108 results about "Boundary values" patented technology

The invention discloses a power grid load prediction method and system based on a large model, and relates to the technical field of power grid load prediction. Comprising the following steps: S1, collecting multi-source load comprehensive data, and carrying out edge processing and data preprocessing; s2, monitoring a sudden change point in real time, constructing a behavior tag sequence, and identifying a high-frequency disturbance point; s3, constructing a prediction input vector of the multi-source load comprehensive data, performing load data prediction, and calculating a physical constraint regularization value; s4, performing dynamic credible interval evaluation, performing real-time judgment on a prediction result according to a credible boundary value interval, and feeding back a regulation and control instruction; and S5, performing error analysis and retraining triggering, comprehensively evaluating prediction accuracy and output stability, and performing model optimization, version management and feedback learning. The problem that the prediction model is easy to vibrate due to high load change speed in a high-density load scene is solved.

The invention relates to a test case generation method and system based on an industrial agent. The method comprises the steps that an industrial data set is acquired, and a system dependency model is constructed; generating a path coverage test sequence through the first agent; generating a boundary value test data set through the second agent; generating abnormal test scene information through a third agent; and generating a test case set based on the path coverage test sequence, the boundary value test data set and the abnormal test scene information. In conclusion, by constructing the system dependency model and utilizing the multi-agent collaborative analysis process logic path, boundary condition and dependency relationship, the multi-surface coverage of the industrial control system test scene is realized, the automation of the test case generation is realized, the effects of improving the generation efficiency and enhancing the comprehensiveness of the test coverage are achieved, and the method is suitable for industrial control system test. Especially for the coverage of boundary conditions, abnormal scenes and concurrent paths, the manual dependence is reduced, and the reliability and consistency of the test are improved.

The invention discloses a carbon quantity prediction method and device for coal-fired unit carbon emission, and a medium, and relates to the technical field of energy data intelligent analysis, and the method comprises the steps: calculating a carbon emission intensity prediction value and a confidence interval boundary value based on a real-time coal quality fusion operation parameter set and a device health index, and generating a dynamic carbon emission prediction package; comparing the dynamic carbon emission prediction packet with CEMS real-time monitoring data, calculating a prediction error rate, extracting a multi-dimensional error feature according to the prediction error rate, and generating a multi-dimensional error feature vector; and based on the multi-dimensional error feature vector, dynamically adjusting an equipment health index correlation factor and a coal quality confidence weight parameter, generating a dynamic correction instruction set, and generating a carbon emission prediction report in combination with a dynamic carbon emission prediction packet. According to the method, dynamic noise reduction, enhancement and feature quantization of the coal flow multispectral image are realized, the high-fidelity coal quality feature vector is directly generated, the problem of coal quality data lag is solved, and the coal quality sudden change response capability is improved.

The invention belongs to the technical field of vehicle software testing, and particularly relates to a test case generation method and system based on big language model and rule collaboration, and the method comprises the steps: analyzing a demand document, and mapping the demand document to a fault ontology model to generate a structured test demand; generating a plurality of groups of fault verification conditions based on equivalence class division and a boundary value analysis method by utilizing a large language model; a multi-dimensional mixed retrieval strategy is adopted to retrieve a reference case from a historical case library; in combination with the verification condition, the reference case and the standardized information base, preposed environment settings, serialized operation instructions and expected responses of the test case are generated in stages; and finally, the case quality is ensured through cooperative verification of a large language model and a rule engine. The problems that a traditional method depends on manpower, the coverage rate is low, errors are prone to occurring, and historical knowledge is difficult to reuse are effectively solved, and the automation degree, accuracy and integrity of BMS test case generation are remarkably improved.

The invention provides an intelligent door and window control method and system, and the method comprises the steps: S1, obtaining multi-source environment data; s2, inputting the multi-source environment data into a fuzzification module, and converting the multi-source environment data into a fuzzy variable through a preset ambiguity function; s3, dynamically generating an environment parameter weight vector according to the real-time scene parameters; s4, inputting the fuzzy variable and the environmental parameter weight vector into a fuzzy inference engine, executing logical operation of a fuzzy rule base, and outputting comprehensive evaluation values of window opening suitability, window closing urgency and shading demand; and S5, generating a final control instruction through a defuzzification strategy, and executing the final control instruction. According to the method, the problem of frequent invalid operation caused by fluctuation of the environmental parameters near the boundary value is solved, the environmental parameter weight vector is dynamically generated based on the time parameters, the user preset mode and the meteorological data confidence factor, adaptation to seasons, time periods, user states and other scenes is achieved, and decision logic can be flexibly adjusted according to actual requirements; and a contradictory decision under a traditional fixed rule is avoided.

The invention provides a self-adaptive regulation and control method for a hydrogen production system based on real-time parameter feedback, and relates to the technical field of process control, the method comprises the following steps: collecting an input instruction signal and a controlled state variable signal of the hydrogen production system, and comparing a fluctuation energy index with a preset identification dead zone threshold; if the fluctuation energy index is lower than the identification dead zone threshold value, the current controller parameter is kept unchanged; if the fluctuation energy index is higher than the identification dead zone threshold value, activating an online parameter identification step; calculating a dynamic confidence interval of process model parameters; judging whether the process model parameters estimated in real time fall into the dynamic confidence interval or not; if the process model parameters fall into the dynamic confidence interval, converting the process model parameters estimated in real time into control parameters of a PID (Proportion Integration Differentiation) controller, and updating the control parameters into a control loop of the hydrogen production system; and if the process model parameter exceeds the dynamic confidence interval, resetting an internal state vector of the recursive algorithm in the online parameter identification step by using a boundary value.

Some embodiments of a method may include: determining a first number by running a learning-based process, wherein the first number is associated with a current sample; obtaining a quantization parameter; determining a quantized value based on at least the quantization parameter for the first number; obtaining a sample set; responsive to determining that the current sample is not in the sample set, outputting the quantized value; and responsive to determining that the current sample is in the sample set, performing several steps comprising: determining a boundary value based on at least the quantization parameter and the first number; determining a second number based on the boundary value; and outputting the second number.

The invention discloses a hardware-in-the-loop automatic test method and system, and the method comprises the steps: receiving a technical specification document and a historical failure case library of a tested device, analyzing a technical specification through a natural language processing model to extract key test parameters, carrying out the correlation analysis of the key test parameters and a fault mode in the historical failure case library, and obtaining a fault mode of the tested device; generating a structured test demand table, wherein the structured test demand table comprises test target priorities, constraint conditions and boundary value definitions; based on the structured test demand table and equipment working condition data acquired in real time, generating a plurality of test cases covering boundary conditions and extreme working conditions by using a genetic algorithm; the test cases covering the boundary conditions and the extreme working conditions are screened out from the multiple test cases to obtain a test case sequence, and each case in the test case sequence comprises a fault injection parameter, an expected response threshold value and an execution priority label. According to the method and system, the test coverage rate and efficiency are improved, and manual intervention is reduced.

Some embodiments of a method may include: determining a first number by running a learning-based process, wherein the first number is associated with a current sample; obtaining a quantization parameter; determining a quantized value based on at least the quantization parameter for the first number; obtaining a sample set; responsive to determining that the current sample is not in the sample set, outputting the quantized value; and responsive to determining that the current sample is in the sample set, performing several steps comprising: determining a boundary value based on at least the quantization parameter and the first number; determining a second number based on the boundary value; and outputting the second number.

The invention discloses a test case generation method, system and device and a storage medium. The method comprises the steps of obtaining a target test scene; based on the target test scene, acquiring a data generation rule corresponding to the test scene from a preset rule base; generating basic test data according with normal business logic according to the business rule; modifying at least one field in the basic test data according to the exception rule to generate exception test data for simulating abnormal input; according to the boundary rule, boundary value analysis is carried out on parameters in the basic test data, and test data used for verifying a parameter limit range is generated; and combining the normal basic test data, the abnormal test data and the boundary test data to generate a test case set.

The application discloses a turbine disc die forging process reasoning method and system, and the method comprises the following steps: (1) selecting key points of a turbine disc which need to be controlled in organization; (2) extracting deformation information of the key points through finite element simulation of the whole forging process of the turbine disc; (3) establishing a target optimization function, and respectively setting requirements for the average grain size and the recrystallization fraction of the key points, and setting corresponding weight values for the final organization targets of the key points; (4) setting boundary values and a penalty function of process parameters according to the actual situation of forging, and when the inferred process exceeds the boundary, the penalty function starts to execute; (5) embedding the target optimization function and the penalty function into a multi-objective optimization algorithm, and simultaneously inferring multiple forging process parameters. The method can achieve the effect of optimizing the grain organization of multiple regions of a forging, and effectively protects a die, thereby providing a theoretical basis and technical support for realizing high-quality forging.

The invention discloses a level set field reconstruction method and system based on a three-dimensional finite element grid and a medium. Relates to the technical field of geometric reconstruction. Positioning a space Cartesian grid area intersected with the boundary surface grid based on a separation axis theory; calculating a geometric space point-surface position relation, calculating a directed projection distance from a space Cartesian grid center to a two-dimensional boundary surface grid, and correcting the positive and negative characteristics of a Cartesian grid level set value based on a three-dimensional eight-direction rapid scanning method, so that the grid level set value is consistent with a region boundary value; according to the method, the geometric reconstruction problem of the deformable body which is complex in appearance and free of explicit analytic expression can be efficiently and accurately solved, a coupling approach between a finite element grid and a Cartesian grid is established under a non-conformal grid system, and the flexibility and universality of explicit calculation of a level set function under the Cartesian grid are improved.

The method for calculating the fastest adjustment time of the primary frequency modulation of the hydroelectric generating set comprises the following steps: step 1, establishing a primary frequency modulation system structure model; 2, constructing a primary frequency modulation system structure model transfer function; 3, establishing a corresponding differential equation according to the model transfer function; 4, converting the differential equation into a standard rhodan form; 5, listing an equation and a boundary value condition of the minimum adjustment time of the primary frequency modulation according to an optimal control principle, and solving a corresponding control law; step 6, solving switching point time and final adjustment time according to the trajectory equation; step 7: algorithm and controller code; and 8, according to the step 7, constructing a corresponding control flow of the fastest primary frequency modulation regulation method. The technical problem to be solved by the invention is to provide the method for calculating the fastest adjustment time of the primary frequency modulation of the hydroelectric generating set so as to realize the most accurate and rapid adjustment of the primary frequency modulation.

The application discloses a kind of based on MPVL algorithm's finite element fast sweep method and device, belong to electromagnetic full-wave simulation technical field, method includes: according to the port type and / or boundary type of electromagnetic field model, construct finite element boundary value equation and finite element variational equation;The finite element boundary value equation is substituted into finite element variational equation to obtain new finite element variational equation;According to the characteristics of wave number in new finite element variational equation, wave number or square root of wave number is extracted as the frequency-related common factor of each term in new finite element variational equation and is arranged, to obtain arrangement equation;Arrange equation is reconstructed as the matrix equation that can use MPVL algorithm to solve, and based on MPVL algorithm, the matrix equation at given expansion frequency point is solved to obtain electromagnetic field value under different excitation.The application realizes fast sweep, and sweep result has higher accuracy.

This invention provides a fast prediction method for airfoil icing shape based on empirical wavelet transform, comprising: acquiring icing meteorological parameters; inputting the icing meteorological parameters into a pre-trained target neural network to predict the empirical wavelet transform boundary values ​​of the icing thickness curve signal under airfoil icing conditions and the first K order Fourier modes of the icing thickness curve signal, where K is a positive integer; constructing a bandpass filter based on the empirical wavelet transform boundary values ​​of the icing thickness curve signal under airfoil icing conditions; obtaining empirical wavelet transform modes of each order based on the bandpass filter and the first K order Fourier modes of the icing thickness curve signal; superimposing the empirical wavelet transform modes of each order to obtain the icing thickness curve signal; and performing a target transform on the icing thickness curve signal to obtain the airfoil icing shape. By implementing this invention, high prediction accuracy can be maintained under different conditions, the prediction generalization ability is enhanced, and the prediction efficiency is improved.

The application discloses a phase loss detection method, a phase loss detection system, a compressor and a refrigeration device. The phase loss detection method comprises the following steps: dividing the running time of a predetermined position stage into several time periods in sequence; detecting the three-phase current of a rotor in each time period and analyzing whether each phase has a phase loss risk; if a certain phase has a phase loss risk in at least two time periods, it is determined that the phase has a phase loss fault; after the predetermined position stage ends, entering a stable running stage, estimating the increment of the rotor position angle in each control period Ts, comparing the increment with a set boundary value, and making further analysis according to the comparison detection result. Different phase loss detection logics are adopted in the predetermined position stage and the stable running stage, the reliability of phase loss detection is effectively improved, single-phase phase loss can be detected, and two-phase phase loss or three-phase phase loss can also be detected.

This invention discloses a high-precision density reversal method and system for gravity anomalies based on regular, stable, and high-speed solution. [Solution] Based on the above-mentioned field source regularization solution, we propose using data with false anomalies at the field source imaging depth as boundary value conditions. Again, by solving the upper space of the first class boundary value problem of Laplace's equation, we calculate gravity anomalies on the ground and perform least-squares optimization iterative solution with the actual ground anomalies to obtain high-precision anomaly imaging data at the field source depth that does not contain false anomalies due to the Gibbs effect. This depth is used as the top interface of the field source, and the top interface is divided into grid prisms to construct the field source morphology. Density inversion is performed on each prism top surface in the frequency domain to obtain the density distribution of the field source morphology. Since this algorithm is implemented in the frequency domain and the inversion is performed at a known field source depth, the present invention is a gravity density inversion method with high speed, high precision, and high resolution characteristics, and provides a new concept for realizing the solution of gravity field source density parameters.

The embodiment of the invention provides a space bounding box calculation method for a three-dimensional model. The method comprises the steps that continuous parameterized expression of the surface of a target three-dimensional model on a parameter domain is determined; according to the continuous parameterized expression, analyzing a plurality of profile curves obtained by projecting the target three-dimensional model on a plurality of mutually orthogonal coordinate planes in a three-dimensional coordinate system; according to the parameterized expressions of the plurality of profile curves, determining two-dimensional bounding boxes respectively corresponding to the plurality of profile curves on the corresponding coordinate planes; and performing extreme value selection operation on the boundary value of the two-dimensional bounding box in the coordinate axis direction to generate a three-dimensional space bounding box of the target three-dimensional model. A projection profile curve is directly analyzed through continuous parameterization expression, the geometric boundary of the model is locked in mathematics essence, the defect that extreme points are missed in discrete sampling is avoided, and the calculation precision is improved; and three-dimensional extremum search is converted into two-dimensional profile curve analysis and two-dimensional bounding box construction on a plurality of orthogonal coordinate planes, so that the calculation complexity is reduced.

The invention discloses an energy system scheduling method and device and a medium, and the method comprises the steps: inputting an energy characteristic parameter corresponding to an energy system into a preset energy prediction model, and outputting a corresponding energy demand prediction result; wherein the energy demand prediction result refers to a confidence interval formed by energy demand prediction values with different confidence degrees under each prediction time step; according to the boundary value of the confidence interval, constructing a power balance constraint relationship of the energy system, and based on the balance constraint relationship, solving a pre-constructed multi-objective optimization function to obtain a plurality of scheduling strategies; and selecting a target scheduling strategy from the plurality of scheduling strategies, and scheduling the energy system through the target scheduling strategy.

The invention relates to a method for calculating a northbound gradient of a meridian component of a sea plumb line deviation, and provides a method for calculating the northbound gradient of the meridian component of the sea plumb line deviation aiming at the compensation requirement of the meridian component of the plumb line deviation of an inertial navigation system under the constraint of the position error of a sea motion carrier. And based on the completely normalized global gravitational potential coefficient, constructing a sea area plumb line deviation meridian component northbound gradient calculation model, and realizing sea area plumb line deviation meridian component northbound gradient calculation. The invention aims to improve the meridian component compensation capability of the vertical line deviation of the inertial navigation system under the constraint of the position error of the motion carrier and improve the navigation precision of the inertial navigation system.

This application relates to the field of gas transportation technology, and provides an equivalent inertia correction method and apparatus for a lumped parameter model of a gas pipeline. The method includes: generating a lumped parameter model of the gas pipeline based on the continuity equation and momentum equation of a one-dimensional compressible gas; generating an equivalent inertia factor based on the equation of state of a real gas under isothermal conditions; wherein the equivalent inertia factor characterizes the nonlinear sensitivity of the compressibility factor of the real gas to pressure; correcting the equivalent inertia of the lumped parameter model based on the equivalent inertia factor; and updating the boundary values ​​of the lumped parameter model after correction based on the fluid sound velocity under the current operating conditions of the gas pipeline and the pipeline length constraint when a system disturbance occurs. The method provided in this application effectively compensates for the inertial distortion caused by lumped averaging of material properties and solves the global phase shift problem under complex operating conditions.

The invention provides a high arch dam earthquake vulnerability analysis method and system based on an extreme gradient lifting tree theory, and the method comprises the steps: obtaining a plurality of pieces of earthquake vibration data meeting the feature requirements of a high arch dam site, and constructing an engineering demand parameter set and an earthquake vibration intensity parameter set; constructing a high arch dam probability earthquake demand model based on an extreme gradient lifting tree model, and fitting a non-linear relationship between engineering demand parameters and earthquake intensity parameters; fitting a change curve of the engineering demand parameters with respect to the acceleration response spectrum value in the first-order natural vibration period based on a scalar model, and extracting boundary values divided by different anti-seismic performance levels; and on the basis of an engineering demand parameter logarithmic normal distribution hypothesis, using the fitted high arch dam probability earthquake demand model to construct an earth surface vulnerability function, and inputting an earth vibration strength parameter and boundary values of different anti-seismic performance levels for calculation to obtain earthquake vulnerability probabilities of the high arch dam under the different anti-seismic performance levels.

This invention discloses an advanced positioning algorithm that, by constructing a statically indeterminate linear equation system, can efficiently integrate data from various stations in a large-scale distributed positioning system and accurately determine the target location in a single calculation. Its innovation lies in utilizing the transformation relationship between polar and rectangular coordinate systems to directly construct a boundary value equation containing mixed variables based on the path difference equation. Through appropriate equation transformation and variable substitution, the algorithm achieves linearization of the boundary value equation, significantly simplifying the calculation process. Furthermore, addressing the decisive influence of the path difference construction method or matrix construction strategy on positioning accuracy, this invention proposes unique path difference construction rules or coefficient matrix construction methods. These rules and methods ensure highly accurate positioning results in practical applications. The new algorithm is not only simple but also significantly improves positioning efficiency, bringing a revolutionary change to large-scale distributed positioning.

The embodiment of the invention discloses a data processing method and device, equipment and a computer readable storage medium. The method comprises the steps of obtaining m amplification boundary values associated with a piecewise function; the first participation device and the second participation device jointly execute the first comparison task to obtain q first Boolean fragments corresponding to the first intermediate parameter; obtaining q'first Boolean fragments from q first Boolean fragments corresponding to the first intermediate parameter according to the value L '; the q'first Boolean fragments are used for being multiplexed to the q first Boolean fragments corresponding to the second intermediate parameter; and executing the piecewise function according to the q first Boolean fragments corresponding to the first intermediate parameter, the q first Boolean fragments corresponding to the second intermediate parameter and the first arithmetic fragment. By adopting the method and the device, not only can computing resources and communication overhead be reduced, but also the training efficiency of the longitudinal federal model can be improved. The embodiment of the invention can be applied to various scenes such as cloud technology, artificial intelligence, intelligent traffic, auxiliary driving, audio and video and the like.

The application discloses a kind of comprehensive energy system interval probability energy flow calculation method and system, the method includes: establishing interval variable model and random variable model;Respectively to random variable model and interval variable model sampling, obtain multidimensional random variable and multidimensional interval variable;Through the HL-MEF agent model of double-layer agent model upper layer calculation determines performance flow, according to the calculation result, output energy flow upper and lower boundary value;According to multidimensional random variable, energy flow upper and lower boundary training the sPCE agent model of double-layer agent model lower layer;Large-scale random variable is regenerated and input to the sPCE agent model of training completion, and the upper and lower boundary values of interval probability energy flow are output.The application designs double-layer agent structure, through the multi-energy flow calculation model based on heterogeneous learner of upper layer, reduces the online sampling burden of lower layer training sPCE agent model, and through sPCE agent model, reduces a large amount of repeated calculation of determining performance flow.

The invention provides a valve opening and closing state real-time tracking and abnormality diagnosis method, which comprises the following steps: comparing the impedance growth rate of each branch with increased roughness with a preset flow distribution rated value to obtain the flow contribution variation of each branch, and determining the interference level of each branch according to the flow contribution variation; the abnormal type of the abnormal valve and the flow of the branch to which the abnormal valve belongs are analyzed, the influence weight between the abnormal type and the flow distribution amount is evaluated by combining the interference level, and the accurate coordinate of the abnormal position is determined; the actual flow at the accurate coordinates of the abnormal position is obtained, the floating range between the expected flow and the actual flow is determined, and the tracking judgment boundary value of the opening and closing state of the valve is determined according to the floating range; the roughness change rate and the opening reading which are collected in real time are processed by tracking and judging boundary values of the opening and closing state of the valve, and whether the opening and closing state of the valve is normal or not is evaluated.

The application discloses a transient electromagnetic inversion method based on deep neural network reparameterization regularization, belongs to the technical research field of geophysical electromagnetic data processing and analysis, and comprises the following steps: constructing an untrained deep neural network; inputting random hidden vectors conforming to the input layer structure of the deep neural network and boundary values of resistivity and stratum thickness required by transient electromagnetic inversion into the deep neural network to output a geological model containing a regularization effect; performing iterative updating on a transient electromagnetic inversion objective function by using a self-adaptive matrix estimation algorithm, judging whether a convergence condition is met, if the convergence condition is not met, taking the weight of each layer of the deep neural network as a variable needing to be updated, iteratively updating the weight of each layer, and making the deep neural network generate a new geological model containing the regularization effect. The application not only makes it easy to apply the regularization effect on the generated geological model in the physical constraint without external training data, but also has good anti-noise performance.

The present invention discloses a method and system for high-precision density inversion of gravity anomalies based on regular stable fast solution. [Solution] Based on the above source regularization solution, we propose using data containing spurious anomalies at the source imaging depth as boundary value conditions. Then, through the upper half-space solution of the first-class boundary value problem of Laplace's equation, we calculate the gravity anomaly on the ground, and perform a least-squares optimization iteration with the actual ground anomaly to obtain high-precision anomaly imaging data at the source depth without spurious anomalies caused by the Gibbs effect. This depth is used as the top interface of the source, and the top interface is divided into grid prisms to construct a source configuration. Density inversion is performed on each prism top surface in the frequency domain to obtain the density distribution of the source configuration. Because this algorithm is implemented in the frequency domain and the inversion is performed at a known source depth, the present invention provides a gravity density inversion method with high speed, high precision, and high resolution, and provides a new concept for solving gravity source density parameters.

The invention provides a single-component thruster temperature measurement method for space application. The method comprises the steps that a programmable digital potentiometer outputs a series of resistance values to simulate resistance value changes of PT100 at different temperatures; calculating a theoretical temperature value Tthreshold of each resistance value through an IEC60751 standard formula, and recording a digital code value Xadc output by an ADC (Analog to Digital Converter) to form a (Xadc, Tthreshold) data pair; drawing a nonlinear relation curve, and dividing the digital code value range into a plurality of continuous intervals according to the curve; carrying out linear regression on the data points of each interval, and fitting a linear function relation of each interval: Tether = Kn * Xadc + Bn; burning each interval boundary value and the corresponding Kn and Bn into a program memory of an on-orbit MCU (Microprogrammed Control Unit); and the in-orbit MCU judges the interval according to the real-time digital code value Xnow, calls the corresponding Kn and Bn, and calculates the current temperature Tcurrent = Kn * Xnow + Bn, so that high-precision, low-resource-occupation, high-reliability and low-cost temperature measurement is realized.

The application discloses a power grid load prediction method and system based on a large model, and relates to the technical field of power grid load prediction.The method comprises the following steps: S1, collecting multi-source load comprehensive data, and performing edge processing and data preprocessing; S2, monitoring a mutation point in real time, constructing a behavior label sequence, and identifying a high-frequency disturbance point; S3, constructing a prediction input vector of the multi-source load comprehensive data, performing load data prediction, and calculating a physical constraint regularization value; S4, performing dynamic credible interval evaluation, judging the prediction result in real time according to a credible boundary value interval, and feeding back a control instruction; and S5, performing error analysis and retraining triggering, comprehensively evaluating prediction accuracy and output stability, and performing model optimization, version management and feedback learning.The method solves the problem that a prediction model is prone to oscillation due to fast load change in a high-density load scene.