113 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 invention discloses a multi-modal standardized knowledge graph automatic generation method and system, particularly relates to the technical field of education knowledge graph automatic generation, and is used for solving the problem of inaccurate knowledge graph generation. According to the method, through preprocessing and feature selection of multi-modal data, text word segmentation, video key frame extraction and image graying, differences between different modal data are eliminated after standardization, then key features of all modals are screened through information gain, normalization processing is carried out, it is ensured that feature vectors have a unified numerical range, and the feature vectors are subjected to multi-modal feature extraction. The method lays a foundation for subsequent feature fusion and knowledge graph generation, calculates the weight through a modal reliability function, considers the importance of each modal in the feature fusion process, generates a unified feature vector, avoids hierarchical conflicts, and automatically updates and dynamically adjusts knowledge points and hierarchical labels thereof according to the dynamic change of data. And the intellectualization, the self-adaptability and the knowledge management efficiency of the graph are improved.

The invention belongs to the technical field of ship control motion prediction, and discloses a ship control motion real-time prediction method and system based on a fusion technology. According to the method, standardization processing is carried out on ship motion data and marine environment data, the marine environment data and the ship motion data are enabled to be input into a constructed ship motion prediction model based on a Transform architecture in the same numerical range, a sliding time window technology is used, online gradient updating is carried out, and the method adapts to new data features; a physical loss item is introduced, and an improved ship motion prediction model based on a Transform architecture is obtained; and carrying out optimization verification to finish nonlinear ship control motion prediction. According to the method, the defects of a traditional model in the aspect of nonlinear processing can be overcome, the reliability of a data driving model can be improved through physical constraints, and powerful technical support is provided for operation control and safe navigation of intelligent ships in the future.

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 provides a method for determining the dynamic response characteristic of a current-limiting resonance elimination device, and belongs to the technical field of current-limiting resonance elimination devices, and the method comprises the steps: firstly, setting an initial condition for an experimental test, and obtaining experimental data including current-limiting current, resonance elimination voltage, temperature rise and the like; and generating comprehensive current-limiting resonance elimination data by changing initial conditions for multiple times. Then, establishing a dynamic response characteristic equation set covering electrical, mechanical, thermal, magnetic and other characteristics, and fitting experimental data to obtain an analytical solution and a numerical range solution; and finally, by taking the analytical solution as a dynamic response characteristic expression and the numerical range solution as a constraint condition, establishing a dynamic response characteristic model of the current-limiting resonance elimination device for performance evaluation and optimization design. The method solves the technical problem that a dynamic response characteristic model of an existing current-limiting resonance elimination device is usually designed and evaluated by adopting a simplified model or an empirical formula, and the actual performance of the device in a complex power grid environment is difficult to reflect accurately.

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.

The invention provides an explanation audio and video generation processing method, device and equipment, and relates to the video processing technology, and the method comprises the steps: obtaining a to-be-analyzed request; wherein the to-be-analyzed request is used for requesting to generate the explanation audio and video, the to-be-analyzed request comprises to-be-processed information, and the to-be-processed information is an arithmetic expression. And performing data analysis on the to-be-processed information, and determining an arithmetic structure and a numerical range. Determining at least one target explanation option template corresponding to the arithmetic structure and the numerical range in a plurality of preset explanation option templates; wherein the explanation option templates in the preset explanation option templates are audio and video templates of different types of explanation rule information, and the explanation rule information is determined according to an arithmetic structure and a numerical range. And according to the at least one target explanation option template and the to-be-processed information, generating an explanation audio / video, and displaying the explanation audio / video. According to the method provided by the invention, the technical problem that the efficiency of explaining the generation mode of the audio and video is relatively low is solved.

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 application provides a trajectory recognition method and device. The method is: when confirming that the circumscribed rectangle of the writing trajectory to be recognized intersects with the circumscribed rectangle of the selection circle, obtaining a data table of the selection circle, the data table including the correspondence between the points on the selection circle and the numerical range corresponding to the points, wherein the endpoint value of the range is the coordinate value of the intersection of the straight line perpendicular to the coordinate axis and the selection circle corresponding to the integer part of the coordinate value of the point; judging whether there is a target point in the writing trajectory that hits the data table; if there is, confirming that the writing trajectory is passed by the selection circle or the writing trajectory is wrapped by the selection circle. This improves the accuracy of trajectory recognition.

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.

The embodiment of the present application discloses a data format conversion device and method, an electronic device, and a computer storage medium. The device includes: a first conversion module, which is used to convert data to be converted in any one of a plurality of preset data formats into intermediate data in an intermediate floating-point format; the intermediate exponent bit width of the intermediate floating-point format is the maximum value of the bit widths of a plurality of configuration exponents corresponding to the plurality of preset formats; the intermediate mantissa bit width of the intermediate floating-point format is the maximum value of the bit widths of a plurality of configuration mantissas corresponding to the plurality of preset data formats; the bit width of the configuration exponent of each data format is used to characterize the numerical range of the data format; the bit width of the configuration mantissa of each data format is used to characterize the data precision of the data format; a second conversion module, which is used to convert the intermediate data into target format data in accordance with a conversion method matching the target format data; the target format of the target format data is any one of the plurality of preset data formats.

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.

The invention provides a data type conversion control method and device, equipment and a computer readable medium. Obtaining a plurality of data types corresponding to the modeling simulation tool and a numerical range of each data type, determining a numerical range sequence of the plurality of data types according to the numerical range of each data type, constructing a plurality of nodes of a type lattice according to the plurality of data types, constructing a connection line sequence of the type lattice according to the numerical range sequence, according to the method, a plurality of data types and numerical range sorting are utilized to construct a type lattice, the type lattice can reflect whether data type conversion can be carried out among the plurality of data types or not and the sequence of data type conversion, all modules can be traversed, and the data type corresponding to each module can be controlled according to the type lattice. Therefore, multiple data types can be smoothly subjected to data type conversion according to the type lattice, data type conversion errors are avoided, and data distortion or precision reduction is avoided.