55 results about "Density curve" patented technology

The invention relates to an electromechanical fault prediction and diagnosis method and system based on big data, and the method comprises the steps: collecting the operation state data of electromechanical equipment in real time, and synchronously obtaining historical associated data; performing dynamic feature extraction on the operation state data and the historical associated data, constructing a sliding mean value feature matrix, and calculating dynamic weights of feature parameters; generating a fusion weight coefficient according to the dynamic weight and a preset fault threshold interval; extracting a distribution density curve of a historical fault occurrence probability, and calculating a dynamic threshold value; performing weighted reconstruction on the sliding mean feature matrix based on the fusion weight coefficient, and outputting a fault type and an occurrence probability through a pre-trained lightweight residual neural network model; and when the fault occurrence probability exceeds a dynamic threshold value adjusted based on a historical fault occurrence probability distribution density curve, generating an electromechanical fault diagnosis result so as to realize the purposes of real-time monitoring of the operation state of the electromechanical equipment and accurate fault prediction and diagnosis.

The invention discloses a method for predicting a favorable area of a thin-layer stacked sand body, and relates to the technical field of exploration and development of oil and gas reservoirs, and the method comprises the steps: generating a synthetic seismic record through well-seismic combination by using interval transit time and a density curve, aligning the synthetic seismic record with an actual seismic record, and building a three-dimensional frame model of a target layer of a research area; performing abnormal value elimination and standardization processing on the logging curve, making a statistical histogram, comparing peak separation degrees of sandstone and mudstone, and selecting the most sensitive curve to distinguish the sandstone and mudstone; and realizing well-side seismic trace waveform dynamic clustering analysis through singular value decomposition, and determining the number of effective samples. Based on the result of waveform indication simulation, the method is combined with the optimized seismic attribute to predict the thin-layer stacked sand body together, a systematic method is provided, the multiplicity of solutions of predicting the thin-layer stacked favorable sand body through the seismic attribute and the uncertainty of depicting the sand body by the sedimentary facies in the area with few wells can be reduced, and the prediction accuracy of the thin-layer stacked favorable sand body is improved. Therefore, the precision and reliability of thin-layer stacked sand body prediction are improved.

The invention relates to the technical field of power equipment safety detection, and discloses a power equipment thermal fault diagnosis method and device, a storage medium and computer equipment, and the method comprises the steps: collecting on-site infrared image data of a transformer substation, constructing an infrared image data set, and carrying out the image enhancement processing; performing target identification and segmentation by using an instance segmentation model combining a cross-stage local network and an attention mechanism, and determining a power equipment region; inputting the gray value of the power equipment area into the deep neural network model to obtain a temperature value of the target power equipment, drawing a probability density curve of the temperature value by using a kernel density estimation method, obtaining a reference temperature of the target power equipment, and calculating a hot spot temperature; and calculating the relative temperature difference and determining a fault state based on a preset fault judgment standard. According to the method, the original data quality is improved, the power equipment is accurately identified and segmented, high-precision temperature prediction is carried out, and finally, full-automatic and reliable fault judgment of the power equipment is completed.

The invention discloses an equipment remaining service life prediction method and system, and belongs to the technical field of equipment state monitoring, and the method comprises the steps: obtaining the whole life cycle degradation data of equipment as a data set; performing data dimension reduction by using an automatic encoder to obtain an equipment health index HI; inputting the HI into a fusion type RUL prediction network model, and predicting a probability density curve of the RUL; wherein the model comprises a probability prediction network, a Wiener process model and a fusion module; the probability prediction network is used for obtaining a probability density curve in a numerical form; the Wiener process model is used for obtaining a probability density curve in an analysis form; and the fusion module is used for dynamically weighting the two curves to realize RUL prediction. According to the method, the RUL probability density curve prediction is realized, the problem that an existing prediction model based on machine learning can only obtain a point prediction result is solved, and the interpretability of the model is improved in combination with a random process.

The invention discloses a method for calculating fatigue life reliability of an asphalt pavement, and relates to the technical field of road engineering. According to the method, actually-measured temperature field historical data and traffic axle load data of an existing asphalt pavement structure are obtained through an asphalt pavement long-term performance observation network, the actually-measured temperature field historical data are extracted to construct a temperature field historical data set, and a temperature cumulative probability distribution curve is generated; then, utilizing traffic axle load data to generate an axle type cumulative probability density curve and an axle load interval cumulative probability density curve, and based on Monte Carlo simulation, performing inverse function sampling in each cumulative probability density curve during each simulation to obtain the structural response of the asphalt pavement structure to determine the standard fatigue life; and determining the reliability of the fatigue life by counting the probability that the standard fatigue life obtained by Monte Carlo simulation is greater than the standard cumulative action times within the design period. According to the method, the fatigue life of the asphalt pavement structure is accurately estimated, and the design reliability of the asphalt pavement structure is effectively guaranteed.

The invention relates to a probabilistic load flow calculation method for new energy and load correlation, and the method specifically comprises the steps: building a probabilistic model of new energy and load based on the distribution types and parameters of the new energy and load, inputting network structure parameters, the probabilistic model information of the new energy and load, and correlation coefficient information, the method comprises the following steps of: setting a median Latin hypercube sampling scale and an iteration number marking variable, then sampling mutually independent random variables such as new energy and load and random variables such as new energy and load with a correlation so as to generate random samples, and finally, based on a Newton-Raphson method, calculating a maximum iteration number of the new energy and the load so as to obtain a maximum iteration number of the new energy and the load. Performing power flow calculation on the obtained random samples in sequence, finally performing statistics on power flow results such as node voltage, line transmission power and line loss rate obtained by each power flow calculation, drawing a probability density curve, and performing probability power flow calculation of new energy and load correlation by using the probability density curve; the method has the advantages of being accurate in calculation, fast and efficient.

The invention provides a data reading method of a memory, storage equipment and a storage medium, and the data reading method of the memory comprises the following steps: carrying out reading operation on a programming unit to obtain target data; according to the bit flipping information of the target data, determining a corresponding failure bit counting exception type; wherein the failure bit counting exception type comprises a first type of exception caused by read voltage offset and a second type of exception caused by trough elevation of a probability density curve of a break-over voltage of a storage unit; in response to the first type of abnormality, adjusting the read voltage; or in response to the second type of exception, performing data refreshing operation or garbage collection operation. By means of the mode, the abnormal type causing reading voltage deviation is judged so as to judge the data state, then the rereading rate of a host is reduced, and the reading effectiveness is improved.

This invention discloses a method, system, and program product for identifying the fault severity of underwater robot thrusters under conditions of relatively mild faults, belonging to the field of underwater robot fault diagnosis technology. The invention first obtains a thrust deviation curve based on the bow angle and lateral thruster control voltage. Then, based on different parts of the curve, it uses difference and multi-time-window sliding Fourier transform methods in the time and frequency domains respectively to obtain multiple thrust losses. Next, it introduces a kernel density estimation method to obtain the time-domain and frequency-domain fault severity probability density curves based on the thrust losses obtained in the time and frequency domains. Finally, it uses a method of averaging at the same location to fuse the curves, and identifies the fault severity based on the fused fault severity probability density curves. This invention achieves high identification accuracy and is particularly suitable for fault severity identification in ocean current environments and under conditions of relatively mild faults.

This invention relates to a method, apparatus, medium, and equipment for analyzing the overpressure characteristic values ​​of combustible gas explosions in marine oil and gas production facilities. The analysis method includes the following steps: geometrically modeling the marine oil and gas production facility; calculating the congestion degree of the process area of ​​the marine oil and gas production facility; calculating the volume of the combustible gas cloud under different filling ratios in the process area of ​​the marine oil and gas production facility; performing transient CFD simulations of combustible gas explosions with varying gas cloud positions and ignition positions to obtain the overpressure value of the protected target changing over time; determining the maximum overpressure value of the protected target under combustible gas explosion conditions; expanding the sample and performing statistical analysis and characteristic value calculations to obtain probability density curves and characteristic values; and determining the explosion-proof level of the protected target according to protection requirements.

The present invention discloses an optimization scheduling method and terminal based on probability box and conditional risk value, which determine the prediction errors corresponding to the output of distributed renewable energy and the load demand in a preset period; determine the corresponding cumulative probability density curve based on each prediction error, and determine the interval set that meets the preset confidence from the cumulative probability density curve based on each prediction error according to the probability box theory; calculate the final uncertainty factor fluctuation interval corresponding to the interval set that meets the preset confidence based on the conditional risk value theory; construct an optimization scheduling model based on the final uncertainty factor fluctuation interval, and use an intelligent optimization algorithm to solve the optimization scheduling model to obtain an optimization scheduling plan, thereby ensuring the integrity of uncertain information, calculating uncertainty situations outside the confidence interval, avoiding the conservatism of traditional interval optimization methods while ensuring the sufficiency of safety constraints, and achieving an effective balance between the economy and safety of the distribution network.

The present application relates to the technical field of rock fracture modeling, and discloses a method and a processor for modeling a multi-scale fracture network of a tight reservoir. The method comprises: obtaining a strike attribute of a fracture; generating a fracture strike conforming to a preset mode according to a cumulative probability density curve of the strike; taking a fracture position interpreted from a core and an imaging logging as hard data of a fracture position in a fracture network; dividing the hard data into seed hard data and correction hard data; correcting a cumulative probability curve of fracture development intensity according to the seed hard data, and generating a fracture element set according to the corrected probability density curve; generating a discrete fracture network through similarity fusion criteria, mechanical cause fusion criteria and intersection criteria; comparing the established discrete fracture network with the correction hard data, and correcting the number of fracture elements and the cumulative probability curve. Through iterative inversion of the seed hard data and the correction hard data, a more optimal multi-scale fracture network model that is more consistent with the hard data is generated.

The present invention discloses a channelized detection method based on robust noise floor estimation, belonging to the field of electronic reconnaissance. The radar signal is filtered, detected, and synthesized to output the detection result. The steps are as follows: First, after the radar signal is successively subjected to AD sampling and polyphase filtering, filtered data is obtained; according to the signal accumulation envelope sequence, an envelope probability density curve is constructed, the position of the sequence extreme point is found, the noise ratio of the sequence is calculated according to the position of the sequence extreme point, and the noise accumulation envelope sequence of each channel is obtained; using the noise accumulation envelope sequence, according to the expression of the cumulative distribution function of the Gaussian distribution, the noise floor mean estimate value and the noise floor variance estimate value are calculated; a reliable false alarm probability is determined by simulation experiments, and the detection threshold is calculated; by comparing the signal accumulation envelope with the detection threshold, the detection VP is obtained.

The invention discloses a variable stiffness composite material wing aeroelastic analysis method containing multi-source uncertainty, which comprises the following steps: carrying out probability space division according to the condition of random uncertainty factors, and determining the value of a random vector at each sampling point and the probability value of a sub-domain; regarding the random vector at each sampling point as a deterministic parameter, and circularly calculating an interval boundary value of a to-be-analyzed aeroelastic response variable at each sample point; based on the interval boundary value, calculating the reliability of the structure at each sample point only under the influence of the interval factors; discretizing the initial condition of the generalized probability density evolution equation to obtain the probability density of the structural reliability in each sub-domain; superposing the probability density of each sub-domain to obtain a probability density curve of the structural reliability considering the multi-source uncertainty; and calculating the reliability of the structure based on the probability density curve. By adopting the technical scheme of the invention, the aeroelasticity analysis containing the multi-source uncertainty of the variable stiffness composite material wing is realized.

The application provides a curve reconstruction method and device based on deep learning, equipment and medium, the method comprises the following steps: obtaining the logging curve data of the non-expanded section, and establishing a training set and a test set; a density curve reconstruction model is established and the training set is used to train the density curve reconstruction model, the density curve reconstruction model is a deep learning model; the test set is used to test the density curve reconstruction model, and a reconstructed density curve is generated, the density curve reconstruction model is adjusted until the correlation of the reconstructed density curve and the original density curve of the non-expanded section and the relative error of the density curve reconstruction model reach a preset condition; the density curve of the target well is reconstructed by using the adjusted density curve reconstruction model. The application simplifies the operation process, especially in complex formation conditions, can accurately reflect the actual situation of the formation, and improves the calculation accuracy of the reservoir parameters.

The present invention relates to the field of data processing, and specifically to a method for improving the data accuracy of a gyro array on a stable platform. The method includes the following steps: S1. Primary screening: A low-pass filter is used to smooth and denoise the signal, and the performance of the gyro unit is quantified in the form of variance. The larger the variance, the more severe the gyro jitter; S2. Secondary screening: In the normality test, a frequency distribution histogram is plotted, the fitting Q-Q plot is visualized, and the probability density curve of the normal distribution is fitted; S3. Temperature drift compensation: The gyro drift value is obtained by the encoder differential velocity closed-loop. In the full temperature range, when the velocity obtained by the encoder differential is 0, the gyro data on the stable two-axis platform is sampled, and then the temperature drift curve after acquisition is fitted. The present invention uses the secondary screening algorithm and the temperature drift compensation algorithm to improve the output data accuracy of the gyro array, and greatly improves the control effect of the stable platform.

The invention discloses a short-term power load probability prediction method based on an improved neural network, and belongs to the technical field of power system load prediction. The method comprises the following steps: acquiring and preprocessing load and related data; the method comprises the following steps: constructing a DCS-CNN-BiLSTM-Attention-QR probability prediction model fusing a convolutional neural network, a bidirectional long short-term memory network, an attention mechanism, quantile regression and kernel density estimation; automatically optimizing key hyper-parameters of the model by utilizing a difference creation search algorithm; carrying out model training by adopting a quantile loss function to obtain load prediction values under different quantiles; and finally, generating a probability density curve of the prediction interval through kernel density estimation. According to the method, deterministic point prediction is expanded into probabilistic interval prediction, the uncertainty of load prediction can be quantified, the model precision and generalization ability are improved through an intelligent optimization algorithm, and richer and more reliable decision information is provided for power system scheduling and risk management.

This invention relates to the field of data processing technology, specifically to an efficient data processing method for communication networks. The method includes: acquiring a network data sequence to be processed; obtaining a feature value sequence based on the differences between adjacent network data in the network data sequence; obtaining suspected drift anomaly data based on the similarity between the normal distribution curve and probability density curve corresponding to the feature value sequence; obtaining an optimal second data window corresponding to each suspected drift anomaly data based on the difference between the feature value corresponding to each network data in each second data window and the mean corresponding to the feature value sequence; obtaining each drift anomaly data based on the first data window and the optimal second data window; and obtaining the target network data sequence based on the optimal second data window. This invention can eliminate the impact of drift anomaly data on detection accuracy, resulting in higher detection accuracy.

The invention provides a distributed energy distribution network weak point identification method and system based on voltage distribution characteristics. The method comprises the following steps: obtaining a DG initial capacity coefficient matrix through a quasi-Monte Carlo method of a Sobol sequence; determining a convergence and dispersion discrimination result matrix of a part of the DG initial capacity coefficient matrix through load flow calculation, training the SVM model, and predicting load flow convergence and dispersion of the remaining part of the DG initial capacity coefficient matrix; screening all capacity coefficient matrixes of load flow convergence in the DG initial capacity coefficient matrix, gradually increasing the DG capacity according to a screening result until the voltage exceeds the upper limit, and recording the response voltage of the power grid system; voltage probability density curve charts of different nodes are obtained in combination with a probability distribution mapping technology and an error optimization fitting algorithm; calculating out-of-limit voltage difference indexes and out-of-limit voltage variance indexes of the nodes and sorting; and identifying the weak point of the power distribution network by combining the voltage probability density curve graph and the index sorting result. According to the method, the weak points of the distributed energy distribution network can be quickly and effectively identified.

The present invention discloses a method for assessing the structural morphology of engineering rock masses. The method involves percussing a hole in the engineering rock mass, recording and storing the drilling parameters in real time during the drilling process. After the hole is formed, a high-definition camera is used to image the entire hole along the depth of the hole. The drilling parameters are filtered. The principal component analysis method is used to reduce the dimension of the drilling parameters to determine two principal components and calculation parameters. The rock mass state in the hole is classified into several types based on the in-hole imaging photos. The principal component density map of each type of rock mass is plotted based on the calculation parameters to determine the first principal component. The probability density curve of the first principal component of each type of rock mass is plotted to divide the first principal component intervals of various types of rock mass. The first principal component value is calculated based on the drilling parameters to determine the rock mass state type. The present invention helps technicians use drilling measurement parameters to quickly and accurately assess the structural morphology of engineering rock masses, providing a decision-making basis for tunnel support, excavation, and mine planning and design.

The invention relates to a data analysis method and device for a wind turbine generator, electronic equipment, a storage medium and a computer program product. The data analysis method comprises the steps of obtaining historical data of target parameters of the wind turbine generator; calculating a target probability density curve of the target parameter based on the historical data of the target parameter; and evaluating the operation performance of the wind turbine generator based on the target probability density curve. Therefore, the evaluation of the operation performance of the wind turbine generator does not depend on any input information, but calculates the probability density curve corresponding to the parameters of the wind turbine generator based on the real operation data of the wind turbine generator in the historical operation process, and then evaluates the operation performance of the wind turbine generator based on the calculated probability density curve. According to the method, the actual operation rule of the wind turbine generator is fully combined, namely the design principle and the control characteristics of the wind turbine generator are fully utilized, the control mode of the wind turbine generator can be truly restored, and the accuracy of evaluating the operation performance of the wind turbine generator is improved.

The invention discloses a weight-free health degree analysis method for industrial equipment, which belongs to the technical field of electric digital data processing, and comprises the following steps: in a health state, according to a data index type for analyzing the health degree, respectively acquiring industrial time sequence data in at least one historical time window of the industrial equipment as reference data, collecting industrial time sequence data in the current time window as comparison data; according to the reference data and the comparison data, probability density curves are obtained through kernel density estimation one by one, so that probability distribution is obtained; and according to the data index type, obtaining JS divergence values for measuring data distribution similarity under the data index type according to the probability distribution of the comparison data and the probability distribution of the at least one piece of corresponding reference data, so as to obtain the health degree of the industrial equipment according to the plurality of JS divergence values. According to the invention, based on kernel density estimation and JS divergence, the distribution difference is quantified, weight setting is not needed, and the sensitivity and consistency of health degree assessment are improved.

The invention discloses a wind turbine generator bearing fault detection method and system. The method comprises the steps of collecting power, wind speed, generator bearing temperature and generator rotating speed of a wind turbine generator in different set days; performing data box separation and quartile distance anomaly detection based on power and wind speed; dividing the operation data matrix with abnormal values removed by day, and fitting the wind speed and power in each sub-matrix into a sigmoid curve as a wind speed and power curve of each day; eliminating data of days in a non-full power generation state based on a wind speed power curve of each day; dividing the operation data matrix in the normal state into a plurality of samples, fitting the generator rotating speed and the generator bearing temperature in each sample into a set nonlinear function, constructing a probability density curve of a degradation value of each sample, obtaining the bearing degradation probability, and if the bearing degradation probability exceeds a set degradation probability threshold, determining that the bearing is not damaged; if yes, determining that a fault exists. According to the invention, the overall operation and maintenance cost is saved, and the power generation efficiency is improved.

The present invention relates to a method for identifying equivalent vehicle loads of a cable-stayed bridge based on the time history curve of cable stress, comprising the following steps: Step S1: Define the maximum time difference between the peaks of adjacent cables in combination with the monitoring cable position of the cable-stayed bridge and the driving speed, and judge the driving direction according to the sequence of the peaks of two adjacent stay cables; Step S2: Identify the peak values of adjacent cables caused by the same vehicle by combining the one-way interval peak identification method in the driving direction; Step S3: Calculate the vehicle speed through the distance between the peak positions and the corresponding time difference between the two outermost cables of the cable-stayed bridge; Step S4: Perform peak staggering elimination based on the vehicle speed in the sub-interval section; Step S5: Identify the equivalent vehicle load in combination with the finite element model; Step S6: According to the equivalent vehicle load identification result obtained in Step S5, directly count the distribution of the equivalent vehicle load to obtain its probability density curve. The present invention can effectively improve the accuracy of equivalent vehicle load identification.

The invention relates to the technical field of electricity market analysis and prediction, in particular to a probabilistic prediction method for the clearing price of an electricity market. The technical problems are that when an electricity market clearing price probabilistic prediction method in the prior art is practically applied, multi-peak distribution caused by market state structural change is difficult to accurately capture, a predicted price probability density curve is often too smooth, and a prediction result has systematic deviation; according to the technical scheme, the probabilistic prediction method for the clearing price of the electricity market comprises a market state mode division step, a mode probability prediction step, a condition price distribution prediction step and a probability distribution synthesis step. According to the method, recognition and division of the market state mode are introduced, and a complex probability prediction problem is decomposed into a plurality of simple sub-problems, so that the multi-peak characteristic of the clearing price probability distribution is more accurately captured, and the prediction precision is improved.

The application provides a shear wave velocity prediction method based on deep learning, comprising the following steps: step 1, obtaining a longitudinal wave velocity curve Vp, a neutron porosity curve CNL, a density curve DEN, a porosity curve POR, a shale content curve SH and a corresponding measured shear wave velocity curve Vs; step 2, pre-processing the logging curves; step 3, constructing a shear wave velocity prediction model; step 4, continuously modifying the model parameters until the model stability and prediction accuracy meet the requirements; and step 5, predicting the shear wave velocity of a well to be predicted by using the shear wave velocity prediction model. The shear wave velocity prediction method based on deep learning can accurately predict the logging shear wave velocity, can replace the empirical formula method and the rock physics modeling method, can accurately and quickly predict the logging shear wave velocity by using the logging curves and logging interpretation results, has strong generalization ability and has practical popularization and application significance.

The present invention belongs to the field of power systems and automation thereof, and particularly relates to a method for determining reserve capacity and allocating its cost taking into account the random fluctuation characteristics of renewable energy. The method comprises the following steps: utilizing historical load / renewable energy forecast data and fitting the probability density curve of each load / renewable energy source based on an uncertainty estimation method of non-parametric kernel density; determining the total reserve capacity by simulating and generating a reserve demand scenario based on the Monte Carlo method; constructing a market clearing model with minimizing the unit operating cost and the rotating reserve cost as the objective function; solving the clearing model with a quadratic programming algorithm to obtain the energy price and the reserve price, and determining the total amount of reserve auxiliary funds to be allocated; calculating the replacement value of each load / renewable energy source based on a VCG mechanism, and allocating the reserve auxiliary fees according to the relative proportion of the value. The present invention can reasonably determine the total reserve capacity and effectively avoid the problem of imbalance between revenue and expenditure, and can be widely applied to the power spot market of the power system.

The invention relates to the technical field of data processing, in particular to an efficient data processing method for a communication network. The method comprises the steps of obtaining a to-be-processed network data sequence; obtaining a characteristic value sequence based on the difference between adjacent network data in the to-be-processed network data sequence, and obtaining each piece of suspected drift anomaly data according to the similarity between a normal distribution curve and a probability density curve corresponding to the characteristic value sequence; obtaining an optimal second data window corresponding to each piece of suspected drift abnormal data according to a difference between a feature value corresponding to each piece of network data in each second data window corresponding to each piece of suspected drift abnormal data and a mean value corresponding to the feature value sequence; according to the first data window and the optimal second data window corresponding to each piece of suspected drift abnormal data, obtaining each piece of drift abnormal data; and obtaining a target network data sequence according to the optimal second data window. The method can eliminate the influence of the drift abnormal data on the detection precision, and enables the detection precision to be higher.

The application discloses a fan vibration online diagnosis method and system based on generalized extreme value distribution, and the method filters old historical vibration time series data, extracts characteristic values and calculates fitting distribution conditions, obtains a generalized extreme value distribution curve of the fan, calculates a cumulative probability density curve of the curve, that is, a fitting model, calculates key point extreme value data according to the curve, enables the fan master control to directly judge a theoretical occurrence probability of a vibration condition at the moment according to the fitting model, flexibly adjusts a control strategy based on the probability, and realizes real-time vibration online diagnosis; the application is simple to implement and low in cost, does not need to additionally add other equipment on the basis of the original, does not need internet and other facilities, only needs to modify a fan master control program code, and meanwhile, the bottom logic is simple and clear, so that the master control corresponding operation logic is convenient to debug and modify.

The application discloses a temperature-pressure decoupling control method based on an isodensity curve and related equipment, and the method comprises the following steps: after determining a target cabin for temperature-pressure control, obtaining an initial state and a target state of the target cabin; judging whether the initial state and the target state are located in the same isodensity curve range based on an isodensity temperature-pressure decoupling control graph of the target cabin; if not, calculating at least two intermediate target states by using the change relationship between the temperature and the pressure under the quantitative premise based on the target temperature and the target pressure in the target state; and performing temperature and pressure control on the liquid in the target cabin based on the at least two intermediate target states, so as to adjust the liquid from the initial state to the target state. By using the isodensity temperature-pressure decoupling control graph of the liquid, the temperature and the pressure of the liquid in the target cabin are adjusted by using the mode of separately controlling the temperature and the pressure, so as to solve the problem that it is difficult to simultaneously and accurately control the temperature and the pressure in the existing high-temperature and high-pressure environment.

The invention discloses a transformer substation pre-disaster emergency resource allocation method based on transmission and distribution collaboration under flood, and belongs to the field of power systems, and the method comprises the steps: obtaining the historical flood data of each transformer substation in a power system, and generating a flood depth probability density curve according to the historical flood data of each transformer substation; before the flood disaster occurs, the rainfall capacity is predicted; the flood depth, the fault probability, the expected economic loss and the expected maintenance time of the transformer substation are speculated by analyzing the flood depth probability density curve of the transformer substation; calculating a characteristic value of the transformer substation through the load capacity of the transformer substation, the expected maintenance time and the expected economic loss; establishing a two-dimensional space by taking the substation fault probability as an abscissa and the characteristic value of the substation as an ordinate, and generating a flood fault scene; a pre-disaster-based transformer substation emergency resource allocation model is established, and before a flood disaster occurs, a key transformer substation is selected to install a dam by taking the minimum economic loss of the transformer substation as a target so as to protect the transformer substation.