data into a data head by adopting a first data structure body, and if the data is the communication trace data generated during repeated calling, recording the data into a data body by adopting a second data structure body, the numerical field in the second data structure body is less than the numerical field of the first data structure body; and storing the communication trace data recorded in the data head and the data body. The invention aims to realize communication trace data compression and storage, the data scale is obviously reduced on the premise of ensuring the trace data to be complete and rich in content, and low-overhead data support is provided for users to analyze bottleneck problems related to large-scale parallel program communication.

The present invention discloses a method and system for automatically generating a multimodal standardized knowledge graph, which specifically relates to the technical field of automatic generation of educational knowledge graphs, and is used to solve the problem of inaccurate knowledge graph generation. The present invention preprocesses and selects features of multimodal data, performs text segmentation, video key frame extraction, and image grayscale conversion, so that different modal data are standardized to eliminate differences between each other, and then screens the key features of each modality through information gain, and performs normalization processing to ensure that the feature vector has a uniform numerical range, laying a foundation for subsequent feature fusion and knowledge graph generation. The weight is calculated through a modal credibility function, and the importance of each modality is considered in the feature fusion process to generate a unified feature vector, avoid hierarchical conflicts, and automatically update according to dynamic changes in data, dynamically adjust knowledge points and their hierarchical labels, thereby improving the intelligence and adaptability of the graph and the efficiency of knowledge management.

The present application provides a function testing method, an apparatus therefor, an electronic device, and a storage medium. The method includes: in response to a calculation instruction of a test formula used in a target function test, obtaining N groups of target timing data from N types of data sources, where the N types of data sources are the data sources corresponding to the timing data required in the test formula, and N is an integer greater than zero; constructing each group of target timing data into a data matrix having a preset data sampling frequency and a preset sampling start time according to the preset data sampling frequency, obtaining N data matrices; processing the N data matrices according to the coordinate mapping relationship between the sampling coordinates of the N types of data sources so as to assimilate the logical structures of the data in the N data matrices; performing an operation on the data in the N assimilated data matrices according to the test formula to obtain an operation result; and when the operation result is within a preset numerical range, determining that the target function test passes. The method of the present application makes the backtest result of the game function more accurate.

The invention relates to the technical field of flow field data processing, solves the technical problem that interpolation precision and reliability are reduced due to the fact that data distribution characteristics cannot be accurately captured in a traditional interpolation method, and particularly relates to a flow field data interpolation method based on an optimal transportation theory. Comprising the following steps: dividing flow field data into N sub-regions and carrying out normalization processing; randomly extracting two batches of data as two discrete data distributions, and constructing discrete distributions according to probability quality; establishing a cost function for measuring data direction information and data value information included in the flow field data; establishing an optimal transportation plan for converting a flow field data interpolation problem into a discrete distribution handling problem, and solving an optimal transportation matrix to obtain an interpolation result; and carrying out reverse normalization on the interpolation result to an original numerical range and carrying out smoothing processing. According to the method, through optimization of the optimal transportation theory, the real physical behavior of the flow field can be better reflected, and the accuracy and credibility of the interpolation result in practical application are improved.

The invention discloses a deep learning electroencephalogram signal identification method and management system. According to the method, original electroencephalogram signal data is converted into a form which can be effectively processed by a machine learning model through feature extraction and conversion. The beneficial effects of the step are mainly embodied in the following aspects: firstly, by converting text data into numerical feature vectors, the model can quantify information in the electroencephalogram signals, which is the basis of a machine learning algorithm for pattern recognition; secondly, the most useful features for the classification task are selected, so that the complexity of the model can be reduced, the calculation efficiency is improved, and the risk of over-fitting is reduced; features are screened through a statistical method or a machine learning method, it can be ensured that the model pays attention to signal attributes most related to electroencephalogram signal classification, and the situation that the learning process of the model is dominated due to the fact that certain feature factor value ranges are large is prevented.

According to the method for training the target model based on reinforcement learning, the target model is used for executing tampering detection on an input image, and the method comprises the steps that a training sample is obtained, the training sample comprises a target image and boundary information of a plurality of truth value boxes, and the truth value boxes are borders of a part of images marked in the target image and comprising tampering content; and inputting the target image into the target model, and outputting boundary information of the plurality of prediction frames. And calculating the overall overlap ratio of the plurality of prediction frames and the plurality of true value frames. Determining a first reward score based on a preset piecewise function according to a numerical range of the overall overlap ratio; the function corresponding to each numerical range of the piecewise function is a constant function, and the value of the corresponding constant function is increased along with the improvement of the overall overlap ratio. And based on a reinforcement learning algorithm, according to the first reward score, updating the target model.

The present invention discloses an automated classification method for regression test error simulation waveforms based on a Mamba neural network architecture, belonging to the field of integrated circuits. The method comprises parsing a simulation waveform file obtained from a regression test, converting a key signal waveform into a discrete sequence of integer waveforms; normalizing the numerical range of the waveform sequence to the 0-1 interval, intercepting a fixed length and inputting it into a Mamba neural network model, with the network outputting two parameters: block size and block starting position; dividing the waveform using the two characteristic parameters output by the network, calculating the thickness, degree of fluctuation, and mean characteristics of each waveform block, and calculating the overall characteristics; performing a fast Fourier transform on the waveform using integer multiples of the waveform block size as a window, and calculating frequency domain features; and merging the extracted features into a multidimensional feature vector and inputting it into a support vector machine to output a final classification result. The present invention can effectively improve the accuracy of diagnosing the cause of regression test failure, replace the manual triage process, and improve verification efficiency.

This disclosure provides a data verification method, apparatus, computer program product, non-transitory computer-readable storage medium, and electronic device. The method involves obtaining a target judgment result and a root hash value. The root hash value is generated by the device being verified according to a preset algorithm and leaf hash values ​​generated based on at least one judgment result. Each leaf hash value corresponds to the judgment result of the target data under a certain judgment condition, and the target judgment result corresponds to the target leaf hash value among the at least one leaf hash value. A corresponding verification hash value is calculated according to the preset algorithm based on the target judgment result. The verification hash value is compared with the root hash value to complete the verification of the target data. Embodiments of this disclosure provide an efficient and secure means of verifying numerical ranges without relying on a centralized system.

The invention discloses an operation platform angle adjusting method and device and electronic equipment, and relates to the technical field of medical equipment and the technical field of computer control, and the method comprises the steps: obtaining an included angle between a first coordinate axis of a base coordinate system and a horizontal plane, and taking the included angle as a first included angle; when the first included angle is not located in the first preset value interval, the corresponding first target lifting mechanism is adjusted to be jacked based on positive and negative of the first included angle, so that the first included angle is located in the first preset value interval; acquiring an included angle between a second coordinate axis of the base coordinate system and the horizontal plane as a second included angle; and when the second included angle is not located in the second preset value interval, the corresponding second target lifting mechanism is adjusted to be jacked based on the plus or minus of the second included angle, so that the second included angle is located in the second preset value interval. By adopting the scheme, the included angle between the coordinate axis of the base coordinate system and the horizontal plane can be flexibly and efficiently adjusted to be within the preset numerical value interval.

The invention provides a meteorological data compression method and device, computer equipment and a storage medium, and belongs to the technical field of meteorological data processing, and the method specifically comprises the steps: dynamically adjusting a storage space according to the actual numerical range of original meteorological data; performing precision control on the original meteorological data to obtain loss data; predicting the loss data by adopting a self-adaptive prediction algorithm, and converting the loss data into a residual sequence in combination with prediction data; and performing entropy coding on the residual sequence to obtain compressed data for transmission. Through the processing scheme provided by the invention, the characteristics of high real-time requirement and large data volume of the traditional meteorological data are fully considered, the meteorological data are compressed, the repeatability of the data is reduced, the redundancy of the data is reduced, and the size of the data is reduced to the greatest extent.

The present invention provides a controller parameter adjustment method and device, which relate to the field of automatic control technology. The method comprises: if it is determined that the control loop of the control system is in a steady-state condition, obtaining the overshoot time series of the control system; calculating the relative damping index according to the overshoot time series, determining the controller parameter adjustment strategy according to the numerical range of the relative damping index and the preset corresponding relationship; adjusting the controller parameters according to the controller parameter adjustment strategy. The device executes the above method. The controller parameter adjustment method and device provided by the embodiment of the present invention do not require precise modeling of the control system, but only need to collect the control system operation data without affecting the normal operation of the system to achieve online index evaluation, and are suitable for online performance evaluation of control systems in industrial sites.

This invention provides a parameter prediction model prediction method, device, and medium based on gradient decision trees, belonging to the field of integrated circuit computer-aided design technology. The method includes: acquiring the topological features of the target network; wherein, the topological features include: fan-out number, number of resistor segments, and number of branches; inputting the topological features into a pre-trained prediction model to obtain the predicted value of total parasitic capacitance and the intermediate predicted value of total parasitic resistance; performing an inverse exponential transformation on the intermediate predicted value of total parasitic resistance to obtain the predicted value of total parasitic resistance. This invention, by performing a logarithmic transformation preprocessing on the target variable of total parasitic resistance and performing an inverse exponential transformation after prediction, can effectively compress the numerical range of total parasitic resistance and optimize the data distribution, thereby accurately restoring the true physical resistance value.

When an arithmetic control unit according to the present invention is to extract high-bit data larger than a bit width for output based on a decimal point position, from a cumulative memory of a fixed bit width in which a decimal point position and a division position are set, the cumulative memory storing a cumulative addition result of a multiply-accumulate operation result between input data and a kernel, in an arithmetic circuit that divides data into higher-order data and lower-order data on the basis of the division position and extracts and outputs the higher-order data and the lower-order data, the number of times that each of the higher-order data and the lower-order data exceed an upper limit and fall below a lower limit of a given numerical range is acquired from a counter, and on the basis of the acquired lower limit count for the lower-order side, and the upper limit count for the lower-order side or the upper limit count for the upper-order side, a decimal point position for calculating the next input data is determined and set in the arithmetic circuit, and on the basis of the lower limit count for the higher-order side, a data distribution of the higher-order data and the lower-order data, and a data distribution of the kernel, the division position for calculating the next input data is determined and set in the arithmetic circuit.

Quantification essentially only readjusts a numerical range, and can be roughly understood as linear mapping. The problem of adding'rough 'two words is that some papers are subjected to nonlinear quantization, but the papers are still subjected to linear quantization in the industrial circle at present. However, it can be obviously seen that the inverse quantization generally has no information loss, and the quantization generally has precision loss. It is also very well understood that the numerical value range which can be stored by float32 is larger than that of uint8, so that a large number of numerical values cannot be represented by uint8 and can only be rounded into uint8 type numerical values. The error of the quantitative model and the full-precision model also comes from a clip operation of rounding off. The invention aims to provide a quantization method for quantization based on a reconstruction quantization operator, which can perform asymmetric quantization on a trained model parameter file and perform reasoning acceleration.

The present invention addresses the problem of calculating the root of a power function. An arithmetic device according to the present invention calculates a root Y of a power function (Y = AX, wherein A is a constant, and X is a prescribed numerical range). The arithmetic device comprises: an input unit into which input data indicating an exponent X is input; a first arithmetic circuit that executes a first arithmetic process on the basis of a linear approximation equation of a graph of a power function in a first numerical region of a numerical range of the exponent X; a second arithmetic circuit that executes a second arithmetic process on the basis of a linear approximation equation of a graph of a power function in a second numerical region of the numerical range, the second numerical region differing from the first numerical region; and a selector that outputs output data indicating the root Y calculated by the first arithmetic circuit when if exponent X indicated by the input data inputted into the input unit belongs to the first numerical region, and outputs output data indicating the root Y calculated by the second arithmetic circuit if the exponent X indicated by the input data inputted into the input unit belongs to the second numerical region.

To provide a data processing method and device which can suppress narrowing of a predictable numerical range, while improving prediction accuracy.SOLUTION: A data processing method includes: an outlier detection step of detecting an outlier from a plurality of data included in training data 31; a predicted value calculation step of creating a first regression model 36 using first training data 35 excluding the outlier from the training data 31 as teaching data, and determining a first predicted value which is a predicted value of an objective variable corresponding to a value of an explanatory variable of each of the excluded outliers, using the first regression model 36; and a data substitution step of substituting the excluded outlier with a value based on the first predicted value, to create substituted outlier training data 38 from the training data 31.SELECTED DRAWING: Figure 1

This invention provides a quantization method for the EltwiseAdd operator in neural networks. The method quantizes the EltwiseAdd operator to obtain the EltwiseAddQuant operator, which can directly perform element-wise addition calculations using fixed-point input activation and directly output fixed-point results. The entire calculation process only involves fixed-point addition, avoiding floating-point multiplication and rounding operations. The method includes: S1. Recording the operator activation range; S2. Updating the numerical range of the EltwiseAdd operator activation and updating the EltwiseAdd quantization calculation method; S3. Performing the overall quantization. The advantages of quantizing the EltwiseAdd operator proposed in this invention include reduced computational resource consumption, faster computation speed, lower power consumption, enhanced compatibility, and improved memory efficiency. These advantages make quantization an effective means of model compression and acceleration, especially suitable for resource-constrained environments and applications requiring rapid response.

The present disclosure provides a task execution method, which relates to the field of artificial intelligence technology, particularly to the fields of large model technology and deep learning technology. The specific implementation solution is as follows: determining the simulation output data of a target sub-model according to the input data of the target sub-model in a deep learning model, where the target sub-model includes at least one target operator, and the target task corresponding to the target sub-model is executed by a first hardware resource; in response to determining that the simulation output data is not within the first numerical range corresponding to the first hardware resource, determining a second hardware resource according to the simulation output data, where the second numerical range corresponding to the second hardware resource is greater than the first numerical range; and using the second hardware resource to execute the target task to process the input data to obtain the target output data of the target sub-model. The present disclosure also provides a task execution device, an electronic device, and a storage medium.

An embodiment of the present application provides a method, device, medium, and program product for sensor value matching, which relates to the technical field of rule matching. The method includes: in response to a rule matching request for a value to be matched, obtaining a target node structure corresponding to the value to be matched from a pre-constructed set of node structures; determining a target value range of the value to be matched relative to the target node structure, obtaining a rule linked list corresponding to the target value range from the target node structure, and determining a rule matching result corresponding to the value to be matched based on the rule linked list. By constructing node structures corresponding to each numerical node, the embodiment of the present application first matches the node structure corresponding to the value to be matched when matching values, and then obtains the corresponding rule linked list from the structure as the matching result according to the numerical size relationship between the value to be matched and the node structure, so that it is not necessary to traverse and match all rules, greatly improving the efficiency of value matching.

A sampler and a device for executing a graph neural network model are disclosed. The sampler is used to complete random sampling of neighbor nodes around a specified node of the graph neural network model, including: a random number generator for generating a plurality of random numbers; a calculation unit for performing a mathematical operation, where the mathematical operation represents dividing a numerical range between a zero value and a first integer value into a plurality of equal sub-intervals based on a second integer value, and obtaining a plurality of third integer values within the plurality of sub-intervals based on the plurality of random numbers, where the first integer value represents the number of all neighbor nodes around the specified node, and the second integer value represents the number of neighbor nodes to be sampled for the specified node; and an execution component for sampling a part of the neighbor nodes from all the neighbor nodes around the specified node according to the plurality of third integer values. The sampler provided by the present disclosure can uniformly sample neighbor nodes around a specified node.

This disclosure relates to a codeword determination method, apparatus, and storage medium, including: generating a codeword based on a first precoding vector and a second precoding vector, wherein the first precoding vector and the second precoding vector are orthogonal, the codeword is used to precode data, and the difference between a first parameter of the first precoding vector and a second parameter of the second precoding vector is within a first numerical range. In the above embodiments, codewords are determined by the difference between the parameters of two orthogonally related precoding vectors, ensuring that the precoding vectors included in the generated codeword are orthogonal, providing a method for selecting precoding vectors with multiple orthogonal options, thereby ensuring the accuracy of the determined codeword, the accuracy of data precoding based on the codeword, and the reliability of communication.

This invention provides a computer program that can reduce the burden of creating lookup tables for determining the flux linkage of electromagnetic devices having windings. [Solution] A computer program that causes a computer to perform a process to create a lookup table for simulating the operation of an electromagnetic device having multiple windings, based on an analysis model of the electromagnetic device, wherein the program transforms the component axes of multiple currents flowing through each winding so as to broaden the numerical range in which the change in flux linkage for the currents flowing through each winding is gradual and narrow the numerical range in which the change in flux linkage is steep, performs a magnetic field analysis based on the analysis model for the multiple currents whose component axes have been transformed, and causes the computer to perform a process to associate the flux linkage calculated by the magnetic field analysis with the currents.

This application relates to a method, apparatus, and computer program product for determining cost data based on machine learning. The method includes: acquiring part data of a target part; constructing a feature vector corresponding to the target part using the part data; determining the similarity between the target part and historical parts by using the distance between the feature vector of the target part and the feature vectors of pre-stored historical parts; constructing learning data using the feature vectors of historical parts and historical cost data, and training a machine learning model using the learning data; and, when the similarity meets a preset numerical range, using the machine learning model to predict the cost using the feature vector of the target part, thereby obtaining the target cost data of the target part. This method can utilize historical parts that are moderately similar to the target part to construct learning data to train the machine learning model, ensuring the reliability of the model's prediction results and thus improving the accuracy of the target cost data.

The present invention belongs to the technical field of ship maneuvering motion prediction, and discloses a real-time prediction method and system for ship maneuvering motion based on fusion technology. The method performs standardized processing on ship motion data and marine environment data, so that the marine environment data and the ship motion data are input into the constructed ship motion prediction model based on the Transformer architecture within the same numerical range, and uses the sliding time window technology to perform online gradient updates to adapt to new data features; introduces physical loss terms to obtain an improved ship motion prediction model based on the Transformer architecture; performs optimization verification to complete the prediction of nonlinear ship maneuvering motion. The present invention can not only solve the shortcomings of traditional models in nonlinear processing, but also improve the reliability of data-driven models through physical constraints, and provide strong technical support for the manipulation control and safe navigation of future intelligent ships.

The invention discloses a multi-scene diffusion calculation method for a chemical pollution source. The method comprises the following steps: (1) parameter input and verification: inputting pollution source parameters, meteorological parameters, calculation region parameters and pollutant types; performing numerical range, data type and logic consistency verification on the input parameters, filling default values when non-key parameters are missing, and prompting correction when the parameters are abnormal; (2) coordinate conversion and caching; (3) calculating diffusion parameters; (4) single-source concentration calculation; (5) multi-source concentration superposition; and (6) result output: outputting a calculation result in the forms of a data table, a scatter diagram and a JSON file. According to the method, multi-scene unified calculation can be provided, an instantaneous point source, a continuous point source, an instantaneous multi-point source and a moving line source are supported, dynamic weather, terrain and pollutant characteristic correction are introduced to improve precision, calculation logic is optimized, and emergency efficiency is ensured.

The invention relates to the technical field of bearing texture optimization, in particular to a sliding bearing surface texture optimization method and system. According to the technical scheme, the load curve of the load of the rotating shaft in the sliding bearing changing along with the operation time and the rotating speed curve of the rotating speed changing along with the operation time are obtained; determining characteristic working conditions of the sliding bearing in the main operation cycle according to the load curve and the rotating speed curve; carrying out feasible region analysis on each texture parameter of the sliding bearing according to the characteristic working condition, taking a numerical interval of each texture parameter as friction performance prediction input of a BP neural network model, and carrying out friction performance optimization in the numerical interval in combination with a genetic algorithm; and determining the parameter combination corresponding to the optimal friction performance as the target texture parameter of the sliding bearing. According to the technical scheme, the optimization efficiency and accuracy are improved when global optimization is carried out on the local texture of the sliding bearing.