190 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 soil hydraulic coupling simulation method and system based on a physical information neural network. The method comprises the following steps: firstly, constructing two improved full-connection neural networks for respectively predicting pore water pressure and deformation response in a soil body; according to a soil hydraulic coupling partial differential equation set, non-dimensionalization control equation loss items of a liquid phase and a solid phase are established based on a fixed stress splitting method, and a total loss function of the liquid phase and the solid phase is established in combination with an initial boundary value condition; and finally, alternately training loss functions of a liquid phase and a solid phase based on a sequential training strategy and a gradient normalization self-adaptive loss balance strategy. According to the method, a sequential training strategy, an improved full-connection neural network and a self-adaptive loss balance scheme are adopted to improve a traditional physical information neural network, and prediction precision and training stability are improved. The trained physical information neural network can be used as an agent model to predict pore water pressure and deformation, and has great application potential.

The invention relates to the technical field of automatic testing, and discloses an automatic testing scheme generation method and device and a medium, and the method comprises the steps: matching a demand action in a test demand with a code entry name corresponding to a code segment, and determining a code risk level of a current demand code block; analyzing the code change frequency and the code dependency degree of the current demand code block according to historical test records in the test domain knowledge base, and determining the test dynamic priority of the current demand code block; constructing a control flow diagram and a data flow diagram of the current demand code block, and detecting risk nodes in the control flow diagram and the data flow diagram; generating a test scene of the current demand code block according to the risk node, and generating a boundary value test rule according to a key constraint condition in the data flow diagram; and querying an environment simulation rule of the current demand code block, and packaging the test scene, the boundary value test rule and the environment simulation rule into an automatic test scheme. According to the invention, the code coverage when the test scheme is generated can be improved.

The invention discloses a potential field data downward continuation method and device, equipment and a medium, and relates to the technical field of geophysical geological resource exploration, and the method comprises the steps: learning a rule in downward continuation through employing a physical information neural network containing a total loss function, and training a better downward continuation model; wherein the total loss function comprises partial differential structure loss, boundary value loss and true value loss; the partial differential structure loss in the gravity field is an error of a product of a Laplacian operator of prediction data and gravity; the partial differential structure loss in the magnetic field is an error of a product of a Laplacian operator of prediction data and magnetic force; the boundary value loss is obtained by predicting data calculation after downward continuation; the true value loss is the error of the data after the downward continuation and the data after the downward continuation of the label, and the trained physical information neural network is utilized to carry out the downward continuation prediction on the actually measured potential field data, so that the high-precision downward continuation of the potential field data is realized.

The invention belongs to the technical field of bubble dynamics and multiphase flow and artificial intelligence, and particularly relates to a high-pressure bubble pulsation load prediction and initial parameter inversion method and system based on artificial intelligence. The underwater high-pressure bubble pulsation process is studied, and interface evolution data in the high-pressure bubble pulsation process are obtained; establishing a compressible VOF-PINN multiphase flow model, and predicting unknown parameters and pulsating load characteristics of bubbles in combination with the initial boundary value; dividing a prediction problem into a positive problem and an inverse problem according to known data; establishing a model which can be used for predicting forward and reverse models; for a positive problem, training and predicting the VOF-PINN model by using a gradient descent optimization algorithm; and for the inverse problem, bubble initial parameter identification is realized. The invention aims to solve the problems that recalculation is needed for changing calculation parameters in numerical simulation, grid division is complex and tedious, high-fidelity data cannot be directly included, and initial parameters in experimental research are uncertain.

The invention relates to the technical field of plate heat exchange, in particular to a calculation method of a plate heat exchanger, which comprises the following steps: S1, converting pressure and temperature data into pressure and enthalpy values, setting boundary conditions, determining a hot side and a cold side of the heat exchanger according to the boundary conditions, and setting non-boundary values by segmentally applying energy conservation to obtain geometric parameters and empirical constants; s2, obtaining physical property parameters of the fluid by using a two-dimensional grid linear interpolation method, and obtaining the physical property parameters of the fluid by taking the enthalpy value and the pressure as coordinates; and S3, determining the effective area required by each section, and obtaining a local heat transfer coefficient and a pressure drop when the volume requirement is met under the fluid physical property parameters and the channel flow velocity. According to the method, the problem that the calculation speed of a calculation program is low when the performance of the brazing sheet heat exchanger is evaluated under different working conditions is solved through unique grid division in the plate heat exchanger and rapid calling of fluid physical properties, the calculation error value is effectively reduced, an accurate check result is obtained within ten iteration steps, and the calculation cost is saved.

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 discloses an examination result evaluation system in the technical field of medical examination evaluation. The system comprises a physiological dynamic recognition module, a critical state interpretation module, a process efficiency optimization module, a health trend prediction module, an examination accuracy improvement module, a result interpretation optimization module, a potential pathology mining module and a health evaluation comprehensive module. According to the method, the depth and the breadth of medical examination and evaluation are remarkably improved by introducing technologies such as a nonlinear dynamic model, fuzzy logic and a machine learning algorithm. Firstly, the application of the physiological dynamic recognition module enables the analysis of time sequence data to be more accurate, and subtle changes of physiological behaviors can be revealed, so that the capability of early recognition of diseases is improved. The introduction of the fuzzy logic shows higher flexibility and accuracy in the aspects of uncertainty and fuzziness of processing test results, and particularly provides finer support for medical decision making in the aspect of judgment of boundary values and critical states.

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 residual terrain correction estimation method based on PINNs and a residual terrain model, and relates to the technical field of geodetic survey, a data set is randomly divided into a training data set and a test data set, a residual terrain gravity anomaly of each data set is obtained through RTM as an output, the network architecture of the PINNs is a full-connection neural network, and the network architecture of the PINNs is a full-connection neural network. Associating the output quantity and the gravitational potential quantity of the RTM model according to a basic equation of physics geodetics to serve as a boundary value condition; and taking the weighted sum of the physical loss function and the data loss function as a total loss function and an initial full-connection neural network weight parameter of the full-connection neural network, training the constructed full-connection neural network through a training data set, and achieving the requirements of small calculation amount, low required data complexity and high precision through the method.

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.

The invention discloses a groundwater environment quality prediction method and system, and relates to the technical field of prediction data processing, and the method comprises the steps: obtaining historical observation data and real-time observation data of a to-be-predicted solute; obtaining a boundary condition, dividing the prediction region into a plurality of sub-regions based on the boundary condition, and generating a plurality of three-dimensional grid points; acquiring geological data corresponding to the three-dimensional grid point, acquiring an influence factor between the solute to be predicted and the geological data based on the correlation model, judging whether the influence factor is within a preset threshold range, and if not, taking the three-dimensional grid point as a grid point to be processed; acquiring a first boundary value and a second boundary value; performing calculation processing on the first boundary value and the second boundary value based on a dispersion calculation model to obtain longitudinal dispersion and transverse dispersion; and obtaining prediction moment data, obtaining a grid prediction result, obtaining a sub-region prediction result, and obtaining a target prediction result. The method has the advantages of being comprehensive, accurate, high in prediction efficiency and high in adaptability.

The present application provides a method and device for processing the worst performance of integrated circuits, electronic equipment, and medium, which are applied to the field of integrated circuit design and reliability assessment technology. By further narrowing the sampling range based on the importance sampling principle near the initially obtained failure boundary, and determining the circuit performance prediction value of the circuit performance normally distributed at the 6-sigma position, and then combining the prediction value with the linear model of the circuit performance to form an optimization problem, the process deviation vector close to the actual situation is solved, and finally the actual circuit performance value at the 6-sigma position is obtained through circuit simulation based on the process deviation vector close to the actual situation. The boundary value of the circuit performance space is quickly and accurately solved to the worst performance close to the actual situation at limited simulation points, which significantly improves the efficiency and accuracy of integrated circuit performance evaluation.

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 provides a method and a device for predicting a parameter adaptability index of a new energy network inverter. The method comprises the following steps: acquiring actual measurement values of inverter parameters at n fixed time intervals, and determining parameter boundary values (namely the maximum value and the minimum value of the actual measurement values in each time interval); calculating a stability sensitivity index based on the measured value and the parameter boundary value, and performing square root operation on the stability sensitivity index to obtain a historical data weight; performing normalization processing on each inverter parameter according to the actually measured value and the parameter boundary value of the inverter parameter to obtain a normalized parameter matrix; and multiplying the normalized parameter matrix by a weight vector formed by historical data weights to obtain a parameter prediction value of the inverter at the next moment. According to the method, the inverter parameters can be accurately optimized by predicting the adaptability index of the new energy grid-forming type inverter parameters, the safe and stable operation level of a new energy base can be improved, and challenges caused by strong fluctuation and uncertainty of new energy power generation can be effectively handled.

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.

The present application is applicable to the technical field of model optimization, and particularly relates to a recommendation result optimization method and apparatus for a recommendation model, and a device and a medium. The method comprises: acquiring a classification boundary value used for classifying a prediction result generated by a recommendation model, and on the basis of the classification boundary value, determining a fuzzy interval that represents a classification error; performing screening on a training data set used during the training of the recommendation model, so as to obtain a target data set, the prediction result of which falls within the fuzzy interval, and performing prediction rule extraction on the target data set, so as to obtain at least one prediction rule; acquiring a prediction result of data to be recommended which corresponds to the recommendation model; and if it is detected that the prediction result of said data is within the fuzzy interval, performing recommendation prediction on said data on the basis of the at least one prediction rule, so as to obtain an optimized recommendation result corresponding to said data. Therefore, a prediction rule is extracted by means of analyzing corresponding data in a training data set, and the prediction rule is used to perform recommendation prediction on uncertain data, thereby improving the accuracy of a recommendation model.

The invention relates to a method and a device for identifying hydrocarbon source rock types in a well area with less offshore sea-land transition facies. The identification method comprises the following steps: S1, acquiring required data; s2, according to the sporopollen phase analysis data, determining a hydrocarbon source rock division threshold value based on the sporopollen phase component content; s3, according to the hydrocarbon source rock division threshold value based on the sporopollen phase component content, determining the threshold value of the biogenic indication parameter of the biomarker compound corresponding to the hydrocarbon source rock and the condensate oil; s4, according to the threshold value of the biogenic indication parameter of the biomarker compound corresponding to the condensate oil, determining the threshold value of the natural gas light hydrocarbon parameter; and S5, drawing a cross plot according to the natural gas light hydrocarbon parameters, and dividing ranges corresponding to different types of hydrocarbon source rocks according to the threshold values of the natural gas light hydrocarbon parameters. According to the identification method and device provided by the invention, the identification accuracy of the hydrocarbon source rock type of the offshore sea-land transition facies sedimentary environment less-well area is remarkably improved, and the problem of limited samples in the less-well area is solved.

The invention belongs to the technical field of power system faults, and discloses a turn-to-turn short circuit fault deduction method, system, equipment and medium, and the turn-to-turn short circuit fault deduction method comprises the steps: determining the critical strain value of the fracture of an insulating material based on the axial and radial tensile test data of a winding, and building a mechanical characteristic database of the dependent variable and the insulation fracture; establishing a Poisson boundary value equation set according to the magnetic vector potential and the current density, solving to obtain the magnetic induction intensity, establishing a circuit equation in a short circuit state based on the primary side voltage of the winding, and analyzing the linear relation between the deformation state of the winding and the electromagnetic load to obtain the maximum axial and radial deformation quantity of the winding; and based on the mechanical characteristic database, the turn-to-turn insulation state and the turn-to-turn short circuit condition are judged by combining the axial and radial maximum deformation quantity of the winding. According to the method, when the winding deformation is small, the occurrence condition of the turn-to-turn short circuit fault can be accurately deduced.

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 provides a method and a device for generating a validity test case of a train number of a vehicle-mounted system. The generation method comprises the following steps: determining the number of bits of the train number of the vehicle-mounted system; determining the character selection of each digit according to the arrangement type of the character corresponding to each digit and the rule requirement that the train number is valid; if the arrangement type of the characters corresponding to the digits is an enumeration type, selecting each character corresponding to the digits one by one as a test case value of the digits; if the arrangement type of the character corresponding to the digit is a continuous type, selecting a boundary value and an equivalence class value of the character corresponding to the digit as a test case value of the digit based on the boundary value and the equivalence class; and combining the test case value corresponding to each digit according to a digit sequence to form a plurality of test cases with effective vehicle numbers of the vehicle-mounted system. According to the invention, the number of test cases is greatly reduced, and the train number validity test efficiency is greatly improved.

The invention is suitable for the technical field of electric power transaction, and provides a multi-target transaction strategy and system for water-wind-light power generation based on an improved hybrid algorithm, and the strategy comprises the following steps: collecting system parameters of a hydropower station, a photovoltaic power station, a wind power station and a pumped storage power station, and determining boundary values of constraint conditions; constructing a multi-objective optimization scheduling model, wherein the multi-objective optimization scheduling model comprises an objective function and a constraint condition; an improved fast non-dominated genetic algorithm and a time-varying multi-objective particle swarm optimization algorithm are adopted, and global optimization is carried out on the multi-objective optimization scheduling model through a double-population strategy; and displaying an optimization result at a client through a graphical interface, wherein the optimization result comprises the power generation plan of each power station, the change of the reservoir capacity and the economic benefit of the system. By combining the genetic algorithm of fast non-dominated sorting and the time-varying multi-objective particle swarm optimization algorithm, global optimization is carried out by adopting a double-population strategy, the advantages of the two algorithms are effectively combined, and the search efficiency is improved by dynamically adjusting the strategy and parameters.

The invention relates to the technical field of seismic engineering, and provides a method and system for solving a seismic wave propagation equation based on a physical information neural network, and the method comprises the steps: determining a space domain, a time domain, an initial condition and a boundary condition of a one-dimensional seismic wave equation; initial boundary value coordinate points are randomly extracted to serve as a training data set constraint network, and Latin hypercube sampling is utilized to generate space-time domain configuration points to meet partial differential equation constraint; a physical information neural network model is established, after space-time coordinates are input, equation residual errors are calculated through automatic differential, and initial boundary value loss and physical residual errors are combined to construct a composite loss function for training; and proposing a self-adaptive region sampling strategy based on residual errors, resampling a high residual error region according to an average residual error value of the configuration points in a training period, and iteratively optimizing network parameters after a newly added point and an initial configuration point are fused. According to the method, by dynamically enhancing the sampling density of a high-error region, the L2 relative error of a prediction solution is reduced by one order of magnitude, the network is effectively prevented from falling into local optimum, the convergence efficiency is remarkably improved, grid discretization or prior data support is not needed, and a high-precision meshless solution scheme is provided for a one-dimensional seismic wave equation.

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 invention discloses a logic drive test case generation method based on a large language model, and belongs to the technical field of software testing. The method comprises the following steps: extracting key information from a code to be tested; based on the key information, a logic driving thinking chain prompt template is adopted to guide a large language model to generate a test case; running the test case and calculating a coverage rate; and judging whether the test case needs to be repaired or not based on the test case operation result and the coverage rate, if yes, repairing the test case by adopting a layered repairing strategy, and if not, outputting the test case. According to the method, a big language model is systematically guided to generate the test case through a logic driving thinking chain, fusion path coverage, condition combination coverage, boundary value analysis and an equivalence class division strategy, and the problems of path missing and incomplete condition combination when a traditional LLM generates the test case are thoroughly solved; and the test case is repaired by adopting a layered repair strategy, so that the manual intervention demand is obviously reduced.

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.